regression_stealer.py 文件源码

def predict_probas(X, w, intercept, multinomial=True):
    """
    Predict probabilities for each class using either a multinomial or a
    one-vs-rest approach
    """

    #print X.shape
    #print w.shape
    #print intercept.shape

    p = safe_sparse_dot(X, w.T, dense_output=True) + intercept

    if multinomial:
        return softmax(p, copy=False)
    else:
        p = p.ravel() if p.shape[1] == 1 else p

        p *= -1
        np.exp(p, p)
        p += 1
        np.reciprocal(p, p)

        if p.ndim == 1:
            return np.vstack([1 - p, p]).T
        else:
            # OvR normalization, like LibLinear's predict_probability
            p /= p.sum(axis=1).reshape((p.shape[0], -1))
            return p