msaf/algorithms/foote/segmenter.py
def compute_nc(X, G):
    """Computes the novelty curve from the self-similarity matrix X and
        the gaussian kernel G."""
    N = X.shape[0]
    M = G.shape[0]
    nc = np.zeros(N)

    for i in range(M // 2, N - M // 2 + 1):
        nc[i] = np.sum(X[i - M // 2:i + M // 2, i - M // 2:i + M // 2] * G)

    # Normalize
    nc += nc.min()
    nc /= nc.max()
    return nc
def compute_nc(X, G):
    """Computes the novelty curve from the self-similarity matrix X and
        the gaussian kernel G."""
    N = X.shape[0]
    M = G.shape[0]
    nc = np.zeros(N)

    for i in range(M // 2, N - M // 2 + 1):
        nc[i] = np.sum(X[i - M // 2:i + M // 2, i - M // 2:i + M // 2] * G)

    # Normalize
    nc += nc.min()
    nc /= nc.max()
    return nc
def compute_nc ( X , G ) : N = X . shape [ 0 ] M = G . shape [ 0 ] nc = np . zeros ( N ) for i in range ( M // 2 , N - M // 2 + 1 ) : nc [ i ] = np . sum ( X [ i - M // 2 : i + M // 2 , i - M // 2 : i + M // 2 ] * G ) # Normalize nc += nc . min ( ) nc /= nc . max ( ) return nc
Computes the novelty curve from the self-similarity matrix X and
        the gaussian kernel G.
Computes the novelty curve from the self - similarity matrix X and the gaussian kernel G .