Python sklearn.linear_model.OrthogonalMatchingPursuit() Examples

The following are 4 code examples of sklearn.linear_model.OrthogonalMatchingPursuit(). 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 also want to check out all available functions/classes of the module sklearn.linear_model , or try the search function .
Example #1
Source File: test_sklearn_glm_regressor_converter.py    From sklearn-onnx with 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
Source File: mnist_estimators.py    From csgm with 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
Source File: OrthogonalMatchingPursuit.py    From mltk-algo-contrib with 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
Source File: test_linear_model.py    From pandas-ml with 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)