本文整理汇总了Python中sklearn.linear_model.ridge.RidgeClassifierCV.predict方法的典型用法代码示例。如果您正苦于以下问题：Python RidgeClassifierCV.predict方法的具体用法？Python RidgeClassifierCV.predict怎么用？Python RidgeClassifierCV.predict使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类sklearn.linear_model.ridge.RidgeClassifierCV的用法示例。
在下文中一共展示了RidgeClassifierCV.predict方法的3个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: test_class_weights_cv
# 需要导入模块: from sklearn.linear_model.ridge import RidgeClassifierCV [as 别名]
# 或者: from sklearn.linear_model.ridge.RidgeClassifierCV import predict [as 别名]
def test_class_weights_cv():
# Test class weights for cross validated ridge classifier.
X = np.array([[-1.0, -1.0], [-1.0, 0], [-0.8, -1.0], [1.0, 1.0], [1.0, 0.0]])
y = [1, 1, 1, -1, -1]
clf = RidgeClassifierCV(class_weight=None, alphas=[0.01, 0.1, 1])
clf.fit(X, y)
# we give a small weights to class 1
clf = RidgeClassifierCV(class_weight={1: 0.001}, alphas=[0.01, 0.1, 1, 10])
clf.fit(X, y)
assert_array_equal(clf.predict([[-0.2, 2]]), np.array([-1]))示例2: _test_ridge_classifiers
# 需要导入模块: from sklearn.linear_model.ridge import RidgeClassifierCV [as 别名]
# 或者: from sklearn.linear_model.ridge.RidgeClassifierCV import predict [as 别名]
def _test_ridge_classifiers(filter_):
n_classes = np.unique(y_iris).shape[0]
n_features = X_iris.shape[1]
for reg in (RidgeClassifier(), RidgeClassifierCV()):
reg.fit(filter_(X_iris), y_iris)
assert_equal(reg.coef_.shape, (n_classes, n_features))
y_pred = reg.predict(filter_(X_iris))
assert_greater(np.mean(y_iris == y_pred), .79)
cv = KFold(5)
reg = RidgeClassifierCV(cv=cv)
reg.fit(filter_(X_iris), y_iris)
y_pred = reg.predict(filter_(X_iris))
assert_true(np.mean(y_iris == y_pred) >= 0.8)示例3: _test_ridge_classifiers
# 需要导入模块: from sklearn.linear_model.ridge import RidgeClassifierCV [as 别名]
# 或者: from sklearn.linear_model.ridge.RidgeClassifierCV import predict [as 别名]
def _test_ridge_classifiers(filter_):
n_classes = np.unique(y_iris).shape[0]
n_features = X_iris.shape[1]
for clf in (RidgeClassifier(), RidgeClassifierCV()):
clf.fit(filter_(X_iris), y_iris)
assert_equal(clf.coef_.shape, (n_classes, n_features))
y_pred = clf.predict(filter_(X_iris))
assert_greater(np.mean(y_iris == y_pred), .79)
n_samples = X_iris.shape[0]
cv = KFold(n_samples, 5)
clf = RidgeClassifierCV(cv=cv)
clf.fit(filter_(X_iris), y_iris)
y_pred = clf.predict(filter_(X_iris))
assert_true(np.mean(y_iris == y_pred) >= 0.8)