ii

git-svn-id: https://scikit-learn.svn.sourceforge.net/svnroot/scikit-learn/trunk@711 22fbfee3-77ab-4535-9bad-27d1bd3bc7d8

scikits/learn/svm.py

@@ -8,14 +8,6 @@ class BaseLibsvm(object):
     support vector machine classification and regression.
 
     Should not be used directly, use derived classes instead
-
-    Parameters
-    ----------
-    X : array-like, shape = [N, D]
-        It will be converted to a floating-point array.
-    y : array, shape = [N]
-        target vector relative to X
-        It will be converted to a floating-point array.
     """
     support_ = np.empty((0,0), dtype=np.float64, order='C')
     dual_coef_ = np.empty((0,0), dtype=np.float64, order='C')
@@ -83,9 +75,8 @@ class BaseLibsvm(object):
         test vectors T.
 
         For a classification model, the predicted class for each
-        sample in T is returned.
-        For a regression model, the function value of T calculated is
-        returned.
+        sample in T is returned.  For a regression model, the function
+        value of T calculated is returned.
 
         For an one-class model, +1 or -1 is returned.
 
@@ -136,6 +127,14 @@ class BaseLibsvm(object):
                       self.nSV_, self.label_,
                       self.probA_, self.probB_)
 
+
+    @property
+    def coef_(self):
+        if self._kernel_types[self.kernel] != 'linear':
+            raise NotImplementedError('coef_ is only available when using a linear kernel')
+        return np.dot(self.dual_coef_, self.support_)
+
+
 ###
 # Public API
 # No processing should go into these classes
@@ -222,12 +221,6 @@ class SVC(BaseLibsvm):
                          cache_size, eps, C, nr_weight, nu, p,
                          shrinking, probability)
 
-    @property
-    def coef_(self):
-        if self._kernel_types[self.kernel] != 'linear':
-            raise NotImplementedError('coef_ is only available when using a linear kernel')
-        return np.dot(self.dual_coef_, self.support_)
-
 class SVR(BaseLibsvm):
     """
     Support Vector Regression.

scikits/learn/tests/test_svm.py

@@ -56,16 +56,15 @@ def test_SVR():
     TODO: simplify this. btw, is it correct ?
     """
 
-    clf = svm.SVR()
+    clf = svm.SVR(kernel='linear')
     clf.fit(X, Y)
     pred = clf.predict(T)
 
-    assert_array_almost_equal(clf.dual_coef_,
-                              [[-0.01441007, -0.51530605, -0.01365979,
-                                0.51569493, 0.01387495, 0.01380604]])
-    print clf.support_
-    assert_array_almost_equal(clf.support_, X)
-    assert_array_almost_equal(pred,[ 1.10001274,  1.86682485,  1.73300377])
+    assert_array_almost_equal(clf.dual_coef_, [[-0.1, 0.1]])
+    assert_array_almost_equal(clf.coef_, [[0.2, 0.2]])
+    assert_array_almost_equal(clf.support_, [[-1, -1], [1, 1]])
+    assert_array_almost_equal(clf.intercept_, [1.5])
+    assert_array_almost_equal(pred, [1.1, 2.3, 2.5])
 
 
 def test_oneclass():
@@ -134,7 +133,9 @@ def test_error():
 def test_LinearSVC():
     clf = svm.LinearSVC()
     clf.fit(X, Y)
+
     assert_array_equal(clf.predict(T), true_result)
+    assert_array_almost_equal(clf.intercept_, [0])
 
     # the same with l1 penalty
     clf = svm.LinearSVC(penalty='L1', dual=False)