em.py 文件源码

def b(self, digital_levels = 250):
    minX, maxX = np.min(self.X), np.max(self.X)

    digital = np.floor((self.X - minX) / (maxX - minX) * (digital_levels - 1)).astype('uint16')
    assert np.max(digital) < digital_levels
    counts = np.ndarray(shape=(self.X.shape[0], digital_levels), dtype='uint8')
    ndcount(digital.T, counts)
    print 'counts done'

    result = one_class_em(counts)

    auc = roc_auc_score(self.cats, result[:, 1])

    predictions = np.argmax(result, axis=1)
    acc = accuracy(predictions, self.cats)

    return acc, auc