Free support barycenters #56
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Thank you @vivienseguy for all the work.
I have several comments that need to be addressed before merging (discussed more in detail below).
But most of all we need a test in the test_ot.py file that call your function and check stuff like the size of the output and reasonable solution.
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| ############################################################################## | ||
| # Compute free support barycenter | ||
| # ------------- |
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---- needs to have the proper length for good documentation generation.
| @@ -26,7 +27,7 @@ def scipy_sparse_to_spmatrix(A): | |||
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| def barycenter(A, M, weights=None, verbose=False, log=False, solver='interior-point'): | |||
| """Compute the entropic regularized wasserstein barycenter of distributions A | |||
| """Compute the Wasserstein barycenter of distributions A | |||
ot/lp/cvx.py
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| Parameters | ||
| ---------- | ||
| data_positions : list of (k_i,d) np.ndarray |
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names in the documentation different from the code : data_positions vs measures_locations
ot/lp/cvx.py
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| Stop threshol on error (>0) | ||
| verbose : bool, optional | ||
| Print information along iterations | ||
| log : bool, optional |
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missing log parameter in the function.
would be nice to return the list of the displacement_square_norm along the iteration in a dictionnary if log=True (similar behavior as barycenter function above that retruns a log)
ot/lp/cvx.py
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| iter_count += 1 | ||
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| return X |
ot/lp/cvx.py
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| @@ -10,6 +10,7 @@ | |||
| import numpy as np | |||
| import scipy as sp | |||
| import scipy.sparse as sps | |||
| import ot | |||
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you shouldn't import pot inside a module.
something with relative path like
from .__init__ import emdis far better since it imports the emd function from the __init__.py
ot/lp/cvx.py
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| @@ -144,3 +145,83 @@ def barycenter(A, M, weights=None, verbose=False, log=False, solver='interior-po | |||
| return b, sol | |||
| else: | |||
| return b | |||
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| def free_support_barycenter(measures_locations, measures_weights, X_init, b, weights=None, numItermax=100, stopThr=1e-6, verbose=False): | |||
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also do b=None if the weights are supposed uniform (needs test an initialization in the function)
| X_init = np.random.normal(0., 1., (k, d)) | ||
| b = np.ones((k,)) / k | ||
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| X = ot.lp.cvx.free_support_barycenter(measures_locations, measures_weights, X_init, b) |
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ot.lp.cvx.free_support_barycenter is very long.
you should import the function in ot.lp __init__.py and add it to __all__ like barycenter so that you can do ot.lp.free_support_barycenter
2-Wasserstein Barycenter algorithm with an example script. Optimization is carried over the locations of the support (not the weights). Only the unregularized case now. Regularized version will come soon.