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添加了SVM无核函数的，注释参考有有核函数的
This commit is contained in:
jiangzhonglian
@@ -123,7 +123,7 @@ def selectJrand(i, m):
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def selectJ(i, oS, Ei): # this is the second choice -heurstic, and calcs Ej
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"""selectJ（）
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"""selectJ（返回最优的j和Ej）
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内循环的启发式方法。
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选择第二个(内循环)alpha的alpha值
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@@ -187,9 +187,6 @@ def updateEk(oS, k):
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Args:
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oS optStruct对象
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k 某一列的行号
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Returns:
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"""
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# 求 误差：预测值-真实值的差
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@@ -214,14 +211,15 @@ def clipAlpha(aj, H, L):
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def innerL(i, oS):
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"""
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"""innerL
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内循环代码
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Args:
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i 具体的某一行
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oS optStruct对象
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Returns:
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0 找不到最优的值
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1 找到了最优的值，并且oS.Cache到缓存中
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"""
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# 求 Ek误差：预测值-真实值的差
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@@ -311,20 +309,29 @@ def smoP(dataMatIn, classLabels, C, toler, maxIter, kTup=('lin', 0)):
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entireSet = True
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alphaPairsChanged = 0
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# 循环遍历：
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# 循环遍历：循环maxIter次 并且 （alphaPairsChanged存在可以改变 or 所有行遍历一遍）
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while (iter < maxIter) and ((alphaPairsChanged > 0) or (entireSet)):
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alphaPairsChanged = 0
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if entireSet: # 在数据集上遍历所有可能的alpha
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# 当entireSet=true or 非边界alpha对没有了；就开始寻找 alpha对，然后决定是否要进行else。
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if entireSet:
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# 在数据集上遍历所有可能的alpha
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for i in range(oS.m):
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# 是否存在alpha对，存在就+1
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alphaPairsChanged += innerL(i, oS)
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print("fullSet, iter: %d i:%d, pairs changed %d" % (iter, i, alphaPairsChanged))
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iter += 1
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else: # 遍历所有的非边界alpha值，也就是不在边界0或C上的值。
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# 对已存在 alpha对，选出非边界的alpha值，进行优化。
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else:
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# 遍历所有的非边界alpha值，也就是不在边界0或C上的值。
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nonBoundIs = nonzero((oS.alphas.A > 0) * (oS.alphas.A < C))[0]
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for i in nonBoundIs:
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alphaPairsChanged += innerL(i, oS)
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print("non-bound, iter: %d i:%d, pairs changed %d" % (iter, i, alphaPairsChanged))
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iter += 1
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# 如果找到alpha对，就优化非边界alpha值，否则，就重新进行寻找，如果寻找一遍 遍历所有的行还是没找到，就退出循环。
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if entireSet:
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entireSet = False # toggle entire set loop
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elif (alphaPairsChanged == 0):
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@@ -438,6 +445,7 @@ if __name__ == "__main__":
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def testRbf(k1=1.3):
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@@ -548,9 +556,6 @@ def calcEkK(oS, k):
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return Ek
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def selectJK(i, oS, Ei): # this is the second choice -heurstic, and calcs Ej
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maxK = -1
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maxDeltaE = 0
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375
src/python/6.SVM/svm-complete_Non-Kernel.py
Normal file
375
src/python/6.SVM/svm-complete_Non-Kernel.py
Normal file
@@ -0,0 +1,375 @@
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#!/usr/bin/python
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# coding:utf8
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"""
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Created on Nov 4, 2010
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Update on 2017-03-21
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Chapter 5 source file for Machine Learing in Action
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@author: Peter/geekidentity/片刻
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"""
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from numpy import *
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import matplotlib.pyplot as plt
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def loadDataSet(fileName):
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"""loadDataSet（对文件进行逐行解析，从而得到第行的类标签和整个数据矩阵）
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Args:
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fileName 文件名
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Returns:
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dataMat 数据矩阵
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labelMat 类标签
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"""
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dataMat = []
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labelMat = []
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fr = open(fileName)
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for line in fr.readlines():
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lineArr = line.strip().split('\t')
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dataMat.append([float(lineArr[0]), float(lineArr[1])])
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labelMat.append(float(lineArr[2]))
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return dataMat, labelMat
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def selectJrand(i, m):
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"""
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随机选择一个整数
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Args:
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i 第一个alpha的下标
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m 所有alpha的数目
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Returns:
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j 返回一个不为i的随机数，在0~m之间的整数值
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"""
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j = i
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while j == i:
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j = int(random.uniform(0, m))
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return j
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def clipAlpha(aj, H, L):
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"""clipAlpha(调整aj的值，使aj处于 L<=aj<=H)
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Args:
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aj 目标值
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H 最大值
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L 最小值
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Returns:
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aj 目标值
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"""
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if aj > H:
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aj = H
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if L > aj:
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aj = L
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return aj
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def calcWs(alphas, dataArr, classLabels):
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"""
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基于alpha计算w值
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Args:
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alphas 拉格朗日乘子
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dataArr feature数据集
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classLabels 目标变量数据集
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Returns:
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wc 回归系数
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"""
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X = mat(dataArr)
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labelMat = mat(classLabels).transpose()
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m, n = shape(X)
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w = zeros((n, 1))
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for i in range(m):
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w += multiply(alphas[i] * labelMat[i], X[i, :].T)
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return w
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'''
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#######********************************
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Non-Kernel VErsions below
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#######********************************
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'''
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class optStruct:
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def __init__(self, dataMatIn, classLabels, C, toler): # Initialize the structure with the parameters
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self.X = dataMatIn
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self.labelMat = classLabels
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self.C = C
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self.tol = toler
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self.m = shape(dataMatIn)[0]
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self.alphas = mat(zeros((self.m, 1)))
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self.b = 0
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self.eCache = mat(zeros((self.m, 2))) # first column is valid flag
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def calcEk(oS, k):
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fXk = float(multiply(oS.alphas, oS.labelMat).T * (oS.X * oS.X[k, :].T)) + oS.b
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Ek = fXk - float(oS.labelMat[k])
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return Ek
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def selectJ(i, oS, Ei): # this is the second choice -heurstic, and calcs Ej
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maxK = -1
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maxDeltaE = 0
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Ej = 0
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oS.eCache[i] = [1, Ei] # set valid #choose the alpha that gives the maximum delta E
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validEcacheList = nonzero(oS.eCache[:, 0].A)[0]
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if (len(validEcacheList)) > 1:
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for k in validEcacheList: # loop through valid Ecache values and find the one that maximizes delta E
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if k == i: continue # don't calc for i, waste of time
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Ek = calcEk(oS, k)
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deltaE = abs(Ei - Ek)
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if (deltaE > maxDeltaE):
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maxK = k
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maxDeltaE = deltaE
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Ej = Ek
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return maxK, Ej
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else: # in this case (first time around) we don't have any valid eCache values
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j = selectJrand(i, oS.m)
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Ej = calcEk(oS, j)
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return j, Ej
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def updateEk(oS, k): # after any alpha has changed update the new value in the cache
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Ek = calcEk(oS, k)
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oS.eCache[k] = [1, Ek]
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def innerL(i, oS):
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Ei = calcEk(oS, i)
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if ((oS.labelMat[i] * Ei < -oS.tol) and (oS.alphas[i] < oS.C)) or (
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(oS.labelMat[i] * Ei > oS.tol) and (oS.alphas[i] > 0)):
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j, Ej = selectJ(i, oS, Ei) # this has been changed from selectJrand
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alphaIold = oS.alphas[i].copy()
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alphaJold = oS.alphas[j].copy()
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if (oS.labelMat[i] != oS.labelMat[j]):
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L = max(0, oS.alphas[j] - oS.alphas[i])
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H = min(oS.C, oS.C + oS.alphas[j] - oS.alphas[i])
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else:
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L = max(0, oS.alphas[j] + oS.alphas[i] - oS.C)
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H = min(oS.C, oS.alphas[j] + oS.alphas[i])
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if L == H:
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print("L==H")
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return 0
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eta = 2.0 * oS.X[i, :] * oS.X[j, :].T - oS.X[i, :] * oS.X[i, :].T - oS.X[j, :] * oS.X[j, :].T
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if eta >= 0:
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print("eta>=0")
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return 0
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oS.alphas[j] -= oS.labelMat[j] * (Ei - Ej) / eta
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oS.alphas[j] = clipAlpha(oS.alphas[j], H, L)
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updateEk(oS, j) # added this for the Ecache
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if (abs(oS.alphas[j] - alphaJold) < 0.00001):
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print("j not moving enough")
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return 0
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oS.alphas[i] += oS.labelMat[j] * oS.labelMat[i] * (alphaJold - oS.alphas[j]) # update i by the same amount as j
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updateEk(oS, i) # added this for the Ecache #the update is in the oppostie direction
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b1 = oS.b - Ei - oS.labelMat[i] * (oS.alphas[i] - alphaIold) * oS.X[i, :] * oS.X[i, :].T - oS.labelMat[j] * (
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oS.alphas[j] - alphaJold) * oS.X[i, :] * oS.X[j, :].T
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b2 = oS.b - Ej - oS.labelMat[i] * (oS.alphas[i] - alphaIold) * oS.X[i, :] * oS.X[j, :].T - oS.labelMat[j] * (
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oS.alphas[j] - alphaJold) * oS.X[j, :] * oS.X[j, :].T
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if (0 < oS.alphas[i]) and (oS.C > oS.alphas[i]):
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oS.b = b1
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elif (0 < oS.alphas[j]) and (oS.C > oS.alphas[j]):
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oS.b = b2
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else:
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oS.b = (b1 + b2) / 2.0
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return 1
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else:
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return 0
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def smoP(dataMatIn, classLabels, C, toler, maxIter): # full Platt SMO
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oS = optStruct(mat(dataMatIn), mat(classLabels).transpose(), C, toler)
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iter = 0
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entireSet = True
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alphaPairsChanged = 0
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while (iter < maxIter) and ((alphaPairsChanged > 0) or (entireSet)):
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alphaPairsChanged = 0
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if entireSet: # go over all
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for i in range(oS.m):
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alphaPairsChanged += innerL(i, oS)
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print("fullSet, iter: %d i:%d, pairs changed %d" % (iter, i, alphaPairsChanged))
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iter += 1
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else: # go over non-bound (railed) alphas
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nonBoundIs = nonzero((oS.alphas.A > 0) * (oS.alphas.A < C))[0]
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for i in nonBoundIs:
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alphaPairsChanged += innerL(i, oS)
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print("non-bound, iter: %d i:%d, pairs changed %d" % (iter, i, alphaPairsChanged))
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iter += 1
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if entireSet:
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entireSet = False # toggle entire set loop
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elif (alphaPairsChanged == 0):
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entireSet = True
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print("iteration number: %d" % iter)
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return oS.b, oS.alphas
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def plotfig_SVM(xArr, yArr, ws, b, alphas):
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"""
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参考地址：
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http://blog.csdn.net/maoersong/article/details/24315633
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http://www.cnblogs.com/JustForCS/p/5283489.html
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http://blog.csdn.net/kkxgx/article/details/6951959
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"""
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xMat = mat(xArr)
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yMat = mat(yArr)
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# b原来是矩阵，先转为数组类型后其数组大小为（1,1），所以后面加[0]，变为(1,)
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b = array(b)[0]
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fig = plt.figure()
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ax = fig.add_subplot(111)
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# 注意flatten的用法
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ax.scatter(xMat[:, 0].flatten().A[0], xMat[:, 1].flatten().A[0])
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# x最大值，最小值根据原数据集dataArr[:, 0]的大小而定
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x = arange(-1.0, 10.0, 0.1)
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# 根据x.w + b = 0 得到，其式子展开为w0.x1 + w1.x2 + b = 0, x2就是y值
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y = (-b-ws[0, 0]*x)/ws[1, 0]
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ax.plot(x, y)
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for i in range(shape(yMat[0, :])[1]):
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if yMat[0, i] > 0:
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ax.plot(xMat[i, 0], xMat[i, 1], 'cx')
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else:
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ax.plot(xMat[i, 0], xMat[i, 1], 'kp')
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# 找到支持向量，并在图中标红
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for i in range(100):
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if alphas[i] > 0.0:
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ax.plot(xMat[i, 0], xMat[i, 1], 'ro')
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plt.show()
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if __name__ == "__main__":
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# 获取特征和目标变量
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dataArr, labelArr = loadDataSet('input/6.SVM/testSet.txt')
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# print labelArr
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# b是常量值， alphas是拉格朗日乘子
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b, alphas = smoP(dataArr, labelArr, 0.6, 0.001, 40)
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print '/n/n/n'
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print 'b=', b
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print 'alphas[alphas>0]=', alphas[alphas > 0]
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print 'shape(alphas[alphas > 0])=', shape(alphas[alphas > 0])
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for i in range(100):
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if alphas[i] > 0:
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print dataArr[i], labelArr[i]
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# 画图
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ws = calcWs(alphas, dataArr, labelArr)
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plotfig_SVM(dataArr, labelArr, ws, b, alphas)
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def testRbf(k1=1.3):
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dataArr, labelArr = loadDataSet('testSetRBF.txt')
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b, alphas = smoP(dataArr, labelArr, 200, 0.0001, 10000, ('rbf', k1)) # C=200 important
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datMat = mat(dataArr)
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labelMat = mat(labelArr).transpose()
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svInd = nonzero(alphas.A > 0)[0]
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sVs = datMat[svInd] # get matrix of only support vectors
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labelSV = labelMat[svInd]
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print("there are %d Support Vectors" % shape(sVs)[0])
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m, n = shape(datMat)
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errorCount = 0
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for i in range(m):
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kernelEval = kernelTrans(sVs, datMat[i, :], ('rbf', k1))
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predict = kernelEval.T * multiply(labelSV, alphas[svInd]) + b
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if sign(predict) != sign(labelArr[i]): errorCount += 1
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print("the training error rate is: %f" % (float(errorCount) / m))
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dataArr, labelArr = loadDataSet('testSetRBF2.txt')
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errorCount = 0
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datMat = mat(dataArr)
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labelMat = mat(labelArr).transpose()
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m, n = shape(datMat)
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for i in range(m):
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kernelEval = kernelTrans(sVs, datMat[i, :], ('rbf', k1))
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predict = kernelEval.T * multiply(labelSV, alphas[svInd]) + b
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if sign(predict) != sign(labelArr[i]): errorCount += 1
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print("the test error rate is: %f" % (float(errorCount) / m))
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def img2vector(filename):
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returnVect = zeros((1, 1024))
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fr = open(filename)
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for i in range(32):
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lineStr = fr.readline()
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for j in range(32):
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returnVect[0, 32 * i + j] = int(lineStr[j])
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return returnVect
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def loadImages(dirName):
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from os import listdir
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hwLabels = []
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print(dirName)
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trainingFileList = listdir(dirName) # load the training set
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m = len(trainingFileList)
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trainingMat = zeros((m, 1024))
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for i in range(m):
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fileNameStr = trainingFileList[i]
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fileStr = fileNameStr.split('.')[0] # take off .txt
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classNumStr = int(fileStr.split('_')[0])
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if classNumStr == 9:
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hwLabels.append(-1)
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else:
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hwLabels.append(1)
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trainingMat[i, :] = img2vector('%s/%s' % (dirName, fileNameStr))
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return trainingMat, hwLabels
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def testDigits(kTup=('rbf', 10)):
|
||||
dataArr, labelArr = loadImages('trainingDigits')
|
||||
b, alphas = smoP(dataArr, labelArr, 200, 0.0001, 10000, kTup)
|
||||
datMat = mat(dataArr)
|
||||
labelMat = mat(labelArr).transpose()
|
||||
svInd = nonzero(alphas.A > 0)[0]
|
||||
sVs = datMat[svInd]
|
||||
labelSV = labelMat[svInd]
|
||||
print("there are %d Support Vectors" % shape(sVs)[0])
|
||||
m, n = shape(datMat)
|
||||
errorCount = 0
|
||||
for i in range(m):
|
||||
kernelEval = kernelTrans(sVs, datMat[i, :], kTup)
|
||||
predict = kernelEval.T * multiply(labelSV, alphas[svInd]) + b
|
||||
if sign(predict) != sign(labelArr[i]): errorCount += 1
|
||||
print("the training error rate is: %f" % (float(errorCount) / m))
|
||||
dataArr, labelArr = loadImages('testDigits')
|
||||
errorCount = 0
|
||||
datMat = mat(dataArr)
|
||||
labelMat = mat(labelArr).transpose()
|
||||
m, n = shape(datMat)
|
||||
for i in range(m):
|
||||
kernelEval = kernelTrans(sVs, datMat[i, :], kTup)
|
||||
predict = kernelEval.T * multiply(labelSV, alphas[svInd]) + b
|
||||
if sign(predict) != sign(labelArr[i]): errorCount += 1
|
||||
print("the test error rate is: %f" % (float(errorCount) / m))
|
||||
|
||||
Reference in New Issue
Block a user