Python sklearn.datasets.make_spd_matrix() Examples

The following are 5 code examples for showing how to use sklearn.datasets.make_spd_matrix(). 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.

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Example 1
Project: qiskit-aqua   Author: Qiskit   File: portfolio.py    License: Apache License 2.0 6 votes vote down vote up
def random_model(n, seed=None):
    """Generate random model (mu, sigma) for portfolio optimization problem.

    Args:
        n (int): number of assets.
        seed (int or None): random seed - if None, will not initialize.

    Returns:
        numpy.narray: expected return vector
        numpy.ndarray: covariance matrix

    """
    if seed:
        aqua_globals.random_seed = seed

    # draw random return values between [0, 1]
    m_u = aqua_globals.random.uniform(size=n, low=0, high=1)

    # construct positive semi-definite covariance matrix
    sigma = make_spd_matrix(n)

    return m_u, sigma 
Example 2
Project: hmmlearn   Author: hmmlearn   File: __init__.py    License: BSD 3-Clause "New" or "Revised" License 6 votes vote down vote up
def make_covar_matrix(covariance_type, n_components, n_features,
                      random_state=None):
    mincv = 0.1
    prng = check_random_state(random_state)
    if covariance_type == 'spherical':
        return (mincv + mincv * prng.random_sample((n_components,))) ** 2
    elif covariance_type == 'tied':
        return (make_spd_matrix(n_features)
                + mincv * np.eye(n_features))
    elif covariance_type == 'diag':
        return (mincv + mincv *
                prng.random_sample((n_components, n_features))) ** 2
    elif covariance_type == 'full':
        return np.array([
            (make_spd_matrix(n_features, random_state=prng)
             + mincv * np.eye(n_features))
            for x in range(n_components)
        ]) 
Example 3
Project: Mastering-Elasticsearch-7.0   Author: PacktPublishing   File: test_samples_generator.py    License: MIT License 5 votes vote down vote up
def test_make_spd_matrix():
    X = make_spd_matrix(n_dim=5, random_state=0)

    assert_equal(X.shape, (5, 5), "X shape mismatch")
    assert_array_almost_equal(X, X.T)

    from numpy.linalg import eig
    eigenvalues, _ = eig(X)
    assert_array_equal(eigenvalues > 0, np.array([True] * 5),
                       "X is not positive-definite") 
Example 4
Project: pyDML   Author: jlsuarezdiaz   File: test_work.py    License: GNU General Public License v3.0 5 votes vote down vote up
def test_Metric(self):
        np.random.seed(28)
        for d in [iris, wine, breast_cancer]:
            X, y = d()
            n, d = X.shape
            M = make_spd_matrix(d)

            metric = Metric(M)
            metric.fit(X, y)
            L = metric.transformer()
            assert_array_almost_equal(L.T.dot(L), M)

            LX1 = metric.transform()
            LX2 = metric.transform(X)

            dl1 = pdist(LX1)
            dl2 = pdist(LX2)
            dm = pdist(X, metric='mahalanobis', VI=M)  # CHecking that d_M = d_L

            assert_array_almost_equal(dm, dl1)
            assert_array_almost_equal(dm, dl2)

            d_, d = L.shape
            e_, e = M.shape

            assert_equal(d, e_)
            assert_equal(d, e)
            assert_equal(d, X.shape[1]) 
Example 5
Project: twitter-stock-recommendation   Author: alvarobartt   File: test_samples_generator.py    License: MIT License 5 votes vote down vote up
def test_make_spd_matrix():
    X = make_spd_matrix(n_dim=5, random_state=0)

    assert_equal(X.shape, (5, 5), "X shape mismatch")
    assert_array_almost_equal(X, X.T)

    from numpy.linalg import eig
    eigenvalues, _ = eig(X)
    assert_array_equal(eigenvalues > 0, np.array([True] * 5),
                       "X is not positive-definite")