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Python linear_model.OrthogonalMatchingPursuit方法代碼示例

本文整理匯總了Python中sklearn.linear_model.OrthogonalMatchingPursuit方法的典型用法代碼示例。如果您正苦於以下問題：Python linear_model.OrthogonalMatchingPursuit方法的具體用法？Python linear_model.OrthogonalMatchingPursuit怎麽用？Python linear_model.OrthogonalMatchingPursuit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.OrthogonalMatchingPursuit方法的4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_orthogonal_matching_pursuit

# 需要導入模塊: from sklearn import linear_model [as 別名]
# 或者: from sklearn.linear_model import OrthogonalMatchingPursuit [as 別名]
def test_model_orthogonal_matching_pursuit(self):
        model, X = fit_regression_model(
            linear_model.OrthogonalMatchingPursuit())
        model_onnx = convert_sklearn(
            model, "orthogonal matching pursuit",
            [("input", FloatTensorType([None, X.shape[1]]))])
        self.assertIsNotNone(model_onnx)
        dump_data_and_model(
            X,
            model,
            model_onnx,
            verbose=False,
            basename="SklearnOrthogonalMatchingPursuit-Dec4",
            allow_failure="StrictVersion("
            "onnxruntime.__version__)"
            "<= StrictVersion('0.2.1')",
        )
開發者ID:onnx，項目名稱:sklearn-onnx，代碼行數:19，代碼來源:test_sklearn_glm_regressor_converter.py

示例2: omp_estimator

# 需要導入模塊: from sklearn import linear_model [as 別名]
# 或者: from sklearn.linear_model import OrthogonalMatchingPursuit [as 別名]
def omp_estimator(hparams):
    """OMP estimator"""
    omp_est = OrthogonalMatchingPursuit(n_nonzero_coefs=hparams.omp_k)
    def estimator(A_val, y_batch_val, hparams):
        x_hat_batch = []
        for i in range(hparams.batch_size):
            y_val = y_batch_val[i]
            omp_est.fit(A_val.T, y_val.reshape(hparams.num_measurements))
            x_hat = omp_est.coef_
            x_hat = np.reshape(x_hat, [-1])
            x_hat = np.maximum(np.minimum(x_hat, 1), 0)
            x_hat_batch.append(x_hat)
        x_hat_batch = np.asarray(x_hat_batch)
        return x_hat_batch
    return estimator
開發者ID:AshishBora，項目名稱:csgm，代碼行數:17，代碼來源:mnist_estimators.py

示例3: __init__

# 需要導入模塊: from sklearn import linear_model [as 別名]
# 或者: from sklearn.linear_model import OrthogonalMatchingPursuit [as 別名]
def __init__(self, options):
        self.handle_options(options)

        params = options.get('params', {})
        out_params = convert_params(
            params,
            floats=['tol'],
            strs=['kernel'],
            ints=['n_nonzero_coefs'],
            bools=['fit_intercept', 'normalize'],
        )

        self.estimator = _OrthogonalMatchingPursuit(**out_params)
開發者ID:splunk，項目名稱:mltk-algo-contrib，代碼行數:15，代碼來源:OrthogonalMatchingPursuit.py

示例4: test_objectmapper

# 需要導入模塊: from sklearn import linear_model [as 別名]
# 或者: from sklearn.linear_model import OrthogonalMatchingPursuit [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)
開發者ID:pandas-ml，項目名稱:pandas-ml，代碼行數:43，代碼來源:test_linear_model.py


注：本文中的sklearn.linear_model.OrthogonalMatchingPursuit方法示例由純淨天空整理自Github/MSDocs等開源代碼及文檔管理平台，相關代碼片段篩選自各路編程大神貢獻的開源項目，源碼版權歸原作者所有，傳播和使用請參考對應項目的License；未經允許，請勿轉載。