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ml/regression/log_regres.py

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2020-02-23 14:14:06 +00:00
#!/usr/bin/env python2
# -*- coding:utf-8 -*-
import numpy as np
import matplotlib.pyplot as plt
# 加载数据
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]))
fr.close()
return dataMat, labelMat
# sigmoid 激活函数
def sigmoid(intX):
return 1.0 / (1 + np.exp(-intX))
# 梯度上升算法
def gradAscent(dataMatIn, classLabels):
dataMatrix = np.mat(dataMatIn)
labelMat = np.mat(classLabels).transpose()
m, n = np.shape(dataMatrix)
alpha = 0.001
maxCycles = 500
weights = np.ones((n, 1))
for k in range(maxCycles):
h = sigmoid(dataMatrix * weights)
error = labelMat - h
weights = weights + alpha * dataMatrix.transpose() * error
return weights.getA()
def plotBeastFit(weights):
dataMat, labelMat = loadDataSet()
dataArr = np.array(dataMat)
n = np.shape(dataArr)[0]
xcord1 = []
xcord2 = []
ycode1 = []
ycode2 = []
for i in range(n):
if int(labelMat[i]) == 1:
xcord1.append(dataArr[i, 1])
ycode1.append(dataArr[i, 2])
else:
xcord2.append(dataArr[i, 1])
ycode2.append(dataArr[i, 2])
fig = plt.figure()
ax = fig.add_subplot(111)
ax.scatter(xcord1, ycode1, s=20, c='red', marker='s', alpha=0.5)
ax.scatter(xcord2, ycode2, s=20, c='green', alpha=0.5)
x = np.arange(-3.0, 3.0, 0.1)
y = (-weights[0] - weights[1] * x) / weights[2]
ax.plot(x, y)
plt.title('BestFit')
plt.xlabel('X1')
plt.ylabel('X2')
plt.show()
# 随机梯度上升算法
def stocGradAscent0(dataMatrix, classLabels):
m, n = np.shape(dataMatrix)
alpha = 0.01
weights = np.ones(n)
for i in range(m):
h = sigmoid(sum(dataMatrix[i] * weights))
error = classLabels[i] - h
weights = weights + alpha * error * dataMatrix[i]
return weights
# 优化后的梯度上升算法
def stocGradAscent1(dataMatrix, classLabels, numIter=150):
m, n = np.shape(dataMatrix)
weights = np.ones(n)
for j in range(numIter):
dataIndex = range(m)
for i in range(m):
# alpha 每次都需要调整
alpha = 4 / (1.0 + j + i) + 0.01
# 随机选取跟新
rangeIndex = int(np.random.uniform(0, len(dataIndex)))
h = sigmoid(sum(dataMatrix[rangeIndex] * weights))
error = classLabels[rangeIndex] - h
weights = weights + alpha * error * dataMatrix[rangeIndex]
return weights
if __name__ == '__main__':
dataMat, labelMat = loadDataSet()
# weights = gradAscent(dataMat, labelMat)
# weights = stocGradAscent0(np.array(dataMat), labelMat)
weights = stocGradAscent1(np.array(dataMat), labelMat)
plotBeastFit(weights)