scikit-learn
diff --git a/‎sklearn/svm/classes.py
Lines changed: 12 additions & 2 deletions b/‎sklearn/svm/classes.py
Lines changed: 12 additions & 2 deletions
diff --git a/‎sklearn/svm/tests/test_svm.py
Lines changed: 40 additions & 0 deletions b/‎sklearn/svm/tests/test_svm.py
Lines changed: 40 additions & 0 deletions
@@ -165,7 +
8000
165,7 @@ def __init__(self, penalty='l2', loss='squared_hinge', dual=True, tol=1e-4,
         self.penalty = penalty
         self.loss = loss
 
-    def fit(self, X, y):
+    def fit(self, X, y, sample_weight=None):
         """Fit the model according to the given training data.
 
         Parameters
@@ -177,6 +177,11 @@ def fit(self, X, y):
         y : array-like, shape = [n_samples]
             Target vector relative to X
 
+        sample_weight : array-like, shape = [n_samples], optional
+                        Array of weights that are assigned to individual
+                        samples. If not provided,
+                        then each sample is given unit weight.
+
         Returns
         -------
         self : object
@@ -210,7 +215,7 @@ def fit(self, X, y):
             X, y, self.C, self.fit_intercept, self.intercept_scaling,
             self.class_weight, self.penalty, self.dual, self.verbose,
             self.max_iter, self.tol, self.random_state, self.multi_class,
-            self.loss)
+            self.loss, sample_weight=sample_weight)
 
         if self.multi_class == "crammer_singer" and len(self.classes_) == 2:
             self.coef_ = (self.coef_[1] - self.coef_[0]).reshape(1, -1)
@@ -341,6 +346,11 @@ def fit(self, X, y, sample_weight=None):
         y : array-like, shape = [n_samples]
             Target vector relative to X
 
+        sample_weight : array-like, shape = [n_samples], optional
+                        Array of weights that are assigned to individual
+                        samples. If not provided,
+                        then each sample is given unit weight.
+
         Returns
         -------
         self : object
 
@@ -655,6 +655,46 @@ def test_linearsvc_crammer_singer():
     assert_array_almost_equal(dec_func, cs_clf.decision_function(iris.data))
 
 
+def test_linearsvc_fit_sampleweight():
+    # check correct result when sample_weight is 1
+    # check that SVR(kernel='linear') and LinearSVC() give
+    # comparable results
+
+    # Test basic routines using LinearSVC
+    n_samples = len(X)
+    unit_weight = np.ones(n_samples)
+    clf = svm.LinearSVC(random_state=0).fit(X, Y)
+    clf_unitweight = svm.LinearSVC(random_state=0).fit(X, Y,
+                                                  sample_weight=unit_weight)
+
+    # sanity check, by default should have intercept
+    assert_true(clf_unitweight.fit_intercept)
+    assert_array_almost_equal(clf_unitweight.intercept_, [0], decimal=3)
+
+    # check if same as sample_weight=None
+    assert_array_equal(clf_unitweight.predict(T), clf.predict(T))
+    assert_allclose(np.linalg.norm(clf.coef_),
+                    np.linalg.norm(clf_unitweight.coef_), 1, 0.0001)
+
+    # check that fit(X)  = fit([X1, X2, X3],sample_weight = [n1, n2, n3]) where
+    # X = X1 repeated n1 times, X2 repeated n2 times and so forth
+
+    random_state = check_random_state(0)
+    random_weight = random_state.randint(0, 10, n_samples)
+    lsvc_unflat = svm.LinearSVC(random_state=0).fit(X, Y,
+                                                    sample_weight=random_weight)
+    pred1 = lsvc_unflat.predict(T)
+
+    X_flat = np.repeat(X, random_weight, axis=0)
+    y_flat = np.repeat(Y, random_weight, axis=0)
+    lsvc_flat = svm.LinearSVC(random_state=0).fit(X_flat, y_flat)
+    pred2 = lsvc_flat.predict(T)
+
+    assert_array_equal(pred1, pred2)
+    assert_allclose(np.linalg.norm(lsvc_unflat.coef_),
+                    np.linalg.norm(lsvc_flat.coef_), 1, 0.0001)
+
+
 def test_crammer_singer_binary():
     # Test Crammer-Singer formulation in the binary case
     X, y = make_classification(n_classes=2, random_state=0)