Python sklearn.datasets.make_spd_matrix() Examples

The following are 5 code examples of sklearn.datasets.make_spd_matrix(). 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.datasets , or try the search function .
Example #1
Source File: portfolio.py    From qiskit-aqua with 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
Source File: __init__.py    From hmmlearn with 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
Source File: test_samples_generator.py    From Mastering-Elasticsearch-7.0 with 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
Source File: test_work.py    From pyDML with 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
Source File: test_samples_generator.py    From twitter-stock-recommendation with 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")