# -*- coding: UTF-8 -*-
# Filename : 03BPTest.py
from numpy import *
import operator
import BackPropgation
import matplotlib.pyplot as plt
# 数据集
dataSet, classLabels = BackPropgation.loadDataSet(
"testSet2.txt") # 初始化时第1列为全1向量, studentTest.txt
dataSet = mat(dataSet)
m, n = shape(dataSet)
SampIn = dataSet.T
hi_wb = ones((m, 1))
hi_input = SampIn * hi_wb
print hi_input
# -*- coding: UTF-8 -*-
# Filename : 03BPTest.py
from numpy import *
import operator
import BackPropgation
import matplotlib.pyplot as plt
# 数据集
dataSet, classLabels = BackPropgation.loadDataSet(
"testSet2.txt") # 初始化时第1列为全1向量, studentTest.txt
dataSet = BackPropgation.normalize(mat(dataSet))
# 绘制数据点
# 重构dataSet数据集
dataMat = mat(ones((shape(dataSet)[0], shape(dataSet)[1])))
dataMat[:, 1] = mat(dataSet)[:, 0]
dataMat[:, 2] = mat(dataSet)[:, 1]
# 绘制数据集散点图
Untils.drawClassScatter(dataMat, transpose(classLabels), False)
# BP神经网络进行数据分类
errRec, WEX, wex = BackPropgation.bpNet(dataSet, classLabels)
# 计算和绘制分类线
x, z = BackPropgation.BPClassfier(-3.0, 3.0, WEX, wex)
Untils.classfyContour(x, x, z)
# 绘制误差曲线
# -*- coding: UTF-8 -*-
# Filename : 03BPTest.py
from numpy import *
import operator
import BackPropgation
import matplotlib.pyplot as plt
# 数据集
dataSet,classLabels = BackPropgation.loadDataSet("testSet2.txt") # 初始化时第1列为全1向量, studentTest.txt
dataSet = mat(dataSet)
m,n=shape(dataSet)
SampIn = dataSet.T
hi_wb = ones((m,1))
hi_input = SampIn*hi_wb
print hi_input
# -*- coding: GBK -*- # Filename :gradDecent.py from numpy import * import operator import Untils import BackPropgation import matplotlib.pyplot as plt # BP神经网络 # 数据集: 列1:截距 1列2:x坐标 列3:y坐标 dataMat,classLabels = BackPropgation.loadDataSet() # 初始化时第1列为全1向量 [m,n] = shape(dataMat) SampIn = mat(BackPropgation.normalize(mat(dataMat)).transpose()) expected = mat(classLabels) # 网络参数 eb = 0.01 # 误差容限 eta = 0.6 # 学习率 mc = 0.8 # 动量因子 maxiter = 1000 # 最大迭代次数 # 构造网络 # 初始化网络 nSampNum = m; # 样本数量 nSampDim = 2; # 样本维度 nHidden = 3; # 隐含层神经元 nOut = 1; # 输出层
# -*- coding: GBK -*- # Filename :gradDecent.py from numpy import * import operator import Untils import BackPropgation import matplotlib.pyplot as plt # BP神经网络 # 数据集: 列1:截距 1列2:x坐标 列3:y坐标 dataMat,classLabels = BackPropgation.loadDataSet() # 初始化时第1列为全1向量 [m,n] = shape(dataMat) SampIn = mat(BackPropgation.normalize(mat(dataMat)).transpose()) expected = mat(classLabels) # 网络参数 eb = 0.01 # 误差容限 eta = 0.6 # 学习率 mc = 0.8 # 动量因子 maxiter = 1000 # 最大迭代次数 # 构造网络 # 初始化网络 nSampNum = m; # 样本数量 nSampDim = 2; # 样本维度 nHidden = 3; # 隐含层神经元 nOut = 1; # 输出层
b = 2*(random.rand(nHidden,1)-1/2)
wex = mat(Untils.mergMatrix(mat(w),mat(b)))
# 输出层参数
W = 2*(random.rand(nOut,nHidden)-1/2)
B = 2*(random.rand(nOut,1)-1/2)
WEX = mat(Untils.mergMatrix(mat(W),mat(B)))
dWEXOld = 0 ; dwexOld = 0
# 训练
iteration = 0;
errRec = [];
for i in range(maxiter):
# 工作信号正向传播
hp = wex*SampIn
tau = BackPropgation.logsig(hp)
tauex = Untils.mergMatrix(tau.T, ones((nSampNum,1))).T
HM = WEX*tauex
out = BackPropgation.logsig(HM)
err = expected - out
sse = BackPropgation.sumsqr(err)
errRec.append(sse);
# 判断是否收敛
iteration = iteration + 1
if sse <= eb:
print "iteration:",i
break;
# 误差信号反向传播
# DELTA和delta为局部梯度
b = 2*(random.rand(nHidden,1)-1/2)
wex = mat(Untils.mergMatrix(mat(w),mat(b)))
# 输出层参数
W = 2*(random.rand(nOut,nHidden)-1/2)
B = 2*(random.rand(nOut,1)-1/2)
WEX = mat(Untils.mergMatrix(mat(W),mat(B)))
dWEXOld = 0 ; dwexOld = 0
# 训练
iteration = 0;
errRec = [];
for i in range(maxiter):
# 工作信号正向传播
hp = wex*SampIn
tau = BackPropgation.logsig(hp)
tauex = Untils.mergMatrix(tau.T, ones((nSampNum,1))).T
HM = WEX*tauex
out = BackPropgation.logsig(HM)
err = expected - out
sse = BackPropgation.sumsqr(err)
errRec.append(sse);
# 判断是否收敛
iteration = iteration + 1
if sse <= eb:
print "iteration:",i
break;
# 误差信号反向传播
# DELTA和delta为局部梯度
# 数据集
dataSet = [[0, 0, 1], [0, 1, 1], [1, 0, 1], [1, 1, 1]]
classLabels = [0, 1, 1, 0]
expected = mat(classLabels)
# 绘制数据点
# 重构dataSet数据集
dataMat = mat(ones((shape(dataSet)[0], shape(dataSet)[1])))
dataMat[:, 1] = mat(dataSet)[:, 0]
dataMat[:, 2] = mat(dataSet)[:, 1]
# 绘制数据集散点图
Untils.drawClassScatter(dataMat, transpose(expected), False)
# BP神经网络进行数据分类
errRec, WEX, wex = BackPropgation.bpNet(dataSet, classLabels)
print errRec, WEX, wex
# 计算和绘制分类线
x = linspace(-0.2, 1.2, 30)
xx = mat(ones((30, 30)))
xx[:, 0:30] = x
yy = xx.T
z = ones((len(xx), len(yy)))
for i in range(len(xx)):
for j in range(len(yy)):
xi = []
tauex = []
tautemp = []
mat(xi.append([xx[i, j], yy[i, j], 1]))
# -*- coding: GBK -*-
# Filename : 03BPTest.py
from numpy import *
import operator
import Untils
import BackPropgation
import matplotlib.pyplot as plt
# 数据集
dataSet,classLabels = BackPropgation.loadDataSet("testSet2.txt") # 初始化时第1列为全1向量, studentTest.txt
dataSet = BackPropgation.normalize(mat(dataSet))
# 绘制数据点
# 重构dataSet数据集
dataMat = mat(ones((shape(dataSet)[0],shape(dataSet)[1])))
dataMat[:,1] = mat(dataSet)[:,0]
dataMat[:,2] = mat(dataSet)[:,1]
# 绘制数据集散点图
Untils.drawClassScatter(dataMat,transpose(classLabels),False)
# BP神经网络进行数据分类
errRec,WEX,wex = BackPropgation.bpNet(dataSet,classLabels)
# 计算和绘制分类线
x,z = BackPropgation.BPClassfier(-3.0,3.0,WEX,wex)
Untils.classfyContour(x,x,z)
# 绘制误差曲线
# 数据集
dataSet = [[0,0,1],[0,1,1],[1,0,1],[1,1,1]]
classLabels = [0,1,1,0]
expected = mat(classLabels)
# 绘制数据点
# 重构dataSet数据集
dataMat = mat(ones((shape(dataSet)[0],shape(dataSet)[1])))
dataMat[:,1] = mat(dataSet)[:,0]
dataMat[:,2] = mat(dataSet)[:,1]
# 绘制数据集散点图
Untils.drawClassScatter(dataMat,transpose(expected),False)
# BP神经网络进行数据分类
errRec,WEX,wex = BackPropgation.bpNet(dataSet,classLabels)
print errRec,WEX,wex
# 计算和绘制分类线
x = linspace(-0.2,1.2,30)
xx = mat(ones((30,30)))
xx[:,0:30] = x
yy = xx.T
z = ones((len(xx),len(yy))) ;
for i in range(len(xx)):
for j in range(len(yy)):
xi = []; tauex=[] ; tautemp=[]
mat(xi.append([xx[i,j],yy[i,j],1]))
hp = wex*(mat(xi).T)
tau = BackPropgation.logistic(hp)