c8abe38d8c4306ad3430cf4c33e7809ad973df3f,Orange/classification/softmax_regression.py,,,#,60

Before Change

//    print(ga)
//    print(gn)

    for lambda_ in [0.1, 0.3, 1, 3, 10]:
        m = SoftmaxRegressionLearner(lambda_=lambda_)
        scores = []
        for tr_ind, te_ind in StratifiedKFold(d.Y.ravel()):
            s = np.mean(m(d[tr_ind])(d[te_ind]) == d[te_ind].Y.ravel())
            scores.append(s)
        print("{:4.1f} {}".format(lambda_, np.mean(scores)))

After Change

    // gradient check
    m = SoftmaxRegressionLearner(lambda_=1.0)
    m.num_classes = 3
    Theta = np.random.randn(3 * 4)
    y = d.Y.ravel().astype(int)
    Y = np.eye(3)[y]

    ga = m.cost_grad(Theta, d.X, Y, y)[1]

Instances