Python BackPropgation.normalize Beispiele

Beispiel #1

Datei: BP_Test01.py Projekt: manji1992/ml_cn_book_code

# -*- 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

Beispiel #2

Datei: 04BP_Dual.py Projekt: wenbo/MLBook

# -*- 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

Beispiel #3

Datei: 04BP_Dual.py Projekt: ttsang529/Machine_Learning_Algorithm_and_Implementation

# -*- 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

Beispiel #4

Datei: 04BPTest.py Projekt: wenbo/MLBook

# -*- 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