regression_stealer.py 文件源码

def logistic_loss(w, X, Y, alpha):
    """
    Implementation of the logistic loss function when Y is a probability
    distribution.

    loss = -SUM_i SUM_k y_ik * log(P[yi == k]) + alpha * ||w||^2
    """
    n_classes = Y.shape[1]
    n_features = X.shape[1]
    intercept = 0

    if n_classes > 2:
        fit_intercept = w.size == (n_classes * (n_features + 1))
        w = w.reshape(n_classes, -1)
        if fit_intercept:
            intercept = w[:, -1]
            w = w[:, :-1]
    else:
        fit_intercept = w.size == (n_features + 1)
        if fit_intercept:
            intercept = w[-1]
            w = w[:-1]

    z = safe_sparse_dot(X, w.T) + intercept

    if n_classes == 2:
        # in the binary case, simply compute the logistic function
        p = np.vstack([log_logistic(-z), log_logistic(z)]).T
    else:
        # compute the logistic function for each class and normalize
        denom = expit(z)
        denom = denom.sum(axis=1).reshape((denom.shape[0], -1))
        p = log_logistic(z)
        loss = - (Y * p).sum()
        loss += np.log(denom).sum()  # Y.sum() = 1
        loss += 0.5 * alpha * squared_norm(w)
        return loss

    loss = - (Y * p).sum() + 0.5 * alpha * squared_norm(w)
    return loss