Пример #1
0
    for i in range(ln):
        XS[i] = HT.dot(train_in[i])
    return XS

def plot(x):
    plt.plot([i for i in range(1,257)], x)
    plt.show()    

if __name__ == '__main__':
    M = 12
    train_in, train_out, test_in, test_out = loaddata()
    #plot(train_in[101])
    print train_in.shape
    #print outputs.shape
    knots = createknots(1,256)
    print knots
    p = len(train_in[0])
    H = basismatrix(knots, p, M)
    XS = transform(H, train_in, M)
    print XS.shape
    print np.linalg.matrix_rank(XS)
    beta = lg.logistic(XS, train_out)
    print beta
    error = 0
    TXS = transform(H, test_in, M)
    for i in range(len(TXS)):
        res = lg.predict(TXS[i], beta)
        if test_out[i] != res:
            error += 1
    print 'error ratio:', float(error)/len(TXS)
Пример #2
0
        row = [1]
        for j in range(len(data[0])):
            seg = []
            if j == 3:
                seg.append(data[i,j])
                row = row + seg
                continue
            for k in range(0,K):
                seg.append(N(data[i,j], k, knots[j]))
            row = row + seg[1:]
        new_matrix[i] = np.array(row)
    return new_matrix

if __name__ == '__main__':
    K = 5
    X, Y = hd.loaddata()
    knots = split(X, K)
    print knots
    H = cookdata(X, K, knots)
    np.savetxt('H.dat', H, delimiter=',')
    print H.shape
    print np.linalg.matrix_rank(H)
    beta = lg.logistic(H, Y)
    print beta
    error = 0
    for i in range(len(H)):
        res = lg.predict(H[i], beta)
        if Y[i] != res:
            error += 1
    print 'error ratio:', float(error)/len(X)