Exemplos de stocGradAscent0 em Python

Exemplo n.º 1

 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)

Exemplo n.º 2

#!/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)

Exemplo n.º 3

Arquivo: execLogClassifier.py Projeto: androiddream/graph-mind

def stocGradAscent0():
    dataArr, labelMat = logRegres.loadDataSet()
    weights = logRegres.stocGradAscent0(array(dataArr), labelMat);
    print weights
    
    logRegres.plotBestFit(weights);

Exemplo n.º 4

Arquivo: plot2D.py Projeto: zicoal/py

'''
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])

Exemplo n.º 5

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)

Exemplo n.º 6

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()

Exemplo n.º 7

# -*- 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()

Exemplo n.º 8

Arquivo: plot2D.py Projeto: rainzhu/MachineLearningStudy

'''
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)

Exemplo n.º 9

'''
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])

Exemplo n.º 10

Arquivo: test.py Projeto: zhumingjun/machine-learning

from numpy import *
import logRegres
dataMat,labelMat=logRegres.loadDataSet();
weights=logRegres.stocGradAscent0(array(dataMat),labelMat)
print(weights)
logRegres.plotBestFit(weights)

Exemplo n.º 11

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))

Exemplo n.º 12

Arquivo: execLogClassifier.py Projeto: binkmust/graph-mind

def stocGradAscent0():
    dataArr, labelMat = logRegres.loadDataSet()
    weights = logRegres.stocGradAscent0(array(dataArr), labelMat)
    print weights

    logRegres.plotBestFit(weights)

Exemplo n.º 13

Arquivo: command.py Projeto: sunbeansoft/MLaction

__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()