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更新注释
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jiangzhonglian
@@ -1,103 +0,0 @@
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#!/usr/bin/env python
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# encoding: utf-8
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from numpy import *
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import matplotlib.pyplot as plt
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import time
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'''
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1、需要安装模块：pip install matplotlib-1.5.0-cp27-none-win_amd64.whl
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由于直接安装会出现问题，所以建议下载whl包进行安装，下载网址：
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https://pypi.python.org/pypi/matplotlib/1.5.0
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2、可以看见画出的图像
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'''
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"""
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@version:
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@author: yangjf
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@license: ApacheCN
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@contact: highfei2011@126.com
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@site: https://github.com/apachecn/MachineLearning
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@software: PyCharm
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@file: logRegression01.py
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@time: 2017/3/3 22:03
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@test result: ok
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"""
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# sigmoid函数
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def sigmoid(inX):
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return 1.0 / (1 + exp(-inX))
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def trainLogRegres(train_x, train_y, opts):
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# 计算训练时间
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startTime = time.time()
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numSamples, numFeatures = shape(train_x)
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alpha = opts['alpha']; maxIter = opts['maxIter']
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weights = ones((numFeatures, 1))
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# 通过梯度下降算法优化
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for k in range(maxIter):
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if opts['optimizeType'] == 'gradDescent': # 梯度下降算法
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output = sigmoid(train_x * weights)
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error = train_y - output
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weights = weights + alpha * train_x.transpose() * error
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elif opts['optimizeType'] == 'stocGradDescent': # 随机梯度下降
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for i in range(numSamples):
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output = sigmoid(train_x[i, :] * weights)
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error = train_y[i, 0] - output
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weights = weights + alpha * train_x[i, :].transpose() * error
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elif opts['optimizeType'] == 'smoothStocGradDescent': # 光滑随机梯度下降
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# 随机选择样本以优化以减少周期波动
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dataIndex = range(numSamples)
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for i in range(numSamples):
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alpha = 4.0 / (1.0 + k + i) + 0.01
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randIndex = int(random.uniform(0, len(dataIndex)))
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output = sigmoid(train_x[randIndex, :] * weights)
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error = train_y[randIndex, 0] - output
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weights = weights + alpha * train_x[randIndex, :].transpose() * error
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del(dataIndex[randIndex]) # 在一次交互期间，删除优化的样品
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else:
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raise NameError('Not support optimize method type!')
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print 'Congratulations, training complete! Took %fs!' % (time.time() - startTime)
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return weights
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#测试给定测试集的训练Logistic回归模型
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def testLogRegres(weights, test_x, test_y):
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numSamples, numFeatures = shape(test_x)
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matchCount = 0
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for i in xrange(numSamples):
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predict = sigmoid(test_x[i, :] * weights)[0, 0] > 0.5
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if predict == bool(test_y[i, 0]):
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matchCount += 1
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accuracy = float(matchCount) / numSamples
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return accuracy
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# 显示你的训练逻辑回归模型只有2-D数据可用
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def showLogRegres(weights, train_x, train_y):
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# 注意：train_x和train_y是垫数据类型
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numSamples, numFeatures = shape(train_x)
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if numFeatures != 3:
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print "抱歉! 我不能绘制，因为你的数据的维度不是2！"
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return 1
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# 画出所有抽样数据
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for i in xrange(numSamples):
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if int(train_y[i, 0]) == 0:
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plt.plot(train_x[i, 1], train_x[i, 2], 'or')
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elif int(train_y[i, 0]) == 1:
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plt.plot(train_x[i, 1], train_x[i, 2], 'ob')
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# 画图操作
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min_x = min(train_x[:, 1])[0, 0]
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max_x = max(train_x[:, 1])[0, 0]
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weights = weights.getA() # 将mat转换为数组
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y_min_x = float(-weights[0] - weights[1] * min_x) / weights[2]
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y_max_x = float(-weights[0] - weights[1] * max_x) / weights[2]
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plt.plot([min_x, max_x], [y_min_x, y_max_x], '-g')
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plt.xlabel('X1'); plt.ylabel('X2')
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#显示图像
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plt.show()
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@@ -1,54 +0,0 @@
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#!/usr/bin/env python
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# encoding: utf-8
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import os
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import sys
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sys.path.append("C:\Python27")
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from numpy import *
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from logRegression01 import *
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"""
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@version:
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@author: yangjf
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@license: ApacheCN
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@contact: highfei2011@126.com
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@site: https://github.com/apachecn/MachineLearning
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@software: PyCharm
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@file: test_logRegression.py
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@time: 2017/3/3 22:09
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@test result: ok
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"""
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def loadData():
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train_x = []
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train_y = []
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# 获取当前文件所在路径
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project_dir = os.getcwdu()
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# 截取字符串至项目名：Test\
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project_dir = project_dir[:project_dir.find("MachineLearning\\") + 15]
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print project_dir
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fileIn = open("%s/testData/Logistic_testdata.txt" % project_dir)
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for line in fileIn.readlines():
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lineArr = line.strip().split()
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train_x.append([1.0, float(lineArr[0]), float(lineArr[1])])
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train_y.append(float(lineArr[2]))
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return mat(train_x), mat(train_y).transpose()
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##第一步: 加载数据
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print "step 1: load data..."
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train_x, train_y = loadData()
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test_x = train_x; test_y = train_y
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##第二步: 训练数据...
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print "step 2: training..."
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opts = {'alpha': 0.01, 'maxIter': 20, 'optimizeType': 'smoothStocGradDescent'}
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optimalWeights = trainLogRegres(train_x, train_y, opts)
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##第三步: 测试
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print "step 3: testing..."
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accuracy = testLogRegres(optimalWeights, test_x, test_y)
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##第四步: 显示结果
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print "step 4: show the result..."
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print 'The classify accuracy is: %.3f%%' % (accuracy * 100)
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showLogRegres(optimalWeights, train_x, train_y)
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@@ -1,164 +0,0 @@
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#!/usr/bin/env python
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# -*- coding:utf-8 -*-
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from numpy import *
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def loadDataSet():
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dataMat = []
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labelMat = []
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fr = open('testSet.txt')
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for line in fr.readlines():
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lineArr = line.strip().split()
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dataMat.append([1.0, float(lineArr[0]), float(lineArr[1])])
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labelMat.append(int(lineArr[2]))
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return dataMat, labelMat
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def sigmoid(inX):
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return 1.0 / (1 + exp(-inX))
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def gradAscent(dataMatIn, classLabels):
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# 转化为矩阵[[1,1,2],[1,1,2]....]
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dataMatrix = mat(dataMatIn) # convert to NumPy matrix
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# 转化为矩阵[[0,1,0,1,0,1.....]]，并转制[[0],[1],[0].....]
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x = mat(classLabels)
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labelMat = x.transpose() # convert to NumPy matrix
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# m->数据量 n->特征数
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m, n = shape(dataMatrix)
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# 步长
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alpha = 0.001
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# 迭代次数
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maxCycles = 500
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# 生成一个长度和特征数相同的矩阵，此处n为3 -> [[1],[1],[1]]
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weights = ones((n, 1))
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for k in range(maxCycles): # heavy on matrix operations
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# 1. dataMatrix * weights 矩阵乘法: [[1,1,2],[1,1,2]....] * [[1],[1],[1]] -> [[]]
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s = dataMatrix * weights
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# 把每个特征与系数的乘积只和带入Sigmoid函数
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h = sigmoid(dataMatrix * weights) # matrix mult
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# [[x,x,x,x,x,......一共一百个误差]]
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error = (labelMat - h) # vector subtraction
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# dataMatrix.transpose() * error 推理略去
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# [[x,x,x,x....一共一百个数],[],[]]
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data_tran = dataMatrix.transpose()
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# [[a,b,c]]
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data_tran_error = data_tran * error
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# weights = weights + alpha * dataMatrix.transpose() * error # matrix mult
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weights = weights + alpha * data_tran_error
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return weights
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# 随机梯度上升
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# 梯度上升优化算法在每次更新数据集时都需要遍历整个数据集，计算复杂都较高
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# 随机梯度上升一次只用一个样本点来更新回归系数
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def stocGradAscent0(dataMatrix, classLabels):
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m, n = shape(dataMatrix)
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alpha = 0.01
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weights = ones(n) # initialize to all ones
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for i in range(m):
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h = sigmoid(sum(dataMatrix[i] * weights))
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error = classLabels[i] - h
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weights = weights + alpha * error * dataMatrix[i]
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return weights
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def plotBestFit(weights):
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import matplotlib.pyplot as plt
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dataMat, labelMat = loadDataSet()
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dataArr = array(dataMat)
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n = shape(dataArr)[0]
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xcord1 = [];
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ycord1 = []
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xcord2 = [];
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ycord2 = []
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for i in range(n):
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if int(labelMat[i]) == 1:
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xcord1.append(dataArr[i, 1]);
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ycord1.append(dataArr[i, 2])
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else:
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xcord2.append(dataArr[i, 1]);
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ycord2.append(dataArr[i, 2])
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fig = plt.figure()
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ax = fig.add_subplot(111)
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ax.scatter(xcord1, ycord1, s=30, c='red', marker='s')
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ax.scatter(xcord2, ycord2, s=30, c='green')
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x = arange(-3.0, 3.0, 0.1)
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y = (-weights[0] - weights[1] * x) / weights[2]
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ax.plot(x, y)
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plt.xlabel('X1');
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plt.ylabel('X2');
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plt.show()
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def stocGradAscent1(dataMatrix, classLabels, numIter=150):
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m, n = shape(dataMatrix)
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weights = ones(n) # initialize to all ones
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for j in range(numIter):
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dataIndex = range(m)
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for i in range(m):
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# 步长在不断减小
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alpha = 4 / (1.0 + j + i) + 0.0001 # apha decreases with iteration, does not
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# 随机选取样本减少周期波动
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randIndex = int(random.uniform(0, len(dataIndex))) # go to 0 because of the constant
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h = sigmoid(sum(dataMatrix[randIndex] * weights))
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error = classLabels[randIndex] - h
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weights = weights + alpha * error * dataMatrix[randIndex]
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del (dataIndex[randIndex])
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return weights
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# a, b = loadDataSet()
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# weights = gradAscent(a, b)
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# plotBestFit(weights)
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#
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######################################################################################################################
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def classifyVector(inX, weights):
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prob = sigmoid(sum(inX * weights))
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if prob > 0.5:
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return 1.0
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else:
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return 0.0
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def colicTest():
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frTrain = open('horseColicTraining.txt');
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frTest = open('horseColicTest.txt')
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trainingSet = [];
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trainingLabels = []
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for line in frTrain.readlines():
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currLine = line.strip().split('\t')
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lineArr = []
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for i in range(21):
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lineArr.append(float(currLine[i]))
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trainingSet.append(lineArr)
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trainingLabels.append(float(currLine[21]))
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trainWeights = stocGradAscent1(array(trainingSet), trainingLabels, 1000)
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errorCount = 0;
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numTestVec = 0.0
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for line in frTest.readlines():
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numTestVec += 1.0
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currLine = line.strip().split('\t')
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lineArr = []
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for i in range(21):
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lineArr.append(float(currLine[i]))
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if int(classifyVector(array(lineArr), trainWeights)) != int(currLine[21]):
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errorCount += 1
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errorRate = (float(errorCount) / numTestVec)
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print "the error rate of this test is: %f" % errorRate
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return errorRate
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def multiTest():
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numTests = 10;
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errorSum = 0.0
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for k in range(numTests):
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errorSum += colicTest()
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print "after %d iterations the average error rate is: %f" % (numTests, errorSum / float(numTests))
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# multiTest()
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colicTest()
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151
src/python/05.Logistic/logistic.py
Normal file
151
src/python/05.Logistic/logistic.py
Normal file
@@ -0,0 +1,151 @@
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#!/usr/bin/python
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# coding: utf8
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'''
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Created on Oct 27, 2010
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Logistic Regression Working Module
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@author: Peter
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'''
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from numpy import *
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import matplotlib.pyplot as plt
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# 解析数据
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def loadDataSet(file_name):
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# dataMat为原始数据， labelMat为原始数据的标签
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dataMat = []; labelMat = []
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fr = open(file_name)
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for line in fr.readlines():
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lineArr = line.strip().split()
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dataMat.append([1.0, float(lineArr[0]), float(lineArr[1])])
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labelMat.append(int(lineArr[2]))
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return dataMat,labelMat
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# sigmoid跳跃函数
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def sigmoid(inX):
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return 1.0/(1+exp(-inX))
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# 正常的处理方案
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def gradAscent(dataMatIn, classLabels):
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# 转化为矩阵[[1,1,2],[1,1,2]....]
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dataMatrix = mat(dataMatIn) #convert to NumPy matrix
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# 转化为矩阵[[0,1,0,1,0,1.....]]，并转制[[0],[1],[0].....]
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# transpose() 行列转制函数
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# 将行矩阵转化为列矩阵 => 矩阵的转置
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labelMat = mat(classLabels).transpose() #convert to NumPy matrix
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# m->数据量 n->特征数
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m,n = shape(dataMatrix)
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# print m, n, '__'*10, shape(dataMatrix.transpose()), '__'*100
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# 步长
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alpha = 0.001
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# 迭代次数
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maxCycles = 500
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# 生成一个长度和特征数相同的矩阵，此处n为3 -> [[1],[1],[1]]
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# 回归系数
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weights = ones((n,1))
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for k in range(maxCycles): #heavy on matrix operations
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# m*3的矩阵 * 3*1的单位矩阵 ＝ m*1的矩阵
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# 那么乘上单位矩阵的意义，就代表：通过公式得到的理论值
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# 参考地址： 矩阵乘法的本质是什么？ https://www.zhihu.com/question/21351965/answer/31050145
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# n*3 * 3*1 = n*1
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h = sigmoid(dataMatrix*weights) #matrix mult
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# labelMat是实际值
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error = (labelMat - h) #vector subtraction
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# 0.001* (3*m)*(m*1) 表示在每一个列上的一个误差情况，最后得出 x1,x2,xn的系数的偏移量
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weights = weights + alpha * dataMatrix.transpose() * error #matrix mult
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return array(weights)
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# 随机梯度上升
|
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# 梯度上升优化算法在每次更新数据集时都需要遍历整个数据集，计算复杂都较高
|
||||
# 随机梯度上升一次只用一个样本点来更新回归系数
|
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def stocGradAscent0(dataMatrix, classLabels):
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m,n = shape(dataMatrix)
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alpha = 0.01
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# n*1的矩阵
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# 函数ones创建一个全1的数组
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weights = ones(n) #initialize to all ones
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for i in range(m):
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# sum(dataMatrix[i]*weights)为了求 f(x)的值， f(x)=a1*x1+b2*x2+..+nn*xn
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h = sigmoid(sum(dataMatrix[i]*weights))
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error = classLabels[i] - h
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# 0.01*(1*1)*(1*n)
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print weights, "*"*10 , dataMatrix[i], "*"*10 , error
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weights = weights + alpha * error * dataMatrix[i]
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return weights
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# 随机梯度上升算法（随机化）
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def stocGradAscent1(dataMatrix, classLabels, numIter=150):
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m,n = shape(dataMatrix)
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weights = ones(n) #initialize to all ones
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# 随机剃度, 循环150,观察是否收敛
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for j in range(numIter):
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# [0, 1, 2 .. m-1]
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dataIndex = range(m)
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for i in range(m):
|
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# i和j的不断增大，导致alpha的值不断减少，但是不为0
|
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alpha = 4/(1.0+j+i)+0.0001 #apha decreases with iteration, does not
|
||||
# 随机产生一个 0～len()之间的一个值
|
||||
randIndex = int(random.uniform(0,len(dataIndex)))#go to 0 because of the constant
|
||||
# sum(dataMatrix[i]*weights)为了求 f(x)的值， f(x)=a1*x1+b2*x2+..+nn*xn
|
||||
h = sigmoid(sum(dataMatrix[randIndex]*weights))
|
||||
error = classLabels[randIndex] - h
|
||||
# print weights, '__h=%s' % h, '__'*20, alpha, '__'*20, error, '__'*20, dataMatrix[randIndex]
|
||||
weights = weights + alpha * error * dataMatrix[randIndex]
|
||||
del(dataIndex[randIndex])
|
||||
return weights
|
||||
|
||||
|
||||
# 可视化展示
|
||||
def plotBestFit(dataArr, labelMat, weights):
|
||||
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的由来，卧槽，是不是没看懂？
|
||||
首先理论上是这个样子的。
|
||||
dataMat.append([1.0, float(lineArr[0]), float(lineArr[1])])
|
||||
w0*x0+w1*x1+w2*x2=f(x)
|
||||
x0最开始就设置为1叻， x2就是我们画图的y值，而f(x)被我们磨合误差给算到w0,w1,w2身上去了
|
||||
所以： w0+w1*x+w2*y=0 => y = (-w0-w1*x)/w2
|
||||
"""
|
||||
y = (-weights[0]-weights[1]*x)/weights[2]
|
||||
ax.plot(x, y)
|
||||
plt.xlabel('X'); plt.ylabel('Y')
|
||||
plt.show()
|
||||
|
||||
|
||||
def main():
|
||||
# project_dir = os.path.dirname(os.path.dirname(os.getcwd()))
|
||||
# 1.收集并准备数据
|
||||
# dataMat, labelMat = loadDataSet("%s/testData/Logistic_testdata.txt" % project_dir)
|
||||
dataMat, labelMat = loadDataSet("testData/Logistic_testdata.txt")
|
||||
|
||||
# print dataMat, '---\n', labelMat
|
||||
# 2.训练模型， f(x)=a1*x1+b2*x2+..+nn*xn中 (a1,b2, .., nn).T的矩阵值
|
||||
# 因为数组没有是复制n份， array的乘法就是乘法
|
||||
dataArr = array(dataMat)
|
||||
# print dataArr
|
||||
# weights = gradAscent(dataArr, labelMat)
|
||||
# weights = stocGradAscent0(dataArr, labelMat)
|
||||
weights = stocGradAscent1(dataArr, labelMat)
|
||||
# print '*'*30, weights
|
||||
|
||||
# 数据可视化
|
||||
plotBestFit(dataArr, labelMat, weights)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Reference in New Issue
Block a user