版权声明:本文为博主原创文章,未经博主允许不得转载。 https://blog.csdn.net/baidu_20183817/article/details/82691495
机器学习算法与Python实践这个系列主要是参考《机器学习实战》这本书。在参考大神的代码自己测试一番。
#################################################
# logRegression: Logistic Regression
# Author : cai
# Date : 2018-09-13
# HomePage : http://blog.csdn.net/
#################################################
from numpy import *
import matplotlib.pyplot as plt
import time
# calculate the sigmoid function
def sigmoid(inX):
return 1.0 / (1 + exp(-inX))
# train a logistic regression model using some optional optimize algorithm
# input: train_x is a mat datatype, each row stands for one sample
# train_y is mat datatype too, each row is the corresponding label
# opts is optimize option include step and maximum number of iterations opts 是一个优化选项,包括步长和最大迭代次数
def trainLogRegres(train_x, train_y, opts):
# 计算训练的起始时间
startTime = time.time()
numSamples, numFeatures = shape(train_x)#train_x的维度(XL,XL) 几行几列
alpha = opts['alpha']
maxIter = opts['maxIter'] #训练次数
weights = ones((numFeatures, 1))
#print 'weights',weights
print 'train_y:',train_y
print '训练次数maxIter:',maxIter
# optimize through gradient descent algorilthm 通过梯度下降算法优化
for k in range(maxIter):
if opts['optimizeType'] == 'gradDescent': # gradient descent algorilthm 梯度下降(gradient descent)
output = sigmoid(train_x * weights)
error = train_y - output
print 'error:',error
print 'output:',output
weights = weights + alpha * train_x.transpose() * error
elif opts['optimizeType'] == 'stocGradDescent': # 随机梯度下降SGD (stochastic gradient descent)
for i in range(numSamples):
output = sigmoid(train_x[i, :] * weights)
error = train_y[i, 0] - output
weights = weights + alpha * train_x[i, :].transpose() * error
elif opts['optimizeType'] == 'smoothStocGradDescent': # smooth stochastic gradient descent 改进的随机梯度下降
# randomly select samples to optimize for reducing cycle fluctuations 随机选择样本优化以减少周期波动
dataIndex = range(numSamples)
for i in range(numSamples):
alpha = 4.0 / (1.0 + k + i) + 0.01
randIndex = int(random.uniform(0, len(dataIndex)))
output = sigmoid(train_x[randIndex, :] * weights)
error = train_y[randIndex, 0] - output
weights = weights + alpha * train_x[randIndex, :].transpose() * error
del(dataIndex[randIndex]) # during one interation, delete the optimized sample
else:
raise NameError('Not support optimize method type!')
print 'Congratulations, training complete! Took %fs!' % (time.time() - startTime)
return weights
# test your trained Logistic Regression model given test set
def testLogRegres(weights, test_x, test_y):
numSamples, numFeatures = shape(test_x)
matchCount = 0
for i in xrange(numSamples):
predict = sigmoid(test_x[i, :] * weights)[0, 0] > 0.5
if predict == bool(test_y[i, 0]):
matchCount += 1
accuracy = float(matchCount) / numSamples
return accuracy
# show your trained logistic regression model only available with 2-D data
def showLogRegres(weights, train_x, train_y):
# notice: train_x and train_y is mat datatype
numSamples, numFeatures = shape(train_x)
if numFeatures != 3:
print "Sorry! I can not draw because the dimension of your data is not 2!"
return 1
# draw all samples
for i in xrange(numSamples):
if int(train_y[i, 0]) == 0:
plt.plot(train_x[i, 1], train_x[i, 2], 'or')
elif int(train_y[i, 0]) == 1:
plt.plot(train_x[i, 1], train_x[i, 2], 'ob')
# draw the classify line
min_x = min(train_x[:, 1])[0, 0]
max_x = max(train_x[:, 1])[0, 0]
weights = weights.getA() # convert mat to array
y_min_x = float(-weights[0] - weights[1] * min_x) / weights[2]
y_max_x = float(-weights[0] - weights[1] * max_x) / weights[2]
plt.plot([min_x, max_x], [y_min_x, y_max_x], '-g')
plt.xlabel('X1'); plt.ylabel('X2')
plt.show()
from numpy import *
import matplotlib.pyplot as plt
import time
def loadData():
train_x = []
train_y = []
fileIn = open('C:\\Users\\root\\Desktopng\\machinelearninginaction-master\\machinelearninginaction-master\\Ch05\\testSet1.txt')
for line in fileIn.readlines():
lineArr = line.strip().split()
train_x.append([1.0, float(lineArr[0]), float(lineArr[1])])
train_y.append(float(lineArr[2]))
return mat(train_x), mat(train_y).transpose() #.transpose()为转置
## step 1: load data 加载数据
print "step 1: load data..."
train_x, train_y = loadData()
test_x = train_x; test_y = train_y
## step 2: training... 训练
print "step 2: training..."
opts = {'alpha': 0.01, 'maxIter': 330, 'optimizeType': 'gradDescent'}
print '选择迭代次数maxIter:',opts['maxIter'],'\n','选择的方法为:optimizeType',opts['optimizeType']
optimalWeights = trainLogRegres(train_x, train_y, opts)
## step 3: testing 测试
print "step 3: testing..."
accuracy = testLogRegres(optimalWeights, test_x, test_y)
## step 4: show the result 显示结果
print "step 4: show the result..."
print 'The classify accuracy is: %.3f%%' % (accuracy * 100)
showLogRegres(optimalWeights, train_x, train_y)
都选择迭代330次,对比三种方法的正确率:
左一图 选择迭代次数maxIter: 330 选择的方法为:梯度下降algorilthm
正确率为:The classify accuracy is: 95.000%
右一图 选择迭代次数maxIter: 330 选择的方法为:随机梯度下降
正确率为:The classify accuracy is: 97.000%
左二图 选择迭代次数maxIter: 330 选择的方法为:平稳随机梯度下降
The classify accuracy is: 95.000%
结论:随机梯度下降的正确率最高达到了97%,更适合此场景下的数据挖掘。