Python感知机算法

# _*_ coding:utf-8 _*_import copyfrom matplotlib import pyplot as pltfrom matplotlib import animationtraining_set = [[(3,3),1],[(4,3),1],[(1,1),-1]]w = [0, 0]b = 0history = []def update(item):    """    update parameters using stochastic gradient descent    :param item: an item which is classified into wrong class    :return: nothing    """    global w, b, history    w[0] += 1 * item[1] * item[0][0]    w[1] += 1 * item[1] * item[0][1]    b += 1 * item[1]    print w, b    history.append([copy.copy(w), b])    # you can uncomment this line to check the process of stochastic gradient descentdef cal(item):    """    calculate the functional distance between 'item' an the dicision surface. output yi(w*xi + b).    :param item:    :return:    """    res = 0    for i in range(len(item[0])):        res += item[0][1] * w[i]    res += b    res *= item[1]    return resdef check():    """    check if the hyperplane can classify the examples correctly    :return: true if it can    """    flag = False    for item in training_set:        if cal(item) <= 0:            flag = True            update(item)    # draw a graph to show the process    if not flag:        print "RESULT: w: " + str(w) + " b: " + str(b)    return flagif __name__ == "__main__":    for i in range(1000):        if not check():            break    # first set up the figure, the axis, and the plot element we want to animate    fig = plt.figure()    ax = plt.axes(xlim=(0, 2), ylim=(-2, 2))    line, = ax.plot([], [], 'g', lw=2)    label = ax.text([], [], '')    # initialization fuction: plot the background of each frame    def init():        line.set_data([], [])        x, y, x_, y_ = [],[],[],[]        for p in training_set:            if p[1] > 0:                x.append(p[0][0])                y.append(p[0][1])            else:                x_.append(p[0][0])                y_.append(p[0][1])        plt.plot(x, y, 'bo', x_, y_, 'rx')        plt.axis([-6, 6, -6, 6])        plt.grid(True)        plt.xlabel('x')        plt.ylabel('y')        plt.title('Perceptron Algorithm (www.hankcs.com)')        return line, label    # animation function.  this is called sequentially    def animate(i):        global history, ax, line, label        w = history[i][0]        b = history[i][1]        if w[1] == 0: return  line, label        x1 = -7        y1 = -(b + w[0] * x1) / w[1]        x2 = 7        y2 = -(b + w[0] * x2) / w[1]        line.set_data([x1, x2], [y1, y2])        x1 = 0        y1 = -(b + w[0] * x1) / w[1]        label.set_text(history[i])        label.set_position([x1, y1])        return line, label    # call the animator.  blit=true means only re-draw the parts that have changed.    print history    anim = animation.FuncAnimation(fig, animate, init_func=init, frames=len(history), interval=1000, repeat=True,                                   blit=True)    plt.show()    anim.save('perceptron.gif', fps=2, writer='imagemagick')