代价函数总是NaN的问题已解决
#!/usr/bin/env python # -*- coding: utf-8 -*- # @Time : 2018/4/3 19:37 # @Author : HJH # @Site : # @File : logistics.py # @Software: PyCharm from numpy import * import numpy as np import matplotlib.pyplot as plt from sklearn.datasets import load_breast_cancer class log(object): def __init__(self): self.W=None def sigmoid(self,X): # longfloat防止溢出,但是并没有什么用 return longfloat(1.0 / (1.0 + exp(-X))) def loss(self,X_train,y_train): m,n=X_train.shape h=self.sigmoid(X_train.dot(self.W)) # print(h) # print((h-y).shape) #此处的loss是矩阵类型,为了便于画图将其中的数取出 loss=(y_train.T.dot(np.log(h))+(1-y_train).T.dot(np.log(1-h)))/-m loss=loss[0,0] dW=X_train.T.dot((h - y_train)) / m # print(dW.shape, '-----------') return loss,dW def train(self,X_train,y_train,learn_rate=0.0005,iters=10000): m,n=X_train.shape # print(m,n) self.W=np.random.rand(n,1) loss_list = [] for i in range(iters): loss,dW=self.loss(X_train,y_train) self.W-=learn_rate*dW loss_list.append(loss) if i % 500 == 0: print('iters = %d,loss = %f' % (i, loss)) return loss_list def predict(self, X_test): m=X_test.shape[0] X_test = np.hstack((X_test, mat(np.ones((m, 1))))) y_pred_list=[] for xx in X_test: y_pred = self.sigmoid(xx.dot(self.W)) # y_pred_list.append(y_pred[0,0]) if y_pred>=0.5: y_pred_list.append(1) else: y_pred_list.append(0) return y_pred_list #从文件中加载数据：特征X，标签label def loadDataSet(): digits=load_breast_cancer() norm_digits=autoNorm(digits.data) X_train = norm_digits[:-10,:] m= X_train.shape[0] #print(m,n) y_total = digits.target.reshape(569,1) #print(y_total.shape) y_train=y_total[:-10,:] #print(m,n) #print(X) X_train = np.hstack((X_train,mat(np.ones((559,1))))) # print(X) #print(y.shape) X_test=norm_digits[-10:,:] X_test=X_test y_test = y_total[-10:, :] return X_train,y_train,X_test,y_test
#将数据归一化(解决代价函数NaN) def autoNorm(X): minVals=X.min(0) maxVals=X.max(0) ranges=maxVals-minVals normDataSet=zeros(shape(X)) m=X.shape[0] normDataSet=X-tile(minVals,(m,1))#在行方向重复minVals m次和列方向上重复minVals 1次 normDataSet=normDataSet/tile(ranges,(m,1)) return normDataSet def plot(loss_list,log): fig = plt.figure() digits = load_breast_cancer() norm_digits = autoNorm(digits.data) x_index = 0 y_index = 1 colors = ['blue', 'red'] plt.subplot(211) for label, color in zip(range(len(digits.target_names)), colors): plt.scatter(norm_digits[digits.target == label, x_index], norm_digits[digits.target == label, y_index], label=digits.target_names[label], c=color) plt.xlabel(digits.feature_names[x_index]) plt.ylabel(digits.feature_names[y_index]) plt.legend(loc='upper left') plt.subplot(212) plt.plot(loss_list, color='blue') plt.xlabel('epochs') plt.ylabel('errors') plt.show() if __name__ == '__main__': X_train,y_train,X_test,y_test=loadDataSet() l=log() loss_list=l.train(X_train,y_train) print(l.predict(X_test)) for i in loss_list: print(i) plot(loss_list,l)
网上的做法：
#!/usr/bin/env python # -*- coding: utf-8 -*- # @Time : 2018/4/4 11:07 # @Author : HJH # @Site : # @File : temp.py # @Software: PyCharm from numpy import * filename='./testSet.txt' #文件目录 def loadDataSet(): #读取数据（这里只有两个特征） dataMat = [] labelMat = [] fr = open(filename) for line in fr.readlines(): lineArr = line.strip().split() dataMat.append([1.0, float(lineArr[0]), float(lineArr[1])]) #前面的1，表示方程的常量。比如两个特征X1,X2，共需要三个参数，W1+W2*X1+W3*X2 labelMat.append(int(lineArr[2])) return dataMat,labelMat def sigmoid(inX): #sigmoid函数 return 1.0/(1+exp(-inX)) def gradAscent(dataMat, labelMat): #梯度上升求最优参数 dataMatrix=mat(dataMat) #将读取的数据转换为矩阵 classLabels=mat(labelMat).transpose() #将读取的数据转换为矩阵 m,n = shape(dataMatrix) alpha = 0.001 #设置梯度的阀值，该值越大梯度上升幅度越大 maxCycles = 500 #设置迭代的次数，一般看实际数据进行设定，有些可能200次就够了 weights = ones((n,1)) #设置初始的参数，并都赋默认值为1。注意这里权重以矩阵形式表示三个参数。 for k in range(maxCycles): h = sigmoid(dataMatrix*weights) error = (classLabels - h) #求导后差值 weights = weights + alpha * dataMatrix.transpose()* error #迭代更新权重 return weights def stocGradAscent0(dataMat, labelMat): #随机梯度上升，当数据量比较大时，每次迭代都选择全量数据进行计算，计算量会非常大。所以采用每次迭代中一次只选择其中的一行数据进行更新权重。 dataMatrix=mat(dataMat) classLabels=labelMat m,n=shape(dataMatrix) alpha=0.01 maxCycles = 500 weights=ones((n,1)) for k in range(maxCycles): for i in range(m): #遍历计算每一行 h = sigmoid(sum(dataMatrix[i] * weights)) error = classLabels[i] - h weights = weights + alpha * error * dataMatrix[i].transpose() return weights def stocGradAscent1(dataMat, labelMat): #改进版随机梯度上升，在每次迭代中随机选择样本来更新权重，并且随迭代次数增加，权重变化越小。 dataMatrix=mat(dataMat) classLabels=labelMat m,n=shape(dataMatrix) weights=ones((n,1)) maxCycles=500 for j in range(maxCycles): #迭代 dataIndex=[i for i in range(m)] for i in range(m): #随机遍历每一行 alpha=4/(1+j+i)+0.0001 #随迭代次数增加，权重变化越小。 randIndex=int(random.uniform(0,len(dataIndex))) #随机抽样 h=sigmoid(sum(dataMatrix[randIndex]*weights)) error=classLabels[randIndex]-h weights=weights+alpha*error*dataMatrix[randIndex].transpose() del(dataIndex[randIndex]) #去除已经抽取的样本 return weights def plotBestFit(weights): #画出最终分类的图 import matplotlib.pyplot as plt 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.xlabel('X1') plt.ylabel('X2') plt.show() if __name__=='__main__': dataMat, labelMat = loadDataSet() weights = gradAscent(dataMat, labelMat).getA() plotBestFit(weights)
test.txt数据集：
-0.017612 14.053064 0 -1.395634 4.662541 1 -0.752157 6.538620 0 -1.322371 7.152853 0 0.423363 11.054677 0 0.406704 7.067335 1 0.667394 12.741452 0 -2.460150 6.866805 1 0.569411 9.548755 0 -0.026632 10.427743 0 0.850433 6.920334 1 1.347183 13.175500 0 1.176813 3.167020 1 -1.781871 9.097953 0 -0.566606 5.749003 1 0.931635 1.589505 1 -0.024205 6.151823 1 -0.036453 2.690988 1 -0.196949 0.444165 1 1.014459 5.754399 1 1.985298 3.230619 1 -1.693453 -0.557540 1 -0.576525 11.778922 0 -0.346811 -1.678730 1 -2.124484 2.672471 1 1.217916 9.597015 0 -0.733928 9.098687 0 -3.642001 -1.618087 1 0.315985 3.523953 1 1.416614 9.619232 0 -0.386323 3.989286 1 0.556921 8.294984 1 1.224863 11.587360 0 -1.347803 -2.406051 1 1.196604 4.951851 1 0.275221 9.543647 0 0.470575 9.332488 0 -1.889567 9.542662 0 -1.527893 12.150579 0 -1.185247 11.309318 0 -0.445678 3.297303 1 1.042222 6.105155 1 -0.618787 10.320986 0 1.152083 0.548467 1 0.828534 2.676045 1 -1.237728 10.549033 0 -0.683565 -2.166125 1 0.229456 5.921938 1 -0.959885 11.555336 0 0.492911 10.993324 0 0.184992 8.721488 0 -0.355715 10.325976 0 -0.397822 8.058397 0 0.824839 13.730343 0 1.507278 5.027866 1 0.099671 6.835839 1 -0.344008 10.717485 0 1.785928 7.718645 1 -0.918801 11.560217 0 -0.364009 4.747300 1 -0.841722 4.119083 1 0.490426 1.960539 1 -0.007194 9.075792 0 0.356107 12.447863 0 0.342578 12.281162 0 -0.810823 -1.466018 1 2.530777 6.476801 1 1.296683 11.607559 0 0.475487 12.040035 0 -0.783277 11.009725 0 0.074798 11.023650 0 -1.337472 0.468339 1 -0.102781 13.763651 0 -0.147324 2.874846 1 0.518389 9.887035 0 1.015399 7.571882 0 -1.658086 -0.027255 1 1.319944 2.171228 1 2.056216 5.019981 1 -0.851633 4.375691 1 -1.510047 6.061992 0 -1.076637 -3.181888 1 1.821096 10.283990 0 3.010150 8.401766 1 -1.099458 1.688274 1 -0.834872 -1.733869 1 -0.846637 3.849075 1 1.400102 12.628781 0 1.752842 5.468166 1 0.078557 0.059736 1 0.089392 -0.715300 1 1.825662 12.693808 0 0.197445 9.744638 0 0.126117 0.922311 1 -0.679797 1.220530 1 0.677983 2.556666 1 0.761349 10.693862 0 -2.168791 0.143632 1 1.388610 9.341997 0 0.317029 14.739025 0