def test_stoc_grade_plot(self): data_set, label_mat = logRegres.loadDataSet() print("\n data_set == %s" % (data_set)) print("\n label_mat == %s" % (label_mat)) weights = logRegres.stocGradAscent0(array(data_set), label_mat) print("\n weights == %s" % (weights)) logRegres.plotBestFit(weights)
#!/usr/bin/python # encoding: utf-8 ''' Created on Nov 28, 2015 @author: yanruibo ''' import logRegres import numpy as np if __name__ == '__main__': dataArr,labelMat = logRegres.loadDataSet() #weights = logRegres.gradAscent(dataArr, labelMat) weights = logRegres.stocGradAscent0(np.array(dataArr), labelMat) print weights #logRegres.plotBestFit(weights.getA()) logRegres.plotBestFit(weights)
def stocGradAscent0(): dataArr, labelMat = logRegres.loadDataSet() weights = logRegres.stocGradAscent0(array(dataArr), labelMat); print weights logRegres.plotBestFit(weights);
''' Created on Oct 6, 2010 @author: Peter ''' from numpy import * import matplotlib import matplotlib.pyplot as plt from matplotlib.patches import Rectangle import logRegres dataMat, labelMat = logRegres.loadDataSet() dataArr = array(dataMat) print dataArr[1] weights = logRegres.stocGradAscent0(dataArr, labelMat) print dataArr[1] n = shape(dataArr)[0] #number of points to create xcord1 = [] ycord1 = [] xcord2 = [] ycord2 = [] markers = [] colors = [] 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])
from numpy import * import logRegres 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])) weights = logRegres.stocGradAscent0(array(dataMat), labelMat) logRegres.plotBestFit(weights)
from numpy import * import logRegres dataarr, labelmat = logRegres.loadDataSet() weights = logRegres.gradAscent(dataarr, labelmat) print(weights) print(weights.getA()) #logRegres.plotBestFit(weights.getA()) weights = logRegres.stocGradAscent0(array(dataarr), labelmat) print(weights) #logRegres.plotBestFit(weights) weights = logRegres.stocGradAscent1(array(dataarr), labelmat) print(weights) #logRegres.plotBestFit(weights) logRegres.multiTest()
# -*- coding: utf-8 -*- from numpy import * import logRegres data, ls = logRegres.loadDataSet() wei1 = logRegres.gradAscent(data, ls) logRegres.plotBestFit(wei1) reload(logRegres) wei2 = logRegres.stocGradAscent0(array(data), ls) logRegres.plotBestFit(wei2) wei3 = logRegres.stocGradAscent1(array(data), ls) logRegres.plotBestFit(wei3) import logRegres logRegres.multiTest()
''' 2016.5.23 @author: zhuyu ''' from numpy import * import matplotlib import matplotlib.pyplot as plt from matplotlib.patches import Rectangle import logRegres dataMat,labelMat=logRegres.loadDataSet() dataArr = array(dataMat) weights = logRegres.stocGradAscent0(dataArr,labelMat) #weights = logRegres.stocGradAscent0(dataArr,labelMat) #weights = logRegres.stocGradAscent0(dataArr,labelMat) n = shape(dataArr)[0] #number of points to create xcord1 = []; ycord1 = [] xcord2 = []; ycord2 = [] markers =[] colors =[] 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(xcord,ycord, c=colors, s=markers)
''' Created on Oct 6, 2010 @author: Peter ''' from numpy import * import matplotlib import matplotlib.pyplot as plt from matplotlib.patches import Rectangle import logRegres dataArr, labelArr = logRegres.loadDataSet() dataArray = array(dataArr) weights = logRegres.stocGradAscent0(dataArray, labelArr) n = shape(dataArray)[0] #number of points to create xcord1 = [] ycord1 = [] xcord2 = [] ycord2 = [] markers = [] colors = [] for i in range(n): if int(labelArr[i]) == 1: xcord1.append(dataArray[i, 1]) ycord1.append(dataArray[i, 2]) else: xcord2.append(dataArray[i, 1]) ycord2.append(dataArray[i, 2])
from numpy import * import logRegres dataMat,labelMat=logRegres.loadDataSet(); weights=logRegres.stocGradAscent0(array(dataMat),labelMat) print(weights) logRegres.plotBestFit(weights)
import logRegres from numpy import * dataArr, labelMat = logRegres.loadDataSet() print(logRegres.gradAscent(dataArr, labelMat)) #打印回归系数 #打印随机梯度上升法拟合的回归系数 print(logRegres.stocGradAscent0(array(dataArr), labelMat)) #打印改进的随机梯度上升法拟合的回归系数 print(logRegres.stocGradAscent1(array(dataArr), labelMat))
def stocGradAscent0(): dataArr, labelMat = logRegres.loadDataSet() weights = logRegres.stocGradAscent0(array(dataArr), labelMat) print weights logRegres.plotBestFit(weights)
__author__ = 'sunbeansoft' import logRegres as lr from numpy import * dataArr, labelMat = lr.loadDataSet() weight = lr.gradAscent(dataArr, labelMat) lr.plotBestFit(weight.getA()) weight = lr.stocGradAscent0(array(dataArr), labelMat) lr.plotBestFit(weight) weight = lr.stocGradAscent1(array(dataArr), labelMat) lr.plotBestFit(weight) lr.multiTest()