Logistic回归代码蜜汁bug（机器学习实战）

[心谭]

先说一下报错，很奇怪。

#报错信息
Traceback (most recent call last):
  File "c:\Users\Asura-Dong\Desktop\Blog\learn\MachineLearning\Unit5\Logistic.py", line 99, in <module>
    weights1 = stocGradAscent1(dataArr,labelMat)
  File "c:\Users\Asura-Dong\Desktop\Blog\learn\MachineLearning\Unit5\Logistic.py", line 78, in stocGradAscent1
    weights = weights+alpha*error*dataMatrix[randIndex]
TypeError: 'numpy.float64' object cannot be interpreted as an integer

为什么会出现这种报错？？？哪里需要float->integer???
代码如下（错误在78行）

from numpy import *
from matplotlib import pyplot as plt

def loadDataSet():
    dataMat = []
    labelMat = []
    fr = open('testSet.txt')
    for line in fr.readlines():
        lineArr = line.strip().split()
        dataMat.append([1.0, float(lineArr[0]), float(lineArr[1])])
        labelMat.append(int(lineArr[2]))
    return dataMat,labelMat

#定义Sigmoid函数
def sigmoid(inX):
    return 1.0/(1+exp(-inX))

def gradAscent(dataMatIn,classLabels):
    dataMatrix = mat(dataMatIn)
    labelMat = mat(classLabels).transpose()
    m,n = shape(dataMatrix) #m个样本，n维/个
    alpha = 0.001#移动的步长
    maxCycles = 500 #迭代次数
    weights = ones((n,1))
    for k in range(maxCycles):
        h = sigmoid(dataMatrix*weights)
        error = (labelMat-h)
        weights = weights + alpha*dataMatrix.T*error
    return weights


def plotBestFit(weights):
    dataMat,labelMat = loadDataSet()
    dataArr = array(dataMat)
    n = 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)
    ax.scatter(xcord1,ycord1,s=30,c='red',marker='s')
    ax.scatter(xcord2,ycord2,s=30,c='green')
    x = arange(-3.0,3.0,0.1)
    y = (-weights[0]-weights[1]*x)/weights[2]
    ax.plot(x,y)
    plt.show()

def stocGradAscent0(dataMatrix,classLabels):
    m,n = shape(dataMatrix)
    alpha = 0.01
    weights = ones(n)
    for i in range(m):
        h = sigmoid(dataMatrix[i]*weights)
        error = classLabels[i]-h 
        #error和h是数值
        weights = weights + alpha*error*dataMatrix[i]
    return weights

def stocGradAscent1(dataMatrix,classLabels,numIter = 150):
    m,n = shape(dataMatrix)
    weights = array([1.0,1.0,1.0])
    for j in range(numIter):
        dataIndex = range(m)
        for i in range(m):
            alpha = 4//(1.0+j+i)+0.01 #注意常数项.保证alpha不断变小，但不为0
            randIndex = int(random.uniform(0,len(dataIndex)))#改动2：随机选取
            h = sigmoid(sum(dataMatrix[randIndex]*weights))
            error = classLabels[randIndex]-h
            weights = weights+alpha*error*dataMatrix[randIndex]
            del(dataIndex[randIndex])
    return weights


if __name__=='__main__':
    dataArr , labelMat = loadDataSet()
    '''
    print("dataArr is:")
    for line in dataArr:
        print(line)

    print("labelMat is:\n",labelMat)
    for line in labelMat:
        print(line)
    weights = stocGradAscent0(dataArr,labelMat)
    print(weights)
    plotBestFit(weights)
    '''
    weights1 = stocGradAscent1(dataArr,labelMat)
    print(weights)
    plotBestFit(weights)

[心谭]

自己来顶一下。 下到的源码也走不通（同样是这里出错）。 说一下环境： python 3.5 win10平台。

[sanGuo_uu]

weights = array([1.0,1.0,1.0]) 我没有array关键词。——name array is not defined 还有 weights = weights+alpha*error*dataMatrix[randIndex] weights类型可以和Int类型加？

[心谭]

@u012536120 二楼，？？？你在说什么？？？

[心谭]

不是有 from numpy import * 了吗。。 什么是weights类型和int类型相加？麻烦注意看一下好吗。 那是array类型。 @sanGuo_uu

[secsilm]

我调试了一下你的代码，（我猜）错误出现在alpha*error*dataMatrix[i]这里，error是一个 float64，而dataMatrix[i]是一个列表，你用float64去乘一个列表是会报错的，建议将dataMatrix转换成numpy array再进行运算，尽量不要 array和list混合运算，因为一个常数去乘一个array和list的差别还是很大的。 另外，你的numpy的版本应该是1.12吧？

[叶上]

可以改成这样： def stocGradAscent0(dataMatrix,classLabels): #随机梯度上升算法 dataMatrix = mat(dataMatrix) m,n = shape(dataMatrix) alpha = 0.01 weights = ones((1,3)) for i in range(m): a=dataMatrix[i].copy() a=a.transpose() h = sigmoid(dot(weights,a)) error =classLabels[i] -h weights = weights +alpha * error * dataMatrix[i] return weights.transpose()

[ash_time]

哈哈 我今天也碰到了，机器学习实战上的吧！ 楼上说的对， weights1 = stocGradAscent1(dataArr,labelMat) 这里改成 weights1 = stocGradAscent1(array(dataArr),labelMat)

[_莫等闲_]

def stoc_grad_ascent(data_matrix_in, class_labels): '''随机梯度上升算法''' m, n = shape(data_matrix_in) alpha = 0.01 weights = ones(n) for i in range(m): h = sigmoid(sum(data_matrix_in[i] * weights)) error = class_labels[i] - h weights = weights + alpha * error * array(data_matrix_in[i]) return asmatrix(weights).transpose() 最后一句把array转成matrix再转置。

[_莫等闲_]

def stoc_grad_ascent(data_matrix_in, class_labels): '''随机梯度上升算法''' m, n = shape(data_matrix_in) alpha = 0.01 weights = ones(n) for i in range(m): h = sigmoid(sum(data_matrix_in[i] * weights)) error = class_labels[i] - h weights = weights + alpha * error * array(data_matrix_in[i]) return asmatrix(weights).transpose() 倒数第二句，把data_matrix_in[i]转成array 最后一句把array转成matrix再转置;