stabThr and pep8

Author: ncourty

ot/bregman.py

@@ -919,7 +919,7 @@ def barycenter(A, M, reg, weights=None, numItermax=1000,
         return geometricBar(weights, UKv)
 
 
-def convolutional_barycenter2d(A, reg, weights=None, numItermax=10000, stopThr=1e-9, verbose=False, log=False):
+def convolutional_barycenter2d(A, reg, weights=None, numItermax=10000, stopThr=1e-9, stabThr=1e-30, verbose=False, log=False):
     """Compute the entropic regularized wasserstein barycenter of distributions A 
     where A is a collection of 2D images. 
 
@@ -948,6 +948,8 @@ def convolutional_barycenter2d(A, reg, weights=None, numItermax=10000, stopThr=1
         Max number of iterations
     stopThr : float, optional
         Stop threshol on error (>0)
+    stabThr : float, optional
+        Stabilization threshold to avoid numerical precision issue 
     verbose : bool, optional
         Print information along iterations
     log : bool, optional
@@ -983,7 +985,6 @@ def convolutional_barycenter2d(A, reg, weights=None, numItermax=10000, stopThr=1
     b = np.zeros_like(A[0, :, :])
     U = np.ones_like(A)
     KV = np.ones_like(A)
-    threshold = 1e-30  # in order to avoids numerical precision issues
 
     cpt = 0
     err = 1
@@ -993,7 +994,7 @@ def convolutional_barycenter2d(A, reg, weights=None, numItermax=10000, stopThr=1
     [Y, X] = np.meshgrid(t, t)
     xi1 = np.exp(-(X - Y)**2 / reg)
 
-    def K(x): 
+    def K(x):
         return np.dot(np.dot(xi1, x), xi1)
 
     while (err > stopThr and cpt < numItermax):
@@ -1003,11 +1004,11 @@ def K(x):
 
         b = np.zeros_like(A[0, :, :])
         for r in range(A.shape[0]):
-            KV[r, :, :] = K(A[r, :, :] / np.maximum(threshold, K(U[r, :, :])))
-            b += weights[r] * np.log(np.maximum(threshold, U[r, :, :] * KV[r, :, :]))
+            KV[r, :, :] = K(A[r, :, :] / np.maximum(stabThr, K(U[r, :, :])))
+            b += weights[r] * np.log(np.maximum(stabThr, U[r, :, :] * KV[r, :, :]))
         b = np.exp(b)
         for r in range(A.shape[0]):
-            U[r, :, :] = b / np.maximum(threshold, KV[r, :, :])
+            U[r, :, :] = b / np.maximum(stabThr, KV[r, :, :])
 
         if cpt % 10 == 1:
             err = np.sum(np.abs(bold - b))