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随机梯度上升的图：

需要注意的是给数据加分类标签时候，画散点图时候直接返回type，而画plot时候返回的是元组 即type3,

此外，数据要以np.array形式给出才能画直线

def plotBestFit(weights,dataMat,labelMat):    dataArr = np.array(dataMat) #注意这里    n = np.shape(dataArr)[0]    xcord1 = []; ycord1 = []    xcord2 = []; ycord2 = []    for i in range(n):        if int(labelMat[i]) == 1:            xcord1.append(dataArr[i,1])            ycord1.append(dataArr[i,2])        else:            xcord2.append(dataArr[i,1])            ycord2.append(dataArr[i,2])    fig = plt.figure()    ax = fig.add_subplot(111)    type1 = ax.scatter(xcord1,ycord1,s=30,c='red',marker='s')    type2 = ax.scatter(xcord2,ycord2,s=30,c='green')    x = np.arange(-3.0,3.0,0.1)    y = (-weights[0]-weights[1]*x)/weights[2] #weights需要是np.array类型    # print(y)    type3, = ax.plot(x,y)    plt.legend([type1, type2, type3], ("Did Not Like", "Liked in Small Doses", "Liked in Large Doses"),loc=2)    plt.xlabel('X1')    plt.ylabel('X2')    plt.savefig('regression.pdf')#保存图片为pdf格式    plt.show()