neurosynth/analysis/stats.py
def pearson(x, y):
    """ Correlates row vector x with each row vector in 2D array y. """
    data = np.vstack((x, y))
    ms = data.mean(axis=1)[(slice(None, None, None), None)]
    datam = data - ms
    datass = np.sqrt(np.sum(datam**2, axis=1))
    temp = np.dot(datam[1:], datam[0].T)
    rs = temp / (datass[1:] * datass[0])
    return rs
def pearson(x, y):
    data = np.vstack((x, y))
    ms = data.mean(axis=1)[(slice(None, None, None), None)]
    datam = data - ms
    datass = np.sqrt(np.sum(datam**2, axis=1))
    temp = np.dot(datam[1:], datam[0].T)
    rs = temp / (datass[1:] * datass[0])
    return rs
def pearson ( x , y ) : data = np . vstack ( ( x , y ) ) ms = data . mean ( axis = 1 ) [ ( slice ( None , None , None ) , None ) ] datam = data - ms datass = np . sqrt ( np . sum ( datam ** 2 , axis = 1 ) ) temp = np . dot ( datam [ 1 : ] , datam [ 0 ] . T ) rs = temp / ( datass [ 1 : ] * datass [ 0 ] ) return rs
Correlates row vector x with each row vector in 2D array y.
Correlates row vector x with each row vector in 2D array y .