Function _make_data

mne/decoding/tests/test_base.py:58–96

Generate some testing data. Parameters ---------- n_samples : int The number of samples. n_features : int The number of features. n_targets : int The number of targets. Returns ------- X : ndarray, shape (n_samples, n_features) The me

(n_samples=1000, n_features=5, n_targets=3)

def _make_data(n_samples=1000, n_features=5, n_targets=3):
    """Generate some testing data.

    Parameters
    ----------
    n_samples : int
        The number of samples.
    n_features : int
        The number of features.
    n_targets : int
        The number of targets.

    Returns
    -------
    X : ndarray, shape (n_samples, n_features)
        The measured data.
    Y : ndarray, shape (n_samples, n_targets)
        The latent variables generating the data.
    A : ndarray, shape (n_features, n_targets)
        The forward model, mapping the latent variables (=Y) to the measured
        data (=X).
    """
    # Define Y latent factors
    np.random.seed(0)
    cov_Y = np.eye(n_targets) * 10 + np.random.rand(n_targets, n_targets)
    cov_Y = (cov_Y + cov_Y.T) / 2.0
    mean_Y = np.random.rand(n_targets)
    Y = np.random.multivariate_normal(mean_Y, cov_Y, size=n_samples)

    # The Forward model
    A = np.random.randn(n_features, n_targets)

    X = Y.dot(A.T)
    X += np.random.randn(n_samples, n_features)  # add noise
    X += np.random.rand(n_features)  # Put an offset
    if n_targets == 1:
        Y = Y[:, 0]

    return X, Y, A


def test_get_coef():