-
Notifications
You must be signed in to change notification settings - Fork 1
/
Copy pathexperiment_1.m
223 lines (186 loc) · 7.47 KB
/
experiment_1.m
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
% 2018/12/05 Uwe Ehret
% Code for experiment 1
clear all
close all
clc
% load data
load data_filtered_A
load edges
%% create variables
% separate the data
RR0 = all_RR0_dBZ_predictors(:,1);
dBZ = all_RR0_dBZ_predictors(:,2);
Decade = all_RR0_dBZ_predictors(:,3);
MoY = all_RR0_dBZ_predictors(:,4);
GWLo = all_RR0_dBZ_predictors(:,5);
logCAPE = all_RR0_dBZ_predictors(:,6);
RH2 = all_RR0_dBZ_predictors(:,7);
TA2 = all_RR0_dBZ_predictors(:,8);
u10 = all_RR0_dBZ_predictors(:,9);
v10 = all_RR0_dBZ_predictors(:,10);
statnum = all_RR0_dBZ_predictors(:,11);
% number of timesteps
num_ts = length(all_DateTime_UTC);
%% Entropy of RR0 with uniform distribution
H_RR0_uniform = log2(length(edges_RR)-1);
%% Entropy of RR0 with real distribution
[pdf_RR0,~] = histcounts(RR0,edges_RR,'Normalization', 'probability');
H_RR0_real = f_entropy(pdf_RR0);
figure;
bar(edges_RR(1:end-1),pdf_RR0,0.9,'histc');
%% Conditional Entropy of RR0 with dBZ as predictor
num_rep = 1;
sample_sizes = [num_ts];
samplingstrategy = 'continuous';
num_sasi = length(sample_sizes);
data = [RR0 dBZ];
edges = cell(1,2);
edges{1} = edges_RR;
edges{2} = edges_dBZ;
[data_binned, data_histcounts] = f_histcounts_anyd(data, edges);
[H_x, ~, ~, H_xgy, DKL_xgy, HPQ_xgy] = f_infomeasures_from_samples(data, edges, data_binned, data_histcounts, sample_sizes, num_rep, samplingstrategy);
% compute the 2-d histogram of YX
% NOTE the following conventions:
% - X is always the target and will be displayed along the vertical axis
% - Y is always the predictor and will be displayed along the horizontal axis
% - the origin of the YX graph is in the lower left corner
figure;
histogram2(dBZ,RR0,edges_dBZ,edges_RR,'FaceColor','flat','Normalization','probability');
xlabel('Y');
ylabel('X');
colorbar;
%% Conditional Entropy of RR0 with RRrad from dBZ-->Marshall-Palmer-->RRrad
% convert the Radar-dBZ to RR
RR0rad = f_dBZ2R_easy_a_b(dBZ,200,1.6);
num_rep = 1;
sample_sizes = [num_ts];
samplingstrategy = 'continuous';
num_sasi = length(sample_sizes);
data = [RR0 RR0rad];
edges = cell(1,2);
edges{1} = edges_RR;
edges{2} = edges_RR;
[data_binned, data_histcounts] = f_histcounts_anyd(data, edges);
[H_x, ~, ~, H_xgy, DKL_xgy, HPQ_xgy] = f_infomeasures_from_samples(data, edges, data_binned, data_histcounts, sample_sizes, num_rep, samplingstrategy);
%% Conditional Entropy of RR0 with dBZ and Decade as predictors
num_rep = 1;
sample_sizes = [num_ts];
samplingstrategy = 'continuous';
num_sasi = length(sample_sizes);
data = [RR0 dBZ Decade];
edges = cell(1,3);
edges{1} = edges_RR;
edges{2} = edges_dBZ;
edges{3} = edges_Decade;
[data_binned, data_histcounts] = f_histcounts_anyd(data, edges);
[H_x, ~, ~, H_xgy, DKL_xgy, HPQ_xgy] = f_infomeasures_from_samples(data, edges, data_binned, data_histcounts, sample_sizes, num_rep, samplingstrategy);
%% Conditional Entropy of RR0 with dBZ and MoY as predictors
num_rep = 1;
sample_sizes = [num_ts];
samplingstrategy = 'continuous';
num_sasi = length(sample_sizes);
data = [RR0 dBZ MoY];
edges = cell(1,3);
edges{1} = edges_RR;
edges{2} = edges_dBZ;
edges{3} = edges_MoY;
[data_binned, data_histcounts] = f_histcounts_anyd(data, edges);
[H_x, ~, ~, H_xgy, DKL_xgy, HPQ_xgy] = f_infomeasures_from_samples(data, edges, data_binned, data_histcounts, sample_sizes, num_rep, samplingstrategy);
%% Conditional Entropy of RR0 with dBZ and GWL0 as predictors
num_rep = 1;
sample_sizes = [num_ts];
samplingstrategy = 'continuous';
num_sasi = length(sample_sizes);
data = [RR0 dBZ GWLo];
edges = cell(1,3);
edges{1} = edges_RR;
edges{2} = edges_dBZ;
edges{3} = edges_GWLo;
[data_binned, data_histcounts] = f_histcounts_anyd(data, edges);
[H_x, ~, ~, H_xgy, DKL_xgy, HPQ_xgy] = f_infomeasures_from_samples(data, edges, data_binned, data_histcounts, sample_sizes, num_rep, samplingstrategy);
%% Conditional Entropy of RR0 with dBZ and logCAPE as predictors
num_rep = 1;
sample_sizes = [num_ts];
samplingstrategy = 'continuous';
num_sasi = length(sample_sizes);
data = [RR0 dBZ logCAPE];
edges = cell(1,3);
edges{1} = edges_RR;
edges{2} = edges_dBZ;
edges{3} = edges_logCAPE;
[data_binned, data_histcounts] = f_histcounts_anyd(data, edges);
[H_x, ~, ~, H_xgy, DKL_xgy, HPQ_xgy] = f_infomeasures_from_samples(data, edges, data_binned, data_histcounts, sample_sizes, num_rep, samplingstrategy);
%% Conditional Entropy of RR0 with dBZ and RH2 as predictors
num_rep = 1;
sample_sizes = [num_ts];
samplingstrategy = 'continuous';
num_sasi = length(sample_sizes);
data = [RR0 dBZ RH2];
edges = cell(1,3);
edges{1} = edges_RR;
edges{2} = edges_dBZ;
edges{3} = edges_RH2;
[data_binned, data_histcounts] = f_histcounts_anyd(data, edges);
[H_x, ~, ~, H_xgy, DKL_xgy, HPQ_xgy] = f_infomeasures_from_samples(data, edges, data_binned, data_histcounts, sample_sizes, num_rep, samplingstrategy);
%% Conditional Entropy of RR0 with dBZ and TA2 as predictors
num_rep = 1;
sample_sizes = [num_ts];
samplingstrategy = 'continuous';
num_sasi = length(sample_sizes);
data = [RR0 dBZ TA2];
edges = cell(1,3);
edges{1} = edges_RR;
edges{2} = edges_dBZ;
edges{3} = edges_TA2;
[data_binned, data_histcounts] = f_histcounts_anyd(data, edges);
[H_x, ~, ~, H_xgy, DKL_xgy, HPQ_xgy] = f_infomeasures_from_samples(data, edges, data_binned, data_histcounts, sample_sizes, num_rep, samplingstrategy);
%% Conditional Entropy of RR0 with dBZ and u10 as predictors
num_rep = 1;
sample_sizes = [num_ts];
samplingstrategy = 'continuous';
num_sasi = length(sample_sizes);
data = [RR0 dBZ u10];
edges = cell(1,3);
edges{1} = edges_RR;
edges{2} = edges_dBZ;
edges{3} = edges_u10;
[data_binned, data_histcounts] = f_histcounts_anyd(data, edges);
[H_x, ~, ~, H_xgy, DKL_xgy, HPQ_xgy] = f_infomeasures_from_samples(data, edges, data_binned, data_histcounts, sample_sizes, num_rep, samplingstrategy);
%% Conditional Entropy of RR0 with dBZ and v10 as predictors
num_rep = 1;
sample_sizes = [num_ts];
samplingstrategy = 'continuous';
num_sasi = length(sample_sizes);
data = [RR0 dBZ v10];
edges = cell(1,3);
edges{1} = edges_RR;
edges{2} = edges_dBZ;
edges{3} = edges_v10;
[data_binned, data_histcounts] = f_histcounts_anyd(data, edges);
[H_x, ~, ~, H_xgy, DKL_xgy, HPQ_xgy] = f_infomeasures_from_samples(data, edges, data_binned, data_histcounts, sample_sizes, num_rep, samplingstrategy);
%% Conditional Entropy of RR0 with dBZ, Decade and GWL0 as predictors
num_rep = 1;
sample_sizes = [num_ts];
samplingstrategy = 'continuous';
num_sasi = length(sample_sizes);
data = [RR0 dBZ Decade GWLo];
edges = cell(1,4);
edges{1} = edges_RR;
edges{2} = edges_dBZ;
edges{3} = edges_Decade;
edges{4} = edges_GWLo;
[data_binned, data_histcounts] = f_histcounts_anyd(data, edges);
[H_x, ~, ~, H_xgy, DKL_xgy, HPQ_xgy] = f_infomeasures_from_samples(data, edges, data_binned, data_histcounts, sample_sizes, num_rep, samplingstrategy);
%% Conditional Entropy of RR0 with dBZ, MoY and GWL0 as predictors
num_rep = 1;
sample_sizes = [num_ts];
samplingstrategy = 'continuous';
num_sasi = length(sample_sizes);
data = [RR0 dBZ MoY GWLo];
edges = cell(1,4);
edges{1} = edges_RR;
edges{2} = edges_dBZ;
edges{3} = edges_MoY;
edges{4} = edges_GWLo;
[data_binned, data_histcounts] = f_histcounts_anyd(data, edges);
[H_x, ~, ~, H_xgy, DKL_xgy, HPQ_xgy] = f_infomeasures_from_samples(data, edges, data_binned, data_histcounts, sample_sizes, num_rep, samplingstrategy);