main.py 文件源码

def train(X, y, k, C):
    n = X.shape[0]
    m = X.shape[1]
    w = np.zeros(m)
    c = cost(X, y, w, C)
    threshold = 1e-5
    for iteration in xrange(10000):
        new_w = np.zeros(m)
        for j in xrange(m):
            sum = 0
            for i in xrange(n):
                sum += y[i] * X[i, j] * (1 - 1 / (1 + np.exp(-y[i] * np.dot(X[i], w))))
            new_w[j] = w[j] + k * sum / np.double(n) - k * C * w[j]
        new_cost = cost(X, y, new_w, C)
        if distance.euclidean(w, new_w) <= threshold:
            return new_w
        c = new_cost
        w = new_w
    return w