本文實例講述了Python SVM(支持向量機)實現方法。分享給大家供大家參考，具體如下：

運行環境

計算過程

st=>start: 開始
e=>end: 結束
op1=>operation: 讀入數據
op2=>operation: 格式化數據
cond=>condition: 是否達到迭代次數
op3=>operation: 尋找超平面分割最小間隔
ccond=>conditon: 數據是否改變
op4=>operation: 輸出結果
st->op1->op2->cond
cond(yes)->op4->e
cond(no)->op3

啊，這markdown flow好難用，我決定就畫到這吧=。=

輸入樣例

/* testSet.txt */3.542485 1.977398 -13.018896 2.556416 -17.551510 -1.580030 12.114999 -0.004466 -18.127113 1.274372 17.108772 -0.986906 18.610639 2.046708 12.326297 0.265213 -13.634009 1.730537 -10.341367 -0.894998 -13.125951 0.293251 -12.123252 -0.783563 -10.887835 -2.797792 -17.139979 -2.329896 11.696414 -1.212496 -18.117032 0.623493 18.497162 -0.266649 14.658191 3.507396 -18.197181 1.545132 11.208047 0.213100 -11.928486 -0.321870 -12.175808 -0.014527 -17.886608 0.461755 13.223038 -0.552392 -13.628502 2.190585 -17.407860 -0.121961 17.286357 0.251077 12.301095 -0.533988 -1-0.232542 -0.547690 -13.457096 -0.082216 -13.023938 -0.057392 -18.015003 0.885325 18.991748 0.923154 17.916831 -1.781735 17.616862 -0.217958 12.450939 0.744967 -17.270337 -2.507834 11.749721 -0.961902 -11.803111 -0.176349 -18.804461 3.044301 11.231257 -0.568573 -12.074915 1.410550 -1-0.743036 -1.736103 -13.536555 3.964960 -18.410143 0.025606 17.382988 -0.478764 16.960661 -0.245353 18.234460 0.701868 18.168618 -0.903835 11.534187 -0.622492 -19.229518 2.066088 17.886242 0.191813 12.893743 -1.643468 -11.870457 -1.040420 -15.286862 -2.358286 16.080573 0.418886 12.544314 1.714165 -16.016004 -3.753712 10.926310 -0.564359 -10.870296 -0.109952 -12.369345 1.375695 -11.363782 -0.254082 -17.279460 -0.189572 11.896005 0.515080 -18.102154 -0.603875 12.529893 0.662657 -11.963874 -0.365233 -18.132048 0.785914 18.245938 0.372366 16.543888 0.433164 1-0.236713 -5.766721 -18.112593 0.295839 19.803425 1.495167 11.497407 -0.552916 -11.336267 -1.632889 -19.205805 -0.586480 11.966279 -1.840439 -18.398012 1.584918 17.239953 -1.764292 17.556201 0.241185 19.015509 0.345019 18.266085 -0.230977 18.545620 2.788799 19.295969 1.346332 12.404234 0.570278 -12.037772 0.021919 -11.727631 -0.453143 -11.979395 -0.050773 -18.092288 -1.372433 11.667645 0.239204 -19.854303 1.365116 17.921057 -1.327587 18.500757 1.492372 11.339746 -0.291183 -13.107511 0.758367 -12.609525 0.902979 -13.263585 1.367898 -12.912122 -0.202359 -11.731786 0.589096 -12.387003 1.573131 -1

代碼實現

# -*- coding:utf-8 -*-#!python3__author__ = 'Wsine'from numpy import *import matplotlib.pyplot as pltimport operatorimport timedef loadDataSet(fileName):  dataMat = []  labelMat = []  with open(fileName) as fr:    for line in fr.readlines():      lineArr = line.strip().split('/t')      dataMat.append([float(lineArr[0]), float(lineArr[1])])      labelMat.append(float(lineArr[2]))  return dataMat, labelMatdef selectJrand(i, m):  j = i  while (j == i):    j = int(random.uniform(0, m))  return jdef clipAlpha(aj, H, L):  if aj > H:    aj = H  if L > aj:    aj = L  return ajclass optStruct:  def __init__(self, dataMatIn, classLabels, C, toler):    self.X = dataMatIn    self.labelMat = classLabels    self.C = C    self.tol = toler    self.m = shape(dataMatIn)[0]    self.alphas = mat(zeros((self.m, 1)))    self.b = 0    self.eCache = mat(zeros((self.m, 2)))def calcEk(oS, k):  fXk = float(multiply(oS.alphas, oS.labelMat).T * (oS.X * oS.X[k, :].T)) + oS.b  Ek = fXk - float(oS.labelMat[k])  return Ekdef selectJ(i, oS, Ei):  maxK = -1  maxDeltaE = 0  Ej = 0  oS.eCache[i] = [1, Ei]  validEcacheList = nonzero(oS.eCache[:, 0].A)[0]  if (len(validEcacheList)) > 1:    for k in validEcacheList:      if k == i:        continue      Ek = calcEk(oS, k)      deltaE = abs(Ei - Ek)      if (deltaE > maxDeltaE):        maxK = k        maxDeltaE = deltaE        Ej = Ek    return maxK, Ej  else:    j = selectJrand(i, oS.m)    Ej = calcEk(oS, j)  return j, Ejdef updateEk(oS, k):  Ek = calcEk(oS, k)  oS.eCache[k] = [1, Ek]def innerL(i, oS):  Ei = calcEk(oS, i)  if ((oS.labelMat[i] * Ei < -oS.tol) and (oS.alphas[i] < oS.C)) or ((oS.labelMat[i] * Ei > oS.tol) and (oS.alphas[i] > 0)):    j, Ej = selectJ(i, oS, Ei)    alphaIold = oS.alphas[i].copy()    alphaJold = oS.alphas[j].copy()    if (oS.labelMat[i] != oS.labelMat[j]):      L = max(0, oS.alphas[j] - oS.alphas[i])      H = min(oS.C, oS.C + oS.alphas[j] - oS.alphas[i])    else:      L = max(0, oS.alphas[j] + oS.alphas[i] - oS.C)      H = min(oS.C, oS.alphas[j] + oS.alphas[i])    if (L == H):      # print("L == H")      return 0    eta = 2.0 * oS.X[i, :] * oS.X[j, :].T - oS.X[i, :] * oS.X[i, :].T - oS.X[j, :] * oS.X[j, :].T    if eta >= 0:      # print("eta >= 0")      return 0    oS.alphas[j] -= oS.labelMat[j] * (Ei - Ej) / eta    oS.alphas[j] = clipAlpha(oS.alphas[j], H, L)    updateEk(oS, j)    if (abs(oS.alphas[j] - alphaJold) < 0.00001):      # print("j not moving enough")      return 0    oS.alphas[i] += oS.labelMat[j] * oS.labelMat[i] * (alphaJold - oS.alphas[j])    updateEk(oS, i)    b1 = oS.b - Ei - oS.labelMat[i] * (oS.alphas[i] - alphaIold) * oS.X[i, :] * oS.X[i, :].T - oS.labelMat[j] * (oS.alphas[j] - alphaJold) * oS.X[i, :] * oS.X[j, :].T    b2 = oS.b - Ei - oS.labelMat[i] * (oS.alphas[i] - alphaIold) * oS.X[i, :] * oS.X[j, :].T - oS.labelMat[j] * (oS.alphas[j] - alphaJold) * oS.X[j, :] * oS.X[j, :].T    if (0 < oS.alphas[i]) and (oS.C > oS.alphas[i]):      oS.b = b1    elif (0 < oS.alphas[j]) and (oS.C > oS.alphas[j]):      oS.b = b2    else:      oS.b = (b1 + b2) / 2.0    return 1  else:    return 0def smoP(dataMatIn, classLabels, C, toler, maxIter, kTup=('lin', 0)):  """  輸入：數據集, 類別標簽, 常數C, 容錯率, 最大循環次數  輸出：目標b, 參數alphas  """  oS = optStruct(mat(dataMatIn), mat(classLabels).transpose(), C, toler)  iterr = 0  entireSet = True  alphaPairsChanged = 0  while (iterr < maxIter) and ((alphaPairsChanged > 0) or (entireSet)):    alphaPairsChanged = 0    if entireSet:      for i in range(oS.m):        alphaPairsChanged += innerL(i, oS)      # print("fullSet, iter: %d i:%d, pairs changed %d" % (iterr, i, alphaPairsChanged))      iterr += 1    else:      nonBoundIs = nonzero((oS.alphas.A > 0) * (oS.alphas.A < C))[0]      for i in nonBoundIs:        alphaPairsChanged += innerL(i, oS)        # print("non-bound, iter: %d i:%d, pairs changed %d" % (iterr, i, alphaPairsChanged))      iterr += 1    if entireSet:      entireSet = False    elif (alphaPairsChanged == 0):      entireSet = True    # print("iteration number: %d" % iterr)  return oS.b, oS.alphasdef calcWs(alphas, dataArr, classLabels):  """  輸入：alphas, 數據集, 類別標簽  輸出：目標w  """  X = mat(dataArr)  labelMat = mat(classLabels).transpose()  m, n = shape(X)  w = zeros((n, 1))  for i in range(m):    w += multiply(alphas[i] * labelMat[i], X[i, :].T)  return wdef plotFeature(dataMat, labelMat, weights, b):  dataArr = array(dataMat)  n = shape(dataArr)[0]  xcord1 = []; ycord1 = []  xcord2 = []; ycord2 = []  for i in range(n):    if int(labelMat[i]) == 1:      xcord1.append(dataArr[i, 0])      ycord1.append(dataArr[i, 1])    else:      xcord2.append(dataArr[i, 0])      ycord2.append(dataArr[i, 1])  fig = plt.figure()  ax = fig.add_subplot(111)  ax.scatter(xcord1, ycord1, s=30, c='red', marker='s')  ax.scatter(xcord2, ycord2, s=30, c='green')  x = arange(2, 7.0, 0.1)  y = (-b[0, 0] * x) - 10 / linalg.norm(weights)  ax.plot(x, y)  plt.xlabel('X1'); plt.ylabel('X2')  plt.show()def main():  trainDataSet, trainLabel = loadDataSet('testSet.txt')  b, alphas = smoP(trainDataSet, trainLabel, 0.6, 0.0001, 40)  ws = calcWs(alphas, trainDataSet, trainLabel)  print("ws = /n", ws)  print("b = /n", b)  plotFeature(trainDataSet, trainLabel, ws, b)if __name__ == '__main__':  start = time.clock()  main()  end = time.clock()  print('finish all in %s' % str(end - start))