From 2c3e732222ee3ecdfd85cf3063fb8c49031741d9 Mon Sep 17 00:00:00 2001 From: vanithakattumuri Date: Tue, 11 Jun 2024 17:27:41 +0900 Subject: [PATCH] #2 updated the error in readtheDocs --- PAMI/correlatedPattern/basic/CoMine.py | 12 +- PAMI/correlatedPattern/basic/CoMinePlus.py | 11 +- PAMI/frequentPattern/basic/Apriori.py | 13 +- PAMI/frequentPattern/basic/Aprioribitset.py | 98 +- PAMI/frequentPattern/basic/ECLAT.py | 9 +- PAMI/frequentPattern/basic/ECLATDiffset.py | 11 +- PAMI/frequentPattern/basic/ECLATbitset.py | 12 +- PAMI/frequentPattern/basic/FPGrowth.py | 9 +- PAMI/frequentPattern/closed/CHARM.py | 8 +- PAMI/frequentPattern/topk/FAE.py | 9 +- PAMI/periodicFrequentPattern/basic/PFECLAT.py | 9 +- .../basic/PFPGrowth.py | 9 +- .../PAMI.correlatedPattern.basic.doctree | Bin 129444 -> 128006 bytes .../PAMI.extras.generateDatabase.doctree | Bin 100598 -> 117644 bytes .../PAMI.frequentPattern.basic.doctree | Bin 224017 -> 221013 bytes .../PAMI.frequentPattern.closed.doctree | Bin 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finalSphinxDocs/_build/html/_modules/PAMI/sequentialPatternMining/basic/prefixSpan.html create mode 100644 finalSphinxDocs/_build/html/_sources/frequentPatternBasicAprioribitset.rst.txt create mode 100644 finalSphinxDocs/_build/html/frequentPatternBasicAprioribitset.html create mode 100644 finalSphinxDocs/frequentPatternBasicAprioribitset.rst diff --git a/PAMI/correlatedPattern/basic/CoMine.py b/PAMI/correlatedPattern/basic/CoMine.py index d4085b59..b49784bb 100644 --- a/PAMI/correlatedPattern/basic/CoMine.py +++ b/PAMI/correlatedPattern/basic/CoMine.py @@ -115,9 +115,7 @@ def traverse(self) -> Tuple[List[int], int]: class CoMine(_ab._correlatedPatterns): """ - - About this algorithm - ==================== + **About this algorithm** :**Description**: CoMine is one of the fundamental algorithm to discover correlated patterns in a transactional database. It is based on the traditional FP-Growth algorithm. This algorithm uses depth-first search technique to find all correlated patterns in a transactional database. @@ -144,8 +142,7 @@ class CoMine(_ab._correlatedPatterns): - **itemSetBuffer** (*list*) -- *it represents the store the items in mining.* - **maxPatternLength** (*int*) -- *it represents the constraint for pattern length.* - Execution methods - ================= + **Execution methods** **Terminal command** @@ -197,10 +194,9 @@ class CoMine(_ab._correlatedPatterns): print("Total ExecutionTime in seconds:", run) - Credits - ======= + **Credits** - The complete program was written by B.Sai Chitra under the supervision of Professor Rage Uday Kiran. + The complete program was written by B.Sai Chitra and revised by Tarun Sreepada under the supervision of Professor Rage Uday Kiran. """ diff --git a/PAMI/correlatedPattern/basic/CoMinePlus.py b/PAMI/correlatedPattern/basic/CoMinePlus.py index 2cf0af86..c9bfcf9c 100644 --- a/PAMI/correlatedPattern/basic/CoMinePlus.py +++ b/PAMI/correlatedPattern/basic/CoMinePlus.py @@ -115,8 +115,7 @@ def traverse(self) -> Tuple[List[int], int]: class CoMine(_ab._correlatedPatterns): """ - About this algorithm - ==================== + **About this algorithm** :**Description**: CoMinePlus is one of the fundamental algorithm to discover correlated patterns in a transactional database. It is based on the traditional FP-Growth algorithm. This algorithm uses depth-first search technique to find all correlated patterns in a transactional database. @@ -143,8 +142,7 @@ class CoMine(_ab._correlatedPatterns): - **itemSetBuffer** (*list*) -- *it represents the store the items in mining.* - **maxPatternLength** (*int*) -- *it represents the constraint for pattern length.* - Execution methods - ================= + **Execution methods** **Terminal command** @@ -196,10 +194,9 @@ class CoMine(_ab._correlatedPatterns): print("Total ExecutionTime in seconds:", run) - Credits - ======= + **Credits** - The complete program was written by B.Sai Chitra and revised by Tarun Sreepads under the supervision of Professor Rage Uday Kiran. + The complete program was written by B.Sai Chitra and revised by Tarun Sreepada under the supervision of Professor Rage Uday Kiran. """ diff --git a/PAMI/frequentPattern/basic/Apriori.py b/PAMI/frequentPattern/basic/Apriori.py index 1ea2accc..72581bf6 100644 --- a/PAMI/frequentPattern/basic/Apriori.py +++ b/PAMI/frequentPattern/basic/Apriori.py @@ -2,7 +2,7 @@ # # **Importing this algorithm into a python program** # -# import PAMI1.frequentPattern.basic.Apriori as alg +# import PAMI.frequentPattern.basic.Apriori as alg # # iFile = 'sampleDB.txt' # @@ -58,8 +58,7 @@ class Apriori(_ab._frequentPatterns): """ - About this algorithm - ==================== + **About this algorithm** :**Description**: Apriori is one of the fundamental algorithm to discover frequent patterns in a transactional database. This program employs apriori property (or downward closure property) to reduce the search space effectively. This algorithm employs breadth-first search technique to find the complete set of frequent patterns in a transactional database. @@ -79,8 +78,7 @@ class Apriori(_ab._frequentPatterns): - **Database** (*list*) -- *To store the transactions of a database in list.* - Execution methods - ================= + **Execution methods** **Terminal command** @@ -101,7 +99,7 @@ class Apriori(_ab._frequentPatterns): .. code-block:: python - import PAMI1.frequentPattern.basic.Apriori as alg + import PAMI.frequentPattern.basic.Apriori as alg iFile = 'sampleDB.txt' @@ -132,8 +130,7 @@ class Apriori(_ab._frequentPatterns): print("Total ExecutionTime in seconds:", run) - Credits - ======= + **Credits** The complete program was written by P. Likhitha and revised by Tarun Sreepada under the supervision of Professor Rage Uday Kiran. diff --git a/PAMI/frequentPattern/basic/Aprioribitset.py b/PAMI/frequentPattern/basic/Aprioribitset.py index b4836e70..53e95ac1 100644 --- a/PAMI/frequentPattern/basic/Aprioribitset.py +++ b/PAMI/frequentPattern/basic/Aprioribitset.py @@ -1,10 +1,13 @@ # AprioriBitset is one of the fundamental algorithm to discover frequent patterns in a transactional database. # # **Importing this algorithm into a python program** -# --------------------------------------------------------- # # import PAMI.frequentPattern.basic.AprioriBitset as alg # +# iFile = 'sampleDB.txt' +# +# minSup = 10 # can also be specified between 0 and 1 +# # obj = alg.AprioriBitset(iFile, minSup) # # obj.mine() @@ -54,43 +57,30 @@ class Aprioribitset(_ab._frequentPatterns): """ - :Description: AprioriBitset is one of the fundamental algorithm to discover frequent patterns in a transactional database. - - :Reference: Mohammed Javeed Zaki: Scalable Algorithms for Association Mining. IEEE Trans. Knowl. Data Eng. 12(3): - 372-390 (2000), https://ieeexplore.ieee.org/document/846291 - - :param iFile: str : - Name of the Input file to mine complete set of frequent patterns - :param oFile: str : - Name of the output file to store complete set of frequent patterns - :param minSup: int or float or str : - The user can specify minSup either in count or proportion of database size. If the program detects the data type of minSup is integer, then it treats minSup is expressed in count. - :param sep: str : - This variable is used to distinguish items from one another in a transaction. The default seperator is tab space. However, the users can override their default separator. + **About this algorithm** - :Attributes: + :**Description**: AprioriBitset is one of the fundamental algorithm to discover frequent patterns in a transactional database. - startTime : float - To record the start time of the mining process + :**Reference**: Mohammed Javeed Zaki: Scalable Algorithms for Association Mining. IEEE Trans. Knowl. Data Eng. 12(3): + 372-390 (2000), https://ieeexplore.ieee.org/document/846291 - endTime : float - To record the completion time of the mining process + :**Parameters**: - **iFile** (*str or URL or dataFrame*) -- *Name of the Input file to mine complete set of frequent patterns.* + - **oFile** (*str*) -- *Name of the output file to store complete set of frequent patterns.* + - **minSup** (*int or float or str*) -- *The user can specify minSup either in count or proportion of database size. If the program detects the data type of minSup is integer, then it treats minSup is expressed in count. Otherwise, it will be treated as float.* + - **sep** (*str*) -- *This variable is used to distinguish items from one another in a transaction. The default seperator is tab space. However, the users can override their default separator.* - finalPatterns : dict - Storing the complete set of patterns in a dictionary variable + :**Attributes**: - **startTime** (*float*) -- *To record the start time of the mining process.* + - **endTime** (*float*) -- *To record the completion time of the mining process.* + - **finalPatterns** (*dict*) -- *Storing the complete set of patterns in a dictionary variable.* + - **memoryUSS** (*float*) -- *To store the total amount of USS memory consumed by the program.* + - **memoryRSS** (*float*) -- *To store the total amount of RSS memory consumed by the program.* + - **Database** (*list*) -- *To store the transactions of a database in list.* - memoryUSS : float - To store the total amount of USS memory consumed by the program - memoryRSS : float - To store the total amount of RSS memory consumed by the program - Database : list - To store the transactions of a database in list + **Execution methods** - - **Methods to execute code on terminal** - ------------------------------------------ + **Terminal command** .. code-block:: console @@ -102,22 +92,26 @@ class Aprioribitset(_ab._frequentPatterns): (.venv) $ python3 AprioriBitset.py sampleDB.txt patterns.txt 10.0 - .. note:: minSup will be considered in percentage of database transactions + .. note:: minSup can be specified in support count or a value between 0 and 1. + + **Calling from a python program** - **Importing this algorithm into a python program** - --------------------------------------------------------- .. code-block:: python - import PAMI.frequentPattern.basic.AprioriBitset as alg + import PAMI.frequentPattern.basic.Aprioribitset as alg + + iFile = 'sampleDB.txt' - obj = alg.AprioriBitset(iFile, minSup) + minSup = 10 # can also be specified between 0 and 1 + + obj = alg.Aprioribitset(iFile, minSup) obj.mine() - frequentPatterns = obj.getPatterns() + frequentPattern = obj.getPatterns() - print("Total number of Frequent Patterns:", len(frequentPatterns)) + print("Total number of Frequent Patterns:", len(frequentPattern)) obj.save(oFile) @@ -135,10 +129,10 @@ class Aprioribitset(_ab._frequentPatterns): print("Total ExecutionTime in seconds:", run) - **Credits:** - ------------------- - The complete program was written by Yudai Masu under the supervision of Professor Rage Uday Kiran. + **Credits** + + The complete program was written by Yudai Masu and revised by Tarun Sreepada under the supervision of Professor Rage Uday Kiran. """ @@ -160,11 +154,8 @@ def _convert(self, value): To convert the user specified minSup value :param value: user specified minSup value - :type value: int - :return: converted type - :rtype: int or float or string """ if type(value) is int: @@ -216,14 +207,9 @@ def _creatingItemSets(self): print("File Not Found") self._minSup = self._convert(self._minSup) - @deprecated( - "It is recommended to use 'mine()' instead of 'startMine()' for mining process. Starting from January 2025, 'startMine()' will be completely terminated.") + @deprecated("It is recommended to use 'mine()' instead of 'startMine()' for mining process. Starting from January 2025, 'startMine()' will be completely terminated.") + def startMine(self): - """ - Frequent pattern mining process will start from here - We start with the scanning the itemSets and store the bitsets respectively. - We form the combinations of single items and check with minSup condition to check the frequency of patterns - """ self.mine() def _bitPacker(self, data, maxIndex): @@ -231,13 +217,9 @@ def _bitPacker(self, data, maxIndex): It takes the data and maxIndex as input and generates integer as output value. :param data: it takes data as input. - :type data: int or float - :param maxIndex: It converts the data into bits By taking the maxIndex value as condition. - :type maxIndex: int - """ packed_bits = 0 for i in data: @@ -248,7 +230,6 @@ def _bitPacker(self, data, maxIndex): def mine(self) -> None: """ Frequent pattern mining process will start from here - # Bitset implementation """ self._startTime = _ab._time.time() @@ -307,6 +288,7 @@ def mine(self) -> None: def getMemoryUSS(self): """ Total amount of USS memory consumed by the mining process will be retrieved from this function + :return: returning USS memory consumed by the mining process :rtype: float """ @@ -316,6 +298,7 @@ def getMemoryUSS(self): def getMemoryRSS(self): """ Total amount of RSS memory consumed by the mining process will be retrieved from this function + :return: returning RSS memory consumed by the mining process :rtype: float """ @@ -325,6 +308,7 @@ def getMemoryRSS(self): def getRuntime(self): """ Calculating the total amount of runtime taken by the mining process + :return: returning total amount of runtime taken by the mining process :rtype: float """ @@ -333,11 +317,9 @@ def getRuntime(self): def getPatternsAsDataFrame(self) -> _ab._pd.DataFrame: """ - Storing final frequent patterns in a dataframe :return: returning frequent patterns in a dataframe - :rtype: pd.DataFrame """ @@ -358,7 +340,6 @@ def getPatternsAsDataFrame(self) -> _ab._pd.DataFrame: def save(self, outFile: str, seperator = "\t" ) -> None: """ - Complete set of frequent patterns will be loaded in to an output file :param outFile: name of the output file @@ -379,6 +360,7 @@ def save(self, outFile: str, seperator = "\t" ) -> None: def getPatterns(self): """ Function to send the set of frequent patterns after completion of the mining process + :return: returning frequent patterns :rtype: dict """ diff --git a/PAMI/frequentPattern/basic/ECLAT.py b/PAMI/frequentPattern/basic/ECLAT.py index d8a029e7..152386e4 100644 --- a/PAMI/frequentPattern/basic/ECLAT.py +++ b/PAMI/frequentPattern/basic/ECLAT.py @@ -56,8 +56,7 @@ class ECLAT(_ab._frequentPatterns): """ - About this algorithm - ==================== + **About this algorithm** :**Description**: ECLAT is one of the fundamental algorithm to discover frequent patterns in a transactional database. @@ -76,8 +75,7 @@ class ECLAT(_ab._frequentPatterns): - **memoryRSS** (*float*) -- *To store the total amount of RSS memory consumed by the program.* - **Database** (*list*) -- *To store the transactions of a database in list.* - Execution methods - ================= + **Execution methods** **Terminal command** @@ -129,8 +127,7 @@ class ECLAT(_ab._frequentPatterns): print("Total ExecutionTime in seconds:", run) - Credits: - ======== + **Credits:** The complete program was written by Kundai and revised by Tarun Sreepada under the supervision of Professor Rage Uday Kiran. diff --git a/PAMI/frequentPattern/basic/ECLATDiffset.py b/PAMI/frequentPattern/basic/ECLATDiffset.py index c36ded2c..decbe75e 100644 --- a/PAMI/frequentPattern/basic/ECLATDiffset.py +++ b/PAMI/frequentPattern/basic/ECLATDiffset.py @@ -58,6 +58,7 @@ class ECLATDiffset(_ab._frequentPatterns): """ + **About this algorithm** :**Description**: ECLATDiffset uses diffset to extract the frequent patterns in a transactional database. @@ -67,7 +68,7 @@ class ECLATDiffset(_ab._frequentPatterns): :**Parameters**: - **iFile** (*str or URL or dataFrame*) -- *Name of the Input file to mine complete set of frequent patterns.* - **oFile** (*str*) -- *Name of the output file to store complete set of frequent patterns* - **minSup** (*int or float or str*) -- *The user can specify minSup either in count or proportion of database size. If the program detects the data type of minSup is integer, then it treats minSup is expressed in count.* - - **sep** (*str*) -- **This variable is used to distinguish items from one another in a transaction. The default seperator is tab space. However, the users can override their default separator.** + - **sep** (*str*) -- *This variable is used to distinguish items from one another in a transaction. The default seperator is tab space. However, the users can override their default separator.* :**Attributes**: - **startTime** (*float*) -- *To record the start time of the mining process.* - **endTime** (*float*) -- *To record the end time of the mining process.* @@ -77,8 +78,7 @@ class ECLATDiffset(_ab._frequentPatterns): - **Database** (*list*) -- *To store the transactions of a database in list.* - Execution methods - ================= + **Execution methods** **Terminal command** @@ -95,7 +95,7 @@ class ECLATDiffset(_ab._frequentPatterns): .. note:: minSup can be specified in support count or a value between 0 and 1. - **Calling from a python program** + **Calling from a python program** .. code-block:: python @@ -130,8 +130,7 @@ class ECLATDiffset(_ab._frequentPatterns): print("Total ExecutionTime in seconds:", run) - Credits: - ======== + **Credits:** The complete program was written by Kundai and revised by Tarun Sreepada under the supervision of Professor Rage Uday Kiran. diff --git a/PAMI/frequentPattern/basic/ECLATbitset.py b/PAMI/frequentPattern/basic/ECLATbitset.py index 979a7cc6..80eaaa5e 100644 --- a/PAMI/frequentPattern/basic/ECLATbitset.py +++ b/PAMI/frequentPattern/basic/ECLATbitset.py @@ -57,6 +57,8 @@ class ECLATbitset(_ab._frequentPatterns): """ + **About this algorithm** + :*Description*: ECLATbitset is one of the fundamental algorithm to discover frequent patterns in a transactional database. :*Reference*: Mohammed Javeed Zaki: Scalable Algorithms for Association Mining. IEEE Trans. Knowl. Data Eng. 12(3): @@ -65,7 +67,7 @@ class ECLATbitset(_ab._frequentPatterns): :**Parameters**: - **iFile** (*str or URL or dataFrame*) -- *Name of the Input file to mine complete set of frequent patterns.* - **oFile** (*str*) -- *Name of the output file to store complete set of frequent patterns* - **minSup** (*int or float or str*) -- *The user can specify minSup either in count or proportion of database size. If the program detects the data type of minSup is integer, then it treats minSup is expressed in count.* - - **sep** (*str*) -- **This variable is used to distinguish items from one another in a transaction. The default seperator is tab space. However, the users can override their default separator.** + - **sep** (*str*) -- *This variable is used to distinguish items from one another in a transaction. The default seperator is tab space. However, the users can override their default separator.* :**Attributes**: - **startTime** (*float*) -- *To record the start time of the mining process.* - **endTime** (*float*) -- *To record the end time of the mining process.* @@ -74,11 +76,12 @@ class ECLATbitset(_ab._frequentPatterns): - **memoryRSS** *(float*) -- *To store the total amount of RSS memory consumed by the program.* - **Database** (*list*) -- *To store the transactions of a database in list.* - Execution methods - ================= + **Execution methods** **Terminal command** + .. code-block:: console + Format: (.venv) $ python3 ECLATbitset.py @@ -124,8 +127,7 @@ class ECLATbitset(_ab._frequentPatterns): print("Total ExecutionTime in seconds:", run) - Credits: - ======== + **Credits:** The complete program was written by Yudai Masu and revised by Tarun Sreepada under the supervision of Professor Rage Uday Kiran. diff --git a/PAMI/frequentPattern/basic/FPGrowth.py b/PAMI/frequentPattern/basic/FPGrowth.py index 6c28b4e6..0ec7d5db 100644 --- a/PAMI/frequentPattern/basic/FPGrowth.py +++ b/PAMI/frequentPattern/basic/FPGrowth.py @@ -115,8 +115,7 @@ def traverse(self) -> Tuple[List[int], int]: class FPGrowth(_fp._frequentPatterns): """ - About this algorithm - ==================== + **About this algorithm** :**Description**: FPGrowth is one of the fundamental algorithm to discover frequent patterns in a transactional database. It stores the database in compressed fp-tree decreasing the memory usage and extracts the patterns from tree.It employs downward closure property to reduce the search space effectively. @@ -138,8 +137,7 @@ class FPGrowth(_fp._frequentPatterns): - **tree** (*class*) -- *it represents the Tree class.* - Execution methods - ================= + **Execution methods** **Terminal command** @@ -191,8 +189,7 @@ class FPGrowth(_fp._frequentPatterns): print("Total ExecutionTime in seconds:", run) - Credits: - ======== + **Credits:** The complete program was written by P. Likhitha and revised by Tarun Sreepada under the supervision of Professor Rage Uday Kiran. diff --git a/PAMI/frequentPattern/closed/CHARM.py b/PAMI/frequentPattern/closed/CHARM.py index ee36ff89..f2f89d20 100644 --- a/PAMI/frequentPattern/closed/CHARM.py +++ b/PAMI/frequentPattern/closed/CHARM.py @@ -58,6 +58,8 @@ class CHARM(_ab._frequentPatterns): """ + **About this algorithm** + :**Description**: CHARM is an algorithm to discover closed frequent patterns in a transactional database. Closed frequent patterns are patterns if there exists no superset that has the same support count as this original itemset. This algorithm employs depth-first search technique to find the complete set of closed frequent patterns in a transactional database. :**Reference**: Mohammed J. Zaki and Ching-Jui Hsiao, CHARM: An Efficient Algorithm for Closed Itemset Mining, @@ -81,8 +83,7 @@ class CHARM(_ab._frequentPatterns): - **hashing** (*dict*) -- *Stores the patterns with their support to check for the closed property.* - Execution methods - ================= + **Execution methods** **Terminal command** @@ -134,8 +135,7 @@ class CHARM(_ab._frequentPatterns): print("Total ExecutionTime in seconds:", run) - Credits: - ======= + **Credits:** The complete program was written by P.Likhitha and revised by Tarun Sreepada under the supervision of Professor Rage Uday Kiran. diff --git a/PAMI/frequentPattern/topk/FAE.py b/PAMI/frequentPattern/topk/FAE.py index c0cc69c9..b3877611 100644 --- a/PAMI/frequentPattern/topk/FAE.py +++ b/PAMI/frequentPattern/topk/FAE.py @@ -57,8 +57,7 @@ class FAE(_ab._frequentPatterns): """ - About this algorithm - ==================== + **About this algorithm** :**Description**: Top - K is and algorithm to discover top frequent patterns in a transactional database. @@ -76,8 +75,7 @@ class FAE(_ab._frequentPatterns): - **memoryUSS** (*float*) -- *To store the total amount of USS memory consumed by the program.* - **memoryRSS** (*float*) -- *To store the total amount of RSS memory consumed by the program.* - Execution methods - ================= + **Execution methods** **Terminal command** @@ -128,8 +126,7 @@ class FAE(_ab._frequentPatterns): print("Total ExecutionTime in seconds:", run) - Credits - ======= + **Credits:** The complete program was written by P. Likhitha and revised by Tarun Sreepada under the supervision of Professor Rage Uday Kiran. diff --git a/PAMI/periodicFrequentPattern/basic/PFECLAT.py b/PAMI/periodicFrequentPattern/basic/PFECLAT.py index fc1a5711..9d909242 100644 --- a/PAMI/periodicFrequentPattern/basic/PFECLAT.py +++ b/PAMI/periodicFrequentPattern/basic/PFECLAT.py @@ -64,8 +64,7 @@ class PFECLAT(_ab._periodicFrequentPatterns): """ - About this algorithm - ==================== + **About this algorithm** :**Description**: PFECLAT is the fundamental approach to mine the periodic-frequent patterns. @@ -102,8 +101,7 @@ class PFECLAT(_ab._periodicFrequentPatterns): - **getPeriodAndSupport()** -- *Calculates the support and period for a list of timestamps.* - **Generation()** -- *Used to implement prefix class equivalence method to generate the periodic patterns recursively* - Execution methods - ================= + **Execution methods** **Terminal command** @@ -156,8 +154,7 @@ class PFECLAT(_ab._periodicFrequentPatterns): print("Total ExecutionTime in seconds:", run) - Credits - ======= + **Credits:** The complete program was written by P. Likhitha and revised by Tarun Sreepada under the supervision of Professor Rage Uday Kiran. diff --git a/PAMI/periodicFrequentPattern/basic/PFPGrowth.py b/PAMI/periodicFrequentPattern/basic/PFPGrowth.py index 87d88b08..3f6df3c7 100644 --- a/PAMI/periodicFrequentPattern/basic/PFPGrowth.py +++ b/PAMI/periodicFrequentPattern/basic/PFPGrowth.py @@ -122,8 +122,7 @@ def traverse(self): class PFPGrowth(_ab._periodicFrequentPatterns): """ - About this algorithm - ==================== + **About this algorithm** :**Description**: PFPGrowth is one of the fundamental algorithm to discover periodic-frequent patterns in a transactional database. @@ -161,8 +160,7 @@ class PFPGrowth(_ab._periodicFrequentPatterns): - **buildTree()** -- *After updating the Database, remaining items will be added into the tree by setting root node as null.* - **convert()** -- *This methos is used to convert the user specified value.* - Execution methods - ================= + **Execution methods** **Terminal command** @@ -216,8 +214,7 @@ class PFPGrowth(_ab._periodicFrequentPatterns): print("Total ExecutionTime in seconds:", run) - Credits - ======= + **Credits** The complete program was written by P. 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zN{_UQHfGRiXxvDKf_W5;HjTE7N)4?XU^1d0bVIu;Js>pFYNyY`xCeXrdbM`o2{?_- zCrWyCA|*tuMj2%tuhruMLa#|f(8@QKz%eab*xP~drsf)?Jl^Z8Z z{9khY&s;yr^#;S;khg?sz&d%}p8a54KeXZ|d~rR#r~~p3>=p8ZyiJ9HrmqtO5C2^Ki+J3O)l!>t P$+(1*2-?#(C$QcD-df)+ diff --git a/finalSphinxDocs/_build/html/PAMI.correlatedPattern.basic.html b/finalSphinxDocs/_build/html/PAMI.correlatedPattern.basic.html index 7d555096..77b4fc41 100644 --- a/finalSphinxDocs/_build/html/PAMI.correlatedPattern.basic.html +++ b/finalSphinxDocs/_build/html/PAMI.correlatedPattern.basic.html @@ -105,6 +105,7 @@

    Submodules class PAMI.correlatedPattern.basic.CoMine.CoMine(iFile: Union[str, DataFrame], minSup: Union[int, float, str], minAllConf: float, sep: str = '\t')[source]

    Bases: _correlatedPatterns

    +

    About this algorithm

    Description:

    CoMine is one of the fundamental algorithm to discover correlated patterns in a transactional database. It is based on the traditional FP-Growth algorithm. This algorithm uses depth-first search technique to find all correlated patterns in a transactional database.

    @@ -140,6 +141,7 @@

    Submodules
    Format:
 
@@ -188,9 +190,8 @@ 
    Submodulesprint("Total ExecutionTime in seconds:", run)
    -
    -

    The complete program was written by B.Sai Chitra under the supervision of Professor Rage Uday Kiran.

    -
    +

    Credits

    +

    The complete program was written by B.Sai Chitra and revised by Tarun Sreepada under the supervision of Professor Rage Uday Kiran.

    getMemoryRSS() → float[source]
    @@ -326,6 +327,7 @@

    Submodules class PAMI.correlatedPattern.basic.CoMinePlus.CoMine(iFile: Union[str, DataFrame], minSup: Union[int, float, str], minAllConf: float, sep: str = '\t')[source]

    Bases: _correlatedPatterns

    +

    About this algorithm

    Description:

    CoMinePlus is one of the fundamental algorithm to discover correlated patterns in a transactional database. It is based on the traditional FP-Growth algorithm. This algorithm uses depth-first search technique to find all correlated patterns in a transactional database.

    @@ -361,6 +363,7 @@

    Submodules
    Format:
 
@@ -409,7 +412,8 @@ 
    Submodulesprint("Total ExecutionTime in seconds:", run)
    -

    The complete program was written by B.Sai Chitra and revised by Tarun Sreepads under the supervision of Professor Rage Uday Kiran.

    +

    Credits

    +

    The complete program was written by B.Sai Chitra and revised by Tarun Sreepada under the supervision of Professor Rage Uday Kiran.

    getMemoryRSS() → float[source]
    diff --git a/finalSphinxDocs/_build/html/PAMI.extras.generateDatabase.html b/finalSphinxDocs/_build/html/PAMI.extras.generateDatabase.html index 4663d1d2..18eb5d07 100644 --- a/finalSphinxDocs/_build/html/PAMI.extras.generateDatabase.html +++ b/finalSphinxDocs/_build/html/PAMI.extras.generateDatabase.html @@ -102,83 +102,156 @@

    Submodules

    PAMI.extras.generateDatabase.generateSpatioTemporalDatabase module

    -
    -class PAMI.extras.generateDatabase.generateSpatioTemporalDatabase.spatioTemporalDatabaseGenerator(xmin: int, xmax: int, ymin: int, ymax: int, maxTimeStamp: int, numberOfItems: int, itemChanceLow: float, itemChanceHigh: float, timeStampChanceLow: float, timeStampChanceHigh: float)[source]
    +
    +class PAMI.extras.generateDatabase.generateSpatioTemporalDatabase.generateTemporalDatabase(numOfTransactions: int, avgLenOfTransactions: int, numItems: int, outputFile: str, x1, y1, x2, y2, percentage: int = 50, sep: str = '\t', typeOfFile: str = 'Database')[source]

    Bases: object

    Description:
    -

    generateSpatioTemporalDatabase is a code used to convert the database into SpatioTemporal database.

    -
    -
    Parameters:
    -
      -

    • xmin – int : -To give minimum value for x

      • -

    • xmax – int : -To give maximum value for x

      • -

    • ymin – int : -To give minimum value for y

      • -

    • ymax – int : -To give maximum value for y

      • -

    • maxTimeStamp – int : -maximum Time Stamp for the database

      • -

    • numberOfItems – int : -number of items in the database

      • -

    • itemChanceLow – int or float : -least chance for item in the database

      • -

    • itemChanceHigh – int or float : -highest chance for item in the database

      • -

    • timeStampChanceLow – int or float : -lowest time stamp value

      • -

    • timeStampChanceHigh – int or float: -highest time stamp value

      • -
    +

    generateTemporalDatabase creates a temporal database and outputs a database or a frame depending on input

    +
    +
    Attributes:
    +
    +
    param numOfTransactions:
    +

    int +number of transactions

    +
    +
    param avgLenOfTransactions:
    +

    int +average length of transactions

    +
    +
    param numItems:
    +

    int +number of items

    +
    +
    param outputFile:
    +

    str +output file name

    +
    +
    param percentage:
    +

    int +percentage of coinToss for TID of temporalDatabase

    +
    +
    param sep:
    +

    str +seperator for database output file

    +
    +
    param typeOfFile:
    +

    str +specify database or dataframe to get corresponding output

    -
    from PAMI.extras.generateDatabase import generateSpatioTemporalDatabase as db
-
-obj = db.generateSpatioTemporalDatabase(0, 100, 0, 100, 10, 10, 0.5, 0.9, 0.5, 0.9)
-
-obj.save(oFile)
+
    +
    Methods:
    +
    +
    getFileName():

    returns filename

    +
    +
    createTemporalFile():

    creates temporal database file or dataframe

    +
    +
    getDatabaseAsDataFrame:

    returns dataframe

    +
    +
    performCoinFlip():

    Perform a coin flip with the given probability

    +
    +
    tuning():

    Tune the arrayLength to match avgLenOfTransactions

    +
    +
    createTemporalFile():

    create Temporal database or dataframe depending on input

    +
    +
    +
    +
    +
    from PAMI.extras.generateDatabase import generateTemporalDatabase as db
 
-obj.createPoint(0,100,0,100) # values can be according to the size of data
+numOfTransactions = 100
+numItems = 15
+avgTransactionLength = 6
+outFileName = 'temporal_ot.txt'
+sep = '     '
+percent = 75
+frameOrBase = "dataframe" # if you want to get dataframe as output
+frameOrBase = "database" # if you want to get database/csv/file as output
 
-obj.saveAsFile("outputFileName") # To create a file
+temporalDB = db.generateTemporalDatabase(numOfTransactions, avgTransactionLength, numItems, outFileName, percent, sep, frameOrBase )
+temporalDB.createTemporalFile()
+print(temporalDB.getDatabaseAsDataFrame())
    -
    -
    -alreadyAdded = {}
    -
    +
    +
    +createTemporalFile() → None[source]
    +

    create Temporal database or dataframe depending on input +:return: None

    +
    -
    -
    -coinFlip = [True, False]
    -
    +
    +
    +generateArray(nums, avg, maxItems, sumRes) → list[source]
    +

    Generate a random array of length n whose values average to m

    +
    +
    Parameters:
    +
      +

    • nums (list) – number of values

      • +

    • avg (float) – average value

      • +

    • maxItems (int) – maximum value

      • +
    +
    +
    Returns:
    +

    random array

    +
    +
    Return type:
    +

    list

    +
    +
    +
    -
    -createPoint(xmin: int, xmax: int, ymin: int, ymax: int) → Tuple[int, int][source]
    -
    +
    +getDatabaseAsDataFrame() → DataFrame[source]
    +

    return dataframe +:return: dataframe +:rtype: pd.DataFrame

    +
    -
    -
    -items = []
    -
    +
    +
    +getFileName() → str[source]
    +

    return filename +:return: filename +:rtype: str

    +
    -
    -
    -outFileName = ''
    +
    +
    +getPoint(x1, y1, x2, y2)[source]
    -
    -saveAsFile(outFileName='', sep='\t') → None[source]
    -
    +
    +performCoinFlip(probability: float) → bool[source]
    +

    Perform a coin flip with the given probability. +:param probability: probability to perform coin flip +:type probability: float +:return: True if coin flip is performed, False otherwise +:rtype: bool

    +
    -
    -
    -timestamp = []
    -
    +
    +
    +tuning(array, sumRes) → list[source]
    +

    Tune the array so that the sum of the values is equal to sumRes

    +
    +
    Parameters:
    +
      +

    • array (list) – list of values

      • +

    • sumRes (int) – the sum of the values in the array to be tuned

      • +
    +
    +
    Returns:
    +

    list of values with the tuned values and the sum of the values in the array to be tuned and sumRes is equal to sumRes

    +
    +
    Return type:
    +

    list

    +
    +
    +

    @@ -265,6 +338,27 @@

    Submodules +
    +generateArray(nums, avg, maxItems, sumRes) → list[source]
    +

    Generate a random array of length n whose values average to m

    +
    +
    Parameters:
    +
      +

    • nums (list) – number of values

      • +

    • avg (float) – average value

      • +

    • maxItems (int) – maximum value

      • +
    +
    +
    Returns:
    +

    random array

    +
    +
    Return type:
    +

    list

    +
    +
    +

    +
    getDatabaseAsDataFrame() → DataFrame[source]
    @@ -299,11 +393,11 @@

    SubmodulesParameters:

    • array (list) – list of values

      • -

    • sumRes (int) – target sum

      • +

    • sumRes (int) – the sum of the values in the array to be tuned

    Returns:
    -

    list of values with the sum equal to sumRes after tuning

    +

    list of values with the tuned values and the sum of the values in the array to be tuned and sumRes is equal to sumRes

    Return type:

    list

    @@ -395,7 +489,7 @@

    Submodules
    -save(filename) → None[source]
    +save(sep, filename) → None[source]

    Save the transactional database to a file

    Parameters:
    diff --git a/finalSphinxDocs/_build/html/PAMI.frequentPattern.basic.html b/finalSphinxDocs/_build/html/PAMI.frequentPattern.basic.html index 757285db..6480baa4 100644 --- a/finalSphinxDocs/_build/html/PAMI.frequentPattern.basic.html +++ b/finalSphinxDocs/_build/html/PAMI.frequentPattern.basic.html @@ -105,6 +105,7 @@

    Submodules class PAMI.frequentPattern.basic.Apriori.Apriori(iFile, minSup, sep='\t')[source]

    Bases: _frequentPatterns

    +

    About this algorithm

    Description:

    Apriori is one of the fundamental algorithm to discover frequent patterns in a transactional database. This program employs apriori property (or downward closure property) to reduce the search space effectively. This algorithm employs breadth-first search technique to find the complete set of frequent patterns in a transactional database.

    @@ -132,6 +133,7 @@

    Submodules
    Format:
 
@@ -147,7 +149,7 @@ 
    Submodules
    import PAMI1.frequentPattern.basic.Apriori as alg
+
    import PAMI.frequentPattern.basic.Apriori as alg
 
 iFile = 'sampleDB.txt'
 
@@ -178,6 +180,7 @@ 
    Submodulesprint("Total ExecutionTime in seconds:", run)
 
    
 
    
+
    Credits
    
 
    The complete program was written by P. Likhitha  and revised by Tarun Sreepada under the supervision of Professor Rage Uday Kiran.
    
 
    
 
    
@@ -290,6 +293,7 @@ 
    Submodules
 class PAMI.frequentPattern.basic.ECLAT.ECLAT(iFile, minSup, sep='\t')[source]
    
 
    Bases: _frequentPatterns
    
+
    About this algorithm
    
 
    
 
    Description:
    
 
    ECLAT is one of the fundamental algorithm to discover frequent patterns in a transactional database.
    
@@ -317,6 +321,7 @@ 
    Submodules
    Format:
 
@@ -363,6 +368,7 @@ 
    Submodulesprint("Total ExecutionTime in seconds:", run)
 
    
 
    
+
    Credits:
    
 
    The complete program was written by Kundai and revised by Tarun Sreepada under the supervision of Professor Rage Uday Kiran.
    
 
    
 
    
@@ -475,6 +481,7 @@ 
    Submodules
 class PAMI.frequentPattern.basic.ECLATDiffset.ECLATDiffset(iFile, minSup, sep='\t')[source]
    
 
    Bases: _frequentPatterns
    
+
    About this algorithm
    
 
    
 
    Description:
    
 
    ECLATDiffset uses diffset to extract the frequent patterns in a transactional database.
    
@@ -488,7 +495,7 @@ 
    SubmodulesAttributes:
@@ -502,6 +509,7 @@ 
    Submodules
    Format:
 
@@ -515,8 +523,8 @@ 
    Submodules
 
    Note
    
 
    minSup can be specified  in support count or a value between 0 and 1.
    
-
    Calling from a python program
    
 
    
+
    Calling from a python program
    
 
    import PAMI.frequentPattern.basic.ECLATDiffset as alg
 
 iFile = 'sampleDB.txt'
@@ -548,6 +556,7 @@ 
    Submodulesprint("Total ExecutionTime in seconds:", run)
 
    
 
    
+
    Credits:
    
 
    The complete program was written by Kundai and revised by Tarun Sreepada under the supervision of Professor Rage Uday Kiran.
    
 
    
 
    
@@ -660,6 +669,7 @@ 
    Submodules
 class PAMI.frequentPattern.basic.ECLATbitset.ECLATbitset(iFile, minSup, sep='\t')[source]
    
 
    Bases: _frequentPatterns
    
+
    About this algorithm
    
 
    
 
    Description:
    
 
    ECLATbitset is one of the fundamental algorithm to discover frequent patterns in a transactional database.
    
@@ -673,7 +683,7 @@ 
    SubmodulesAttributes:
@@ -687,13 +697,17 @@ 
    Submodules
    Format:
+
+(.venv) $ python3 ECLATbitset.py <inputFile> <outputFile> <minSup>
+
+Example Usage:
+
+(.venv) $ python3 ECLATbitset.py sampleDB.txt patterns.txt 10.0
+
    
+

    Note

    minSup can be specified in support count or a value between 0 and 1.

    @@ -730,6 +744,7 @@

    Submodulesprint("Total ExecutionTime in seconds:", run)

    +

    Credits:

    The complete program was written by Yudai Masu and revised by Tarun Sreepada under the supervision of Professor Rage Uday Kiran.

    @@ -845,6 +860,7 @@

    Submodules class PAMI.frequentPattern.basic.FPGrowth.FPGrowth(iFile, minSup, sep='\t')[source]

    Bases: _frequentPatterns

    +

    About this algorithm

    Description:

    FPGrowth is one of the fundamental algorithm to discover frequent patterns in a transactional database. It stores the database in compressed fp-tree decreasing the memory usage and extracts the patterns from tree.It employs downward closure property to reduce the search space effectively.

    @@ -874,6 +890,7 @@

    Submodules
    Format:
 
@@ -920,6 +937,7 @@ 
    Submodulesprint("Total ExecutionTime in seconds:", run)
    +

    Credits:

    The complete program was written by P. Likhitha and revised by Tarun Sreepada under the supervision of Professor Rage Uday Kiran.

    diff --git a/finalSphinxDocs/_build/html/PAMI.frequentPattern.closed.html b/finalSphinxDocs/_build/html/PAMI.frequentPattern.closed.html index 66da8eb6..859818cf 100644 --- a/finalSphinxDocs/_build/html/PAMI.frequentPattern.closed.html +++ b/finalSphinxDocs/_build/html/PAMI.frequentPattern.closed.html @@ -105,6 +105,7 @@

    Submodules class PAMI.frequentPattern.closed.CHARM.CHARM(iFile, minSup, sep='\t')[source]

    Bases: _frequentPatterns

    +

    About this algorithm

    Description:

    CHARM is an algorithm to discover closed frequent patterns in a transactional database. Closed frequent patterns are patterns if there exists no superset that has the same support count as this original itemset. This algorithm employs depth-first search technique to find the complete set of closed frequent patterns in a transactional database.

    @@ -137,6 +138,7 @@

    Submodules
    Format:
 
@@ -183,6 +185,7 @@ 
    Submodulesprint("Total ExecutionTime in seconds:", run)
    +

    Credits:

    The complete program was written by P.Likhitha and revised by Tarun Sreepada under the supervision of Professor Rage Uday Kiran.

    diff --git a/finalSphinxDocs/_build/html/PAMI.frequentPattern.topk.html b/finalSphinxDocs/_build/html/PAMI.frequentPattern.topk.html index 90b6273b..1cb040a8 100644 --- a/finalSphinxDocs/_build/html/PAMI.frequentPattern.topk.html +++ b/finalSphinxDocs/_build/html/PAMI.frequentPattern.topk.html @@ -105,6 +105,7 @@

    Submodules class PAMI.frequentPattern.topk.FAE.FAE(iFile, k, sep='\t')[source]

    Bases: _frequentPatterns

    +

    About this algorithm

    Description:

    Top - K is and algorithm to discover top frequent patterns in a transactional database.

    @@ -131,6 +132,7 @@

    Submodules
    Format:
 
@@ -177,6 +179,7 @@ 
    Submodulesprint("Total ExecutionTime in seconds:", run)
    +

    Credits:

    The complete program was written by P. Likhitha and revised by Tarun Sreepada under the supervision of Professor Rage Uday Kiran.

    diff --git a/finalSphinxDocs/_build/html/PAMI.html b/finalSphinxDocs/_build/html/PAMI.html index 5500dbaa..8c5fcf9b 100644 --- a/finalSphinxDocs/_build/html/PAMI.html +++ b/finalSphinxDocs/_build/html/PAMI.html @@ -807,17 +807,17 @@

    SubpackagesSubpackages

    Bases: _periodicFrequentPatterns

    +

    About this algorithm

    Description:

    PFECLAT is the fundamental approach to mine the periodic-frequent patterns.

    @@ -153,6 +154,7 @@

    Submodules
    Format:
 
@@ -201,6 +203,7 @@ 
    Submodulesprint("Total ExecutionTime in seconds:", run)
    +

    Credits:

    The complete program was written by P. Likhitha and revised by Tarun Sreepada under the supervision of Professor Rage Uday Kiran.

    @@ -315,6 +318,7 @@

    Submodules class PAMI.periodicFrequentPattern.basic.PFPGrowth.PFPGrowth(iFile, minSup, maxPer, sep='\t')[source]

    Bases: _periodicFrequentPatterns

    +

    About this algorithm

    Description:

    PFPGrowth is one of the fundamental algorithm to discover periodic-frequent patterns in a transactional database.

    @@ -363,6 +367,7 @@

    Submodules
    Format:
 
@@ -411,6 +416,7 @@ 
    Submodulesprint("Total ExecutionTime in seconds:", run)
    +

    Credits

    The complete program was written by P. Likhitha and revised by Tarun Sreepada under the supervision of Professor Rage Uday Kiran.

    diff --git a/finalSphinxDocs/_build/html/PAMI.sequentialPatternMining.basic.html b/finalSphinxDocs/_build/html/PAMI.sequentialPatternMining.basic.html index a44784f8..d66506f2 100644 --- a/finalSphinxDocs/_build/html/PAMI.sequentialPatternMining.basic.html +++ b/finalSphinxDocs/_build/html/PAMI.sequentialPatternMining.basic.html @@ -99,896 +99,17 @@

    PAMI.sequentialPatternMining.basic package

    Submodules

    -
    -

    PAMI.sequentialPatternMining.basic.SPADE module

    -
    -
    -class PAMI.sequentialPatternMining.basic.SPADE.SPADE(iFile, minSup, sep='\t')[source]
    -

    Bases: _sequentialPatterns

    -
    -
    Description:
    -
      -

    • SPADE is one of the fundamental algorithm to discover sequential frequent patterns in a transactional database.

      • -

    • This program employs SPADE property (or downward closure property) to reduce the search space effectively.

      • -

    • This algorithm employs breadth-first search technique when 1-2 length patterns and depth-first serch when above 3 length patterns to find the complete set of frequent patterns in a transactional database.

      • -
    -
    -
    Reference:
    -

    Mohammed J. Zaki. 2001. SPADE: An Efficient Algorithm for Mining Frequent Sequences. Mach. Learn. 42, 1-2 (January 2001), 31-60. DOI=10.1023/A:1007652502315 http://dx.doi.org/10.1023/A:1007652502315

    -
    -
    Parameters:
    -
      -

    • iFile – str : -Name of the Input file to mine complete set of Sequential frequent patterns

      • -

    • oFile – str : -Name of the output file to store complete set of Sequential frequent patterns

      • -

    • minSup – float or int or str : -minSup measure constraints the minimum number of transactions in a database where a pattern must appear -Example: minSup=10 will be treated as integer, while minSup=10.0 will be treated as float

      • -

    • sep – str : -This variable is used to distinguish items from one another in a transaction. The default seperator is tab space. However, the users can override their default separator.

      • -
    -
    -
    Attributes:
    -
    -
    iFilestr

    Input file name or path of the input file

    -
    -
    oFilestr

    Name of the output file or the path of output file

    -
    -
    minSup: float or int or str

    The user can specify minSup either in count or proportion of database size. -If the program detects the data type of minSup is integer, then it treats minSup is expressed in count. -Otherwise, it will be treated as float. -Example: minSup=10 will be treated as integer, while minSup=10.0 will be treated as float

    -
    -
    sepstr

    This variable is used to distinguish items from one another in a transaction. The default seperator is tab space or . -However, the users can override their default separator.

    -
    -
    startTime:float

    To record the start time of the mining process

    -
    -
    endTime:float

    To record the completion time of the mining process

    -
    -
    finalPatterns: dict

    Storing the complete set of patterns in a dictionary variable

    -
    -
    memoryUSSfloat

    To store the total amount of USS memory consumed by the program

    -
    -
    memoryRSSfloat

    To store the total amount of RSS memory consumed by the program

    -
    -
    Databaselist

    To store the transactions of a database in list

    -
    -
    _xLenDatabase: dict

    To store the datas in different sequence separated by sequence, rownumber, length.

    -
    -
    _xLenDatabaseSamedict

    To store the datas in same sequence separated by sequence, rownumber, length.

    -
    -
    -
    -
    Methods:
    -
    -
    startMine()

    Mining process will start from here

    -
    -
    getPatterns()

    Complete set of patterns will be retrieved with this function

    -
    -
    savePatterns(oFile)

    Complete set of frequent patterns will be loaded in to an output file

    -
    -
    getPatternsAsDataFrame()

    Complete set of frequent patterns will be loaded in to a dataframe

    -
    -
    getMemoryUSS()

    Total amount of USS memory consumed by the mining process will be retrieved from this function

    -
    -
    getMemoryRSS()

    Total amount of RSS memory consumed by the mining process will be retrieved from this function

    -
    -
    getRuntime()

    Total amount of runtime taken by the mining process will be retrieved from this function

    -
    -
    candidateToFrequent(candidateList)

    Generates frequent patterns from the candidate patterns

    -
    -
    frequentToCandidate(frequentList, length)

    Generates candidate patterns from the frequent patterns

    -
    -
    -
    -
    -
    Format:
-
-(.venv) $ python3 SPADE.py <inputFile> <outputFile> <minSup>
-
-Example usage:
-
-(.venv) $ python3 SPADE.py sampleDB.txt patterns.txt 10.0
-
-
-        .. note:: minSup will be considered in times of minSup and count of database transactions
-
    -
    -
    import PAMI.sequentialPatternMining.basic.SPADE as alg
-
-obj = alg.SPADE(iFile, minSup)
-
-obj.startMine()
-
-sequentialPatternMining = obj.getPatterns()
-
-print("Total number of Frequent Patterns:", len(frequentPatterns))
-
-obj.save(oFile)
-
-Df = obj.getPatternInDataFrame()
-
-memUSS = obj.getMemoryUSS()
-
-print("Total Memory in USS:", memUSS)
-
-memRSS = obj.getMemoryRSS()
-
-print("Total Memory in RSS", memRSS)
-
-run = obj.getRuntime()
-
-print("Total ExecutionTime in seconds:", run)
-
    -
    -
    -

    The complete program was written by Suzuki Shota under the supervision of Professor Rage Uday Kiran.

    -
    -
    -
    -Mine()[source]
    -

    Frequent pattern mining process will start from here

    -
    - -
    -
    -getMemoryRSS()[source]
    -

    Total amount of RSS memory consumed by the mining process will be retrieved from this function

    -
    -
    Returns:
    -

    returning RSS memory consumed by the mining process

    -
    -
    Return type:
    -

    float

    -
    -
    -
    - -
    -
    -getMemoryUSS()[source]
    -

    Total amount of USS memory consumed by the mining process will be retrieved from this function

    -
    -
    Returns:
    -

    returning USS memory consumed by the mining process

    -
    -
    Return type:
    -

    float

    -
    -
    -
    - -
    -
    -getPatterns()[source]
    -

    Function to send the set of frequent patterns after completion of the mining process

    -
    -
    Returns:
    -

    returning frequent patterns

    -
    -
    Return type:
    -

    dict

    -
    -
    -
    - -
    -
    -getPatternsAsDataFrame()[source]
    -

    Storing final frequent patterns in a dataframe

    -
    -
    Returns:
    -

    returning frequent patterns in a dataframe

    -
    -
    Return type:
    -

    pd.DataFrame

    -
    -
    -
    - -
    -
    -getRuntime()[source]
    -

    Calculating the total amount of runtime taken by the mining process

    -
    -
    Returns:
    -

    returning total amount of runtime taken by the mining process

    -
    -
    Return type:
    -

    float

    -
    -
    -
    - -
    -
    -make1LenDatabase()[source]
    -

    To make 1 length frequent patterns by breadth-first search technique and update Database to sequential database

    -
    - -
    -
    -make2LenDatabase()[source]
    -

    To make 2 length frequent patterns by joining two one length patterns by breadth-first search technique and update xlen Database to sequential database

    -
    - -
    -
    -make3LenDatabase()[source]
    -

    To call each 2 length patterns to make 3 length frequent patterns depth-first search technique

    -
    - -
    -
    -makeNextRow(bs, latestWord, latestWord2)[source]
    -

    To make pattern row when two patterns have the latest word in different sequence

    -

    :param bs : previous pattern without the latest one -:param latestWord : latest word of one previous pattern -:param latestWord2 : latest word of other previous pattern

    -
    - -
    -
    -makeNextRowSame(bs, latestWord, latestWord2)[source]
    -

    To make pattern row when one pattern have the latestWord1 in different sequence and other(latestWord2) in same

    -

    :param bs : previous pattern without the latest one -:param latestWord : latest word of one previous pattern in same sequence -:param latestWord2 : latest word of other previous pattern in different sequence

    -
    - -
    -
    -makeNextRowSame2(bs, latestWord, latestWord2)[source]
    -

    To make pattern row when two patterns have the latest word in same sequence

    -

    :param bs : previous pattern without the latest one -:param latestWord : latest word of one previous pattern -:param latestWord2 : latest word of the other previous pattern

    -
    - -
    -
    -makeNextRowSame3(bs, latestWord, latestWord2)[source]
    -

    To make pattern row when two patterns have the latest word in different sequence and both latest word is in same sequence

    -

    :param bs : previous pattern without the latest one -:param latestWord : latest word of one previous pattern -:param latestWord2 : latest word of other previous pattern

    -
    - -
    -
    -makexLenDatabase(rowLen, bs, latestWord)[source]
    -

    To make “rowLen” length frequent patterns from pattern which the latest word is in same seq by joining “rowLen”-1 length patterns by depth-first search technique and update xlenDatabase to sequential database

    -
    -
    Parameters:
    -

    rowLen – row length of patterns.

    -
    -
    -

    :param bs : patterns without the latest one -:param latestWord : latest word of patterns

    -
    - -
    -
    -makexLenDatabaseSame(rowLen, bs, latestWord)[source]
    -

    To make 3 or more length frequent patterns from pattern which the latest word is in different seq by depth-first search technique and update xlenDatabase to sequential database

    -
    -
    Parameters:
    -

    rowLen – row length of previous patterns.

    -
    -
    -

    :param bs : previous patterns without the latest one -:param latestWord : latest word of previous patterns

    -
    - -
    -
    -printResults()[source]
    -

    This function is used to prnt the results

    -
    - -
    -
    -save(outFile)[source]
    -

    Complete set of frequent patterns will be loaded in to an output file

    -
    -
    Parameters:
    -

    outFile (csv file) – name of the output file

    -
    -
    -
    - -
    -
    -startMine()[source]
    -

    Frequent pattern mining process will start from here

    -
    - -
    - +
    +

    PAMI.sequentialPatternMining.basic.SPADE module

    -
    -

    PAMI.sequentialPatternMining.basic.SPAM module

    -
    -
    -class PAMI.sequentialPatternMining.basic.SPAM.SPAM(iFile, minSup, sep='\t')[source]
    -

    Bases: _sequentialPatterns

    -
    -
    Description:
    -

    SPAM is one of the fundamental algorithm to discover sequential frequent patterns in a transactional database. -This program employs SPAM property (or downward closure property) to reduce the search space effectively. -This algorithm employs breadth-first search technique to find the complete set of frequent patterns in a sequential database.

    -
    -
    Reference:
    -
      -

    1. Ayres, J. Gehrke, T.Yiu, and J. Flannick. Sequential Pattern Mining Using Bitmaps. In Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Edmonton, Alberta, Canada, July 2002.

      2. -
    -
    -
    Parameters:
    -
      -

    • iFile – str : -Name of the Input file to mine complete set of Sequential frequent patterns

      • -

    • oFile – str : -Name of the output file to store complete set of Sequential frequent patterns

      • -

    • minSup – float or int or str : -minSup measure constraints the minimum number of transactions in a database where a pattern must appear -Example: minSup=10 will be treated as integer, while minSup=10.0 will be treated as float

      • -

    • sep – str : -This variable is used to distinguish items from one another in a transaction. The default seperator is tab space. However, the users can override their default separator.

      • -
    -
    -
    Attributes:
    -
    -
    iFilestr

    Input file name or path of the input file

    -
    -
    oFilestr

    Name of the output file or the path of output file

    -
    -
    minSupfloat or int or str

    The user can specify minSup either in count or proportion of database size. -If the program detects the data type of minSup is integer, then it treats minSup is expressed in count. -Otherwise, it will be treated as float. -Example: minSup=10 will be treated as integer, while minSup=10.0 will be treated as float

    -
    -
    sepstr

    This variable is used to distinguish items from one another in a transaction. The default seperator is tab space or . -However, the users can override their default separator.

    -
    -
    startTimefloat

    To record the start time of the mining process

    -
    -
    endTimefloat

    To record the completion time of the mining process

    -
    -
    finalPatternsdict

    Storing the complete set of patterns in a dictionary variable

    -
    -
    memoryUSSfloat

    To store the total amount of USS memory consumed by the program

    -
    -
    memoryRSSfloat

    To store the total amount of RSS memory consumed by the program

    -
    -
    Databaselist

    To store the sequences of a database in list

    -
    -
    _idDatabasedict

    To store the sequences of a database by bit map

    -
    -
    _maxSeqLen:

    the maximum length of subsequence in sequence.

    -
    -
    -
    -
    Methods:
    -
    -
    _creatingItemSets():

    Storing the complete sequences of the database/input file in a database variable

    -
    -
    _convert(value):

    To convert the user specified minSup value

    -
    -
    make2BitDatabase():

    To make 1 length frequent patterns by breadth-first search technique and update Database to sequential database

    -
    -
    DfsPruning(items,sStep,iStep):

    the main algorithm of spam. This can search sstep and istep items and find next patterns, its sstep, and its istep. And call this function again by using them. Recursion until there are no more items available for exploration.

    -
    -
    Sstep(s):

    To convert bit to ssteo bit.The first time you get 1, you set it to 0 and subsequent ones to 1.(like 010101=>001111, 00001001=>00000111)

    -
    -
    startMine()

    Mining process will start from here

    -
    -
    getPatterns()

    Complete set of patterns will be retrieved with this function

    -
    -
    savePatterns(oFile)

    Complete set of frequent patterns will be loaded in to a output file

    -
    -
    getPatternsAsDataFrame()

    Complete set of frequent patterns will be loaded in to a dataframe

    -
    -
    getMemoryUSS()

    Total amount of USS memory consumed by the mining process will be retrieved from this function

    -
    -
    getMemoryRSS()

    Total amount of RSS memory consumed by the mining process will be retrieved from this function

    -
    -
    getRuntime()

    Total amount of runtime taken by the mining process will be retrieved from this function

    -
    -
    candidateToFrequent(candidateList)

    Generates frequent patterns from the candidate patterns

    -
    -
    frequentToCandidate(frequentList, length)

    Generates candidate patterns from the frequent patterns

    -
    -
    -
    -
    -
    Format:
-
-(.venv) $ python3 SPAM.py <inputFile> <outputFile> <minSup> (<separator>)
-
-Examples usage:
-
-(.venv) $ python3 SPAM.py sampleDB.txt patterns.txt 10.0
-
-
-        .. note:: minSup will be considered in times of minSup and count of database transactions
-
    -
    -
    -

    import PAMI.sequentialPatternMining.basic.SPAM as alg

    -

    obj = alg.SPAM(iFile, minSup)

    -

    obj.startMine()

    -

    sequentialPatternMining = obj.getPatterns()

    -

    print(“Total number of Frequent Patterns:”, len(frequentPatterns))

    -

    obj.savePatterns(oFile)

    -

    Df = obj.getPatternInDataFrame()

    -

    memUSS = obj.getMemoryUSS()

    -

    print(“Total Memory in USS:”, memUSS)

    -

    memRSS = obj.getMemoryRSS()

    -

    print(“Total Memory in RSS”, memRSS)

    -

    run = obj.getRuntime()

    -

    print(“Total ExecutionTime in seconds:”, run)

    -
    -
    -

    The complete program was written by Shota Suzuki under the supervision of Professor Rage Uday Kiran.

    -
    -
    -
    -DfsPruning(items, sStep, iStep)[source]
    -

    the main algorithm of spam. This can search sstep and istep items and find next patterns, its sstep, and its istep. And call this function again by using them. Recursion until there are no more items available for exploration.

    -
    -
    Attributes:
    -

    -
    -
    -
    itemsstr

    The pattrens I got before

    -
    -
    sSteplist

    Items presumed to have “sstep” relationship with “items”.(sstep is What appears later like a-b and a-c)

    -
    -
    iSteplist

    Items presumed to have “istep” relationship with “items”(istep is What appears in same time like ab and ac)

    -
    -
    -
    - -
    -
    -Sstep(s)[source]
    -

    To convert bit to Sstep bit.The first time you get 1, you set it to 0 and subsequent ones to 1.(like 010101=>001111, 00001001=>00000111)

    -
    -
    :param s:list

    to store each bit sequence

    -
    -
    -
    -
    Returns:
    -

    nextS:list to store the bit sequence converted by sstep

    -
    -
    -
    - -
    -
    -countSup(n)[source]
    -

    count support

    -
    -
    :param n:list

    to store each bit sequence

    -
    -
    -
    -
    Returns:
    -

    count: int support of this list

    -
    -
    -
    - -
    -
    -getMemoryRSS()[source]
    -

    Total amount of RSS memory consumed by the mining process will be retrieved from this function -:return: returning RSS memory consumed by the mining process -:rtype: float

    -
    - -
    -
    -getMemoryUSS()[source]
    -

    Total amount of USS memory consumed by the mining process will be retrieved from this function -:return: returning USS memory consumed by the mining process -:rtype: float

    -
    - -
    -
    -getPatterns()[source]
    -

    Function to send the set of frequent patterns after completion of the mining process -:return: returning frequent patterns -:rtype: dict

    -
    - -
    -
    -getPatternsAsDataFrame()[source]
    -

    Storing final frequent patterns in a dataframe -:return: returning frequent patterns in a dataframe -:rtype: pd.DataFrame

    -
    - -
    -
    -getRuntime()[source]
    -

    Calculating the total amount of runtime taken by the mining process -:return: returning total amount of runtime taken by the mining process -:rtype: float

    -
    - -
    -
    -make2BitDatabase()[source]
    -

    To make 1 length frequent patterns by breadth-first search technique and update Database to sequential database

    -
    - -
    -
    -printResults()[source]
    -

    This function is used to print the results

    -
    - -
    -
    -save(outFile)[source]
    -

    Complete set of frequent patterns will be loaded in to an output file -:param outFile: name of the output file -:type outFile: file

    -
    - -
    -
    -startMine()[source]
    -

    Frequent pattern mining process will start from here

    -
    - -
    - +
    +

    PAMI.sequentialPatternMining.basic.SPAM module

    -
    -

    PAMI.sequentialPatternMining.basic.abstract module

    +
    +

    PAMI.sequentialPatternMining.basic.abstract module

    -
    -

    PAMI.sequentialPatternMining.basic.prefixSpan module

    -
    -
    -class PAMI.sequentialPatternMining.basic.prefixSpan.prefixSpan(iFile, minSup, sep='\t')[source]
    -

    Bases: _sequentialPatterns

    -
    -
    Description:
    -
      -

    • Prefix Span is one of the fundamental algorithm to discover sequential frequent patterns in a transactional database.

      • -

    • This program employs Prefix Span property (or downward closure property) to reduce the search space effectively.

      • -

    • This algorithm employs depth-first search technique to find the complete set of frequent patterns in a transactional database.

      • -
    -
    -
    Reference:
    -
      -

    1. Pei, J. Han, B. Mortazavi-Asl, J. Wang, H. Pinto, Q. Chen, U. Dayal, M. Hsu: Mining Sequential Patterns by Pattern-Growth: The PrefixSpan Approach. IEEE Trans. Knowl. Data Eng. 16(11): 1424-1440 (2004)

      2. -
    -
    -
    Parameters:
    -
      -

    • iFile – str : -Name of the Input file to mine complete set of Sequential frequent patterns

      • -

    • oFile – str : -Name of the output file to store complete set of Sequential frequent patterns

      • -

    • minSup – float or int or str : -minSup measure constraints the minimum number of transactions in a database where a pattern must appear -Example: minSup=10 will be treated as integer, while minSup=10.0 will be treated as float

      • -

    • sep – str : -This variable is used to distinguish items from one another in a transaction. The default seperator is tab space. However, the users can override their default separator.

      • -
    -
    -
    Attributes:
    -
    -
    iFilestr

    Input file name or path of the input file

    -
    -
    oFilestr

    Name of the output file or the path of output file

    -
    -
    minSupfloat or int or str

    The user can specify minSup either in count or proportion of database size. -If the program detects the data type of minSup is integer, then it treats minSup is expressed in count. -Otherwise, it will be treated as float. -Example: minSup=10 will be treated as integer, while minSup=10.0 will be treated as float

    -
    -
    sepstr

    This variable is used to distinguish items from one another in a transaction. The default seperator is tab space or . -However, the users can override their default separator.

    -
    -
    startTimefloat

    To record the start time of the mining process

    -
    -
    endTimefloat

    To record the completion time of the mining process

    -
    -
    finalPatternsdict

    Storing the complete set of patterns in a dictionary variable

    -
    -
    memoryUSSfloat

    To store the total amount of USS memory consumed by the program

    -
    -
    memoryRSSfloat

    To store the total amount of RSS memory consumed by the program

    -
    -
    Databaselist

    To store the transactions of a database in list

    -
    -
    -
    -
    Methods:
    -
    -
    startMine()

    Mining process will start from here

    -
    -
    getPatterns()

    Complete set of patterns will be retrieved with this function

    -
    -
    savePatterns(oFile)

    Complete set of frequent patterns will be loaded in to a output file

    -
    -
    getPatternsAsDataFrame()

    Complete set of frequent patterns will be loaded in to a dataframe

    -
    -
    getMemoryUSS()

    Total amount of USS memory consumed by the mining process will be retrieved from this function

    -
    -
    getMemoryRSS()

    Total amount of RSS memory consumed by the mining process will be retrieved from this function

    -
    -
    getRuntime()

    Total amount of runtime taken by the mining process will be retrieved from this function

    -
    -
    candidateToFrequent(candidateList)

    Generates frequent patterns from the candidate patterns

    -
    -
    frequentToCandidate(frequentList, length)

    Generates candidate patterns from the frequent patterns

    -
    -
    -
    -
    -
    Format:
-
-(.venv) $ python3 prefixSpan.py <inputFile> <outputFile> <minSup>
-
-Example usage:
-
-(.venv) $ python3 prefixSpan.py sampleDB.txt patterns.txt 10
-
-
-        .. note:: minSup will be considered in support count or frequency
-
    -
    -
    import PAMI.frequentPattern.basic.prefixSpan as alg
-
-obj = alg.prefixSpan(iFile, minSup)
-
-obj.startMine()
-
-frequentPatterns = obj.getPatterns()
-
-print("Total number of Frequent Patterns:", len(frequentPatterns))
-
-obj.save(oFile)
-
-Df = obj.getPatternInDataFrame()
-
-memUSS = obj.getMemoryUSS()
-
-print("Total Memory in USS:", memUSS)
-
-memRSS = obj.getMemoryRSS()
-
-print("Total Memory in RSS", memRSS)
-
-run = obj.getRuntime()
-
-print("Total ExecutionTime in seconds:", run)
-
    -
    -
    -

    The complete program was written by Suzuki Shota under the supervision of Professor Rage Uday Kiran.

    -
    -
    -
    -Mine()[source]
    -

    Frequent pattern mining process will start from here

    -
    - -
    -
    -getMemoryRSS()[source]
    -

    Total amount of RSS memory consumed by the mining process will be retrieved from this function

    -
    -
    Returns:
    -

    returning RSS memory consumed by the mining process

    -
    -
    Return type:
    -

    float

    -
    -
    -
    - -
    -
    -getMemoryUSS()[source]
    -

    Total amount of USS memory consumed by the mining process will be retrieved from this function

    -
    -
    Returns:
    -

    returning USS memory consumed by the mining process

    -
    -
    Return type:
    -

    float

    -
    -
    -
    - -
    -
    -getPatterns()[source]
    -

    Function to send the set of frequent patterns after completion of the mining process

    -
    -
    Returns:
    -

    returning frequent patterns

    -
    -
    Return type:
    -

    dict

    -
    -
    -
    - -
    -
    -getPatternsAsDataFrame()[source]
    -

    Storing final frequent patterns in a dataframe

    -
    -
    Returns:
    -

    returning frequent patterns in a dataframe

    -
    -
    Return type:
    -

    pd.DataFrame

    -
    -
    -
    - -
    -
    -getRuntime()[source]
    -

    Calculating the total amount of runtime taken by the mining process

    -
    -
    Returns:
    -

    returning total amount of runtime taken by the mining process

    -
    -
    Return type:
    -

    float

    -
    -
    -
    - -
    -
    -getSameSeq(startrow)[source]
    -
    -

    To get words in the latest sequence

    -
    -
    -
    Parameters:
    -

    startrow – the patterns get before

    -
    -
    -
    - -
    -
    -makeNext(sepDatabase, startrow)[source]
    -

    To get next pattern by adding head word to next sequence of startrow

    -
    -
    Parameters:
    -
      -

    • sepDatabase – dict -what words and rows startrow have to add it.

      • -

    • startrow – the patterns get before

      • -
    -
    -
    -
    - -
    -
    -makeNextSame(sepDatabase, startrow)[source]
    -
    -

    To get next pattern by adding head word to the latest sequence of startrow

    -
    -
    -
    Parameters:
    -
      -

    • sepDatabase – dict -what words and rows startrow have to add it

      • -

    • startrow – the patterns get before

      • -
    -
    -
    -
    - -
    -
    -makeSeqDatabaseFirst(database)[source]
    -

    To make 1 length sequence dataset list which start from same word. It was stored only 1 from 1 line.

    -
    -
    Parameters:
    -

    database – To store the transactions of a database in list

    -
    -
    -
    - -
    -
    -makeSeqDatabaseSame(database, startrow)[source]
    -

    To make sequence dataset list which start from same word(head). It was stored only 1 from 1 line. -And it separated by having head in the latest sequence of startrow or not.

    -
    -
    Parameters:
    -
      -

    • database – To store the transactions of a database in list

      • -

    • startrow – the patterns get before

      • -
    -
    -
    -
    - -
    -
    -makeSupDatabase(database, head)[source]
    -

    To delete not frequent words without words in the latest sequence

    -
    -
    Parameters:
    -

    database – list -database of lines having same startrow and head word

    -
    -
    -
    -
    :param head:list

    words in the latest sequence

    -
    -
    -
    -
    Returns:
    -

    changed database

    -
    -
    -
    - -
    -
    -printResults()[source]
    -

    This function is used to print the results

    -
    - -
    -
    -save(outFile)[source]
    -

    Complete set of frequent patterns will be loaded in to an output file

    -
    -
    Parameters:
    -

    outFile (csv file) – name of the output file

    -
    -
    -
    - -
    -
    -serchSame(database, startrow, give)[source]
    -

    To get 2 or more length patterns in same sequence.

    -
    -
    Parameters:
    -
      -

    • database – list -To store the transactions of a database in list which have same startrow and head word

      • -

    • startrow – list -the patterns get before

      • -

    • give – list -the word in the latest sequence of startrow

      • -
    -
    -
    -
    - -
    -
    -startMine()[source]
    -

    Frequent pattern mining process will start from here

    -
    - -
    - +
    +

    PAMI.sequentialPatternMining.basic.prefixSpan module

    Module contents

    diff --git a/finalSphinxDocs/_build/html/PAMI.sequentialPatternMining.closed.html b/finalSphinxDocs/_build/html/PAMI.sequentialPatternMining.closed.html index 8a9b52b8..a4e61dd2 100644 --- a/finalSphinxDocs/_build/html/PAMI.sequentialPatternMining.closed.html +++ b/finalSphinxDocs/_build/html/PAMI.sequentialPatternMining.closed.html @@ -99,11 +99,11 @@

    PAMI.sequentialPatternMining.closed package

    Submodules

    -
    -

    PAMI.sequentialPatternMining.closed.abstract module

    +
    +

    PAMI.sequentialPatternMining.closed.abstract module

    -
    -

    PAMI.sequentialPatternMining.closed.bide module

    +
    +

    PAMI.sequentialPatternMining.closed.bide module

    Module contents

    diff --git a/finalSphinxDocs/_build/html/PAMI.sequentialPatternMining.html b/finalSphinxDocs/_build/html/PAMI.sequentialPatternMining.html index a8837d82..2c1e6d33 100644 --- a/finalSphinxDocs/_build/html/PAMI.sequentialPatternMining.html +++ b/finalSphinxDocs/_build/html/PAMI.sequentialPatternMining.html @@ -102,17 +102,17 @@

    SubpackagesPAMI.sequentialPatternMining.basic package
  1. PAMI.sequentialPatternMining.closed package
    2. diff --git a/finalSphinxDocs/_build/html/_modules/PAMI/correlatedPattern/basic/CoMine.html b/finalSphinxDocs/_build/html/_modules/PAMI/correlatedPattern/basic/CoMine.html index d9035771..2e397c94 100644 --- a/finalSphinxDocs/_build/html/_modules/PAMI/correlatedPattern/basic/CoMine.html +++ b/finalSphinxDocs/_build/html/_modules/PAMI/correlatedPattern/basic/CoMine.html @@ -211,9 +211,7 @@

    Source code for PAMI.correlatedPattern.basic.CoMine

    [docs]class CoMine(_ab._correlatedPatterns): """ - - About this algorithm - ==================== + **About this algorithm** :**Description**: CoMine is one of the fundamental algorithm to discover correlated patterns in a transactional database. It is based on the traditional FP-Growth algorithm. This algorithm uses depth-first search technique to find all correlated patterns in a transactional database. @@ -240,8 +238,7 @@

    Source code for PAMI.correlatedPattern.basic.CoMine

    - **itemSetBuffer** (*list*) -- *it represents the store the items in mining.* - **maxPatternLength** (*int*) -- *it represents the constraint for pattern length.* - Execution methods - ================= + **Execution methods** **Terminal command** @@ -293,10 +290,9 @@

    Source code for PAMI.correlatedPattern.basic.CoMine

    print("Total ExecutionTime in seconds:", run) - Credits - ======= + **Credits** - The complete program was written by B.Sai Chitra under the supervision of Professor Rage Uday Kiran. + The complete program was written by B.Sai Chitra and revised by Tarun Sreepada under the supervision of Professor Rage Uday Kiran. """ diff --git a/finalSphinxDocs/_build/html/_modules/PAMI/correlatedPattern/basic/CoMinePlus.html b/finalSphinxDocs/_build/html/_modules/PAMI/correlatedPattern/basic/CoMinePlus.html index 76e1870c..32834dc9 100644 --- a/finalSphinxDocs/_build/html/_modules/PAMI/correlatedPattern/basic/CoMinePlus.html +++ b/finalSphinxDocs/_build/html/_modules/PAMI/correlatedPattern/basic/CoMinePlus.html @@ -211,8 +211,7 @@

    Source code for PAMI.correlatedPattern.basic.CoMinePlus

    [docs]class CoMine(_ab._correlatedPatterns): """ - About this algorithm - ==================== + **About this algorithm** :**Description**: CoMinePlus is one of the fundamental algorithm to discover correlated patterns in a transactional database. It is based on the traditional FP-Growth algorithm. This algorithm uses depth-first search technique to find all correlated patterns in a transactional database. @@ -239,8 +238,7 @@

    Source code for PAMI.correlatedPattern.basic.CoMinePlus

    - **itemSetBuffer** (*list*) -- *it represents the store the items in mining.* - **maxPatternLength** (*int*) -- *it represents the constraint for pattern length.* - Execution methods - ================= + **Execution methods** **Terminal command** @@ -292,10 +290,9 @@

    Source code for PAMI.correlatedPattern.basic.CoMinePlus

    print("Total ExecutionTime in seconds:", run) - Credits - ======= + **Credits** - The complete program was written by B.Sai Chitra and revised by Tarun Sreepads under the supervision of Professor Rage Uday Kiran. + The complete program was written by B.Sai Chitra and revised by Tarun Sreepada under the supervision of Professor Rage Uday Kiran. """ diff --git a/finalSphinxDocs/_build/html/_modules/PAMI/extras/generateDatabase/generateSpatioTemporalDatabase.html b/finalSphinxDocs/_build/html/_modules/PAMI/extras/generateDatabase/generateSpatioTemporalDatabase.html index 8eed9c94..e14ef048 100644 --- a/finalSphinxDocs/_build/html/_modules/PAMI/extras/generateDatabase/generateSpatioTemporalDatabase.html +++ b/finalSphinxDocs/_build/html/_modules/PAMI/extras/generateDatabase/generateSpatioTemporalDatabase.html @@ -94,20 +94,22 @@

    Source code for PAMI.extras.generateDatabase.generateSpatioTemporalDatabase

    -# generateSpatioTemporalDatabase is a code used to convert the database into SpatioTemporal database.
+# generateTemporalDatabase is a code used to convert the database into Temporal database.
 #
-#   **Importing this algorithm into a python program**
-#   --------------------------------------------------------
+#  **Importing this algorithm into a python program**
+#  --------------------------------------------------------
 #
-#             from PAMI.extras.generateDatabase import generateSpatioTemporalDatabase as db
+#             from PAMI.extras.generateDatabase import generateTemporalDatabase as db
 #
-#             obj = db.generateSpatioTemporalDatabase(0, 100, 0, 100, 10, 10, 0.5, 0.9, 0.5, 0.9)
+#             obj = db.generateTemporalDatabase(100, 10, 6, oFile, %, "\t")
 #
 #             obj.save()
 #
-#             obj.createPoint(0,100,0,100) # values can be according to the size of data
+#             obj.getFileName("outputFileName") # to create a file
 #
-#             obj.saveAsFile("outputFileName") # To create a file
+#             obj.getDatabaseAsDataFrame("outputFileName") # to convert database into dataframe
+#
+#             obj.createTemporalFile("outputFileName") # to get outputfile
 #
 
 
@@ -129,130 +131,297 @@ 
    Source code for PAMI.extras.generateDatabase.generateSpatioTemporalDatabase<
      You should have received a copy of the GNU General Public License
      along with this program.  If not, see <https://www.gnu.org/licenses/>.
 """
-import random as rand
-from typing import List, Dict, Tuple, Set, Union, Any, Generator
-import pandas
+
+from typing import Tuple, List, Union
+import pandas as pd
+import numpy as np
+import random
 import sys
+import os
 
-
    [docs]class spatioTemporalDatabaseGenerator():
+
    [docs]class generateTemporalDatabase:
     """
-
-    :Description:   generateSpatioTemporalDatabase is a code used to convert the database into SpatioTemporal database.
-
-    :param  xmin: int :
-            To give minimum value for x
-    :param  xmax: int :
-            To give maximum value for x
-    :param  ymin: int :
-            To give minimum value for y
-    :param  ymax: int :
-             To give maximum value for y
-    :param maxTimeStamp: int :
-            maximum Time Stamp for the database
-    :param numberOfItems: int :
-            number of items in the database
-    :param itemChanceLow: int or float :
-            least chance for item in the database
-    :param itemChanceHigh: int or float :
-            highest chance for item in the database
-    :param timeStampChanceLow: int or float :
-            lowest time stamp value
-    :param timeStampChanceHigh: int or float:
-            highest time stamp value
+    :Description:   generateTemporalDatabase creates a temporal database and outputs a database or a frame depending on input
+
+    :Attributes:
+        :param numOfTransactions: int
+            number of transactions
+        :param avgLenOfTransactions: int
+            average length of transactions
+        :param numItems: int
+            number of items
+        :param outputFile: str
+            output file name
+        :param percentage: int
+            percentage of coinToss for TID of temporalDatabase
+        :param sep: str
+            seperator for database output file
+        :param typeOfFile: str
+            specify database or dataframe to get corresponding output
+
+    :Methods:
+        getFileName():
+            returns filename
+        createTemporalFile():
+            creates temporal database file or dataframe
+        getDatabaseAsDataFrame:
+            returns dataframe
+        performCoinFlip():
+            Perform a coin flip with the given probability
+        tuning():
+            Tune the arrayLength to match avgLenOfTransactions
+        createTemporalFile():
+            create Temporal database or dataframe depending on input
 
     **Importing this algorithm into a python program**
     --------------------------------------------------------
     .. code-block:: python
 
-            from PAMI.extras.generateDatabase import generateSpatioTemporalDatabase as db
+            from PAMI.extras.generateDatabase import generateTemporalDatabase as db
 
-            obj = db.generateSpatioTemporalDatabase(0, 100, 0, 100, 10, 10, 0.5, 0.9, 0.5, 0.9)
+            numOfTransactions = 100
+            numItems = 15
+            avgTransactionLength = 6
+            outFileName = 'temporal_ot.txt'
+            sep = '\t'
+            percent = 75
+            frameOrBase = "dataframe" # if you want to get dataframe as output
+            frameOrBase = "database" # if you want to get database/csv/file as output
 
-            obj.save(oFile)
+            temporalDB = db.generateTemporalDatabase(numOfTransactions, avgTransactionLength, numItems, outFileName, percent, sep, frameOrBase )
+            temporalDB.createTemporalFile()
+            print(temporalDB.getDatabaseAsDataFrame())
 
-            obj.createPoint(0,100,0,100) # values can be according to the size of data
+    """
 
-            obj.saveAsFile("outputFileName") # To create a file
+
    [docs]    def getPoint(self, x1, y1, x2, y2):
+        return (np.random.randint(x1, x2), np.random.randint(y1, y2))
    
+
+    def __init__(self, numOfTransactions: int, avgLenOfTransactions: int, 
+                 numItems: int, outputFile: str, x1, y1, x2, y2, percentage: int=50,
+                 sep: str='\t', typeOfFile: str="Database") -> None:
+        
+        """
+        :Description:   Initialize the generateTemporalDatabase class
+
+        :Attributes:
+            :param numOfTransactions: int
+                number of transactions
+            :param avgLenOfTransactions: int
+                average length of transactions
+            :param numItems: int
+                number of items
+            :param outputFile: str
+                output file name
+            :param percentage: int
+                percentage of coinToss for TID of temporalDatabase
+            :param sep: str
+                seperator for database output file
+            :param typeOfFile: str
+                specify database or dataframe to get corresponding output
+
+        :Methods:
+            getFileName():
+                returns filename
+            createTemporalFile():
+                creates temporal database file or dataframe
+            getDatabaseAsDataFrame:
+                returns dataframe
+            performCoinFlip():
+                Perform a coin flip with the given probability
+            tuning():
+                Tune the arrayLength to match avgLenOfTransactions
+            createTemporalFile():
+                create Temporal database or dataframe depending on input
+        
+        """
+
+        self.numOfTransactions = numOfTransactions
+        self.avgLenOfTransactions = avgLenOfTransactions
+        self.numItems = numItems
+        self.outputFile = outputFile
+        if percentage > 1:
+            self.percentage = percentage / 100
+        else:
+            self.percentage = percentage
+        self.sep = sep
+        self.typeOfFile = typeOfFile.lower()
+        self.x1 = x1
+        self.y1 = y1
+        self.x2 = x2
+        self.y2 = y2
+
+        numPoints = (x2 - x1) * (y2 - y1)
+        if numItems > numPoints:
+            raise ValueError("Number of points is less than the number of lines * average items per line")
+        
+        self.itemPoint = {}
+        usedPoints = set()
+
+        for i in range(1, numItems + 1):
+            # self.itemPoint[i] = (np.random.randint(x1, x2), np.random.randint(y1, y2))
+            point = self.getPoint(x1, y1, x2, y2)
+            while point in usedPoints:
+                point = self.getPoint(x1, y1, x2, y2)
+            self.itemPoint[i] = point
+
+
    [docs]    def getFileName(self) -> str:
+        """
+        return filename
+        :return: filename
+        :rtype: str
+        """
+        return self.outputFile
    
+
+
    [docs]    def getDatabaseAsDataFrame(self) -> pd.DataFrame:
+        """
+        return dataframe
+        :return: dataframe
+        :rtype: pd.DataFrame
+        """
+        return self.df
    
+    
+
    [docs]    def performCoinFlip(self, probability: float) -> bool:
+        """
+        Perform a coin flip with the given probability.
+        :param probability: probability to perform coin flip
+        :type probability: float
+        :return: True if coin flip is performed, False otherwise
+        :rtype: bool
+        """
+        result = np.random.choice([0, 1], p=[1 - probability, probability])
+        return result == 1
    
+
+
+
    [docs]    def tuning(self, array, sumRes) -> list:
+        """
+        Tune the array so that the sum of the values is equal to sumRes
+
+        :param array: list of values
+
+        :type array: list
+
+        :param sumRes: the sum of the values in the array to be tuned
+
+        :type sumRes: int
+
+        :return: list of values with the tuned values and the sum of the values in the array to be tuned and sumRes is equal to sumRes
+
+        :rtype: list
+        """
+
+        while np.sum(array) != sumRes:
+            # get index of largest value
+            randIndex = np.random.randint(0, len(array))
+            # if sum is too large, decrease the largest value
+            if np.sum(array) > sumRes:
+                array[randIndex] -= 1
+            # if sum is too small, increase the smallest value
+            else:
+                minIndex = np.argmin(array)
+                array[randIndex] += 1
+        return array
    
+        
+
+
    [docs]    def generateArray(self, nums, avg, maxItems, sumRes) -> list:
+        """
+        Generate a random array of length n whose values average to m
+
+        :param nums: number of values
+
+        :type nums: list
+
+        :param avg: average value
+
+        :type avg: float
+
+        :param maxItems: maximum value
+
+        :type maxItems: int
+
+        :return: random array
+
+        :rtype: list
+        """
+
+        # generate n random values
+        values = np.random.randint(1, maxItems, nums)
+
+        # sumRes = nums * avg
 
-    """
+        self.tuning(values, sumRes)
+
+        # if any value is less than 1, increase it and tune the array again
+        while np.any(values < 1):
+            for i in range(nums):
+                if values[i] < 1:
+                    values[i] += 1
+            self.tuning(values, sumRes)
+
+        while np.any(values > maxItems):
+            for i in range(nums):
+                if values[i] > maxItems:
+                    values[i] -= 1
+            self.tuning(values, sumRes)
+
+
+        # if all values are same then randomly increase one value and decrease another
+        while np.all(values == values[0]):
+            values[np.random.randint(0, nums)] += 1
+            self.tuning(values, sumRes)
+
+        return values
    
+
+
    [docs]    def createTemporalFile(self) -> None:
+        """
+        create Temporal database or dataframe depending on input
+        :return: None
+        """
+
+        lines = [i for i in range(self.numOfTransactions) if self.performCoinFlip(self.percentage)]
+        values = self.generateArray(len(lines), self.avgLenOfTransactions, self.numItems, self.avgLenOfTransactions * self.numOfTransactions)
+        # print(values, sum(values), self.avgLenOfTransactions * self.numOfTransactions, sum(values)/self.numOfTransactions)
+        # print(lines)
+
+        form = list(zip(lines, values))
+
+        database = [[] for i in range(self.numOfTransactions)]
+
+        for i in range(len(form)):
+            database[form[i][0]] = np.random.choice(range(1, self.numItems + 1), form[i][1], replace=False).tolist()
+
+            database[form[i][0]] = [str(self.itemPoint[i]) for i in database[form[i][0]]]
+            database[form[i][0]] = self.sep.join(database[form[i][0]])
+
+        self.df = pd.DataFrame({'Timestamp': [i+1 for i in range(self.numOfTransactions)], 'Transactions': database})
+        print(self.df)
    
+
+if __name__ == '__main__':
+    numOfTransactions = 100
+    numItems = 20
+    avgTransactionLength = 6
+    outFileName = 'temporal_out.txt'
+    sep = '\t'
+    frameOrBase = "database"
+
+    temporalDB = generateTemporalDatabase(numOfTransactions, avgTransactionLength, numItems, outFileName,1,1,10,10)
+
+    temporalDB.createTemporalFile()
+
+    # numOfTransactions = 100
+    # numItems = 15
+    # avgTransactionLength = 6
+    # outFileName = 'temporal_ot.txt'
+    # sep = '\t'
+    # percent = 75
+    # frameOrBase = "dataframe"
+
+    # temporalDB = generateTemporalDatabase(numOfTransactions, avgTransactionLength, numItems, outFileName, percent, sep, frameOrBase )
+    # temporalDB.createTemporalFile()
+    # print(temporalDB.getDatabaseAsDataFrame())
 
-    coinFlip = [True, False]
-    timestamp = list()
-    items = list()
-    alreadyAdded = set()
-    outFileName=""
-
-
    [docs]    def createPoint(self, xmin: int, xmax: int, ymin: int, ymax: int) -> Tuple[int, int]:
-        x = rand.randint(xmin, xmax)
-        y = rand.randint(ymin, ymax)
-        coordinate = tuple([x, y])
-        return coordinate
    
-
-    def __init__(self,xmin: int,xmax: int,ymin: int,ymax: int,maxTimeStamp: int,numberOfItems: int, itemChanceLow: float,
-                 itemChanceHigh: float, timeStampChanceLow: float,
-                 timeStampChanceHigh: float) -> None:
-        coinFlip = [True, False]
-        timeStamp = 1
-        self.timeStampList = list()
-        self.itemList = list()
-
-        while timeStamp != maxTimeStamp + 1:
-            itemSet=list()
-            for i in range(1, numberOfItems+1):
-                #rand1=rand.rand(itemChanceLow,itemChanceHigh)
-                #rand2 = rand.rand(timeStampChanceLow, timeStampChanceHigh)
-                if rand.choices(coinFlip, weights=[itemChanceLow,itemChanceHigh], k=1)[0]:
-                    coordinate=self.createPoint(xmin, xmax, ymin, ymax)
-                    coordinate=tuple(coordinate)
-                    if coordinate not in self.alreadyAdded:
-                        coordinate=list(coordinate)
-                        itemSet.append(coordinate)
-                        coordinate=tuple(coordinate)
-                        self.alreadyAdded.add(coordinate)
-            if itemSet != []:
-                self.timeStampList.append(
-                    timeStamp)
-                self.itemList.append(
-                    itemSet)
-            if rand.choices(coinFlip, weights=[itemChanceLow,itemChanceHigh], k=1)[0]:
-                 timeStamp += 1
-        self.outFileName = "temporal_" + str(maxTimeStamp // 1000) + \
-                           "KI" + str(numberOfItems) + "C" + str(itemChanceLow) + "T" + str(timeStampChanceLow) + ".csv"
-
-
-
-
-
    [docs]    def saveAsFile(self, outFileName="", sep="\t") -> None:
-        if outFileName != "":
-            self.outFileName = outFileName
-
-        file = open(
-            self.outFileName, "w")
-
-        for i in range(len(self.timeStampList)):
-            file.write(
-                str(self.timeStampList[i]))
-            for j in range(len(self.itemList[i])):
-                file.write(
-                    sep + str(self.itemList[i][j]))
-            file.write('\n')
-
-        file.close()
    
-
-
-if __name__ == "__main__":
-    xmin=0
-    xmax=100
-    ymin=0
-    ymax=100
-    maxTimeStamp = 10
-    numberOfItems = 10
-    itemChanceLow = 0.5
-    itemChanceHigh = 0.9
-    timeStampChanceLow = 0.5
-    timeStampChanceHigh = 0.9
-    obj = spatioTemporalDatabaseGenerator(sys.argv[1], sys.argv[2], sys.argv[3], sys.argv[4])
-    obj.saveAsFile(sys.argv[5])
+    # obj = generateTemporalDatabase(sys.argv[1], sys.argv[2], sys.argv[3], sys.argv[4])
+    # obj.createTemporalFile(sys.argv[5])
    diff --git a/finalSphinxDocs/_build/html/_modules/PAMI/extras/generateDatabase/generateTemporalDatabase.html b/finalSphinxDocs/_build/html/_modules/PAMI/extras/generateDatabase/generateTemporalDatabase.html index 404e48ac..075fb114 100644 --- a/finalSphinxDocs/_build/html/_modules/PAMI/extras/generateDatabase/generateTemporalDatabase.html +++ b/finalSphinxDocs/_build/html/_modules/PAMI/extras/generateDatabase/generateTemporalDatabase.html @@ -279,35 +279,76 @@

    Source code for PAMI.extras.generateDatabase.generateTemporalDatabase

    :type array: list - :param sumRes: target sum + :param sumRes: the sum of the values in the array to be tuned :type sumRes: int - :return: list of values with the sum equal to sumRes after tuning + :return: list of values with the tuned values and the sum of the values in the array to be tuned and sumRes is equal to sumRes :rtype: list """ - # first generate a random array of length n whose values average to m - values = np.random.randint(1, self.numItems, len(array)) - - while np.sum(values) != sumRes: + while np.sum(array) != sumRes: # get index of largest value + randIndex = np.random.randint(0, len(array)) # if sum is too large, decrease the largest value - if np.sum(values) > sumRes: - maxIndex = np.argmax(values) - values[maxIndex] -= 1 + if np.sum(array) > sumRes: + array[randIndex] -= 1 # if sum is too small, increase the smallest value else: - minIndex = np.argmin(values) - values[minIndex] += 1 - - # get location of all values greater than numItems + minIndex = np.argmin(array) + array[randIndex] += 1 + return array
    - for i in range(len(array)): - array[i][1] = values[i] - return array
    +
    [docs] def generateArray(self, nums, avg, maxItems, sumRes) -> list: + """ + Generate a random array of length n whose values average to m + + :param nums: number of values + + :type nums: list + + :param avg: average value + + :type avg: float + + :param maxItems: maximum value + + :type maxItems: int + + :return: random array + + :rtype: list + """ + + # generate n random values + values = np.random.randint(1, maxItems, nums) + + # sumRes = nums * avg + + self.tuning(values, sumRes) + + # if any value is less than 1, increase it and tune the array again + while np.any(values < 1): + for i in range(nums): + if values[i] < 1: + values[i] += 1 + self.tuning(values, sumRes) + + while np.any(values > maxItems): + for i in range(nums): + if values[i] > maxItems: + values[i] -= 1 + self.tuning(values, sumRes) + + + # if all values are same then randomly increase one value and decrease another + while np.all(values == values[0]): + values[np.random.randint(0, nums)] += 1 + self.tuning(values, sumRes) + + return values
    [docs] def createTemporalFile(self) -> None: """ @@ -315,38 +356,21 @@

    Source code for PAMI.extras.generateDatabase.generateTemporalDatabase

    :return: None """ - db = [] - lineSize = [] - for i in range(self.numOfTransactions): - db.append([i]) - if self.performCoinFlip(self.percentage): - lineSize.append([i,0]) - - # make it so that sum of lineSize[1] equal to numTransactions * avgLenOfTransactions - sumRes = self.numOfTransactions * self.avgLenOfTransactions - self.tuning(lineSize, sumRes) - - for i in range(len(lineSize)): - if lineSize[i][1] > self.numItems: - raise ValueError("Error: Either increase numItems or decrease avgLenOfTransactions or modify percentage") - line = np.random.choice(range(1, self.numItems + 1), lineSize[i][1], replace=False) - db[lineSize[i][0]].extend(line) + lines = [i for i in range(self.numOfTransactions) if self.performCoinFlip(self.percentage)] + values = self.generateArray(len(lines), self.avgLenOfTransactions, self.numItems, self.avgLenOfTransactions * self.numOfTransactions) + # print(values, sum(values), self.avgLenOfTransactions * self.numOfTransactions, sum(values)/self.numOfTransactions) + # print(lines) - if self.typeOfFile == "database": - with open(self.outputFile, "w") as outFile: - for line in db: - outFile.write(self.sep.join(map(str, line)) + '\n') - outFile.close() + form = list(zip(lines, values)) - if self.typeOfFile == "dataframe": - data = { - 'timestamp': [line[0] for line in db], - 'transactions': pd.Series([line[1:] for line in db]) - } - self.df = pd.DataFrame(data) + database = [[] for i in range(self.numOfTransactions)] - print("Temporal database created successfully")
    + for i in range(len(form)): + database[form[i][0]] = np.random.choice(range(1, self.numItems + 1), form[i][1], replace=False).tolist().sort() + database[form[i][0]] = self.sep.join([str(i) for i in database[form[i][0]]]) + self.df = pd.DataFrame({'Timestamp': [i+1 for i in range(self.numOfTransactions)], 'Transactions': database}) + print(self.df)
    if __name__ == '__main__': numOfTransactions = 100 @@ -360,20 +384,20 @@

    Source code for PAMI.extras.generateDatabase.generateTemporalDatabase

    temporalDB.createTemporalFile() - numOfTransactions = 100 - numItems = 15 - avgTransactionLength = 6 - outFileName = 'temporal_ot.txt' - sep = '\t' - percent = 75 - frameOrBase = "dataframe" + # numOfTransactions = 100 + # numItems = 15 + # avgTransactionLength = 6 + # outFileName = 'temporal_ot.txt' + # sep = '\t' + # percent = 75 + # frameOrBase = "dataframe" - temporalDB = generateTemporalDatabase(numOfTransactions, avgTransactionLength, numItems, outFileName, percent, sep, frameOrBase ) - temporalDB.createTemporalFile() - print(temporalDB.getDatabaseAsDataFrame()) + # temporalDB = generateTemporalDatabase(numOfTransactions, avgTransactionLength, numItems, outFileName, percent, sep, frameOrBase ) + # temporalDB.createTemporalFile() + # print(temporalDB.getDatabaseAsDataFrame()) - obj = generateTemporalDatabase(sys.argv[1], sys.argv[2], sys.argv[3], sys.argv[4]) - obj.createTemporalFile(sys.argv[5]) + # obj = generateTemporalDatabase(sys.argv[1], sys.argv[2], sys.argv[3], sys.argv[4]) + # obj.createTemporalFile(sys.argv[5])
    diff --git a/finalSphinxDocs/_build/html/_modules/PAMI/extras/generateDatabase/generateTransactionalDatabase.html b/finalSphinxDocs/_build/html/_modules/PAMI/extras/generateDatabase/generateTransactionalDatabase.html index f3bf415a..c69912bd 100644 --- a/finalSphinxDocs/_build/html/_modules/PAMI/extras/generateDatabase/generateTransactionalDatabase.html +++ b/finalSphinxDocs/_build/html/_modules/PAMI/extras/generateDatabase/generateTransactionalDatabase.html @@ -268,7 +268,7 @@

    Source code for PAMI.extras.generateDatabase.generateTransactionalDatabaseline = np.random.choice(range(1, self.numItems + 1), value, replace=False) self.db.append(line) -
    [docs] def save(self, filename) -> None: +
    [docs] def save(self, sep, filename) -> None: """ Save the transactional database to a file @@ -281,7 +281,7 @@

    Source code for PAMI.extras.generateDatabase.generateTransactionalDatabasewith open(filename, 'w') as f: for line in self.db: - f.write(','.join(map(str, line)) + '\n')

    + f.write(sep.join(map(str, line)) + '\n')
    [docs] def getTransactions(self) -> pd.DataFrame: """ diff --git a/finalSphinxDocs/_build/html/_modules/PAMI/frequentPattern/basic/Apriori.html b/finalSphinxDocs/_build/html/_modules/PAMI/frequentPattern/basic/Apriori.html index 05addeab..0125f195 100644 --- a/finalSphinxDocs/_build/html/_modules/PAMI/frequentPattern/basic/Apriori.html +++ b/finalSphinxDocs/_build/html/_modules/PAMI/frequentPattern/basic/Apriori.html @@ -98,7 +98,7 @@

    Source code for PAMI.frequentPattern.basic.Apriori

    # # **Importing this algorithm into a python program** # -# import PAMI1.frequentPattern.basic.Apriori as alg +# import PAMI.frequentPattern.basic.Apriori as alg # # iFile = 'sampleDB.txt' # @@ -154,8 +154,7 @@

    Source code for PAMI.frequentPattern.basic.Apriori

    [docs]class Apriori(_ab._frequentPatterns): """ - About this algorithm - ==================== + **About this algorithm** :**Description**: Apriori is one of the fundamental algorithm to discover frequent patterns in a transactional database. This program employs apriori property (or downward closure property) to reduce the search space effectively. This algorithm employs breadth-first search technique to find the complete set of frequent patterns in a transactional database. @@ -175,8 +174,7 @@

    Source code for PAMI.frequentPattern.basic.Apriori

    - **Database** (*list*) -- *To store the transactions of a database in list.* - Execution methods - ================= + **Execution methods** **Terminal command** @@ -197,7 +195,7 @@

    Source code for PAMI.frequentPattern.basic.Apriori

    .. code-block:: python - import PAMI1.frequentPattern.basic.Apriori as alg + import PAMI.frequentPattern.basic.Apriori as alg iFile = 'sampleDB.txt' @@ -228,8 +226,7 @@

    Source code for PAMI.frequentPattern.basic.Apriori

    print("Total ExecutionTime in seconds:", run) - Credits - ======= + **Credits** The complete program was written by P. Likhitha and revised by Tarun Sreepada under the supervision of Professor Rage Uday Kiran. diff --git a/finalSphinxDocs/_build/html/_modules/PAMI/frequentPattern/basic/Aprioribitset.html b/finalSphinxDocs/_build/html/_modules/PAMI/frequentPattern/basic/Aprioribitset.html new file mode 100644 index 00000000..3279bff4 --- /dev/null +++ b/finalSphinxDocs/_build/html/_modules/PAMI/frequentPattern/basic/Aprioribitset.html @@ -0,0 +1,521 @@ + + + + + + PAMI.frequentPattern.basic.Aprioribitset — PAMI 2024.04.23 documentation + + + + + + + + + + + + + + + + + +
    + + +
    + +
    +
    +
    +
      +
      • +
    • Module code
      • +
    • PAMI.frequentPattern.basic.Aprioribitset
      • +
    • +
      • +
    +
    +
    +
    +
    + +

    Source code for PAMI.frequentPattern.basic.Aprioribitset

    +# AprioriBitset is one of the fundamental algorithm to discover frequent patterns in a transactional database.
+#
+# **Importing this algorithm into a python program**
+#
+#             import PAMI.frequentPattern.basic.AprioriBitset as alg
+#
+#             iFile = 'sampleDB.txt'
+#
+#             minSup = 10  # can also be specified between 0 and 1
+#
+#             obj = alg.AprioriBitset(iFile, minSup)
+#
+#             obj.mine()
+#
+#             frequentPatterns = obj.getPatterns()
+#
+#             print("Total number of Frequent Patterns:", len(frequentPatterns))
+#
+#             obj.save(oFile)
+#
+#             Df = obj.getPatternInDataFrame()
+#
+#             memUSS = obj.getMemoryUSS()
+#
+#             print("Total Memory in USS:", memUSS)
+#
+#             memRSS = obj.getMemoryRSS()
+#
+#             print("Total Memory in RSS", memRSS)
+#
+#             run = obj.getRuntime()
+#
+#             print("Total ExecutionTime in seconds:", run)
+#
+
+
+__copyright__ = """
+Copyright (C)  2021 Rage Uday Kiran
+
+     This program is free software: you can redistribute it and/or modify
+     it under the terms of the GNU General Public License as published by
+     the Free Software Foundation, either version 3 of the License, or
+     (at your option) any later version.
+
+     This program is distributed in the hope that it will be useful,
+     but WITHOUT ANY WARRANTY; without even the implied warranty of
+     MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+     GNU General Public License for more details.
+
+     You should have received a copy of the GNU General Public License
+     along with this program.  If not, see <https://www.gnu.org/licenses/>.
+"""
+
+from PAMI.frequentPattern.basic import abstract as _ab
+from deprecated import deprecated
+
+
+
    [docs]class Aprioribitset(_ab._frequentPatterns):
+    """
+    **About this algorithm**
+
+    :**Description**:  AprioriBitset is one of the fundamental algorithm to discover frequent patterns in a transactional database.
+
+    :**Reference**:  Mohammed Javeed Zaki: Scalable Algorithms for Association Mining. IEEE Trans. Knowl. Data Eng. 12(3):
+                     372-390 (2000), https://ieeexplore.ieee.org/document/846291
+
+    :**Parameters**:    - **iFile** (*str or URL or dataFrame*) -- *Name of the Input file to mine complete set of frequent patterns.*
+                        - **oFile** (*str*) -- *Name of the output file to store complete set of frequent patterns.*
+                        - **minSup** (*int or float or str*) -- *The user can specify minSup either in count or proportion of database size. If the program detects the data type of minSup is integer, then it treats minSup is expressed in count. Otherwise, it will be treated as float.*
+                        - **sep** (*str*) -- *This variable is used to distinguish items from one another in a transaction. The default seperator is tab space. However, the users can override their default separator.*
+
+    :**Attributes**:    - **startTime** (*float*) -- *To record the start time of the mining process.*
+                        - **endTime** (*float*) -- *To record the completion time of the mining process.*
+                        - **finalPatterns** (*dict*) -- *Storing the complete set of patterns in a dictionary variable.*
+                        - **memoryUSS** (*float*) -- *To store the total amount of USS memory consumed by the program.*
+                        - **memoryRSS** (*float*) -- *To store the total amount of RSS memory consumed by the program.*
+                        - **Database** (*list*) -- *To store the transactions of a database in list.*
+
+
+
+    **Execution methods**
+
+    **Terminal command**
+
+    .. code-block:: console
+
+      Format:
+
+      (.venv) $ python3 AprioriBitset.py <inputFile> <outputFile> <minSup>
+
+      Example Usage:
+
+      (.venv) $ python3 AprioriBitset.py sampleDB.txt patterns.txt 10.0
+
+    .. note:: minSup can be specified  in support count or a value between 0 and 1.
+
+
+    **Calling from a python program**
+
+    .. code-block:: python
+
+            import PAMI.frequentPattern.basic.Aprioribitset as alg
+
+            iFile = 'sampleDB.txt'
+
+            minSup = 10  # can also be specified between 0 and 1
+
+            obj = alg.Aprioribitset(iFile, minSup)
+
+            obj.mine()
+
+            frequentPattern = obj.getPatterns()
+
+            print("Total number of Frequent Patterns:", len(frequentPattern))
+
+            obj.save(oFile)
+
+            Df = obj.getPatternInDataFrame()
+
+            memUSS = obj.getMemoryUSS()
+
+            print("Total Memory in USS:", memUSS)
+
+            memRSS = obj.getMemoryRSS()
+
+            print("Total Memory in RSS", memRSS)
+
+            run = obj.getRuntime()
+
+            print("Total ExecutionTime in seconds:", run)
+
+
+    **Credits**
+
+    The complete program was written by Yudai Masu and revised by Tarun Sreepada under the supervision of Professor Rage Uday Kiran.
+
+    """
+
+    _startTime = float()
+    _endTime = float()
+    _finalPatterns = {}
+    _iFile = " "
+    _oFile = " "
+    _sep = " "
+    _minSup = str()
+    _memoryUSS = float()
+    _memoryRSS = float()
+    _Database = []
+    _mapSupport = {}
+    _lno = 0
+
+    def _convert(self, value):
+        """
+        To convert the user specified minSup value
+
+        :param value: user specified minSup value
+        :type value: int
+        :return: converted type
+        :rtype: int or float or string
+        """
+        if type(value) is int:
+            value = int(value)
+        if type(value) is float:
+            value = (len(self._Database) * value)
+        if type(value) is str:
+            if '.' in value:
+                value = float(value)
+                value = (len(self._Database) * value)
+            else:
+                value = int(value)
+        return value
+
+    def _creatingItemSets(self):
+        """
+        Storing the complete transactions of the database/input file in a database variable
+        """
+        self._Database = []
+        self._mapSupport = {}
+        if isinstance(self._iFile, _ab._pd.DataFrame):
+            if self._iFile.empty:
+                print("its empty..")
+            i = self._iFile.columns.values.tolist()
+            if 'Transactions' in i:
+                self._Database = self._iFile['Transactions'].tolist()
+                self._Database = [x.split(self._sep) for x in self._Database]
+            else:
+                print("The column name should be Transactions and each line should be separated by tab space or a seperator specified by the user")
+
+        if isinstance(self._iFile, str):
+            if _ab._validators.url(self._iFile):
+                data = _ab._urlopen(self._iFile)
+                for line in data:
+                    line.strip()
+                    line = line.decode("utf-8")
+                    temp = [i.rstrip() for i in line.split(self._sep)]
+                    temp = [x for x in temp if x]
+                    self._Database.append(temp)
+            else:
+                try:
+                    with open(self._iFile, 'r') as f:
+                        for line in f:
+                            self._lno += 1
+                            splitter = [i.rstrip() for i in line.split(self._sep)]
+                            splitter = [x for x in splitter if x]
+                            self._Database.append(splitter)
+                except IOError:
+                    print("File Not Found")
+        self._minSup = self._convert(self._minSup)
+
+
    [docs]    @deprecated("It is recommended to use 'mine()' instead of 'startMine()' for mining process. Starting from January 2025, 'startMine()' will be completely terminated.")
+
+    def startMine(self):
+        self.mine()
    
+
+    def _bitPacker(self, data, maxIndex):
+        """
+        It takes the data and maxIndex as input and generates integer as output value.
+
+        :param data: it takes data as input.
+        :type data: int or float
+        :param maxIndex: It converts the data into bits By taking the maxIndex value as condition.
+        :type maxIndex: int
+        """
+        packed_bits = 0
+        for i in data:
+            packed_bits |= 1 << (maxIndex - i)
+
+        return packed_bits
+
+
    [docs]    def mine(self) -> None:
+        """
+        Frequent pattern mining process will start from here
+        """
+        self._startTime = _ab._time.time()
+
+        self._Database = []
+
+        self._creatingItemSets()
+
+        items = {}
+        index = 0
+        for line in self._Database:
+            for item in line:
+                if tuple([item]) in items:
+                    items[tuple([item])].append(index)
+                else:
+                    items[tuple([item])] = [index]
+            index += 1
+
+        # sort by length in descending order
+        items = dict(sorted(items.items(), key=lambda x: len(x[1]), reverse=True))
+        cands = []
+        for key in items:
+            if len(items[key]) >= self._minSup:
+                self._finalPatterns[key] = len(items[key])
+                cands.append(key)
+                items[key] = self._bitPacker(items[key], index)
+                # print(key, items[key])
+            else:
+                break
+
+        while cands:
+            newCands = []
+            for i in range(len(cands)):
+                for j in range(i + 1, len(cands)):
+                    if cands[i][:-1] == cands[j][:-1]:
+                        newCand = tuple(cands[i] + tuple([cands[j][-1]]))
+                        intersection = items[tuple([newCand[0]])]
+                        for k in range(1, len(newCand)):
+                            intersection &= items[tuple([newCand[k]])]
+                        count = int.bit_count(intersection)
+                        if count >= self._minSup:
+                            newCands.append(newCand)
+                            self._finalPatterns[newCand] = count
+                    else:
+                        break
+
+            cands = newCands
+
+        self._endTime = _ab._time.time()
+        process = _ab._psutil.Process(_ab._os.getpid())
+        self._memoryUSS = float()
+        self._memoryRSS = float()
+        self._memoryUSS = process.memory_full_info().uss
+        self._memoryRSS = process.memory_info().rss
+        print("Frequent patterns were generated successfully using Apriori algorithm ")
    
+
+
    [docs]    def getMemoryUSS(self):
+        """
+        Total amount of USS memory consumed by the mining process will be retrieved from this function
+
+        :return: returning USS memory consumed by the mining process
+        :rtype: float
+        """
+
+        return self._memoryUSS
    
+
+
    [docs]    def getMemoryRSS(self):
+        """
+        Total amount of RSS memory consumed by the mining process will be retrieved from this function
+
+        :return: returning RSS memory consumed by the mining process
+        :rtype: float
+        """
+
+        return self._memoryRSS
    
+
+
    [docs]    def getRuntime(self):
+        """
+        Calculating the total amount of runtime taken by the mining process
+
+        :return: returning total amount of runtime taken by the mining process
+        :rtype: float
+        """
+
+        return self._endTime - self._startTime
    
+
+
    [docs]    def getPatternsAsDataFrame(self) -> _ab._pd.DataFrame:
+        """
+        Storing final frequent patterns in a dataframe
+
+        :return: returning frequent patterns in a dataframe
+        :rtype: pd.DataFrame
+
+        """
+
+        # time = _ab._time.time()
+        # dataFrame = {}
+        # data = []
+        # for a, b in self._finalPatterns.items():
+        #     # data.append([a.replace('\t', ' '), b])
+        #     data.append([" ".join(a), b])
+        #     dataFrame = _ab._pd.DataFrame(data, columns=['Patterns', 'Support'])
+        # print("Time taken to convert the frequent patterns into DataFrame is: ", _ab._time.time() - time)
+
+        dataFrame = _ab._pd.DataFrame(list([[" ".join(x), y] for x,y in self._finalPatterns.items()]), columns=['Patterns', 'Support'])
+        # dataFrame = _ab._pd.DataFrame(list(self._finalPatterns.items()), columns=['Patterns', 'Support'])
+
+        return dataFrame
    
+
+
    [docs]    def save(self, outFile: str, seperator = "\t" ) -> None:
+        """
+        Complete set of frequent patterns will be loaded in to an output file
+
+        :param outFile: name of the output file
+        :type outFile: csvfile
+        :return: None
+        """
+
+        # self._oFile = outFile
+        # writer = open(self._oFile, 'w+')
+        # for x, y in self._finalPatterns.items():
+        #     patternsAndSupport = x.strip() + ":" + str(y[0])
+        #     writer.write("%s \n" % patternsAndSupport)
+        with open(outFile, 'w') as f:
+            for x, y in self._finalPatterns.items():
+                x = seperator.join(x)
+                f.write(f"{x}:{y}\n")
    
+
+
    [docs]    def getPatterns(self):
+        """
+        Function to send the set of frequent patterns after completion of the mining process
+
+        :return: returning frequent patterns
+        :rtype: dict
+        """
+        return self._finalPatterns
    
+
+
    [docs]    def printResults(self):
+        """
+        This function is used to print the result
+        """
+        print("Total number of Frequent Patterns:", len(self.getPatterns()))
+        print("Total Memory in USS:", self.getMemoryUSS())
+        print("Total Memory in RSS", self.getMemoryRSS())
+        print("Total ExecutionTime in ms:", self.getRuntime())
    
+
+
+if __name__ == "__main__":
+    _ap = str()
+    if len(_ab._sys.argv) == 4 or len(_ab._sys.argv) == 5:
+        if len(_ab._sys.argv) == 5:
+            _ap = Aprioribitset(_ab._sys.argv[1], _ab._sys.argv[3], _ab._sys.argv[4])
+        if len(_ab._sys.argv) == 4:
+            _ap = Aprioribitset(_ab._sys.argv[1], _ab._sys.argv[3])
+        _ap.startMine()
+        _ap.mine()
+        print("Total number of Frequent Patterns:", len(_ap.getPatterns()))
+        _ap.save(_ab._sys.argv[2])
+        print("Total Memory in USS:", _ap.getMemoryUSS())
+        print("Total Memory in RSS", _ap.getMemoryRSS())
+        print("Total ExecutionTime in ms:", _ap.getRuntime())
+    else:
+        print("Error! The number of input parameters do not match the total number of parameters provided")
+
+
    + +
    +
    +
    + +
    + +
    +

    © Copyright 2024, RAGE Uday Kiran.

    +
    + + Built with Sphinx using a + theme + provided by Read the Docs. + + +
    +
    +
    +
    +
    + + + + \ No newline at end of file diff --git a/finalSphinxDocs/_build/html/_modules/PAMI/frequentPattern/basic/ECLAT.html b/finalSphinxDocs/_build/html/_modules/PAMI/frequentPattern/basic/ECLAT.html index 24b88f1c..b06a9e84 100644 --- a/finalSphinxDocs/_build/html/_modules/PAMI/frequentPattern/basic/ECLAT.html +++ b/finalSphinxDocs/_build/html/_modules/PAMI/frequentPattern/basic/ECLAT.html @@ -152,8 +152,7 @@

    Source code for PAMI.frequentPattern.basic.ECLAT

    [docs]class ECLAT(_ab._frequentPatterns): """ - About this algorithm - ==================== + **About this algorithm** :**Description**: ECLAT is one of the fundamental algorithm to discover frequent patterns in a transactional database. @@ -172,8 +171,7 @@

    Source code for PAMI.frequentPattern.basic.ECLAT

    - **memoryRSS** (*float*) -- *To store the total amount of RSS memory consumed by the program.* - **Database** (*list*) -- *To store the transactions of a database in list.* - Execution methods - ================= + **Execution methods** **Terminal command** @@ -225,8 +223,7 @@

    Source code for PAMI.frequentPattern.basic.ECLAT

    print("Total ExecutionTime in seconds:", run) - Credits: - ======== + **Credits:** The complete program was written by Kundai and revised by Tarun Sreepada under the supervision of Professor Rage Uday Kiran. diff --git a/finalSphinxDocs/_build/html/_modules/PAMI/frequentPattern/basic/ECLATDiffset.html b/finalSphinxDocs/_build/html/_modules/PAMI/frequentPattern/basic/ECLATDiffset.html index 7eedfebf..7d2c5b4e 100644 --- a/finalSphinxDocs/_build/html/_modules/PAMI/frequentPattern/basic/ECLATDiffset.html +++ b/finalSphinxDocs/_build/html/_modules/PAMI/frequentPattern/basic/ECLATDiffset.html @@ -154,6 +154,7 @@

    Source code for PAMI.frequentPattern.basic.ECLATDiffset

    [docs]class ECLATDiffset(_ab._frequentPatterns): """ + **About this algorithm** :**Description**: ECLATDiffset uses diffset to extract the frequent patterns in a transactional database. @@ -163,7 +164,7 @@

    Source code for PAMI.frequentPattern.basic.ECLATDiffset

    :**Parameters**: - **iFile** (*str or URL or dataFrame*) -- *Name of the Input file to mine complete set of frequent patterns.* - **oFile** (*str*) -- *Name of the output file to store complete set of frequent patterns* - **minSup** (*int or float or str*) -- *The user can specify minSup either in count or proportion of database size. If the program detects the data type of minSup is integer, then it treats minSup is expressed in count.* - - **sep** (*str*) -- **This variable is used to distinguish items from one another in a transaction. The default seperator is tab space. However, the users can override their default separator.** + - **sep** (*str*) -- *This variable is used to distinguish items from one another in a transaction. The default seperator is tab space. However, the users can override their default separator.* :**Attributes**: - **startTime** (*float*) -- *To record the start time of the mining process.* - **endTime** (*float*) -- *To record the end time of the mining process.* @@ -173,8 +174,7 @@

    Source code for PAMI.frequentPattern.basic.ECLATDiffset

    - **Database** (*list*) -- *To store the transactions of a database in list.* - Execution methods - ================= + **Execution methods** **Terminal command** @@ -191,7 +191,7 @@

    Source code for PAMI.frequentPattern.basic.ECLATDiffset

    .. note:: minSup can be specified in support count or a value between 0 and 1. - **Calling from a python program** + **Calling from a python program** .. code-block:: python @@ -226,8 +226,7 @@

    Source code for PAMI.frequentPattern.basic.ECLATDiffset

    print("Total ExecutionTime in seconds:", run) - Credits: - ======== + **Credits:** The complete program was written by Kundai and revised by Tarun Sreepada under the supervision of Professor Rage Uday Kiran. diff --git a/finalSphinxDocs/_build/html/_modules/PAMI/frequentPattern/basic/ECLATbitset.html b/finalSphinxDocs/_build/html/_modules/PAMI/frequentPattern/basic/ECLATbitset.html index d84dbd19..041b24cc 100644 --- a/finalSphinxDocs/_build/html/_modules/PAMI/frequentPattern/basic/ECLATbitset.html +++ b/finalSphinxDocs/_build/html/_modules/PAMI/frequentPattern/basic/ECLATbitset.html @@ -153,6 +153,8 @@

    Source code for PAMI.frequentPattern.basic.ECLATbitset

    [docs]class ECLATbitset(_ab._frequentPatterns): """ + **About this algorithm** + :*Description*: ECLATbitset is one of the fundamental algorithm to discover frequent patterns in a transactional database. :*Reference*: Mohammed Javeed Zaki: Scalable Algorithms for Association Mining. IEEE Trans. Knowl. Data Eng. 12(3): @@ -161,7 +163,7 @@

    Source code for PAMI.frequentPattern.basic.ECLATbitset

    :**Parameters**: - **iFile** (*str or URL or dataFrame*) -- *Name of the Input file to mine complete set of frequent patterns.* - **oFile** (*str*) -- *Name of the output file to store complete set of frequent patterns* - **minSup** (*int or float or str*) -- *The user can specify minSup either in count or proportion of database size. If the program detects the data type of minSup is integer, then it treats minSup is expressed in count.* - - **sep** (*str*) -- **This variable is used to distinguish items from one another in a transaction. The default seperator is tab space. However, the users can override their default separator.** + - **sep** (*str*) -- *This variable is used to distinguish items from one another in a transaction. The default seperator is tab space. However, the users can override their default separator.* :**Attributes**: - **startTime** (*float*) -- *To record the start time of the mining process.* - **endTime** (*float*) -- *To record the end time of the mining process.* @@ -170,11 +172,12 @@

    Source code for PAMI.frequentPattern.basic.ECLATbitset

    - **memoryRSS** *(float*) -- *To store the total amount of RSS memory consumed by the program.* - **Database** (*list*) -- *To store the transactions of a database in list.* - Execution methods - ================= + **Execution methods** **Terminal command** + .. code-block:: console + Format: (.venv) $ python3 ECLATbitset.py <inputFile> <outputFile> <minSup> @@ -220,8 +223,7 @@

    Source code for PAMI.frequentPattern.basic.ECLATbitset

    print("Total ExecutionTime in seconds:", run) - Credits: - ======== + **Credits:** The complete program was written by Yudai Masu and revised by Tarun Sreepada under the supervision of Professor Rage Uday Kiran. diff --git a/finalSphinxDocs/_build/html/_modules/PAMI/frequentPattern/basic/FPGrowth.html b/finalSphinxDocs/_build/html/_modules/PAMI/frequentPattern/basic/FPGrowth.html index 1735c04b..aa83b951 100644 --- a/finalSphinxDocs/_build/html/_modules/PAMI/frequentPattern/basic/FPGrowth.html +++ b/finalSphinxDocs/_build/html/_modules/PAMI/frequentPattern/basic/FPGrowth.html @@ -211,8 +211,7 @@

    Source code for PAMI.frequentPattern.basic.FPGrowth

    [docs]class FPGrowth(_fp._frequentPatterns): """ - About this algorithm - ==================== + **About this algorithm** :**Description**: FPGrowth is one of the fundamental algorithm to discover frequent patterns in a transactional database. It stores the database in compressed fp-tree decreasing the memory usage and extracts the patterns from tree.It employs downward closure property to reduce the search space effectively. @@ -234,8 +233,7 @@

    Source code for PAMI.frequentPattern.basic.FPGrowth

    - **tree** (*class*) -- *it represents the Tree class.* - Execution methods - ================= + **Execution methods** **Terminal command** @@ -287,8 +285,7 @@

    Source code for PAMI.frequentPattern.basic.FPGrowth

    print("Total ExecutionTime in seconds:", run) - Credits: - ======== + **Credits:** The complete program was written by P. Likhitha and revised by Tarun Sreepada under the supervision of Professor Rage Uday Kiran. diff --git a/finalSphinxDocs/_build/html/_modules/PAMI/frequentPattern/closed/CHARM.html b/finalSphinxDocs/_build/html/_modules/PAMI/frequentPattern/closed/CHARM.html index 266b191f..6ebaaa05 100644 --- a/finalSphinxDocs/_build/html/_modules/PAMI/frequentPattern/closed/CHARM.html +++ b/finalSphinxDocs/_build/html/_modules/PAMI/frequentPattern/closed/CHARM.html @@ -154,6 +154,8 @@

    Source code for PAMI.frequentPattern.closed.CHARM

    [docs]class CHARM(_ab._frequentPatterns): """ + **About this algorithm** + :**Description**: CHARM is an algorithm to discover closed frequent patterns in a transactional database. Closed frequent patterns are patterns if there exists no superset that has the same support count as this original itemset. This algorithm employs depth-first search technique to find the complete set of closed frequent patterns in a transactional database. :**Reference**: Mohammed J. Zaki and Ching-Jui Hsiao, CHARM: An Efficient Algorithm for Closed Itemset Mining, @@ -177,8 +179,7 @@

    Source code for PAMI.frequentPattern.closed.CHARM

    - **hashing** (*dict*) -- *Stores the patterns with their support to check for the closed property.* - Execution methods - ================= + **Execution methods** **Terminal command** @@ -230,8 +231,7 @@

    Source code for PAMI.frequentPattern.closed.CHARM

    print("Total ExecutionTime in seconds:", run) - Credits: - ======= + **Credits:** The complete program was written by P.Likhitha and revised by Tarun Sreepada under the supervision of Professor Rage Uday Kiran. diff --git a/finalSphinxDocs/_build/html/_modules/PAMI/frequentPattern/topk/FAE.html b/finalSphinxDocs/_build/html/_modules/PAMI/frequentPattern/topk/FAE.html index 074b7021..f5330cd5 100644 --- a/finalSphinxDocs/_build/html/_modules/PAMI/frequentPattern/topk/FAE.html +++ b/finalSphinxDocs/_build/html/_modules/PAMI/frequentPattern/topk/FAE.html @@ -153,8 +153,7 @@

    Source code for PAMI.frequentPattern.topk.FAE

    [docs]class FAE(_ab._frequentPatterns): """ - About this algorithm - ==================== + **About this algorithm** :**Description**: Top - K is and algorithm to discover top frequent patterns in a transactional database. @@ -172,8 +171,7 @@

    Source code for PAMI.frequentPattern.topk.FAE

    - **memoryUSS** (*float*) -- *To store the total amount of USS memory consumed by the program.* - **memoryRSS** (*float*) -- *To store the total amount of RSS memory consumed by the program.* - Execution methods - ================= + **Execution methods** **Terminal command** @@ -224,8 +222,7 @@

    Source code for PAMI.frequentPattern.topk.FAE

    print("Total ExecutionTime in seconds:", run) - Credits - ======= + **Credits:** The complete program was written by P. Likhitha and revised by Tarun Sreepada under the supervision of Professor Rage Uday Kiran. diff --git a/finalSphinxDocs/_build/html/_modules/PAMI/periodicFrequentPattern/basic/PFECLAT.html b/finalSphinxDocs/_build/html/_modules/PAMI/periodicFrequentPattern/basic/PFECLAT.html index 8dd87729..60cfc77a 100644 --- a/finalSphinxDocs/_build/html/_modules/PAMI/periodicFrequentPattern/basic/PFECLAT.html +++ b/finalSphinxDocs/_build/html/_modules/PAMI/periodicFrequentPattern/basic/PFECLAT.html @@ -160,8 +160,7 @@

    Source code for PAMI.periodicFrequentPattern.basic.PFECLAT

    [docs]class PFECLAT(_ab._periodicFrequentPatterns): """ - About this algorithm - ==================== + **About this algorithm** :**Description**: PFECLAT is the fundamental approach to mine the periodic-frequent patterns. @@ -198,8 +197,7 @@

    Source code for PAMI.periodicFrequentPattern.basic.PFECLAT

    - **getPeriodAndSupport()** -- *Calculates the support and period for a list of timestamps.* - **Generation()** -- *Used to implement prefix class equivalence method to generate the periodic patterns recursively* - Execution methods - ================= + **Execution methods** **Terminal command** @@ -252,8 +250,7 @@

    Source code for PAMI.periodicFrequentPattern.basic.PFECLAT

    print("Total ExecutionTime in seconds:", run) - Credits - ======= + **Credits:** The complete program was written by P. Likhitha and revised by Tarun Sreepada under the supervision of Professor Rage Uday Kiran. diff --git a/finalSphinxDocs/_build/html/_modules/PAMI/periodicFrequentPattern/basic/PFPGrowth.html b/finalSphinxDocs/_build/html/_modules/PAMI/periodicFrequentPattern/basic/PFPGrowth.html index 6875ab7e..a3b44cb3 100644 --- a/finalSphinxDocs/_build/html/_modules/PAMI/periodicFrequentPattern/basic/PFPGrowth.html +++ b/finalSphinxDocs/_build/html/_modules/PAMI/periodicFrequentPattern/basic/PFPGrowth.html @@ -218,8 +218,7 @@

    Source code for PAMI.periodicFrequentPattern.basic.PFPGrowth

    [docs]class PFPGrowth(_ab._periodicFrequentPatterns): """ - About this algorithm - ==================== + **About this algorithm** :**Description**: PFPGrowth is one of the fundamental algorithm to discover periodic-frequent patterns in a transactional database. @@ -257,8 +256,7 @@

    Source code for PAMI.periodicFrequentPattern.basic.PFPGrowth

    - **buildTree()** -- *After updating the Database, remaining items will be added into the tree by setting root node as null.* - **convert()** -- *This methos is used to convert the user specified value.* - Execution methods - ================= + **Execution methods** **Terminal command** @@ -312,8 +310,7 @@

    Source code for PAMI.periodicFrequentPattern.basic.PFPGrowth

    print("Total ExecutionTime in seconds:", run) - Credits - ======= + **Credits** The complete program was written by P. Likhitha and revised by Tarun Sreepada under the supervision of Professor Rage Uday Kiran. diff --git a/finalSphinxDocs/_build/html/_modules/PAMI/sequentialPatternMining/basic/SPADE.html b/finalSphinxDocs/_build/html/_modules/PAMI/sequentialPatternMining/basic/SPADE.html deleted file mode 100644 index a18716a0..00000000 --- a/finalSphinxDocs/_build/html/_modules/PAMI/sequentialPatternMining/basic/SPADE.html +++ /dev/null @@ -1,1008 +0,0 @@ - - - - - - PAMI.sequentialPatternMining.basic.SPADE — PAMI 2024.04.23 documentation - - - - - - - - - - - - - - - - - -
    - - -
    - -
    -
    -
    -
      -
      • -
    • Module code
      • -
    • PAMI.sequentialPatternMining.basic.SPADE
      • -
    • -
      • -
    -
    -
    -
    -
    - -

    Source code for PAMI.sequentialPatternMining.basic.SPADE

    -# SPADE is one of the fundamental algorithm to discover sequential frequent patterns in a transactional database.
-# This program employs SPADE property (or downward closure property) to  reduce the search space effectively.
-# This algorithm employs breadth-first search technique when 1-2 length patterns and depth-first search when above 3 length patterns to find the complete set of frequent patterns in a transactional database.
-#
-#
-# **Importing this algorithm into a python program**
-# --------------------------------------------------------
-#
-#
-#             import PAMI.sequentialPatternMining.basic.SPADE as alg
-#
-#             obj = alg.SPADE(iFile, minSup)
-#
-#             obj.startMine()
-#
-#             sequentialPatternMining = obj.getPatterns()
-#
-#             print("Total number of Frequent Patterns:", len(frequentPatterns))
-#
-#             obj.save(oFile)
-#
-#             Df = obj.getPatternInDataFrame()
-#
-#             memUSS = obj.getMemoryUSS()
-#
-#             print("Total Memory in USS:", memUSS)
-#
-#             memRSS = obj.getMemoryRSS()
-#
-#             print("Total Memory in RSS", memRSS)
-#
-#             run = obj.getRuntime()
-#
-
-
-
-
-__copyright__ = """
- Copyright (C)  2021 Rage Uday Kiran
-
-     This program is free software: you can redistribute it and/or modify
-     it under the terms of the GNU General Public License as published by
-     the Free Software Foundation, either version 3 of the License, or
-     (at your option) any later version.
-
-     This program is distributed in the hope that it will be useful,
-     but WITHOUT ANY WARRANTY; without even the implied warranty of
-     MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-     GNU General Public License for more details.
-
-     You should have received a copy of the GNU General Public License
-     along with this program.  If not, see <https://www.gnu.org/licenses/>.
-     Copyright (C)  2021 Rage Uday Kiran
-
-"""
-
-
-import pandas as pd
-from deprecated import deprecated
-
-from PAMI.sequentialPatternMining.basic import abstract as _ab
-
-_ab._sys.setrecursionlimit(10000)
-
-
    [docs]class SPADE(_ab._sequentialPatterns):
-    """
-    :Description:
-
-        * SPADE is one of the fundamental algorithm to discover sequential frequent patterns in a transactional database.
-        * This program employs SPADE property (or downward closure property) to  reduce the search space effectively.
-        * This algorithm employs breadth-first search technique when 1-2 length patterns and depth-first serch when above 3 length patterns to find the complete set of frequent patterns in a transactional database.
-
-    :Reference:   Mohammed J. Zaki. 2001. SPADE: An Efficient Algorithm for Mining Frequent Sequences. Mach. Learn. 42, 1-2 (January 2001), 31-60. DOI=10.1023/A:1007652502315 http://dx.doi.org/10.1023/A:1007652502315
-
-    :param  iFile: str :
-                   Name of the Input file to mine complete set of  Sequential frequent patterns
-    :param  oFile: str :
-                   Name of the output file to store complete set of  Sequential frequent patterns
-    :param  minSup: float or int or str :
-                    minSup measure constraints the minimum number of transactions in a database where a pattern must appear
-                    Example: minSup=10 will be treated as integer, while minSup=10.0 will be treated as float
-    :param  sep: str :
-                   This variable is used to distinguish items from one another in a transaction. The default seperator is tab space. However, the users can override their default separator.
-
-    :Attributes:
-
-            iFile : str
-                Input file name or path of the input file
-            oFile : str
-                Name of the output file or the path of output file
-            minSup: float or int or str
-                The user can specify minSup either in count or proportion of database size.
-                If the program detects the data type of minSup is integer, then it treats minSup is expressed in count.
-                Otherwise, it will be treated as float.
-                Example: minSup=10 will be treated as integer, while minSup=10.0 will be treated as float
-            sep : str
-                This variable is used to distinguish items from one another in a transaction. The default seperator is tab space or \t.
-                However, the users can override their default separator.
-            startTime:float
-                To record the start time of the mining process
-            endTime:float
-                To record the completion time of the mining process
-            finalPatterns: dict
-                Storing the complete set of patterns in a dictionary variable
-            memoryUSS : float
-                To store the total amount of USS memory consumed by the program
-            memoryRSS : float
-                To store the total amount of RSS memory consumed by the program
-            Database : list
-                To store the transactions of a database in list
-            _xLenDatabase: dict
-                To store the datas in different sequence separated by sequence, rownumber, length.
-            _xLenDatabaseSame : dict
-                To store the datas in same sequence separated by sequence, rownumber, length.
-
-    :Methods:
-
-            startMine()
-                Mining process will start from here
-            getPatterns()
-                Complete set of patterns will be retrieved with this function
-            savePatterns(oFile)
-                Complete set of frequent patterns will be loaded in to an output file
-            getPatternsAsDataFrame()
-                Complete set of frequent patterns will be loaded in to a dataframe
-            getMemoryUSS()
-                Total amount of USS memory consumed by the mining process will be retrieved from this function
-            getMemoryRSS()
-                Total amount of RSS memory consumed by the mining process will be retrieved from this function
-            getRuntime()
-                Total amount of runtime taken by the mining process will be retrieved from this function
-            candidateToFrequent(candidateList)
-                Generates frequent patterns from the candidate patterns
-            frequentToCandidate(frequentList, length)
-                Generates candidate patterns from the frequent patterns
-
-    **Methods to execute code on terminal**
-    -------------------------------------------
-    .. code-block:: console
-
-
-       Format:
-
-       (.venv) $ python3 SPADE.py <inputFile> <outputFile> <minSup>
-
-       Example usage:
-
-       (.venv) $ python3 SPADE.py sampleDB.txt patterns.txt 10.0
-
-
-               .. note:: minSup will be considered in times of minSup and count of database transactions
-
-    **Importing this algorithm into a python program**
-    ----------------------------------------------------
-    .. code-block:: python
-
-            import PAMI.sequentialPatternMining.basic.SPADE as alg
-
-            obj = alg.SPADE(iFile, minSup)
-
-            obj.startMine()
-
-            sequentialPatternMining = obj.getPatterns()
-
-            print("Total number of Frequent Patterns:", len(frequentPatterns))
-
-            obj.save(oFile)
-
-            Df = obj.getPatternInDataFrame()
-
-            memUSS = obj.getMemoryUSS()
-
-            print("Total Memory in USS:", memUSS)
-
-            memRSS = obj.getMemoryRSS()
-
-            print("Total Memory in RSS", memRSS)
-
-            run = obj.getRuntime()
-
-            print("Total ExecutionTime in seconds:", run)
-
-    **Credits:**
-    ---------------
-
-              The complete program was written by Suzuki Shota under the supervision of Professor Rage  Uday Kiran.
-
-    """
-
-    _minSup = float()
-    _startTime = float()
-    _endTime = float()
-    _finalPatterns = {}
-    _iFile = " "
-    _oFile = " "
-    _sep = " "
-    _memoryUSS = float()
-    _memoryRSS = float()
-    _Database = []
-    _xLenDatabase={}
-    _xLenDatabaseSame = {}
-    def _creatingItemSets(self):
-        """
-        Storing the complete transactions of the database/input file in a database variable
-        """
-        self._Database = []
-
-        if isinstance(self._iFile, _ab._pd.DataFrame):
-            temp = []
-            if self._iFile.empty:
-                print("its empty..")
-            i = self._iFile.columns.values.tolist()
-            if 'Transactions' in i:
-                temp = self._iFile['Transactions'].tolist()
-            if "tid" in i:
-                temp2=self._iFile[''].tolist()
-            addList=[]
-            addList.append(temp[0])
-            for k in range(len(temp)-1):
-                if temp2[k]==temp[k+1]:
-                    addList.append(temp[k+1])
-                else:
-                    self._Database.append(addList)
-                    addList=[]
-                    addList.append(temp[k+1])
-            self._Database.append(addList)
-        if isinstance(self._iFile, str):
-            if _ab._validators.url(self._iFile):
-                data = _ab._urlopen(self._iFile)
-                for line in data:
-                    line.strip()
-                    line = line.decode("utf-8")
-                    temp = [i.rstrip() for i in line.split(self._sep)]
-                    temp = [x for x in temp if x]
-                    temp.pop()
-                    self._Database.append(temp)
-            else:
-                try:
-                    with open(self._iFile, 'r', encoding='utf-8') as f:
-                        for line in f:
-                            line.strip()
-                            temp = [i.rstrip() for i in line.split('-1')]
-                            temp = [x for x in temp if x ]
-                            temp.pop()
-
-                            seq = []
-                            for i in temp:
-                                k = -2
-                                if len(i)>1:
-                                    seq.append(list(sorted(set(i.split()))))
-
-                                else:
-                                    seq.append(i)
-
-                            self._Database.append(seq)
-
-                except IOError:
-                    print("File Not Found")
-                    quit()
-
-    def _convert(self, value):
-        """
-        To convert the user specified minSup value
-
-        :param value: user specified minSup value
-        :return: converted type
-        """
-        if type(value) is int:
-            value = int(value)
-        if type(value) is float:
-            value = (len(self._Database) * value)
-        if type(value) is str:
-            if '.' in value:
-                value = float(value)
-                value = (len(self._Database) * value)
-            else:
-                value = int(value)
-        return value
-
-
-
    [docs]    def make1LenDatabase(self):
-        """
-        To make 1 length frequent patterns by breadth-first search technique   and update Database to sequential database
-        """
-
-        idDatabase={}
-        alreadyInData=[]
-        lineNumber=0
-        alreadyInLine={}
-        for line in self._Database:
-            seqNumber=0
-            for seq in line:
-
-                for data in seq:
-                    if data in alreadyInData:
-                        if lineNumber in alreadyInLine[data]:
-                            idDatabase[data][lineNumber].append(seqNumber)
-                        else:
-                            idDatabase[data][lineNumber] = []
-                            idDatabase[data][lineNumber].append(seqNumber)
-                            alreadyInLine[data].append(lineNumber)
-                    else:
-                        idDatabase[data]={}
-                        idDatabase[data][lineNumber]=[]
-                        idDatabase[data][lineNumber].append(seqNumber)
-                        alreadyInData.append(data)
-                        alreadyInLine[data]=[]
-                        alreadyInLine[data].append(lineNumber)
-
-
-
-                seqNumber+=1
-            lineNumber+=1
-
-        newDatabase={i :idDatabase[i] for i in idDatabase.keys()}
-        for key in idDatabase.keys():
-            if len(idDatabase[key].keys())<self._minSup:
-                newDatabase.pop(key)
-            else:
-                self._finalPatterns[str(key)]=len(idDatabase[key].keys())
-        self._Database=newDatabase
    
-
-
-
    [docs]    def make2LenDatabase(self):
-        """
-        To make 2 length frequent patterns by joining two one length patterns by breadth-first search technique  and update xlen Database to sequential database
-        """
-        self._xLenDatabase = {}
-        keyList=[i for i in self._Database.keys()]
-        nextDatabase={i:{} for i in self._Database.keys()}
-        nextDatabaseSame={i:{} for i in self._Database.keys()}
-        keyNumber=-1
-        for key1 in keyList:
-            keyNumber+=1
-            for key2 in keyList[keyNumber:]:
-                if key1!=key2:
-
-
-                    if len(self._Database[key1].keys())>=len(self._Database[key1].keys()):
-                        nextDatabase[key1][key2] = {}
-                        nextDatabase[key2][key1] = {}
-                        nextDatabaseSame[key1][key2] = {}
-
-                        for seq in self._Database[key2].keys():
-                            if seq in self._Database[key1].keys():
-                                x=[i for i in self._Database[key1][seq] if i >self._Database[key2][seq][0]]
-                                if len(x) != 0:
-                                    nextDatabase[key2][key1][seq]=x
-                                x = [i for i in self._Database[key2][seq] if i > self._Database[key1][seq][0]]
-                                if len(x) != 0:
-                                    nextDatabase[key1][key2][seq]=x
-                                x=list(sorted(set(self._Database[key1][seq])&set(self._Database[key2][seq])))
-                                if len(x)!=0:
-                                    nextDatabaseSame[key1][key2][seq]=x
-                    else:
-                        nextDatabase[key1][key2] = {}
-                        nextDatabase[key2][key1] = {}
-                        nextDatabaseSame[key1][key2] = {}
-
-                        for seq in self._Database[key1].keys():
-                            x = [i for i in self._Database[key1][seq] if
-                                                                    i > self._Database[key2][seq][0]]
-                            if len(x)!=0:
-                                nextDatabase[key2][key1][seq]=0
-                            x = [i for i in self._Database[key2][seq] if
-                                                                    i > self._Database[key1][seq][0]]
-                            if len(x)!=0:
-                                nextDatabase[key1][key2][seq]=x
-                            x= list(
-                                sorted(set(self._Database[key1][seq]) & set(self._Database[key2][seq])))
-                            if len(x)!=0:
-                                nextDatabaseSame[key1][key2][seq]=x
-                else:
-                    nextDatabase[key1][key2] = {}
-                    for seq in self._Database[key2].keys():
-                        if len(self._Database[key1][seq])>=2:
-                            nextDatabase[key1][key2][seq]= self._Database[key2][seq][1:]
-        self._xLenDatabase[2] = {tuple([i]): {} for i in nextDatabase.keys()}
-        for key1 in nextDatabase.keys():
-            for key2 in nextDatabase[key1].keys():
-                if len(nextDatabase[key1][key2].keys())>=self._minSup:
-                    self._finalPatterns[str((key1,-1,key2,-1))]=len(nextDatabase[key1][key2].keys())
-                    self._xLenDatabase[2][tuple([key1])][key2]=nextDatabase[key1][key2]
-        self._xLenDatabaseSame[2]={tuple([i]): {} for i in nextDatabaseSame.keys()}
-        for key1 in nextDatabaseSame.keys():
-            for key2 in nextDatabaseSame[key1].keys():
-                if len(nextDatabaseSame[key1][key2].keys()) >= self._minSup:
-                    self._finalPatterns[str((key1,key2,-1))]=len(nextDatabaseSame[key1][key2].keys())
-                    self._xLenDatabaseSame[2][tuple([key1])][key2]={i:nextDatabaseSame[key1][key2][i] for i in nextDatabaseSame[key1][key2].keys()}
-                    self._xLenDatabaseSame[2][tuple([key2])][key1] = {i: nextDatabaseSame[key1][key2][i] for i in nextDatabaseSame[key1][key2].keys()}
    
-
-
    [docs]    def make3LenDatabase(self):
-        """
-        To call each 2 length patterns to make 3 length frequent patterns depth-first search technique
-        """
-        for i in self._xLenDatabase[2].keys():
-            for k in self._xLenDatabase[2][i].keys():
-                self.makexLenDatabase(2,i,k)
-        for i in self._xLenDatabaseSame[2].keys():
-            for k in self._xLenDatabaseSame[2][i].keys():
-                self.makexLenDatabaseSame(2,i,k)
    
-
-
    [docs]    def makexLenDatabase(self, rowLen, bs, latestWord):
-            """
-            To make "rowLen" length frequent patterns from pattern which the latest word is in same seq  by joining "rowLen"-1 length patterns by depth-first search technique  and update xlenDatabase to sequential database
-
-            :param rowLen: row length of patterns.
-            :param bs : patterns without the latest one
-            :param latestWord : latest word of patterns
-            """
-            if rowLen+1 not in self._xLenDatabase:
-                self._xLenDatabase[rowLen+1]={}
-                self._xLenDatabaseSame[rowLen+1]={}
-            for latestWord2 in self._xLenDatabase[rowLen][bs].keys():
-                if latestWord != latestWord2:
-
-                    if len(self._xLenDatabase[rowLen][bs][latestWord].keys()) <= len(self._xLenDatabase[rowLen][bs][latestWord2].keys()):
-                        next={}
-                        next2={}
-                        nextSame={}
-
-                        for seq in self._xLenDatabase[rowLen][bs][latestWord].keys():
-                            if seq in self._xLenDatabase[rowLen][bs][latestWord2].keys():
-                                if self._xLenDatabase[rowLen][bs][latestWord2][seq]!=[] and self._xLenDatabase[rowLen][bs][latestWord][seq]!=[]:
-                                    x = [i for i in self._xLenDatabase[rowLen][bs][latestWord2][seq] if
-                                            i > self._xLenDatabase[rowLen][bs][latestWord][seq][0]]
-                                    if len(x) != 0:
-                                        next[seq] = x
-                                    x = [i for i in self._xLenDatabase[rowLen][bs][latestWord][seq] if
-                                            i > self._xLenDatabase[rowLen][bs][latestWord2][seq][0]]
-                                    if len(x) != 0:
-                                        next2[seq] = x
-                                    x = list(sorted(set(self._xLenDatabase[rowLen][bs][latestWord][seq]) & set(
-                                        self._xLenDatabase[rowLen][bs][latestWord2][seq])))
-                                    if len(x) != 0:
-                                        nextSame[seq] = x
-                        if len(next)>=self._minSup:
-                            nextRow,nextbs= self.makeNextRow(bs,latestWord,latestWord2)
-                            if str(nextRow) not in self._finalPatterns.keys():
-                                self._finalPatterns[str(nextRow)] = len(next)
-                                if nextbs not in self._xLenDatabase[rowLen + 1]:
-                                    self._xLenDatabase[rowLen + 1][nextbs]={}
-                                self._xLenDatabase[rowLen+1][nextbs][latestWord2]={i:next[i] for i in next }
-                                self.makexLenDatabase(rowLen+1,nextbs,latestWord2)
-                        if len(next2)>=self._minSup:
-                            nextRow,nextbs = self.makeNextRow(bs, latestWord2, latestWord)
-                            if str(nextRow) not in self._finalPatterns.keys():
-                                if nextbs not in self._xLenDatabase[rowLen + 1]:
-                                    self._xLenDatabase[rowLen + 1][nextbs]={}
-                                self._finalPatterns[str(nextRow)] = len(next2)
-                                self._xLenDatabase[rowLen+1][nextbs][latestWord] = {i:next2[i] for i in next2 }
-                                self.makexLenDatabase(rowLen+1, nextbs, latestWord)
-                        if len(nextSame) >= self._minSup:
-                            nextRow,nextbs ,nextlste= self.makeNextRowSame3(bs, latestWord, latestWord2)
-                            if str(nextRow) not in self._finalPatterns.keys():
-                                if nextbs not in self._xLenDatabaseSame[rowLen + 1]:
-                                    self._xLenDatabaseSame[rowLen + 1][nextbs]={}
-                                self._finalPatterns[str(nextRow)] = len(nextSame)
-                                self._xLenDatabaseSame[rowLen+1][nextbs][nextlste]={i:nextSame[i] for i in nextSame }
-                                self.makexLenDatabaseSame(rowLen+1, nextbs, nextlste)
-
-                    else:
-                        next = {}
-                        next2 = {}
-                        nextSame = {}
-
-                        for seq in self._xLenDatabase[rowLen][bs][latestWord2].keys():
-                            if seq in self._xLenDatabase[rowLen][bs][latestWord].keys():
-                                if self._xLenDatabase[rowLen][bs][latestWord2][seq] != [] and self._xLenDatabase[rowLen][bs][latestWord][seq] != []:
-                                    x = [i for i in self._xLenDatabase[rowLen][bs][latestWord2][seq] if i > self._xLenDatabase[rowLen][bs][latestWord][seq][0]]
-                                    if len(x)!=0:
-                                        next[seq]=x
-                                    x= [i for i in self._xLenDatabase[rowLen][bs][latestWord][seq] if i > self._xLenDatabase[rowLen][bs][latestWord2][seq][0]]
-                                    if len(x)!=0:
-                                        next2[seq]=x
-                                    x= list(sorted(set(self._xLenDatabase[rowLen][bs][latestWord][seq]) & set(
-                                            self._xLenDatabase[rowLen][bs][latestWord2][seq])))
-                                    if len(x)!=0:
-                                        nextSame[seq]=x
-                        if len(next) >= self._minSup:
-                            nextRow,nextbs = self.makeNextRow(bs, latestWord, latestWord2)
-                            if str(nextRow) not in self._finalPatterns.keys():
-                                if nextbs not in self._xLenDatabase[rowLen + 1]:
-                                    self._xLenDatabase[rowLen+1][nextbs]={}
-                                self._finalPatterns[str(nextRow)] = len(next)
-                                self._xLenDatabase[rowLen + 1][nextbs][latestWord2] ={i:next[i] for i in next }
-                                self.makexLenDatabase(rowLen+1, nextbs, latestWord2)
-                        if len(next2) >= self._minSup:
-                            nextRow,nextbs = self.makeNextRow(bs, latestWord2, latestWord)
-                            if str(nextRow) not in self._finalPatterns.keys():
-                                if nextbs not in self._xLenDatabase[rowLen + 1]:
-                                    self._xLenDatabase[rowLen + 1][nextbs]={}
-                                self._finalPatterns[str(nextRow)] = len(next2)
-                                self._xLenDatabase[rowLen+1][nextbs][latestWord] = {i:next2[i] for i in next2 }
-                                self.makexLenDatabase(rowLen+1, nextbs, latestWord)
-                        if len(nextSame) >= self._minSup:
-                            nextRow,nextbs,nextlate = self.makeNextRowSame3(bs,latestWord,latestWord2)
-                            if str(nextRow) not in self._finalPatterns.keys():
-                                if nextbs not in self._xLenDatabaseSame[rowLen + 1]:
-                                    self._xLenDatabaseSame[rowLen + 1][nextbs]={}
-                                self._finalPatterns[str(nextRow)] = len(nextSame)
-                                self._xLenDatabaseSame[rowLen+1][nextbs][nextlate] = {i:nextSame[i] for i in nextSame}
-                                self.makexLenDatabaseSame(rowLen+1, nextbs, nextlate)
-
-                else:
-                    next= {}
-                    for seq in self._xLenDatabase[rowLen][bs][latestWord2].keys():
-                        if len(self._xLenDatabase[rowLen][bs][latestWord][seq])>=2:
-                            next[seq]= self._xLenDatabase[rowLen][bs][latestWord][seq][1:]
-                    if len(next) >= self._minSup:
-                        nextRow, nextbs= self.makeNextRow(bs,latestWord,latestWord2)
-                        if str(nextRow) not in self._finalPatterns.keys():
-                            if nextbs not in self._xLenDatabase[rowLen+1]:
-                                self._xLenDatabase[rowLen+1][nextbs]={}
-                            self._finalPatterns[str(nextRow)] = len(next)
-                            self._xLenDatabase[rowLen+1][nextbs][latestWord2] ={i:next[i] for i in next }
-                            self.makexLenDatabase(rowLen+1, nextbs, latestWord2)
-            if bs in self._xLenDatabaseSame[rowLen]:
-                for latestWord2 in self._xLenDatabaseSame[rowLen][bs]:
-
-
-                        if len(self._xLenDatabase[rowLen][bs][latestWord].keys()) <= len(
-                                self._xLenDatabaseSame[rowLen][bs][latestWord2].keys()):
-                            next = {}
-
-                            for seq in self._xLenDatabase[rowLen][bs][latestWord].keys():
-                                if seq in self._xLenDatabaseSame[rowLen][bs][latestWord2].keys():
-                                    if self._xLenDatabaseSame[rowLen][bs][latestWord2][seq] != []:
-                                        x= [i for i in self._xLenDatabase[rowLen][bs][latestWord][seq] if i > self._xLenDatabaseSame[rowLen][bs][latestWord2][seq][0]]
-                                        if len(x) != 0:
-                                            next[seq] = x
-                            if len(next) >= self._minSup:
-
-                                nextRow ,nextbs= self.makeNextRowSame(bs, latestWord2, latestWord)
-                                if str(nextRow) not in self._finalPatterns.keys():
-                                    if nextbs not in self._xLenDatabase[rowLen + 1]:
-                                        self._xLenDatabase[rowLen + 1][nextbs] = {}
-                                    self._finalPatterns[str(nextRow)] = len(next)
-                                    self._xLenDatabase[rowLen + 1][nextbs][latestWord] = {i:next[i] for i in next }
-                                    self.makexLenDatabase(rowLen + 1, nextbs, latestWord)
-
-                        else:
-                            next = {}
-                            for seq in self._xLenDatabaseSame[rowLen][bs][latestWord2].keys():
-                                if seq in self._xLenDatabase[rowLen][bs][latestWord].keys():
-                                    if self._xLenDatabaseSame[rowLen][bs][latestWord2][seq] != [] :
-                                        x= [i for i in self._xLenDatabase[rowLen][bs][latestWord][seq] if
-                                                         i > self._xLenDatabaseSame[rowLen][bs][latestWord2][seq][0]]
-                                        if len(x) != 0:
-                                            next[seq] = x
-                            if len(next) >= self._minSup:
-                                nextRow,nextbs = self.makeNextRowSame(bs, latestWord2, latestWord)
-                                if str(nextRow) not in self._finalPatterns.keys():
-                                    if nextbs not in self._xLenDatabase[rowLen + 1]:
-                                        self._xLenDatabase[rowLen + 1][nextbs] = {}
-                                    self._finalPatterns[str(nextRow)] = len(next)
-                                    self._xLenDatabase[rowLen+1][nextbs][latestWord] = {i:next[i] for i in next }
-                                    self.makexLenDatabase(rowLen + 1, nextbs,latestWord)
    
-
-
    [docs]    def makexLenDatabaseSame(self, rowLen, bs, latestWord):
-        """
-        To make 3 or more length frequent patterns from pattern which the latest word is in different seq  by depth-first search technique  and update xlenDatabase to sequential database
-
-        :param rowLen: row length of previous patterns.
-        :param bs : previous patterns without the latest one
-        :param latestWord : latest word of previous patterns
-        """
-        if rowLen + 1 not in self._xLenDatabase:
-            self._xLenDatabase[rowLen + 1] = {}
-            self._xLenDatabaseSame[rowLen + 1] = {}
-        if bs in self._xLenDatabase[rowLen]:
-            for latestWord2 in self._xLenDatabase[rowLen][bs]:
-                if len(self._xLenDatabaseSame[rowLen][bs][latestWord].keys()) <= len(self._xLenDatabase[rowLen][bs][latestWord2].keys()):
-                    next = {}
-
-                    for seq in self._xLenDatabaseSame[rowLen][bs][latestWord].keys():
-                        if seq in self._xLenDatabase[rowLen][bs][latestWord2].keys():
-                            if self._xLenDatabaseSame[rowLen][bs][latestWord][seq] != []:
-                                x= [i for i in self._xLenDatabase[rowLen][bs][latestWord2][seq] if
-                                             i > self._xLenDatabaseSame[rowLen][bs][latestWord][seq][0]]
-                                if len(x) != 0:
-                                    next[seq] = x
-                    if len(next) >= self._minSup:
-                        nextRow ,nextbs= self.makeNextRowSame(bs, latestWord, latestWord2)
-                        if str(nextRow) not in self._finalPatterns.keys():
-                            if nextbs not in self._xLenDatabase[rowLen + 1]:
-                                self._xLenDatabase[rowLen + 1][nextbs] = {}
-                            self._finalPatterns[str(nextRow)] = len(next)
-                            self._xLenDatabase[rowLen + 1][nextbs][latestWord2]= {i:next[i] for i in next}
-                            self.makexLenDatabase(rowLen + 1, nextbs, latestWord2)
-
-                else:
-                    next = {}
-
-                    for seq in self._xLenDatabase[rowLen][bs][latestWord2].keys():
-                        if seq in self._xLenDatabaseSame[rowLen][bs][latestWord].keys():
-                            if self._xLenDatabaseSame[rowLen][bs][latestWord][seq] != []:
-                                x= [i for i in self._xLenDatabase[rowLen][bs][latestWord2][seq] if
-                                             i > self._xLenDatabaseSame[rowLen][bs][latestWord][seq][0]]
-                                if len(x) != 0:
-                                    next[seq] = x
-                    if len(next) >= self._minSup:
-                        nextRow,nextbs = self.makeNextRowSame(bs, latestWord, latestWord2)
-                        if str(nextRow) not in self._finalPatterns.keys():
-                            if nextbs not in self._xLenDatabase[rowLen + 1]:
-                                self._xLenDatabase[rowLen + 1][nextbs] = {}
-                            self._finalPatterns[str(nextRow)] = len(next)
-                            self._xLenDatabase[rowLen + 1][nextbs][latestWord2] = {i:next[i] for i in next}
-                            self.makexLenDatabase(rowLen + 1,nextbs, latestWord2)
-        if bs in self._xLenDatabaseSame[rowLen]:
-            for latestWord2 in self._xLenDatabaseSame[rowLen][bs]:
-                if latestWord2!=latestWord:
-                    if len(self._xLenDatabaseSame[rowLen][bs][latestWord].keys()) <= len(
-                            self._xLenDatabaseSame[rowLen][bs][latestWord2].keys()):
-                        next = {}
-
-                        for seq in self._xLenDatabaseSame[rowLen][bs][latestWord].keys():
-                            if seq in self._xLenDatabaseSame[rowLen][bs][latestWord2].keys():
-                                x= list(sorted(set(self._xLenDatabaseSame[rowLen][bs][latestWord][seq]) & set(
-                                        self._xLenDatabaseSame[rowLen][bs][latestWord2][seq])))
-                                if len(x) != 0:
-                                    next[seq] = x
-                        if len(next) >= self._minSup:
-
-                                nextRow, nextbs,nextLate= self.makeNextRowSame2(bs, latestWord, latestWord2)
-                                if str(nextRow) not in self._finalPatterns.keys():
-                                    if nextbs not in self._xLenDatabaseSame[rowLen+1]:
-                                        self._xLenDatabaseSame[rowLen + 1][nextbs] = {}
-
-                                    self._finalPatterns[str(nextRow)] = len(next)
-                                    self._xLenDatabaseSame[rowLen + 1][nextbs][nextLate] = {i:next[i] for i in next}
-                                    self.makexLenDatabaseSame(rowLen + 1, nextbs, nextLate)
-                    else:
-                        next = {}
-
-                        for seq in self._xLenDatabaseSame[rowLen][bs][latestWord2].keys():
-                            if seq in self._xLenDatabaseSame[rowLen][bs][latestWord].keys():
-                                x= list(sorted(set(self._xLenDatabaseSame[rowLen][bs][latestWord][seq]) & set(
-                                        self._xLenDatabaseSame[rowLen][bs][latestWord2][seq])))
-                                if len(x) != 0:
-                                    next[seq] = x
-                        if len(next) >= self._minSup:
-
-                                nextRow, nextbs,nextLate= self.makeNextRowSame2(bs, latestWord, latestWord2)
-                                if str(nextRow) not in self._finalPatterns.keys():
-                                    if nextbs not in self._xLenDatabaseSame[rowLen+1]:
-                                        self._xLenDatabaseSame[rowLen + 1][nextbs] = {}
-
-                                    self._finalPatterns[str(nextRow)] = len(next)
-                                    self._xLenDatabaseSame[rowLen + 1][nextbs][nextLate] = {i:next[i] for i in next}
-                                    self.makexLenDatabaseSame(rowLen + 1, nextbs, nextLate)
    
-
-
    [docs]    def makeNextRow(self,bs, latestWord, latestWord2):
-        """
-        To make pattern row when two patterns have the latest word in different sequence
-
-        :param bs : previous pattern without the latest one
-        :param latestWord : latest word of one previous pattern
-        :param latestWord2 : latest word of other previous pattern
-        """
-
-        bs=bs+(-1,latestWord)
-        bs2=bs+(-1,latestWord2,-1)
-        return bs2,bs
    
-
-
    [docs]    def makeNextRowSame(self,bs, latestWord, latestWord2):
-        """
-        To make pattern row when one pattern have the latestWord1 in different sequence and other(latestWord2) in same
-
-        :param bs : previous pattern without the latest one
-        :param latestWord : latest word of one previous pattern in same sequence
-        :param latestWord2 : latest word of other previous pattern in different sequence
-        """
-
-        bs=list(bs)
-        x=1
-        x2=[latestWord,]
-        while bs:
-            x=bs.pop()
-            if x!=-1:
-                x2.append(x)
-            else:
-                break
-        x2=list(sorted(set(x2)))
-        if len(bs)!=0:
-            bs=tuple(bs)+(-1,)+tuple(x2)
-        else:
-            bs=tuple(x2)
-        bs2=tuple(bs)+(-1,latestWord2,-1)
-        return bs2,bs
    
-
-
-
    [docs]    def makeNextRowSame2(self,bs, latestWord, latestWord2):
-        """
-        To make pattern row when two patterns have the latest word in same sequence
-
-        :param bs : previous pattern without the latest one
-        :param latestWord : latest word of one previous pattern
-        :param latestWord2 : latest word of the other previous pattern
-        """
-
-        bs = list(bs)
-        x = 1
-        x2 = [latestWord, latestWord2]
-        while bs:
-            x = bs.pop()
-            if x != -1:
-                x2.append(x)
-            else:
-                break
-        x2 = list(sorted(set(x2)))
-        x3 = x2.pop()
-        if len(bs)!=0:
-            bs = tuple(bs)+(-1,)+ tuple(x2)
-        else:
-            bs = tuple(x2)
-        bs2 = tuple(bs) + (x3, -1)
-
-        return bs2, bs, x3
    
-
-
-
    [docs]    def makeNextRowSame3(self, bs, latestWord, latestWord2):
-        """
-        To make pattern row when two patterns have the latest word in different sequence and both latest word is in same sequence
-
-        :param bs : previous pattern without the latest one
-        :param latestWord : latest word of one previous pattern
-        :param latestWord2 : latest word of other previous pattern
-        """
-
-        x = list(sorted({latestWord, latestWord2}))
-        x2 = x.pop()
-        x3=x.pop()
-        bs = bs + (-1,x3)
-        bs2 = bs + (x2,)
-        return  bs2,bs,x2
    
-
-
    [docs]    @deprecated("It is recommended to use mine() instead of startMine() for mining process")
-    def startMine(self):
-        """
-        Frequent pattern mining process will start from here
-        """
-        self._Database = []
-        self._startTime = _ab._time.time()
-        self._creatingItemSets()
-        self._minSup = self._convert(self._minSup)
-        self.make1LenDatabase()
-        self.make2LenDatabase()
-        self.make3LenDatabase()
-        self._endTime = _ab._time.time()
-        process = _ab._psutil.Process(_ab._os.getpid())
-        self._memoryUSS = float()
-        self._memoryRSS = float()
-        self._memoryUSS = process.memory_full_info().uss
-        self._memoryRSS = process.memory_info().rss
-        print("Sequential Frequent patterns were generated successfully using SPADE algorithm ")
    
-
-
    [docs]    def Mine(self):
-        """
-        Frequent pattern mining process will start from here
-        """
-        self._Database = []
-        self._startTime = _ab._time.time()
-        self._creatingItemSets()
-        self._minSup = self._convert(self._minSup)
-        self.make1LenDatabase()
-        self.make2LenDatabase()
-        self.make3LenDatabase()
-        self._endTime = _ab._time.time()
-        process = _ab._psutil.Process(_ab._os.getpid())
-        self._memoryUSS = float()
-        self._memoryRSS = float()
-        self._memoryUSS = process.memory_full_info().uss
-        self._memoryRSS = process.memory_info().rss
-        print("Sequential Frequent patterns were generated successfully using SPADE algorithm ")
    
-
-
    [docs]    def getMemoryUSS(self):
-        """
-        Total amount of USS memory consumed by the mining process will be retrieved from this function
-
-        :return: returning USS memory consumed by the mining process
-        :rtype: float
-        """
-
-        return self._memoryUSS
    
-
-
    [docs]    def getMemoryRSS(self):
-        """
-        Total amount of RSS memory consumed by the mining process will be retrieved from this function
-
-        :return: returning RSS memory consumed by the mining process
-        :rtype: float
-        """
-
-        return self._memoryRSS
    
-
-
    [docs]    def getRuntime(self):
-        """
-        Calculating the total amount of runtime taken by the mining process
-
-        :return: returning total amount of runtime taken by the mining process
-        :rtype: float
-        """
-
-        return self._endTime - self._startTime
    
-
-
    [docs]    def getPatternsAsDataFrame(self):
-        """
-        Storing final frequent patterns in a dataframe
-
-        :return: returning frequent patterns in a dataframe
-        :rtype: pd.DataFrame
-        """
-
-        dataFrame = {}
-        data = []
-        for a, b in self._finalPatterns.items():
-            data.append([a, b])
-            dataFrame = _ab._pd.DataFrame(data, columns=['Patterns', 'Support'])
-        return dataFrame
    
-
-
    [docs]    def save(self, outFile):
-        """
-        Complete set of frequent patterns will be loaded in to an output file
-
-        :param outFile: name of the output file
-        :type outFile: csv file
-        """
-        self._oFile = outFile
-        writer = open(self._oFile, 'w+')
-        for x, y in self._finalPatterns.items():
-            s1 = x + ":" + str(y)
-            writer.write("%s \n" % s1)
    
-
-
    [docs]    def getPatterns(self):
-        """
-        Function to send the set of frequent patterns after completion of the mining process
-
-        :return: returning frequent patterns
-        :rtype: dict
-        """
-        return self._finalPatterns
    
-
-
    [docs]    def printResults(self):
-        """
-        This function is used to prnt the results
-        """
-        print("Total number of Frequent Patterns:", len(self.getPatterns()))
-        print("Total Memory in USS:", self.getMemoryUSS())
-        print("Total Memory in RSS", self.getMemoryRSS())
-        print("Total ExecutionTime in ms:", self.getRuntime())
    
-
-
-if __name__ == "__main__":
-    _ap = str()
-    if len(_ab._sys.argv) == 4 or len(_ab._sys.argv) == 5:
-        if len(_ab._sys.argv) == 5:
-            _ap = SPADE(_ab._sys.argv[1], _ab._sys.argv[3], _ab._sys.argv[4])
-        if len(_ab._sys.argv) == 4:
-            _ap = SPADE(_ab._sys.argv[1], _ab._sys.argv[3])
-        _ap.startMine()
-        _Patterns = _ap.getPatterns()
-        print("Total number of Frequent Patterns:", len(_Patterns))
-        _ap.savePatterns(_ab._sys.argv[2])
-        _memUSS = _ap.getMemoryUSS()
-        print("Total Memory in USS:", _memUSS)
-        _memRSS = _ap.getMemoryRSS()
-        print("Total Memory in RSS", _memRSS)
-        _run = _ap.getRuntime()
-        print("Total ExecutionTime in ms:", _run)
-    else:
-        _ap = SPADE('text3.txt' ,80, '\t')
-        _ap.startMine()
-        _Patterns = _ap.getPatterns()
-        _memUSS = _ap.getMemoryUSS()
-        print("Total Memory in USS:", _memUSS)
-        _memRSS = _ap.getMemoryRSS()
-        print("Total Memory in RSS", _memRSS)
-        _run = _ap.getRuntime()
-        print("Total ExecutionTime in ms:", _run)
-        print("Total number of Frequent Patterns:", len(_Patterns))
-        print("Error! The number of input parameters do not match the total number of parameters provided")
-
    - -
    -
    -
    - -
    - -
    -

    © Copyright 2024, RAGE Uday Kiran.

    -
    - - Built with Sphinx using a - theme - provided by Read the Docs. - - -
    -
    -
    -
    -
    - - - - \ No newline at end of file diff --git a/finalSphinxDocs/_build/html/_modules/PAMI/sequentialPatternMining/basic/SPAM.html b/finalSphinxDocs/_build/html/_modules/PAMI/sequentialPatternMining/basic/SPAM.html deleted file mode 100644 index e9a19ab7..00000000 --- a/finalSphinxDocs/_build/html/_modules/PAMI/sequentialPatternMining/basic/SPAM.html +++ /dev/null @@ -1,649 +0,0 @@ - - - - - - PAMI.sequentialPatternMining.basic.SPAM — PAMI 2024.04.23 documentation - - - - - - - - - - - - - - - - - -
    - - -
    - -
    -
    -
    -
      -
      • -
    • Module code
      • -
    • PAMI.sequentialPatternMining.basic.SPAM
      • -
    • -
      • -
    -
    -
    -
    -
    - -

    Source code for PAMI.sequentialPatternMining.basic.SPAM

    -# SPAM is one of the fundamental algorithm to discover sequential frequent patterns in a transactional database.
-# This program employs SPAM property (or downward closure property) to  reduce the search space effectively.
-#  This algorithm employs breadth-first search technique  to find the complete set of frequent patterns in a sequential database.
-# **Importing this algorithm into a python program**
-# --------------------------------------------------------
-#
-#
-#             import PAMI.sequentialPatternMining.basic.SPAM as alg
-#
-#             obj = alg.SPAM(iFile, minSup)
-#
-#             obj.startMine()
-#
-#             sequentialPatternMining = obj.getPatterns()
-#
-#             print("Total number of Frequent Patterns:", len(frequentPatterns))
-#
-#             obj.save(oFile)
-#
-#             Df = obj.getPatternInDataFrame()
-#
-#             memUSS = obj.getMemoryUSS()
-#
-#             print("Total Memory in USS:", memUSS)
-#
-#             memRSS = obj.getMemoryRSS()
-#
-#             print("Total Memory in RSS", memRSS)
-#
-#             run = obj.getRuntime()
-#
-#             print("Total ExecutionTime in seconds:", run)
-#
-
-
-
-__copyright__ = """
- Copyright (C)  2021 Rage Uday Kiran
-
-     This program is free software: you can redistribute it and/or modify
-     it under the terms of the GNU General Public License as published by
-     the Free Software Foundation, either version 3 of the License, or
-     (at your option) any later version.
-
-     This program is distributed in the hope that it will be useful,
-     but WITHOUT ANY WARRANTY; without even the implied warranty of
-     MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-     GNU General Public License for more details.
-
-     You should have received a copy of the GNU General Public License
-     along with this program.  If not, see <https://www.gnu.org/licenses/>.
-     Copyright (C)  2021 Rage Uday Kiran
-
-"""
-
-
-import pandas as pd
-from deprecated import deprecated
-
-from PAMI.sequentialPatternMining.basic import abstract as _ab
-_ab._sys.setrecursionlimit(10000)
-
-
    [docs]class SPAM(_ab._sequentialPatterns):
-    """
-    :Description:    SPAM is one of the fundamental algorithm to discover sequential frequent patterns in a transactional database.
-                     This program employs SPAM property (or downward closure property) to  reduce the search space effectively.
-                     This algorithm employs breadth-first search technique  to find the complete set of frequent patterns in a sequential database.
-
-    :Reference:   J. Ayres, J. Gehrke, T.Yiu, and J. Flannick. Sequential Pattern Mining Using Bitmaps. In Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Edmonton, Alberta, Canada, July 2002.
-
-    :param  iFile: str :
-                   Name of the Input file to mine complete set of  Sequential frequent patterns
-    :param  oFile: str :
-                   Name of the output file to store complete set of  Sequential frequent patterns
-    :param  minSup: float or int or str :
-                    minSup measure constraints the minimum number of transactions in a database where a pattern must appear
-                    Example: minSup=10 will be treated as integer, while minSup=10.0 will be treated as float
-    :param  sep: str :
-                   This variable is used to distinguish items from one another in a transaction. The default seperator is tab space. However, the users can override their default separator.
-
-    :Attributes:
-
-            iFile : str
-                Input file name or path of the input file
-            oFile : str
-                Name of the output file or the path of output file
-            minSup : float or int or str
-                The user can specify minSup either in count or proportion of database size.
-                If the program detects the data type of minSup is integer, then it treats minSup is expressed in count.
-                Otherwise, it will be treated as float.
-                Example: minSup=10 will be treated as integer, while minSup=10.0 will be treated as float
-            sep : str
-                This variable is used to distinguish items from one another in a transaction. The default seperator is tab space or \t.
-                However, the users can override their default separator.
-            startTime : float
-                To record the start time of the mining process
-            endTime : float
-                To record the completion time of the mining process
-            finalPatterns : dict
-                Storing the complete set of patterns in a dictionary variable
-            memoryUSS : float
-                To store the total amount of USS memory consumed by the program
-            memoryRSS : float
-                To store the total amount of RSS memory consumed by the program
-            Database : list
-                To store the sequences of a database in list
-            _idDatabase : dict
-                To store the sequences of a database by bit map
-            _maxSeqLen:
-                the maximum length of subsequence in sequence.
-
-    :Methods:
-
-            _creatingItemSets():
-                Storing the complete sequences of the database/input file in a database variable
-            _convert(value):
-                To convert the user specified minSup value
-            make2BitDatabase():
-                To make 1 length frequent patterns by breadth-first search technique   and update Database to sequential database
-            DfsPruning(items,sStep,iStep):
-                the main algorithm of spam. This can search sstep and istep items and find next patterns, its sstep, and its istep. And call this function again by using them. Recursion until there are no more items available for exploration.
-            Sstep(s):
-                To convert bit to ssteo bit.The first time you get 1, you set it to 0 and subsequent ones to 1.(like 010101=>001111, 00001001=>00000111)
-            startMine()
-                Mining process will start from here
-            getPatterns()
-                Complete set of patterns will be retrieved with this function
-            savePatterns(oFile)
-                Complete set of frequent patterns will be loaded in to a output file
-            getPatternsAsDataFrame()
-                Complete set of frequent patterns will be loaded in to a dataframe
-            getMemoryUSS()
-                Total amount of USS memory consumed by the mining process will be retrieved from this function
-            getMemoryRSS()
-                Total amount of RSS memory consumed by the mining process will be retrieved from this function
-            getRuntime()
-                Total amount of runtime taken by the mining process will be retrieved from this function
-            candidateToFrequent(candidateList)
-                Generates frequent patterns from the candidate patterns
-            frequentToCandidate(frequentList, length)
-                Generates candidate patterns from the frequent patterns
-
-
-    **Executing the code on terminal**:
-    ----------------------------------------
-    .. code-block:: console
-
-
-       Format:
-
-       (.venv) $ python3 SPAM.py <inputFile> <outputFile> <minSup> (<separator>)
-
-       Examples usage:
-
-       (.venv) $ python3 SPAM.py sampleDB.txt patterns.txt 10.0
-
-
-               .. note:: minSup will be considered in times of minSup and count of database transactions
-
-    **Sample run of the importing code**:
-    -------------------------------------
-            import PAMI.sequentialPatternMining.basic.SPAM as alg
-
-            obj = alg.SPAM(iFile, minSup)
-
-            obj.startMine()
-
-            sequentialPatternMining = obj.getPatterns()
-
-            print("Total number of Frequent Patterns:", len(frequentPatterns))
-
-            obj.savePatterns(oFile)
-
-            Df = obj.getPatternInDataFrame()
-
-            memUSS = obj.getMemoryUSS()
-
-            print("Total Memory in USS:", memUSS)
-
-            memRSS = obj.getMemoryRSS()
-
-            print("Total Memory in RSS", memRSS)
-
-            run = obj.getRuntime()
-
-            print("Total ExecutionTime in seconds:", run)
-
-    **Credits**:
-    ------------
-            The complete program was written by Shota Suzuki  under the supervision of Professor Rage Uday Kiran.
-    """
-
-    _minSup = float()
-    _startTime = float()
-    _endTime = float()
-    _finalPatterns = {}
-    _iFile = " "
-    _oFile = " "
-    _sep = " "
-    _memoryUSS = float()
-    _memoryRSS = float()
-    _Database = []
-    _idDatabase={}
-    _maxSeqLen=0
-    def _creatingItemSets(self):
-        """
-        Storing the complete sequences of the database/input file in a database variable
-        """
-        self._Database = []
-
-        if isinstance(self._iFile, _ab._pd.DataFrame):
-            temp = []
-            if self._iFile.empty:
-                print("its empty..")
-            i = self._iFile.columns.values.tolist()
-            if 'Transactions' in i:
-                temp = self._iFile['Transactions'].tolist()
-            if "tid" in i:
-                temp2=self._iFile[''].tolist()
-            addList=[]
-            addList.append(temp[0])
-            for k in range(len(temp)-1):
-                if temp2[k]==temp[k+1]:
-                    addList.append(temp[k+1])
-                else:
-                    self._Database.append(addList)
-                    addList=[]
-                    addList.append(temp[k+1])
-            self._Database.append(addList)
-        if isinstance(self._iFile, str):
-            if _ab._validators.url(self._iFile):
-                data = _ab._urlopen(self._iFile)
-                for line in data:
-                    line.strip()
-                    line = line.decode("utf-8")
-                    temp = [i.rstrip() for i in line.split(self._sep)]
-                    temp = [x for x in temp if x]
-                    temp.pop()
-                    self._Database.append(temp)
-            else:
-                try:
-                    with open(self._iFile, 'r', encoding='utf-8') as f:
-                        for line in f:
-                            line.strip()
-                            temp = [i.rstrip() for i in line.split('-1')]
-                            temp = [x for x in temp if x ]
-                            temp.pop()
-
-                            seq = []
-                            for i in temp:
-                                k = -2
-                                if len(i)>1:
-                                    seq.append(list(sorted(set(i.split()))))
-
-                                else:
-                                    seq.append(i)
-
-                            self._Database.append(seq)
-
-                except IOError:
-                    print("File Not Found")
-                    quit()
-
-    def _convert(self, value):
-        """
-        To convert the user specified minSup value
-
-        :param value: user specified minSup value
-        :return: converted type
-        """
-        if type(value) is int:
-            value = int(value)
-        if type(value) is float:
-            value = (len(self._Database) * value)
-        if type(value) is str:
-            if '.' in value:
-                value = float(value)
-                value = (len(self._Database) * value)
-            else:
-                value = int(value)
-        return value
-
-
-
    [docs]    def make2BitDatabase(self):
-        """
-        To make 1 length frequent patterns by breadth-first search technique   and update Database to sequential database
-        """
-        self._maxSeqLen=max([len(i) for i in self._Database])
-        lineNumber=0
-        idDatabase={}
-        for line in self._Database:
-            seqNumber=1
-            for seq in line:
-
-                for data in seq:
-                    if data in idDatabase:
-                        while lineNumber+1!=len(idDatabase[data]):
-                            idDatabase[data].append(0)
-                        idDatabase[data][lineNumber]+=int(2**(self._maxSeqLen-seqNumber))
-
-                    else:
-                        idDatabase[data]=[]
-                        while lineNumber+1!=len(idDatabase[data]):
-                            idDatabase[data].append(0)
-                        idDatabase[data][lineNumber]+=(int(2 ** (self._maxSeqLen-seqNumber)))
-
-                seqNumber+=1
-            lineNumber+=1
-        for key,val in idDatabase.items():
-
-            sup=self.countSup(val)
-            while lineNumber+1!=len(idDatabase[key]):
-                            idDatabase[key].append(0)
-            if sup>=self._minSup:
-                self._finalPatterns[str(key)+self._sep+"-2"]=sup
-                self._idDatabase[str(key)]=val
    
-
-
    [docs]    def DfsPruning(self,items,sStep,iStep):
-        """
-        the main algorithm of spam. This can search sstep and istep items and find next patterns, its sstep, and its istep. And call this function again by using them. Recursion until there are no more items available for exploration.
-
-        :Attributes:
-
-        items : str
-            The pattrens I got before
-        sStep : list
-            Items presumed to have "sstep" relationship with "items".(sstep is What appears later like a-b and a-c)
-        iStep : list
-            Items presumed to have "istep" relationship with "items"(istep is What appears in same time like ab and ac)
-
-        """
-        Snext=[]
-        Inext=[]
-        ns = self.Sstep(self._idDatabase[items])
-        for i in sStep:
-            nnext=[]
-            for k in  range(len(self._idDatabase[items])):
-                nandi=ns[k] & self._idDatabase[i][k]
-                nnext.append(nandi)
-
-
-            sup=self.countSup(nnext)
-            if sup>=self._minSup:
-                key=items+self._sep+"-1"+self._sep+i
-                self._finalPatterns[key+self._sep+"-1"+self._sep+"-2"]=sup
-                self._idDatabase[key]=nnext
-                Snext.append(i)
-
-        for i in Snext:
-            key = items+self._sep+"-1"+self._sep+i
-            self.DfsPruning(key,Snext,[k for k in Snext if self._Database.index(i)<self._Database.index(k)])
-        for i in iStep:
-            nnext = []
-
-            for k in range(len(self._idDatabase[items])):
-                nandi = self._idDatabase[items][k] & self._idDatabase[i][k]
-                nnext.append(nandi)
-            sup=self.countSup(nnext)
-            if sup>=self._minSup:
-                key=items+self._sep+str(i)
-                self._finalPatterns[key+self._sep+"-1"+self._sep+"-2"]=sup
-                self._idDatabase[key]=nnext
-                Inext.append(i)
-        for i in Inext:
-            key = items +self._sep +str(i)
-            self.DfsPruning(key,Snext,[k for k in Inext if self._Database.index(i)<self._Database.index(k)])
    
-
-
    [docs]    def Sstep(self,s):
-        """
-        To convert bit to Sstep bit.The first time you get 1, you set it to 0 and subsequent ones to 1.(like 010101=>001111, 00001001=>00000111)
-
-
-        :param s:list
-            to store each bit sequence
-        :return:
-            nextS:list to store the bit sequence converted by sstep
-
-        """
-        nextS=[]
-        for bins in s:
-            binS=str(bin(bins))
-
-
-            LenNum=2
-            for i in range(len(binS)-2):
-                if binS[LenNum] == "1":
-
-                    binS = binS[:LenNum] + "0" + binS[LenNum + 1:]
-                    while len(binS)-1!=LenNum:
-                        LenNum += 1
-                        binS = binS[:LenNum] + "1" + binS[LenNum + 1:]
-                    break
-                LenNum+=1
-            nextS.append(int(binS, 0))
-
-
-        return nextS
    
-
-
    [docs]    def countSup(self,n):
-        """
-        count support
-
-        :param n:list
-                to store each bit sequence
-        :return:
-            count: int support of this list
-        """
-        count=0
-        for i in n:
-            if "1" in str(bin(i)):
-                count+=1
-        return count
    
-
-
    [docs]    def startMine(self):
-        """
-        Frequent pattern mining process will start from here
-        """
-        self._Database = []
-        self._startTime = _ab._time.time()
-        self._creatingItemSets()
-        self._minSup = self._convert(self._minSup)
-        self.make2BitDatabase()
-        self._Database = [i for i in self._idDatabase.keys()]
-        for i in self._Database:
-            x=[]
-            for j in self._Database:
-                if self._Database.index(i)<self._Database.index(j):
-                    x.append(j)
-
-            self.DfsPruning(i,self._Database,x)
-        self._endTime = _ab._time.time()
-        process = _ab._psutil.Process(_ab._os.getpid())
-        self._memoryUSS = float()
-        self._memoryRSS = float()
-        self._memoryUSS = process.memory_full_info().uss
-        self._memoryRSS = process.memory_info().rss
-        print("Frequent patterns were generated successfully using Apriori algorithm ")
    
-
-
    [docs]    def getMemoryUSS(self):
-        """Total amount of USS memory consumed by the mining process will be retrieved from this function
-        :return: returning USS memory consumed by the mining process
-        :rtype: float
-        """
-
-        return self._memoryUSS
    
-
-
    [docs]    def getMemoryRSS(self):
-        """Total amount of RSS memory consumed by the mining process will be retrieved from this function
-        :return: returning RSS memory consumed by the mining process
-        :rtype: float
-        """
-
-        return self._memoryRSS
    
-
-
    [docs]    def getRuntime(self):
-        """Calculating the total amount of runtime taken by the mining process
-        :return: returning total amount of runtime taken by the mining process
-        :rtype: float
-        """
-
-        return self._endTime - self._startTime
    
-
-
    [docs]    def getPatternsAsDataFrame(self):
-        """Storing final frequent patterns in a dataframe
-        :return: returning frequent patterns in a dataframe
-        :rtype: pd.DataFrame
-        """
-
-        dataFrame = {}
-        data = []
-        for a, b in self._finalPatterns.items():
-            data.append([a, b])
-            dataFrame = _ab._pd.DataFrame(data, columns=['Patterns', 'Support'])
-        return dataFrame
    
-
-
    [docs]    def save(self, outFile):
-        """Complete set of frequent patterns will be loaded in to an output file
-        :param outFile: name of the output file
-        :type outFile: file
-        """
-        self._oFile = outFile
-        writer = open(self._oFile, 'w+')
-        for x, y in self._finalPatterns.items():
-            s1 = x + ":" + str(y)
-            writer.write("%s \n" % s1)
    
-
-
    [docs]    def getPatterns(self):
-        """ Function to send the set of frequent patterns after completion of the mining process
-        :return: returning frequent patterns
-        :rtype: dict
-        """
-        return self._finalPatterns
    
-
-
    [docs]    def printResults(self):
-        """
-        This function is used to print the results
-        """
-        print("Total number of Frequent Patterns:", len(self.getPatterns()))
-        print("Total Memory in USS:", self.getMemoryUSS())
-        print("Total Memory in RSS", self.getMemoryRSS())
-        print("Total ExecutionTime in ms:", self.getRuntime())
    
-
-
-if __name__ == "__main__":
-    _ap = str()
-    if len(_ab._sys.argv) == 4 or len(_ab._sys.argv) == 5:
-        if len(_ab._sys.argv) == 5:
-            _ap = SPAM(_ab._sys.argv[1], _ab._sys.argv[3], _ab._sys.argv[4])
-        if len(_ab._sys.argv) == 4:
-            _ap = SPAM(_ab._sys.argv[1], _ab._sys.argv[3])
-        _ap.startMine()
-        _Patterns = _ap.getPatterns()
-        print("Total number of Frequent Patterns:", len(_Patterns))
-        _ap.savePatterns(_ab._sys.argv[2])
-        _memUSS = _ap.getMemoryUSS()
-        print("Total Memory in USS:", _memUSS)
-        _memRSS = _ap.getMemoryRSS()
-        print("Total Memory in RSS", _memRSS)
-        _run = _ap.getRuntime()
-        print("Total ExecutionTime in ms:", _run)
-    else:
-
-        print("Error! The number of input parameters do not match the total number of parameters provided")
-
    - -
    -
    -
    - -
    - -
    -

    © Copyright 2024, RAGE Uday Kiran.

    -
    - - Built with Sphinx using a - theme - provided by Read the Docs. - - -
    -
    -
    -
    -
    - - - - \ No newline at end of file diff --git a/finalSphinxDocs/_build/html/_modules/PAMI/sequentialPatternMining/basic/prefixSpan.html b/finalSphinxDocs/_build/html/_modules/PAMI/sequentialPatternMining/basic/prefixSpan.html deleted file mode 100644 index 3d3ca088..00000000 --- a/finalSphinxDocs/_build/html/_modules/PAMI/sequentialPatternMining/basic/prefixSpan.html +++ /dev/null @@ -1,781 +0,0 @@ - - - - - - PAMI.sequentialPatternMining.basic.prefixSpan — PAMI 2024.04.23 documentation - - - - - - - - - - - - - - - - - -
    - - -
    - -
    -
    -
    -
      -
      • -
    • Module code
      • -
    • PAMI.sequentialPatternMining.basic.prefixSpan
      • -
    • -
      • -
    -
    -
    -
    -
    - -

    Source code for PAMI.sequentialPatternMining.basic.prefixSpan

    -# Prefix Span is one of the fundamental algorithm to discover sequential frequent patterns in a transactional database.
-# This program employs Prefix Span property (or downward closure property) to  reduce the search space effectively.
-# This algorithm employs depth-first search technique to find the complete set of frequent patterns in a transactional database.
-#
-# **Importing this algorithm into a python program**
-# --------------------------------------------------------
-#
-#
-#             import PAMI.frequentPattern.basic.prefixSpan as alg
-#
-#             obj = alg.prefixSpan(iFile, minSup,oFile,sep)
-#
-#             obj.startMine()
-#
-#             frequentPatterns = obj.getPatterns()
-#
-#             print("Total number of Frequent Patterns:", len(frequentPatterns))
-#
-#             obj.save(oFile)
-#
-#             Df = obj.getPatternInDataFrame()
-#
-#             memUSS = obj.getMemoryUSS()
-#
-#             print("Total Memory in USS:", memUSS)
-#
-#             memRSS = obj.getMemoryRSS()
-#
-#             print("Total Memory in RSS", memRSS)
-#
-#             run = obj.getRuntime()
-#
-#             print("Total ExecutionTime in seconds:", run)
-#
-
-
-
-__copyright__ = """
- Copyright (C)  2021 Rage Uday Kiran
-
-     This program is free software: you can redistribute it and/or modify
-     it under the terms of the GNU General Public License as published by
-     the Free Software Foundation, either version 3 of the License, or
-     (at your option) any later version.
-
-     This program is distributed in the hope that it will be useful,
-     but WITHOUT ANY WARRANTY; without even the implied warranty of
-     MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-     GNU General Public License for more details.
-
-     You should have received a copy of the GNU General Public License
-     along with this program.  If not, see <https://www.gnu.org/licenses/>.
-     Copyright (C)  2021 Rage Uday Kiran
-"""
-
-import pandas as pd
-from deprecated import deprecated
-
-from PAMI.sequentialPatternMining.basic import abstract as _ab
-import copy
-import re
-_ab._sys.setrecursionlimit(10000)
-
-
    [docs]class prefixSpan(_ab._sequentialPatterns):
-    """
-    :Description:
-        * Prefix Span is one of the fundamental algorithm to discover sequential frequent patterns in a transactional database.
-        * This program employs Prefix Span property (or downward closure property) to  reduce the search space effectively.
-        * This algorithm employs depth-first search technique to find the complete set of frequent patterns in a transactional database.
-
-    :Reference:   J. Pei, J. Han, B. Mortazavi-Asl, J. Wang, H. Pinto, Q. Chen, U. Dayal, M. Hsu: Mining Sequential Patterns by Pattern-Growth: The PrefixSpan Approach. IEEE Trans. Knowl. Data Eng. 16(11): 1424-1440 (2004)
-
-    :param  iFile: str :
-                   Name of the Input file to mine complete set of Sequential frequent patterns
-    :param  oFile: str :
-                   Name of the output file to store complete set of Sequential frequent patterns
-    :param  minSup: float or int or str :
-                    minSup measure constraints the minimum number of transactions in a database where a pattern must appear
-                    Example: minSup=10 will be treated as integer, while minSup=10.0 will be treated as float
-    :param  sep: str :
-                   This variable is used to distinguish items from one another in a transaction. The default seperator is tab space. However, the users can override their default separator.
-
-    :Attributes:
-
-        iFile : str
-            Input file name or path of the input file
-        oFile : str
-            Name of the output file or the path of output file
-        minSup : float or int or str
-            The user can specify minSup either in count or proportion of database size.
-            If the program detects the data type of minSup is integer, then it treats minSup is expressed in count.
-            Otherwise, it will be treated as float.
-            Example: minSup=10 will be treated as integer, while minSup=10.0 will be treated as float
-        sep : str
-            This variable is used to distinguish items from one another in a transaction. The default seperator is tab space or \t.
-            However, the users can override their default separator.
-        startTime : float
-            To record the start time of the mining process
-        endTime : float
-            To record the completion time of the mining process
-        finalPatterns : dict
-            Storing the complete set of patterns in a dictionary variable
-        memoryUSS : float
-            To store the total amount of USS memory consumed by the program
-        memoryRSS : float
-            To store the total amount of RSS memory consumed by the program
-        Database : list
-            To store the transactions of a database in list
-
-    :Methods:
-
-        startMine()
-            Mining process will start from here
-        getPatterns()
-            Complete set of patterns will be retrieved with this function
-        savePatterns(oFile)
-            Complete set of frequent patterns will be loaded in to a output file
-        getPatternsAsDataFrame()
-            Complete set of frequent patterns will be loaded in to a dataframe
-        getMemoryUSS()
-            Total amount of USS memory consumed by the mining process will be retrieved from this function
-        getMemoryRSS()
-            Total amount of RSS memory consumed by the mining process will be retrieved from this function
-        getRuntime()
-            Total amount of runtime taken by the mining process will be retrieved from this function
-        candidateToFrequent(candidateList)
-            Generates frequent patterns from the candidate patterns
-        frequentToCandidate(frequentList, length)
-            Generates candidate patterns from the frequent patterns
-
-    **Methods to execute code on terminal**
-    ------------------------------------------
-    .. code-block:: console
-
-
-       Format:
-
-       (.venv) $ python3 prefixSpan.py <inputFile> <outputFile> <minSup>
-
-       Example usage:
-
-       (.venv) $ python3 prefixSpan.py sampleDB.txt patterns.txt 10
-
-
-               .. note:: minSup will be considered in support count or frequency
-
-
-    **Importing this algorithm into a python program**
-    -----------------------------------------------------
-    .. code-block:: python
-
-            import PAMI.frequentPattern.basic.prefixSpan as alg
-
-            obj = alg.prefixSpan(iFile, minSup)
-
-            obj.startMine()
-
-            frequentPatterns = obj.getPatterns()
-
-            print("Total number of Frequent Patterns:", len(frequentPatterns))
-
-            obj.save(oFile)
-
-            Df = obj.getPatternInDataFrame()
-
-            memUSS = obj.getMemoryUSS()
-
-            print("Total Memory in USS:", memUSS)
-
-            memRSS = obj.getMemoryRSS()
-
-            print("Total Memory in RSS", memRSS)
-
-            run = obj.getRuntime()
-
-            print("Total ExecutionTime in seconds:", run)
-
-    **Credits:**
-    ---------------
-
-              The complete program was written by Suzuki Shota under the supervision of Professor Rage  Uday Kiran.
-    """
-
-    _minSup = float()
-    _startTime = float()
-    _endTime = float()
-    _finalPatterns = {}
-    _iFile = " "
-    _oFile = " "
-    _sep = " "
-    _memoryUSS = float()
-    _memoryRSS = float()
-    _Database = []
-    _sepDatabase={}
-    def _creatingItemSets(self):
-        """
-        Storing the complete transactions of the database/input file in a database variable
-        """
-        self._Database = []
-
-        if isinstance(self._iFile, _ab._pd.DataFrame):
-            temp = []
-            if self._iFile.empty:
-                print("its empty..")
-            i = self._iFile.columns.values.tolist()
-            if 'Transactions' in i:
-                temp = self._iFile['Transactions'].tolist()
-
-            for k in temp:
-                self._Database.append(set(k))
-        if isinstance(self._iFile, str):
-            if _ab._validators.url(self._iFile):
-                data = _ab._urlopen(self._iFile)
-                for line in data:
-                    line.strip()
-                    line = line.decode("utf-8")
-                    temp = [i.rstrip() for i in line.split(self._sep)]
-                    temp = [x for x in temp if x]
-                    self._Database.append(set(temp))
-            else:
-                try:
-                    with open(self._iFile, 'r', encoding='utf-8') as f:
-                        for line in f:
-                            line.strip()
-                            temp = [i.rstrip() for i in line.split(':')]
-                            temp = [x for x in temp if x ]
-
-                            seq = []
-                            for i in temp:
-                                if len(i)>1:
-                                   for i in list(sorted(set(i.split()))):
-                                       seq.append(i)
-                                   seq.append(":")
-
-                                else:
-                                    seq.append(i)
-                                    seq.append(":")
-                            self._Database.append(seq)
-
-
-                except IOError:
-                    print("File Not Found")
-                    quit()
-
-    def _convert(self, value):
-        """
-        To convert the user specified minSup value
-
-        :param value: user specified minSup value
-        :return: converted type
-        """
-        if type(value) is int:
-            value = int(value)
-        if type(value) is float:
-            value = (len(self._Database) * value)
-        if type(value) is str:
-            if '.' in value:
-                value = float(value)
-                value = (len(self._Database) * value)
-            else:
-                value = int(value)
-        return value
-
    [docs]    def makeNext(self,sepDatabase,startrow):
-        """
-        To get next pattern by adding head word to next sequence of startrow
-
-        :param sepDatabase: dict
-            what words and rows startrow have to add it.
-        :param startrow:
-            the patterns get before
-        """
-        for head in sepDatabase.keys():
-            newrow=[i for i in startrow]
-
-            if len(sepDatabase[head])>=self._minSup:
-                if newrow!=[]:
-                    newrow.append(":")
-                newrow.append(head)
-                newrow.append(":")
-                if str(newrow) not in self._finalPatterns:
-                    self._finalPatterns[str(newrow)]=len(sepDatabase[head])
-                    give = []
-                    give.append(head)
-                    sepDatabase[head] = self.makeSupDatabase(sepDatabase[head], give)
-                    newrow.pop()
-                    self.makeSeqDatabaseSame(sepDatabase[head], newrow)
-                elif len(sepDatabase[head]) > self._finalPatterns[str(newrow)]:
-                    self._finalPatterns[str(newrow)] = len(sepDatabase[head])
-                    give = []
-                    give.append(head)
-                    sepDatabase[head] = self.makeSupDatabase(sepDatabase[head], give)
-                    newrow.pop()
-                    self.makeSeqDatabaseSame(sepDatabase[head], newrow)
    
-
-
-
-
    [docs]    def makeSupDatabase(self,database,head):
-        """
-        To delete not frequent words without words in the latest sequence
-
-        :param database: list
-            database of lines having same startrow and head word
-        :param head:list
-         words in the latest sequence
-        :return: changed database
-        """
-
-        supDatabase={}
-        alreadyInData=[]
-        newDatabase = []
-        for line in database:
-            alreadyInLine = []
-            for data in line:
-                    if data not in alreadyInLine:
-                        if data not in alreadyInData:
-                            supDatabase[data]=1
-                            alreadyInData.append(data)
-                        else:
-                            supDatabase[data]+=1
-                        alreadyInLine.append(data)
-        for line in database:
-                newLine=[]
-                for i in line:
-                    if supDatabase[i]>=self._minSup or i in head:
-                        if len(newLine)>1:
-                            if (newLine[-1]!=":" or i!=":"):
-                                newLine.append(i)
-                        else:
-                            newLine.append(i)
-                newDatabase.append(newLine)
-
-        return newDatabase
    
-
-
    [docs]    def makeNextSame(self,sepDatabase,startrow):
-        """
-         To get next pattern by adding head word to the latest sequence of startrow
-
-        :param sepDatabase: dict
-            what words and rows startrow have to add it
-        :param startrow:
-            the patterns get before
-        """
-        for head in sepDatabase.keys():
-
-            if len(sepDatabase[head])>=self._minSup:
-                newrow = startrow.copy()
-                newrow.append(head)
-                newrow.append(":")
-                if str(newrow) not in self._finalPatterns.keys():
-                    self._finalPatterns[str(newrow)]=len(sepDatabase[head])
-                    if ":" in startrow:
-                        give = self.getSameSeq(startrow)
-                    else:
-                        give = startrow.copy()
-                    give.append(head)
-                    sepDatabase[head] = self.makeSupDatabase(sepDatabase[head], give)
-                    newrow.pop()
-                    self.makeSeqDatabaseSame(sepDatabase[head], newrow)
-                elif len(sepDatabase[head])>self._finalPatterns[str(newrow)]:
-                    self._finalPatterns[str(newrow)] = len(sepDatabase[head])
-                    if ":" in startrow:
-                        give = self.getSameSeq(startrow)
-                    else:
-                        give = startrow.copy()
-                    give.append(head)
-                    sepDatabase[head] = self.makeSupDatabase(sepDatabase[head], give)
-                    newrow.pop()
-                    self.makeSeqDatabaseSame(sepDatabase[head], newrow)
    
-
    [docs]    def makeSeqDatabaseFirst(self,database):
-        """
-        To make 1 length sequence dataset list which start from same word. It was stored only 1 from 1 line.
-
-        :param database:
-            To store the transactions of a database in list
-        """
-        startrow=[]
-        seqDatabase={}
-
-        for line in database:
-            alreadyInLine=[]
-            for data in range(len(line)):
-                if line[data] not in alreadyInLine and line[data]!=":":
-                    if line[data] not in seqDatabase.keys():
-                        seqDatabase[line[data]]=[]
-                        seqDatabase[line[data]].append(line[data+1:])
-                        alreadyInLine.append(line[data])
-                    else:
-                        seqDatabase[line[data]].append(line[data+1:])
-                        alreadyInLine.append(line[data])
-
-        if len(seqDatabase)>0:
-            self.makeNext(seqDatabase,startrow)
    
-
-
    [docs]    def serchSame(self,database,startrow,give):
-        """
-        To get 2 or more length patterns in same sequence.
-
-        :param database: list
-            To store the transactions of a database in list which have same startrow and head word
-        :param startrow: list
-            the patterns get before
-        :param give: list
-            the word in the latest sequence of startrow
-        """
-        sepDatabaseSame={}
-        sepDatabaseSame[startrow[-1]]=[]
-        for line in database:
-            addLine=0
-            i=0
-            if len(line)>1:
-                while line[i]!=":":
-                    if line[i]==startrow[-1]:
-                        sepDatabaseSame[startrow[-1]].append(line[i+1:])
-                        addLine=1
-                        break
-                    i+=1
-                if addLine!=1:
-                    ok=[]
-                    while i <len(line):
-                        if line[i]==":":
-                            ok=[]
-                        elif line[i]==startrow[-1]:
-                            ok.append("sk1")
-                        for x in give:
-                            if x==line[i]:
-                                ok.append(x)
-                        if len(ok)==1+len(give):
-                            sepDatabaseSame[startrow[-1]].append(line[i+1:])
-                            break
-                        i+=1
-        startrow2=[startrow[0]]
-        startrow.append(":")
-        if str(startrow) not in self._finalPatterns.keys():
-                self.makeNextSame(sepDatabaseSame,startrow2)
-        elif self._finalPatterns[str(startrow)]<len(sepDatabaseSame[startrow[-2]]):
-            self.makeNextSame(sepDatabaseSame,startrow2)
-        return sepDatabaseSame[startrow[-2]]
    
-
-
    [docs]    def getSameSeq(self,startrow):
-        """
-         To get words in the latest sequence
-        :param startrow: the patterns get before
-
-        """
-        give = []
-        newrow = startrow.copy()
-        while newrow[-1] != ":":
-            y = newrow.pop()
-            give.append(y)
-        return give
    
-
-
    [docs]    def makeSeqDatabaseSame(self,database,startrow):
-        """
-        To make sequence dataset list which start from same word(head). It was stored only 1 from 1 line.
-        And it separated by having head in the latest sequence of startrow or not.
-
-        :param database:
-                To store the transactions of a database in list
-        :param startrow: the patterns get before
-
-        """
-        seqDatabase={}
-        seqDatabaseSame={}
-        for line in database:
-            if len(line)>1:
-                alreadyInLine=[]
-                i = 0
-                while line[i] != ":":
-                        if line[i] not in seqDatabaseSame:
-                            if ":" in startrow:
-                                give=self.getSameSeq(startrow)
-                            else:
-                                give=startrow.copy()
-                            newrow= [startrow[-1], line[i]]
-                            seqDatabaseSame[line[i]] = self.serchSame(database, newrow,give)
-
-                        i += 1
-                same=0
-                while len(line)>i:
-                    if line[i]!=":":
-                        if line[i] not in alreadyInLine:
-                            if line[i] not in seqDatabase:
-                                seqDatabase[line[i]]=[]
-                            seqDatabase[line[i]].append(line[i + 1:])
-                            alreadyInLine.append(line[i])
-                        if line[i]==startrow[-1]:
-                            same=1
-
-                        elif same==1 and line[i] not in seqDatabaseSame:
-                            if ":" in startrow:
-                                give=self.getSameSeq(startrow)
-                            else:
-                                give=startrow.copy()
-                            newrow= [startrow[-1], line[i]]
-                            seqDatabaseSame[line[i]] = self.serchSame(database, newrow,give)
-
-                    else:
-                        same=0
-                    i+=1
-
-
-        if len(seqDatabase)!=0:
-            self.makeNext(seqDatabase,startrow)
-        if len(seqDatabaseSame)!=0:
-            self.makeNextSame(seqDatabaseSame,startrow)
    
-
-
    [docs]    @deprecated("It is recommended to use mine() instead of startMine() for mining process")
-    def startMine(self):
-        """
-        Frequent pattern mining process will start from here
-        """
-        self._Database = []
-        self._startTime = _ab._time.time()
-        self._creatingItemSets()
-        self._Database=self.makeSupDatabase(self._Database,"")
-        self._minSup = self._convert(self._minSup)
-        self.makeSeqDatabaseFirst(self._Database)
-        self._endTime = _ab._time.time()
-        process = _ab._psutil.Process(_ab._os.getpid())
-        self._memoryUSS = float()
-        self._memoryRSS = float()
-        self._memoryUSS = process.memory_full_info().uss
-        self._memoryRSS = process.memory_info().rss
-        print("Frequent patterns were generated successfully using prefixSpan algorithm ")
    
-
-
    [docs]    def Mine(self):
-        """
-        Frequent pattern mining process will start from here
-        """
-        self._Database = []
-        self._startTime = _ab._time.time()
-        self._creatingItemSets()
-        self._Database=self.makeSupDatabase(self._Database,"")
-        self._minSup = self._convert(self._minSup)
-        self.makeSeqDatabaseFirst(self._Database)
-        self._endTime = _ab._time.time()
-        process = _ab._psutil.Process(_ab._os.getpid())
-        self._memoryUSS = float()
-        self._memoryRSS = float()
-        self._memoryUSS = process.memory_full_info().uss
-        self._memoryRSS = process.memory_info().rss
-        print("Frequent patterns were generated successfully using prefixSpan algorithm ")
    
-
-
    [docs]    def getMemoryUSS(self):
-        """
-        Total amount of USS memory consumed by the mining process will be retrieved from this function
-
-        :return: returning USS memory consumed by the mining process
-        :rtype: float
-        """
-
-        return self._memoryUSS
    
-
-
    [docs]    def getMemoryRSS(self):
-        """
-        Total amount of RSS memory consumed by the mining process will be retrieved from this function
-
-        :return: returning RSS memory consumed by the mining process
-        :rtype: float
-        """
-
-        return self._memoryRSS
    
-
-
    [docs]    def getRuntime(self):
-        """
-        Calculating the total amount of runtime taken by the mining process
-
-        :return: returning total amount of runtime taken by the mining process
-        :rtype: float
-        """
-
-        return self._endTime - self._startTime
    
-
-
    [docs]    def getPatternsAsDataFrame(self):
-        """
-        Storing final frequent patterns in a dataframe
-
-        :return: returning frequent patterns in a dataframe
-        :rtype: pd.DataFrame
-        """
-
-        dataFrame = {}
-        data = []
-        for a, b in self._finalPatterns.items():
-            data.append([a, b])
-            dataFrame = _ab._pd.DataFrame(data, columns=['Patterns', 'Support'])
-        return dataFrame
    
-
-
    [docs]    def save(self, outFile):
-        """
-        Complete set of frequent patterns will be loaded in to an output file
-
-        :param outFile: name of the output file
-        :type outFile: csv file
-        """
-        self._oFile = outFile
-        writer = open(self._oFile, 'w+')
-        for x, y in self._finalPatterns.items():
-            pattern=""
-            x=re.sub("[\['\]]","",x)
-            for i in x.split(","):
-                
-                pattern=pattern+"\t"+str(i)
-            s1 = pattern  + str(y)
-            writer.write("%s \n" % s1)
    
-
-
    [docs]    def getPatterns(self):
-        """
-        Function to send the set of frequent patterns after completion of the mining process
-
-        :return: returning frequent patterns
-        :rtype: dict
-        """
-        return self._finalPatterns
    
-
-
    [docs]    def printResults(self):
-        """
-        This function is used to print the results
-        """
-        print("Total number of Frequent Patterns:", len(self.getPatterns()))
-        print("Total Memory in USS:", self.getMemoryUSS())
-        print("Total Memory in RSS", self.getMemoryRSS())
-        print("Total ExecutionTime in ms:", self.getRuntime())
    
-
-
-if __name__ == "__main__":
-    _ap = str()
-    if len(_ab._sys.argv) == 4 or len(_ab._sys.argv) == 5:
-        if len(_ab._sys.argv) == 5:
-            _ap = prefixSpan(_ab._sys.argv[1], _ab._sys.argv[3], _ab._sys.argv[4])
-        if len(_ab._sys.argv) == 4:
-            _ap = prefixSpan(_ab._sys.argv[1], _ab._sys.argv[3])
-        _ap.startMine()
-        _Patterns = _ap.getPatterns()
-        print("Total number of Frequent Patterns:", len(_Patterns))
-        _ap.savePatterns(_ab._sys.argv[2])
-        _memUSS = _ap.getMemoryUSS()
-        print("Total Memory in USS:", _memUSS)
-        _memRSS = _ap.getMemoryRSS()
-        print("Total Memory in RSS", _memRSS)
-        _run = _ap.getRuntime()
-        print("Total ExecutionTime in ms:", _run)
-    else:
-        _ap = prefixSpan('retail.txt',1000, ' ')
-        _ap.startMine()
-        _Patterns = _ap.getPatterns()
-        _memUSS = _ap.getMemoryUSS()
-        print("Total Memory in USS:", _memUSS)
-        _memRSS = _ap.getMemoryRSS()
-        print("Total Memory in RSS", _memRSS)
-        _run = _ap.getRuntime()
-        print("Total ExecutionTime in ms:", _run)
-        print("Total number of Frequent Patterns:", len(_Patterns))
-        print("Error! The number of input parameters do not match the total number of parameters provided")
-        _ap.save("priOut2.txt")
-
    - -
    -
    -
    - -
    - -
    -

    © Copyright 2024, RAGE Uday Kiran.

    -
    - - Built with Sphinx using a - theme - provided by Read the Docs. - - -
    -
    -
    -
    -
    - - - - \ No newline at end of file diff --git a/finalSphinxDocs/_build/html/_modules/index.html b/finalSphinxDocs/_build/html/_modules/index.html index ee3430b7..8d2e0182 100644 --- a/finalSphinxDocs/_build/html/_modules/index.html +++ b/finalSphinxDocs/_build/html/_modules/index.html @@ -160,6 +160,7 @@

    All modules for which code is available

  2. PAMI.faultTolerantFrequentPattern.basic.FTApriori
  3. PAMI.faultTolerantFrequentPattern.basic.FTFPGrowth
  4. PAMI.frequentPattern.basic.Apriori
    5. +
  5. PAMI.frequentPattern.basic.Aprioribitset
  6. PAMI.frequentPattern.basic.ECLAT
  7. PAMI.frequentPattern.basic.ECLATDiffset
  8. PAMI.frequentPattern.basic.ECLATbitset
    9. @@ -226,9 +227,6 @@

    All modules for which code is available

  9. PAMI.recurringPattern.basic.RPGrowth
  10. PAMI.relativeFrequentPattern.basic.RSFPGrowth
  11. PAMI.relativeHighUtilityPattern.basic.RHUIM
    12. -
  12. PAMI.sequentialPatternMining.basic.SPADE
    13. -
  13. PAMI.sequentialPatternMining.basic.SPAM
    14. -
  14. PAMI.sequentialPatternMining.basic.prefixSpan
  15. PAMI.stablePeriodicFrequentPattern.basic.SPPEclat
  16. PAMI.stablePeriodicFrequentPattern.basic.SPPGrowth
  17. PAMI.stablePeriodicFrequentPattern.topK.TSPIN
    18. diff --git a/finalSphinxDocs/_build/html/_sources/frequentPatternBasicAprioribitset.rst.txt b/finalSphinxDocs/_build/html/_sources/frequentPatternBasicAprioribitset.rst.txt new file mode 100644 index 00000000..796aa946 --- /dev/null +++ b/finalSphinxDocs/_build/html/_sources/frequentPatternBasicAprioribitset.rst.txt @@ -0,0 +1,7 @@ +Aprioribitset +------------- + +.. automodule:: PAMI.frequentPattern.basic.Aprioribitset + :members: + :undoc-members: + :show-inheritance: \ No newline at end of file diff --git a/finalSphinxDocs/_build/html/_sources/frequentPatternMining.rst.txt b/finalSphinxDocs/_build/html/_sources/frequentPatternMining.rst.txt index 75ca2ed1..2c5f19d9 100644 --- a/finalSphinxDocs/_build/html/_sources/frequentPatternMining.rst.txt +++ b/finalSphinxDocs/_build/html/_sources/frequentPatternMining.rst.txt @@ -5,6 +5,7 @@ Basic :maxdepth: 1 frequentPatternBasicApriori + frequentPatternBasicAprioribitset frequentPatternBasicECLAT frequentPatternBasicECLATDiffset frequentPatternBasicECLATbitset diff --git a/finalSphinxDocs/_build/html/correlatedPatternBasicCoMine.html b/finalSphinxDocs/_build/html/correlatedPatternBasicCoMine.html index e537d938..1ccddffb 100644 --- a/finalSphinxDocs/_build/html/correlatedPatternBasicCoMine.html +++ b/finalSphinxDocs/_build/html/correlatedPatternBasicCoMine.html @@ -120,6 +120,7 @@
    class PAMI.correlatedPattern.basic.CoMine.CoMine(iFile: Union[str, DataFrame], minSup: Union[int, float, str], minAllConf: float, sep: str = '\t')[source]

    Bases: _correlatedPatterns

    +

    About this algorithm

    Description:

    CoMine is one of the fundamental algorithm to discover correlated patterns in a transactional database. It is based on the traditional FP-Growth algorithm. This algorithm uses depth-first search technique to find all correlated patterns in a transactional database.

    @@ -155,6 +156,7 @@
    +

    Execution methods

    Terminal command

    Format:
 
@@ -203,9 +205,8 @@
 print("Total ExecutionTime in seconds:", run)
    -
    -

    The complete program was written by B.Sai Chitra under the supervision of Professor Rage Uday Kiran.

    -
    +

    Credits

    +

    The complete program was written by B.Sai Chitra and revised by Tarun Sreepada under the supervision of Professor Rage Uday Kiran.

    getMemoryRSS() → float[source]
    diff --git a/finalSphinxDocs/_build/html/correlatedPatternBasicCoMinePlus.html b/finalSphinxDocs/_build/html/correlatedPatternBasicCoMinePlus.html index 1a63bb5e..4954854e 100644 --- a/finalSphinxDocs/_build/html/correlatedPatternBasicCoMinePlus.html +++ b/finalSphinxDocs/_build/html/correlatedPatternBasicCoMinePlus.html @@ -120,6 +120,7 @@
    class PAMI.correlatedPattern.basic.CoMinePlus.CoMine(iFile: Union[str, DataFrame], minSup: Union[int, float, str], minAllConf: float, sep: str = '\t')[source]

    Bases: _correlatedPatterns

    +

    About this algorithm

    Description:

    CoMinePlus is one of the fundamental algorithm to discover correlated patterns in a transactional database. It is based on the traditional FP-Growth algorithm. This algorithm uses depth-first search technique to find all correlated patterns in a transactional database.

    @@ -155,6 +156,7 @@
    +

    Execution methods

    Terminal command

    Format:
 
@@ -203,7 +205,8 @@
 print("Total ExecutionTime in seconds:", run)
    -

    The complete program was written by B.Sai Chitra and revised by Tarun Sreepads under the supervision of Professor Rage Uday Kiran.

    +

    Credits

    +

    The complete program was written by B.Sai Chitra and revised by Tarun Sreepada under the supervision of Professor Rage Uday Kiran.

    getMemoryRSS() → float[source]
    diff --git a/finalSphinxDocs/_build/html/frequentPatternBasicApriori.html b/finalSphinxDocs/_build/html/frequentPatternBasicApriori.html index 05598e36..0d602992 100644 --- a/finalSphinxDocs/_build/html/frequentPatternBasicApriori.html +++ b/finalSphinxDocs/_build/html/frequentPatternBasicApriori.html @@ -21,7 +21,7 @@ - + @@ -49,6 +49,7 @@
  18. Frequent Pattern mining
    • Basic
      • Apriori
        • +
      • Aprioribitset
      • ECLAT
      • ECLATDiffset
      • ECLATbitset
        • @@ -128,6 +129,7 @@
        class PAMI.frequentPattern.basic.Apriori.Apriori(iFile, minSup, sep='\t')[source]

        Bases: _frequentPatterns

        +

        About this algorithm

        Description:

        Apriori is one of the fundamental algorithm to discover frequent patterns in a transactional database. This program employs apriori property (or downward closure property) to reduce the search space effectively. This algorithm employs breadth-first search technique to find the complete set of frequent patterns in a transactional database.

        @@ -155,6 +157,7 @@
    19. +

    Execution methods

    Terminal command

    Format:
 
@@ -170,7 +173,7 @@
 
    minSup can be specified  in support count or a value between 0 and 1.

    Calling from a python program

    -
    import PAMI1.frequentPattern.basic.Apriori as alg
+
    import PAMI.frequentPattern.basic.Apriori as alg
 
 iFile = 'sampleDB.txt'
 
@@ -201,6 +204,7 @@
 print("Total ExecutionTime in seconds:", run)
 
    
 
    
+
    Credits
    
 
    The complete program was written by P. Likhitha  and revised by Tarun Sreepada under the supervision of Professor Rage Uday Kiran.
    
 
    
 
    
@@ -313,7 +317,7 @@
    diff --git a/finalSphinxDocs/_build/html/frequentPatternBasicAprioribitset.html b/finalSphinxDocs/_build/html/frequentPatternBasicAprioribitset.html new file mode 100644 index 00000000..c2f0fe59 --- /dev/null +++ b/finalSphinxDocs/_build/html/frequentPatternBasicAprioribitset.html @@ -0,0 +1,346 @@ + + + + + + + Aprioribitset — PAMI 2024.04.23 documentation + + + + + + + + + + + + + + + + + + + +
    + + +
    + +
    +
    + +
    +
    + +
    +

    Aprioribitset

    +
    +
    +class PAMI.frequentPattern.basic.Aprioribitset.Aprioribitset(iFile, minSup, sep='\t')[source]
    +

    Bases: _frequentPatterns

    +

    About this algorithm

    +
    +
    Description:
    +

    AprioriBitset is one of the fundamental algorithm to discover frequent patterns in a transactional database.

    +
    +
    Reference:
    +

    Mohammed Javeed Zaki: Scalable Algorithms for Association Mining. IEEE Trans. Knowl. Data Eng. 12(3): +372-390 (2000), https://ieeexplore.ieee.org/document/846291

    +
    +
    Parameters:
    +
      +

    • iFile (str or URL or dataFrame) – Name of the Input file to mine complete set of frequent patterns.

      • +

    • oFile (str) – Name of the output file to store complete set of frequent patterns.

      • +

    • minSup (int or float or str) – The user can specify minSup either in count or proportion of database size. If the program detects the data type of minSup is integer, then it treats minSup is expressed in count. Otherwise, it will be treated as float.

      • +

    • sep (str) – This variable is used to distinguish items from one another in a transaction. The default seperator is tab space. However, the users can override their default separator.

      • +
    +
    +
    Attributes:
    +
      +

    • startTime (float) – To record the start time of the mining process.

      • +

    • endTime (float) – To record the completion time of the mining process.

      • +

    • finalPatterns (dict) – Storing the complete set of patterns in a dictionary variable.

      • +

    • memoryUSS (float) – To store the total amount of USS memory consumed by the program.

      • +

    • memoryRSS (float) – To store the total amount of RSS memory consumed by the program.

      • +

    • Database (list) – To store the transactions of a database in list.

      • +
    +
    +
    +

    Execution methods

    +

    Terminal command

    +
    Format:
+
+(.venv) $ python3 AprioriBitset.py <inputFile> <outputFile> <minSup>
+
+Example Usage:
+
+(.venv) $ python3 AprioriBitset.py sampleDB.txt patterns.txt 10.0
+
    +
    +
    +

    Note

    +

    minSup can be specified in support count or a value between 0 and 1.

    +
    +

    Calling from a python program

    +
    import PAMI.frequentPattern.basic.Aprioribitset as alg
+
+iFile = 'sampleDB.txt'
+
+minSup = 10  # can also be specified between 0 and 1
+
+obj = alg.Aprioribitset(iFile, minSup)
+
+obj.mine()
+
+frequentPattern = obj.getPatterns()
+
+print("Total number of Frequent Patterns:", len(frequentPattern))
+
+obj.save(oFile)
+
+Df = obj.getPatternInDataFrame()
+
+memUSS = obj.getMemoryUSS()
+
+print("Total Memory in USS:", memUSS)
+
+memRSS = obj.getMemoryRSS()
+
+print("Total Memory in RSS", memRSS)
+
+run = obj.getRuntime()
+
+print("Total ExecutionTime in seconds:", run)
+
    +
    +

    Credits

    +

    The complete program was written by Yudai Masu and revised by Tarun Sreepada under the supervision of Professor Rage Uday Kiran.

    +
    +
    +getMemoryRSS()[source]
    +

    Total amount of RSS memory consumed by the mining process will be retrieved from this function

    +
    +
    Returns:
    +

    returning RSS memory consumed by the mining process

    +
    +
    Return type:
    +

    float

    +
    +
    +
    + +
    +
    +getMemoryUSS()[source]
    +

    Total amount of USS memory consumed by the mining process will be retrieved from this function

    +
    +
    Returns:
    +

    returning USS memory consumed by the mining process

    +
    +
    Return type:
    +

    float

    +
    +
    +
    + +
    +
    +getPatterns()[source]
    +

    Function to send the set of frequent patterns after completion of the mining process

    +
    +
    Returns:
    +

    returning frequent patterns

    +
    +
    Return type:
    +

    dict

    +
    +
    +
    + +
    +
    +getPatternsAsDataFrame() → DataFrame[source]
    +

    Storing final frequent patterns in a dataframe

    +
    +
    Returns:
    +

    returning frequent patterns in a dataframe

    +
    +
    Return type:
    +

    pd.DataFrame

    +
    +
    +
    + +
    +
    +getRuntime()[source]
    +

    Calculating the total amount of runtime taken by the mining process

    +
    +
    Returns:
    +

    returning total amount of runtime taken by the mining process

    +
    +
    Return type:
    +

    float

    +
    +
    +
    + +
    +
    +mine() → None[source]
    +

    Frequent pattern mining process will start from here

    +
    + +
    +
    +printResults()[source]
    +

    This function is used to print the result

    +
    + +
    +
    +save(outFile: str, seperator='\t') → None[source]
    +

    Complete set of frequent patterns will be loaded in to an output file

    +
    +
    Parameters:
    +

    outFile (csvfile) – name of the output file

    +
    +
    Returns:
    +

    None

    +
    +
    +
    + +
    +
    +startMine()[source]
    +

    Code for the mining process will start from this function

    +
    + +
    + +
    + + +
    +
    + +
    +
    +
    +
    + + + + \ No newline at end of file diff --git a/finalSphinxDocs/_build/html/frequentPatternBasicECLAT.html b/finalSphinxDocs/_build/html/frequentPatternBasicECLAT.html index a379ea81..e72ebb32 100644 --- a/finalSphinxDocs/_build/html/frequentPatternBasicECLAT.html +++ b/finalSphinxDocs/_build/html/frequentPatternBasicECLAT.html @@ -22,7 +22,7 @@ - + @@ -49,6 +49,7 @@
  19. Frequent Pattern mining
    • Basic
      • Apriori
        • +
      • Aprioribitset
      • ECLAT
      • ECLATDiffset
      • ECLATbitset
        • @@ -128,6 +129,7 @@
        class PAMI.frequentPattern.basic.ECLAT.ECLAT(iFile, minSup, sep='\t')[source]

        Bases: _frequentPatterns

        +

        About this algorithm

        Description:

        ECLAT is one of the fundamental algorithm to discover frequent patterns in a transactional database.

        @@ -155,6 +157,7 @@

    20. +

    Execution methods

    Terminal command

    Format:
 
@@ -201,6 +204,7 @@
 print("Total ExecutionTime in seconds:", run)
    +

    Credits:

    The complete program was written by Kundai and revised by Tarun Sreepada under the supervision of Professor Rage Uday Kiran.

    @@ -312,7 +316,7 @@
    diff --git a/finalSphinxDocs/_build/html/frequentPatternBasicECLATDiffset.html b/finalSphinxDocs/_build/html/frequentPatternBasicECLATDiffset.html index 3db4c6f6..6cfa29ab 100644 --- a/finalSphinxDocs/_build/html/frequentPatternBasicECLATDiffset.html +++ b/finalSphinxDocs/_build/html/frequentPatternBasicECLATDiffset.html @@ -49,6 +49,7 @@
  20. Frequent Pattern mining
    • Basic
      • Apriori
        • +
      • Aprioribitset
      • ECLAT
      • ECLATDiffset
      • ECLATbitset
        • @@ -128,6 +129,7 @@
        class PAMI.frequentPattern.basic.ECLATDiffset.ECLATDiffset(iFile, minSup, sep='\t')[source]

        Bases: _frequentPatterns

        +

        About this algorithm

        Description:

        ECLATDiffset uses diffset to extract the frequent patterns in a transactional database.

        @@ -141,7 +143,7 @@

      • iFile (str or URL or dataFrame) – Name of the Input file to mine complete set of frequent patterns.

      • oFile (str) – Name of the output file to store complete set of frequent patterns

      • minSup (int or float or str) – The user can specify minSup either in count or proportion of database size. If the program detects the data type of minSup is integer, then it treats minSup is expressed in count.

        • -

      • sep (str) – This variable is used to distinguish items from one another in a transaction. The default seperator is tab space. However, the users can override their default separator.

        • +

      • sep (str) – This variable is used to distinguish items from one another in a transaction. The default seperator is tab space. However, the users can override their default separator.

    21. Attributes:
    @@ -155,6 +157,7 @@
    +

    Execution methods

    Terminal command

    Format:
 
@@ -168,8 +171,8 @@
 
    
 
    Note
    
 
    minSup can be specified  in support count or a value between 0 and 1.
    
-
    Calling from a python program
    
 
    
+
    Calling from a python program
    
 
    import PAMI.frequentPattern.basic.ECLATDiffset as alg
 
 iFile = 'sampleDB.txt'
@@ -201,6 +204,7 @@
 print("Total ExecutionTime in seconds:", run)
 
    
 
    
+
    Credits:
    
 
    The complete program was written by Kundai and revised by Tarun Sreepada under the supervision of Professor Rage Uday Kiran.
    
 
    
 
    
diff --git a/finalSphinxDocs/_build/html/frequentPatternBasicECLATbitset.html b/finalSphinxDocs/_build/html/frequentPatternBasicECLATbitset.html
index 928cc791..e37ec9ab 100644
--- a/finalSphinxDocs/_build/html/frequentPatternBasicECLATbitset.html
+++ b/finalSphinxDocs/_build/html/frequentPatternBasicECLATbitset.html
@@ -49,6 +49,7 @@
 
  21. Frequent Pattern mining
      
 
    • Basic
        
 
      • Apriori
        • 
+
      • Aprioribitset
        • 
 
      • ECLAT
        • 
 
      • ECLATDiffset
        • 
 
      • ECLATbitset
        • 
@@ -128,6 +129,7 @@
 
        
 class PAMI.frequentPattern.basic.ECLATbitset.ECLATbitset(iFile, minSup, sep='\t')[source]
        
 
        Bases: _frequentPatterns
        
+
        About this algorithm
        
 
        
 
        Description:
        
 
        ECLATbitset is one of the fundamental algorithm to discover frequent patterns in a transactional database.
        
@@ -141,7 +143,7 @@
 
      • iFile (str or URL or dataFrame) – Name of the Input file to mine complete set of frequent patterns.
        • 
 
      • oFile (str) – Name of the output file to store complete set of frequent patterns
        • 
 
      • minSup (int or float or str) – The user can specify minSup either in count or proportion of database size. If the program detects the data type of minSup is integer, then it treats minSup is expressed in count.
        • 
-
      • sep (str) – This variable is used to distinguish items from one another in a transaction. The default seperator is tab space. However, the users can override their default separator.
        • 
+
      • sep (str) – This variable is used to distinguish items from one another in a transaction. The default seperator is tab space. However, the users can override their default separator.
        •

    22. Attributes:
    @@ -155,13 +157,17 @@
    +

    Execution methods

    Terminal command

    -
    -

    Format:

    -

    (.venv) $ python3 ECLATbitset.py <inputFile> <outputFile> <minSup>

    -

    Example Usage:

    -

    (.venv) $ python3 ECLATbitset.py sampleDB.txt patterns.txt 10.0

    -
    +
    Format:
+
+(.venv) $ python3 ECLATbitset.py <inputFile> <outputFile> <minSup>
+
+Example Usage:
+
+(.venv) $ python3 ECLATbitset.py sampleDB.txt patterns.txt 10.0
+
    +

    Note

    minSup can be specified in support count or a value between 0 and 1.

    @@ -198,6 +204,7 @@ print("Total ExecutionTime in seconds:", run)
    +

    Credits:

    The complete program was written by Yudai Masu and revised by Tarun Sreepada under the supervision of Professor Rage Uday Kiran.

    diff --git a/finalSphinxDocs/_build/html/frequentPatternBasicFPGrowth.html b/finalSphinxDocs/_build/html/frequentPatternBasicFPGrowth.html index 67150b8c..4dcaef25 100644 --- a/finalSphinxDocs/_build/html/frequentPatternBasicFPGrowth.html +++ b/finalSphinxDocs/_build/html/frequentPatternBasicFPGrowth.html @@ -49,6 +49,7 @@
  22. Frequent Pattern mining
    • Basic
      • Apriori
        • +
      • Aprioribitset
      • ECLAT
      • ECLATDiffset
      • ECLATbitset
        • @@ -128,6 +129,7 @@
        class PAMI.frequentPattern.basic.FPGrowth.FPGrowth(iFile, minSup, sep='\t')[source]

        Bases: _frequentPatterns

        +

        About this algorithm

        Description:

        FPGrowth is one of the fundamental algorithm to discover frequent patterns in a transactional database. It stores the database in compressed fp-tree decreasing the memory usage and extracts the patterns from tree.It employs downward closure property to reduce the search space effectively.

        @@ -157,6 +159,7 @@

    23. +

    Execution methods

    Terminal command

    Format:
 
@@ -203,6 +206,7 @@
 print("Total ExecutionTime in seconds:", run)
    +

    Credits:

    The complete program was written by P. Likhitha and revised by Tarun Sreepada under the supervision of Professor Rage Uday Kiran.

    diff --git a/finalSphinxDocs/_build/html/frequentPatternMining.html b/finalSphinxDocs/_build/html/frequentPatternMining.html index 6ad3f9e1..1dd0198d 100644 --- a/finalSphinxDocs/_build/html/frequentPatternMining.html +++ b/finalSphinxDocs/_build/html/frequentPatternMining.html @@ -49,6 +49,7 @@
  23. Frequent Pattern mining
    • Basic
      • Apriori
        • +
      • Aprioribitset
      • ECLAT
      • ECLATDiffset
      • ECLATbitset
        • @@ -149,6 +150,7 @@

        Basic
        • Apriori
          • +
        • Aprioribitset
        • ECLAT
        • ECLATDiffset
        • ECLATbitset
          • diff --git a/finalSphinxDocs/_build/html/frequentPatternTopkFAE.html b/finalSphinxDocs/_build/html/frequentPatternTopkFAE.html index b09deece..02c20dc2 100644 --- a/finalSphinxDocs/_build/html/frequentPatternTopkFAE.html +++ b/finalSphinxDocs/_build/html/frequentPatternTopkFAE.html @@ -124,6 +124,7 @@
          class PAMI.frequentPattern.topk.FAE.FAE(iFile, k, sep='\t')[source]

          Bases: _frequentPatterns

          +

          About this algorithm

          Description:

          Top - K is and algorithm to discover top frequent patterns in a transactional database.

          @@ -150,6 +151,7 @@

    24. +

    Execution methods

    Terminal command

    Format:
 
@@ -196,6 +198,7 @@
 print("Total ExecutionTime in seconds:", run)
    +

    Credits:

    The complete program was written by P. Likhitha and revised by Tarun Sreepada under the supervision of Professor Rage Uday Kiran.

    diff --git a/finalSphinxDocs/_build/html/frequentPatternclosedCHARM.html b/finalSphinxDocs/_build/html/frequentPatternclosedCHARM.html index b7c6d2c2..a76bc153 100644 --- a/finalSphinxDocs/_build/html/frequentPatternclosedCHARM.html +++ b/finalSphinxDocs/_build/html/frequentPatternclosedCHARM.html @@ -124,6 +124,7 @@
    class PAMI.frequentPattern.closed.CHARM.CHARM(iFile, minSup, sep='\t')[source]

    Bases: _frequentPatterns

    +

    About this algorithm

    Description:

    CHARM is an algorithm to discover closed frequent patterns in a transactional database. Closed frequent patterns are patterns if there exists no superset that has the same support count as this original itemset. This algorithm employs depth-first search technique to find the complete set of closed frequent patterns in a transactional database.

    @@ -156,6 +157,7 @@
    +

    Execution methods

    Terminal command

    Format:
 
@@ -202,6 +204,7 @@
 print("Total ExecutionTime in seconds:", run)
    +

    Credits:

    The complete program was written by P.Likhitha and revised by Tarun Sreepada under the supervision of Professor Rage Uday Kiran.

    diff --git a/finalSphinxDocs/_build/html/genindex.html b/finalSphinxDocs/_build/html/genindex.html index cf99d730..7f4743fa 100644 --- a/finalSphinxDocs/_build/html/genindex.html +++ b/finalSphinxDocs/_build/html/genindex.html @@ -171,8 +171,6 @@

    A

  24. (PAMI.partialPeriodicFrequentPattern.basic.GPFgrowth.Tree method), [1]
    25. -
  25. alreadyAdded (PAMI.extras.generateDatabase.generateSpatioTemporalDatabase.spatioTemporalDatabaseGenerator attribute) -
  26. another() (PAMI.subgraphMining.basic.edge.Edge method)
      @@ -180,6 +178,8 @@

      A

  27. Apriori (class in PAMI.frequentPattern.basic.Apriori), [1] +
    28. +
  28. Aprioribitset (class in PAMI.frequentPattern.basic.Aprioribitset)
  29. avg_transaction_length (PAMI.extras.syntheticDataGenerator.temporalDatabaseGen.CreateSyntheticTemporal attribute)
    30. @@ -222,8 +222,6 @@

    C

  30. CHARM (class in PAMI.frequentPattern.closed.CHARM), [1]
  31. CMine (class in PAMI.coveragePattern.basic.CMine), [1] -
    32. -
  32. coinFlip (PAMI.extras.generateDatabase.generateSpatioTemporalDatabase.spatioTemporalDatabaseGenerator attribute)
  33. CoMine (class in PAMI.correlatedPattern.basic.CoMine), [1] @@ -277,8 +275,6 @@

    C

  34. (PAMI.subgraphMining.topK.DFSCode.DfsCode method)
    35. -
  35. countSup() (PAMI.sequentialPatternMining.basic.SPAM.SPAM method), [1] -
  36. CPFPMiner (class in PAMI.periodicFrequentPattern.closed.CPFPMiner), [1]
  37. CPPG (class in PAMI.coveragePattern.basic.CPPG), [1] @@ -301,10 +297,10 @@

    C

  38. createGeoreferentialTemporalDatabase() (PAMI.extras.syntheticDataGenerator.createSyntheticGeoreferentialTemporal.createGeoreferentialTemporalDatabase method)
    39. - - + -
  39. createPoint() (PAMI.extras.generateDatabase.generateSpatioTemporalDatabase.spatioTemporalDatabaseGenerator method) -
  40. createPrefixTree() (PAMI.localPeriodicPattern.basic.LPPGrowth.Tree method), [1]
      @@ -357,8 +351,12 @@

      C

  41. createTemporalDatabase() (PAMI.extras.syntheticDataGenerator.createSyntheticTemporal.createSyntheticTemporal method)
    42. -
  42. createTemporalFile() (PAMI.extras.generateDatabase.generateTemporalDatabase.generateTemporalDatabase method) +
  43. createTemporalFile() (PAMI.extras.generateDatabase.generateSpatioTemporalDatabase.generateTemporalDatabase method) + +
  44. createTransaction() (PAMI.highUtilitySpatialPattern.topk.TKSHUIM.Dataset method), [1]
  45. createTransactional() (PAMI.extras.DF2DB.denseDF2DB_dump.DenseFormatDF method) @@ -430,8 +428,6 @@

    D

  46. DFSCode (class in PAMI.subgraphMining.basic.dfsCode)
  47. DfsCode (class in PAMI.subgraphMining.topK.DFSCode) -
    48. -
  48. DfsPruning() (PAMI.sequentialPatternMining.basic.SPAM.SPAM method), [1]
  49. DfsThread (class in PAMI.subgraphMining.topK.DFSThread)
    50. @@ -641,8 +637,14 @@

    G

  50. GenerateAndPrintItemPairs() (PAMI.extras.syntheticDataGenerator.utilityDatabase.UtilityDataGenerator method)
    51. -
  51. generateArray() (PAMI.extras.generateDatabase.generateTransactionalDatabase.generateTransactionalDatabase method) +
  52. generateArray() (PAMI.extras.generateDatabase.generateSpatioTemporalDatabase.generateTemporalDatabase method) + +
  53. generateConditionalTree() (PAMI.frequentPattern.pyspark.parallelFPGrowth.Tree method), [1]
  54. GenerateExternalUtilityData() (PAMI.extras.syntheticDataGenerator.utilityDatabase.UtilityDataGenerator method) @@ -663,8 +665,12 @@

    G

  55. generateTemporal (class in PAMI.extras.syntheticDataGenerator.generateTemporal)
    56. -
  56. generateTemporalDatabase (class in PAMI.extras.generateDatabase.generateTemporalDatabase) +
  57. generateTemporalDatabase (class in PAMI.extras.generateDatabase.generateSpatioTemporalDatabase) + +
  58. generateTransactional (class in PAMI.extras.syntheticDataGenerator.generateTransactional)
  59. generateTransactionalDatabase (class in PAMI.extras.generateDatabase.generateTransactionalDatabase) @@ -779,9 +785,11 @@

    G

  60. (PAMI.partialPeriodicFrequentPattern.basic.GPFgrowth.Node method), [1]
    61. -
  61. getDatabaseAsDataFrame() (PAMI.extras.generateDatabase.generateTemporalDatabase.generateTemporalDatabase method) +
  62. getDatabaseAsDataFrame() (PAMI.extras.generateDatabase.generateSpatioTemporalDatabase.generateTemporalDatabase method)
    63. @@ -883,6 +891,8 @@

    G

  63. (PAMI.extras.DF2DB.sparseDF2DBPlus.SparseFormatDFPlus method)
  64. (PAMI.extras.DF2DB.SparseFormatDF.SparseFormatDF method) +
    65. +
  65. (PAMI.extras.generateDatabase.generateSpatioTemporalDatabase.generateTemporalDatabase method)
  66. (PAMI.extras.generateDatabase.generateTemporalDatabase.generateTemporalDatabase method)
    67. @@ -1029,6 +1039,8 @@

    G

  67. (PAMI.faultTolerantFrequentPattern.basic.FTFPGrowth.FTFPGrowth method), [1]
  68. (PAMI.frequentPattern.basic.Apriori.Apriori method), [1] +
    69. +
  69. (PAMI.frequentPattern.basic.Aprioribitset.Aprioribitset method)
  70. (PAMI.frequentPattern.basic.ECLAT.ECLAT method), [1]
    71. @@ -1161,12 +1173,6 @@

    G

  71. (PAMI.relativeFrequentPattern.basic.RSFPGrowth.RSFPGrowth method), [1]
  72. (PAMI.relativeHighUtilityPattern.basic.RHUIM.RHUIM method), [1] -
    73. -
  73. (PAMI.sequentialPatternMining.basic.prefixSpan.prefixSpan method), [1] -
    74. -
  74. (PAMI.sequentialPatternMining.basic.SPADE.SPADE method), [1] -
    75. -
  75. (PAMI.sequentialPatternMining.basic.SPAM.SPAM method), [1]
  76. (PAMI.stablePeriodicFrequentPattern.basic.SPPEclat.SPPEclat method), [1]
    77. @@ -1223,6 +1229,8 @@

    G

  77. (PAMI.faultTolerantFrequentPattern.basic.FTFPGrowth.FTFPGrowth method), [1]
  78. (PAMI.frequentPattern.basic.Apriori.Apriori method), [1] +
    79. +
  79. (PAMI.frequentPattern.basic.Aprioribitset.Aprioribitset method)
  80. (PAMI.frequentPattern.basic.ECLAT.ECLAT method), [1]
    81. @@ -1355,12 +1363,6 @@

    G

  81. (PAMI.relativeFrequentPattern.basic.RSFPGrowth.RSFPGrowth method), [1]
  82. (PAMI.relativeHighUtilityPattern.basic.RHUIM.RHUIM method), [1] -
    83. -
  83. (PAMI.sequentialPatternMining.basic.prefixSpan.prefixSpan method), [1] -
    84. -
  84. (PAMI.sequentialPatternMining.basic.SPADE.SPADE method), [1] -
    85. -
  85. (PAMI.sequentialPatternMining.basic.SPAM.SPAM method), [1]
  86. (PAMI.stablePeriodicFrequentPattern.basic.SPPEclat.SPPEclat method), [1]
    87. @@ -1515,6 +1517,8 @@

    G

  87. (PAMI.faultTolerantFrequentPattern.basic.FTFPGrowth.FTFPGrowth method), [1]
  88. (PAMI.frequentPattern.basic.Apriori.Apriori method), [1] +
    89. +
  89. (PAMI.frequentPattern.basic.Aprioribitset.Aprioribitset method)
  90. (PAMI.frequentPattern.basic.ECLAT.ECLAT method), [1]
    91. @@ -1647,12 +1651,6 @@

    G

  91. (PAMI.relativeFrequentPattern.basic.RSFPGrowth.RSFPGrowth method), [1]
  92. (PAMI.relativeHighUtilityPattern.basic.RHUIM.RHUIM method), [1] -
    93. -
  93. (PAMI.sequentialPatternMining.basic.prefixSpan.prefixSpan method), [1] -
    94. -
  94. (PAMI.sequentialPatternMining.basic.SPADE.SPADE method), [1] -
    95. -
  95. (PAMI.sequentialPatternMining.basic.SPAM.SPAM method), [1]
  96. (PAMI.stablePeriodicFrequentPattern.basic.SPPEclat.SPPEclat method), [1]
    97. @@ -1707,6 +1705,8 @@

    G

  97. (PAMI.faultTolerantFrequentPattern.basic.FTFPGrowth.FTFPGrowth method), [1]
  98. (PAMI.frequentPattern.basic.Apriori.Apriori method), [1] +
    99. +
  99. (PAMI.frequentPattern.basic.Aprioribitset.Aprioribitset method)
  100. (PAMI.frequentPattern.basic.ECLAT.ECLAT method), [1]
    101. @@ -1843,12 +1843,6 @@

    G

  101. (PAMI.relativeFrequentPattern.basic.RSFPGrowth.RSFPGrowth method), [1]
  102. (PAMI.relativeHighUtilityPattern.basic.RHUIM.RHUIM method), [1] -
    103. -
  103. (PAMI.sequentialPatternMining.basic.prefixSpan.prefixSpan method), [1] -
    104. -
  104. (PAMI.sequentialPatternMining.basic.SPADE.SPADE method), [1] -
    105. -
  105. (PAMI.sequentialPatternMining.basic.SPAM.SPAM method), [1]
  106. (PAMI.stablePeriodicFrequentPattern.basic.SPPEclat.SPPEclat method), [1]
    107. @@ -1904,6 +1898,8 @@

    G

  107. getPmus() (PAMI.highUtilitySpatialPattern.topk.TKSHUIM.Transaction method), [1] +
    108. +
  108. getPoint() (PAMI.extras.generateDatabase.generateSpatioTemporalDatabase.generateTemporalDatabase method)
  109. getps() (in module PAMI.partialPeriodicPattern.pyspark.parallel3PGrowth) @@ -1939,6 +1935,8 @@

    G

  110. (PAMI.faultTolerantFrequentPattern.basic.FTFPGrowth.FTFPGrowth method), [1]
  111. (PAMI.frequentPattern.basic.Apriori.Apriori method), [1] +
    112. +
  112. (PAMI.frequentPattern.basic.Aprioribitset.Aprioribitset method)
  113. (PAMI.frequentPattern.basic.ECLAT.ECLAT method), [1]
    114. @@ -2071,12 +2069,6 @@

    G

  114. (PAMI.relativeFrequentPattern.basic.RSFPGrowth.RSFPGrowth method), [1]
  115. (PAMI.relativeHighUtilityPattern.basic.RHUIM.RHUIM method), [1] -
    116. -
  116. (PAMI.sequentialPatternMining.basic.prefixSpan.prefixSpan method), [1] -
    117. -
  117. (PAMI.sequentialPatternMining.basic.SPADE.SPADE method), [1] -
    118. -
  118. (PAMI.sequentialPatternMining.basic.SPAM.SPAM method), [1]
  119. (PAMI.stablePeriodicFrequentPattern.basic.SPPEclat.SPPEclat method), [1]
    120. @@ -2119,8 +2111,6 @@

    G

  120. (PAMI.weightedUncertainFrequentPattern.basic.WUFIM.WUFIM method)
    121. -
  121. getSameSeq() (PAMI.sequentialPatternMining.basic.prefixSpan.prefixSpan method), [1] -
  122. getSequenceSize() (PAMI.extras.dbStats.SequentialDatabase.SequentialDatabase method)
    123. -
  123. items (PAMI.extras.generateDatabase.generateSpatioTemporalDatabase.spatioTemporalDatabaseGenerator attribute) -
    124. @@ -2531,36 +2519,6 @@

    M

  124. (in module PAMI.highUtilitySpatialPattern.topk.TKSHUIM), [1]
    125. -
  125. make1LenDatabase() (PAMI.sequentialPatternMining.basic.SPADE.SPADE method), [1] -
    126. -
  126. make2BitDatabase() (PAMI.sequentialPatternMining.basic.SPAM.SPAM method), [1] -
    127. -
  127. make2LenDatabase() (PAMI.sequentialPatternMining.basic.SPADE.SPADE method), [1] -
    128. -
  128. make3LenDatabase() (PAMI.sequentialPatternMining.basic.SPADE.SPADE method), [1] -
    129. -
  129. makeNext() (PAMI.sequentialPatternMining.basic.prefixSpan.prefixSpan method), [1] -
    130. -
  130. makeNextRow() (PAMI.sequentialPatternMining.basic.SPADE.SPADE method), [1] -
    131. -
  131. makeNextRowSame() (PAMI.sequentialPatternMining.basic.SPADE.SPADE method), [1] -
    132. -
  132. makeNextRowSame2() (PAMI.sequentialPatternMining.basic.SPADE.SPADE method), [1] -
    133. -
  133. makeNextRowSame3() (PAMI.sequentialPatternMining.basic.SPADE.SPADE method), [1] -
    134. -
  134. makeNextSame() (PAMI.sequentialPatternMining.basic.prefixSpan.prefixSpan method), [1] -
    135. -
  135. makeSeqDatabaseFirst() (PAMI.sequentialPatternMining.basic.prefixSpan.prefixSpan method), [1] -
    136. -
  136. makeSeqDatabaseSame() (PAMI.sequentialPatternMining.basic.prefixSpan.prefixSpan method), [1] -
    137. -
  137. makeSupDatabase() (PAMI.sequentialPatternMining.basic.prefixSpan.prefixSpan method), [1] -
    138. -
  138. makexLenDatabase() (PAMI.sequentialPatternMining.basic.SPADE.SPADE method), [1] -
    139. -
  139. makexLenDatabaseSame() (PAMI.sequentialPatternMining.basic.SPADE.SPADE method), [1] -
  140. mapNeighbours() (PAMI.georeferencedPartialPeriodicPattern.basic.STEclat.STEclat method), [1]
  141. mine() (PAMI.stablePeriodicFrequentPattern.basic.SPPEclat.SPPEclat method), [1] @@ -2996,6 +2952,8 @@

    M

  142. PAMI.frequentPattern.basic.abstract
  143. PAMI.frequentPattern.basic.Apriori, [1] +
    144. +
  144. PAMI.frequentPattern.basic.Aprioribitset
  145. PAMI.frequentPattern.basic.ECLAT, [1]
    146. @@ -3340,20 +3298,8 @@

    M

  146. PAMI.sequentialPatternMining
  147. PAMI.sequentialPatternMining.basic -
    148. -
  148. PAMI.sequentialPatternMining.basic.abstract -
    149. -
  149. PAMI.sequentialPatternMining.basic.prefixSpan, [1] -
    150. -
  150. PAMI.sequentialPatternMining.basic.SPADE, [1] -
    151. -
  151. PAMI.sequentialPatternMining.basic.SPAM, [1]
  152. PAMI.sequentialPatternMining.closed -
    153. -
  153. PAMI.sequentialPatternMining.closed.abstract -
    154. -
  154. PAMI.sequentialPatternMining.closed.bide, [1]
  155. PAMI.stablePeriodicFrequentPattern
    156. @@ -3568,8 +3514,6 @@

    O

  156. (PAMI.subgraphMining.topK.DFSCode.DfsCode method)
    157. -
  157. outFileName (PAMI.extras.generateDatabase.generateSpatioTemporalDatabase.spatioTemporalDatabaseGenerator attribute) -
  158. output() (PAMI.highUtilitySpatialPattern.topk.TKSHUIM.TKSHUIM method), [1]
    159. @@ -4296,6 +4240,13 @@

    P

    +
  159. + PAMI.frequentPattern.basic.Aprioribitset + +
  160. @@ -5061,6 +5012,8 @@

    P

  161. module
    162. + +
    • PAMI.partialPeriodicPattern.basic.Gabstract @@ -5089,8 +5042,6 @@

      P

    • module, [1]
    - -
    • PAMI.partialPeriodicPattern.closed @@ -5502,34 +5453,6 @@

      P

      • -
    • - PAMI.sequentialPatternMining.basic.abstract - -
      • -
    • - PAMI.sequentialPatternMining.basic.prefixSpan - -
      • -
    • - PAMI.sequentialPatternMining.basic.SPADE - -
      • -
    • - PAMI.sequentialPatternMining.basic.SPAM - -
    • @@ -5537,20 +5460,6 @@

      P

      • -
    • - PAMI.sequentialPatternMining.closed.abstract - -
      • -
    • - PAMI.sequentialPatternMining.closed.bide - -
    • @@ -6045,9 +5954,11 @@

      P

    • (class in PAMI.partialPeriodicPattern.topk.abstract)
    -
  162. performCoinFlip() (PAMI.extras.generateDatabase.generateTemporalDatabase.generateTemporalDatabase method) +
  163. performCoinFlip() (PAMI.extras.generateDatabase.generateSpatioTemporalDatabase.generateTemporalDatabase method)
    164. @@ -6134,8 +6045,6 @@

    P

  164. predictedClass2Transaction (class in PAMI.extras.uncertaindb_convert) -
    165. -
  165. prefixSpan (class in PAMI.sequentialPatternMining.basic.prefixSpan), [1]
  166. prefixUtility (PAMI.highUtilitySpatialPattern.topk.TKSHUIM.Transaction attribute), [1]
    167. @@ -6157,6 +6066,8 @@

    P

  167. (PAMI.faultTolerantFrequentPattern.basic.FTFPGrowth.FTFPGrowth method), [1]
  168. (PAMI.frequentPattern.basic.Apriori.Apriori method), [1] +
    169. +
  169. (PAMI.frequentPattern.basic.Aprioribitset.Aprioribitset method)
  170. (PAMI.frequentPattern.basic.ECLAT.ECLAT method), [1]
    171. @@ -6287,12 +6198,6 @@

    P

  171. (PAMI.relativeFrequentPattern.basic.RSFPGrowth.RSFPGrowth method), [1]
  172. (PAMI.relativeHighUtilityPattern.basic.RHUIM.RHUIM method), [1] -
    173. -
  173. (PAMI.sequentialPatternMining.basic.prefixSpan.prefixSpan method), [1] -
    174. -
  174. (PAMI.sequentialPatternMining.basic.SPADE.SPADE method), [1] -
    175. -
  175. (PAMI.sequentialPatternMining.basic.SPAM.SPAM method), [1]
  176. (PAMI.stablePeriodicFrequentPattern.basic.SPPEclat.SPPEclat method), [1]
    177. @@ -6575,6 +6480,8 @@

    S

  177. (PAMI.faultTolerantFrequentPattern.basic.FTFPGrowth.FTFPGrowth method), [1]
  178. (PAMI.frequentPattern.basic.Apriori.Apriori method), [1] +
    179. +
  179. (PAMI.frequentPattern.basic.Aprioribitset.Aprioribitset method)
  180. (PAMI.frequentPattern.basic.ECLAT.ECLAT method), [1]
    181. @@ -6707,12 +6614,6 @@

    S

  181. (PAMI.relativeFrequentPattern.basic.RSFPGrowth.RSFPGrowth method), [1]
  182. (PAMI.relativeHighUtilityPattern.basic.RHUIM.RHUIM method), [1] -
    183. -
  183. (PAMI.sequentialPatternMining.basic.prefixSpan.prefixSpan method), [1] -
    184. -
  184. (PAMI.sequentialPatternMining.basic.SPADE.SPADE method), [1] -
    185. -
  185. (PAMI.sequentialPatternMining.basic.SPAM.SPAM method), [1]
  186. (PAMI.stablePeriodicFrequentPattern.basic.SPPEclat.SPPEclat method), [1]
    187. @@ -6755,8 +6656,6 @@

    S

  187. (PAMI.weightedUncertainFrequentPattern.basic.WUFIM.WUFIM method)
    188. -
  188. saveAsFile() (PAMI.extras.generateDatabase.generateSpatioTemporalDatabase.spatioTemporalDatabaseGenerator method) -
  189. saveAsTemporalDB() (PAMI.extras.imageProcessing.imagery2Databases.createDatabase method)
  190. saveAsTransactionalDB() (PAMI.extras.imageProcessing.imagery2Databases.createDatabase method) @@ -6775,12 +6674,12 @@

    S

  191. savePattern() (PAMI.subgraphMining.topK.tkg.TKG method)
    192. + + - -
  192. SPADE (class in PAMI.sequentialPatternMining.basic.SPADE), [1] -
    193. -
  193. SPAM (class in PAMI.sequentialPatternMining.basic.SPAM), [1] -
  194. SparseFormatDF (class in PAMI.extras.DF2DB.SparseFormatDF)
  195. SparseFormatDFPlus (class in PAMI.extras.DF2DB.sparseDF2DBPlus) @@ -6852,16 +6745,12 @@

    S

  196. SpatialECLAT (class in PAMI.georeferencedFrequentPattern.basic.SpatialECLAT), [1] -
    197. -
  197. spatioTemporalDatabaseGenerator (class in PAMI.extras.generateDatabase.generateSpatioTemporalDatabase)
  198. SPPEclat (class in PAMI.stablePeriodicFrequentPattern.basic.SPPEclat), [1]
  199. SPPGrowth (class in PAMI.stablePeriodicFrequentPattern.basic.SPPGrowth), [1]
  200. SPPList (PAMI.stablePeriodicFrequentPattern.basic.SPPGrowth.SPPGrowth attribute), [1] -
    201. -
  201. Sstep() (PAMI.sequentialPatternMining.basic.SPAM.SPAM method), [1]
  202. startConvert() (PAMI.extras.fuzzyTransformation.temporalToFuzzy.temporalToFuzzy method) @@ -6883,6 +6772,8 @@

    S

  203. (PAMI.faultTolerantFrequentPattern.basic.FTFPGrowth.FTFPGrowth method), [1]
  204. (PAMI.frequentPattern.basic.Apriori.Apriori method), [1] +
    205. +
  205. (PAMI.frequentPattern.basic.Aprioribitset.Aprioribitset method)
  206. (PAMI.frequentPattern.basic.ECLAT.ECLAT method), [1]
    207. @@ -7007,12 +6898,6 @@

    S

  207. (PAMI.relativeFrequentPattern.basic.RSFPGrowth.RSFPGrowth method), [1]
  208. (PAMI.relativeHighUtilityPattern.basic.RHUIM.RHUIM method), [1] -
    209. -
  209. (PAMI.sequentialPatternMining.basic.prefixSpan.prefixSpan method), [1] -
    210. -
  210. (PAMI.sequentialPatternMining.basic.SPADE.SPADE method), [1] -
    211. -
  211. (PAMI.sequentialPatternMining.basic.SPAM.SPAM method), [1]
  212. (PAMI.stablePeriodicFrequentPattern.basic.SPPEclat.SPPEclat method), [1]
    213. @@ -7090,8 +6975,6 @@

    T

  213. THREADED_DYNAMIC_SEARCH (PAMI.subgraphMining.topK.tkg.TKG attribute)
  214. tidToBitset() (PAMI.coveragePattern.basic.CMine.CMine method), [1] -
    215. -
  215. timestamp (PAMI.extras.generateDatabase.generateSpatioTemporalDatabase.spatioTemporalDatabaseGenerator attribute)
  216. TKG (class in PAMI.subgraphMining.topK.tkg)
    217. @@ -7109,10 +6992,10 @@

    T

  217. totalTransactions (PAMI.extras.syntheticDataGenerator.syntheticUtilityDatabase.syntheticUtilityDatabase attribute)
    218. - - +
    • TransactionalDatabase (class in PAMI.extras.dbStats.TransactionalDatabase)
      • -
    • tuning() (PAMI.extras.generateDatabase.generateTemporalDatabase.generateTemporalDatabase method) +
    • tuning() (PAMI.extras.generateDatabase.generateSpatioTemporalDatabase.generateTemporalDatabase method)
        +
      • (PAMI.extras.generateDatabase.generateTemporalDatabase.generateTemporalDatabase method) +
      • (PAMI.extras.generateDatabase.generateTransactionalDatabase.generateTransactionalDatabase method)
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zdnNac?)PE0!+JgVF_{BF>>EV=gOD)23gnjkYxD$vi-HTlEm#4)s#}@f@oFQD=FOd( zu1-3?0joST&QY9S*qBBRL7Z)#J4p`**0gLN$jyfnIsRkU0ZK zQF5MyWv1XOk&yR!>q6---l@}Q-sJP{XOM*Hk-}T``%mx&Z}rE&M$dOuw#`$u@*(fP oWH0pqe5!>GU)X6hJWUtqVcN9^(EO-ist<-|-#@1R2U0Zkt(E7))c^nh diff --git a/finalSphinxDocs/_build/html/periodicFrequentPatternbasicPFECLAT.html b/finalSphinxDocs/_build/html/periodicFrequentPatternbasicPFECLAT.html index 55912d12..04858c20 100644 --- a/finalSphinxDocs/_build/html/periodicFrequentPatternbasicPFECLAT.html +++ b/finalSphinxDocs/_build/html/periodicFrequentPatternbasicPFECLAT.html @@ -127,6 +127,7 @@
        class PAMI.periodicFrequentPattern.basic.PFECLAT.PFECLAT(iFile, minSup, maxPer, sep='\t')[source]

        Bases: _periodicFrequentPatterns

        +

        About this algorithm

        Description:

        PFECLAT is the fundamental approach to mine the periodic-frequent patterns.

        @@ -175,6 +176,7 @@
    +

    Execution methods

    Terminal command

    Format:
 
@@ -223,6 +225,7 @@
 print("Total ExecutionTime in seconds:", run)
    +

    Credits:

    The complete program was written by P. Likhitha and revised by Tarun Sreepada under the supervision of Professor Rage Uday Kiran.

    diff --git a/finalSphinxDocs/_build/html/periodicFrequentPatternbasicPFPGrowth.html b/finalSphinxDocs/_build/html/periodicFrequentPatternbasicPFPGrowth.html index d3f3a8ac..21b072ff 100644 --- a/finalSphinxDocs/_build/html/periodicFrequentPatternbasicPFPGrowth.html +++ b/finalSphinxDocs/_build/html/periodicFrequentPatternbasicPFPGrowth.html @@ -127,6 +127,7 @@
    class PAMI.periodicFrequentPattern.basic.PFPGrowth.PFPGrowth(iFile, minSup, maxPer, sep='\t')[source]

    Bases: _periodicFrequentPatterns

    +

    About this algorithm

    Description:

    PFPGrowth is one of the fundamental algorithm to discover periodic-frequent patterns in a transactional database.

    @@ -175,6 +176,7 @@
    +

    Execution methods

    Terminal command

    Format:
 
@@ -223,6 +225,7 @@
 print("Total ExecutionTime in seconds:", run)
    +

    Credits

    The complete program was written by P. Likhitha and revised by Tarun Sreepada under the supervision of Professor Rage Uday Kiran.

    diff --git a/finalSphinxDocs/_build/html/py-modindex.html b/finalSphinxDocs/_build/html/py-modindex.html index 362bf46f..bde0d997 100644 --- a/finalSphinxDocs/_build/html/py-modindex.html +++ b/finalSphinxDocs/_build/html/py-modindex.html @@ -617,6 +617,11 @@

    Python Module Index

        PAMI.frequentPattern.basic.Apriori + + +     + PAMI.frequentPattern.basic.Aprioribitset +     @@ -1477,41 +1482,11 @@

    Python Module Index

        PAMI.sequentialPatternMining.basic - - -     - PAMI.sequentialPatternMining.basic.abstract - - - -     - PAMI.sequentialPatternMining.basic.prefixSpan - - - -     - PAMI.sequentialPatternMining.basic.SPADE - - - -     - PAMI.sequentialPatternMining.basic.SPAM -     PAMI.sequentialPatternMining.closed - - -     - PAMI.sequentialPatternMining.closed.abstract - - - -     - PAMI.sequentialPatternMining.closed.bide -     diff --git a/finalSphinxDocs/_build/html/searchindex.js b/finalSphinxDocs/_build/html/searchindex.js index 47b4d360..1ba6e09f 100644 --- a/finalSphinxDocs/_build/html/searchindex.js +++ b/finalSphinxDocs/_build/html/searchindex.js @@ -1 +1 @@ -Search.setIndex({"docnames": ["ContiguousFrequentPatterns1", "CorrelatedPatternMining1", "CoveragePatternMining1", "FaultTolerantPatternMining1", "FrequentPatternWithMultipleMinimumSupport1", "FuzzyCorrelatedPatternMining1", "FuzzyFrequentPatternMining1", "FuzzyGeoReferencedFrequentPatternMining1", "FuzzyGeoReferencedPeriodicFrequentPatternMining1", "FuzzyPeriodicFrequentPatternMining1", "GeoReferencedFrequentPatternMining1", "GeoReferencedFrequentSequencePatternMining1", "GeoReferencedPartialPeriodicPatternMining1", "GeoReferencedPeriodicFrequentPatternMining1", "HighUtilityFrequentPatternMining1", "HighUtilityGeo-referencedFrequentPatternMining1", "HighUtilityPatternMining1", "HighUtilitySpatialPatternMining1", "LocalPeriodicPatternMining1", "MultiplePartialPeriodicPatternMining1", "PAMI", "PAMI.AssociationRules", "PAMI.AssociationRules.basic", "PAMI.correlatedPattern", "PAMI.correlatedPattern.basic", "PAMI.coveragePattern", "PAMI.coveragePattern.basic", "PAMI.extras", "PAMI.extras.DF2DB", "PAMI.extras.calculateMISValues", "PAMI.extras.dbStats", "PAMI.extras.fuzzyTransformation", "PAMI.extras.generateDatabase", "PAMI.extras.graph", "PAMI.extras.image2Database", "PAMI.extras.imageProcessing", "PAMI.extras.messaging", "PAMI.extras.neighbours", "PAMI.extras.sampleDatasets", "PAMI.extras.stats", "PAMI.extras.syntheticDataGenerator", "PAMI.extras.visualize", "PAMI.faultTolerantFrequentPattern", "PAMI.faultTolerantFrequentPattern.basic", "PAMI.frequentPattern", "PAMI.frequentPattern.basic", "PAMI.frequentPattern.closed", "PAMI.frequentPattern.cuda", "PAMI.frequentPattern.maximal", "PAMI.frequentPattern.pyspark", "PAMI.frequentPattern.topk", "PAMI.fuzzyCorrelatedPattern", "PAMI.fuzzyCorrelatedPattern.basic", "PAMI.fuzzyFrequentPattern", "PAMI.fuzzyFrequentPattern.basic", "PAMI.fuzzyGeoreferencedFrequentPattern", "PAMI.fuzzyGeoreferencedFrequentPattern.basic", "PAMI.fuzzyGeoreferencedPeriodicFrequentPattern", "PAMI.fuzzyGeoreferencedPeriodicFrequentPattern.basic", "PAMI.fuzzyPartialPeriodicPatterns", "PAMI.fuzzyPartialPeriodicPatterns.basic", "PAMI.fuzzyPeriodicFrequentPattern", "PAMI.fuzzyPeriodicFrequentPattern.basic", "PAMI.geoReferencedPeriodicFrequentPattern", "PAMI.geoReferencedPeriodicFrequentPattern.basic", "PAMI.georeferencedFrequentPattern", "PAMI.georeferencedFrequentPattern.basic", "PAMI.georeferencedFrequentSequencePattern", "PAMI.georeferencedPartialPeriodicPattern", "PAMI.georeferencedPartialPeriodicPattern.basic", "PAMI.highUtilityFrequentPattern", "PAMI.highUtilityFrequentPattern.basic", "PAMI.highUtilityGeoreferencedFrequentPattern", "PAMI.highUtilityGeoreferencedFrequentPattern.basic", "PAMI.highUtilityPattern", "PAMI.highUtilityPattern.basic", "PAMI.highUtilityPattern.parallel", "PAMI.highUtilityPatternsInStreams", "PAMI.highUtilitySpatialPattern", "PAMI.highUtilitySpatialPattern.basic", "PAMI.highUtilitySpatialPattern.topk", "PAMI.localPeriodicPattern", "PAMI.localPeriodicPattern.basic", "PAMI.multipleMinimumSupportBasedFrequentPattern", "PAMI.multipleMinimumSupportBasedFrequentPattern.basic", "PAMI.partialPeriodicFrequentPattern", "PAMI.partialPeriodicFrequentPattern.basic", "PAMI.partialPeriodicPattern", "PAMI.partialPeriodicPattern.basic", "PAMI.partialPeriodicPattern.closed", "PAMI.partialPeriodicPattern.maximal", "PAMI.partialPeriodicPattern.pyspark", "PAMI.partialPeriodicPattern.topk", "PAMI.partialPeriodicPatternInMultipleTimeSeries", "PAMI.periodicCorrelatedPattern", "PAMI.periodicCorrelatedPattern.basic", "PAMI.periodicFrequentPattern", "PAMI.periodicFrequentPattern.basic", "PAMI.periodicFrequentPattern.closed", "PAMI.periodicFrequentPattern.cuda", "PAMI.periodicFrequentPattern.maximal", "PAMI.periodicFrequentPattern.pyspark", "PAMI.periodicFrequentPattern.topk", "PAMI.periodicFrequentPattern.topk.TopkPFP", "PAMI.periodicFrequentPattern.topk.kPFPMiner", "PAMI.recurringPattern", "PAMI.recurringPattern.basic", "PAMI.relativeFrequentPattern", "PAMI.relativeFrequentPattern.basic", "PAMI.relativeHighUtilityPattern", "PAMI.relativeHighUtilityPattern.basic", "PAMI.sequence", "PAMI.sequentialPatternMining", "PAMI.sequentialPatternMining.basic", "PAMI.sequentialPatternMining.closed", "PAMI.stablePeriodicFrequentPattern", "PAMI.stablePeriodicFrequentPattern.basic", "PAMI.stablePeriodicFrequentPattern.topK", "PAMI.subgraphMining", "PAMI.subgraphMining.basic", "PAMI.subgraphMining.topK", "PAMI.uncertainFaultTolerantFrequentPattern", "PAMI.uncertainFrequentPattern", "PAMI.uncertainFrequentPattern.basic", "PAMI.uncertainGeoreferencedFrequentPattern", "PAMI.uncertainGeoreferencedFrequentPattern.basic", "PAMI.uncertainPeriodicFrequentPattern", "PAMI.uncertainPeriodicFrequentPattern.basic", "PAMI.weightedFrequentNeighbourhoodPattern", "PAMI.weightedFrequentNeighbourhoodPattern.basic", "PAMI.weightedFrequentPattern", "PAMI.weightedFrequentPattern.basic", "PAMI.weightedFrequentRegularPattern", "PAMI.weightedFrequentRegularPattern.basic", "PAMI.weightedUncertainFrequentPattern", "PAMI.weightedUncertainFrequentPattern.basic", "PartialPeriodicFrequentPatternMining1", "PartialPeriodicPatternMining1", "PeriodicCorrelatedPatternMining1", "PeriodicFrequentPatternMining1", "RecurringPatternMining1", "RelativeHighUtilityPatternMining1", "SequentialFrequentPatternMining1", "StablePeriodicPatternMining1", "UncertainFrequentPatternMining1", "UncertainGeoReferencedFrequentPatternMining1", "UncertainPeriodicFrequentPatternMining1", "WeightedFrequentNeighbourhoodPatternMining1", "WeightedFrequentPatternMining1", "WeightedFrequentRegularPatternMining1", "contiguousFrequentPatterns", "contiguousPatternMining", "correlatedPatternBasicCoMine", "correlatedPatternBasicCoMinePlus", "correlatedPatternMining", "coveragePatternBasicCMine", "coveragePatternBasicCPPG", "coveragePatternMining", "faultTolerantFrequentPatternBasicFTApriori", "faultTolerantFrequentPatternBasicFTFPGrowth", "faultTolerantPatternMining", "frequent1", "frequentPatternBasicApriori", "frequentPatternBasicECLAT", "frequentPatternBasicECLATDiffset", "frequentPatternBasicECLATbitset", "frequentPatternBasicFPGrowth", "frequentPatternCUDAcuApriori", "frequentPatternCUDAcuAprioriBit", "frequentPatternCUDAcuAprioriGCT", 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"multipleMinimumSupportBasedFrequentPatternBasicCFPGrowthPlus", "multiplePartialPeriodicPatternMining", "multipleTimeseriesPatternMining", "partialPeriodicFrequentPatternMining", "partialPeriodicFrequentPatternbasicGPFgrowth", "partialPeriodicFrequentPatternbasicPPF_DFS", "partialPeriodicPatternInMultipleTimeSeriesPPGrowth", "partialPeriodicPatternMining", "partialPeriodicPatternbasicGThreePGrowth", "partialPeriodicPatternbasicPPPGrowth", "partialPeriodicPatternbasicPPP_ECLAT", "partialPeriodicPatternclosedPPPClose", "partialPeriodicPatternmaximalMax3PGrowth", "partialPeriodicPatterntopkk3PMiner", "periodicCorrelatedPatternMining", "periodicCorrelatedPatternbasicEPCPGrowth", "periodicFrequentPatternMining", "periodicFrequentPatternbasicPFECLAT", "periodicFrequentPatternbasicPFPGrowth", "periodicFrequentPatternbasicPFPGrowthPlus", "periodicFrequentPatternbasicPFPMC", "periodicFrequentPatternbasicPSGrowth", "periodicFrequentPatternclosedCPFPMiner", "periodicFrequentPatternmaximalMaxPFGrowth", "periodicFrequentPatterntopkTopkPFPTopkPFP", "periodicFrequentPatterntopkkPFPMinerkPFPMiner", "recurringPatternMining", "recurringPatternbasicRPGrowth", "relativeFrequent", "relativeFrequentPattern", "relativeFrequentPatternBasicRSFPGrowth", "relativeHighUtilityPatternBasicRHUIM", "relativeHighUtilityPatternMining", "sequentialFrequentPatternMining", "sequentialPatternMining", "sequentialPatternMiningBasicSPADE", "sequentialPatternMiningBasicSPAM", "sequentialPatternMiningBasicprefixSpan", "sequentialPatternMiningClosedbide", "stablePeriodicFrequentPatternbasicSPPEclat", "stablePeriodicFrequentPatternbasicSPPGrowth", "stablePeriodicFrequentPatterntopKTSPIN", "stablePeriodicPatternMining", "temporalPatternMining", "transactionalPatternMining", "uncertainFrequentPatternBasicCUFPTree", "uncertainFrequentPatternBasicPUFGrowth", "uncertainFrequentPatternBasicTUFP", "uncertainFrequentPatternBasicTubeP", "uncertainFrequentPatternBasicTubeS", "uncertainFrequentPatternBasicUFGrowth", "uncertainFrequentPatternBasicUVECLAT", "uncertainFrequentPatternMining", "uncertainGeoReferencedFrequentPatternMining", "uncertainGeoreferencedFrequentPatternBasicGFPGrowth", "uncertainPatternMining", "uncertainPeriodicFrequentPatternBasicUPFPGrowth", "uncertainPeriodicFrequentPatternBasicUPFPGrowthPlus", "uncertainPeriodicFrequentPatternMining", "utilityPatternMining", "weightedFrequentNeighbourhoodPatternBasicSWFPGrowth", "weightedFrequentNeighbourhoodPatternMining", "weightedFrequentPatternBasicWFIM", "weightedFrequentPatternMining", "weightedFrequentRegularPatternBasicWFRIMiner", "weightedFrequentRegularPatternMining"], "filenames": ["ContiguousFrequentPatterns1.rst", "CorrelatedPatternMining1.rst", "CoveragePatternMining1.rst", "FaultTolerantPatternMining1.rst", "FrequentPatternWithMultipleMinimumSupport1.rst", "FuzzyCorrelatedPatternMining1.rst", "FuzzyFrequentPatternMining1.rst", "FuzzyGeoReferencedFrequentPatternMining1.rst", 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"PAMI.multipleMinimumSupportBasedFrequentPattern.rst", "PAMI.multipleMinimumSupportBasedFrequentPattern.basic.rst", "PAMI.partialPeriodicFrequentPattern.rst", "PAMI.partialPeriodicFrequentPattern.basic.rst", "PAMI.partialPeriodicPattern.rst", "PAMI.partialPeriodicPattern.basic.rst", "PAMI.partialPeriodicPattern.closed.rst", "PAMI.partialPeriodicPattern.maximal.rst", "PAMI.partialPeriodicPattern.pyspark.rst", "PAMI.partialPeriodicPattern.topk.rst", "PAMI.partialPeriodicPatternInMultipleTimeSeries.rst", "PAMI.periodicCorrelatedPattern.rst", "PAMI.periodicCorrelatedPattern.basic.rst", "PAMI.periodicFrequentPattern.rst", "PAMI.periodicFrequentPattern.basic.rst", "PAMI.periodicFrequentPattern.closed.rst", "PAMI.periodicFrequentPattern.cuda.rst", "PAMI.periodicFrequentPattern.maximal.rst", "PAMI.periodicFrequentPattern.pyspark.rst", "PAMI.periodicFrequentPattern.topk.rst", "PAMI.periodicFrequentPattern.topk.TopkPFP.rst", "PAMI.periodicFrequentPattern.topk.kPFPMiner.rst", "PAMI.recurringPattern.rst", "PAMI.recurringPattern.basic.rst", "PAMI.relativeFrequentPattern.rst", "PAMI.relativeFrequentPattern.basic.rst", "PAMI.relativeHighUtilityPattern.rst", "PAMI.relativeHighUtilityPattern.basic.rst", "PAMI.sequence.rst", "PAMI.sequentialPatternMining.rst", "PAMI.sequentialPatternMining.basic.rst", "PAMI.sequentialPatternMining.closed.rst", "PAMI.stablePeriodicFrequentPattern.rst", "PAMI.stablePeriodicFrequentPattern.basic.rst", "PAMI.stablePeriodicFrequentPattern.topK.rst", "PAMI.subgraphMining.rst", "PAMI.subgraphMining.basic.rst", "PAMI.subgraphMining.topK.rst", "PAMI.uncertainFaultTolerantFrequentPattern.rst", "PAMI.uncertainFrequentPattern.rst", "PAMI.uncertainFrequentPattern.basic.rst", "PAMI.uncertainGeoreferencedFrequentPattern.rst", "PAMI.uncertainGeoreferencedFrequentPattern.basic.rst", "PAMI.uncertainPeriodicFrequentPattern.rst", "PAMI.uncertainPeriodicFrequentPattern.basic.rst", "PAMI.weightedFrequentNeighbourhoodPattern.rst", "PAMI.weightedFrequentNeighbourhoodPattern.basic.rst", "PAMI.weightedFrequentPattern.rst", "PAMI.weightedFrequentPattern.basic.rst", "PAMI.weightedFrequentRegularPattern.rst", "PAMI.weightedFrequentRegularPattern.basic.rst", "PAMI.weightedUncertainFrequentPattern.rst", "PAMI.weightedUncertainFrequentPattern.basic.rst", "PartialPeriodicFrequentPatternMining1.rst", "PartialPeriodicPatternMining1.rst", "PeriodicCorrelatedPatternMining1.rst", "PeriodicFrequentPatternMining1.rst", "RecurringPatternMining1.rst", "RelativeHighUtilityPatternMining1.rst", "SequentialFrequentPatternMining1.rst", "StablePeriodicPatternMining1.rst", "UncertainFrequentPatternMining1.rst", "UncertainGeoReferencedFrequentPatternMining1.rst", "UncertainPeriodicFrequentPatternMining1.rst", "WeightedFrequentNeighbourhoodPatternMining1.rst", "WeightedFrequentPatternMining1.rst", "WeightedFrequentRegularPatternMining1.rst", "contiguousFrequentPatterns.rst", "contiguousPatternMining.rst", "correlatedPatternBasicCoMine.rst", "correlatedPatternBasicCoMinePlus.rst", "correlatedPatternMining.rst", "coveragePatternBasicCMine.rst", "coveragePatternBasicCPPG.rst", "coveragePatternMining.rst", "faultTolerantFrequentPatternBasicFTApriori.rst", "faultTolerantFrequentPatternBasicFTFPGrowth.rst", "faultTolerantPatternMining.rst", "frequent1.rst", "frequentPatternBasicApriori.rst", "frequentPatternBasicECLAT.rst", "frequentPatternBasicECLATDiffset.rst", "frequentPatternBasicECLATbitset.rst", "frequentPatternBasicFPGrowth.rst", "frequentPatternCUDAcuApriori.rst", "frequentPatternCUDAcuAprioriBit.rst", "frequentPatternCUDAcuAprioriGCT.rst", "frequentPatternCUDAcuAprioriTID.rst", "frequentPatternCUDAcuECLAT.rst", "frequentPatternCUDAcuECLATBit.rst", "frequentPatternCUDAcuECLATGCT.rst", "frequentPatternMaximalmaxFPGrowth.rst", "frequentPatternMining.rst", "frequentPatternPysparkParallelApriori.rst", "frequentPatternPysparkParallelECLAT.rst", "frequentPatternPysparkParallelFPGrowth.rst", "frequentPatternTopkFAE.rst", "frequentPatternWithMultipleMinimumSupport.rst", "frequentPatternclosedCHARM.rst", "fuzzyCorrelatedPatternMining.rst", "fuzzyCorrelatedPatternbasicFCPGrowth.rst", "fuzzyFrequentPatternMining.rst", "fuzzyFrequentPatternbasicFFIMiner.rst", "fuzzyGeoReferencedFrequentPatternMining.rst", "fuzzyGeoReferencedPeriodicFrequentPatternMining.rst", "fuzzyGeoreferencedFrequentPatternbasicFFSPMiner.rst", "fuzzyGeoreferencedPeriodicFrequentPatternbasicFGPFPMiner.rst", "fuzzyPatternMining.rst", "fuzzyPeriodicFrequentPatternMining.rst", "fuzzyPeriodicFrequentPatternbasicFPFPMiner.rst", "geoReferencedFrequentPatternMining.rst", "geoReferencedFrequentSequencePatternMining.rst", "geoReferencedPartialPeriodicPatternMining.rst", "geoReferencedPatternMining.rst", "geoReferencedPeriodicFrequentPatternMining.rst", "geoReferencedPeriodicFrequentPatternbasicGPFPMiner.rst", "georeferencedFrequentPatternbasicFSPGrowth.rst", "georeferencedFrequentPatternbasicSpatialECLAT.rst", "georeferencedPartialPeriodicPatternbasicSTEclat.rst", "highUtilityFrequentPatternBasicHUFIM.rst", "highUtilityFrequentPatternMining.rst", "highUtilityGeo-referencedFrequentPatternMining.rst", "highUtilityGeoreferencedFrequentPatternBasicSHUFIM.rst", "highUtilityPatternBasicEFIM.rst", "highUtilityPatternBasicHMiner.rst", "highUtilityPatternBasicUPGrowth.rst", "highUtilityPatternMining.rst", "highUtilitySpatialPatternBasicHDSHUIM.rst", "highUtilitySpatialPatternBasicSHUIM.rst", "highUtilitySpatialPatternMining.rst", "highUtilitySpatialPatternTopkTKSHUIM.rst", "index.rst", "localPeriodicPatternMining.rst", "localPeriodicPatternbasicLPPGrowth.rst", "localPeriodicPatternbasicLPPMBreadth.rst", "localPeriodicPatternbasicLPPMDepth.rst", "modules.rst", "multipleMinimumSupportBasedFrequentPatternBasicCFPGrowth.rst", "multipleMinimumSupportBasedFrequentPatternBasicCFPGrowthPlus.rst", "multiplePartialPeriodicPatternMining.rst", "multipleTimeseriesPatternMining.rst", "partialPeriodicFrequentPatternMining.rst", "partialPeriodicFrequentPatternbasicGPFgrowth.rst", "partialPeriodicFrequentPatternbasicPPF_DFS.rst", "partialPeriodicPatternInMultipleTimeSeriesPPGrowth.rst", "partialPeriodicPatternMining.rst", "partialPeriodicPatternbasicGThreePGrowth.rst", "partialPeriodicPatternbasicPPPGrowth.rst", "partialPeriodicPatternbasicPPP_ECLAT.rst", "partialPeriodicPatternclosedPPPClose.rst", "partialPeriodicPatternmaximalMax3PGrowth.rst", "partialPeriodicPatterntopkk3PMiner.rst", "periodicCorrelatedPatternMining.rst", "periodicCorrelatedPatternbasicEPCPGrowth.rst", "periodicFrequentPatternMining.rst", "periodicFrequentPatternbasicPFECLAT.rst", "periodicFrequentPatternbasicPFPGrowth.rst", "periodicFrequentPatternbasicPFPGrowthPlus.rst", "periodicFrequentPatternbasicPFPMC.rst", "periodicFrequentPatternbasicPSGrowth.rst", "periodicFrequentPatternclosedCPFPMiner.rst", "periodicFrequentPatternmaximalMaxPFGrowth.rst", "periodicFrequentPatterntopkTopkPFPTopkPFP.rst", "periodicFrequentPatterntopkkPFPMinerkPFPMiner.rst", "recurringPatternMining.rst", "recurringPatternbasicRPGrowth.rst", "relativeFrequent.rst", "relativeFrequentPattern.rst", "relativeFrequentPatternBasicRSFPGrowth.rst", "relativeHighUtilityPatternBasicRHUIM.rst", "relativeHighUtilityPatternMining.rst", "sequentialFrequentPatternMining.rst", "sequentialPatternMining.rst", "sequentialPatternMiningBasicSPADE.rst", "sequentialPatternMiningBasicSPAM.rst", "sequentialPatternMiningBasicprefixSpan.rst", "sequentialPatternMiningClosedbide.rst", "stablePeriodicFrequentPatternbasicSPPEclat.rst", "stablePeriodicFrequentPatternbasicSPPGrowth.rst", "stablePeriodicFrequentPatterntopKTSPIN.rst", "stablePeriodicPatternMining.rst", "temporalPatternMining.rst", "transactionalPatternMining.rst", "uncertainFrequentPatternBasicCUFPTree.rst", "uncertainFrequentPatternBasicPUFGrowth.rst", "uncertainFrequentPatternBasicTUFP.rst", "uncertainFrequentPatternBasicTubeP.rst", "uncertainFrequentPatternBasicTubeS.rst", "uncertainFrequentPatternBasicUFGrowth.rst", "uncertainFrequentPatternBasicUVECLAT.rst", "uncertainFrequentPatternMining.rst", "uncertainGeoReferencedFrequentPatternMining.rst", "uncertainGeoreferencedFrequentPatternBasicGFPGrowth.rst", "uncertainPatternMining.rst", "uncertainPeriodicFrequentPatternBasicUPFPGrowth.rst", "uncertainPeriodicFrequentPatternBasicUPFPGrowthPlus.rst", "uncertainPeriodicFrequentPatternMining.rst", "utilityPatternMining.rst", "weightedFrequentNeighbourhoodPatternBasicSWFPGrowth.rst", "weightedFrequentNeighbourhoodPatternMining.rst", "weightedFrequentPatternBasicWFIM.rst", "weightedFrequentPatternMining.rst", "weightedFrequentRegularPatternBasicWFRIMiner.rst", "weightedFrequentRegularPatternMining.rst"], "titles": ["Contiguous Frequent Patterns", "Correlated Pattern Mining", "Coverage Pattern Mining", "Fault-Tolerant Frequent Pattern Mining", "Frequent pattern With Multiple Minimum Support", "Fuzzy Correlated Pattern Mining", "Fuzzy Frequent Pattern Mining", "Fuzzy Geo-referenced Frequent Pattern Mining", "Fuzzy Geo-referenced Periodic Frequent Pattern Mining", "Fuzzy Periodic Frequent Pattern Mining", "Geo-referenced Frequent Pattern Mining", "Geo-referenced Frequent Sequence Pattern mining", "Geo-referenced Partial Periodic Pattern Mining", "Geo-referenced Periodic Frequent Pattern Mining", "High-Utility Frequent Pattern Mining", "High-Utility Geo-referenced Frequent Pattern Mining", "High-Utility Pattern mining", "High-Utility Spatial Pattern Mining", "Local Periodic Pattern Mining", "Multiple Partial Periodic Pattern Mining", "PAMI package", "PAMI.AssociationRules package", "PAMI.AssociationRules.basic package", "PAMI.correlatedPattern package", "PAMI.correlatedPattern.basic package", "PAMI.coveragePattern package", "PAMI.coveragePattern.basic package", "PAMI.extras package", "PAMI.extras.DF2DB package", "PAMI.extras.calculateMISValues package", "PAMI.extras.dbStats package", "PAMI.extras.fuzzyTransformation package", "PAMI.extras.generateDatabase package", "PAMI.extras.graph package", "PAMI.extras.image2Database package", "PAMI.extras.imageProcessing package", "PAMI.extras.messaging package", "PAMI.extras.neighbours package", "PAMI.extras.sampleDatasets package", "PAMI.extras.stats package", "PAMI.extras.syntheticDataGenerator package", "PAMI.extras.visualize package", "PAMI.faultTolerantFrequentPattern package", "PAMI.faultTolerantFrequentPattern.basic package", "PAMI.frequentPattern package", "PAMI.frequentPattern.basic package", "PAMI.frequentPattern.closed package", "PAMI.frequentPattern.cuda package", "PAMI.frequentPattern.maximal package", "PAMI.frequentPattern.pyspark package", "PAMI.frequentPattern.topk package", "PAMI.fuzzyCorrelatedPattern package", "PAMI.fuzzyCorrelatedPattern.basic package", "PAMI.fuzzyFrequentPattern package", "PAMI.fuzzyFrequentPattern.basic package", "PAMI.fuzzyGeoreferencedFrequentPattern package", "PAMI.fuzzyGeoreferencedFrequentPattern.basic package", "PAMI.fuzzyGeoreferencedPeriodicFrequentPattern package", "PAMI.fuzzyGeoreferencedPeriodicFrequentPattern.basic package", "PAMI.fuzzyPartialPeriodicPatterns package", "PAMI.fuzzyPartialPeriodicPatterns.basic package", "PAMI.fuzzyPeriodicFrequentPattern package", "PAMI.fuzzyPeriodicFrequentPattern.basic package", "PAMI.geoReferencedPeriodicFrequentPattern package", "PAMI.geoReferencedPeriodicFrequentPattern.basic package", "PAMI.georeferencedFrequentPattern package", "PAMI.georeferencedFrequentPattern.basic package", "PAMI.georeferencedFrequentSequencePattern package", "PAMI.georeferencedPartialPeriodicPattern package", "PAMI.georeferencedPartialPeriodicPattern.basic package", "PAMI.highUtilityFrequentPattern package", "PAMI.highUtilityFrequentPattern.basic package", "PAMI.highUtilityGeoreferencedFrequentPattern package", "PAMI.highUtilityGeoreferencedFrequentPattern.basic package", "PAMI.highUtilityPattern package", "PAMI.highUtilityPattern.basic package", "PAMI.highUtilityPattern.parallel package", "PAMI.highUtilityPatternsInStreams package", "PAMI.highUtilitySpatialPattern package", "PAMI.highUtilitySpatialPattern.basic package", "PAMI.highUtilitySpatialPattern.topk package", "PAMI.localPeriodicPattern package", "PAMI.localPeriodicPattern.basic package", "PAMI.multipleMinimumSupportBasedFrequentPattern package", "PAMI.multipleMinimumSupportBasedFrequentPattern.basic package", "PAMI.partialPeriodicFrequentPattern package", "PAMI.partialPeriodicFrequentPattern.basic package", "PAMI.partialPeriodicPattern package", "PAMI.partialPeriodicPattern.basic package", "PAMI.partialPeriodicPattern.closed package", "PAMI.partialPeriodicPattern.maximal package", "PAMI.partialPeriodicPattern.pyspark package", "PAMI.partialPeriodicPattern.topk package", "PAMI.partialPeriodicPatternInMultipleTimeSeries package", "PAMI.periodicCorrelatedPattern package", "PAMI.periodicCorrelatedPattern.basic package", "PAMI.periodicFrequentPattern package", "PAMI.periodicFrequentPattern.basic package", "PAMI.periodicFrequentPattern.closed package", "PAMI.periodicFrequentPattern.cuda package", "PAMI.periodicFrequentPattern.maximal package", "PAMI.periodicFrequentPattern.pyspark package", "PAMI.periodicFrequentPattern.topk package", "PAMI.periodicFrequentPattern.topk.TopkPFP package", "PAMI.periodicFrequentPattern.topk.kPFPMiner package", "PAMI.recurringPattern package", "PAMI.recurringPattern.basic package", "PAMI.relativeFrequentPattern package", "PAMI.relativeFrequentPattern.basic package", "PAMI.relativeHighUtilityPattern package", "PAMI.relativeHighUtilityPattern.basic package", "PAMI.sequence package", "PAMI.sequentialPatternMining package", "PAMI.sequentialPatternMining.basic package", "PAMI.sequentialPatternMining.closed package", "PAMI.stablePeriodicFrequentPattern package", "PAMI.stablePeriodicFrequentPattern.basic package", "PAMI.stablePeriodicFrequentPattern.topK package", "PAMI.subgraphMining package", "PAMI.subgraphMining.basic package", "PAMI.subgraphMining.topK package", "PAMI.uncertainFaultTolerantFrequentPattern package", "PAMI.uncertainFrequentPattern package", "PAMI.uncertainFrequentPattern.basic package", "PAMI.uncertainGeoreferencedFrequentPattern package", "PAMI.uncertainGeoreferencedFrequentPattern.basic package", "PAMI.uncertainPeriodicFrequentPattern package", "PAMI.uncertainPeriodicFrequentPattern.basic package", "PAMI.weightedFrequentNeighbourhoodPattern package", "PAMI.weightedFrequentNeighbourhoodPattern.basic package", "PAMI.weightedFrequentPattern package", "PAMI.weightedFrequentPattern.basic package", "PAMI.weightedFrequentRegularPattern package", "PAMI.weightedFrequentRegularPattern.basic package", "PAMI.weightedUncertainFrequentPattern package", "PAMI.weightedUncertainFrequentPattern.basic package", "Partial Periodic Frequent Pattern Mining", "Partial Periodic Pattern Mining", "Periodic correlated pattern mining", "Periodic Frequent Pattern Mining", "Recurring Pattern Mining", "Relative High-Utility Pattern Mining", "Sequential Frequent Pattern mining", "Stable Periodic Pattern Mining", "Uncertain Frequent Pattern mining", "Uncertain Geo-Referenced Frequent Pattern mining", "Uncertain Periodic Frequent Pattern mining", "Weighted Frequent Neighbourhood Pattern Mining", "Weighted Frequent Pattern Mining", "Weighted Frequent Regular Pattern Mining", "<no title>", "Contiguous Patterns", "CoMine", "CoMinePlus", "Basic", "CMine", "CPPG", "Basic", "FTApriori", "FTFPGrowth", "Basic", "Frequent Pattern mining", "Apriori", "ECLAT", "ECLATDiffset", "ECLATbitset", "FPGrowth", "cuApriori", "cuAprioriBit", "cudaAprioriGCT", "cudaAprioriTID", "cuEclat", "cuEclatBit", "cudaEclatGCT", "MaxFPGrowth", "Basic", "parallelApriori", "parallelECLAT", "parallelFPGrowth", "FAE", "Basic", "CHARM", "Basic", "FCPGrowth", "Basic", "FFIMiner", "Basic", "Basic", "FFSPMiner", "FGPFPMiner", "Fuzzy Pattern Mining", "Basic", "FPFPMiner", "Basic", "<no title>", "Basic", "Geo-referenced Pattern Mining", "Basic", "GPFPMiner", "FSPGrowth", "SpatialECLAT", "STEclat", "HUFIM", "Basic", "Basic", "SHUFIM", "EFIM", "HMiner", "UPGrowth", "Basic", "HDSHUIM", "SHUIM", "Basic", "TKSHUIM", "Welcome to PAMI\u2019s documentation!", "Basic", "LPPGrowth", "LPPMBreadth", "LPPMDepth", "PAMI", "CFPGrowth", "CFPGrowthPlus", "Basic", "Multiple Timeseries", "Basic", "GPFgrowth", "PPF_DFS", "PPGrowth", "Basic", "GThreePGrowth", "PPPGrowth", "PPP_ECLAT", "PPPClose", "Max3PGrowth", "k3PMiner", "Basic", "EPCPGrowth", "Basic", "PFECLAT", "PFPGrowth", "PFPGrowthPlus", "PFPMC", "PSGrowth", "CPFPMiner", "MaxPFGrowth", "TopkPFP", "kPFPMiner", "Basic", "RPGrowth", "Relative Frequent Pattern", "Basic", "RSFPGrowth", "RHUIM", "Basic", "Basic", "Sequential Database", "SPADE", "SPAM", "prefixSpan", "bide", "SPPEclat", "SPPGrowth", "TSPIN", "Basic", "Temporal Database", "Transactional Database", "CUFPTree", "PUFGrowth", "TUFP", "TubeP", "TubeS", "UFGrowth", "UVECLAT", "Basic", "Basic", "GFPGrowth", "Uncertain Database", "UPFPGrowth", "UPFPGrowthPlus", "Basic", "Utility Pattern mining", "SWFPGrowth", "Basic", "WFIM", "Basic", "WFRIMiner", "Basic"], "terms": {"ar": [1, 2, 6, 7, 8, 9, 11, 14, 15, 18, 46, 54, 56, 58, 62, 71, 73, 75, 79, 80, 82, 86, 88, 90, 91, 97, 100, 110, 113, 116, 119, 123, 125, 127, 135, 136, 138, 142, 147, 148, 149, 154, 157, 181, 184, 185, 186, 187, 188, 189, 190, 191, 192, 194, 196, 202, 203, 204, 205, 206, 211, 213, 215, 216, 217, 218, 223, 224, 225, 229, 230, 231, 233, 235, 238, 240, 241, 242, 244, 252, 254, 255, 257, 260, 266, 267, 268, 269, 270, 271, 272, 275, 276, 277, 278, 280, 282, 284, 286], "specif": [1, 13, 119, 120, 136, 138, 139, 140, 143, 154, 197, 224, 235, 237, 247, 263], "type": [1, 5, 24, 26, 28, 29, 30, 32, 39, 40, 43, 45, 46, 48, 49, 50, 52, 54, 56, 58, 60, 62, 64, 66, 69, 71, 73, 75, 76, 79, 80, 82, 84, 86, 88, 89, 90, 91, 92, 93, 95, 97, 98, 100, 101, 103, 104, 106, 108, 110, 113, 116, 117, 119, 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157, 203, 235, 249, 250, 254], "usag": [2, 14, 24, 26, 30, 39, 40, 43, 45, 46, 48, 49, 50, 52, 54, 56, 58, 60, 62, 64, 66, 69, 71, 73, 75, 76, 79, 80, 82, 97, 100, 101, 106, 108, 110, 113, 116, 120, 121, 123, 125, 127, 129, 131, 133, 135, 138, 152, 153, 155, 156, 157, 158, 159, 162, 163, 164, 165, 166, 174, 176, 177, 178, 179, 181, 183, 185, 188, 189, 190, 192, 198, 200, 201, 202, 203, 205, 206, 207, 208, 210, 211, 213, 216, 217, 218, 235, 238, 239, 241, 244, 248, 249, 250, 251, 252, 256, 257, 258, 260, 261, 266, 267, 268, 269, 270, 272, 275, 276, 277, 278, 280, 281, 283, 285], "manufactur": [2, 4, 6, 9, 143, 149, 157, 180, 184, 191, 249, 250, 263, 286], "social": [2, 157], "network": [2, 4, 9, 49, 137, 139, 148, 157, 176, 180, 191, 228, 237, 249, 250, 284], "approach": [3, 4, 5, 43, 45, 88, 91, 97, 113, 121, 123, 143, 159, 160, 166, 180, 182, 229, 230, 231, 238, 240, 241, 258, 263, 266], "aim": [3, 12, 16, 26, 73, 79, 80, 116, 138, 143, 155, 160, 195, 205, 209, 211, 213, 235, 260, 261, 263], "discov": [3, 7, 10, 11, 12, 14, 16, 24, 26, 43, 45, 46, 48, 49, 50, 52, 56, 60, 62, 66, 69, 73, 75, 76, 78, 79, 80, 84, 86, 88, 89, 90, 91, 92, 93, 95, 97, 98, 100, 101, 103, 104, 106, 110, 113, 116, 117, 121, 123, 125, 127, 129, 131, 133, 135, 138, 139, 140, 143, 152, 153, 155, 156, 158, 159, 160, 162, 163, 165, 166, 174, 176, 177, 178, 179, 181, 183, 186, 188, 192, 193, 194, 195, 200, 201, 203, 205, 209, 210, 211, 213, 220, 225, 227, 229, 230, 231, 232, 233, 234, 235, 236, 237, 239, 240, 242, 243, 244, 245, 246, 247, 248, 252, 256, 257, 258, 260, 262, 263, 266, 267, 268, 269, 270, 271, 272, 275, 277, 278, 281, 283, 285], "larg": [3, 45, 56, 66, 79, 80, 90, 97, 98, 100, 104, 110, 160, 161, 162, 188, 200, 210, 213, 233, 240, 243, 244, 246, 252], "contain": [3, 10, 33, 71, 73, 75, 76, 79, 80, 110, 119, 144, 145, 146, 160, 193, 196, 202, 205, 206, 211, 213, 223, 252, 264, 265, 273, 274, 279, 280], "both": [3, 12, 80, 113, 138, 139, 160, 195, 213, 235, 237, 256], "uncertain": 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206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 251, 252, 253, 254, 255, 256, 257, 258, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 281, 282, 283, 284, 285, 286], "python": [20, 24, 26, 45, 46, 50, 56, 58, 86, 88, 92, 97, 98, 101, 103, 116, 119, 120, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 155, 162, 163, 164, 165, 166, 179, 181, 188, 189, 225, 226, 231, 234, 238, 239, 243, 245, 260, 266, 267, 268, 269, 270, 275, 277, 278, 281, 283, 285], "librari": 20, "class": [24, 26, 27, 28, 29, 30, 31, 32, 33, 35, 36, 37, 39, 40, 41, 43, 45, 46, 48, 49, 50, 52, 54, 56, 58, 60, 62, 64, 66, 69, 71, 73, 75, 76, 78, 79, 80, 82, 84, 86, 88, 89, 90, 91, 92, 93, 95, 97, 98, 100, 101, 103, 104, 106, 108, 110, 113, 116, 117, 119, 120, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 155, 156, 158, 159, 162, 163, 164, 165, 166, 174, 176, 177, 178, 179, 181, 183, 185, 188, 189, 192, 198, 200, 201, 202, 205, 206, 207, 208, 210, 211, 213, 216, 217, 218, 220, 221, 225, 226, 227, 229, 230, 231, 232, 233, 234, 236, 238, 239, 240, 241, 242, 243, 244, 245, 246, 248, 251, 252, 256, 257, 258, 260, 261, 262, 266, 267, 268, 269, 270, 271, 272, 275, 277, 278, 281, 283, 285], "ifil": [24, 26, 27, 29, 30, 31, 33, 37, 39, 41, 43, 45, 46, 48, 49, 50, 52, 54, 56, 58, 60, 62, 64, 66, 69, 71, 73, 75, 76, 78, 79, 80, 82, 84, 86, 88, 89, 90, 91, 92, 93, 95, 97, 98, 100, 101, 103, 104, 106, 108, 110, 113, 116, 117, 119, 120, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 155, 156, 158, 159, 162, 163, 164, 165, 166, 174, 176, 177, 178, 179, 181, 183, 185, 188, 189, 192, 198, 200, 201, 202, 205, 206, 207, 208, 210, 211, 213, 216, 217, 218, 220, 221, 225, 226, 227, 229, 230, 231, 232, 233, 234, 236, 238, 239, 240, 241, 242, 243, 244, 245, 246, 248, 251, 252, 256, 257, 258, 260, 261, 262, 266, 267, 268, 269, 270, 271, 272, 275, 277, 278, 281, 283, 285], "union": [24, 28, 30, 39, 43, 62, 71, 82, 88, 100, 101, 108, 129, 152, 153, 159, 192, 202, 217, 218, 229, 244, 251, 281], "str": [24, 26, 27, 28, 29, 30, 31, 32, 33, 35, 36, 37, 39, 40, 43, 45, 46, 48, 49, 50, 52, 54, 56, 58, 60, 62, 64, 66, 69, 71, 73, 75, 76, 78, 79, 80, 82, 84, 86, 88, 89, 90, 91, 92, 93, 95, 97, 98, 100, 101, 103, 104, 106, 108, 110, 113, 116, 117, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 155, 156, 158, 159, 162, 163, 164, 165, 166, 174, 176, 177, 178, 179, 181, 183, 185, 188, 189, 192, 198, 200, 201, 202, 205, 206, 207, 208, 210, 211, 213, 216, 217, 218, 220, 221, 225, 226, 227, 229, 230, 231, 232, 233, 234, 236, 238, 239, 240, 241, 242, 243, 244, 245, 246, 248, 251, 252, 256, 257, 258, 260, 261, 262, 266, 267, 268, 269, 270, 271, 272, 275, 277, 278, 281, 283, 285], "datafram": [24, 26, 27, 28, 29, 30, 32, 33, 35, 39, 40, 43, 45, 46, 48, 49, 50, 52, 54, 56, 58, 60, 62, 64, 66, 69, 71, 73, 75, 76, 79, 80, 82, 84, 86, 88, 89, 90, 91, 92, 93, 95, 97, 98, 100, 101, 103, 104, 106, 108, 110, 113, 116, 117, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 155, 156, 158, 159, 162, 163, 164, 165, 166, 174, 176, 177, 178, 179, 181, 183, 185, 188, 189, 192, 198, 200, 201, 202, 205, 206, 207, 208, 210, 211, 213, 216, 217, 218, 220, 221, 225, 226, 227, 229, 230, 231, 232, 233, 234, 236, 238, 239, 240, 241, 242, 243, 244, 245, 246, 248, 251, 252, 256, 257, 258, 260, 261, 262, 266, 267, 268, 269, 270, 271, 272, 275, 277, 278, 281, 283, 285], "minsup": [24, 26, 33, 43, 45, 46, 48, 49, 52, 54, 56, 58, 60, 62, 64, 66, 71, 73, 75, 79, 84, 86, 93, 95, 97, 98, 100, 101, 103, 108, 110, 113, 116, 119, 120, 121, 123, 125, 127, 129, 131, 133, 135, 139, 152, 153, 156, 158, 159, 162, 163, 164, 165, 166, 174, 176, 177, 178, 181, 183, 185, 188, 189, 192, 198, 200, 202, 205, 207, 210, 211, 220, 221, 225, 226, 227, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 251, 252, 256, 257, 258, 260, 261, 266, 267, 268, 269, 270, 271, 272, 275, 277, 278, 281, 283, 285], "int": [24, 26, 27, 28, 29, 30, 32, 33, 35, 37, 39, 40, 43, 45, 46, 48, 49, 50, 52, 54, 56, 58, 60, 62, 64, 66, 69, 71, 73, 75, 76, 79, 80, 82, 84, 86, 88, 89, 90, 91, 92, 93, 95, 97, 98, 100, 101, 103, 104, 106, 108, 110, 113, 116, 117, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 155, 156, 158, 159, 162, 163, 164, 165, 166, 174, 176, 177, 178, 179, 181, 183, 185, 188, 189, 192, 198, 200, 201, 202, 205, 206, 207, 208, 210, 211, 213, 216, 217, 218, 220, 221, 225, 226, 227, 229, 230, 231, 232, 233, 234, 236, 238, 239, 240, 241, 242, 243, 244, 245, 246, 248, 251, 252, 256, 257, 258, 260, 261, 262, 266, 267, 268, 269, 270, 271, 272, 275, 277, 278, 281, 283, 285], "float": [24, 26, 27, 28, 30, 32, 35, 39, 40, 43, 45, 46, 48, 49, 50, 52, 54, 56, 58, 60, 62, 64, 66, 69, 71, 73, 75, 76, 78, 79, 80, 82, 84, 86, 88, 89, 90, 91, 92, 93, 95, 97, 98, 100, 101, 103, 104, 106, 108, 110, 113, 116, 117, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 155, 156, 158, 159, 162, 163, 164, 165, 166, 174, 176, 177, 178, 179, 181, 183, 185, 188, 189, 192, 198, 200, 201, 202, 205, 206, 207, 208, 210, 211, 213, 216, 217, 218, 220, 221, 225, 226, 227, 229, 230, 231, 232, 233, 234, 236, 238, 239, 240, 241, 242, 243, 244, 245, 246, 248, 251, 252, 256, 257, 258, 260, 261, 262, 266, 267, 268, 269, 270, 271, 272, 275, 277, 278, 281, 283, 285], "minallconf": [24, 52, 95, 152, 153, 183, 236], "sep": [24, 26, 27, 29, 30, 31, 32, 35, 39, 40, 43, 45, 46, 48, 49, 50, 52, 54, 56, 58, 60, 62, 64, 66, 69, 71, 73, 75, 76, 78, 79, 80, 82, 84, 86, 88, 89, 90, 91, 92, 93, 95, 97, 98, 100, 101, 103, 104, 106, 108, 110, 113, 116, 117, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 155, 156, 158, 159, 162, 163, 164, 165, 166, 174, 176, 177, 178, 179, 181, 183, 185, 188, 189, 190, 192, 196, 198, 200, 201, 202, 205, 206, 207, 208, 210, 211, 213, 216, 217, 218, 220, 221, 223, 225, 226, 227, 229, 230, 231, 232, 233, 234, 236, 238, 239, 240, 241, 242, 243, 244, 245, 246, 248, 251, 252, 255, 256, 257, 258, 260, 261, 262, 264, 265, 266, 267, 268, 269, 270, 271, 272, 275, 276, 277, 278, 280, 281, 283, 285], "t": [24, 26, 27, 30, 31, 32, 37, 39, 40, 43, 45, 46, 48, 49, 50, 52, 54, 56, 60, 62, 64, 66, 69, 71, 73, 75, 76, 78, 79, 80, 82, 84, 86, 88, 89, 90, 91, 92, 93, 95, 97, 98, 100, 101, 103, 104, 106, 108, 110, 113, 116, 117, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 155, 156, 158, 159, 162, 163, 164, 165, 166, 174, 176, 177, 178, 179, 181, 183, 185, 188, 190, 192, 198, 200, 201, 202, 205, 206, 207, 208, 210, 211, 213, 216, 217, 218, 220, 221, 225, 226, 227, 229, 230, 231, 232, 233, 234, 236, 238, 239, 240, 241, 242, 243, 244, 245, 246, 248, 251, 252, 256, 257, 258, 260, 261, 262, 266, 267, 268, 269, 270, 271, 272, 275, 276, 277, 278, 280, 281, 283, 285], "sourc": [24, 26, 27, 28, 29, 30, 31, 32, 33, 35, 36, 37, 39, 40, 41, 43, 45, 46, 48, 49, 50, 52, 54, 56, 58, 60, 62, 64, 66, 69, 71, 73, 75, 76, 78, 79, 80, 82, 84, 86, 88, 89, 90, 91, 92, 93, 95, 97, 98, 100, 101, 103, 104, 106, 108, 110, 113, 116, 117, 119, 120, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 155, 156, 158, 159, 162, 163, 164, 165, 166, 174, 176, 177, 178, 179, 181, 183, 185, 188, 189, 192, 198, 200, 201, 202, 205, 206, 207, 208, 210, 211, 213, 216, 217, 218, 220, 221, 225, 226, 227, 229, 230, 231, 232, 233, 234, 236, 238, 239, 240, 241, 242, 243, 244, 245, 246, 248, 251, 252, 256, 257, 258, 260, 261, 262, 266, 267, 268, 269, 270, 271, 272, 275, 277, 278, 281, 283, 285], "_correlatedpattern": [24, 152, 153], "descript": [24, 26, 27, 28, 29, 30, 31, 32, 33, 35, 39, 40, 43, 45, 46, 48, 49, 50, 52, 54, 56, 58, 60, 62, 64, 66, 69, 71, 73, 75, 76, 78, 79, 80, 82, 84, 86, 88, 89, 90, 91, 92, 93, 95, 97, 98, 100, 101, 103, 104, 106, 108, 110, 113, 116, 117, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 155, 156, 158, 159, 162, 163, 164, 165, 166, 174, 176, 177, 178, 179, 181, 183, 185, 188, 189, 192, 198, 200, 201, 202, 205, 206, 207, 208, 210, 211, 213, 216, 217, 218, 220, 221, 225, 226, 227, 229, 230, 232, 233, 234, 236, 238, 239, 240, 241, 242, 243, 244, 245, 246, 248, 251, 252, 256, 257, 258, 260, 261, 262, 266, 267, 268, 269, 270, 271, 272, 275, 277, 278, 280, 281, 283, 285], "fundament": [24, 43, 45, 48, 49, 69, 84, 88, 91, 93, 97, 100, 101, 106, 113, 121, 123, 131, 133, 152, 153, 158, 159, 162, 163, 165, 166, 174, 178, 201, 220, 227, 229, 230, 231, 238, 239, 240, 241, 242, 244, 248, 256, 257, 258, 266, 267, 268, 269, 271, 272, 283, 285], "algorithm": [24, 26, 33, 43, 45, 46, 48, 49, 50, 52, 54, 60, 64, 66, 69, 71, 75, 76, 78, 79, 80, 82, 84, 86, 89, 90, 92, 93, 95, 97, 98, 100, 101, 103, 104, 106, 108, 110, 113, 117, 119, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 155, 156, 158, 159, 162, 163, 165, 166, 174, 176, 177, 178, 179, 181, 183, 185, 198, 200, 201, 202, 206, 207, 208, 210, 211, 216, 217, 218, 220, 221, 225, 226, 227, 232, 233, 234, 236, 239, 240, 242, 243, 244, 245, 246, 248, 251, 252, 256, 257, 258, 262, 264, 265, 266, 267, 268, 269, 270, 271, 272, 275, 276, 278, 281, 283, 285], "correl": [24, 52, 95, 152, 153, 154, 182, 183, 190, 214, 235, 236, 264, 265], "fp": [24, 28, 43, 45, 49, 131, 152, 153, 159, 166, 178, 283], "growth": [24, 48, 49, 75, 82, 86, 90, 97, 100, 113, 127, 152, 153, 174, 178, 208, 216, 225, 233, 242, 244, 258, 278], "depth": [24, 46, 82, 89, 98, 113, 119, 152, 153, 181, 216, 217, 218, 232, 243, 256, 258], "first": [24, 45, 46, 49, 75, 80, 82, 86, 89, 98, 110, 113, 119, 152, 153, 162, 178, 181, 206, 213, 216, 217, 218, 223, 225, 232, 243, 252, 256, 257, 258, 264, 276], "search": [24, 43, 45, 46, 49, 54, 56, 58, 62, 75, 76, 82, 84, 89, 98, 113, 119, 121, 131, 133, 152, 153, 158, 159, 162, 166, 176, 177, 178, 181, 185, 188, 189, 192, 214, 216, 217, 218, 221, 232, 243, 256, 257, 258, 283, 285], "lee": [24, 97, 127, 152, 153, 239, 277], "y": [24, 32, 33, 43, 45, 71, 97, 104, 116, 117, 119, 120, 152, 153, 159, 166, 202, 238, 246, 261, 262], "kim": [24, 152, 153], "w": [24, 79, 116, 127, 152, 153, 211, 260, 277], "cao": [24, 152, 153], "d": [24, 152, 153, 255, 264, 265], "han": [24, 43, 45, 84, 113, 152, 153, 158, 159, 166, 220, 258], "j": [24, 43, 45, 46, 48, 71, 73, 75, 76, 79, 80, 82, 84, 86, 88, 89, 91, 97, 110, 113, 116, 119, 120, 123, 127, 131, 152, 153, 159, 166, 174, 181, 202, 205, 206, 211, 213, 216, 217, 218, 220, 225, 231, 232, 240, 252, 256, 257, 258, 260, 266, 277, 283], "2003": [24, 45, 152, 153, 164], "icdm": [24, 123, 152, 153, 270], "pp": [24, 45, 49, 56, 62, 69, 79, 80, 93, 97, 110, 116, 129, 131, 133, 152, 153, 162, 176, 188, 192, 201, 210, 213, 227, 242, 252, 260, 281, 283, 285], "581": [24, 152, 153], "584": [24, 152, 153], "paramet": [24, 26, 27, 28, 29, 31, 32, 33, 35, 40, 43, 45, 46, 48, 49, 50, 52, 54, 56, 58, 60, 62, 64, 66, 69, 71, 73, 75, 76, 78, 79, 80, 82, 84, 86, 88, 89, 90, 91, 92, 93, 95, 97, 98, 100, 101, 103, 104, 106, 108, 110, 113, 116, 117, 119, 120, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 155, 156, 158, 159, 162, 163, 164, 165, 166, 174, 176, 177, 178, 179, 181, 183, 185, 188, 189, 192, 198, 200, 201, 202, 205, 206, 207, 208, 210, 211, 213, 216, 217, 218, 220, 221, 225, 226, 227, 229, 230, 231, 232, 233, 234, 236, 238, 239, 240, 241, 242, 243, 244, 245, 246, 248, 251, 252, 256, 257, 258, 260, 261, 262, 266, 267, 268, 269, 270, 272, 275, 277, 278, 281, 283, 285], "name": [24, 26, 27, 28, 29, 30, 31, 32, 33, 37, 39, 40, 43, 45, 46, 48, 49, 50, 52, 54, 56, 58, 60, 62, 64, 66, 69, 71, 73, 75, 76, 78, 79, 80, 82, 84, 86, 88, 89, 90, 91, 92, 93, 95, 97, 98, 100, 101, 103, 104, 106, 108, 110, 113, 116, 117, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 155, 156, 158, 159, 162, 163, 164, 165, 166, 174, 176, 177, 178, 179, 181, 183, 185, 188, 189, 192, 198, 200, 201, 202, 205, 206, 207, 208, 210, 211, 213, 216, 217, 218, 220, 221, 225, 226, 227, 229, 230, 231, 232, 233, 234, 236, 238, 239, 240, 241, 242, 243, 244, 245, 246, 248, 251, 252, 256, 257, 258, 260, 261, 262, 266, 267, 268, 269, 270, 271, 272, 275, 276, 277, 278, 281, 283, 285], "input": [24, 26, 27, 29, 30, 31, 32, 33, 37, 39, 40, 43, 45, 46, 48, 49, 50, 52, 54, 56, 58, 60, 62, 64, 66, 69, 71, 73, 75, 76, 78, 79, 80, 82, 84, 86, 88, 89, 90, 91, 92, 93, 95, 97, 98, 100, 101, 103, 104, 106, 108, 110, 113, 116, 117, 119, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 155, 156, 158, 159, 162, 163, 164, 165, 166, 174, 176, 177, 178, 179, 181, 183, 185, 188, 189, 192, 198, 200, 201, 202, 205, 206, 207, 208, 210, 211, 213, 216, 217, 218, 220, 221, 225, 226, 227, 229, 230, 231, 232, 233, 234, 236, 238, 239, 240, 241, 242, 243, 244, 245, 246, 248, 251, 252, 256, 257, 258, 260, 261, 262, 266, 267, 268, 269, 270, 271, 272, 275, 277, 278, 281, 283, 285], "file": [24, 26, 27, 28, 29, 30, 31, 32, 33, 37, 39, 40, 43, 45, 46, 48, 49, 50, 52, 54, 56, 58, 60, 62, 64, 66, 69, 71, 73, 75, 76, 78, 79, 80, 82, 84, 86, 88, 89, 90, 91, 92, 93, 95, 97, 98, 100, 101, 103, 104, 106, 108, 110, 113, 116, 117, 119, 120, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 155, 156, 158, 159, 162, 163, 164, 165, 166, 174, 176, 177, 178, 179, 181, 183, 185, 188, 189, 192, 198, 200, 201, 202, 205, 206, 207, 208, 210, 211, 213, 216, 217, 218, 220, 221, 225, 226, 227, 229, 230, 231, 232, 233, 234, 236, 238, 239, 240, 241, 242, 243, 244, 245, 246, 248, 251, 252, 256, 257, 258, 260, 261, 262, 266, 267, 268, 269, 270, 271, 272, 275, 277, 278, 281, 283, 285], "complet": [24, 26, 28, 29, 30, 31, 39, 40, 43, 45, 46, 48, 49, 50, 52, 54, 56, 58, 60, 62, 64, 66, 69, 71, 73, 75, 76, 78, 79, 80, 82, 84, 86, 88, 89, 90, 91, 92, 93, 95, 97, 98, 100, 101, 103, 104, 106, 108, 110, 113, 116, 117, 121, 123, 125, 127, 129, 131, 133, 135, 136, 152, 153, 155, 156, 158, 159, 162, 163, 164, 165, 166, 174, 176, 177, 178, 179, 181, 183, 185, 188, 189, 192, 198, 200, 201, 202, 205, 206, 207, 208, 210, 211, 213, 216, 217, 218, 220, 221, 224, 225, 226, 227, 229, 230, 231, 232, 233, 234, 236, 238, 239, 240, 241, 242, 243, 244, 245, 246, 248, 251, 252, 256, 257, 258, 260, 261, 262, 266, 267, 268, 269, 270, 271, 272, 275, 276, 277, 278, 281, 283, 285], "ofil": [24, 26, 27, 28, 29, 30, 31, 32, 35, 37, 39, 43, 45, 46, 48, 49, 50, 52, 54, 56, 58, 60, 62, 64, 66, 69, 71, 73, 75, 76, 78, 79, 80, 82, 84, 86, 88, 89, 90, 91, 92, 93, 95, 97, 98, 100, 101, 103, 104, 106, 108, 110, 113, 116, 117, 119, 120, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 155, 156, 158, 159, 162, 163, 164, 165, 166, 174, 176, 177, 178, 179, 181, 183, 185, 188, 189, 192, 198, 200, 201, 202, 205, 206, 207, 208, 210, 211, 213, 216, 217, 218, 220, 221, 225, 226, 227, 229, 230, 231, 232, 233, 234, 236, 238, 239, 240, 241, 242, 243, 244, 245, 246, 248, 251, 252, 256, 257, 258, 260, 261, 262, 266, 267, 268, 269, 270, 271, 272, 275, 277, 278, 281, 283, 285], "output": [24, 26, 27, 28, 29, 30, 31, 32, 37, 39, 40, 43, 45, 46, 48, 49, 50, 52, 54, 56, 58, 60, 62, 64, 66, 69, 71, 73, 75, 76, 78, 79, 80, 82, 84, 86, 88, 89, 90, 91, 92, 93, 95, 97, 98, 100, 101, 103, 104, 106, 108, 110, 113, 116, 117, 119, 120, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 155, 156, 158, 159, 162, 163, 164, 165, 166, 174, 176, 177, 178, 179, 181, 183, 185, 188, 189, 192, 198, 200, 201, 202, 205, 206, 207, 208, 210, 211, 213, 216, 217, 218, 220, 221, 225, 226, 227, 229, 230, 231, 232, 233, 234, 236, 238, 239, 240, 241, 242, 243, 244, 245, 246, 248, 251, 252, 256, 257, 258, 260, 261, 262, 266, 267, 268, 269, 270, 271, 272, 275, 277, 278, 281, 283, 285], "store": [24, 26, 27, 29, 30, 31, 33, 37, 39, 40, 43, 45, 46, 48, 49, 50, 52, 54, 56, 58, 60, 62, 64, 66, 69, 71, 73, 75, 76, 78, 79, 80, 82, 84, 86, 88, 89, 90, 91, 92, 93, 95, 97, 98, 100, 101, 103, 104, 106, 108, 110, 113, 116, 117, 119, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 155, 156, 158, 159, 162, 163, 164, 165, 166, 174, 176, 177, 178, 179, 181, 183, 185, 188, 189, 192, 198, 200, 201, 202, 205, 206, 207, 208, 210, 211, 213, 216, 217, 218, 220, 221, 223, 225, 226, 227, 229, 230, 231, 232, 233, 234, 236, 238, 239, 240, 241, 242, 243, 244, 245, 246, 248, 251, 252, 256, 257, 258, 260, 261, 262, 266, 267, 268, 269, 270, 271, 272, 275, 277, 278, 281, 283, 285], "user": [24, 26, 27, 28, 29, 31, 37, 40, 43, 45, 46, 48, 49, 50, 52, 54, 56, 58, 60, 62, 64, 66, 69, 71, 73, 75, 76, 78, 79, 80, 82, 84, 86, 88, 89, 90, 91, 92, 93, 95, 97, 98, 100, 101, 103, 104, 106, 108, 110, 113, 116, 117, 121, 123, 125, 127, 129, 131, 133, 135, 139, 142, 143, 152, 153, 155, 156, 158, 159, 162, 163, 164, 165, 166, 174, 176, 177, 178, 179, 181, 183, 185, 188, 189, 190, 192, 198, 200, 201, 202, 205, 206, 207, 208, 210, 211, 213, 216, 217, 218, 220, 221, 225, 226, 227, 229, 230, 231, 232, 233, 234, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 248, 251, 252, 254, 256, 257, 258, 260, 261, 262, 263, 266, 267, 268, 269, 270, 271, 272, 275, 276, 277, 278, 280, 281, 283, 285], "specifi": [24, 29, 32, 33, 37, 40, 43, 45, 46, 48, 49, 50, 52, 54, 56, 58, 60, 62, 64, 66, 69, 71, 73, 75, 78, 79, 80, 86, 88, 89, 90, 91, 92, 93, 95, 97, 98, 100, 101, 103, 104, 106, 113, 116, 117, 119, 120, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 158, 159, 162, 163, 164, 165, 166, 174, 176, 177, 178, 179, 181, 183, 185, 188, 189, 192, 198, 200, 201, 202, 205, 206, 207, 208, 210, 211, 225, 226, 227, 229, 230, 231, 232, 233, 234, 236, 238, 239, 240, 241, 242, 243, 244, 245, 246, 248, 256, 257, 258, 260, 261, 262, 266, 267, 268, 269, 270, 271, 272, 275, 277, 278, 281, 283, 285], "either": [24, 29, 40, 43, 45, 46, 48, 49, 52, 54, 56, 58, 60, 62, 64, 66, 69, 71, 73, 75, 78, 79, 86, 88, 89, 90, 91, 92, 93, 95, 97, 98, 100, 101, 106, 113, 116, 117, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 158, 159, 162, 163, 164, 165, 166, 174, 176, 177, 178, 181, 183, 185, 188, 189, 192, 198, 200, 201, 202, 205, 207, 210, 211, 225, 226, 227, 229, 230, 231, 232, 233, 236, 238, 239, 240, 241, 242, 243, 244, 248, 256, 257, 258, 260, 261, 262, 266, 267, 268, 269, 270, 271, 272, 275, 277, 278, 280, 281, 283, 285], "count": [24, 29, 43, 45, 46, 48, 49, 50, 52, 54, 56, 58, 60, 62, 64, 66, 69, 71, 73, 75, 78, 79, 84, 86, 88, 89, 90, 91, 92, 93, 95, 97, 98, 100, 101, 103, 104, 106, 110, 113, 116, 117, 120, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 158, 159, 162, 163, 164, 165, 166, 174, 176, 177, 178, 179, 181, 183, 185, 188, 189, 192, 198, 200, 201, 202, 205, 207, 210, 211, 220, 221, 225, 226, 227, 229, 230, 231, 232, 233, 234, 236, 238, 239, 240, 241, 242, 243, 244, 245, 246, 248, 252, 256, 257, 258, 260, 261, 262, 266, 267, 268, 269, 270, 271, 272, 275, 277, 278, 281, 283, 285], "proport": [24, 29, 43, 45, 46, 48, 49, 52, 54, 56, 58, 60, 62, 64, 66, 69, 71, 73, 75, 79, 86, 88, 89, 90, 91, 92, 93, 95, 97, 98, 100, 101, 106, 113, 116, 117, 121, 123, 125, 127, 129, 131, 133, 135, 136, 152, 153, 158, 159, 162, 163, 164, 165, 166, 174, 176, 177, 178, 181, 183, 185, 188, 189, 192, 198, 200, 201, 202, 205, 207, 210, 211, 224, 225, 226, 227, 229, 230, 231, 232, 233, 236, 238, 239, 240, 241, 242, 243, 244, 248, 256, 257, 258, 260, 261, 262, 266, 267, 268, 269, 270, 271, 272, 275, 277, 278, 281, 283, 285], "size": [24, 29, 30, 32, 33, 39, 43, 45, 46, 48, 49, 52, 54, 56, 58, 60, 62, 64, 66, 69, 71, 73, 75, 76, 79, 86, 88, 89, 90, 91, 92, 93, 95, 97, 98, 100, 101, 106, 113, 116, 117, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 158, 159, 162, 163, 164, 165, 166, 174, 176, 177, 178, 181, 183, 185, 188, 189, 192, 198, 200, 201, 202, 205, 207, 210, 211, 225, 226, 227, 229, 230, 231, 232, 233, 236, 238, 239, 240, 241, 242, 243, 244, 248, 256, 257, 258, 260, 261, 262, 266, 267, 268, 269, 270, 271, 272, 275, 277, 278, 281, 283, 285], "If": [24, 29, 43, 45, 46, 48, 49, 52, 54, 56, 58, 60, 62, 64, 66, 69, 71, 73, 75, 79, 88, 89, 90, 91, 92, 93, 95, 97, 98, 100, 101, 106, 113, 116, 117, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 158, 159, 162, 163, 164, 165, 166, 174, 176, 177, 178, 181, 183, 185, 188, 189, 190, 192, 198, 200, 201, 202, 205, 207, 210, 211, 227, 229, 230, 231, 232, 233, 236, 238, 239, 240, 241, 242, 243, 244, 248, 256, 257, 258, 260, 261, 262, 266, 267, 268, 269, 270, 271, 272, 275, 276, 277, 278, 281, 283, 285], "program": [24, 26, 29, 30, 37, 39, 40, 43, 45, 46, 48, 49, 50, 52, 54, 56, 58, 60, 62, 64, 66, 69, 71, 73, 75, 76, 78, 79, 80, 82, 84, 86, 88, 89, 90, 91, 92, 93, 95, 97, 98, 100, 101, 103, 104, 106, 108, 110, 113, 116, 117, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 155, 156, 158, 159, 162, 163, 164, 165, 166, 174, 176, 177, 178, 179, 181, 183, 185, 188, 189, 192, 198, 200, 201, 202, 205, 206, 207, 208, 210, 211, 213, 216, 217, 218, 220, 221, 225, 226, 227, 229, 230, 231, 232, 233, 234, 236, 238, 239, 240, 241, 242, 243, 244, 245, 246, 248, 251, 252, 256, 257, 258, 260, 261, 262, 266, 267, 268, 269, 270, 271, 272, 275, 277, 278, 281, 283, 285], "integ": [24, 29, 30, 43, 45, 46, 48, 49, 52, 54, 56, 58, 60, 62, 64, 66, 69, 71, 73, 75, 78, 79, 80, 82, 88, 89, 90, 91, 92, 93, 95, 97, 98, 100, 101, 106, 110, 113, 116, 117, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 158, 159, 162, 163, 164, 165, 166, 174, 176, 177, 178, 181, 183, 185, 188, 189, 192, 198, 200, 201, 202, 205, 207, 210, 211, 216, 223, 227, 229, 230, 231, 232, 233, 236, 238, 239, 240, 241, 242, 243, 244, 248, 252, 256, 257, 258, 260, 261, 262, 264, 265, 266, 267, 268, 269, 270, 271, 272, 275, 277, 278, 281, 283, 285], "treat": [24, 29, 43, 45, 46, 48, 49, 52, 54, 56, 58, 60, 62, 64, 66, 69, 71, 73, 75, 79, 88, 89, 90, 91, 92, 93, 95, 97, 98, 100, 101, 106, 110, 113, 116, 117, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 158, 159, 162, 163, 164, 165, 166, 174, 176, 177, 178, 181, 183, 185, 188, 189, 192, 198, 200, 201, 202, 205, 207, 210, 211, 227, 229, 230, 231, 232, 233, 236, 238, 239, 240, 241, 242, 243, 244, 248, 252, 256, 257, 258, 260, 261, 262, 266, 267, 268, 269, 270, 271, 272, 275, 277, 278, 281, 283, 285], "express": [24, 29, 43, 45, 46, 48, 49, 52, 54, 56, 58, 60, 62, 64, 66, 69, 71, 73, 75, 79, 88, 89, 90, 91, 92, 93, 95, 97, 98, 100, 101, 106, 113, 116, 117, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 158, 159, 162, 163, 164, 165, 166, 174, 176, 177, 178, 181, 183, 185, 188, 189, 192, 198, 200, 201, 202, 205, 207, 210, 211, 227, 229, 230, 231, 232, 233, 236, 238, 239, 240, 241, 242, 243, 244, 248, 256, 257, 258, 260, 261, 262, 266, 267, 268, 269, 270, 271, 272, 275, 277, 278, 281, 283, 285], "rang": [24, 30, 39, 40, 52, 84, 123, 125, 127, 129, 131, 135, 152, 153, 183, 220, 221, 266, 275, 276, 277, 278, 281, 283], "0": [24, 26, 27, 30, 32, 33, 39, 40, 43, 45, 46, 48, 49, 50, 52, 54, 56, 64, 66, 69, 75, 76, 80, 82, 86, 88, 89, 90, 91, 92, 93, 95, 97, 98, 100, 101, 104, 106, 108, 110, 113, 116, 117, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 155, 156, 158, 159, 162, 163, 164, 165, 166, 174, 176, 177, 178, 179, 181, 183, 188, 190, 196, 198, 200, 201, 206, 213, 216, 217, 218, 225, 226, 227, 229, 230, 231, 232, 233, 236, 238, 239, 240, 241, 242, 243, 244, 246, 248, 251, 252, 256, 257, 258, 260, 261, 262, 266, 267, 268, 269, 270, 271, 272, 275, 276, 277, 278, 280, 281, 283, 285], "1": [24, 30, 39, 43, 45, 46, 52, 56, 62, 75, 76, 79, 82, 84, 86, 88, 91, 97, 108, 113, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 158, 162, 163, 164, 165, 166, 181, 183, 188, 190, 192, 196, 211, 216, 217, 218, 220, 223, 225, 226, 229, 230, 238, 239, 242, 251, 255, 256, 257, 258, 264, 265, 266, 267, 268, 269, 270, 272, 275, 276, 277, 278, 280, 281, 283, 285], "variabl": [24, 26, 27, 29, 30, 31, 37, 39, 43, 45, 46, 48, 49, 50, 52, 54, 56, 58, 60, 62, 64, 66, 69, 71, 73, 75, 76, 78, 79, 80, 82, 84, 86, 88, 89, 90, 91, 92, 93, 95, 97, 98, 100, 101, 103, 104, 106, 108, 110, 113, 116, 117, 121, 123, 125, 127, 129, 131, 133, 135, 137, 152, 153, 155, 156, 158, 159, 162, 163, 164, 165, 166, 174, 176, 177, 178, 179, 181, 183, 185, 188, 189, 192, 198, 200, 201, 202, 205, 206, 207, 208, 210, 211, 213, 216, 217, 218, 220, 221, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 236, 238, 239, 240, 241, 242, 243, 244, 245, 246, 248, 251, 252, 256, 257, 258, 260, 261, 262, 266, 267, 268, 269, 270, 271, 272, 275, 277, 278, 281, 283, 285], "distinguish": [24, 26, 27, 29, 31, 37, 43, 45, 46, 48, 49, 50, 52, 54, 56, 58, 60, 62, 64, 66, 69, 71, 73, 75, 76, 78, 79, 80, 82, 84, 86, 88, 89, 90, 91, 92, 93, 95, 97, 98, 100, 101, 103, 104, 106, 108, 110, 113, 116, 117, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 155, 156, 158, 159, 162, 163, 164, 165, 166, 174, 176, 177, 178, 179, 181, 183, 185, 188, 189, 190, 192, 198, 200, 201, 202, 205, 206, 207, 208, 210, 211, 213, 216, 217, 218, 220, 221, 225, 226, 227, 229, 230, 231, 232, 233, 234, 236, 238, 239, 240, 241, 242, 243, 244, 245, 246, 248, 251, 252, 256, 257, 258, 260, 261, 262, 266, 267, 268, 269, 270, 271, 272, 275, 276, 277, 278, 280, 281, 283, 285], "anoth": [24, 26, 27, 29, 31, 37, 43, 45, 46, 48, 49, 50, 52, 54, 56, 58, 60, 62, 64, 66, 69, 71, 73, 75, 76, 78, 79, 80, 82, 84, 86, 88, 89, 90, 91, 92, 93, 95, 97, 98, 100, 101, 103, 104, 106, 108, 110, 113, 116, 117, 119, 120, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 155, 156, 158, 159, 162, 163, 164, 165, 166, 174, 176, 177, 178, 179, 181, 183, 185, 188, 189, 192, 198, 200, 201, 202, 205, 206, 207, 208, 210, 211, 213, 216, 217, 218, 220, 221, 225, 226, 227, 229, 230, 231, 232, 233, 234, 236, 238, 239, 240, 241, 242, 243, 244, 245, 246, 248, 251, 252, 256, 257, 258, 260, 261, 262, 266, 267, 268, 269, 270, 271, 272, 275, 277, 278, 281, 283, 285], "default": [24, 26, 27, 29, 30, 31, 37, 39, 43, 45, 46, 48, 49, 50, 52, 54, 56, 58, 60, 62, 64, 66, 69, 71, 73, 75, 76, 78, 79, 80, 82, 84, 86, 88, 89, 90, 91, 92, 93, 95, 97, 98, 100, 101, 103, 104, 106, 108, 110, 113, 116, 117, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 155, 156, 158, 159, 162, 163, 164, 165, 166, 174, 176, 177, 178, 179, 181, 183, 185, 188, 189, 190, 192, 198, 200, 201, 202, 205, 206, 207, 208, 210, 211, 213, 216, 217, 218, 220, 221, 223, 225, 226, 227, 229, 230, 231, 232, 233, 234, 236, 238, 239, 240, 241, 242, 243, 244, 245, 246, 248, 251, 252, 256, 257, 258, 260, 261, 262, 264, 265, 266, 267, 268, 269, 270, 271, 272, 275, 276, 277, 278, 280, 281, 283, 285], "seper": [24, 26, 27, 29, 31, 32, 37, 40, 43, 45, 46, 48, 49, 50, 52, 54, 56, 58, 60, 62, 64, 66, 69, 71, 73, 75, 76, 78, 79, 80, 82, 84, 86, 88, 89, 90, 91, 92, 93, 95, 97, 98, 100, 101, 103, 104, 106, 108, 110, 113, 116, 117, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 155, 156, 158, 162, 163, 164, 165, 166, 174, 176, 177, 178, 179, 181, 183, 185, 188, 189, 190, 192, 198, 200, 201, 202, 205, 206, 207, 208, 210, 211, 213, 216, 217, 218, 220, 221, 223, 225, 226, 227, 229, 230, 231, 232, 233, 234, 236, 238, 239, 240, 241, 242, 243, 244, 245, 246, 248, 251, 252, 255, 256, 257, 258, 260, 261, 262, 264, 265, 266, 267, 268, 269, 270, 271, 272, 275, 276, 277, 278, 280, 281, 283, 285], "tab": [24, 26, 27, 29, 30, 31, 37, 39, 43, 45, 46, 48, 49, 50, 52, 54, 56, 58, 60, 62, 64, 66, 69, 71, 73, 75, 76, 78, 79, 80, 82, 84, 86, 88, 89, 90, 91, 92, 93, 95, 97, 98, 100, 101, 103, 104, 106, 108, 110, 113, 116, 117, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 155, 156, 158, 159, 162, 163, 164, 165, 166, 174, 176, 177, 178, 179, 181, 183, 185, 188, 189, 190, 192, 198, 200, 201, 202, 205, 206, 207, 208, 210, 211, 213, 216, 217, 218, 220, 221, 223, 225, 226, 227, 229, 230, 231, 232, 233, 234, 236, 238, 239, 240, 241, 242, 243, 244, 245, 246, 248, 251, 252, 255, 256, 257, 258, 260, 261, 262, 264, 265, 266, 267, 268, 269, 270, 271, 272, 275, 276, 277, 278, 280, 281, 283, 285], "overrid": [24, 26, 27, 29, 31, 37, 43, 45, 46, 48, 49, 50, 52, 54, 56, 58, 60, 62, 64, 66, 69, 71, 73, 75, 76, 78, 79, 80, 82, 84, 86, 88, 89, 90, 91, 92, 93, 95, 97, 98, 100, 101, 103, 104, 106, 108, 110, 113, 116, 117, 120, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 155, 156, 158, 159, 162, 163, 164, 165, 166, 174, 176, 177, 178, 179, 181, 183, 185, 188, 189, 190, 192, 198, 200, 201, 202, 205, 206, 207, 208, 210, 211, 213, 216, 217, 218, 220, 221, 225, 226, 227, 229, 230, 231, 232, 233, 234, 236, 238, 239, 240, 241, 242, 243, 244, 245, 246, 248, 251, 252, 256, 257, 258, 260, 261, 262, 266, 267, 268, 269, 270, 271, 272, 275, 276, 277, 278, 280, 281, 283, 285], "separ": [24, 26, 27, 29, 30, 31, 37, 39, 43, 45, 46, 48, 49, 50, 52, 54, 56, 58, 60, 62, 64, 66, 69, 71, 73, 75, 76, 78, 79, 80, 82, 84, 86, 88, 89, 90, 91, 92, 93, 95, 97, 98, 100, 101, 103, 104, 106, 108, 110, 113, 116, 117, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 155, 156, 158, 159, 162, 163, 164, 165, 166, 174, 176, 177, 178, 179, 181, 183, 185, 188, 189, 190, 192, 198, 200, 201, 202, 205, 206, 207, 208, 210, 211, 213, 216, 217, 218, 220, 221, 225, 226, 227, 229, 230, 231, 232, 233, 234, 236, 238, 239, 240, 241, 242, 243, 244, 245, 246, 248, 251, 252, 256, 257, 258, 260, 261, 262, 264, 265, 266, 267, 268, 269, 270, 271, 272, 275, 276, 277, 278, 280, 281, 283, 285], "memoryuss": [24, 26, 43, 45, 46, 48, 49, 50, 64, 66, 69, 75, 76, 78, 80, 84, 86, 88, 89, 90, 91, 92, 93, 95, 97, 98, 100, 101, 103, 104, 106, 108, 113, 116, 117, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 155, 156, 158, 159, 162, 163, 164, 165, 166, 174, 176, 177, 178, 179, 181, 198, 200, 201, 213, 220, 221, 225, 226, 227, 229, 230, 231, 232, 233, 234, 236, 238, 239, 240, 241, 242, 243, 244, 245, 246, 248, 251, 256, 257, 258, 260, 261, 262, 266, 267, 268, 269, 270, 271, 272, 275, 277, 278, 281, 283, 285], "To": [24, 26, 28, 29, 30, 32, 33, 39, 43, 45, 46, 48, 49, 50, 52, 54, 56, 58, 60, 62, 64, 66, 69, 71, 73, 75, 78, 79, 80, 82, 84, 86, 88, 89, 90, 91, 92, 93, 95, 97, 98, 100, 101, 103, 104, 106, 108, 110, 113, 116, 117, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 155, 156, 158, 159, 162, 163, 164, 165, 166, 174, 176, 177, 178, 179, 181, 183, 185, 188, 189, 192, 198, 200, 201, 202, 205, 206, 207, 208, 210, 211, 213, 216, 217, 218, 220, 221, 225, 227, 229, 230, 231, 232, 233, 234, 236, 238, 239, 240, 241, 242, 243, 244, 245, 246, 248, 251, 252, 256, 257, 258, 260, 261, 262, 266, 267, 268, 269, 270, 271, 272, 275, 277, 278, 281, 283, 285], "total": [24, 26, 28, 30, 32, 39, 40, 43, 45, 46, 48, 49, 50, 52, 54, 56, 58, 60, 62, 64, 66, 69, 71, 73, 75, 78, 79, 80, 82, 84, 86, 88, 89, 90, 91, 92, 93, 95, 97, 98, 100, 101, 103, 104, 106, 108, 110, 113, 116, 117, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 155, 156, 158, 159, 162, 163, 164, 165, 166, 174, 176, 177, 178, 179, 181, 183, 185, 188, 189, 190, 192, 196, 198, 200, 201, 202, 205, 206, 207, 208, 210, 211, 213, 216, 217, 218, 220, 221, 225, 226, 227, 229, 230, 231, 232, 233, 234, 236, 238, 239, 240, 241, 242, 243, 244, 245, 246, 248, 249, 250, 251, 252, 256, 257, 258, 260, 261, 262, 266, 267, 268, 269, 270, 271, 272, 275, 276, 277, 278, 280, 281, 283, 285], "amount": [24, 26, 43, 45, 46, 48, 49, 50, 52, 54, 56, 58, 60, 62, 64, 66, 69, 71, 73, 75, 78, 79, 80, 82, 84, 86, 88, 89, 90, 91, 92, 93, 95, 97, 98, 100, 101, 103, 104, 106, 108, 110, 113, 116, 117, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 155, 156, 158, 159, 162, 163, 164, 165, 166, 174, 176, 177, 178, 179, 181, 183, 185, 188, 189, 192, 198, 200, 201, 202, 205, 206, 207, 208, 210, 211, 213, 216, 217, 218, 220, 221, 225, 226, 227, 229, 230, 231, 232, 233, 234, 236, 238, 239, 240, 241, 242, 243, 244, 245, 246, 248, 251, 252, 256, 257, 258, 260, 261, 262, 266, 267, 268, 269, 270, 271, 272, 275, 277, 278, 281, 283, 285], "uss": [24, 26, 28, 43, 45, 46, 48, 49, 50, 52, 54, 56, 58, 60, 62, 64, 66, 69, 71, 73, 75, 76, 78, 79, 80, 82, 84, 86, 88, 89, 90, 91, 92, 93, 95, 97, 98, 100, 101, 103, 104, 106, 108, 110, 113, 116, 117, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 155, 156, 158, 159, 162, 163, 164, 165, 166, 174, 176, 177, 178, 179, 181, 183, 185, 188, 189, 192, 198, 200, 201, 202, 205, 206, 207, 208, 210, 211, 213, 216, 217, 218, 220, 221, 225, 226, 227, 229, 230, 231, 232, 233, 234, 236, 238, 239, 240, 241, 242, 243, 244, 245, 246, 248, 251, 252, 256, 257, 258, 260, 261, 262, 266, 267, 268, 269, 270, 271, 272, 275, 277, 278, 281, 283, 285], "memori": [24, 26, 28, 33, 43, 45, 46, 48, 49, 50, 52, 54, 56, 58, 60, 62, 64, 66, 69, 71, 73, 75, 76, 78, 79, 80, 82, 84, 86, 88, 89, 90, 91, 92, 93, 95, 97, 98, 100, 101, 103, 104, 106, 108, 110, 113, 116, 117, 120, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 155, 156, 158, 159, 162, 163, 164, 165, 166, 174, 176, 177, 178, 179, 181, 183, 185, 188, 189, 192, 198, 200, 201, 202, 205, 206, 207, 208, 210, 211, 213, 216, 217, 218, 220, 221, 225, 226, 227, 229, 230, 231, 232, 233, 234, 236, 238, 239, 240, 241, 242, 243, 244, 245, 246, 248, 251, 252, 256, 257, 258, 260, 261, 262, 266, 267, 268, 269, 270, 271, 272, 275, 277, 278, 281, 283, 285], "consum": [24, 26, 43, 45, 46, 48, 49, 50, 52, 54, 56, 58, 60, 62, 64, 66, 69, 71, 73, 75, 78, 79, 80, 82, 84, 86, 88, 89, 90, 91, 92, 93, 95, 97, 98, 100, 101, 103, 104, 106, 108, 110, 113, 116, 117, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 155, 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285], "rss": [24, 26, 28, 43, 45, 46, 48, 49, 50, 52, 54, 56, 58, 60, 62, 64, 66, 69, 71, 73, 75, 76, 78, 79, 80, 82, 84, 86, 88, 89, 90, 91, 92, 93, 95, 97, 98, 100, 101, 103, 104, 106, 108, 110, 113, 116, 117, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 155, 156, 158, 159, 162, 163, 164, 165, 166, 174, 176, 177, 178, 179, 181, 183, 185, 188, 189, 192, 198, 200, 201, 202, 205, 206, 207, 208, 210, 211, 213, 216, 217, 218, 220, 221, 225, 226, 227, 229, 230, 231, 232, 233, 234, 236, 238, 239, 240, 241, 242, 243, 244, 245, 246, 248, 251, 252, 256, 257, 258, 260, 261, 262, 266, 267, 268, 269, 270, 271, 272, 275, 277, 278, 281, 283, 285], "starttim": [24, 26, 43, 45, 46, 48, 49, 50, 52, 54, 56, 58, 60, 62, 64, 66, 69, 71, 73, 75, 78, 79, 80, 82, 84, 86, 88, 89, 90, 91, 92, 93, 95, 97, 98, 100, 101, 103, 104, 106, 108, 110, 113, 116, 117, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 155, 156, 158, 159, 162, 163, 164, 165, 166, 174, 176, 177, 178, 179, 181, 183, 185, 188, 189, 192, 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64, 66, 69, 71, 73, 75, 78, 79, 80, 82, 84, 86, 88, 89, 90, 91, 92, 93, 95, 97, 98, 100, 101, 103, 104, 106, 108, 110, 113, 116, 117, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 155, 156, 158, 159, 162, 163, 164, 165, 166, 174, 176, 177, 178, 179, 181, 183, 185, 188, 189, 192, 198, 200, 201, 202, 205, 206, 207, 208, 210, 211, 213, 216, 217, 218, 220, 221, 227, 229, 230, 231, 232, 233, 234, 236, 238, 239, 240, 241, 242, 243, 244, 245, 246, 248, 251, 252, 256, 257, 258, 260, 261, 262, 266, 267, 268, 269, 270, 271, 272, 275, 277, 278, 281, 283, 285], "given": [24, 27, 28, 29, 32, 33, 40, 52, 54, 56, 58, 60, 62, 64, 66, 69, 71, 73, 75, 76, 79, 80, 86, 90, 108, 110, 119, 152, 153, 183, 185, 188, 189, 192, 198, 200, 201, 202, 205, 206, 207, 208, 210, 211, 213, 233, 249, 250, 251, 252], "minimum": [24, 26, 27, 29, 30, 32, 39, 43, 50, 54, 56, 58, 60, 73, 75, 76, 78, 79, 80, 82, 84, 86, 88, 89, 90, 91, 92, 97, 98, 100, 101, 106, 108, 110, 113, 116, 119, 121, 123, 125, 127, 129, 131, 135, 139, 152, 153, 155, 156, 158, 179, 180, 185, 188, 189, 205, 208, 210, 211, 213, 214, 216, 217, 218, 220, 221, 225, 229, 230, 231, 232, 233, 234, 237, 240, 241, 242, 243, 244, 248, 251, 252, 256, 257, 258, 260, 261, 265, 266, 275, 277, 278, 281, 283], "ratio": [24, 52, 86, 136, 152, 153, 183, 224, 226], "should": [24, 52, 119, 152, 153, 183], "list": [24, 26, 27, 28, 30, 32, 35, 39, 40, 43, 45, 46, 48, 49, 52, 54, 56, 58, 60, 62, 64, 66, 69, 71, 73, 75, 79, 80, 82, 84, 86, 88, 90, 91, 92, 93, 95, 97, 100, 101, 103, 104, 106, 108, 110, 113, 116, 117, 119, 120, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 155, 156, 158, 159, 162, 163, 164, 165, 166, 174, 178, 181, 183, 185, 188, 189, 192, 198, 200, 201, 202, 205, 206, 207, 208, 210, 211, 213, 216, 220, 221, 225, 227, 229, 230, 231, 233, 234, 236, 238, 239, 240, 241, 242, 244, 245, 246, 248, 251, 252, 256, 257, 258, 260, 261, 262, 266, 267, 268, 269, 270, 271, 272, 275, 277, 278, 281, 283, 285], "mapsupport": [24, 43, 45, 46, 48, 84, 86, 88, 90, 91, 93, 95, 97, 100, 101, 106, 108, 116, 117, 123, 125, 127, 129, 131, 133, 135, 152, 153, 159, 166, 174, 181, 220, 221, 226, 227, 229, 230, 231, 233, 236, 238, 239, 240, 241, 242, 244, 248, 251, 260, 261, 262, 266, 267, 268, 269, 270, 271, 272, 275, 277, 278, 281, 283, 285], "dictionari": [24, 26, 27, 30, 31, 39, 43, 45, 46, 48, 49, 50, 54, 64, 66, 69, 73, 75, 76, 78, 79, 80, 82, 84, 86, 88, 89, 90, 91, 92, 93, 95, 97, 98, 100, 101, 103, 104, 106, 108, 113, 116, 117, 119, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 155, 156, 158, 159, 162, 163, 164, 165, 166, 174, 176, 177, 178, 179, 181, 185, 198, 200, 201, 205, 211, 213, 216, 220, 221, 225, 227, 229, 230, 231, 232, 233, 234, 236, 238, 239, 240, 241, 242, 243, 244, 245, 246, 248, 251, 256, 257, 258, 260, 261, 262, 266, 267, 268, 269, 270, 271, 272, 275, 277, 278, 281, 283, 285], "maintain": [24, 43, 45, 46, 48, 49, 82, 84, 86, 88, 90, 91, 93, 95, 97, 100, 101, 106, 108, 116, 117, 123, 125, 127, 129, 131, 133, 135, 152, 153, 159, 166, 174, 178, 181, 216, 220, 221, 225, 226, 227, 229, 230, 231, 233, 236, 238, 239, 240, 241, 242, 244, 248, 251, 260, 261, 262, 266, 267, 268, 269, 270, 271, 272, 275, 277, 278, 281, 283, 285], "lno": [24, 43, 48, 49, 84, 86, 88, 90, 91, 93, 95, 97, 100, 101, 104, 106, 108, 116, 117, 123, 125, 127, 129, 131, 133, 135, 152, 153, 159, 174, 176, 177, 178, 220, 221, 226, 227, 229, 230, 231, 233, 236, 238, 239, 240, 241, 242, 244, 246, 248, 251, 260, 261, 262, 266, 267, 268, 269, 270, 271, 272, 275, 278, 281, 283, 285], "tree": [24, 43, 45, 46, 48, 49, 71, 73, 75, 79, 80, 82, 84, 86, 88, 90, 91, 93, 95, 97, 100, 101, 106, 108, 110, 116, 117, 123, 125, 127, 129, 131, 133, 135, 152, 153, 159, 166, 174, 178, 181, 202, 205, 206, 208, 211, 213, 216, 220, 221, 225, 227, 229, 230, 231, 233, 236, 238, 239, 240, 241, 242, 244, 248, 251, 252, 260, 261, 262, 266, 267, 268, 269, 270, 271, 272, 275, 277, 278, 281, 283, 285], "itemsetcount": [24, 46, 48, 90, 93, 95, 97, 100, 101, 106, 108, 116, 117, 123, 125, 127, 135, 152, 153, 174, 181, 227, 233, 236, 238, 239, 240, 241, 242, 244, 248, 251, 260, 261, 262, 266, 267, 268, 269, 270, 271, 272, 275, 278], "finalpattern": [24, 26, 31, 43, 45, 46, 48, 49, 50, 64, 66, 69, 78, 80, 82, 84, 86, 88, 89, 90, 91, 92, 93, 95, 97, 98, 100, 101, 103, 104, 106, 108, 113, 116, 117, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 155, 156, 158, 159, 162, 163, 164, 165, 166, 174, 176, 177, 178, 179, 181, 198, 200, 201, 213, 216, 217, 218, 220, 221, 225, 226, 227, 229, 230, 231, 232, 233, 234, 236, 238, 239, 240, 241, 242, 243, 244, 245, 246, 248, 251, 256, 257, 258, 260, 261, 262, 266, 267, 268, 269, 270, 271, 272, 275, 277, 278, 281, 283, 285], "dict": [24, 26, 27, 30, 31, 33, 39, 43, 45, 46, 48, 49, 50, 52, 54, 56, 58, 60, 62, 64, 66, 69, 71, 73, 75, 76, 78, 79, 80, 82, 84, 86, 88, 89, 90, 91, 92, 93, 95, 97, 98, 100, 101, 103, 104, 106, 108, 110, 113, 116, 117, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 155, 156, 158, 159, 162, 163, 164, 165, 166, 174, 176, 177, 178, 179, 181, 183, 185, 188, 189, 192, 198, 200, 201, 202, 205, 206, 207, 208, 210, 211, 213, 216, 217, 218, 220, 221, 225, 226, 227, 229, 230, 231, 232, 233, 234, 236, 238, 239, 240, 241, 242, 243, 244, 245, 246, 248, 251, 252, 256, 257, 258, 260, 261, 262, 266, 267, 268, 269, 270, 271, 272, 275, 277, 278, 281, 283, 285], "itemsetbuff": [24, 54, 56, 58, 60, 62, 108, 152, 153, 185, 188, 189, 251], "maxpatternlength": [24, 108, 152, 153, 251], "constraint": [24, 73, 79, 101, 108, 110, 113, 116, 139, 143, 152, 153, 205, 211, 237, 251, 252, 256, 257, 258, 260, 261, 263], "length": [24, 27, 30, 32, 39, 40, 43, 75, 79, 82, 108, 113, 121, 123, 125, 127, 135, 152, 153, 158, 207, 210, 216, 217, 218, 251, 256, 257, 258, 266, 267, 268, 269, 270, 271, 272, 275, 278], "termin": [24, 26, 45, 46, 50, 56, 93, 97, 98, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 155, 162, 163, 164, 165, 166, 179, 181, 188, 227, 238, 239, 243, 266, 267, 268, 269, 270, 272, 275, 277, 278, 281, 283, 285], "command": [24, 26, 45, 46, 50, 56, 93, 97, 98, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 155, 162, 163, 164, 165, 166, 179, 181, 188, 227, 238, 239, 243, 266, 267, 268, 269, 270, 272, 275, 277, 278, 281, 283, 285], "format": [24, 26, 30, 39, 40, 43, 45, 46, 48, 49, 50, 52, 54, 56, 58, 60, 62, 64, 66, 69, 71, 73, 75, 79, 80, 82, 84, 86, 88, 89, 90, 91, 92, 93, 95, 97, 98, 100, 101, 103, 104, 106, 108, 110, 113, 116, 117, 119, 120, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 155, 156, 158, 159, 162, 163, 164, 165, 166, 174, 176, 177, 178, 179, 181, 183, 185, 188, 189, 190, 192, 196, 198, 200, 201, 202, 205, 206, 207, 208, 210, 211, 213, 216, 217, 218, 220, 221, 223, 225, 226, 227, 229, 230, 231, 232, 233, 234, 236, 238, 239, 240, 241, 242, 243, 244, 245, 246, 248, 251, 252, 255, 256, 257, 258, 260, 261, 262, 264, 265, 266, 267, 268, 269, 270, 271, 272, 275, 276, 277, 278, 280, 281, 283, 285], "venv": [24, 26, 30, 39, 40, 43, 45, 46, 48, 49, 50, 52, 54, 56, 58, 60, 62, 64, 66, 69, 71, 73, 75, 79, 80, 82, 84, 88, 89, 91, 93, 97, 98, 100, 101, 103, 104, 106, 108, 110, 113, 116, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 155, 156, 158, 159, 162, 163, 164, 165, 166, 174, 176, 177, 178, 179, 181, 183, 185, 188, 189, 192, 198, 200, 201, 202, 205, 206, 207, 208, 210, 211, 213, 216, 217, 218, 220, 221, 227, 230, 231, 232, 238, 239, 240, 241, 242, 243, 244, 245, 246, 248, 251, 252, 256, 257, 258, 260, 261, 266, 267, 268, 269, 270, 272, 275, 277, 278, 281, 283, 285], "python3": [24, 26, 30, 39, 40, 43, 45, 46, 48, 49, 50, 52, 54, 56, 58, 60, 62, 64, 66, 69, 71, 73, 75, 79, 80, 82, 84, 86, 88, 89, 90, 91, 92, 93, 95, 97, 98, 100, 101, 103, 104, 106, 108, 110, 113, 116, 117, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 155, 156, 158, 159, 162, 163, 164, 165, 166, 174, 176, 177, 178, 179, 181, 183, 185, 188, 189, 192, 198, 200, 201, 202, 205, 206, 207, 208, 210, 211, 213, 216, 217, 218, 220, 221, 225, 226, 227, 229, 230, 231, 232, 233, 234, 236, 238, 239, 240, 241, 242, 243, 244, 245, 246, 248, 251, 252, 256, 257, 258, 260, 261, 262, 266, 267, 268, 269, 270, 271, 272, 275, 277, 278, 281, 283, 285], "py": [24, 26, 30, 39, 40, 43, 45, 46, 48, 49, 50, 52, 54, 56, 58, 60, 62, 64, 66, 69, 71, 73, 75, 79, 80, 82, 84, 86, 88, 89, 90, 91, 92, 93, 95, 97, 98, 100, 101, 103, 104, 106, 108, 110, 113, 116, 117, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 155, 156, 158, 159, 162, 163, 164, 165, 166, 174, 176, 177, 178, 179, 181, 183, 185, 188, 189, 192, 198, 200, 201, 202, 205, 206, 207, 210, 211, 213, 216, 217, 218, 220, 221, 225, 226, 227, 229, 230, 231, 232, 233, 234, 236, 238, 239, 240, 241, 242, 243, 244, 245, 246, 248, 251, 252, 256, 257, 258, 260, 261, 262, 266, 267, 268, 269, 270, 271, 272, 275, 277, 278, 281, 283, 285], "inputfil": [24, 26, 27, 30, 39, 43, 45, 46, 48, 49, 50, 52, 54, 56, 58, 60, 62, 64, 66, 69, 71, 73, 75, 76, 79, 80, 82, 84, 86, 88, 89, 90, 91, 93, 95, 97, 98, 100, 101, 103, 104, 106, 108, 110, 113, 116, 117, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 155, 156, 158, 159, 162, 163, 164, 165, 166, 174, 176, 177, 178, 179, 181, 183, 185, 188, 189, 192, 198, 200, 201, 202, 205, 206, 207, 208, 210, 211, 213, 216, 217, 218, 220, 221, 225, 226, 227, 229, 230, 231, 232, 233, 236, 238, 239, 240, 241, 242, 243, 244, 245, 246, 248, 251, 252, 256, 257, 258, 260, 261, 262, 266, 267, 268, 269, 270, 271, 272, 275, 277, 278, 281, 283, 285], "outputfil": [24, 26, 27, 28, 30, 32, 35, 39, 40, 43, 45, 46, 48, 49, 50, 52, 54, 56, 58, 60, 62, 64, 66, 69, 71, 73, 75, 76, 79, 80, 82, 84, 86, 88, 89, 90, 91, 93, 95, 97, 98, 100, 101, 103, 104, 106, 108, 110, 113, 116, 117, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 155, 156, 158, 159, 162, 163, 164, 165, 166, 174, 176, 177, 178, 179, 181, 183, 185, 188, 189, 192, 198, 200, 201, 202, 205, 206, 207, 208, 210, 211, 213, 216, 217, 218, 220, 221, 225, 226, 227, 229, 230, 231, 232, 233, 236, 238, 239, 240, 241, 242, 243, 244, 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188, 189, 192, 198, 200, 201, 202, 205, 206, 207, 208, 210, 211, 213, 227, 232, 233, 236, 240, 241, 242, 243, 244, 248, 252, 260, 261, 262, 266, 271, 275, 277, 278], "txt": [24, 26, 30, 32, 39, 40, 43, 45, 46, 48, 49, 50, 52, 54, 56, 58, 60, 62, 64, 66, 69, 71, 73, 75, 79, 80, 82, 84, 86, 88, 89, 90, 91, 92, 93, 95, 97, 98, 100, 101, 103, 104, 106, 108, 110, 113, 116, 117, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 155, 156, 158, 159, 162, 163, 164, 165, 166, 174, 176, 177, 178, 179, 181, 183, 185, 188, 189, 192, 198, 200, 201, 202, 205, 206, 207, 208, 210, 211, 213, 216, 217, 218, 220, 221, 225, 226, 227, 229, 230, 231, 232, 233, 234, 236, 238, 239, 240, 241, 242, 243, 244, 245, 246, 248, 251, 252, 256, 257, 258, 260, 261, 262, 266, 267, 268, 269, 270, 271, 272, 275, 277, 278, 281, 283, 285], "25": [24, 152, 153], "2": [24, 40, 48, 50, 52, 54, 56, 58, 60, 62, 69, 73, 79, 80, 88, 89, 91, 97, 98, 100, 106, 108, 113, 116, 121, 127, 129, 152, 153, 174, 179, 183, 185, 188, 189, 190, 192, 196, 201, 205, 211, 213, 223, 229, 230, 232, 240, 241, 242, 243, 244, 248, 251, 256, 258, 260, 264, 265, 276, 277, 278, 280, 281], "call": [24, 26, 45, 46, 50, 56, 58, 78, 80, 82, 97, 98, 113, 119, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 155, 162, 163, 164, 165, 166, 179, 181, 188, 189, 218, 238, 239, 243, 256, 257, 266, 267, 268, 269, 270, 275, 276, 277, 278, 281, 283, 285], "alg": [24, 26, 30, 39, 43, 45, 46, 48, 49, 50, 52, 54, 56, 58, 60, 62, 64, 66, 69, 71, 73, 75, 79, 80, 82, 84, 86, 88, 89, 90, 91, 92, 93, 95, 97, 98, 100, 101, 103, 104, 106, 108, 110, 113, 116, 117, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 155, 156, 158, 159, 162, 163, 164, 165, 166, 174, 176, 177, 178, 179, 181, 183, 185, 188, 189, 192, 198, 200, 201, 202, 205, 206, 207, 208, 210, 211, 213, 216, 217, 218, 220, 221, 225, 226, 227, 229, 230, 231, 232, 233, 234, 236, 238, 239, 240, 241, 242, 243, 244, 245, 246, 248, 251, 252, 256, 257, 258, 260, 261, 262, 266, 267, 268, 269, 270, 271, 272, 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283, 285], "number": [24, 26, 30, 32, 39, 40, 43, 45, 46, 48, 49, 50, 52, 54, 56, 58, 60, 62, 64, 66, 69, 71, 73, 75, 76, 79, 80, 82, 84, 86, 88, 89, 90, 91, 92, 93, 95, 97, 98, 100, 101, 103, 104, 106, 108, 110, 113, 116, 117, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 155, 156, 158, 159, 162, 163, 164, 165, 166, 174, 176, 177, 178, 179, 181, 183, 185, 188, 189, 192, 198, 200, 201, 202, 205, 206, 207, 208, 210, 211, 213, 216, 217, 218, 220, 221, 225, 226, 227, 229, 230, 231, 232, 233, 234, 236, 238, 239, 240, 241, 242, 243, 244, 245, 246, 248, 251, 252, 256, 257, 258, 260, 261, 262, 264, 265, 266, 267, 268, 269, 270, 271, 272, 275, 277, 278, 280, 281, 283, 285], "len": [24, 26, 43, 45, 46, 48, 49, 50, 52, 54, 56, 58, 60, 62, 64, 66, 69, 71, 73, 75, 79, 82, 84, 86, 88, 89, 90, 91, 92, 93, 95, 97, 98, 100, 101, 103, 104, 106, 108, 110, 113, 116, 117, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 155, 156, 158, 159, 162, 163, 164, 165, 166, 174, 176, 177, 178, 179, 181, 183, 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71, 73, 75, 76, 78, 79, 80, 82, 84, 86, 88, 89, 90, 91, 92, 93, 95, 97, 98, 100, 101, 103, 104, 106, 108, 110, 113, 116, 117, 119, 120, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 155, 156, 158, 159, 162, 163, 164, 165, 166, 174, 176, 177, 178, 179, 181, 183, 185, 188, 189, 192, 198, 200, 201, 202, 205, 206, 207, 208, 210, 211, 213, 216, 217, 218, 220, 221, 225, 226, 227, 229, 230, 231, 232, 233, 234, 236, 238, 239, 240, 241, 242, 243, 244, 245, 246, 248, 251, 252, 256, 257, 258, 260, 261, 262, 266, 267, 268, 269, 270, 271, 272, 275, 277, 278, 281, 283, 285], "return": [24, 26, 27, 28, 29, 30, 32, 33, 37, 39, 40, 43, 45, 46, 48, 49, 50, 52, 54, 56, 58, 60, 62, 64, 66, 69, 71, 73, 75, 76, 79, 80, 82, 84, 86, 88, 89, 90, 91, 92, 93, 95, 97, 98, 100, 101, 103, 104, 106, 108, 110, 113, 116, 117, 119, 120, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 155, 156, 158, 159, 162, 163, 164, 165, 166, 174, 176, 177, 178, 179, 181, 183, 185, 188, 189, 192, 198, 200, 201, 202, 205, 206, 207, 208, 210, 211, 213, 216, 217, 218, 220, 221, 225, 226, 227, 229, 230, 231, 232, 233, 234, 236, 238, 239, 240, 241, 242, 243, 244, 245, 246, 248, 251, 252, 256, 257, 258, 260, 261, 262, 266, 267, 268, 269, 270, 271, 272, 275, 277, 278, 281, 283, 285], "tupl": [24, 26, 32, 43, 49, 82, 97, 100, 152, 153, 155, 158, 178, 217, 218, 239, 240, 242, 244], "send": [24, 26, 36, 43, 45, 46, 48, 49, 50, 52, 54, 56, 58, 60, 62, 64, 66, 69, 71, 73, 75, 79, 80, 82, 84, 86, 88, 89, 90, 91, 92, 93, 95, 97, 98, 100, 101, 103, 104, 106, 108, 110, 113, 116, 117, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 155, 156, 158, 159, 162, 163, 165, 166, 174, 176, 177, 178, 179, 181, 183, 185, 188, 189, 192, 198, 200, 201, 202, 205, 206, 207, 208, 210, 211, 213, 216, 217, 218, 220, 221, 225, 226, 227, 229, 230, 231, 232, 233, 234, 236, 238, 239, 240, 241, 242, 243, 244, 245, 246, 248, 251, 252, 256, 257, 258, 261, 262, 266, 267, 268, 269, 270, 271, 272, 275, 277, 278, 281, 283, 285], "after": [24, 26, 32, 39, 43, 45, 46, 48, 49, 50, 52, 54, 56, 58, 60, 62, 64, 66, 69, 71, 73, 75, 79, 80, 82, 84, 86, 88, 89, 90, 91, 92, 93, 95, 97, 98, 100, 101, 103, 104, 106, 108, 110, 113, 116, 117, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 155, 156, 158, 159, 162, 163, 164, 165, 166, 174, 176, 177, 178, 179, 181, 183, 185, 188, 189, 192, 198, 200, 201, 202, 205, 206, 207, 208, 210, 211, 213, 216, 217, 218, 220, 221, 225, 226, 227, 229, 230, 231, 232, 233, 234, 236, 238, 239, 240, 241, 242, 243, 244, 245, 246, 248, 251, 252, 256, 257, 258, 260, 261, 262, 266, 267, 268, 269, 270, 271, 272, 275, 277, 278, 281, 283, 285], "final": [24, 26, 43, 45, 46, 48, 49, 50, 52, 54, 56, 58, 60, 62, 64, 66, 69, 71, 73, 75, 76, 79, 80, 82, 84, 86, 88, 89, 90, 91, 92, 93, 95, 97, 98, 100, 101, 103, 104, 106, 108, 110, 113, 116, 117, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 155, 156, 158, 159, 162, 163, 164, 165, 166, 174, 176, 177, 178, 179, 181, 183, 185, 188, 189, 192, 198, 200, 201, 202, 205, 206, 207, 208, 210, 211, 213, 216, 217, 218, 220, 221, 225, 226, 227, 229, 230, 231, 232, 233, 234, 236, 238, 239, 240, 241, 242, 243, 244, 245, 246, 248, 251, 252, 256, 257, 258, 260, 261, 262, 266, 267, 268, 269, 270, 271, 272, 275, 277, 278, 281, 283, 285], "pd": [24, 26, 29, 32, 40, 43, 45, 46, 48, 49, 50, 52, 54, 56, 58, 60, 62, 64, 66, 69, 71, 73, 75, 76, 79, 80, 82, 84, 86, 88, 89, 90, 91, 92, 93, 95, 97, 98, 100, 101, 103, 104, 106, 108, 110, 113, 116, 117, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 155, 156, 158, 159, 162, 163, 164, 165, 166, 174, 176, 177, 178, 179, 181, 183, 185, 188, 189, 192, 198, 200, 201, 202, 205, 206, 207, 208, 210, 211, 213, 216, 217, 218, 220, 221, 225, 226, 227, 229, 230, 231, 232, 233, 234, 236, 238, 239, 240, 241, 242, 243, 244, 245, 246, 248, 251, 252, 256, 257, 258, 260, 261, 262, 266, 267, 268, 269, 270, 271, 272, 275, 277, 278, 281, 283, 285], "calcul": [24, 26, 29, 30, 43, 45, 46, 48, 49, 50, 52, 54, 56, 58, 60, 62, 64, 66, 69, 71, 73, 75, 79, 80, 82, 84, 86, 88, 89, 90, 91, 92, 93, 95, 97, 98, 100, 101, 103, 104, 106, 108, 110, 113, 116, 117, 120, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 155, 156, 158, 159, 162, 163, 164, 165, 166, 174, 176, 177, 178, 179, 181, 183, 185, 188, 189, 192, 198, 200, 201, 202, 205, 206, 207, 208, 210, 211, 213, 216, 217, 218, 220, 221, 225, 226, 227, 229, 230, 231, 232, 233, 234, 236, 238, 239, 240, 241, 242, 243, 244, 245, 246, 248, 251, 252, 256, 257, 258, 260, 261, 262, 266, 267, 268, 269, 270, 271, 272, 275, 277, 278, 281, 283, 285], "runtim": [24, 26, 33, 43, 45, 46, 48, 49, 50, 52, 54, 56, 58, 60, 62, 64, 66, 69, 71, 73, 75, 76, 78, 79, 80, 82, 84, 86, 88, 89, 90, 91, 92, 93, 95, 97, 98, 100, 101, 103, 104, 106, 108, 110, 113, 116, 117, 120, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 155, 156, 158, 159, 162, 163, 164, 165, 166, 174, 176, 177, 178, 179, 181, 183, 185, 188, 189, 192, 198, 200, 201, 202, 205, 206, 207, 208, 210, 211, 213, 216, 217, 218, 220, 221, 225, 226, 227, 229, 230, 231, 232, 233, 234, 236, 238, 239, 240, 241, 242, 243, 244, 245, 246, 248, 251, 252, 256, 257, 258, 260, 261, 262, 266, 267, 268, 269, 270, 271, 272, 275, 277, 278, 281, 283, 285], "taken": [24, 26, 43, 45, 46, 48, 49, 50, 52, 54, 56, 58, 60, 62, 64, 66, 69, 71, 73, 75, 78, 79, 80, 82, 84, 86, 88, 89, 90, 91, 92, 93, 95, 97, 98, 100, 101, 103, 104, 106, 108, 110, 113, 116, 117, 120, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 155, 156, 158, 159, 162, 163, 164, 165, 166, 174, 176, 177, 178, 179, 181, 183, 185, 188, 189, 192, 198, 200, 201, 202, 205, 206, 207, 208, 210, 211, 213, 216, 217, 218, 220, 221, 225, 226, 227, 229, 230, 231, 232, 233, 234, 236, 238, 239, 240, 241, 242, 243, 244, 245, 246, 248, 251, 252, 256, 257, 258, 260, 261, 262, 266, 267, 268, 269, 270, 271, 272, 275, 277, 278, 281, 283, 285], "none": [24, 26, 27, 28, 29, 30, 31, 32, 33, 35, 36, 39, 40, 43, 45, 52, 54, 56, 62, 71, 75, 79, 80, 82, 84, 88, 95, 97, 100, 101, 108, 110, 117, 119, 120, 123, 127, 129, 131, 133, 135, 152, 153, 155, 156, 158, 159, 162, 163, 164, 165, 166, 183, 185, 188, 192, 202, 206, 208, 210, 211, 213, 216, 217, 218, 220, 221, 229, 230, 231, 236, 238, 239, 240, 241, 242, 244, 251, 252, 262, 266, 267, 268, 269, 277, 281, 283, 285], "main": [24, 26, 31, 43, 45, 52, 73, 75, 79, 80, 84, 86, 88, 90, 91, 92, 97, 100, 103, 104, 108, 113, 120, 123, 125, 127, 131, 148, 152, 153, 155, 159, 166, 183, 205, 207, 210, 211, 213, 220, 221, 226, 229, 230, 231, 233, 234, 240, 244, 245, 246, 251, 257, 266, 267, 268, 269, 270, 271, 272, 275, 277, 278, 283, 284], "method": [24, 26, 27, 30, 31, 32, 33, 37, 39, 40, 43, 49, 52, 54, 56, 58, 60, 62, 64, 66, 69, 71, 73, 75, 76, 78, 79, 80, 82, 84, 86, 88, 89, 90, 91, 92, 93, 95, 97, 98, 100, 101, 103, 104, 106, 108, 110, 113, 116, 117, 119, 120, 123, 125, 127, 129, 131, 133, 135, 152, 153, 155, 159, 176, 178, 183, 185, 188, 189, 192, 198, 200, 201, 202, 205, 206, 207, 208, 210, 211, 213, 216, 217, 218, 220, 221, 225, 226, 227, 229, 230, 231, 232, 233, 234, 236, 238, 239, 240, 241, 242, 243, 244, 245, 246, 248, 251, 252, 256, 257, 258, 260, 261, 262, 266, 267, 268, 269, 270, 271, 272, 275, 277, 278, 281, 283, 285], "printresult": [24, 26, 43, 45, 46, 48, 49, 50, 52, 54, 56, 58, 60, 62, 64, 66, 69, 71, 73, 75, 76, 79, 80, 82, 84, 86, 88, 89, 90, 91, 92, 93, 95, 97, 98, 100, 101, 103, 104, 106, 108, 110, 113, 116, 117, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 155, 156, 158, 159, 162, 163, 164, 165, 166, 174, 176, 177, 178, 179, 181, 183, 185, 188, 189, 192, 198, 200, 201, 202, 205, 206, 207, 208, 210, 211, 213, 216, 217, 218, 220, 221, 225, 226, 227, 229, 230, 231, 232, 233, 234, 236, 238, 239, 240, 241, 242, 243, 244, 245, 246, 248, 251, 252, 256, 257, 258, 260, 261, 262, 266, 267, 268, 269, 270, 271, 272, 275, 277, 278, 281, 283, 285], "result": [24, 26, 27, 30, 39, 43, 45, 46, 48, 49, 50, 52, 54, 56, 58, 60, 62, 64, 66, 69, 71, 73, 75, 76, 79, 80, 82, 84, 86, 88, 89, 90, 91, 92, 93, 95, 97, 98, 100, 101, 103, 106, 108, 110, 113, 116, 117, 119, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 155, 156, 158, 159, 162, 163, 164, 165, 166, 174, 177, 178, 179, 181, 183, 185, 188, 189, 192, 198, 200, 201, 202, 205, 206, 207, 208, 210, 211, 213, 216, 217, 218, 220, 221, 225, 226, 227, 229, 230, 231, 232, 233, 234, 236, 238, 239, 240, 241, 242, 243, 244, 245, 248, 251, 252, 256, 257, 258, 260, 261, 262, 266, 267, 268, 269, 270, 271, 272, 275, 277, 278, 281, 283, 285], "recurs": [24, 71, 73, 75, 79, 80, 86, 88, 97, 110, 113, 116, 119, 123, 152, 153, 202, 205, 206, 208, 211, 213, 226, 231, 238, 241, 252, 257, 260, 270], "node": [24, 49, 75, 82, 86, 88, 90, 91, 93, 95, 97, 100, 101, 106, 116, 117, 123, 125, 127, 135, 152, 153, 176, 177, 178, 208, 216, 225, 227, 229, 230, 233, 236, 239, 240, 242, 244, 248, 261, 262, 266, 267, 268, 269, 270, 271, 275, 277, 278], "root": [24, 49, 82, 86, 88, 90, 91, 93, 95, 97, 100, 101, 106, 116, 117, 123, 125, 127, 135, 152, 153, 178, 216, 225, 227, 229, 230, 233, 236, 239, 240, 242, 244, 248, 261, 262, 266, 267, 268, 269, 270, 271, 275, 277, 278], "build": [24, 49, 75, 152, 153, 178, 208], "ani": [24, 26, 82, 86, 97, 98, 100, 101, 103, 120, 148, 152, 153, 155, 156, 216, 217, 218, 225, 226, 238, 239, 240, 241, 242, 243, 244, 245, 264, 284], "current": [24, 80, 152, 153, 213, 276], "being": [24, 152, 153, 264], "_node": [24, 152, 153], "save": [24, 26, 27, 28, 29, 30, 32, 33, 35, 37, 39, 40, 43, 45, 46, 48, 49, 50, 52, 54, 56, 58, 60, 62, 64, 66, 69, 71, 73, 75, 76, 78, 79, 80, 82, 84, 86, 88, 89, 90, 91, 92, 93, 95, 97, 98, 100, 101, 103, 104, 106, 108, 110, 113, 116, 117, 119, 120, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 155, 156, 158, 159, 162, 163, 164, 165, 166, 174, 176, 177, 178, 179, 181, 183, 185, 188, 189, 192, 198, 200, 201, 202, 205, 206, 207, 208, 210, 211, 213, 216, 217, 218, 220, 221, 225, 226, 227, 229, 230, 231, 232, 233, 234, 236, 238, 239, 240, 241, 242, 243, 244, 245, 246, 248, 251, 252, 256, 257, 258, 260, 261, 262, 264, 265, 266, 267, 268, 269, 270, 271, 272, 275, 277, 278, 281, 283, 285], "outfil": [24, 26, 28, 29, 43, 45, 46, 48, 49, 50, 52, 54, 56, 58, 60, 62, 64, 66, 69, 71, 73, 75, 76, 79, 80, 82, 84, 86, 88, 89, 90, 91, 92, 93, 95, 97, 98, 100, 101, 103, 104, 106, 108, 110, 113, 116, 117, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 155, 156, 158, 159, 162, 163, 164, 165, 166, 174, 176, 177, 178, 179, 181, 183, 185, 188, 189, 192, 198, 200, 201, 202, 205, 206, 207, 208, 210, 211, 213, 216, 217, 218, 220, 221, 225, 226, 227, 229, 230, 231, 232, 233, 234, 236, 238, 239, 240, 241, 242, 243, 244, 245, 246, 248, 251, 252, 256, 257, 258, 260, 261, 262, 266, 267, 268, 269, 270, 271, 275, 277, 278, 281, 283, 285], "an": [24, 26, 28, 29, 43, 45, 46, 48, 49, 50, 52, 54, 56, 58, 62, 64, 69, 71, 73, 75, 76, 78, 79, 80, 82, 84, 86, 88, 92, 95, 97, 98, 108, 110, 113, 116, 117, 119, 121, 123, 125, 127, 129, 131, 133, 135, 139, 152, 153, 155, 156, 158, 159, 162, 163, 164, 165, 166, 174, 176, 177, 178, 179, 181, 183, 185, 188, 189, 190, 192, 196, 198, 201, 202, 205, 206, 207, 208, 210, 211, 213, 216, 217, 218, 220, 223, 225, 226, 229, 231, 234, 236, 237, 238, 239, 240, 241, 242, 243, 249, 250, 251, 252, 255, 256, 257, 258, 260, 261, 262, 266, 267, 268, 269, 270, 271, 272, 275, 276, 277, 278, 280, 281, 283, 285], "startmin": [24, 26, 43, 45, 46, 48, 49, 50, 52, 54, 56, 58, 60, 62, 64, 66, 69, 71, 73, 75, 76, 78, 79, 80, 82, 84, 86, 88, 89, 90, 91, 92, 93, 95, 97, 98, 100, 101, 103, 104, 106, 108, 110, 113, 116, 117, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 155, 156, 158, 159, 162, 163, 164, 165, 166, 174, 176, 177, 178, 179, 181, 183, 185, 188, 189, 192, 198, 200, 201, 202, 205, 206, 207, 208, 210, 211, 213, 216, 217, 218, 220, 221, 225, 226, 227, 229, 230, 231, 232, 233, 234, 236, 238, 239, 240, 241, 242, 243, 244, 245, 246, 248, 251, 252, 256, 257, 258, 260, 261, 262, 266, 267, 268, 269, 271, 275, 277, 278, 281, 283, 285], "revis": [24, 45, 46, 50, 97, 153, 162, 163, 164, 165, 166, 179, 181, 238, 239], "tarun": [24, 45, 46, 50, 75, 97, 153, 162, 163, 164, 165, 166, 179, 181, 238, 239], "sreepad": [24, 153], "code": [24, 29, 32, 35, 56, 78, 80, 86, 88, 92, 97, 101, 103, 116, 119, 153, 225, 226, 231, 234, 238, 239, 245, 260], "minrf": [26, 155, 156], "minc": [26, 155, 156], "maxor": [26, 155, 156], "_coveragepattern": [26, 155, 156], "coverag": [26, 155, 156, 157, 214, 265], "bhargav": [26, 155, 156], "sripada": [26, 155], "polep": [26, 155], "krishna": [26, 69, 84, 86, 88, 89, 93, 101, 129, 155, 156, 201, 221, 225, 227, 231, 232, 281], "reddi": [26, 69, 71, 84, 86, 88, 93, 97, 101, 110, 129, 155, 156, 201, 202, 221, 225, 227, 231, 242, 252, 281], "banner": [26, 155], "advertis": [26, 155], "placement": [26, 155], "www": [26, 54, 88, 89, 95, 106, 155, 185, 231, 232, 236, 248], "companion": [26, 155], "volum": [26, 86, 123, 155, 225, 266], "2011": [26, 84, 123, 155, 221, 266, 272], "131": [26, 155], "132": [26, 155], "__http": [26, 155], "dl": [26, 92, 155, 234], "acm": [26, 45, 49, 75, 92, 113, 123, 155, 164, 178, 208, 234, 257, 272], "org": [26, 43, 45, 46, 50, 75, 76, 84, 86, 88, 90, 91, 92, 97, 98, 100, 104, 113, 116, 117, 121, 123, 125, 127, 131, 155, 159, 162, 163, 164, 165, 166, 179, 181, 206, 208, 220, 221, 225, 229, 230, 233, 234, 239, 240, 242, 243, 244, 246, 256, 261, 262, 266, 268, 269, 270, 272, 275, 277, 278, 283], "doi": [26, 43, 45, 46, 49, 50, 52, 56, 62, 69, 71, 75, 76, 79, 80, 82, 84, 86, 88, 91, 92, 93, 97, 104, 110, 113, 116, 117, 121, 123, 125, 127, 129, 131, 133, 155, 159, 162, 164, 166, 176, 179, 181, 183, 188, 192, 201, 202, 206, 208, 210, 213, 216, 217, 218, 220, 221, 225, 227, 229, 230, 234, 239, 240, 242, 246, 252, 256, 261, 262, 266, 268, 269, 270, 272, 275, 277, 278, 281, 283, 285], "10": [26, 27, 32, 37, 40, 43, 45, 46, 49, 50, 52, 54, 56, 62, 64, 66, 69, 71, 73, 75, 76, 79, 80, 82, 84, 86, 88, 89, 90, 91, 92, 93, 95, 97, 98, 100, 101, 103, 104, 106, 110, 113, 116, 117, 121, 123, 125, 127, 129, 131, 133, 135, 155, 156, 158, 159, 162, 163, 164, 165, 166, 176, 177, 178, 179, 181, 183, 185, 188, 190, 192, 196, 198, 200, 201, 202, 205, 206, 208, 210, 211, 213, 216, 217, 218, 220, 221, 225, 226, 227, 229, 230, 231, 232, 233, 234, 236, 238, 239, 240, 241, 242, 243, 244, 245, 246, 248, 252, 256, 257, 258, 260, 261, 262, 266, 267, 268, 269, 270, 271, 272, 275, 277, 278, 280, 281, 283, 285], "1145": [26, 45, 75, 84, 88, 91, 123, 155, 162, 164, 208, 221, 229, 230, 272], "1963192": [26, 155], "1963259": [26, 155], "everi": [26, 78, 80, 82, 86, 92, 97, 98, 100, 101, 108, 155, 156, 216, 225, 240, 241, 242, 243, 244, 251, 264, 276, 280], "must": [26, 78, 80, 86, 92, 97, 98, 100, 101, 108, 110, 113, 155, 156, 190, 223, 240, 241, 242, 243, 244, 251, 252, 256, 257, 258, 264, 265, 276, 280], "least": [26, 32, 108, 155, 156, 251], "maximum": [26, 30, 32, 39, 40, 62, 71, 73, 75, 76, 79, 80, 82, 86, 90, 97, 98, 100, 101, 103, 106, 110, 113, 116, 117, 127, 129, 139, 155, 156, 192, 202, 205, 206, 208, 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"uncertainFrequentPatternBasicTubeS", "uncertainFrequentPatternBasicUFGrowth", "uncertainFrequentPatternBasicUVECLAT", "uncertainFrequentPatternMining", "uncertainGeoReferencedFrequentPatternMining", "uncertainGeoreferencedFrequentPatternBasicGFPGrowth", "uncertainPatternMining", "uncertainPeriodicFrequentPatternBasicUPFPGrowth", "uncertainPeriodicFrequentPatternBasicUPFPGrowthPlus", "uncertainPeriodicFrequentPatternMining", "utilityPatternMining", "weightedFrequentNeighbourhoodPatternBasicSWFPGrowth", "weightedFrequentNeighbourhoodPatternMining", "weightedFrequentPatternBasicWFIM", "weightedFrequentPatternMining", "weightedFrequentRegularPatternBasicWFRIMiner", "weightedFrequentRegularPatternMining"], "filenames": ["ContiguousFrequentPatterns1.rst", "CorrelatedPatternMining1.rst", "CoveragePatternMining1.rst", "FaultTolerantPatternMining1.rst", "FrequentPatternWithMultipleMinimumSupport1.rst", "FuzzyCorrelatedPatternMining1.rst", "FuzzyFrequentPatternMining1.rst", 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"PAMI.extras.graph.rst", "PAMI.extras.image2Database.rst", "PAMI.extras.imageProcessing.rst", "PAMI.extras.messaging.rst", "PAMI.extras.neighbours.rst", "PAMI.extras.sampleDatasets.rst", "PAMI.extras.stats.rst", "PAMI.extras.syntheticDataGenerator.rst", "PAMI.extras.visualize.rst", "PAMI.faultTolerantFrequentPattern.rst", "PAMI.faultTolerantFrequentPattern.basic.rst", "PAMI.frequentPattern.rst", "PAMI.frequentPattern.basic.rst", "PAMI.frequentPattern.closed.rst", "PAMI.frequentPattern.cuda.rst", "PAMI.frequentPattern.maximal.rst", "PAMI.frequentPattern.pyspark.rst", "PAMI.frequentPattern.topk.rst", "PAMI.fuzzyCorrelatedPattern.rst", "PAMI.fuzzyCorrelatedPattern.basic.rst", "PAMI.fuzzyFrequentPattern.rst", "PAMI.fuzzyFrequentPattern.basic.rst", "PAMI.fuzzyGeoreferencedFrequentPattern.rst", "PAMI.fuzzyGeoreferencedFrequentPattern.basic.rst", "PAMI.fuzzyGeoreferencedPeriodicFrequentPattern.rst", "PAMI.fuzzyGeoreferencedPeriodicFrequentPattern.basic.rst", 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"PAMI.periodicFrequentPattern.topk.kPFPMiner.rst", "PAMI.recurringPattern.rst", "PAMI.recurringPattern.basic.rst", "PAMI.relativeFrequentPattern.rst", "PAMI.relativeFrequentPattern.basic.rst", "PAMI.relativeHighUtilityPattern.rst", "PAMI.relativeHighUtilityPattern.basic.rst", "PAMI.sequence.rst", "PAMI.sequentialPatternMining.rst", "PAMI.sequentialPatternMining.basic.rst", "PAMI.sequentialPatternMining.closed.rst", "PAMI.stablePeriodicFrequentPattern.rst", "PAMI.stablePeriodicFrequentPattern.basic.rst", "PAMI.stablePeriodicFrequentPattern.topK.rst", "PAMI.subgraphMining.rst", "PAMI.subgraphMining.basic.rst", "PAMI.subgraphMining.topK.rst", "PAMI.uncertainFaultTolerantFrequentPattern.rst", "PAMI.uncertainFrequentPattern.rst", "PAMI.uncertainFrequentPattern.basic.rst", "PAMI.uncertainGeoreferencedFrequentPattern.rst", "PAMI.uncertainGeoreferencedFrequentPattern.basic.rst", "PAMI.uncertainPeriodicFrequentPattern.rst", "PAMI.uncertainPeriodicFrequentPattern.basic.rst", "PAMI.weightedFrequentNeighbourhoodPattern.rst", "PAMI.weightedFrequentNeighbourhoodPattern.basic.rst", "PAMI.weightedFrequentPattern.rst", "PAMI.weightedFrequentPattern.basic.rst", "PAMI.weightedFrequentRegularPattern.rst", "PAMI.weightedFrequentRegularPattern.basic.rst", "PAMI.weightedUncertainFrequentPattern.rst", "PAMI.weightedUncertainFrequentPattern.basic.rst", "PartialPeriodicFrequentPatternMining1.rst", "PartialPeriodicPatternMining1.rst", "PeriodicCorrelatedPatternMining1.rst", "PeriodicFrequentPatternMining1.rst", "RecurringPatternMining1.rst", "RelativeHighUtilityPatternMining1.rst", "SequentialFrequentPatternMining1.rst", "StablePeriodicPatternMining1.rst", "UncertainFrequentPatternMining1.rst", "UncertainGeoReferencedFrequentPatternMining1.rst", "UncertainPeriodicFrequentPatternMining1.rst", "WeightedFrequentNeighbourhoodPatternMining1.rst", "WeightedFrequentPatternMining1.rst", "WeightedFrequentRegularPatternMining1.rst", "contiguousFrequentPatterns.rst", "contiguousPatternMining.rst", "correlatedPatternBasicCoMine.rst", "correlatedPatternBasicCoMinePlus.rst", "correlatedPatternMining.rst", "coveragePatternBasicCMine.rst", "coveragePatternBasicCPPG.rst", "coveragePatternMining.rst", "faultTolerantFrequentPatternBasicFTApriori.rst", "faultTolerantFrequentPatternBasicFTFPGrowth.rst", "faultTolerantPatternMining.rst", "frequent1.rst", "frequentPatternBasicApriori.rst", "frequentPatternBasicAprioribitset.rst", "frequentPatternBasicECLAT.rst", "frequentPatternBasicECLATDiffset.rst", "frequentPatternBasicECLATbitset.rst", "frequentPatternBasicFPGrowth.rst", "frequentPatternCUDAcuApriori.rst", "frequentPatternCUDAcuAprioriBit.rst", "frequentPatternCUDAcuAprioriGCT.rst", "frequentPatternCUDAcuAprioriTID.rst", "frequentPatternCUDAcuECLAT.rst", "frequentPatternCUDAcuECLATBit.rst", "frequentPatternCUDAcuECLATGCT.rst", "frequentPatternMaximalmaxFPGrowth.rst", "frequentPatternMining.rst", "frequentPatternPysparkParallelApriori.rst", "frequentPatternPysparkParallelECLAT.rst", "frequentPatternPysparkParallelFPGrowth.rst", "frequentPatternTopkFAE.rst", "frequentPatternWithMultipleMinimumSupport.rst", "frequentPatternclosedCHARM.rst", "fuzzyCorrelatedPatternMining.rst", "fuzzyCorrelatedPatternbasicFCPGrowth.rst", "fuzzyFrequentPatternMining.rst", "fuzzyFrequentPatternbasicFFIMiner.rst", "fuzzyGeoReferencedFrequentPatternMining.rst", "fuzzyGeoReferencedPeriodicFrequentPatternMining.rst", "fuzzyGeoreferencedFrequentPatternbasicFFSPMiner.rst", "fuzzyGeoreferencedPeriodicFrequentPatternbasicFGPFPMiner.rst", "fuzzyPatternMining.rst", "fuzzyPeriodicFrequentPatternMining.rst", "fuzzyPeriodicFrequentPatternbasicFPFPMiner.rst", "geoReferencedFrequentPatternMining.rst", "geoReferencedFrequentSequencePatternMining.rst", "geoReferencedPartialPeriodicPatternMining.rst", "geoReferencedPatternMining.rst", "geoReferencedPeriodicFrequentPatternMining.rst", "geoReferencedPeriodicFrequentPatternbasicGPFPMiner.rst", "georeferencedFrequentPatternbasicFSPGrowth.rst", "georeferencedFrequentPatternbasicSpatialECLAT.rst", "georeferencedPartialPeriodicPatternbasicSTEclat.rst", "highUtilityFrequentPatternBasicHUFIM.rst", "highUtilityFrequentPatternMining.rst", "highUtilityGeo-referencedFrequentPatternMining.rst", "highUtilityGeoreferencedFrequentPatternBasicSHUFIM.rst", "highUtilityPatternBasicEFIM.rst", "highUtilityPatternBasicHMiner.rst", "highUtilityPatternBasicUPGrowth.rst", "highUtilityPatternMining.rst", "highUtilitySpatialPatternBasicHDSHUIM.rst", "highUtilitySpatialPatternBasicSHUIM.rst", "highUtilitySpatialPatternMining.rst", "highUtilitySpatialPatternTopkTKSHUIM.rst", "index.rst", "localPeriodicPatternMining.rst", "localPeriodicPatternbasicLPPGrowth.rst", "localPeriodicPatternbasicLPPMBreadth.rst", "localPeriodicPatternbasicLPPMDepth.rst", "modules.rst", "multipleMinimumSupportBasedFrequentPatternBasicCFPGrowth.rst", "multipleMinimumSupportBasedFrequentPatternBasicCFPGrowthPlus.rst", "multiplePartialPeriodicPatternMining.rst", "multipleTimeseriesPatternMining.rst", "partialPeriodicFrequentPatternMining.rst", "partialPeriodicFrequentPatternbasicGPFgrowth.rst", "partialPeriodicFrequentPatternbasicPPF_DFS.rst", "partialPeriodicPatternInMultipleTimeSeriesPPGrowth.rst", "partialPeriodicPatternMining.rst", "partialPeriodicPatternbasicGThreePGrowth.rst", "partialPeriodicPatternbasicPPPGrowth.rst", "partialPeriodicPatternbasicPPP_ECLAT.rst", "partialPeriodicPatternclosedPPPClose.rst", "partialPeriodicPatternmaximalMax3PGrowth.rst", "partialPeriodicPatterntopkk3PMiner.rst", "periodicCorrelatedPatternMining.rst", "periodicCorrelatedPatternbasicEPCPGrowth.rst", "periodicFrequentPatternMining.rst", "periodicFrequentPatternbasicPFECLAT.rst", "periodicFrequentPatternbasicPFPGrowth.rst", "periodicFrequentPatternbasicPFPGrowthPlus.rst", "periodicFrequentPatternbasicPFPMC.rst", "periodicFrequentPatternbasicPSGrowth.rst", "periodicFrequentPatternclosedCPFPMiner.rst", "periodicFrequentPatternmaximalMaxPFGrowth.rst", "periodicFrequentPatterntopkTopkPFPTopkPFP.rst", "periodicFrequentPatterntopkkPFPMinerkPFPMiner.rst", "recurringPatternMining.rst", "recurringPatternbasicRPGrowth.rst", "relativeFrequent.rst", "relativeFrequentPattern.rst", "relativeFrequentPatternBasicRSFPGrowth.rst", "relativeHighUtilityPatternBasicRHUIM.rst", "relativeHighUtilityPatternMining.rst", "sequentialFrequentPatternMining.rst", "sequentialPatternMining.rst", "sequentialPatternMiningBasicSPADE.rst", "sequentialPatternMiningBasicSPAM.rst", "sequentialPatternMiningBasicprefixSpan.rst", "sequentialPatternMiningClosedbide.rst", "stablePeriodicFrequentPatternbasicSPPEclat.rst", "stablePeriodicFrequentPatternbasicSPPGrowth.rst", "stablePeriodicFrequentPatterntopKTSPIN.rst", "stablePeriodicPatternMining.rst", "temporalPatternMining.rst", "transactionalPatternMining.rst", "uncertainFrequentPatternBasicCUFPTree.rst", "uncertainFrequentPatternBasicPUFGrowth.rst", "uncertainFrequentPatternBasicTUFP.rst", "uncertainFrequentPatternBasicTubeP.rst", "uncertainFrequentPatternBasicTubeS.rst", "uncertainFrequentPatternBasicUFGrowth.rst", "uncertainFrequentPatternBasicUVECLAT.rst", "uncertainFrequentPatternMining.rst", "uncertainGeoReferencedFrequentPatternMining.rst", "uncertainGeoreferencedFrequentPatternBasicGFPGrowth.rst", "uncertainPatternMining.rst", "uncertainPeriodicFrequentPatternBasicUPFPGrowth.rst", "uncertainPeriodicFrequentPatternBasicUPFPGrowthPlus.rst", "uncertainPeriodicFrequentPatternMining.rst", "utilityPatternMining.rst", "weightedFrequentNeighbourhoodPatternBasicSWFPGrowth.rst", "weightedFrequentNeighbourhoodPatternMining.rst", "weightedFrequentPatternBasicWFIM.rst", "weightedFrequentPatternMining.rst", "weightedFrequentRegularPatternBasicWFRIMiner.rst", "weightedFrequentRegularPatternMining.rst"], "titles": ["Contiguous Frequent Patterns", "Correlated Pattern Mining", "Coverage Pattern Mining", "Fault-Tolerant Frequent Pattern Mining", "Frequent pattern With Multiple Minimum Support", "Fuzzy Correlated Pattern Mining", "Fuzzy Frequent Pattern Mining", "Fuzzy Geo-referenced Frequent Pattern Mining", "Fuzzy Geo-referenced Periodic Frequent Pattern Mining", "Fuzzy Periodic Frequent Pattern Mining", "Geo-referenced Frequent Pattern Mining", "Geo-referenced Frequent Sequence Pattern mining", "Geo-referenced Partial Periodic Pattern Mining", "Geo-referenced Periodic Frequent Pattern Mining", "High-Utility Frequent Pattern Mining", "High-Utility Geo-referenced Frequent Pattern Mining", "High-Utility Pattern mining", "High-Utility Spatial Pattern Mining", "Local Periodic Pattern Mining", "Multiple Partial Periodic Pattern Mining", "PAMI package", "PAMI.AssociationRules package", "PAMI.AssociationRules.basic package", "PAMI.correlatedPattern package", "PAMI.correlatedPattern.basic package", "PAMI.coveragePattern package", "PAMI.coveragePattern.basic package", "PAMI.extras package", "PAMI.extras.DF2DB package", "PAMI.extras.calculateMISValues package", "PAMI.extras.dbStats package", "PAMI.extras.fuzzyTransformation package", "PAMI.extras.generateDatabase package", "PAMI.extras.graph package", "PAMI.extras.image2Database package", "PAMI.extras.imageProcessing package", "PAMI.extras.messaging package", "PAMI.extras.neighbours package", "PAMI.extras.sampleDatasets package", "PAMI.extras.stats package", "PAMI.extras.syntheticDataGenerator package", "PAMI.extras.visualize package", "PAMI.faultTolerantFrequentPattern package", "PAMI.faultTolerantFrequentPattern.basic package", "PAMI.frequentPattern package", "PAMI.frequentPattern.basic package", "PAMI.frequentPattern.closed package", "PAMI.frequentPattern.cuda package", "PAMI.frequentPattern.maximal package", "PAMI.frequentPattern.pyspark package", "PAMI.frequentPattern.topk package", "PAMI.fuzzyCorrelatedPattern package", "PAMI.fuzzyCorrelatedPattern.basic package", "PAMI.fuzzyFrequentPattern package", "PAMI.fuzzyFrequentPattern.basic package", "PAMI.fuzzyGeoreferencedFrequentPattern package", "PAMI.fuzzyGeoreferencedFrequentPattern.basic package", "PAMI.fuzzyGeoreferencedPeriodicFrequentPattern package", "PAMI.fuzzyGeoreferencedPeriodicFrequentPattern.basic package", "PAMI.fuzzyPartialPeriodicPatterns package", "PAMI.fuzzyPartialPeriodicPatterns.basic package", "PAMI.fuzzyPeriodicFrequentPattern package", "PAMI.fuzzyPeriodicFrequentPattern.basic package", "PAMI.geoReferencedPeriodicFrequentPattern package", "PAMI.geoReferencedPeriodicFrequentPattern.basic package", "PAMI.georeferencedFrequentPattern package", "PAMI.georeferencedFrequentPattern.basic package", "PAMI.georeferencedFrequentSequencePattern package", "PAMI.georeferencedPartialPeriodicPattern package", "PAMI.georeferencedPartialPeriodicPattern.basic package", "PAMI.highUtilityFrequentPattern package", "PAMI.highUtilityFrequentPattern.basic package", "PAMI.highUtilityGeoreferencedFrequentPattern package", "PAMI.highUtilityGeoreferencedFrequentPattern.basic package", "PAMI.highUtilityPattern package", "PAMI.highUtilityPattern.basic package", "PAMI.highUtilityPattern.parallel package", "PAMI.highUtilityPatternsInStreams package", "PAMI.highUtilitySpatialPattern package", "PAMI.highUtilitySpatialPattern.basic package", "PAMI.highUtilitySpatialPattern.topk package", "PAMI.localPeriodicPattern package", "PAMI.localPeriodicPattern.basic package", "PAMI.multipleMinimumSupportBasedFrequentPattern package", "PAMI.multipleMinimumSupportBasedFrequentPattern.basic package", "PAMI.partialPeriodicFrequentPattern package", "PAMI.partialPeriodicFrequentPattern.basic package", "PAMI.partialPeriodicPattern package", "PAMI.partialPeriodicPattern.basic package", "PAMI.partialPeriodicPattern.closed package", "PAMI.partialPeriodicPattern.maximal package", "PAMI.partialPeriodicPattern.pyspark package", "PAMI.partialPeriodicPattern.topk package", "PAMI.partialPeriodicPatternInMultipleTimeSeries package", "PAMI.periodicCorrelatedPattern package", "PAMI.periodicCorrelatedPattern.basic package", "PAMI.periodicFrequentPattern package", "PAMI.periodicFrequentPattern.basic package", "PAMI.periodicFrequentPattern.closed package", "PAMI.periodicFrequentPattern.cuda package", "PAMI.periodicFrequentPattern.maximal package", "PAMI.periodicFrequentPattern.pyspark package", "PAMI.periodicFrequentPattern.topk package", "PAMI.periodicFrequentPattern.topk.TopkPFP package", "PAMI.periodicFrequentPattern.topk.kPFPMiner package", "PAMI.recurringPattern package", "PAMI.recurringPattern.basic package", "PAMI.relativeFrequentPattern package", "PAMI.relativeFrequentPattern.basic package", "PAMI.relativeHighUtilityPattern package", "PAMI.relativeHighUtilityPattern.basic package", "PAMI.sequence package", "PAMI.sequentialPatternMining package", "PAMI.sequentialPatternMining.basic package", "PAMI.sequentialPatternMining.closed package", "PAMI.stablePeriodicFrequentPattern package", "PAMI.stablePeriodicFrequentPattern.basic package", "PAMI.stablePeriodicFrequentPattern.topK package", "PAMI.subgraphMining package", "PAMI.subgraphMining.basic package", "PAMI.subgraphMining.topK package", "PAMI.uncertainFaultTolerantFrequentPattern package", "PAMI.uncertainFrequentPattern package", "PAMI.uncertainFrequentPattern.basic package", "PAMI.uncertainGeoreferencedFrequentPattern package", "PAMI.uncertainGeoreferencedFrequentPattern.basic package", "PAMI.uncertainPeriodicFrequentPattern package", "PAMI.uncertainPeriodicFrequentPattern.basic package", "PAMI.weightedFrequentNeighbourhoodPattern package", "PAMI.weightedFrequentNeighbourhoodPattern.basic package", "PAMI.weightedFrequentPattern package", "PAMI.weightedFrequentPattern.basic package", "PAMI.weightedFrequentRegularPattern package", "PAMI.weightedFrequentRegularPattern.basic package", "PAMI.weightedUncertainFrequentPattern package", "PAMI.weightedUncertainFrequentPattern.basic package", "Partial Periodic Frequent Pattern Mining", "Partial Periodic Pattern Mining", "Periodic correlated pattern mining", "Periodic Frequent Pattern Mining", "Recurring Pattern Mining", "Relative High-Utility Pattern Mining", "Sequential Frequent Pattern mining", "Stable Periodic Pattern Mining", "Uncertain Frequent Pattern mining", "Uncertain Geo-Referenced Frequent Pattern mining", "Uncertain Periodic Frequent Pattern mining", "Weighted Frequent Neighbourhood Pattern Mining", "Weighted Frequent Pattern Mining", "Weighted Frequent Regular Pattern Mining", "<no title>", "Contiguous Patterns", "CoMine", "CoMinePlus", "Basic", "CMine", "CPPG", "Basic", "FTApriori", "FTFPGrowth", "Basic", "Frequent Pattern mining", "Apriori", "Aprioribitset", "ECLAT", "ECLATDiffset", "ECLATbitset", "FPGrowth", "cuApriori", "cuAprioriBit", "cudaAprioriGCT", "cudaAprioriTID", "cuEclat", "cuEclatBit", "cudaEclatGCT", "MaxFPGrowth", "Basic", "parallelApriori", "parallelECLAT", "parallelFPGrowth", "FAE", "Basic", "CHARM", "Basic", "FCPGrowth", "Basic", "FFIMiner", "Basic", "Basic", "FFSPMiner", "FGPFPMiner", "Fuzzy Pattern Mining", "Basic", "FPFPMiner", "Basic", "<no title>", "Basic", "Geo-referenced Pattern Mining", "Basic", "GPFPMiner", "FSPGrowth", "SpatialECLAT", "STEclat", "HUFIM", "Basic", "Basic", "SHUFIM", "EFIM", "HMiner", "UPGrowth", "Basic", "HDSHUIM", "SHUIM", "Basic", "TKSHUIM", "Welcome to PAMI\u2019s documentation!", "Basic", "LPPGrowth", "LPPMBreadth", "LPPMDepth", "PAMI", "CFPGrowth", "CFPGrowthPlus", "Basic", "Multiple Timeseries", "Basic", "GPFgrowth", "PPF_DFS", "PPGrowth", "Basic", "GThreePGrowth", "PPPGrowth", "PPP_ECLAT", "PPPClose", "Max3PGrowth", "k3PMiner", "Basic", "EPCPGrowth", "Basic", "PFECLAT", "PFPGrowth", "PFPGrowthPlus", "PFPMC", "PSGrowth", "CPFPMiner", "MaxPFGrowth", "TopkPFP", "kPFPMiner", "Basic", "RPGrowth", "Relative Frequent Pattern", "Basic", "RSFPGrowth", "RHUIM", "Basic", "Basic", "Sequential Database", "SPADE", "SPAM", "prefixSpan", "bide", "SPPEclat", "SPPGrowth", "TSPIN", "Basic", "Temporal Database", "Transactional 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274], "tufp": [20, 122, 270, 274], "tubep": [20, 122, 274], "tube": [20, 122, 274], "ufgrowth": [20, 122, 274], "uveclat": [20, 122, 274], "uncertaingeoreferencedfrequentpattern": [20, 220, 276], "gfpgrowth": [20, 124, 275], "uncertainperiodicfrequentpattern": [20, 220, 278, 279], "upfpgrowth": [20, 126, 280], "upfpgrowthplu": [20, 126, 280], "weightedfrequentneighbourhoodpattern": [20, 220, 282], "swfpgrowth": [20, 128, 283], "weightedfrequentpattern": [20, 220, 284], "wfim": [20, 130, 285], "weightedfrequentregularpattern": [20, 220, 286], "wfrimin": [20, 132, 287], "weighteduncertainfrequentpattern": [20, 220], "wufim": [20, 134], "pattern": [20, 24, 26, 27, 28, 29, 31, 33, 43, 45, 46, 48, 49, 50, 52, 54, 56, 58, 60, 62, 64, 66, 69, 71, 73, 75, 76, 78, 79, 80, 82, 84, 86, 88, 89, 90, 91, 92, 93, 95, 97, 98, 100, 101, 103, 104, 106, 108, 110, 116, 117, 119, 120, 121, 123, 125, 127, 129, 131, 133, 135, 150, 152, 153, 154, 155, 156, 157, 158, 159, 160, 162, 163, 164, 165, 166, 167, 175, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 192, 193, 194, 195, 196, 198, 199, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 252, 253, 254, 255, 256, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 282, 283, 284, 285, 286, 287], "python": [20, 24, 26, 45, 46, 50, 56, 58, 86, 88, 92, 97, 98, 101, 103, 116, 119, 120, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 155, 162, 163, 164, 165, 166, 167, 180, 182, 189, 190, 226, 227, 232, 235, 239, 240, 244, 246, 261, 267, 268, 269, 270, 271, 276, 278, 279, 282, 284, 286], "librari": 20, "class": [24, 26, 27, 28, 29, 30, 31, 32, 33, 35, 36, 37, 39, 40, 41, 43, 45, 46, 48, 49, 50, 52, 54, 56, 58, 60, 62, 64, 66, 69, 71, 73, 75, 76, 78, 79, 80, 82, 84, 86, 88, 89, 90, 91, 92, 93, 95, 97, 98, 100, 101, 103, 104, 106, 108, 110, 116, 117, 119, 120, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 155, 156, 158, 159, 162, 163, 164, 165, 166, 167, 175, 177, 178, 179, 180, 182, 184, 186, 189, 190, 193, 199, 201, 202, 203, 206, 207, 208, 209, 211, 212, 214, 217, 218, 219, 221, 222, 226, 227, 228, 230, 231, 232, 233, 234, 235, 237, 239, 240, 241, 242, 243, 244, 245, 246, 247, 249, 252, 253, 261, 262, 263, 267, 268, 269, 270, 271, 272, 273, 276, 278, 279, 282, 284, 286], "ifil": [24, 26, 27, 29, 30, 31, 33, 37, 39, 41, 43, 45, 46, 48, 49, 50, 52, 54, 56, 58, 60, 62, 64, 66, 69, 71, 73, 75, 76, 78, 79, 80, 82, 84, 86, 88, 89, 90, 91, 92, 93, 95, 97, 98, 100, 101, 103, 104, 106, 108, 110, 116, 117, 119, 120, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 155, 156, 158, 159, 162, 163, 164, 165, 166, 167, 175, 177, 178, 179, 180, 182, 184, 186, 189, 190, 193, 199, 201, 202, 203, 206, 207, 208, 209, 211, 212, 214, 217, 218, 219, 221, 222, 226, 227, 228, 230, 231, 232, 233, 234, 235, 237, 239, 240, 241, 242, 243, 244, 245, 246, 247, 249, 252, 253, 261, 262, 263, 267, 268, 269, 270, 271, 272, 273, 276, 278, 279, 282, 284, 286], "union": [24, 28, 30, 39, 43, 62, 71, 82, 88, 100, 101, 108, 129, 152, 153, 159, 193, 203, 218, 219, 230, 245, 252, 282], "str": [24, 26, 27, 28, 29, 30, 31, 32, 33, 35, 36, 37, 39, 40, 43, 45, 46, 48, 49, 50, 52, 54, 56, 58, 60, 62, 64, 66, 69, 71, 73, 75, 76, 78, 79, 80, 82, 84, 86, 88, 89, 90, 91, 92, 93, 95, 97, 98, 100, 101, 103, 104, 106, 108, 110, 116, 117, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 155, 156, 158, 159, 162, 163, 164, 165, 166, 167, 175, 177, 178, 179, 180, 182, 184, 186, 189, 190, 193, 199, 201, 202, 203, 206, 207, 208, 209, 211, 212, 214, 217, 218, 219, 221, 222, 226, 227, 228, 230, 231, 232, 233, 234, 235, 237, 239, 240, 241, 242, 243, 244, 245, 246, 247, 249, 252, 253, 261, 262, 263, 267, 268, 269, 270, 271, 272, 273, 276, 278, 279, 282, 284, 286], "datafram": [24, 26, 27, 28, 29, 30, 32, 33, 35, 39, 40, 43, 45, 46, 48, 49, 50, 52, 54, 56, 58, 60, 62, 64, 66, 69, 71, 73, 75, 76, 79, 80, 82, 84, 86, 88, 89, 90, 91, 92, 93, 95, 97, 98, 100, 101, 103, 104, 106, 108, 110, 116, 117, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 155, 156, 158, 159, 162, 163, 164, 165, 166, 167, 175, 177, 178, 179, 180, 182, 184, 186, 189, 190, 193, 199, 201, 202, 203, 206, 207, 208, 209, 211, 212, 214, 217, 218, 219, 221, 222, 226, 227, 228, 230, 231, 232, 233, 234, 235, 237, 239, 240, 241, 242, 243, 244, 245, 246, 247, 249, 252, 253, 261, 262, 263, 267, 268, 269, 270, 271, 272, 273, 276, 278, 279, 282, 284, 286], "minsup": [24, 26, 33, 43, 45, 46, 48, 49, 52, 54, 56, 58, 60, 62, 64, 66, 71, 73, 75, 79, 84, 86, 93, 95, 97, 98, 100, 101, 103, 108, 110, 116, 119, 120, 121, 123, 125, 127, 129, 131, 133, 135, 139, 152, 153, 156, 158, 159, 162, 163, 164, 165, 166, 167, 175, 177, 178, 179, 182, 184, 186, 189, 190, 193, 199, 201, 203, 206, 208, 211, 212, 221, 222, 226, 227, 228, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 252, 253, 261, 262, 267, 268, 269, 270, 271, 272, 273, 276, 278, 279, 282, 284, 286], "int": [24, 26, 27, 28, 29, 30, 32, 33, 35, 37, 39, 40, 43, 45, 46, 48, 49, 50, 52, 54, 56, 58, 60, 62, 64, 66, 69, 71, 73, 75, 76, 79, 80, 82, 84, 86, 88, 89, 90, 91, 92, 93, 95, 97, 98, 100, 101, 103, 104, 106, 108, 110, 116, 117, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 155, 156, 158, 159, 162, 163, 164, 165, 166, 167, 175, 177, 178, 179, 180, 182, 184, 186, 189, 190, 193, 199, 201, 202, 203, 206, 207, 208, 209, 211, 212, 214, 217, 218, 219, 221, 222, 226, 227, 228, 230, 231, 232, 233, 234, 235, 237, 239, 240, 241, 242, 243, 244, 245, 246, 247, 249, 252, 253, 261, 262, 263, 267, 268, 269, 270, 271, 272, 273, 276, 278, 279, 282, 284, 286], "float": [24, 26, 27, 28, 30, 32, 35, 39, 40, 43, 45, 46, 48, 49, 50, 52, 54, 56, 58, 60, 62, 64, 66, 69, 71, 73, 75, 76, 78, 79, 80, 82, 84, 86, 88, 89, 90, 91, 92, 93, 95, 97, 98, 100, 101, 103, 104, 106, 108, 110, 116, 117, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 155, 156, 158, 159, 162, 163, 164, 165, 166, 167, 175, 177, 178, 179, 180, 182, 184, 186, 189, 190, 193, 199, 201, 202, 203, 206, 207, 208, 209, 211, 212, 214, 217, 218, 219, 221, 222, 226, 227, 228, 230, 231, 232, 233, 234, 235, 237, 239, 240, 241, 242, 243, 244, 245, 246, 247, 249, 252, 253, 261, 262, 263, 267, 268, 269, 270, 271, 272, 273, 276, 278, 279, 282, 284, 286], "minallconf": [24, 52, 95, 152, 153, 184, 237], "sep": [24, 26, 27, 29, 30, 31, 32, 35, 39, 40, 43, 45, 46, 48, 49, 50, 52, 54, 56, 58, 60, 62, 64, 66, 69, 71, 73, 75, 76, 78, 79, 80, 82, 84, 86, 88, 89, 90, 91, 92, 93, 95, 97, 98, 100, 101, 103, 104, 106, 108, 110, 116, 117, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 155, 156, 158, 159, 162, 163, 164, 165, 166, 167, 175, 177, 178, 179, 180, 182, 184, 186, 189, 190, 191, 193, 197, 199, 201, 202, 203, 206, 207, 208, 209, 211, 212, 214, 217, 218, 219, 221, 222, 224, 226, 227, 228, 230, 231, 232, 233, 234, 235, 237, 239, 240, 241, 242, 243, 244, 245, 246, 247, 249, 252, 253, 256, 261, 262, 263, 265, 266, 267, 268, 269, 270, 271, 272, 273, 276, 277, 278, 279, 281, 282, 284, 286], "t": [24, 26, 27, 30, 31, 32, 37, 39, 40, 43, 45, 46, 48, 49, 50, 52, 54, 56, 60, 62, 64, 66, 69, 71, 73, 75, 76, 78, 79, 80, 82, 84, 86, 88, 89, 90, 91, 92, 93, 95, 97, 98, 100, 101, 103, 104, 106, 108, 110, 116, 117, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 155, 156, 158, 159, 162, 163, 164, 165, 166, 167, 175, 177, 178, 179, 180, 182, 184, 186, 189, 191, 193, 199, 201, 202, 203, 206, 207, 208, 209, 211, 212, 214, 217, 218, 219, 221, 222, 226, 227, 228, 230, 231, 232, 233, 234, 235, 237, 239, 240, 241, 242, 243, 244, 245, 246, 247, 249, 252, 253, 261, 262, 263, 267, 268, 269, 270, 271, 272, 273, 276, 277, 278, 279, 281, 282, 284, 286], "sourc": [24, 26, 27, 28, 29, 30, 31, 32, 33, 35, 36, 37, 39, 40, 41, 43, 45, 46, 48, 49, 50, 52, 54, 56, 58, 60, 62, 64, 66, 69, 71, 73, 75, 76, 78, 79, 80, 82, 84, 86, 88, 89, 90, 91, 92, 93, 95, 97, 98, 100, 101, 103, 104, 106, 108, 110, 116, 117, 119, 120, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 155, 156, 158, 159, 162, 163, 164, 165, 166, 167, 175, 177, 178, 179, 180, 182, 184, 186, 189, 190, 193, 199, 201, 202, 203, 206, 207, 208, 209, 211, 212, 214, 217, 218, 219, 221, 222, 226, 227, 228, 230, 231, 232, 233, 234, 235, 237, 239, 240, 241, 242, 243, 244, 245, 246, 247, 249, 252, 253, 261, 262, 263, 267, 268, 269, 270, 271, 272, 273, 276, 278, 279, 282, 284, 286], "_correlatedpattern": [24, 152, 153], "about": [24, 26, 30, 39, 45, 46, 50, 97, 119, 152, 153, 155, 162, 163, 164, 165, 166, 167, 180, 182, 239, 240], "algorithm": [24, 26, 33, 43, 45, 46, 48, 49, 50, 52, 54, 60, 64, 66, 69, 71, 75, 76, 78, 79, 80, 82, 84, 86, 89, 90, 92, 93, 95, 97, 98, 100, 101, 103, 104, 106, 108, 110, 117, 119, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 155, 156, 158, 159, 162, 163, 164, 165, 166, 167, 175, 177, 178, 179, 180, 182, 184, 186, 199, 201, 202, 203, 207, 208, 209, 211, 212, 217, 218, 219, 221, 222, 226, 227, 228, 233, 234, 235, 237, 239, 240, 241, 243, 244, 245, 246, 247, 249, 252, 253, 263, 265, 266, 267, 268, 269, 270, 271, 272, 273, 276, 277, 279, 282, 284, 286], "descript": [24, 26, 27, 28, 29, 30, 31, 32, 33, 35, 39, 40, 43, 45, 46, 48, 49, 50, 52, 54, 56, 58, 60, 62, 64, 66, 69, 71, 73, 75, 76, 78, 79, 80, 82, 84, 86, 88, 89, 90, 91, 92, 93, 95, 97, 98, 100, 101, 103, 104, 106, 108, 110, 116, 117, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 155, 156, 158, 159, 162, 163, 164, 165, 166, 167, 175, 177, 178, 179, 180, 182, 184, 186, 189, 190, 193, 199, 201, 202, 203, 206, 207, 208, 209, 211, 212, 214, 217, 218, 219, 221, 222, 226, 227, 228, 230, 231, 233, 234, 235, 237, 239, 240, 241, 242, 243, 244, 245, 246, 247, 249, 252, 253, 261, 262, 263, 267, 268, 269, 270, 271, 272, 273, 276, 278, 279, 281, 282, 284, 286], "fundament": [24, 43, 45, 48, 49, 69, 84, 88, 91, 93, 97, 100, 101, 106, 121, 123, 131, 133, 152, 153, 158, 159, 162, 163, 164, 166, 167, 175, 179, 202, 221, 228, 230, 231, 232, 239, 240, 241, 242, 243, 245, 249, 267, 268, 269, 270, 272, 273, 284, 286], "correl": [24, 52, 95, 152, 153, 154, 183, 184, 191, 215, 236, 237, 265, 266], "fp": [24, 28, 43, 45, 49, 131, 152, 153, 159, 167, 179, 284], "growth": [24, 48, 49, 75, 82, 86, 90, 97, 100, 127, 152, 153, 175, 179, 209, 217, 226, 234, 243, 245, 279], "depth": [24, 46, 82, 89, 98, 119, 152, 153, 182, 217, 218, 219, 233, 244], "first": [24, 45, 46, 49, 75, 80, 82, 86, 89, 98, 110, 119, 152, 153, 162, 179, 182, 207, 214, 217, 218, 219, 224, 226, 233, 244, 253, 265, 277], "search": [24, 43, 45, 46, 49, 54, 56, 58, 62, 75, 76, 82, 84, 89, 98, 119, 121, 131, 133, 152, 153, 158, 159, 162, 167, 177, 178, 179, 182, 186, 189, 190, 193, 215, 217, 218, 219, 222, 233, 244, 284, 286], "lee": [24, 97, 127, 152, 153, 240, 278], "y": [24, 33, 43, 45, 71, 97, 104, 116, 117, 119, 120, 152, 153, 159, 167, 203, 239, 247, 262, 263], "kim": [24, 152, 153], "w": [24, 79, 116, 127, 152, 153, 212, 261, 278], "cao": [24, 152, 153], "d": [24, 152, 153, 256, 265, 266], "han": [24, 43, 45, 84, 152, 153, 158, 159, 167, 221], "j": [24, 43, 45, 46, 48, 71, 73, 75, 76, 79, 80, 82, 84, 86, 88, 89, 91, 97, 110, 116, 119, 120, 123, 127, 131, 152, 153, 159, 167, 175, 182, 203, 206, 207, 212, 214, 217, 218, 219, 221, 226, 232, 233, 241, 253, 261, 267, 278, 284], "2003": [24, 45, 152, 153, 165], "icdm": [24, 123, 152, 153, 271], "pp": [24, 45, 49, 56, 62, 69, 79, 80, 93, 97, 110, 116, 129, 131, 133, 152, 153, 162, 177, 189, 193, 202, 211, 214, 228, 243, 253, 261, 282, 284, 286], "581": [24, 152, 153], "584": [24, 152, 153], "paramet": [24, 26, 27, 28, 29, 31, 32, 33, 35, 40, 43, 45, 46, 48, 49, 50, 52, 54, 56, 58, 60, 62, 64, 66, 69, 71, 73, 75, 76, 78, 79, 80, 82, 84, 86, 88, 89, 90, 91, 92, 93, 95, 97, 98, 100, 101, 103, 104, 106, 108, 110, 116, 117, 119, 120, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 155, 156, 158, 159, 162, 163, 164, 165, 166, 167, 175, 177, 178, 179, 180, 182, 184, 186, 189, 190, 193, 199, 201, 202, 203, 206, 207, 208, 209, 211, 212, 214, 217, 218, 219, 221, 222, 226, 227, 228, 230, 231, 232, 233, 234, 235, 237, 239, 240, 241, 242, 243, 244, 245, 246, 247, 249, 252, 253, 261, 262, 263, 267, 268, 269, 270, 271, 273, 276, 278, 279, 282, 284, 286], "name": [24, 26, 27, 28, 29, 30, 31, 32, 33, 37, 39, 40, 43, 45, 46, 48, 49, 50, 52, 54, 56, 58, 60, 62, 64, 66, 69, 71, 73, 75, 76, 78, 79, 80, 82, 84, 86, 88, 89, 90, 91, 92, 93, 95, 97, 98, 100, 101, 103, 104, 106, 108, 110, 116, 117, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 155, 156, 158, 159, 162, 163, 164, 165, 166, 167, 175, 177, 178, 179, 180, 182, 184, 186, 189, 190, 193, 199, 201, 202, 203, 206, 207, 208, 209, 211, 212, 214, 217, 218, 219, 221, 222, 226, 227, 228, 230, 231, 232, 233, 234, 235, 237, 239, 240, 241, 242, 243, 244, 245, 246, 247, 249, 252, 253, 261, 262, 263, 267, 268, 269, 270, 271, 272, 273, 276, 277, 278, 279, 282, 284, 286], "input": [24, 26, 27, 29, 30, 31, 32, 33, 37, 39, 40, 43, 45, 46, 48, 49, 50, 52, 54, 56, 58, 60, 62, 64, 66, 69, 71, 73, 75, 76, 78, 79, 80, 82, 84, 86, 88, 89, 90, 91, 92, 93, 95, 97, 98, 100, 101, 103, 104, 106, 108, 110, 116, 117, 119, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 155, 156, 158, 159, 162, 163, 164, 165, 166, 167, 175, 177, 178, 179, 180, 182, 184, 186, 189, 190, 193, 199, 201, 202, 203, 206, 207, 208, 209, 211, 212, 214, 217, 218, 219, 221, 222, 226, 227, 228, 230, 231, 232, 233, 234, 235, 237, 239, 240, 241, 242, 243, 244, 245, 246, 247, 249, 252, 253, 261, 262, 263, 267, 268, 269, 270, 271, 272, 273, 276, 278, 279, 282, 284, 286], "file": [24, 26, 27, 28, 29, 30, 31, 32, 33, 37, 39, 40, 43, 45, 46, 48, 49, 50, 52, 54, 56, 58, 60, 62, 64, 66, 69, 71, 73, 75, 76, 78, 79, 80, 82, 84, 86, 88, 89, 90, 91, 92, 93, 95, 97, 98, 100, 101, 103, 104, 106, 108, 110, 116, 117, 119, 120, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 155, 156, 158, 159, 162, 163, 164, 165, 166, 167, 175, 177, 178, 179, 180, 182, 184, 186, 189, 190, 193, 199, 201, 202, 203, 206, 207, 208, 209, 211, 212, 214, 217, 218, 219, 221, 222, 226, 227, 228, 230, 231, 232, 233, 234, 235, 237, 239, 240, 241, 242, 243, 244, 245, 246, 247, 249, 252, 253, 261, 262, 263, 267, 268, 269, 270, 271, 272, 273, 276, 278, 279, 282, 284, 286], "complet": [24, 26, 28, 29, 30, 31, 39, 40, 43, 45, 46, 48, 49, 50, 52, 54, 56, 58, 60, 62, 64, 66, 69, 71, 73, 75, 76, 78, 79, 80, 82, 84, 86, 88, 89, 90, 91, 92, 93, 95, 97, 98, 100, 101, 103, 104, 106, 108, 110, 116, 117, 121, 123, 125, 127, 129, 131, 133, 135, 136, 152, 153, 155, 156, 158, 159, 162, 163, 164, 165, 166, 167, 175, 177, 178, 179, 180, 182, 184, 186, 189, 190, 193, 199, 201, 202, 203, 206, 207, 208, 209, 211, 212, 214, 217, 218, 219, 221, 222, 225, 226, 227, 228, 230, 231, 232, 233, 234, 235, 237, 239, 240, 241, 242, 243, 244, 245, 246, 247, 249, 252, 253, 261, 262, 263, 267, 268, 269, 270, 271, 272, 273, 276, 277, 278, 279, 282, 284, 286], "ofil": [24, 26, 27, 28, 29, 30, 31, 35, 37, 39, 43, 45, 46, 48, 49, 50, 52, 54, 56, 58, 60, 62, 64, 66, 69, 71, 73, 75, 76, 78, 79, 80, 82, 84, 86, 88, 89, 90, 91, 92, 93, 95, 97, 98, 100, 101, 103, 104, 106, 108, 110, 116, 117, 119, 120, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 155, 156, 158, 159, 162, 163, 164, 165, 166, 167, 175, 177, 178, 179, 180, 182, 184, 186, 189, 190, 193, 199, 201, 202, 203, 206, 207, 208, 209, 211, 212, 214, 217, 218, 219, 221, 222, 226, 227, 228, 230, 231, 232, 233, 234, 235, 237, 239, 240, 241, 242, 243, 244, 245, 246, 247, 249, 252, 253, 261, 262, 263, 267, 268, 269, 270, 271, 272, 273, 276, 278, 279, 282, 284, 286], "output": [24, 26, 27, 28, 29, 30, 31, 32, 37, 39, 40, 43, 45, 46, 48, 49, 50, 52, 54, 56, 58, 60, 62, 64, 66, 69, 71, 73, 75, 76, 78, 79, 80, 82, 84, 86, 88, 89, 90, 91, 92, 93, 95, 97, 98, 100, 101, 103, 104, 106, 108, 110, 116, 117, 119, 120, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 155, 156, 158, 159, 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233, 234, 235, 237, 239, 240, 241, 242, 243, 244, 245, 246, 247, 249, 252, 261, 262, 263, 267, 268, 269, 270, 271, 272, 273, 276, 278, 279, 282, 284, 286], "maintain": [24, 43, 45, 46, 48, 49, 82, 84, 86, 88, 90, 91, 93, 95, 97, 100, 101, 106, 108, 116, 117, 123, 125, 127, 129, 131, 133, 135, 152, 153, 159, 167, 175, 179, 182, 217, 221, 222, 226, 227, 228, 230, 231, 232, 234, 237, 239, 240, 241, 242, 243, 245, 249, 252, 261, 262, 263, 267, 268, 269, 270, 271, 272, 273, 276, 278, 279, 282, 284, 286], "lno": [24, 43, 48, 49, 84, 86, 88, 90, 91, 93, 95, 97, 100, 101, 104, 106, 108, 116, 117, 123, 125, 127, 129, 131, 133, 135, 152, 153, 159, 175, 177, 178, 179, 221, 222, 227, 228, 230, 231, 232, 234, 237, 239, 240, 241, 242, 243, 245, 247, 249, 252, 261, 262, 263, 267, 268, 269, 270, 271, 272, 273, 276, 279, 282, 284, 286], "tree": [24, 43, 45, 46, 48, 49, 71, 73, 75, 79, 80, 82, 84, 86, 88, 90, 91, 93, 95, 97, 100, 101, 106, 108, 110, 116, 117, 123, 125, 127, 129, 131, 133, 135, 152, 153, 159, 167, 175, 179, 182, 203, 206, 207, 209, 212, 214, 217, 221, 222, 226, 228, 230, 231, 232, 234, 237, 239, 240, 241, 242, 243, 245, 249, 252, 253, 261, 262, 263, 267, 268, 269, 270, 271, 272, 273, 276, 278, 279, 282, 284, 286], "itemsetcount": [24, 46, 48, 90, 93, 95, 97, 100, 101, 106, 108, 116, 117, 123, 125, 127, 135, 152, 153, 175, 182, 228, 234, 237, 239, 240, 241, 242, 243, 245, 249, 252, 261, 262, 263, 267, 268, 269, 270, 271, 272, 273, 276, 279], "finalpattern": [24, 26, 31, 43, 45, 46, 48, 49, 50, 64, 66, 69, 78, 80, 82, 84, 86, 88, 89, 90, 91, 92, 93, 95, 97, 98, 100, 101, 103, 104, 106, 108, 116, 117, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 155, 156, 158, 159, 162, 163, 164, 165, 166, 167, 175, 177, 178, 179, 180, 182, 199, 201, 202, 214, 217, 218, 219, 221, 222, 226, 227, 228, 230, 231, 232, 233, 234, 235, 237, 239, 240, 241, 242, 243, 244, 245, 246, 247, 249, 252, 261, 262, 263, 267, 268, 269, 270, 271, 272, 273, 276, 278, 279, 282, 284, 286], "dict": [24, 26, 27, 30, 31, 33, 39, 43, 45, 46, 48, 49, 50, 52, 54, 56, 58, 60, 62, 64, 66, 69, 71, 73, 75, 76, 78, 79, 80, 82, 84, 86, 88, 89, 90, 91, 92, 93, 95, 97, 98, 100, 101, 103, 104, 106, 108, 110, 116, 117, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 155, 156, 158, 159, 162, 163, 164, 165, 166, 167, 175, 177, 178, 179, 180, 182, 184, 186, 189, 190, 193, 199, 201, 202, 203, 206, 207, 208, 209, 211, 212, 214, 217, 218, 219, 221, 222, 226, 227, 228, 230, 231, 232, 233, 234, 235, 237, 239, 240, 241, 242, 243, 244, 245, 246, 247, 249, 252, 253, 261, 262, 263, 267, 268, 269, 270, 271, 272, 273, 276, 278, 279, 282, 284, 286], "itemsetbuff": [24, 54, 56, 58, 60, 62, 108, 152, 153, 186, 189, 190, 252], "maxpatternlength": [24, 108, 152, 153, 252], "constraint": [24, 73, 79, 101, 108, 110, 116, 139, 143, 152, 153, 206, 212, 238, 252, 253, 261, 262, 264], "length": [24, 27, 30, 32, 39, 40, 43, 75, 79, 82, 108, 121, 123, 125, 127, 135, 152, 153, 158, 208, 211, 217, 218, 219, 252, 267, 268, 269, 270, 271, 272, 273, 276, 279], "execut": [24, 30, 39, 45, 46, 50, 58, 80, 82, 86, 89, 91, 92, 97, 100, 101, 103, 106, 152, 153, 162, 163, 164, 165, 166, 167, 180, 182, 190, 217, 218, 219, 233, 239, 240, 245, 246, 249], "method": [24, 26, 27, 30, 31, 32, 33, 37, 39, 40, 43, 45, 46, 49, 50, 52, 54, 56, 58, 60, 62, 64, 66, 69, 71, 73, 75, 76, 78, 79, 80, 82, 84, 86, 88, 89, 90, 91, 92, 93, 95, 97, 98, 100, 101, 103, 104, 106, 108, 110, 116, 117, 119, 120, 123, 125, 127, 129, 131, 133, 135, 152, 153, 155, 159, 162, 163, 164, 165, 166, 167, 177, 179, 180, 182, 184, 186, 189, 190, 193, 199, 201, 202, 203, 206, 207, 208, 209, 211, 212, 214, 217, 218, 219, 221, 222, 226, 227, 228, 230, 231, 232, 233, 234, 235, 237, 239, 240, 241, 242, 243, 244, 245, 246, 247, 249, 252, 253, 261, 262, 263, 267, 268, 269, 270, 271, 272, 273, 276, 278, 279, 282, 284, 286], "termin": [24, 26, 45, 46, 50, 56, 93, 97, 98, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 155, 162, 163, 164, 165, 166, 167, 180, 182, 189, 228, 239, 240, 244, 267, 268, 269, 270, 271, 273, 276, 278, 279, 282, 284, 286], "command": [24, 26, 45, 46, 50, 56, 93, 97, 98, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 155, 162, 163, 164, 165, 166, 167, 180, 182, 189, 228, 239, 240, 244, 267, 268, 269, 270, 271, 273, 276, 278, 279, 282, 284, 286], "format": [24, 26, 30, 39, 40, 43, 45, 46, 48, 49, 50, 52, 54, 56, 58, 60, 62, 64, 66, 69, 71, 73, 75, 79, 80, 82, 84, 86, 88, 89, 90, 91, 92, 93, 95, 97, 98, 100, 101, 103, 104, 106, 108, 110, 116, 117, 119, 120, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 155, 156, 158, 159, 162, 163, 164, 165, 166, 167, 175, 177, 178, 179, 180, 182, 184, 186, 189, 190, 191, 193, 197, 199, 201, 202, 203, 206, 207, 208, 209, 211, 212, 214, 217, 218, 219, 221, 222, 224, 226, 227, 228, 230, 231, 232, 233, 234, 235, 237, 239, 240, 241, 242, 243, 244, 245, 246, 247, 249, 252, 253, 256, 261, 262, 263, 265, 266, 267, 268, 269, 270, 271, 272, 273, 276, 277, 278, 279, 281, 282, 284, 286], "venv": [24, 26, 30, 39, 40, 43, 45, 46, 48, 49, 50, 52, 54, 56, 58, 60, 62, 64, 66, 69, 71, 73, 75, 79, 80, 82, 84, 88, 89, 91, 93, 97, 98, 100, 101, 103, 104, 106, 108, 110, 116, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 155, 156, 158, 159, 162, 163, 164, 165, 166, 167, 175, 177, 178, 179, 180, 182, 184, 186, 189, 190, 193, 199, 201, 202, 203, 206, 207, 208, 209, 211, 212, 214, 217, 218, 219, 221, 222, 228, 231, 232, 233, 239, 240, 241, 242, 243, 244, 245, 246, 247, 249, 252, 253, 261, 262, 267, 268, 269, 270, 271, 273, 276, 278, 279, 282, 284, 286], "python3": [24, 26, 30, 39, 40, 43, 45, 46, 48, 49, 50, 52, 54, 56, 58, 60, 62, 64, 66, 69, 71, 73, 75, 79, 80, 82, 84, 86, 88, 89, 90, 91, 92, 93, 95, 97, 98, 100, 101, 103, 104, 106, 108, 110, 116, 117, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 155, 156, 158, 159, 162, 163, 164, 165, 166, 167, 175, 177, 178, 179, 180, 182, 184, 186, 189, 190, 193, 199, 201, 202, 203, 206, 207, 208, 209, 211, 212, 214, 217, 218, 219, 221, 222, 226, 227, 228, 230, 231, 232, 233, 234, 235, 237, 239, 240, 241, 242, 243, 244, 245, 246, 247, 249, 252, 253, 261, 262, 263, 267, 268, 269, 270, 271, 272, 273, 276, 278, 279, 282, 284, 286], "py": [24, 26, 30, 39, 40, 43, 45, 46, 48, 49, 50, 52, 54, 56, 58, 60, 62, 64, 66, 69, 71, 73, 75, 79, 80, 82, 84, 86, 88, 89, 90, 91, 92, 93, 95, 97, 98, 100, 101, 103, 104, 106, 108, 110, 116, 117, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 155, 156, 158, 159, 162, 163, 164, 165, 166, 167, 175, 177, 178, 179, 180, 182, 184, 186, 189, 190, 193, 199, 201, 202, 203, 206, 207, 208, 211, 212, 214, 217, 218, 219, 221, 222, 226, 227, 228, 230, 231, 232, 233, 234, 235, 237, 239, 240, 241, 242, 243, 244, 245, 246, 247, 249, 252, 253, 261, 262, 263, 267, 268, 269, 270, 271, 272, 273, 276, 278, 279, 282, 284, 286], "inputfil": [24, 26, 27, 30, 39, 43, 45, 46, 48, 49, 50, 52, 54, 56, 58, 60, 62, 64, 66, 69, 71, 73, 75, 76, 79, 80, 82, 84, 86, 88, 89, 90, 91, 93, 95, 97, 98, 100, 101, 103, 104, 106, 108, 110, 116, 117, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 155, 156, 158, 159, 162, 163, 164, 165, 166, 167, 175, 177, 178, 179, 180, 182, 184, 186, 189, 190, 193, 199, 201, 202, 203, 206, 207, 208, 209, 211, 212, 214, 217, 218, 219, 221, 222, 226, 227, 228, 230, 231, 232, 233, 234, 237, 239, 240, 241, 242, 243, 244, 245, 246, 247, 249, 252, 253, 261, 262, 263, 267, 268, 269, 270, 271, 272, 273, 276, 278, 279, 282, 284, 286], "outputfil": [24, 26, 27, 28, 30, 32, 35, 39, 40, 43, 45, 46, 48, 49, 50, 52, 54, 56, 58, 60, 62, 64, 66, 69, 71, 73, 75, 76, 79, 80, 82, 84, 86, 88, 89, 90, 91, 93, 95, 97, 98, 100, 101, 103, 104, 106, 108, 110, 116, 117, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 155, 156, 158, 159, 162, 163, 164, 165, 166, 167, 175, 177, 178, 179, 180, 182, 184, 186, 189, 190, 193, 199, 201, 202, 203, 206, 207, 208, 209, 211, 212, 214, 217, 218, 219, 221, 222, 226, 227, 228, 230, 231, 232, 233, 234, 237, 239, 240, 241, 242, 243, 244, 245, 246, 247, 249, 252, 253, 261, 262, 263, 267, 268, 269, 270, 271, 272, 273, 276, 278, 279, 282, 284, 286], "exampl": [24, 26, 30, 39, 40, 43, 45, 46, 48, 49, 50, 52, 54, 56, 58, 60, 62, 64, 66, 69, 71, 73, 75, 79, 80, 82, 84, 86, 88, 89, 90, 91, 92, 93, 95, 97, 98, 100, 101, 103, 104, 106, 108, 110, 116, 117, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 155, 156, 158, 159, 162, 163, 164, 165, 166, 167, 175, 177, 178, 179, 180, 182, 184, 186, 189, 190, 191, 193, 197, 199, 201, 202, 203, 206, 207, 208, 209, 211, 212, 214, 217, 218, 219, 221, 222, 224, 226, 227, 228, 230, 231, 232, 233, 234, 235, 237, 239, 240, 241, 242, 243, 244, 245, 246, 247, 249, 252, 253, 256, 261, 262, 263, 265, 266, 267, 268, 269, 270, 271, 272, 273, 276, 277, 278, 279, 281, 282, 284, 286], "sampletdb": [24, 26, 48, 52, 54, 56, 58, 60, 62, 64, 66, 69, 71, 73, 75, 79, 80, 89, 90, 93, 95, 97, 98, 100, 101, 106, 110, 116, 117, 123, 125, 127, 135, 152, 153, 155, 156, 175, 184, 186, 189, 190, 193, 199, 201, 202, 203, 206, 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246, 247, 249, 252, 253, 261, 262, 263, 267, 268, 269, 270, 271, 272, 273, 276, 278, 279, 282, 284, 286], "tupl": [24, 26, 43, 49, 82, 97, 100, 152, 153, 155, 158, 179, 218, 219, 240, 241, 243, 245], "send": [24, 26, 36, 43, 45, 46, 48, 49, 50, 52, 54, 56, 58, 60, 62, 64, 66, 69, 71, 73, 75, 79, 80, 82, 84, 86, 88, 89, 90, 91, 92, 93, 95, 97, 98, 100, 101, 103, 104, 106, 108, 110, 116, 117, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 155, 156, 158, 159, 162, 163, 164, 166, 167, 175, 177, 178, 179, 180, 182, 184, 186, 189, 190, 193, 199, 201, 202, 203, 206, 207, 208, 209, 211, 212, 214, 217, 218, 219, 221, 222, 226, 227, 228, 230, 231, 232, 233, 234, 235, 237, 239, 240, 241, 242, 243, 244, 245, 246, 247, 249, 252, 253, 262, 263, 267, 268, 269, 270, 271, 272, 273, 276, 278, 279, 282, 284, 286], "after": [24, 26, 39, 43, 45, 46, 48, 49, 50, 52, 54, 56, 58, 60, 62, 64, 66, 69, 71, 73, 75, 79, 80, 82, 84, 86, 88, 89, 90, 91, 92, 93, 95, 97, 98, 100, 101, 103, 104, 106, 108, 110, 116, 117, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 155, 156, 158, 159, 162, 163, 164, 165, 166, 167, 175, 177, 178, 179, 180, 182, 184, 186, 189, 190, 193, 199, 201, 202, 203, 206, 207, 208, 209, 211, 212, 214, 217, 218, 219, 221, 222, 226, 227, 228, 230, 231, 232, 233, 234, 235, 237, 239, 240, 241, 242, 243, 244, 245, 246, 247, 249, 252, 253, 261, 262, 263, 267, 268, 269, 270, 271, 272, 273, 276, 278, 279, 282, 284, 286], "final": [24, 26, 43, 45, 46, 48, 49, 50, 52, 54, 56, 58, 60, 62, 64, 66, 69, 71, 73, 75, 76, 79, 80, 82, 84, 86, 88, 89, 90, 91, 92, 93, 95, 97, 98, 100, 101, 103, 104, 106, 108, 110, 116, 117, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 155, 156, 158, 159, 162, 163, 164, 165, 166, 167, 175, 177, 178, 179, 180, 182, 184, 186, 189, 190, 193, 199, 201, 202, 203, 206, 207, 208, 209, 211, 212, 214, 217, 218, 219, 221, 222, 226, 227, 228, 230, 231, 232, 233, 234, 235, 237, 239, 240, 241, 242, 243, 244, 245, 246, 247, 249, 252, 253, 261, 262, 263, 267, 268, 269, 270, 271, 272, 273, 276, 278, 279, 282, 284, 286], "pd": [24, 26, 29, 32, 40, 43, 45, 46, 48, 49, 50, 52, 54, 56, 58, 60, 62, 64, 66, 69, 71, 73, 75, 76, 79, 80, 82, 84, 86, 88, 89, 90, 91, 92, 93, 95, 97, 98, 100, 101, 103, 104, 106, 108, 110, 116, 117, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 155, 156, 158, 159, 162, 163, 164, 165, 166, 167, 175, 177, 178, 179, 180, 182, 184, 186, 189, 190, 193, 199, 201, 202, 203, 206, 207, 208, 209, 211, 212, 214, 217, 218, 219, 221, 222, 226, 227, 228, 230, 231, 232, 233, 234, 235, 237, 239, 240, 241, 242, 243, 244, 245, 246, 247, 249, 252, 253, 261, 262, 263, 267, 268, 269, 270, 271, 272, 273, 276, 278, 279, 282, 284, 286], "calcul": [24, 26, 29, 30, 43, 45, 46, 48, 49, 50, 52, 54, 56, 58, 60, 62, 64, 66, 69, 71, 73, 75, 79, 80, 82, 84, 86, 88, 89, 90, 91, 92, 93, 95, 97, 98, 100, 101, 103, 104, 106, 108, 110, 116, 117, 120, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 155, 156, 158, 159, 162, 163, 164, 165, 166, 167, 175, 177, 178, 179, 180, 182, 184, 186, 189, 190, 193, 199, 201, 202, 203, 206, 207, 208, 209, 211, 212, 214, 217, 218, 219, 221, 222, 226, 227, 228, 230, 231, 232, 233, 234, 235, 237, 239, 240, 241, 242, 243, 244, 245, 246, 247, 249, 252, 253, 261, 262, 263, 267, 268, 269, 270, 271, 272, 273, 276, 278, 279, 282, 284, 286], "runtim": [24, 26, 33, 43, 45, 46, 48, 49, 50, 52, 54, 56, 58, 60, 62, 64, 66, 69, 71, 73, 75, 76, 78, 79, 80, 82, 84, 86, 88, 89, 90, 91, 92, 93, 95, 97, 98, 100, 101, 103, 104, 106, 108, 110, 116, 117, 120, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 155, 156, 158, 159, 162, 163, 164, 165, 166, 167, 175, 177, 178, 179, 180, 182, 184, 186, 189, 190, 193, 199, 201, 202, 203, 206, 207, 208, 209, 211, 212, 214, 217, 218, 219, 221, 222, 226, 227, 228, 230, 231, 232, 233, 234, 235, 237, 239, 240, 241, 242, 243, 244, 245, 246, 247, 249, 252, 253, 261, 262, 263, 267, 268, 269, 270, 271, 272, 273, 276, 278, 279, 282, 284, 286], "taken": [24, 26, 43, 45, 46, 48, 49, 50, 52, 54, 56, 58, 60, 62, 64, 66, 69, 71, 73, 75, 78, 79, 80, 82, 84, 86, 88, 89, 90, 91, 92, 93, 95, 97, 98, 100, 101, 103, 104, 106, 108, 110, 116, 117, 120, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 155, 156, 158, 159, 162, 163, 164, 165, 166, 167, 175, 177, 178, 179, 180, 182, 184, 186, 189, 190, 193, 199, 201, 202, 203, 206, 207, 208, 209, 211, 212, 214, 217, 218, 219, 221, 222, 226, 227, 228, 230, 231, 232, 233, 234, 235, 237, 239, 240, 241, 242, 243, 244, 245, 246, 247, 249, 252, 253, 261, 262, 263, 267, 268, 269, 270, 271, 272, 273, 276, 278, 279, 282, 284, 286], "none": [24, 26, 27, 28, 29, 30, 31, 32, 33, 35, 36, 39, 40, 43, 45, 52, 54, 56, 62, 71, 75, 79, 80, 82, 84, 88, 95, 97, 100, 101, 108, 110, 117, 119, 120, 123, 127, 129, 131, 133, 135, 152, 153, 155, 156, 158, 159, 162, 163, 164, 165, 166, 167, 184, 186, 189, 193, 203, 207, 209, 211, 212, 214, 217, 218, 219, 221, 222, 230, 231, 232, 237, 239, 240, 241, 242, 243, 245, 252, 253, 263, 267, 268, 269, 270, 278, 282, 284, 286], "main": [24, 26, 31, 43, 45, 52, 73, 75, 79, 80, 84, 86, 88, 90, 91, 92, 97, 100, 103, 104, 108, 120, 123, 125, 127, 131, 148, 152, 153, 155, 159, 167, 184, 206, 208, 211, 212, 214, 221, 222, 227, 230, 231, 232, 234, 235, 241, 245, 246, 247, 252, 267, 268, 269, 270, 271, 272, 273, 276, 278, 279, 284, 285], "printresult": [24, 26, 43, 45, 46, 48, 49, 50, 52, 54, 56, 58, 60, 62, 64, 66, 69, 71, 73, 75, 76, 79, 80, 82, 84, 86, 88, 89, 90, 91, 92, 93, 95, 97, 98, 100, 101, 103, 104, 106, 108, 110, 116, 117, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 155, 156, 158, 159, 162, 163, 164, 165, 166, 167, 175, 177, 178, 179, 180, 182, 184, 186, 189, 190, 193, 199, 201, 202, 203, 206, 207, 208, 209, 211, 212, 214, 217, 218, 219, 221, 222, 226, 227, 228, 230, 231, 232, 233, 234, 235, 237, 239, 240, 241, 242, 243, 244, 245, 246, 247, 249, 252, 253, 261, 262, 263, 267, 268, 269, 270, 271, 272, 273, 276, 278, 279, 282, 284, 286], "result": [24, 26, 27, 30, 39, 43, 45, 46, 48, 49, 50, 52, 54, 56, 58, 60, 62, 64, 66, 69, 71, 73, 75, 76, 79, 80, 82, 84, 86, 88, 89, 90, 91, 92, 93, 95, 97, 98, 100, 101, 103, 106, 108, 110, 116, 117, 119, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 155, 156, 158, 159, 162, 163, 164, 165, 166, 167, 175, 178, 179, 180, 182, 184, 186, 189, 190, 193, 199, 201, 202, 203, 206, 207, 208, 209, 211, 212, 214, 217, 218, 219, 221, 222, 226, 227, 228, 230, 231, 232, 233, 234, 235, 237, 239, 240, 241, 242, 243, 244, 245, 246, 249, 252, 253, 261, 262, 263, 267, 268, 269, 270, 271, 272, 273, 276, 278, 279, 282, 284, 286], "recurs": [24, 71, 73, 75, 79, 80, 86, 88, 97, 110, 116, 119, 123, 152, 153, 203, 206, 207, 209, 212, 214, 227, 232, 239, 242, 253, 261, 271], "node": [24, 49, 75, 82, 86, 88, 90, 91, 93, 95, 97, 100, 101, 106, 116, 117, 123, 125, 127, 135, 152, 153, 177, 178, 179, 209, 217, 226, 228, 230, 231, 234, 237, 240, 241, 243, 245, 249, 262, 263, 267, 268, 269, 270, 271, 272, 276, 278, 279], "root": [24, 49, 82, 86, 88, 90, 91, 93, 95, 97, 100, 101, 106, 116, 117, 123, 125, 127, 135, 152, 153, 179, 217, 226, 228, 230, 231, 234, 237, 240, 241, 243, 245, 249, 262, 263, 267, 268, 269, 270, 271, 272, 276, 278, 279], "build": [24, 49, 75, 152, 153, 179, 209], "ani": [24, 26, 82, 86, 97, 98, 100, 101, 103, 120, 148, 152, 153, 155, 156, 217, 218, 219, 226, 227, 239, 240, 241, 242, 243, 244, 245, 246, 265, 285], "current": [24, 80, 152, 153, 214, 277], "being": [24, 152, 153, 265], "_node": [24, 152, 153], "save": [24, 26, 27, 28, 29, 30, 32, 33, 35, 37, 39, 40, 43, 45, 46, 48, 49, 50, 52, 54, 56, 58, 60, 62, 64, 66, 69, 71, 73, 75, 76, 78, 79, 80, 82, 84, 86, 88, 89, 90, 91, 92, 93, 95, 97, 98, 100, 101, 103, 104, 106, 108, 110, 116, 117, 119, 120, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 155, 156, 158, 159, 162, 163, 164, 165, 166, 167, 175, 177, 178, 179, 180, 182, 184, 186, 189, 190, 193, 199, 201, 202, 203, 206, 207, 208, 209, 211, 212, 214, 217, 218, 219, 221, 222, 226, 227, 228, 230, 231, 232, 233, 234, 235, 237, 239, 240, 241, 242, 243, 244, 245, 246, 247, 249, 252, 253, 261, 262, 263, 265, 266, 267, 268, 269, 270, 271, 272, 273, 276, 278, 279, 282, 284, 286], "outfil": [24, 26, 28, 29, 43, 45, 46, 48, 49, 50, 52, 54, 56, 58, 60, 62, 64, 66, 69, 71, 73, 75, 76, 79, 80, 82, 84, 86, 88, 89, 90, 91, 92, 93, 95, 97, 98, 100, 101, 103, 104, 106, 108, 110, 116, 117, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 155, 156, 158, 159, 162, 163, 164, 165, 166, 167, 175, 177, 178, 179, 180, 182, 184, 186, 189, 190, 193, 199, 201, 202, 203, 206, 207, 208, 209, 211, 212, 214, 217, 218, 219, 221, 222, 226, 227, 228, 230, 231, 232, 233, 234, 235, 237, 239, 240, 241, 242, 243, 244, 245, 246, 247, 249, 252, 253, 261, 262, 263, 267, 268, 269, 270, 271, 272, 276, 278, 279, 282, 284, 286], "an": [24, 26, 28, 29, 43, 45, 46, 48, 49, 50, 52, 54, 56, 58, 62, 64, 69, 71, 73, 75, 76, 78, 79, 80, 82, 84, 86, 88, 92, 95, 97, 98, 108, 110, 116, 117, 119, 121, 123, 125, 127, 129, 131, 133, 135, 139, 152, 153, 155, 156, 158, 159, 162, 163, 164, 165, 166, 167, 175, 177, 178, 179, 180, 182, 184, 186, 189, 190, 191, 193, 197, 199, 202, 203, 206, 207, 208, 209, 211, 212, 214, 217, 218, 219, 221, 224, 226, 227, 230, 232, 235, 237, 238, 239, 240, 241, 242, 243, 244, 250, 251, 252, 253, 256, 261, 262, 263, 267, 268, 269, 270, 271, 272, 273, 276, 277, 278, 279, 281, 282, 284, 286], "startmin": [24, 26, 43, 45, 46, 48, 49, 50, 52, 54, 56, 58, 60, 62, 64, 66, 69, 71, 73, 75, 76, 78, 79, 80, 82, 84, 86, 88, 89, 90, 91, 92, 93, 95, 97, 98, 100, 101, 103, 104, 106, 108, 110, 116, 117, 121, 123, 125, 127, 129, 131, 133, 135, 152, 153, 155, 156, 158, 159, 162, 163, 164, 165, 166, 167, 175, 177, 178, 179, 180, 182, 184, 186, 189, 190, 193, 199, 201, 202, 203, 206, 207, 208, 209, 211, 212, 214, 217, 218, 219, 221, 222, 226, 227, 228, 230, 231, 232, 233, 234, 235, 237, 239, 240, 241, 242, 243, 244, 245, 246, 247, 249, 252, 253, 261, 262, 263, 267, 268, 269, 270, 272, 276, 278, 279, 282, 284, 286], "code": [24, 29, 35, 56, 78, 80, 86, 88, 92, 97, 101, 103, 116, 119, 153, 163, 226, 227, 232, 235, 239, 240, 246, 261], "minrf": [26, 155, 156], "minc": [26, 155, 156], "maxor": [26, 155, 156], "_coveragepattern": [26, 155, 156], "coverag": [26, 155, 156, 157, 215, 266], "bhargav": [26, 155, 156], "sripada": [26, 155], "polep": [26, 155], "krishna": [26, 69, 84, 86, 88, 89, 93, 101, 129, 155, 156, 202, 222, 226, 228, 232, 233, 282], "reddi": [26, 69, 71, 84, 86, 88, 93, 97, 101, 110, 129, 155, 156, 202, 203, 222, 226, 228, 232, 243, 253, 282], "banner": [26, 155], "advertis": [26, 155], "placement": [26, 155], "www": [26, 54, 88, 89, 95, 106, 155, 186, 232, 233, 237, 249], "companion": [26, 155], "volum": [26, 86, 123, 155, 226, 267], "2011": [26, 84, 123, 155, 222, 267, 273], "131": [26, 155], "132": [26, 155], "__http": [26, 155], "dl": [26, 92, 155, 235], "acm": [26, 45, 49, 75, 92, 123, 155, 165, 179, 209, 235, 273], "org": [26, 43, 45, 46, 50, 75, 76, 84, 86, 88, 90, 91, 92, 97, 98, 100, 104, 116, 117, 121, 123, 125, 127, 131, 155, 159, 162, 163, 164, 165, 166, 167, 180, 182, 207, 209, 221, 222, 226, 230, 231, 234, 235, 240, 241, 243, 244, 245, 247, 262, 263, 267, 269, 270, 271, 273, 276, 278, 279, 284], "doi": [26, 43, 45, 46, 49, 50, 52, 56, 62, 69, 71, 75, 76, 79, 80, 82, 84, 86, 88, 91, 92, 93, 97, 104, 110, 116, 117, 121, 123, 125, 127, 129, 131, 133, 155, 159, 162, 165, 167, 177, 180, 182, 184, 189, 193, 202, 203, 207, 209, 211, 214, 217, 218, 219, 221, 222, 226, 228, 230, 231, 235, 240, 241, 243, 247, 253, 262, 263, 267, 269, 270, 271, 273, 276, 278, 279, 282, 284, 286], "10": [26, 27, 37, 40, 43, 45, 46, 49, 50, 52, 54, 56, 62, 64, 66, 69, 71, 73, 75, 76, 79, 80, 82, 84, 86, 88, 89, 90, 91, 92, 93, 95, 97, 98, 100, 101, 103, 104, 106, 110, 116, 117, 121, 123, 125, 127, 129, 131, 133, 135, 155, 156, 158, 159, 162, 163, 164, 165, 166, 167, 177, 178, 179, 180, 182, 184, 186, 189, 191, 193, 197, 199, 201, 202, 203, 206, 207, 209, 211, 212, 214, 217, 218, 219, 221, 222, 226, 227, 228, 230, 231, 232, 233, 234, 235, 237, 239, 240, 241, 242, 243, 244, 245, 246, 247, 249, 253, 261, 262, 263, 267, 268, 269, 270, 271, 272, 273, 276, 278, 279, 281, 282, 284, 286], "1145": [26, 45, 75, 84, 88, 91, 123, 155, 162, 165, 209, 222, 230, 231, 273], "1963192": [26, 155], "1963259": [26, 155], "everi": [26, 78, 80, 82, 86, 92, 97, 98, 100, 101, 108, 155, 156, 217, 226, 241, 242, 243, 244, 245, 252, 265, 277, 281], "must": [26, 78, 80, 86, 92, 97, 98, 100, 101, 108, 110, 155, 156, 191, 224, 241, 242, 243, 244, 245, 252, 253, 265, 266, 277, 281], "least": [26, 108, 155, 156, 252], "maximum": [26, 30, 32, 39, 40, 62, 71, 73, 75, 76, 79, 80, 82, 86, 90, 97, 98, 100, 101, 103, 106, 110, 116, 117, 127, 129, 139, 155, 156, 193, 203, 206, 207, 209, 211, 212, 214, 217, 218, 219, 226, 227, 234, 238, 239, 240, 241, 242, 243, 244, 245, 246, 249, 253, 261, 262, 263, 278, 279, 282], "two": [26, 40, 71, 73, 75, 79, 80, 82, 86, 97, 98, 100, 101, 103, 110, 155, 156, 203, 206, 207, 209, 212, 214, 217, 218, 219, 224, 226, 227, 239, 240, 241, 242, 243, 244, 245, 246, 253, 265, 277], "reappear": [26, 82, 86, 97, 98, 100, 101, 103, 155, 156, 217, 218, 219, 226, 227, 239, 240, 241, 242, 243, 244, 245, 246], "4": [26, 52, 58, 69, 82, 88, 89, 90, 93, 95, 97, 98, 100, 101, 106, 116, 117, 123, 127, 131, 155, 156, 184, 190, 191, 197, 202, 217, 218, 219, 224, 228, 232, 233, 234, 237, 241, 242, 243, 244, 245, 249, 261, 262, 263, 265, 267, 277, 278, 279, 281, 284], "7": [26, 155, 156, 191, 265, 277, 281], "5": [26, 40, 64, 66, 69, 82, 86, 91, 97, 101, 116, 123, 133, 155, 156, 191, 197, 199, 201, 202, 217, 218, 219, 224, 226, 227, 242, 261, 269, 270, 277, 281, 286], "p": [26, 40, 43, 45, 46, 48, 50, 52, 56, 64, 69, 71, 73, 75, 76, 79, 80, 82, 84, 86, 88, 89, 90, 92, 93, 95, 97, 98, 100, 101, 103, 104, 110, 116, 117, 121, 123, 125, 127, 129, 131, 133, 155, 156, 158, 159, 162, 167, 175, 180, 182, 184, 189, 199, 202, 203, 206, 207, 211, 212, 214, 217, 218, 219, 221, 222, 226, 228, 230, 231, 232, 233, 234, 235, 237, 239, 240, 241, 242, 243, 244, 245, 246, 247, 253, 261, 262, 263, 267, 268, 269, 270, 271, 272, 273, 276, 278, 279, 282, 284, 286], "likhitha": [26, 40, 43, 45, 46, 48, 50, 60, 69, 84, 88, 89, 90, 92, 93, 95, 97, 98, 100, 103, 104, 116, 117, 121, 123, 125, 127, 129, 131, 133, 155, 156, 158, 159, 162, 167, 175, 180, 182, 202, 221, 222, 228, 230, 231, 233, 234, 235, 237, 239, 240, 241, 242, 243, 244, 245, 246, 247, 261, 262, 263, 267, 268, 269, 270, 271, 272, 273, 276, 278, 279, 282, 284, 286], "creatingcoverageitem": [26, 155], "creat": [26, 28, 29, 32, 35, 37, 40, 49, 75, 79, 80, 82, 86, 119, 120, 155, 179, 191, 197, 208, 211, 214, 217, 218, 219, 224, 226, 256, 265, 266, 277, 281], "_databas": [26, 155], "coveragetiddata": [26, 155], "tid": [26, 32, 40, 52, 54, 56, 58, 60, 62, 82, 86, 91, 101, 104, 155, 184, 186, 189, 190, 191, 193, 197, 217, 226, 227, 247, 265, 266, 277, 281], "genpattern": [26, 155], "prefix": [26, 46, 49, 52, 54, 56, 58, 60, 62, 75, 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203, 206, 207, 208, 209, 211, 212, 214, 253], "knowledg": [43, 45, 75, 123, 133, 159, 165, 167, 209, 268, 272, 277, 286], "53": [43, 45, 79, 159, 167, 212], "87": [43, 45, 159, 167], "2004": [43, 45, 84, 159, 167, 221], "1023": [43, 45, 159, 167], "scan": [43, 45, 73, 79, 80, 84, 86, 88, 90, 91, 92, 93, 95, 97, 100, 101, 103, 104, 106, 108, 116, 117, 123, 125, 127, 129, 131, 133, 135, 159, 166, 206, 212, 214, 221, 222, 227, 228, 230, 231, 232, 234, 235, 237, 239, 240, 241, 242, 243, 245, 246, 247, 249, 252, 261, 262, 263, 267, 268, 269, 270, 271, 272, 273, 276, 278, 279, 282, 284, 286], "frequentoneitem": [43, 64, 66, 69, 84, 92, 103, 104, 108, 123, 125, 129, 131, 133, 135, 159, 199, 201, 202, 221, 222, 235, 246, 247, 252, 267, 268, 269, 270, 271, 272, 273, 276, 282, 284, 286], "getpatternindatafram": [43, 45, 49, 50, 84, 86, 92, 103, 104, 121, 133, 159, 162, 163, 164, 165, 166, 167, 177, 178, 179, 180, 221, 222, 227, 235, 246, 247, 286], "_frequentpattern": [45, 46, 48, 49, 50, 84, 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163, 164, 166, 207], "eng": [45, 163, 164, 166], "12": [45, 163, 164, 166], "372": [45, 163, 164, 166], "390": [45, 163, 164, 166], "2000": [45, 163, 164, 166], "ieeexplor": [45, 50, 90, 98, 100, 163, 164, 166, 180, 234, 244, 245], "document": [45, 50, 90, 98, 100, 163, 164, 166, 180, 234, 244, 245], "846291": [45, 163, 164, 166], "kundai": [45, 58, 164, 165], "diffset": [45, 165], "kdd": [45, 75, 165, 209], "03": [45, 165], "proceed": [45, 46, 75, 88, 91, 123, 131, 165, 182, 209, 230, 231, 271, 273, 284], "ninth": [45, 165], "sigkdd": [45, 75, 165, 209], "intern": [45, 49, 56, 62, 69, 75, 79, 80, 88, 91, 93, 98, 103, 110, 116, 123, 129, 131, 133, 165, 177, 189, 193, 202, 209, 211, 214, 228, 230, 231, 244, 246, 253, 261, 271, 281, 282, 284, 286], "confer": [45, 49, 56, 62, 69, 75, 79, 80, 88, 91, 93, 98, 103, 110, 123, 129, 131, 133, 165, 177, 179, 189, 193, 202, 209, 211, 214, 228, 230, 231, 244, 246, 253, 268, 271, 272, 282, 284, 286], "august": [45, 165], "page": [45, 86, 88, 91, 123, 165, 215, 226, 230, 231, 267, 273], "326": [45, 165], "335": [45, 165], "956750": [45, 165], "956788": [45, 165], "yudai": [45, 49, 163, 166, 177, 178, 179], "masu": [45, 49, 163, 166, 177, 178, 179], "implement": [45, 49, 86, 88, 97, 116, 166, 177, 227, 232, 239, 242, 261], "we": [45, 54, 56, 58, 62, 166, 186, 189, 190, 193, 250, 251, 266, 277], "check": [45, 46, 71, 73, 75, 79, 80, 90, 91, 97, 108, 110, 119, 166, 182, 203, 206, 207, 212, 214, 234, 239, 241, 242, 252, 253], "superset": [46, 73, 182, 206], "same": [46, 49, 52, 54, 56, 58, 60, 62, 179, 182, 184, 186, 189, 190, 193, 197, 265], "origin": [46, 80, 120, 123, 125, 127, 135, 182, 214, 267, 268, 269, 270, 271, 272, 273, 276, 278, 279], "ching": [46, 182], "jui": [46, 182], "hsiao": [46, 182], "2002": [46, 182], "siam": [46, 131, 182, 284], "sdm": [46, 182], "457": [46, 182], "473": [46, 182], "1137": [46, 131, 182, 284], "9781611972726": [46, 182], "27": [46, 182], "tidlist": [46, 66, 82, 86, 88, 92, 97, 103, 104, 116, 182, 201, 217, 226, 227, 232, 235, 239, 242, 246, 247, 261], "hash": [46, 97, 182, 239, 242], "equival": [46, 64, 66, 86, 88, 97, 116, 123, 182, 199, 201, 227, 232, 239, 242, 261, 273], "maxfp": [48, 175], "grahn": [48, 175], "zhu": [48, 175], "enc": [48, 175], "concordia": [48, 175], "ca": [48, 175], "paper": [48, 106, 175, 249], "hpdm03": [48, 175], "pdf": [48, 52, 88, 89, 95, 106, 108, 131, 175, 184, 232, 233, 237, 249, 252, 284], "maxper": [48, 52, 54, 56, 58, 60, 62, 64, 66, 75, 79, 82, 86, 93, 95, 97, 98, 100, 101, 103, 106, 108, 116, 117, 127, 129, 131, 139, 175, 184, 186, 189, 190, 193, 199, 201, 208, 211, 212, 217, 218, 219, 226, 227, 228, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 249, 252, 261, 262, 263, 278, 279, 282, 284], "functon": [48, 175], "numwork": [49, 91, 101, 177, 178, 179], "li": [49, 54, 177, 179, 186, 277], "l": [49, 121, 177], "zeng": [49, 177], "q": [49, 177], "he": [49, 177], "z": [49, 75, 79, 177, 208, 211], "shi": [49, 177], "mapreduc": [49, 177], 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71, 73, 75, 76, 79, 80, 110, 119, 184, 186, 189, 190, 193, 199, 201, 202, 203, 206, 207, 208, 209, 211, 212, 214, 253], "track": [52, 54, 56, 58, 60, 62, 75, 79, 184, 186, 189, 190, 193, 208, 211], "low": [52, 54, 56, 62, 80, 184, 191, 193, 214], "region": [52, 54, 56, 58, 60, 62, 184, 189, 193], "mapitemsmidsum": [52, 54, 56, 60, 62, 184, 193], "middl": [52, 54, 56, 62, 184, 193], "mapitemshighsum": [52, 54, 56, 62, 184, 193], "mapitemsum": [52, 54, 56, 58, 60, 62, 184, 186, 189, 190, 193], "mapitemregion": [52, 54, 56, 58, 60, 62, 184, 189, 193], "jointcnt": [52, 54, 62, 184, 193], "ffi": [52, 54, 56, 58, 60, 62, 184, 186, 189, 190, 193], "construct": [52, 54, 56, 58, 60, 62, 75, 79, 88, 91, 97, 119, 120, 123, 125, 127, 135, 184, 186, 189, 190, 193, 208, 209, 211, 224, 230, 231, 241, 265, 266, 267, 268, 269, 270, 271, 272, 273, 276, 278, 279], "buffers": [52, 54, 56, 58, 60, 62, 184, 186, 189, 190, 193], "buffer": [52, 54, 56, 58, 60, 62, 184, 186, 189, 190, 193], "itembuff": [52, 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186, 267], "wei": [54, 75, 101, 123, 135, 186, 209, 267], "ting": [54, 186], "fournier": [54, 66, 71, 75, 76, 82, 89, 93, 110, 116, 117, 127, 135, 186, 201, 203, 207, 217, 218, 219, 228, 233, 253, 261, 262, 263, 279], "viger": [54, 66, 71, 75, 76, 82, 93, 110, 116, 117, 127, 135, 186, 201, 203, 207, 217, 218, 219, 228, 253, 261, 262, 263, 279], "philipp": [54, 66, 89, 103, 127, 135, 186, 201, 233, 246, 279], "tzung": [54, 123, 135, 186, 267], "fast": [54, 75, 76, 123, 186, 207, 271], "29": [54, 186], "2373": [54, 186], "2379": [54, 186], "3233": [54, 186], "IFS": [54, 186], "151936": [54, 186], "researchg": [54, 186], "net": [54, 186], "286510908_a_fast_algorithm_for_mining_fuzzy_frequent_itemset": [54, 186], "fmfile": [54, 186], "joinscnt": [54, 56, 58, 60, 62, 186, 189, 190], "compareitem": [54, 60, 186], "o1": [54, 60, 186], "o2": [54, 60, 186], "ascend": [54, 60, 186], "ffiminermin": 54, "fuzzymembership": 54, "nfile": [56, 58, 64, 66, 69, 71, 73, 78, 79, 80, 125, 129, 189, 190, 199, 201, 202, 203, 206, 211, 212, 214, 276, 282], "_fuzzyspatialfrequentpattern": [56, 58, 189, 190], "veena": [56, 79, 125, 127, 189, 212, 276, 279], "chithra": [56, 189], "u": [56, 62, 71, 80, 82, 88, 89, 95, 97, 104, 106, 110, 116, 131, 189, 193, 203, 214, 217, 218, 219, 232, 233, 237, 243, 247, 249, 253, 261, 284], "agarw": [56, 189], "zettsu": [56, 66, 69, 79, 90, 93, 97, 100, 101, 125, 127, 129, 189, 201, 202, 212, 228, 234, 239, 245, 276, 278, 279, 282], "quantit": [56, 60, 62, 79, 80, 189, 193, 211, 214, 281], "spatiotempor": [56, 66, 69, 73, 79, 80, 129, 189, 201, 202, 206, 211, 212, 214, 282], "2021": [56, 79, 80, 97, 110, 127, 189, 211, 214, 239, 253, 278], "fuzz": [56, 62, 189, 193], "fuzz45933": [56, 189], "9494594": [56, 189], "neighbor": [56, 58, 75, 79, 119, 147, 189, 190, 208, 211, 283], "intersect": [56, 58, 73, 79, 80, 189, 190, 206, 212, 214], "neighbourx": [56, 58, 189, 190], "neighbouri": [56, 58, 189, 190], "common": [56, 58, 64, 66, 69, 73, 79, 80, 189, 190, 199, 201, 202, 206, 212, 214, 265], "samplen": [56, 58, 64, 66, 69, 73, 75, 79, 80, 189, 190, 199, 201, 202, 206, 209, 211, 212, 214], "fuzzyspatialfrequentpattern": [56, 189], "block": [56, 86, 88, 92, 101, 103, 116, 226, 227, 232, 235, 246, 261], "consol": 56, "kwangwari": 58, "generategraph": 58, "_fuzzypartialperiodicpattern": 60, "irregulat": 60, "mapitemsgsum": 60, "mapitemshsum": 60, "f3pmine": 60, "palla": [60, 92, 125, 127, 235, 276, 279], "_fuzzyperiodicfrequentpattern": [62, 193], "2020": [62, 66, 82, 90, 98, 100, 101, 123, 193, 201, 217, 218, 219, 234, 244, 245, 269, 270], "glasgow": [62, 193], "uk": [62, 193], "fuzz48607": [62, 193], "9177579": [62, 193], "maxtid": [62, 193], "lasttid": [62, 193], "last": [62, 80, 82, 86, 119, 193, 214, 217, 218, 219, 226, 227], "itemstoregion": [62, 193], "il": 62, "_georeferencedperiodicfrequentpattern": [64, 199], "extens": [64, 66, 119, 199, 201, 250, 251], "\u00e9clat": [64, 199], "stand": [64, 66, 199, 201], "cluster": [64, 66, 199, 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125, 127, 129, 131, 133, 135], "correlatedpattern": [23, 24], "comin": [24, 152], "comineplu": [24, 153], "coveragepattern": [25, 26], "cmine": [26, 155], "cppg": [26, 156], "extra": [27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41], "convertmultitsintofuzzi": 27, "generatelatexgraphfil": 27, "plotpointonmap": 27, "plotpointonmap_dump": 27, "scatterplotspatialpoint": 27, "topkpattern": 27, "uncertaindb_convert": 27, "df2db": 28, "df2dbplu": 28, "denseformatdf": 28, "sparseformatdf": 28, "createtdb": 28, "densedf2dbplu": 28, "densedf2db_dump": 28, "sparsedf2dbplu": 28, "calculatemisvalu": 29, "usingbeta": 29, "usingsd": 29, "dbstat": 30, "fuzzydatabas": [30, 40], "multipletimeseriesfuzzydatabasestat": 30, "sequentialdatabas": [30, 39], "temporaldatabas": [30, 39, 40], "transactionaldatabas": [30, 39, 40], "uncertaintemporaldatabas": 30, "uncertaintransactionaldatabas": 30, "utilitydatabas": [30, 39, 40], "fuzzytransform": 31, "temporaltofuzzi": 31, "transactionaltofuzzi": 31, "utilitytofuzzi": 31, "generatedatabas": 32, "generatespatiotemporaldatabas": 32, "generatetemporaldatabas": 32, "generatetransactionaldatabas": 32, "graph": [33, 41, 119, 120], "df2fig": 33, "df2tex": 33, "plotlinegraphfromdictionari": 33, "plotlinegraphsfromdatafram": 33, "visualizefuzzypattern": 33, "visualizepattern": 33, "image2databas": 34, "imageprocess": 35, "imagery2databas": 35, "messag": 36, "discord": 36, "gmail": 36, "neighbour": 37, "findneighborsusingeuclideandistanceforpointinfo": 37, "findneighboursusingeuclidean": 37, "findneighboursusinggeodes": 37, "sampledataset": 38, "stat": 39, "graphdatabas": 39, "syntheticdatagener": 40, "createsyntheticgeoreferentialtempor": 40, "createsyntheticgeoreferentialtransact": 40, "createsyntheticgeoreferentialuncertaintransact": 40, "createsynthetictempor": 40, "createsynthetictransact": 40, "createsyntheticuncertaintempor": 40, "createsyntheticuncertaintransact": 40, "createsyntheticutil": 40, "generatetempor": 40, "generatetransact": 40, "generateuncertaintempor": 40, "generateuncertaintransact": 40, "generateutilitytempor": 40, "generateutilitytransact": 40, "georeferencedtemporaldatabas": 40, "georeferencedtransactionaldatabas": 40, "syntheticutilitydatabas": 40, "temporaldatabasegen": 40, "visual": 41, "faulttolerantfrequentpattern": [42, 43], "ftapriori": [43, 158], "ftfpgrowth": [43, 159], "frequentpattern": [44, 45, 46, 47, 48, 49, 50], "apriori": [45, 162], "eclat": [45, 164], "eclatdiffset": [45, 165], "eclatbitset": [45, 166], "fpgrowth": [45, 167], "close": [46, 89, 98, 114, 176, 229, 238, 255], "charm": [46, 182], "cuda": [47, 99, 176, 229], "cuapriori": [47, 168], "cuaprioribit": [47, 169], "cueclat": [47, 172], "cueclatbit": [47, 173], "cudaapriorigct": [47, 170], "cudaaprioritid": [47, 171], "cudaeclatgct": [47, 174], "maxim": [48, 90, 100, 176, 229, 238], "maxfpgrowth": [48, 175], "pyspark": [49, 91, 101, 176], "parallelapriori": [49, 177], "paralleleclat": [49, 178], "parallelfpgrowth": [49, 179], "topk": [50, 80, 92, 102, 103, 104, 117, 120, 229, 264], "fae": [50, 180], "fuzzycorrelatedpattern": [51, 52], "fcpgrowth": [52, 184], "fuzzyfrequentpattern": [53, 54], "ffimin": [54, 186], "ffiminer_old": 54, "fuzzygeoreferencedfrequentpattern": [55, 56], "ffspminer": [56, 189], "ffspminer_old": 56, "fuzzygeoreferencedperiodicfrequentpattern": [57, 58], "fgpfpminer": [58, 190], "fgpfpminer_old": 58, "fuzzypartialperiodicpattern": [59, 60], "f3pminer": 60, "fuzzyperiodicfrequentpattern": [61, 62], "fpfpminer": [62, 193], "fpfpminer_old": 62, "georeferencedperiodicfrequentpattern": [63, 64], "gpfpminer": [64, 199], "georeferencedfrequentpattern": [65, 66], "fspgrowth": [66, 200], "spatialeclat": [66, 201], "georeferencedfrequentsequencepattern": 67, "georeferencedpartialperiodicpattern": [68, 69], "steclat": [69, 202], "highutilityfrequentpattern": [70, 71], "hufim": [71, 203], "highutilitygeoreferencedfrequentpattern": [72, 73], "shufim": [73, 206], "highutilitypattern": [74, 75, 76], "efim": [75, 207], "hminer": [75, 208], "upgrowth": [75, 209], "efimparallel": [75, 76], "parallel": 76, "highutilitypatternsinstream": 77, "hupm": 77, "shugrowth": 77, "highutilityspatialpattern": [78, 79, 80], "hdshuim": [79, 211], "shuim": [79, 212], "tkshuim": [80, 214], "localperiodicpattern": [81, 82], "lppgrowth": [82, 217], "lppmbreadth": [82, 218], "lppmdepth": [82, 219], "multipleminimumsupportbasedfrequentpattern": [83, 84], "cfpgrowth": [84, 221], "cfpgrowthplu": [84, 222], "partialperiodicfrequentpattern": [85, 86], "gpfgrowth": [86, 226], "ppf_df": [86, 227], "partialperiodicpattern": [87, 88, 89, 90, 91, 92], "gthreepgrowth": [88, 230], "gabstract": 88, "pppgrowth": [88, 231], "ppp_eclat": [88, 232], "pppclose": [89, 233], "max3pgrowth": [90, 234], "parallel3pgrowth": 91, "k3pminer": [92, 235], "partialperiodicpatterninmultipletimeseri": 93, "ppgrowth": [93, 228], "periodiccorrelatedpattern": [94, 95], "epcpgrowth": [95, 237], "periodicfrequentpattern": [96, 97, 98, 99, 100, 101, 102, 103, 104], "pfeclat": [97, 239], "pfpgrowth": [97, 240], "pfpgrowthplu": [97, 241], "pfpmc": [97, 242], "psgrowth": [97, 243], "parallelpfpgrowth": [97, 101], "cpfpminer": [98, 244], "cugpfmin": 99, "gpfminerbit": 99, "maxpfgrowth": [100, 245], "topkpfp": [103, 246], "kpfpminer": [104, 247], "recurringpattern": [105, 106], "rpgrowth": [106, 249], "relativefrequentpattern": [107, 108], "rsfpgrowth": [108, 252], "relativehighutilitypattern": [109, 110], "rhuim": [110, 253], "sequentialpatternmin": [112, 113, 114], "spade": [113, 257], "spam": [113, 258], "prefixspan": [113, 259], "bide": [114, 260], "stableperiodicfrequentpattern": [115, 116, 117], "sppeclat": [116, 261], "sppgrowth": [116, 262], "sppgrowthdump": 116, "tspin": [117, 263], "subgraphmin": [118, 119, 120], "dfscode": [119, 120], "edg": [119, 120], "extendededg": [119, 120], "frequentsubgraph": [119, 120], "gspan": 119, "sparsetriangularmatrix": [119, 120], "vertex": [119, 120], "dfsthread": 120, "tkg": 120, "uncertainfaulttolerantfrequentpattern": 121, "vbftmine": 121, "uncertainfrequentpattern": [122, 123], "cufptre": [123, 267], "pufgrowth": [123, 268], "tufp": [123, 269], "tubep": [123, 270], "tube": [123, 271], "ufgrowth": [123, 272], "uveclat": [123, 273], "uncertaingeoreferencedfrequentpattern": [124, 125], "gfpgrowth": [125, 276], "uncertainperiodicfrequentpattern": [126, 127], "upfpgrowth": [127, 278], "upfpgrowthplu": [127, 279], "weightedfrequentneighbourhoodpattern": [128, 129], "swfpgrowth": [129, 282], "weightedfrequentpattern": [130, 131], "wfim": [131, 284], "weightedfrequentregularpattern": [132, 133], "wfrimin": [133, 286], "weighteduncertainfrequentpattern": [134, 135], "wufim": 135, "recur": 140, "rel": [141, 250], "sequenti": [142, 256], "stabl": 143, "uncertain": [144, 145, 146, 277], "weight": [147, 148, 149], "neighbourhood": 147, "regular": 149, "aprioribitset": 163, "top": [176, 213, 238], "k": [176, 213, 238], "welcom": 215, "s": 215, "document": 215, "indic": 215, "tabl": 215, "timeseri": 224, "databas": [256, 265, 266, 277], "tempor": 265, "transact": 266}, "envversion": {"sphinx.domains.c": 2, "sphinx.domains.changeset": 1, "sphinx.domains.citation": 1, "sphinx.domains.cpp": 6, "sphinx.domains.index": 1, "sphinx.domains.javascript": 2, "sphinx.domains.math": 2, "sphinx.domains.python": 3, "sphinx.domains.rst": 2, "sphinx.domains.std": 2, "sphinx.ext.viewcode": 1, "sphinx.ext.intersphinx": 1, "sphinx": 56}}) \ No newline at end of file diff --git a/finalSphinxDocs/_build/html/sequentialPatternMiningBasicSPADE.html b/finalSphinxDocs/_build/html/sequentialPatternMiningBasicSPADE.html index 6356c10e..fcd913b8 100644 --- a/finalSphinxDocs/_build/html/sequentialPatternMiningBasicSPADE.html +++ b/finalSphinxDocs/_build/html/sequentialPatternMiningBasicSPADE.html @@ -112,314 +112,8 @@
    -
    -

    SPADE

    -
    -
    -class PAMI.sequentialPatternMining.basic.SPADE.SPADE(iFile, minSup, sep='\t')[source]
    -

    Bases: _sequentialPatterns

    -
    -
    Description:
    -
      -

    • SPADE is one of the fundamental algorithm to discover sequential frequent patterns in a transactional database.

      • -

    • This program employs SPADE property (or downward closure property) to reduce the search space effectively.

      • -

    • This algorithm employs breadth-first search technique when 1-2 length patterns and depth-first serch when above 3 length patterns to find the complete set of frequent patterns in a transactional database.

      • -
    -
    -
    Reference:
    -

    Mohammed J. Zaki. 2001. SPADE: An Efficient Algorithm for Mining Frequent Sequences. Mach. Learn. 42, 1-2 (January 2001), 31-60. DOI=10.1023/A:1007652502315 http://dx.doi.org/10.1023/A:1007652502315

    -
    -
    Parameters:
    -
      -

    • iFile – str : -Name of the Input file to mine complete set of Sequential frequent patterns

      • -

    • oFile – str : -Name of the output file to store complete set of Sequential frequent patterns

      • -

    • minSup – float or int or str : -minSup measure constraints the minimum number of transactions in a database where a pattern must appear -Example: minSup=10 will be treated as integer, while minSup=10.0 will be treated as float

      • -

    • sep – str : -This variable is used to distinguish items from one another in a transaction. The default seperator is tab space. However, the users can override their default separator.

      • -
    -
    -
    Attributes:
    -
    -
    iFilestr

    Input file name or path of the input file

    -
    -
    oFilestr

    Name of the output file or the path of output file

    -
    -
    minSup: float or int or str

    The user can specify minSup either in count or proportion of database size. -If the program detects the data type of minSup is integer, then it treats minSup is expressed in count. -Otherwise, it will be treated as float. -Example: minSup=10 will be treated as integer, while minSup=10.0 will be treated as float

    -
    -
    sepstr

    This variable is used to distinguish items from one another in a transaction. The default seperator is tab space or . -However, the users can override their default separator.

    -
    -
    startTime:float

    To record the start time of the mining process

    -
    -
    endTime:float

    To record the completion time of the mining process

    -
    -
    finalPatterns: dict

    Storing the complete set of patterns in a dictionary variable

    -
    -
    memoryUSSfloat

    To store the total amount of USS memory consumed by the program

    -
    -
    memoryRSSfloat

    To store the total amount of RSS memory consumed by the program

    -
    -
    Databaselist

    To store the transactions of a database in list

    -
    -
    _xLenDatabase: dict

    To store the datas in different sequence separated by sequence, rownumber, length.

    -
    -
    _xLenDatabaseSamedict

    To store the datas in same sequence separated by sequence, rownumber, length.

    -
    -
    -
    -
    Methods:
    -
    -
    startMine()

    Mining process will start from here

    -
    -
    getPatterns()

    Complete set of patterns will be retrieved with this function

    -
    -
    savePatterns(oFile)

    Complete set of frequent patterns will be loaded in to an output file

    -
    -
    getPatternsAsDataFrame()

    Complete set of frequent patterns will be loaded in to a dataframe

    -
    -
    getMemoryUSS()

    Total amount of USS memory consumed by the mining process will be retrieved from this function

    -
    -
    getMemoryRSS()

    Total amount of RSS memory consumed by the mining process will be retrieved from this function

    -
    -
    getRuntime()

    Total amount of runtime taken by the mining process will be retrieved from this function

    -
    -
    candidateToFrequent(candidateList)

    Generates frequent patterns from the candidate patterns

    -
    -
    frequentToCandidate(frequentList, length)

    Generates candidate patterns from the frequent patterns

    -
    -
    -
    -
    -
    Format:
-
-(.venv) $ python3 SPADE.py <inputFile> <outputFile> <minSup>
-
-Example usage:
-
-(.venv) $ python3 SPADE.py sampleDB.txt patterns.txt 10.0
-
-
-        .. note:: minSup will be considered in times of minSup and count of database transactions
-
    -
    -
    import PAMI.sequentialPatternMining.basic.SPADE as alg
-
-obj = alg.SPADE(iFile, minSup)
-
-obj.startMine()
-
-sequentialPatternMining = obj.getPatterns()
-
-print("Total number of Frequent Patterns:", len(frequentPatterns))
-
-obj.save(oFile)
-
-Df = obj.getPatternInDataFrame()
-
-memUSS = obj.getMemoryUSS()
-
-print("Total Memory in USS:", memUSS)
-
-memRSS = obj.getMemoryRSS()
-
-print("Total Memory in RSS", memRSS)
-
-run = obj.getRuntime()
-
-print("Total ExecutionTime in seconds:", run)
-
    -
    -
    -

    The complete program was written by Suzuki Shota under the supervision of Professor Rage Uday Kiran.

    -
    -
    -
    -Mine()[source]
    -

    Frequent pattern mining process will start from here

    -
    - -
    -
    -getMemoryRSS()[source]
    -

    Total amount of RSS memory consumed by the mining process will be retrieved from this function

    -
    -
    Returns:
    -

    returning RSS memory consumed by the mining process

    -
    -
    Return type:
    -

    float

    -
    -
    -
    - -
    -
    -getMemoryUSS()[source]
    -

    Total amount of USS memory consumed by the mining process will be retrieved from this function

    -
    -
    Returns:
    -

    returning USS memory consumed by the mining process

    -
    -
    Return type:
    -

    float

    -
    -
    -
    - -
    -
    -getPatterns()[source]
    -

    Function to send the set of frequent patterns after completion of the mining process

    -
    -
    Returns:
    -

    returning frequent patterns

    -
    -
    Return type:
    -

    dict

    -
    -
    -
    - -
    -
    -getPatternsAsDataFrame()[source]
    -

    Storing final frequent patterns in a dataframe

    -
    -
    Returns:
    -

    returning frequent patterns in a dataframe

    -
    -
    Return type:
    -

    pd.DataFrame

    -
    -
    -
    - -
    -
    -getRuntime()[source]
    -

    Calculating the total amount of runtime taken by the mining process

    -
    -
    Returns:
    -

    returning total amount of runtime taken by the mining process

    -
    -
    Return type:
    -

    float

    -
    -
    -
    - -
    -
    -make1LenDatabase()[source]
    -

    To make 1 length frequent patterns by breadth-first search technique and update Database to sequential database

    -
    - -
    -
    -make2LenDatabase()[source]
    -

    To make 2 length frequent patterns by joining two one length patterns by breadth-first search technique and update xlen Database to sequential database

    -
    - -
    -
    -make3LenDatabase()[source]
    -

    To call each 2 length patterns to make 3 length frequent patterns depth-first search technique

    -
    - -
    -
    -makeNextRow(bs, latestWord, latestWord2)[source]
    -

    To make pattern row when two patterns have the latest word in different sequence

    -

    :param bs : previous pattern without the latest one -:param latestWord : latest word of one previous pattern -:param latestWord2 : latest word of other previous pattern

    -
    - -
    -
    -makeNextRowSame(bs, latestWord, latestWord2)[source]
    -

    To make pattern row when one pattern have the latestWord1 in different sequence and other(latestWord2) in same

    -

    :param bs : previous pattern without the latest one -:param latestWord : latest word of one previous pattern in same sequence -:param latestWord2 : latest word of other previous pattern in different sequence

    -
    - -
    -
    -makeNextRowSame2(bs, latestWord, latestWord2)[source]
    -

    To make pattern row when two patterns have the latest word in same sequence

    -

    :param bs : previous pattern without the latest one -:param latestWord : latest word of one previous pattern -:param latestWord2 : latest word of the other previous pattern

    -
    - -
    -
    -makeNextRowSame3(bs, latestWord, latestWord2)[source]
    -

    To make pattern row when two patterns have the latest word in different sequence and both latest word is in same sequence

    -

    :param bs : previous pattern without the latest one -:param latestWord : latest word of one previous pattern -:param latestWord2 : latest word of other previous pattern

    -
    - -
    -
    -makexLenDatabase(rowLen, bs, latestWord)[source]
    -

    To make “rowLen” length frequent patterns from pattern which the latest word is in same seq by joining “rowLen”-1 length patterns by depth-first search technique and update xlenDatabase to sequential database

    -
    -
    Parameters:
    -

    rowLen – row length of patterns.

    -
    -
    -

    :param bs : patterns without the latest one -:param latestWord : latest word of patterns

    -
    - -
    -
    -makexLenDatabaseSame(rowLen, bs, latestWord)[source]
    -

    To make 3 or more length frequent patterns from pattern which the latest word is in different seq by depth-first search technique and update xlenDatabase to sequential database

    -
    -
    Parameters:
    -

    rowLen – row length of previous patterns.

    -
    -
    -

    :param bs : previous patterns without the latest one -:param latestWord : latest word of previous patterns

    -
    - -
    -
    -printResults()[source]
    -

    This function is used to prnt the results

    -
    - -
    -
    -save(outFile)[source]
    -

    Complete set of frequent patterns will be loaded in to an output file

    -
    -
    Parameters:
    -

    outFile (csv file) – name of the output file

    -
    -
    -
    - -
    -
    -startMine()[source]
    -

    Frequent pattern mining process will start from here

    -
    - -
    - +
    +

    SPADE

    diff --git a/finalSphinxDocs/_build/html/sequentialPatternMiningBasicSPAM.html b/finalSphinxDocs/_build/html/sequentialPatternMiningBasicSPAM.html index 38e7917f..e25f10a4 100644 --- a/finalSphinxDocs/_build/html/sequentialPatternMiningBasicSPAM.html +++ b/finalSphinxDocs/_build/html/sequentialPatternMiningBasicSPAM.html @@ -112,247 +112,8 @@
    -
    -

    SPAM

    -
    -
    -class PAMI.sequentialPatternMining.basic.SPAM.SPAM(iFile, minSup, sep='\t')[source]
    -

    Bases: _sequentialPatterns

    -
    -
    Description:
    -

    SPAM is one of the fundamental algorithm to discover sequential frequent patterns in a transactional database. -This program employs SPAM property (or downward closure property) to reduce the search space effectively. -This algorithm employs breadth-first search technique to find the complete set of frequent patterns in a sequential database.

    -
    -
    Reference:
    -
      -

    1. Ayres, J. Gehrke, T.Yiu, and J. Flannick. Sequential Pattern Mining Using Bitmaps. In Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Edmonton, Alberta, Canada, July 2002.

      2. -
    -
    -
    Parameters:
    -
      -

    • iFile – str : -Name of the Input file to mine complete set of Sequential frequent patterns

      • -

    • oFile – str : -Name of the output file to store complete set of Sequential frequent patterns

      • -

    • minSup – float or int or str : -minSup measure constraints the minimum number of transactions in a database where a pattern must appear -Example: minSup=10 will be treated as integer, while minSup=10.0 will be treated as float

      • -

    • sep – str : -This variable is used to distinguish items from one another in a transaction. The default seperator is tab space. However, the users can override their default separator.

      • -
    -
    -
    Attributes:
    -
    -
    iFilestr

    Input file name or path of the input file

    -
    -
    oFilestr

    Name of the output file or the path of output file

    -
    -
    minSupfloat or int or str

    The user can specify minSup either in count or proportion of database size. -If the program detects the data type of minSup is integer, then it treats minSup is expressed in count. -Otherwise, it will be treated as float. -Example: minSup=10 will be treated as integer, while minSup=10.0 will be treated as float

    -
    -
    sepstr

    This variable is used to distinguish items from one another in a transaction. The default seperator is tab space or . -However, the users can override their default separator.

    -
    -
    startTimefloat

    To record the start time of the mining process

    -
    -
    endTimefloat

    To record the completion time of the mining process

    -
    -
    finalPatternsdict

    Storing the complete set of patterns in a dictionary variable

    -
    -
    memoryUSSfloat

    To store the total amount of USS memory consumed by the program

    -
    -
    memoryRSSfloat

    To store the total amount of RSS memory consumed by the program

    -
    -
    Databaselist

    To store the sequences of a database in list

    -
    -
    _idDatabasedict

    To store the sequences of a database by bit map

    -
    -
    _maxSeqLen:

    the maximum length of subsequence in sequence.

    -
    -
    -
    -
    Methods:
    -
    -
    _creatingItemSets():

    Storing the complete sequences of the database/input file in a database variable

    -
    -
    _convert(value):

    To convert the user specified minSup value

    -
    -
    make2BitDatabase():

    To make 1 length frequent patterns by breadth-first search technique and update Database to sequential database

    -
    -
    DfsPruning(items,sStep,iStep):

    the main algorithm of spam. This can search sstep and istep items and find next patterns, its sstep, and its istep. And call this function again by using them. Recursion until there are no more items available for exploration.

    -
    -
    Sstep(s):

    To convert bit to ssteo bit.The first time you get 1, you set it to 0 and subsequent ones to 1.(like 010101=>001111, 00001001=>00000111)

    -
    -
    startMine()

    Mining process will start from here

    -
    -
    getPatterns()

    Complete set of patterns will be retrieved with this function

    -
    -
    savePatterns(oFile)

    Complete set of frequent patterns will be loaded in to a output file

    -
    -
    getPatternsAsDataFrame()

    Complete set of frequent patterns will be loaded in to a dataframe

    -
    -
    getMemoryUSS()

    Total amount of USS memory consumed by the mining process will be retrieved from this function

    -
    -
    getMemoryRSS()

    Total amount of RSS memory consumed by the mining process will be retrieved from this function

    -
    -
    getRuntime()

    Total amount of runtime taken by the mining process will be retrieved from this function

    -
    -
    candidateToFrequent(candidateList)

    Generates frequent patterns from the candidate patterns

    -
    -
    frequentToCandidate(frequentList, length)

    Generates candidate patterns from the frequent patterns

    -
    -
    -
    -
    -
    Format:
-
-(.venv) $ python3 SPAM.py <inputFile> <outputFile> <minSup> (<separator>)
-
-Examples usage:
-
-(.venv) $ python3 SPAM.py sampleDB.txt patterns.txt 10.0
-
-
-        .. note:: minSup will be considered in times of minSup and count of database transactions
-
    -
    -
    -

    import PAMI.sequentialPatternMining.basic.SPAM as alg

    -

    obj = alg.SPAM(iFile, minSup)

    -

    obj.startMine()

    -

    sequentialPatternMining = obj.getPatterns()

    -

    print(“Total number of Frequent Patterns:”, len(frequentPatterns))

    -

    obj.savePatterns(oFile)

    -

    Df = obj.getPatternInDataFrame()

    -

    memUSS = obj.getMemoryUSS()

    -

    print(“Total Memory in USS:”, memUSS)

    -

    memRSS = obj.getMemoryRSS()

    -

    print(“Total Memory in RSS”, memRSS)

    -

    run = obj.getRuntime()

    -

    print(“Total ExecutionTime in seconds:”, run)

    -
    -
    -

    The complete program was written by Shota Suzuki under the supervision of Professor Rage Uday Kiran.

    -
    -
    -
    -DfsPruning(items, sStep, iStep)[source]
    -

    the main algorithm of spam. This can search sstep and istep items and find next patterns, its sstep, and its istep. And call this function again by using them. Recursion until there are no more items available for exploration.

    -
    -
    Attributes:
    -

    -
    -
    -
    itemsstr

    The pattrens I got before

    -
    -
    sSteplist

    Items presumed to have “sstep” relationship with “items”.(sstep is What appears later like a-b and a-c)

    -
    -
    iSteplist

    Items presumed to have “istep” relationship with “items”(istep is What appears in same time like ab and ac)

    -
    -
    -
    - -
    -
    -Sstep(s)[source]
    -

    To convert bit to Sstep bit.The first time you get 1, you set it to 0 and subsequent ones to 1.(like 010101=>001111, 00001001=>00000111)

    -
    -
    :param s:list

    to store each bit sequence

    -
    -
    -
    -
    Returns:
    -

    nextS:list to store the bit sequence converted by sstep

    -
    -
    -
    - -
    -
    -countSup(n)[source]
    -

    count support

    -
    -
    :param n:list

    to store each bit sequence

    -
    -
    -
    -
    Returns:
    -

    count: int support of this list

    -
    -
    -
    - -
    -
    -getMemoryRSS()[source]
    -

    Total amount of RSS memory consumed by the mining process will be retrieved from this function -:return: returning RSS memory consumed by the mining process -:rtype: float

    -
    - -
    -
    -getMemoryUSS()[source]
    -

    Total amount of USS memory consumed by the mining process will be retrieved from this function -:return: returning USS memory consumed by the mining process -:rtype: float

    -
    - -
    -
    -getPatterns()[source]
    -

    Function to send the set of frequent patterns after completion of the mining process -:return: returning frequent patterns -:rtype: dict

    -
    - -
    -
    -getPatternsAsDataFrame()[source]
    -

    Storing final frequent patterns in a dataframe -:return: returning frequent patterns in a dataframe -:rtype: pd.DataFrame

    -
    - -
    -
    -getRuntime()[source]
    -

    Calculating the total amount of runtime taken by the mining process -:return: returning total amount of runtime taken by the mining process -:rtype: float

    -
    - -
    -
    -make2BitDatabase()[source]
    -

    To make 1 length frequent patterns by breadth-first search technique and update Database to sequential database

    -
    - -
    -
    -printResults()[source]
    -

    This function is used to print the results

    -
    - -
    -
    -save(outFile)[source]
    -

    Complete set of frequent patterns will be loaded in to an output file -:param outFile: name of the output file -:type outFile: file

    -
    - -
    -
    -startMine()[source]
    -

    Frequent pattern mining process will start from here

    -
    - -
    - +
    +

    SPAM

    diff --git a/finalSphinxDocs/_build/html/sequentialPatternMiningBasicprefixSpan.html b/finalSphinxDocs/_build/html/sequentialPatternMiningBasicprefixSpan.html index 88c227bd..49cb1547 100644 --- a/finalSphinxDocs/_build/html/sequentialPatternMiningBasicprefixSpan.html +++ b/finalSphinxDocs/_build/html/sequentialPatternMiningBasicprefixSpan.html @@ -112,342 +112,8 @@
    -
    -

    prefixSpan

    -
    -
    -class PAMI.sequentialPatternMining.basic.prefixSpan.prefixSpan(iFile, minSup, sep='\t')[source]
    -

    Bases: _sequentialPatterns

    -
    -
    Description:
    -
      -

    • Prefix Span is one of the fundamental algorithm to discover sequential frequent patterns in a transactional database.

      • -

    • This program employs Prefix Span property (or downward closure property) to reduce the search space effectively.

      • -

    • This algorithm employs depth-first search technique to find the complete set of frequent patterns in a transactional database.

      • -
    -
    -
    Reference:
    -
      -

    1. Pei, J. Han, B. Mortazavi-Asl, J. Wang, H. Pinto, Q. Chen, U. Dayal, M. Hsu: Mining Sequential Patterns by Pattern-Growth: The PrefixSpan Approach. IEEE Trans. Knowl. Data Eng. 16(11): 1424-1440 (2004)

      2. -
    -
    -
    Parameters:
    -
      -

    • iFile – str : -Name of the Input file to mine complete set of Sequential frequent patterns

      • -

    • oFile – str : -Name of the output file to store complete set of Sequential frequent patterns

      • -

    • minSup – float or int or str : -minSup measure constraints the minimum number of transactions in a database where a pattern must appear -Example: minSup=10 will be treated as integer, while minSup=10.0 will be treated as float

      • -

    • sep – str : -This variable is used to distinguish items from one another in a transaction. The default seperator is tab space. However, the users can override their default separator.

      • -
    -
    -
    Attributes:
    -
    -
    iFilestr

    Input file name or path of the input file

    -
    -
    oFilestr

    Name of the output file or the path of output file

    -
    -
    minSupfloat or int or str

    The user can specify minSup either in count or proportion of database size. -If the program detects the data type of minSup is integer, then it treats minSup is expressed in count. -Otherwise, it will be treated as float. -Example: minSup=10 will be treated as integer, while minSup=10.0 will be treated as float

    -
    -
    sepstr

    This variable is used to distinguish items from one another in a transaction. The default seperator is tab space or . -However, the users can override their default separator.

    -
    -
    startTimefloat

    To record the start time of the mining process

    -
    -
    endTimefloat

    To record the completion time of the mining process

    -
    -
    finalPatternsdict

    Storing the complete set of patterns in a dictionary variable

    -
    -
    memoryUSSfloat

    To store the total amount of USS memory consumed by the program

    -
    -
    memoryRSSfloat

    To store the total amount of RSS memory consumed by the program

    -
    -
    Databaselist

    To store the transactions of a database in list

    -
    -
    -
    -
    Methods:
    -
    -
    startMine()

    Mining process will start from here

    -
    -
    getPatterns()

    Complete set of patterns will be retrieved with this function

    -
    -
    savePatterns(oFile)

    Complete set of frequent patterns will be loaded in to a output file

    -
    -
    getPatternsAsDataFrame()

    Complete set of frequent patterns will be loaded in to a dataframe

    -
    -
    getMemoryUSS()

    Total amount of USS memory consumed by the mining process will be retrieved from this function

    -
    -
    getMemoryRSS()

    Total amount of RSS memory consumed by the mining process will be retrieved from this function

    -
    -
    getRuntime()

    Total amount of runtime taken by the mining process will be retrieved from this function

    -
    -
    candidateToFrequent(candidateList)

    Generates frequent patterns from the candidate patterns

    -
    -
    frequentToCandidate(frequentList, length)

    Generates candidate patterns from the frequent patterns

    -
    -
    -
    -
    -
    Format:
-
-(.venv) $ python3 prefixSpan.py <inputFile> <outputFile> <minSup>
-
-Example usage:
-
-(.venv) $ python3 prefixSpan.py sampleDB.txt patterns.txt 10
-
-
-        .. note:: minSup will be considered in support count or frequency
-
    -
    -
    import PAMI.frequentPattern.basic.prefixSpan as alg
-
-obj = alg.prefixSpan(iFile, minSup)
-
-obj.startMine()
-
-frequentPatterns = obj.getPatterns()
-
-print("Total number of Frequent Patterns:", len(frequentPatterns))
-
-obj.save(oFile)
-
-Df = obj.getPatternInDataFrame()
-
-memUSS = obj.getMemoryUSS()
-
-print("Total Memory in USS:", memUSS)
-
-memRSS = obj.getMemoryRSS()
-
-print("Total Memory in RSS", memRSS)
-
-run = obj.getRuntime()
-
-print("Total ExecutionTime in seconds:", run)
-
    -
    -
    -

    The complete program was written by Suzuki Shota under the supervision of Professor Rage Uday Kiran.

    -
    -
    -
    -Mine()[source]
    -

    Frequent pattern mining process will start from here

    -
    - -
    -
    -getMemoryRSS()[source]
    -

    Total amount of RSS memory consumed by the mining process will be retrieved from this function

    -
    -
    Returns:
    -

    returning RSS memory consumed by the mining process

    -
    -
    Return type:
    -

    float

    -
    -
    -
    - -
    -
    -getMemoryUSS()[source]
    -

    Total amount of USS memory consumed by the mining process will be retrieved from this function

    -
    -
    Returns:
    -

    returning USS memory consumed by the mining process

    -
    -
    Return type:
    -

    float

    -
    -
    -
    - -
    -
    -getPatterns()[source]
    -

    Function to send the set of frequent patterns after completion of the mining process

    -
    -
    Returns:
    -

    returning frequent patterns

    -
    -
    Return type:
    -

    dict

    -
    -
    -
    - -
    -
    -getPatternsAsDataFrame()[source]
    -

    Storing final frequent patterns in a dataframe

    -
    -
    Returns:
    -

    returning frequent patterns in a dataframe

    -
    -
    Return type:
    -

    pd.DataFrame

    -
    -
    -
    - -
    -
    -getRuntime()[source]
    -

    Calculating the total amount of runtime taken by the mining process

    -
    -
    Returns:
    -

    returning total amount of runtime taken by the mining process

    -
    -
    Return type:
    -

    float

    -
    -
    -
    - -
    -
    -getSameSeq(startrow)[source]
    -
    -

    To get words in the latest sequence

    -
    -
    -
    Parameters:
    -

    startrow – the patterns get before

    -
    -
    -
    - -
    -
    -makeNext(sepDatabase, startrow)[source]
    -

    To get next pattern by adding head word to next sequence of startrow

    -
    -
    Parameters:
    -
      -

    • sepDatabase – dict -what words and rows startrow have to add it.

      • -

    • startrow – the patterns get before

      • -
    -
    -
    -
    - -
    -
    -makeNextSame(sepDatabase, startrow)[source]
    -
    -

    To get next pattern by adding head word to the latest sequence of startrow

    -
    -
    -
    Parameters:
    -
      -

    • sepDatabase – dict -what words and rows startrow have to add it

      • -

    • startrow – the patterns get before

      • -
    -
    -
    -
    - -
    -
    -makeSeqDatabaseFirst(database)[source]
    -

    To make 1 length sequence dataset list which start from same word. It was stored only 1 from 1 line.

    -
    -
    Parameters:
    -

    database – To store the transactions of a database in list

    -
    -
    -
    - -
    -
    -makeSeqDatabaseSame(database, startrow)[source]
    -

    To make sequence dataset list which start from same word(head). It was stored only 1 from 1 line. -And it separated by having head in the latest sequence of startrow or not.

    -
    -
    Parameters:
    -
      -

    • database – To store the transactions of a database in list

      • -

    • startrow – the patterns get before

      • -
    -
    -
    -
    - -
    -
    -makeSupDatabase(database, head)[source]
    -

    To delete not frequent words without words in the latest sequence

    -
    -
    Parameters:
    -

    database – list -database of lines having same startrow and head word

    -
    -
    -
    -
    :param head:list

    words in the latest sequence

    -
    -
    -
    -
    Returns:
    -

    changed database

    -
    -
    -
    - -
    -
    -printResults()[source]
    -

    This function is used to print the results

    -
    - -
    -
    -save(outFile)[source]
    -

    Complete set of frequent patterns will be loaded in to an output file

    -
    -
    Parameters:
    -

    outFile (csv file) – name of the output file

    -
    -
    -
    - -
    -
    -serchSame(database, startrow, give)[source]
    -

    To get 2 or more length patterns in same sequence.

    -
    -
    Parameters:
    -
      -

    • database – list -To store the transactions of a database in list which have same startrow and head word

      • -

    • startrow – list -the patterns get before

      • -

    • give – list -the word in the latest sequence of startrow

      • -
    -
    -
    -
    - -
    -
    -startMine()[source]
    -

    Frequent pattern mining process will start from here

    -
    - -
    - +
    +

    prefixSpan

    diff --git a/finalSphinxDocs/_build/html/sequentialPatternMiningClosedbide.html b/finalSphinxDocs/_build/html/sequentialPatternMiningClosedbide.html index 476acd97..03bbef7e 100644 --- a/finalSphinxDocs/_build/html/sequentialPatternMiningClosedbide.html +++ b/finalSphinxDocs/_build/html/sequentialPatternMiningClosedbide.html @@ -110,8 +110,8 @@
    -
    -

    bide

    +
    +

    bide

    diff --git a/finalSphinxDocs/frequentPatternBasicAprioribitset.rst b/finalSphinxDocs/frequentPatternBasicAprioribitset.rst new file mode 100644 index 00000000..796aa946 --- /dev/null +++ b/finalSphinxDocs/frequentPatternBasicAprioribitset.rst @@ -0,0 +1,7 @@ +Aprioribitset +------------- + +.. automodule:: PAMI.frequentPattern.basic.Aprioribitset + :members: + :undoc-members: + :show-inheritance: \ No newline at end of file diff --git a/finalSphinxDocs/frequentPatternMining.rst b/finalSphinxDocs/frequentPatternMining.rst index 75ca2ed1..2c5f19d9 100644 --- a/finalSphinxDocs/frequentPatternMining.rst +++ b/finalSphinxDocs/frequentPatternMining.rst @@ -5,6 +5,7 @@ Basic :maxdepth: 1 frequentPatternBasicApriori + frequentPatternBasicAprioribitset frequentPatternBasicECLAT frequentPatternBasicECLATDiffset frequentPatternBasicECLATbitset