Beispiel #1
0
def plot_svm():
    num_points = 10
    (X, y, f) = random_linearly_separable_data(num_points, bounds)
    (w_svm, vectors) = linsep_svm(X, y)
    
    x_plot = X[:,0]
    y_plot = X[:,1]
    s = 20*np.ones(num_points)
    s[vectors] = 60

    pla = PLA(bounds=bounds)
    pla.fit(X, y)
    w_pla = pla.weights
    
    # test values for b
    print("Average b:",  w_svm[0])
    each_b = y[vectors] - np.dot(X[vectors], w_svm[1:])
    print("Individual b's:",  each_b)

    (x_f, y_f) = weights_to_mxb_2D(f, bounds)
    (x_w, y_w) = weights_to_mxb_2D(w_svm, bounds)
    (x_p, y_p) = weights_to_mxb_2D(w_pla, bounds)
    c = np.where(y==1, 'r', 'b')
    plt.scatter(x_plot,y_plot, s, c=c)
    plt.plot(x_f, y_f, 'k-.')
    plt.plot(x_w, y_w, 'b')
    plt.plot(x_p, y_p, 'r--')
    plt.xlim([-1, 1])
    plt.ylim([-1, 1])
    plt.grid()
    plt.show()
Beispiel #2
0
def answers():
    num_points = 10
    num_experiments = 1000
    times_svm_better = 0.0
    total_num_vectors = 0.0

    for i in range(num_experiments):
        (X, y, f) = random_linearly_separable_data(num_points, bounds)
        (w_svm, vectors) = linsep_svm(X, y)

        pla = PLA(bounds=bounds)
        pla.fit(X, y)
        w_pla = pla.weights

        E_svm = linear_randomized_Eout(f, w_svm, bounds)
        E_pla = linear_randomized_Eout(f, w_pla, bounds)
        if E_svm < E_pla:
            times_svm_better += 1
        total_num_vectors += len(vectors)

    print("Number of points used:            ", num_points)
    print("Proportion of times SVM beats PLA:", times_svm_better/num_experiments)
    print("Average number of support vectors:", total_num_vectors/num_experiments)