def gmm_cause(points, k=4, p1=2, p2=2):
    """Init a root cause with a Gaussian Mixture Model w/ a spherical covariance type."""
    g = GMM(k, covariance_type="spherical")
    g.fit(np.random.randn(300, 1))

    g.means_ = p1 * np.random.randn(k, 1)
    g.covars_ = np.power(abs(p2 * np.random.randn(k, 1) + 1), 2)
    g.weights_ = abs(np.random.rand(k))
    g.weights_ = g.weights_ / sum(g.weights_)
    return g.sample(points)[0].reshape(-1)
def gmm_cause(points, k=4, p1=2, p2=2):
    """Init a root cause with a Gaussian Mixture Model w/ a spherical covariance type."""
    g = GMM(k, covariance_type="spherical")
    g.fit(np.random.randn(300, 1))

    g.means_ = p1 * np.random.randn(k, 1)
    g.covars_ = np.power(abs(p2 * np.random.randn(k, 1) + 1), 2)
    g.weights_ = abs(np.random.rand(k))
    g.weights_ = g.weights_ / sum(g.weights_)
    return g.sample(points)[0].reshape(-1)
def gmm_cause ( points , k = 4 , p1 = 2 , p2 = 2 ) : g = GMM ( k , covariance_type = "spherical" ) g . fit ( np . random . randn ( 300 , 1 ) ) g . means_ = p1 * np . random . randn ( k , 1 ) g . covars_ = np . power ( abs ( p2 * np . random . randn ( k , 1 ) + 1 ) , 2 ) g . weights_ = abs ( np . random . rand ( k ) ) g . weights_ = g . weights_ / sum ( g . weights_ ) return g . sample ( points ) [ 0 ] . reshape ( - 1 )
Init a root cause with a Gaussian Mixture Model w/ a spherical covariance type.
Init a root cause with a Gaussian Mixture Model w / a spherical covariance type .