Replaced abbreviated 'w.r.t' to 'with regards to'

Author: eltermann

doc/modules/clustering.rst

@@ -1034,8 +1034,8 @@ Advantages
 Drawbacks
 ~~~~~~~~~
 
-- The previously introduced metrics are **not normalized w.r.t. random
-  labeling**: this means that depending on the number of samples,
+- The previously introduced metrics are **not normalized with regards to
+  random labeling**: this means that depending on the number of samples,
   clusters and ground truth classes, a completely random labeling will
   not always yield the same values for homogeneity, completeness and
   hence v-measure. In particular **random labeling won't yield zero

doc/modules/dp-derivation.rst

@@ -194,8 +194,8 @@ The updates
 
 The updates for mu essentially are just weighted expectations of
 :math:`X` regularized by the prior. We can see this by taking the
-gradient of the bound w.r.t. :math:`\nu_{\mu}` and setting it to zero. The
-gradient is
+gradient of the bound with regards to :math:`\nu_{\mu}` and setting it to zero.
+The gradient is
 
 .. math::
 

doc/modules/ensemble.rst

@@ -629,8 +629,8 @@ the parameter ``loss``:
       target values.
     * Huber (``'huber'``): Another robust loss function that combines
       least squares and least absolute deviation; use ``alpha`` to
-      control the sensitivity w.r.t. outliers (see [F2001]_ for more
-      details).
+      control the sensitivity with regards to outliers (see [F2001]_ for
+      more details).
     * Quantile (``'quantile'``): A loss function for quantile regression.
       Use ``0 < alpha < 1`` to specify the quantile. This loss function
       can be used to create prediction intervals

doc/modules/feature_extraction.rst

@@ -640,7 +640,7 @@ for languages that use white-spaces for word separation as it generates
 significantly less noisy features than the raw ``char`` variant in
 that case. For such languages it can increase both the predictive
 accuracy and convergence speed of classifiers trained using such
-features while retaining the robustness w.r.t. misspellings and
+features while retaining the robustness with regards to misspellings and
 word derivations.
 
 While some local positioning information can be preserved by extracting

examples/applications/plot_species_distribution_modeling.py

@@ -193,7 +193,7 @@ def plot_species_distribution(species=["bradypus_variegatus_0",
         pl.title(species.name)
         pl.axis('equal')
 
-        # Compute AUC w.r.t. background points
+        # Compute AUC with regards to background points
         pred_background = Z[background_points[0], background_points[1]]
         pred_test = clf.decision_function((species.cov_test - mean)
                                           / std)[:, 0]

sklearn/cluster/_k_means.c

@@ -202,8 +202,8 @@ def _mini_batch_update_csr(X, np.ndarray[DOUBLE, ndim=1] x_squared_norms,
             # no new sample: leave this center as it stands
             continue
 
-        # rescale the old center to reflect it previous accumulated
-        # weight w.r.t. the new data that will be incrementally contributed
+        # rescale the old center to reflect it previous accumulated weight
+        # with regards to the new data that will be incrementally contributed
         if compute_squared_diff:
             old_center[:] = centers[center_idx]
         centers[center_idx] *= old_count

sklearn/cluster/_k_means.pyx

@@ -576,7 +576,7 @@ class KMeans(BaseEstimator, ClusterMixin, TransformerMixin):
         Precompute distances (faster but takes more memory).
 
     tol : float, optional default: 1e-4
-        Relative tolerance w.r.t. inertia to declare convergence
+        Relative tolerance with regards to inertia to declare convergence
 
     n_jobs : int
         The number of jobs to use for the computation. This works by breaking

sklearn/cluster/k_means_.py

@@ -14,7 +14,7 @@ def generate_clustered_data(seed=0, n_clusters=3, n_features=2,
     prng = np.random.RandomState(seed)
 
     # the data is voluntary shifted away from zero to check clustering
-    # algorithm robustness w.r.t. non centered data
+    # algorithm robustness with regards to non centered data
     means = np.array([[1,  1, 1, 0],
                       [-1, -1, 0, 1],
                       [1, -1, 1, 1],