Add UNHAP solver #18
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Add UNHAP solver #18
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according to review Co-authored-by: Thomas Moreau <[email protected]>
according to review Co-authored-by: Thomas Moreau <[email protected]>
All class names in CamelCase Co-authored-by: Thomas Moreau <[email protected]>
tomMoral
reviewed
Jul 30, 2024
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| moment_matching: boolean, `default=False` | ||
| If set to False, baseline, alpha and kernel parameters are randomly | ||
| chosen. If set to True, baseline, alpha and kernel parameters are | ||
| chosen using the smart init strategy. | ||
| The smart init strategy is only implemented | ||
| for truncated gaussian and raised_cosine kernels. |
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This should probably have the same structure as init in the other solvers?
| Baseline parameter of the Hawkes process. | ||
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| param_baseline_noise : `tensor`, shape (n_dim) | ||
| Baseline parameter of the Hawkes process. |
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| marks: list of vector of marks (size number of events) | ||
| square_int_marks: integral of the square mark in the left part of the loss | ||
| rho: list of vector of size (number of events) |
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Does not match the signature
| return grad_baseline_noise | ||
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| def get_grad_eta_mixture(precomputations, baseline, alpha, kernel, |
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Why do we separate the gradient computation for each parameter?
It seems that several quantities are estimated multiple times?
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| self.opt_intens.step() | ||
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| if i % self.batch_rho == 0: |
| @@ -3,6 +3,8 @@ | |||
| import torch | |||
| from scipy.linalg import toeplitz | |||
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| from fadin.utils.utils import convert_float_tensor | |||
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| @numba.jit(nopython=True, cache=True) | |||
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do we still use numba in the repo?
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In this PR, we add UNHaP solver and examples related to it