Python sklearn.linear_model.OrthogonalMatchingPursuit() Examples

The following are 4 code examples for showing how to use sklearn.linear_model.OrthogonalMatchingPursuit(). These examples are extracted from open source projects. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example.

You may check out the related API usage on the sidebar.

You may also want to check out all available functions/classes of the module sklearn.linear_model , or try the search function .

Example 1
Project: sklearn-onnx   Author: onnx   File: test_sklearn_glm_regressor_converter.py    License: MIT License 6 votes vote down vote up
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')",
        ) 
Example 2
Project: csgm   Author: AshishBora   File: mnist_estimators.py    License: MIT License 5 votes vote down vote up
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 
Example 3
Project: mltk-algo-contrib   Author: splunk   File: OrthogonalMatchingPursuit.py    License: Apache License 2.0 5 votes vote down vote up
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) 
Example 4
Project: pandas-ml   Author: pandas-ml   File: test_linear_model.py    License: BSD 3-Clause "New" or "Revised" License 5 votes vote down vote up
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)