FOBI.py 文件源码

def FOBI(X):
    """Fourth Order Blind Identification technique is used.
    The function returns the unmixing matrix.
    X is assumed to be centered and whitened.
    The paper by J. Cardaso is in itself the best resource out there for it.
    SOURCE SEPARATION USING HIGHER ORDER MOMENTS - Jean-Francois Cardoso""" 

    rows = X.shape[0]
    n = X.shape[1]
    # Initializing the weighted covariance matrix which will hold the fourth order information
    weightedCovMatrix = np.zeros([rows, rows]) 

    # Approximating the expectation by diving with the number of data points
    for signal in X.T:
        norm = np.linalg.norm(signal)
        weightedCovMatrix += norm*norm*np.outer(signal, signal)

    weightedCovMatrix /= n

    # Doing the eigen value decomposition
    eigValue, eigVector = np.linalg.eigh(weightedCovMatrix)

    # print eigVector
    return eigVector