Python matplotlib.mlab.GaussianKDE() Examples

def test_kde_bandwidth_method(self):

        np.random.seed(8765678)
        n_basesample = 50
        xn = np.random.randn(n_basesample)

        # Default
        gkde = mlab.GaussianKDE(xn)
        # Supply a callable
        gkde2 = mlab.GaussianKDE(xn, 'scott')
        # Supply a scalar
        gkde3 = mlab.GaussianKDE(xn, bw_method=gkde.factor)

        xs = np.linspace(-7, 7, 51)
        kdepdf = gkde.evaluate(xs)
        kdepdf2 = gkde2.evaluate(xs)
        assert kdepdf.all() == kdepdf2.all()
        kdepdf3 = gkde3.evaluate(xs)
        assert kdepdf.all() == kdepdf3.all() 

def test_kde_bandwidth_method(self):

        np.random.seed(8765678)
        n_basesample = 50
        xn = np.random.randn(n_basesample)

        # Default
        gkde = mlab.GaussianKDE(xn)
        # Supply a callable
        gkde2 = mlab.GaussianKDE(xn, 'scott')
        # Supply a scalar
        gkde3 = mlab.GaussianKDE(xn, bw_method=gkde.factor)

        xs = np.linspace(-7, 7, 51)
        kdepdf = gkde.evaluate(xs)
        kdepdf2 = gkde2.evaluate(xs)
        assert_almost_equal(kdepdf.all(), kdepdf2.all())
        kdepdf3 = gkde3.evaluate(xs)
        assert_almost_equal(kdepdf.all(), kdepdf3.all()) 

def test_kde_bandwidth_method(self):

        np.random.seed(8765678)
        n_basesample = 50
        xn = np.random.randn(n_basesample)

        # Default
        gkde = mlab.GaussianKDE(xn)
        # Supply a callable
        gkde2 = mlab.GaussianKDE(xn, 'scott')
        # Supply a scalar
        gkde3 = mlab.GaussianKDE(xn, bw_method=gkde.factor)

        xs = np.linspace(-7, 7, 51)
        kdepdf = gkde.evaluate(xs)
        kdepdf2 = gkde2.evaluate(xs)
        assert kdepdf.all() == kdepdf2.all()
        kdepdf3 = gkde3.evaluate(xs)
        assert kdepdf.all() == kdepdf3.all() 

def test_kde_bandwidth_method(self):

        np.random.seed(8765678)
        n_basesample = 50
        xn = np.random.randn(n_basesample)

        # Default
        gkde = mlab.GaussianKDE(xn)
        # Supply a callable
        gkde2 = mlab.GaussianKDE(xn, 'scott')
        # Supply a scalar
        gkde3 = mlab.GaussianKDE(xn, bw_method=gkde.factor)

        xs = np.linspace(-7, 7, 51)
        kdepdf = gkde.evaluate(xs)
        kdepdf2 = gkde2.evaluate(xs)
        assert kdepdf.all() == kdepdf2.all()
        kdepdf3 = gkde3.evaluate(xs)
        assert kdepdf.all() == kdepdf3.all() 

def test_kde_bandwidth_method(self):

        np.random.seed(8765678)
        n_basesample = 50
        xn = np.random.randn(n_basesample)

        # Default
        gkde = mlab.GaussianKDE(xn)
        # Supply a callable
        gkde2 = mlab.GaussianKDE(xn, 'scott')
        # Supply a scalar
        gkde3 = mlab.GaussianKDE(xn, bw_method=gkde.factor)

        xs = np.linspace(-7, 7, 51)
        kdepdf = gkde.evaluate(xs)
        kdepdf2 = gkde2.evaluate(xs)
        assert_almost_equal(kdepdf.all(), kdepdf2.all())
        kdepdf3 = gkde3.evaluate(xs)
        assert_almost_equal(kdepdf.all(), kdepdf3.all()) 

def test_wrong_bw_method(self):
        """Test the error message that should be called when bw is invalid."""
        np.random.seed(8765678)
        n_basesample = 50
        data = np.random.randn(n_basesample)
        with pytest.raises(ValueError):
            mlab.GaussianKDE(data, bw_method="invalid") 

def test_scalar_empty_dataset(self):
        """Use an empty array as the dataset and test the scalar's cov factor
        """
        with pytest.raises(ValueError):
            mlab.GaussianKDE([], bw_method=5) 

def test_scalar_covariance_dataset(self):
        """Use a dataset and test a scalar's cov factor
        """
        np.random.seed(8765678)
        n_basesample = 50
        multidim_data = [np.random.randn(n_basesample) for i in range(5)]

        kde = mlab.GaussianKDE(multidim_data, bw_method=0.5)
        assert kde.covariance_factor() == 0.5 

def test_callable_singledim_dataset(self):
        """Use a single-dimensional array as the dataset and test the
        callable's cov factor"""
        np.random.seed(8765678)
        n_basesample = 50
        multidim_data = np.random.randn(n_basesample)

        kde = mlab.GaussianKDE(multidim_data, bw_method='silverman')
        y_expected = 0.48438841363348911
        assert_almost_equal(kde.covariance_factor(), y_expected, 7) 

def test_wrong_bw_method(self):
        """Test the error message that should be called when bw is invalid."""
        np.random.seed(8765678)
        n_basesample = 50
        data = np.random.randn(n_basesample)
        with pytest.raises(ValueError):
            mlab.GaussianKDE(data, bw_method="invalid") 

def test_evaluate_equal_dim_and_num_lt(self):
        """Test when line 3810 fails"""
        x1 = np.arange(3, 10, 2)
        x2 = np.arange(3, 8, 2)
        kde = mlab.GaussianKDE(x1)
        y_expected = [0.08797252, 0.11774109, 0.11774109]
        y = kde.evaluate(x2)
        np.testing.assert_array_almost_equal(y, y_expected, 7) 

def test_scott_singledim_dataset(self):
        """Use a single-dimensional array as the dataset and test scott's
        output"""
        x1 = np.array([-7, -5, 1, 4, 5])
        mygauss = mlab.GaussianKDE(x1, "scott")
        y_expected = 0.72477966367769553
        assert_almost_equal(mygauss.covariance_factor(), y_expected, 7) 

def test_scalar_empty_dataset(self):
        """Use an empty array as the dataset and test the scalar's cov factor
        """
        with pytest.raises(ValueError):
            mlab.GaussianKDE([], bw_method=5) 

def test_scalar_covariance_dataset(self):
        """Use a dataset and test a scalar's cov factor
        """
        np.random.seed(8765678)
        n_basesample = 50
        multidim_data = [np.random.randn(n_basesample) for i in range(5)]

        kde = mlab.GaussianKDE(multidim_data, bw_method=0.5)
        assert kde.covariance_factor() == 0.5 

def test_callable_singledim_dataset(self):
        """Use a single-dimensional array as the dataset and test the
        callable's cov factor"""
        np.random.seed(8765678)
        n_basesample = 50
        multidim_data = np.random.randn(n_basesample)

        kde = mlab.GaussianKDE(multidim_data, bw_method='silverman')
        y_expected = 0.48438841363348911
        assert_almost_equal(kde.covariance_factor(), y_expected, 7) 

def test_evaluate_inv_dim(self):
        """ Invert the dimensions. i.e., Give the dataset a dimension of
        1 [3,2,4], and the points will have a dimension of 3 [[3],[2],[4]].
        ValueError should be raised"""
        np.random.seed(8765678)
        n_basesample = 50
        multidim_data = np.random.randn(n_basesample)
        kde = mlab.GaussianKDE(multidim_data)
        x2 = [[1], [2], [3]]
        with pytest.raises(ValueError):
            kde.evaluate(x2) 

def test_evaluate_inv_dim(self):
        """ Invert the dimensions. i.e., Give the dataset a dimension of
        1 [3,2,4], and the points will have a dimension of 3 [[3],[2],[4]].
        ValueError should be raised"""
        np.random.seed(8765678)
        n_basesample = 50
        multidim_data = np.random.randn(n_basesample)
        kde = mlab.GaussianKDE(multidim_data)
        x2 = [[1], [2], [3]]
        with pytest.raises(ValueError):
            kde.evaluate(x2) 

def test_scott_singledim_dataset(self):
        """Use a single-dimensional array as the dataset and test scott's
        output"""
        x1 = np.array([-7, -5, 1, 4, 5])
        mygauss = mlab.GaussianKDE(x1, "scott")
        y_expected = 0.72477966367769553
        assert_almost_equal(mygauss.covariance_factor(), y_expected, 7) 

def test_scott_multidim_dataset(self):
        """Use a multi-dimensional array as the dataset and test scott's output
        """
        x1 = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
        with pytest.raises(np.linalg.LinAlgError):
            mlab.GaussianKDE(x1, "scott") 

def test_silverman_singledim_dataset(self):
        """Use a single dimension list as the dataset and test silverman's
        output."""
        x1 = np.array([-7, -5, 1, 4, 5])
        mygauss = mlab.GaussianKDE(x1, "silverman")
        y_expected = 0.76770389927475502
        assert_almost_equal(mygauss.covariance_factor(), y_expected, 7) 

def test_single_dataset_element(self):
        """Pass a single dataset element into the GaussianKDE class."""
        with pytest.raises(ValueError):
            mlab.GaussianKDE([42]) 

def test_no_data(self):
        """Pass no data into the GaussianKDE class."""
        with pytest.raises(ValueError):
            mlab.GaussianKDE([]) 

def test_gaussian_kde_covariance_caching(self):
        x1 = np.array([-7, -5, 1, 4, 5], dtype=float)
        xs = np.linspace(-10, 10, num=5)
        # These expected values are from scipy 0.10, before some changes to
        # gaussian_kde. They were not compared with any external reference.
        y_expected = [0.02463386, 0.04689208, 0.05395444, 0.05337754,
                      0.01664475]

        # set it to the default bandwidth.
        kde2 = mlab.GaussianKDE(x1, 'scott')
        y2 = kde2(xs)

        np.testing.assert_array_almost_equal(y_expected, y2, decimal=7) 

def test_kde_integer_input(self):
        """Regression test for #1181."""
        x1 = np.arange(5)
        kde = mlab.GaussianKDE(x1)
        y_expected = [0.13480721, 0.18222869, 0.19514935, 0.18222869,
                      0.13480721]
        np.testing.assert_array_almost_equal(kde(x1), y_expected, decimal=6) 

def test_evaluate_equal_dim_and_num_lt(self):
        """Test when line 3810 fails"""
        x1 = np.arange(3, 10, 2)
        x2 = np.arange(3, 8, 2)
        kde = mlab.GaussianKDE(x1)
        y_expected = [0.08797252, 0.11774109, 0.11774109]
        y = kde.evaluate(x2)
        np.testing.assert_array_almost_equal(y, y_expected, 7)


#*****************************************************************
#***************************************************************** 

def test_evaluate_dim_and_num(self):
        """ Tests if evaluated against a one by one array"""
        x1 = np.arange(3, 10, 2)
        x2 = np.array([3])
        kde = mlab.GaussianKDE(x1)
        y_expected = [0.08797252]
        y = kde.evaluate(x2)
        np.testing.assert_array_almost_equal(y, y_expected, 7) 

def test_evaluate_inv_dim(self):
        """ Invert the dimensions. i.e., Give the dataset a dimension of
        1 [3,2,4], and the points will have a dimension of 3 [[3],[2],[4]].
        ValueError should be raised"""
        np.random.seed(8765678)
        n_basesample = 50
        multidim_data = np.random.randn(n_basesample)
        kde = mlab.GaussianKDE(multidim_data)
        x2 = [[1], [2], [3]]
        assert_raises(ValueError, kde.evaluate, x2) 

def test_evaluate_diff_dim(self):
        """Test the evaluate method when the dim's of dataset and points are
        different dimensions"""
        x1 = np.arange(3, 10, 2)
        kde = mlab.GaussianKDE(x1)
        x2 = np.arange(3, 12, 2)
        y_expected = [
            0.08797252, 0.11774109, 0.11774109, 0.08797252, 0.0370153
        ]
        y = kde.evaluate(x2)
        np.testing.assert_array_almost_equal(y, y_expected, 7) 

def test_wrong_bw_method(self):
        """Test the error message that should be called when bw is invalid."""
        np.random.seed(8765678)
        n_basesample = 50
        data = np.random.randn(n_basesample)
        assert_raises(ValueError, mlab.GaussianKDE, data, bw_method="invalid") 

def test_callable_singledim_dataset(self):
        """Use a single-dimensional array as the dataset and test the
        callable's cov factor"""
        np.random.seed(8765678)
        n_basesample = 50
        multidim_data = np.random.randn(n_basesample)

        kde = mlab.GaussianKDE(multidim_data, bw_method='silverman')
        y_expected = 0.48438841363348911
        assert_almost_equal(kde.covariance_factor(), y_expected, 7)