本文整理匯總了Python中sklearn.linear_model.LarsCV方法的典型用法代碼示例。如果您正苦於以下問題：Python linear_model.LarsCV方法的具體用法？Python linear_model.LarsCV怎麽用？Python linear_model.LarsCV使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類sklearn.linear_model的用法示例。
在下文中一共展示了linear_model.LarsCV方法的3個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: test_model_lars_cv
# 需要導入模塊: from sklearn import linear_model [as 別名]
# 或者: from sklearn.linear_model import LarsCV [as 別名]
def test_model_lars_cv(self):
model, X = fit_regression_model(linear_model.LarsCV())
model_onnx = convert_sklearn(
model, "lars",
[("input", FloatTensorType([None, X.shape[1]]))])
self.assertIsNotNone(model_onnx)
dump_data_and_model(
X,
model,
model_onnx,
basename="SklearnLarsCV-Dec4",
allow_failure="StrictVersion("
"onnxruntime.__version__)"
"<= StrictVersion('0.2.1')",
) 示例2: test_objectmapper
# 需要導入模塊: from sklearn import linear_model [as 別名]
# 或者: from sklearn.linear_model import LarsCV [as 別名]
def test_objectmapper(self):
df = pdml.ModelFrame([])
self.assertIs(df.linear_model.ARDRegression, lm.ARDRegression)
self.assertIs(df.linear_model.BayesianRidge, lm.BayesianRidge)
self.assertIs(df.linear_model.ElasticNet, lm.ElasticNet)
self.assertIs(df.linear_model.ElasticNetCV, lm.ElasticNetCV)
self.assertIs(df.linear_model.HuberRegressor, lm.HuberRegressor)
self.assertIs(df.linear_model.Lars, lm.Lars)
self.assertIs(df.linear_model.LarsCV, lm.LarsCV)
self.assertIs(df.linear_model.Lasso, lm.Lasso)
self.assertIs(df.linear_model.LassoCV, lm.LassoCV)
self.assertIs(df.linear_model.LassoLars, lm.LassoLars)
self.assertIs(df.linear_model.LassoLarsCV, lm.LassoLarsCV)
self.assertIs(df.linear_model.LassoLarsIC, lm.LassoLarsIC)
self.assertIs(df.linear_model.LinearRegression, lm.LinearRegression)
self.assertIs(df.linear_model.LogisticRegression, lm.LogisticRegression)
self.assertIs(df.linear_model.LogisticRegressionCV, lm.LogisticRegressionCV)
self.assertIs(df.linear_model.MultiTaskLasso, lm.MultiTaskLasso)
self.assertIs(df.linear_model.MultiTaskElasticNet, lm.MultiTaskElasticNet)
self.assertIs(df.linear_model.MultiTaskLassoCV, lm.MultiTaskLassoCV)
self.assertIs(df.linear_model.MultiTaskElasticNetCV, lm.MultiTaskElasticNetCV)
self.assertIs(df.linear_model.OrthogonalMatchingPursuit, lm.OrthogonalMatchingPursuit)
self.assertIs(df.linear_model.OrthogonalMatchingPursuitCV, lm.OrthogonalMatchingPursuitCV)
self.assertIs(df.linear_model.PassiveAggressiveClassifier, lm.PassiveAggressiveClassifier)
self.assertIs(df.linear_model.PassiveAggressiveRegressor, lm.PassiveAggressiveRegressor)
self.assertIs(df.linear_model.Perceptron, lm.Perceptron)
self.assertIs(df.linear_model.RandomizedLasso, lm.RandomizedLasso)
self.assertIs(df.linear_model.RandomizedLogisticRegression, lm.RandomizedLogisticRegression)
self.assertIs(df.linear_model.RANSACRegressor, lm.RANSACRegressor)
self.assertIs(df.linear_model.Ridge, lm.Ridge)
self.assertIs(df.linear_model.RidgeClassifier, lm.RidgeClassifier)
self.assertIs(df.linear_model.RidgeClassifierCV, lm.RidgeClassifierCV)
self.assertIs(df.linear_model.RidgeCV, lm.RidgeCV)
self.assertIs(df.linear_model.SGDClassifier, lm.SGDClassifier)
self.assertIs(df.linear_model.SGDRegressor, lm.SGDRegressor)
self.assertIs(df.linear_model.TheilSenRegressor, lm.TheilSenRegressor) 示例3: test_lars_precompute
# 需要導入模塊: from sklearn import linear_model [as 別名]
# 或者: from sklearn.linear_model import LarsCV [as 別名]
def test_lars_precompute():
# Check for different values of precompute
X, y = diabetes.data, diabetes.target
G = np.dot(X.T, X)
for classifier in [linear_model.Lars, linear_model.LarsCV,
linear_model.LassoLarsIC]:
clf = classifier(precompute=G)
output_1 = ignore_warnings(clf.fit)(X, y).coef_
for precompute in [True, False, 'auto', None]:
clf = classifier(precompute=precompute)
output_2 = clf.fit(X, y).coef_
assert_array_almost_equal(output_1, output_2, decimal=8)