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遗传算法的神经网络python实现代码详解编程语言

• 编程笔记

遗传算法的神经网络python实现代码 

## {{{ http://code.activestate.com/recipes/578241/ (r1) 
from operator import itemgetter, attrgetter 
import math 
import random 
import string 
import timeit 
from timeit import Timer as t 
import matplotlib.pyplot as plt 
import numpy as np 
  
def sigmoid (x): 
  return math.tanh(x) 
  
def makeMatrix ( I, J, fill=0.0): 
  m = [] 
  for i in range(I): 
    m.append([fill]*J) 
  return m 
    
def randomizeMatrix ( matrix, a, b): 
  for i in range ( len (matrix) ): 
    for j in range ( len (matrix[0]) ): 
      matrix[i][j] = random.uniform(a,b) 
  
class NN: 
  def __init__(self, NI, NH, NO): 
    self.ni = NI 
    self.nh = NH 
    self.no = NO 
    self.ai = [1.0]*self.ni 
    self.ah = [1.0]*self.nh 
    self.ao = [1.0]*self.no 
    self.wi = [ [0.0]*self.nh for i in range(self.ni) ] 
    self.wo = [ [0.0]*self.no for j in range(self.nh) ] 
    randomizeMatrix ( self.wi, -0.2, 0.2 ) 
    randomizeMatrix ( self.wo, -2.0, 2.0 ) 
  
  def runNN (self, inputs): 
    if len(inputs) != self.ni: 
      print 'incorrect number of inputs' 
    for i in range(self.ni): 
      self.ai[i] = inputs[i] 
    for j in range(self.nh): 
      self.ah[j] = sigmoid(sum([ self.ai[i]*self.wi[i][j] for i in range(self.ni) ])) 
    for k in range(self.no): 
      self.ao[k] = sigmoid(sum([ self.ah[j]*self.wo[j][k] for j in range(self.nh) ])) 
    return self.ao 
  
  def weights(self): 
    print 'Input weights:' 
    for i in range(self.ni): 
      print self.wi[i] 
    print 
    print 'Output weights:' 
    for j in range(self.nh): 
      print self.wo[j] 
    print '' 
  
  def test(self, patterns): 
    results, targets = [], [] 
    for p in patterns: 
      inputs = p[0] 
      rounded = [ round(i) for i in self.runNN(inputs) ] 
      if rounded == p[1]: result = '+++++' 
      else: result = '-----' 
      print '%s %s %s %s %s %s %s' %( 'Inputs:', p[0], '-->', str(self.runNN(inputs)).rjust(65), 'Target', p[1], result) 
      results+= self.runNN(inputs) 
      targets += p[1] 
    return results, targets 
  
  def sumErrors (self): 
    error = 0.0 
    for p in pat: 
      inputs = p[0] 
      targets = p[1] 
      self.runNN(inputs) 
      error += self.calcError(targets) 
    inverr = 1.0/error 
    return inverr 
  
  def calcError (self, targets): 
    error = 0.0 
    for k in range(len(targets)): 
      error += 0.5 * (targets[k]-self.ao[k])**2 
    return error 
  
  def assignWeights (self, weights, I): 
    io = 0 
    for i in range(self.ni): 
      for j in range(self.nh): 
        self.wi[i][j] = weights[I][io][i][j] 
    io = 1 
    for j in range(self.nh): 
      for k in range(self.no): 
        self.wo[j][k] = weights[I][io][j][k] 
  
  def testWeights (self, weights, I): 
    same = [] 
    io = 0 
    for i in range(self.ni): 
      for j in range(self.nh): 
        if self.wi[i][j] != weights[I][io][i][j]: 
          same.append(('I',i,j, round(self.wi[i][j],2),round(weights[I][io][i][j],2),round(self.wi[i][j] - weights[I][io][i][j],2))) 
  
    io = 1 
    for j in range(self.nh): 
      for k in range(self.no): 
        if self.wo[j][k] !=  weights[I][io][j][k]: 
          same.append((('O',j,k), round(self.wo[j][k],2),round(weights[I][io][j][k],2),round(self.wo[j][k] - weights[I][io][j][k],2))) 
    if same != []: 
      print same 
  
def roulette (fitnessScores): 
  cumalativeFitness = 0.0 
  r = random.random() 
  for i in range(len(fitnessScores)): 
    cumalativeFitness += fitnessScores[i] 
    if cumalativeFitness > r: 
      return i 
        
def calcFit (numbers):  # each fitness is a fraction of the total error 
  total, fitnesses = sum(numbers), [] 
  for i in range(len(numbers)):           
    fitnesses.append(numbers[i]/total) 
  return fitnesses 
  
# takes a population of NN objects 
def pairPop (pop): 
  weights, errors = [], [] 
  for i in range(len(pop)):                 # for each individual 
    weights.append([pop[i].wi,pop[i].wo])   # append input & output weights of individual to list of all pop weights 
    errors.append(pop[i].sumErrors())       # append 1/sum(MSEs) of individual to list of pop errors 
  fitnesses = calcFit(errors)               # fitnesses are a fraction of the total error 
  for i in range(int(pop_size*0.15)): 
    print str(i).zfill(2), '1/sum(MSEs)', str(errors[i]).rjust(15), str(int(errors[i]*graphical_error_scale)*'-').rjust(20), 'fitness'.rjust(12), str(fitnesses[i]).rjust(17), str(int(fitnesses[i]*1000)*'-').rjust(20) 
  del pop 
  return zip(weights, errors,fitnesses)            # weights become item[0] and fitnesses[1] in this way fitness is paired with its weight in a tuple 
    
def rankPop (newpopW,pop): 
  errors, copy = [], []           # a fresh pop of NN's are assigned to a list of len pop_size 
  #pop = [NN(ni,nh,no)]*pop_size # this does not work as they are all copies of eachother 
  pop = [NN(ni,nh,no) for i in range(pop_size) ] 
  for i in range(pop_size): copy.append(newpopW[i]) 
  for i in range(pop_size):  
    pop[i].assignWeights(newpopW, i)                                    # each individual is assigned the weights generated from previous iteration 
    pop[i].testWeights(newpopW, i) 
  for i in range(pop_size):  
    pop[i].testWeights(newpopW, i) 
  pairedPop = pairPop(pop)                                              # the fitness of these weights is calculated and tupled with the weights 
  rankedPop = sorted(pairedPop, key = itemgetter(-1), reverse = True)   # weights are sorted in descending order of fitness (fittest first) 
  errors = [ eval(repr(x[1])) for x in rankedPop ] 
  return rankedPop, eval(repr(rankedPop[0][1])), float(sum(errors))/float(len(errors)) 
  
def iteratePop (rankedPop): 
  rankedWeights = [ item[0] for item in rankedPop] 
  fitnessScores = [ item[-1] for item in rankedPop] 
  newpopW = [ eval(repr(x)) for x in rankedWeights[:int(pop_size*0.15)] ] 
  while len(newpopW) <= pop_size:                                       # Breed two randomly selected but different chromos until pop_size reached 
    ch1, ch2 = [], [] 
    index1 = roulette(fitnessScores)                                    
    index2 = roulette(fitnessScores) 
    while index1 == index2:                                             # ensures different chromos are used for breeeding 
      index2 = roulette(fitnessScores) 
    #index1, index2 = 3,4 
    ch1.extend(eval(repr(rankedWeights[index1]))) 
    ch2.extend(eval(repr(rankedWeights[index2]))) 
    if random.random() < crossover_rate: 
      ch1, ch2 = crossover(ch1, ch2) 
    mutate(ch1) 
    mutate(ch2) 
    newpopW.append(ch1) 
    newpopW.append(ch2) 
  return newpopW 
  
graphical_error_scale = 100 
max_iterations = 4000 
pop_size = 100 
mutation_rate = 0.1 
crossover_rate = 0.8 
ni, nh, no = 4,6,1 
  
def main (): 
  # Rank first random population 
  pop = [ NN(ni,nh,no) for i in range(pop_size) ] # fresh pop 
  pairedPop = pairPop(pop) 
  rankedPop = sorted(pairedPop, key = itemgetter(-1), reverse = True) # THIS IS CORRECT 
  # Keep iterating new pops until max_iterations 
  iters = 0 
  tops, avgs = [], [] 
  while iters != max_iterations: 
    if iters%1 == 0: 
      print 'Iteration'.rjust(150), iters 
    newpopW = iteratePop(rankedPop) 
    rankedPop, toperr, avgerr = rankPop(newpopW,pop) 
    tops.append(toperr) 
    avgs.append(avgerr) 
    iters+=1 
    
  # test a NN with the fittest weights 
  tester = NN (ni,nh,no) 
  fittestWeights = [ x[0] for x in rankedPop ] 
  tester.assignWeights(fittestWeights, 0) 
  results, targets = tester.test(testpat) 
  x = np.arange(0,150) 
  title2 = 'Test after '+str(iters)+' iterations' 
  plt.title(title2) 
  plt.ylabel('Node output') 
  plt.xlabel('Instances') 
  plt.plot( results, 'xr', linewidth = 0.5) 
  plt.plot( targets, 's', color = 'black',linewidth = 3) 
  #lines = plt.plot( results, 'sg') 
  plt.annotate(s='Target Values', xy = (110, 0),color = 'black', family = 'sans-serif', size  ='small') 
  plt.annotate(s='Test Values', xy = (110, 0.5),color = 'red', family = 'sans-serif', size  ='small', weight = 'bold') 
  plt.figure(2) 
  plt.subplot(121) 
  plt.title('Top individual error evolution') 
  plt.ylabel('Inverse error') 
  plt.xlabel('Iterations') 
  plt.plot( tops, '-g', linewidth = 1) 
  plt.subplot(122) 
  plt.plot( avgs, '-g', linewidth = 1) 
  plt.title('Population average error evolution') 
  plt.ylabel('Inverse error') 
  plt.xlabel('Iterations') 
    
  plt.show() 
    
  print 'max_iterations',max_iterations,'/tpop_size',pop_size,'pop_size*0.15',int(pop_size*0.15),'/tmutation_rate',mutation_rate,'crossover_rate',crossover_rate,'ni, nh, no',ni, nh, no 
  
def crossover (m1, m2): 
  r = random.randint(0, (ni*nh)+(nh*no) ) # ni*nh+nh*no  = total weights 
  output1 = [ [[0.0]*nh]*ni ,[[0.0]*no]*nh ] 
  output2 = [ [[0.0]*nh]*ni ,[[0.0]*no]*nh ] 
  for i in range(len(m1)): 
    for j in range(len(m1[i])): 
      for k in range(len(m1[i][j])): 
        if r >= 0: 
          output1[i][j][k] = m1[i][j][k] 
          output2[i][j][k] = m2[i][j][k] 
        elif r < 0: 
          output1[i][j][k] = m2[i][j][k] 
          output2[i][j][k] = m1[i][j][k] 
        r -=1 
  return output1, output2 
  
def mutate (m): 
  # could include a constant to control 
  # how much the weight is mutated by 
  for i in range(len(m)): 
    for j in range(len(m[i])): 
      for k in range(len(m[i][j])): 
        if random.random() < mutation_rate: 
            m[i][j][k] = random.uniform(-2.0,2.0) 
    
if __name__ == "__main__": 
    main() 
pat = [ 
  [[5.1, 3.5, 1.4, 0.2], [-1], ['Iris-setosa']] , 
  [[4.9, 3.0, 1.4, 0.2], [-1], ['Iris-setosa']] , 
  [[4.7, 3.2, 1.3, 0.2], [-1], ['Iris-setosa']] , 
  [[5.4, 3.9, 1.7, 0.4], [-1], ['Iris-setosa']] , 
  [[4.6, 3.4, 1.4, 0.3], [-1], ['Iris-setosa']] , 
  [[5.0, 3.4, 1.5, 0.2], [-1], ['Iris-setosa']] , 
  [[4.4, 2.9, 1.4, 0.2], [-1], ['Iris-setosa']] , 
  [[4.9, 3.1, 1.5, 0.1], [-1], ['Iris-setosa']] , 
  [[5.4, 3.7, 1.5, 0.2], [-1], ['Iris-setosa']] , 
  [[4.8, 3.4, 1.6, 0.2], [-1], ['Iris-setosa']] , 
  [[4.8, 3.0, 1.4, 0.1], [-1], ['Iris-setosa']] , 
  [[4.3, 3.0, 1.1, 0.1], [-1], ['Iris-setosa']] , 
  [[5.8, 4.0, 1.2, 0.2], [-1], ['Iris-setosa']] , 
  [[5.7, 4.4, 1.5, 0.4], [-1], ['Iris-setosa']] , 
  [[5.4, 3.9, 1.3, 0.4], [-1], ['Iris-setosa']] , 
  [[5.1, 3.5, 1.4, 0.3], [-1], ['Iris-setosa']] , 
  [[5.7, 3.8, 1.7, 0.3], [-1], ['Iris-setosa']] , 
  [[5.1, 3.8, 1.5, 0.3], [-1], ['Iris-setosa']] , 
  [[5.4, 3.4, 1.7, 0.2], [-1], ['Iris-setosa']] , 
  [[5.1, 3.7, 1.5, 0.4], [-1], ['Iris-setosa']] , 
  [[4.6, 3.6, 1.0, 0.2], [-1], ['Iris-setosa']] , 
  [[5.1, 3.3, 1.7, 0.5], [-1], ['Iris-setosa']] , 
  [[4.8, 3.4, 1.9, 0.2], [-1], ['Iris-setosa']] , 
  [[5.0, 3.0, 1.6, 0.2], [-1], ['Iris-setosa']] , 
  [[5.0, 3.4, 1.6, 0.4], [-1], ['Iris-setosa']] , 
  [[5.2, 3.5, 1.5, 0.2], [-1], ['Iris-setosa']] , 
  [[5.2, 3.4, 1.4, 0.2], [-1], ['Iris-setosa']] , 
  [[4.7, 3.2, 1.6, 0.2], [-1], ['Iris-setosa']] , 
  [[4.8, 3.1, 1.6, 0.2], [-1], ['Iris-setosa']] , 
  [[5.4, 3.4, 1.5, 0.4], [-1], ['Iris-setosa']] , 
  [[5.2, 4.1, 1.5, 0.1], [-1], ['Iris-setosa']] , 
  [[5.5, 4.2, 1.4, 0.2], [-1], ['Iris-setosa']] , 
  [[4.9, 3.1, 1.5, 0.1], [-1], ['Iris-setosa']] , 
  [[5.0, 3.2, 1.2, 0.2], [-1], ['Iris-setosa']] , 
  [[5.5, 3.5, 1.3, 0.2], [-1], ['Iris-setosa']] , 
  [[4.9, 3.1, 1.5, 0.1], [-1], ['Iris-setosa']] , 
  [[4.4, 3.0, 1.3, 0.2], [-1], ['Iris-setosa']] , 
  [[5.1, 3.4, 1.5, 0.2], [-1], ['Iris-setosa']] , 
  [[5.0, 3.5, 1.3, 0.3], [-1], ['Iris-setosa']] , 
  [[4.5, 2.3, 1.3, 0.3], [-1], ['Iris-setosa']] , 
  [[4.4, 3.2, 1.3, 0.2], [-1], ['Iris-setosa']] , 
  [[5.0, 3.5, 1.6, 0.6], [-1], ['Iris-setosa']] , 
  [[5.1, 3.8, 1.9, 0.4], [-1], ['Iris-setosa']] , 
  [[4.8, 3.0, 1.4, 0.3], [-1], ['Iris-setosa']] , 
  [[5.1, 3.8, 1.6, 0.2], [-1], ['Iris-setosa']] , 
  [[4.6, 3.2, 1.4, 0.2], [-1], ['Iris-setosa']] , 
  [[5.3, 3.7, 1.5, 0.2], [-1], ['Iris-setosa']] , 
  [[5.0, 3.3, 1.4, 0.2], [-1], ['Iris-setosa']] , 
  [[7.0, 3.2, 4.7, 1.4], [0], ['Iris-versicolor']] , 
  [[6.4, 3.2, 4.5, 1.5], [0], ['Iris-versicolor']] , 
  [[6.9, 3.1, 4.9, 1.5], [0], ['Iris-versicolor']] , 
  [[5.5, 2.3, 4.0, 1.3], [0], ['Iris-versicolor']] , 
  [[6.5, 2.8, 4.6, 1.5], [0], ['Iris-versicolor']] , 
  [[5.7, 2.8, 4.5, 1.3], [0], ['Iris-versicolor']] , 
  [[6.3, 3.3, 4.7, 1.6], [0], ['Iris-versicolor']] , 
  [[4.9, 2.4, 3.3, 1.0], [0], ['Iris-versicolor']] , 
  [[6.6, 2.9, 4.6, 1.3], [0], ['Iris-versicolor']] , 
  [[5.2, 2.7, 3.9, 1.4], [0], ['Iris-versicolor']] , 
  [[5.0, 2.0, 3.5, 1.0], [0], ['Iris-versicolor']] , 
  [[5.9, 3.0, 4.2, 1.5], [0], ['Iris-versicolor']] , 
  [[6.0, 2.2, 4.0, 1.0], [0], ['Iris-versicolor']] , 
  [[6.1, 2.9, 4.7, 1.4], [0], ['Iris-versicolor']] , 
  [[5.6, 2.9, 3.6, 1.3], [0], ['Iris-versicolor']] , 
  [[6.7, 3.1, 4.4, 1.4], [0], ['Iris-versicolor']] , 
  [[5.6, 3.0, 4.5, 1.5], [0], ['Iris-versicolor']] , 
  [[5.8, 2.7, 4.1, 1.0], [0], ['Iris-versicolor']] , 
  [[6.2, 2.2, 4.5, 1.5], [0], ['Iris-versicolor']] , 
  [[5.6, 2.5, 3.9, 1.1], [0], ['Iris-versicolor']] , 
  [[5.9, 3.2, 4.8, 1.8], [0], ['Iris-versicolor']] , 
  [[6.1, 2.8, 4.0, 1.3], [0], ['Iris-versicolor']] , 
  [[6.3, 2.5, 4.9, 1.5], [0], ['Iris-versicolor']] , 
  [[6.1, 2.8, 4.7, 1.2], [0], ['Iris-versicolor']] , 
  [[6.4, 2.9, 4.3, 1.3], [0], ['Iris-versicolor']] , 
  [[6.6, 3.0, 4.4, 1.4], [0], ['Iris-versicolor']] , 
  [[6.8, 2.8, 4.8, 1.4], [0], ['Iris-versicolor']] , 
  [[6.7, 3.0, 5.0, 1.7], [0], ['Iris-versicolor']] , 
  [[6.0, 2.9, 4.5, 1.5], [0], ['Iris-versicolor']] , 
  [[5.7, 2.6, 3.5, 1.0], [0], ['Iris-versicolor']] , 
  [[5.5, 2.4, 3.8, 1.1], [0], ['Iris-versicolor']] , 
  [[5.5, 2.4, 3.7, 1.0], [0], ['Iris-versicolor']] , 
  [[5.8, 2.7, 3.9, 1.2], [0], ['Iris-versicolor']] , 
  [[6.0, 2.7, 5.1, 1.6], [0], ['Iris-versicolor']] , 
  [[5.4, 3.0, 4.5, 1.5], [0], ['Iris-versicolor']] , 
  [[6.0, 3.4, 4.5, 1.6], [0], ['Iris-versicolor']] , 
  [[6.7, 3.1, 4.7, 1.5], [0], ['Iris-versicolor']] , 
  [[6.3, 2.3, 4.4, 1.3], [0], ['Iris-versicolor']] , 
  [[5.6, 3.0, 4.1, 1.3], [0], ['Iris-versicolor']] , 
  [[6.1, 3.0, 4.6, 1.4], [0], ['Iris-versicolor']] , 
  [[5.8, 2.6, 4.0, 1.2], [0], ['Iris-versicolor']] , 
  [[5.0, 2.3, 3.3, 1.0], [0], ['Iris-versicolor']] , 
  [[5.6, 2.7, 4.2, 1.3], [0], ['Iris-versicolor']] , 
  [[5.7, 3.0, 4.2, 1.2], [0], ['Iris-versicolor']] , 
  [[5.7, 2.9, 4.2, 1.3], [0], ['Iris-versicolor']] , 
  [[6.2, 2.9, 4.3, 1.3], [0], ['Iris-versicolor']] , 
  [[5.1, 2.5, 3.0, 1.1], [0], ['Iris-versicolor']] , 
  [[5.7, 2.8, 4.1, 1.3], [0], ['Iris-versicolor']] , 
  [[6.3, 3.3, 6.0, 2.5], [1], ['Iris-virginica']] , 
  [[5.8, 2.7, 5.1, 1.9], [1], ['Iris-virginica']] , 
  [[7.1, 3.0, 5.9, 2.1], [1], ['Iris-virginica']] , 
  [[6.3, 2.9, 5.6, 1.8], [1], ['Iris-virginica']] , 
  [[6.5, 3.0, 5.8, 2.2], [1], ['Iris-virginica']] , 
  [[7.6, 3.0, 6.6, 2.1], [1], ['Iris-virginica']] , 
  [[4.9, 2.5, 4.5, 1.7], [1], ['Iris-virginica']] , 
  [[7.3, 2.9, 6.3, 1.8], [1], ['Iris-virginica']] , 
  [[6.7, 2.5, 5.8, 1.8], [1], ['Iris-virginica']] , 
  [[7.2, 3.6, 6.1, 2.5], [1], ['Iris-virginica']] , 
  [[6.5, 3.2, 5.1, 2.0], [1], ['Iris-virginica']] , 
  [[6.4, 2.7, 5.3, 1.9], [1], ['Iris-virginica']] , 
  [[6.8, 3.0, 5.5, 2.1], [1], ['Iris-virginica']] , 
  [[5.7, 2.5, 5.0, 2.0], [1], ['Iris-virginica']] , 
  [[5.8, 2.8, 5.1, 2.4], [1], ['Iris-virginica']] , 
  [[7.7, 3.8, 6.7, 2.2], [1], ['Iris-virginica']] , 
  [[7.7, 2.6, 6.9, 2.3], [1], ['Iris-virginica']] , 
  [[6.0, 2.2, 5.0, 1.5], [1], ['Iris-virginica']] , 
  [[6.9, 3.2, 5.7, 2.3], [1], ['Iris-virginica']] , 
  [[5.6, 2.8, 4.9, 2.0], [1], ['Iris-virginica']] , 
  [[7.7, 2.8, 6.7, 2.0], [1], ['Iris-virginica']] , 
  [[6.3, 2.7, 4.9, 1.8], [1], ['Iris-virginica']] , 
  [[6.7, 3.3, 5.7, 2.1], [1], ['Iris-virginica']] , 
  [[7.2, 3.2, 6.0, 1.8], [1], ['Iris-virginica']] , 
  [[6.2, 2.8, 4.8, 1.8], [1], ['Iris-virginica']] , 
  [[6.1, 3.0, 4.9, 1.8], [1], ['Iris-virginica']] , 
  [[6.4, 2.8, 5.6, 2.1], [1], ['Iris-virginica']] , 
  [[7.2, 3.0, 5.8, 1.6], [1], ['Iris-virginica']] , 
  [[7.4, 2.8, 6.1, 1.9], [1], ['Iris-virginica']] , 
  [[7.9, 3.8, 6.4, 2.0], [1], ['Iris-virginica']] , 
  [[6.4, 2.8, 5.6, 2.2], [1], ['Iris-virginica']] , 
  [[6.3, 2.8, 5.1, 1.5], [1], ['Iris-virginica']] , 
  [[6.1, 2.6, 5.6, 1.4], [1], ['Iris-virginica']] , 
  [[7.7, 3.0, 6.1, 2.3], [1], ['Iris-virginica']] , 
  [[6.3, 3.4, 5.6, 2.4], [1], ['Iris-virginica']] , 
  [[6.4, 3.1, 5.5, 1.8], [1], ['Iris-virginica']] , 
  [[6.0, 3.0, 4.8, 1.8], [1], ['Iris-virginica']] , 
  [[6.9, 3.1, 5.4, 2.1], [1], ['Iris-virginica']] , 
  [[6.7, 3.1, 5.6, 2.4], [1], ['Iris-virginica']] , 
  [[6.9, 3.1, 5.1, 2.3], [1], ['Iris-virginica']] , 
  [[5.8, 2.7, 5.1, 1.9], [1], ['Iris-virginica']] , 
  [[6.8, 3.2, 5.9, 2.3], [1], ['Iris-virginica']] , 
  [[6.7, 3.3, 5.7, 2.5], [1], ['Iris-virginica']] , 
  [[6.7, 3.0, 5.2, 2.3], [1], ['Iris-virginica']] , 
  [[6.3, 2.5, 5.0, 1.9], [1], ['Iris-virginica']] , 
  [[6.5, 3.0, 5.2, 2.0], [1], ['Iris-virginica']] , 
  [[6.2, 3.4, 5.4, 2.3], [1], ['Iris-virginica']] , 
  [[5.9, 3.0, 5.1, 1.8], [1], ['Iris-virginica']] 
] 
  
testpat = [ 
  [[5.1, 3.5, 1.4, 0.2], [-1], ['Iris-setosa']] , 
  [[4.9, 3.0, 1.4, 0.2], [-1], ['Iris-setosa']] , 
  [[4.7, 3.2, 1.3, 0.2], [-1], ['Iris-setosa']] , 
  [[5.4, 3.9, 1.7, 0.4], [-1], ['Iris-setosa']] , 
  [[4.6, 3.4, 1.4, 0.3], [-1], ['Iris-setosa']] , 
  [[5.0, 3.4, 1.5, 0.2], [-1], ['Iris-setosa']] , 
  [[4.4, 2.9, 1.4, 0.2], [-1], ['Iris-setosa']] , 
  [[4.9, 3.1, 1.5, 0.1], [-1], ['Iris-setosa']] , 
  [[5.4, 3.7, 1.5, 0.2], [-1], ['Iris-setosa']] , 
  [[4.8, 3.4, 1.6, 0.2], [-1], ['Iris-setosa']] , 
  [[4.8, 3.0, 1.4, 0.1], [-1], ['Iris-setosa']] , 
  [[4.3, 3.0, 1.1, 0.1], [-1], ['Iris-setosa']] , 
  [[5.8, 4.0, 1.2, 0.2], [-1], ['Iris-setosa']] , 
  [[5.7, 4.4, 1.5, 0.4], [-1], ['Iris-setosa']] , 
  [[5.4, 3.9, 1.3, 0.4], [-1], ['Iris-setosa']] , 
  [[5.1, 3.5, 1.4, 0.3], [-1], ['Iris-setosa']] , 
  [[5.7, 3.8, 1.7, 0.3], [-1], ['Iris-setosa']] , 
  [[5.1, 3.8, 1.5, 0.3], [-1], ['Iris-setosa']] , 
  [[5.4, 3.4, 1.7, 0.2], [-1], ['Iris-setosa']] , 
  [[5.1, 3.7, 1.5, 0.4], [-1], ['Iris-setosa']] , 
  [[4.6, 3.6, 1.0, 0.2], [-1], ['Iris-setosa']] , 
  [[5.1, 3.3, 1.7, 0.5], [-1], ['Iris-setosa']] , 
  [[4.8, 3.4, 1.9, 0.2], [-1], ['Iris-setosa']] , 
  [[5.0, 3.0, 1.6, 0.2], [-1], ['Iris-setosa']] , 
  [[5.0, 3.4, 1.6, 0.4], [-1], ['Iris-setosa']] , 
  [[5.2, 3.5, 1.5, 0.2], [-1], ['Iris-setosa']] , 
  [[5.2, 3.4, 1.4, 0.2], [-1], ['Iris-setosa']] , 
  [[4.7, 3.2, 1.6, 0.2], [-1], ['Iris-setosa']] , 
  [[4.8, 3.1, 1.6, 0.2], [-1], ['Iris-setosa']] , 
  [[5.4, 3.4, 1.5, 0.4], [-1], ['Iris-setosa']] , 
  [[5.2, 4.1, 1.5, 0.1], [-1], ['Iris-setosa']] , 
  [[5.5, 4.2, 1.4, 0.2], [-1], ['Iris-setosa']] , 
  [[4.9, 3.1, 1.5, 0.1], [-1], ['Iris-setosa']] , 
  [[5.0, 3.2, 1.2, 0.2], [-1], ['Iris-setosa']] , 
  [[5.5, 3.5, 1.3, 0.2], [-1], ['Iris-setosa']] , 
  [[4.9, 3.1, 1.5, 0.1], [-1], ['Iris-setosa']] , 
  [[4.4, 3.0, 1.3, 0.2], [-1], ['Iris-setosa']] , 
  [[5.1, 3.4, 1.5, 0.2], [-1], ['Iris-setosa']] , 
  [[5.0, 3.5, 1.3, 0.3], [-1], ['Iris-setosa']] , 
  [[4.5, 2.3, 1.3, 0.3], [-1], ['Iris-setosa']] , 
  [[4.4, 3.2, 1.3, 0.2], [-1], ['Iris-setosa']] , 
  [[5.0, 3.5, 1.6, 0.6], [-1], ['Iris-setosa']] , 
  [[5.1, 3.8, 1.9, 0.4], [-1], ['Iris-setosa']] , 
  [[4.8, 3.0, 1.4, 0.3], [-1], ['Iris-setosa']] , 
  [[5.1, 3.8, 1.6, 0.2], [-1], ['Iris-setosa']] , 
  [[4.6, 3.2, 1.4, 0.2], [-1], ['Iris-setosa']] , 
  [[5.3, 3.7, 1.5, 0.2], [-1], ['Iris-setosa']] , 
  [[5.0, 3.3, 1.4, 0.2], [-1], ['Iris-setosa']] , 
  [[7.0, 3.2, 4.7, 1.4], [0], ['Iris-versicolor']] , 
  [[6.4, 3.2, 4.5, 1.5], [0], ['Iris-versicolor']] , 
  [[6.9, 3.1, 4.9, 1.5], [0], ['Iris-versicolor']] , 
  [[5.5, 2.3, 4.0, 1.3], [0], ['Iris-versicolor']] , 
  [[6.5, 2.8, 4.6, 1.5], [0], ['Iris-versicolor']] , 
  [[5.7, 2.8, 4.5, 1.3], [0], ['Iris-versicolor']] , 
  [[6.3, 3.3, 4.7, 1.6], [0], ['Iris-versicolor']] , 
  [[4.9, 2.4, 3.3, 1.0], [0], ['Iris-versicolor']] , 
  [[6.6, 2.9, 4.6, 1.3], [0], ['Iris-versicolor']] , 
  [[5.2, 2.7, 3.9, 1.4], [0], ['Iris-versicolor']] , 
  [[5.0, 2.0, 3.5, 1.0], [0], ['Iris-versicolor']] , 
  [[5.9, 3.0, 4.2, 1.5], [0], ['Iris-versicolor']] , 
  [[6.0, 2.2, 4.0, 1.0], [0], ['Iris-versicolor']] , 
  [[6.1, 2.9, 4.7, 1.4], [0], ['Iris-versicolor']] , 
  [[5.6, 2.9, 3.6, 1.3], [0], ['Iris-versicolor']] , 
  [[6.7, 3.1, 4.4, 1.4], [0], ['Iris-versicolor']] , 
  [[5.6, 3.0, 4.5, 1.5], [0], ['Iris-versicolor']] , 
  [[5.8, 2.7, 4.1, 1.0], [0], ['Iris-versicolor']] , 
  [[6.2, 2.2, 4.5, 1.5], [0], ['Iris-versicolor']] , 
  [[5.6, 2.5, 3.9, 1.1], [0], ['Iris-versicolor']] , 
  [[5.9, 3.2, 4.8, 1.8], [0], ['Iris-versicolor']] , 
  [[6.1, 2.8, 4.0, 1.3], [0], ['Iris-versicolor']] , 
  [[6.3, 2.5, 4.9, 1.5], [0], ['Iris-versicolor']] , 
  [[6.1, 2.8, 4.7, 1.2], [0], ['Iris-versicolor']] , 
  [[6.4, 2.9, 4.3, 1.3], [0], ['Iris-versicolor']] , 
  [[6.6, 3.0, 4.4, 1.4], [0], ['Iris-versicolor']] , 
  [[6.8, 2.8, 4.8, 1.4], [0], ['Iris-versicolor']] , 
  [[6.7, 3.0, 5.0, 1.7], [0], ['Iris-versicolor']] , 
  [[6.0, 2.9, 4.5, 1.5], [0], ['Iris-versicolor']] , 
  [[5.7, 2.6, 3.5, 1.0], [0], ['Iris-versicolor']] , 
  [[5.5, 2.4, 3.8, 1.1], [0], ['Iris-versicolor']] , 
  [[5.5, 2.4, 3.7, 1.0], [0], ['Iris-versicolor']] , 
  [[5.8, 2.7, 3.9, 1.2], [0], ['Iris-versicolor']] , 
  [[6.0, 2.7, 5.1, 1.6], [0], ['Iris-versicolor']] , 
  [[5.4, 3.0, 4.5, 1.5], [0], ['Iris-versicolor']] , 
  [[6.0, 3.4, 4.5, 1.6], [0], ['Iris-versicolor']] , 
  [[6.7, 3.1, 4.7, 1.5], [0], ['Iris-versicolor']] , 
  [[6.3, 2.3, 4.4, 1.3], [0], ['Iris-versicolor']] , 
  [[5.6, 3.0, 4.1, 1.3], [0], ['Iris-versicolor']] , 
  [[6.1, 3.0, 4.6, 1.4], [0], ['Iris-versicolor']] , 
  [[5.8, 2.6, 4.0, 1.2], [0], ['Iris-versicolor']] , 
  [[5.0, 2.3, 3.3, 1.0], [0], ['Iris-versicolor']] , 
  [[5.6, 2.7, 4.2, 1.3], [0], ['Iris-versicolor']] , 
  [[5.7, 3.0, 4.2, 1.2], [0], ['Iris-versicolor']] , 
  [[5.7, 2.9, 4.2, 1.3], [0], ['Iris-versicolor']] , 
  [[6.2, 2.9, 4.3, 1.3], [0], ['Iris-versicolor']] , 
  [[5.1, 2.5, 3.0, 1.1], [0], ['Iris-versicolor']] , 
  [[5.7, 2.8, 4.1, 1.3], [0], ['Iris-versicolor']] , 
  [[6.3, 3.3, 6.0, 2.5], [1], ['Iris-virginica']] , 
  [[5.8, 2.7, 5.1, 1.9], [1], ['Iris-virginica']] , 
  [[7.1, 3.0, 5.9, 2.1], [1], ['Iris-virginica']] , 
  [[6.3, 2.9, 5.6, 1.8], [1], ['Iris-virginica']] , 
  [[6.5, 3.0, 5.8, 2.2], [1], ['Iris-virginica']] , 
  [[7.6, 3.0, 6.6, 2.1], [1], ['Iris-virginica']] , 
  [[4.9, 2.5, 4.5, 1.7], [1], ['Iris-virginica']] , 
  [[7.3, 2.9, 6.3, 1.8], [1], ['Iris-virginica']] , 
  [[6.7, 2.5, 5.8, 1.8], [1], ['Iris-virginica']] , 
  [[7.2, 3.6, 6.1, 2.5], [1], ['Iris-virginica']] , 
  [[6.5, 3.2, 5.1, 2.0], [1], ['Iris-virginica']] , 
  [[6.4, 2.7, 5.3, 1.9], [1], ['Iris-virginica']] , 
  [[6.8, 3.0, 5.5, 2.1], [1], ['Iris-virginica']] , 
  [[5.7, 2.5, 5.0, 2.0], [1], ['Iris-virginica']] , 
  [[5.8, 2.8, 5.1, 2.4], [1], ['Iris-virginica']] , 
  [[7.7, 3.8, 6.7, 2.2], [1], ['Iris-virginica']] , 
  [[7.7, 2.6, 6.9, 2.3], [1], ['Iris-virginica']] , 
  [[6.0, 2.2, 5.0, 1.5], [1], ['Iris-virginica']] , 
  [[6.9, 3.2, 5.7, 2.3], [1], ['Iris-virginica']] , 
  [[5.6, 2.8, 4.9, 2.0], [1], ['Iris-virginica']] , 
  [[7.7, 2.8, 6.7, 2.0], [1], ['Iris-virginica']] , 
  [[6.3, 2.7, 4.9, 1.8], [1], ['Iris-virginica']] , 
  [[6.7, 3.3, 5.7, 2.1], [1], ['Iris-virginica']] , 
  [[7.2, 3.2, 6.0, 1.8], [1], ['Iris-virginica']] , 
  [[6.2, 2.8, 4.8, 1.8], [1], ['Iris-virginica']] , 
  [[6.1, 3.0, 4.9, 1.8], [1], ['Iris-virginica']] , 
  [[6.4, 2.8, 5.6, 2.1], [1], ['Iris-virginica']] , 
  [[7.2, 3.0, 5.8, 1.6], [1], ['Iris-virginica']] , 
  [[7.4, 2.8, 6.1, 1.9], [1], ['Iris-virginica']] , 
  [[7.9, 3.8, 6.4, 2.0], [1], ['Iris-virginica']] , 
  [[6.4, 2.8, 5.6, 2.2], [1], ['Iris-virginica']] , 
  [[6.3, 2.8, 5.1, 1.5], [1], ['Iris-virginica']] , 
  [[6.1, 2.6, 5.6, 1.4], [1], ['Iris-virginica']] , 
  [[7.7, 3.0, 6.1, 2.3], [1], ['Iris-virginica']] , 
  [[6.3, 3.4, 5.6, 2.4], [1], ['Iris-virginica']] , 
  [[6.4, 3.1, 5.5, 1.8], [1], ['Iris-virginica']] , 
  [[6.0, 3.0, 4.8, 1.8], [1], ['Iris-virginica']] , 
  [[6.9, 3.1, 5.4, 2.1], [1], ['Iris-virginica']] , 
  [[6.7, 3.1, 5.6, 2.4], [1], ['Iris-virginica']] , 
  [[6.9, 3.1, 5.1, 2.3], [1], ['Iris-virginica']] , 
  [[5.8, 2.7, 5.1, 1.9], [1], ['Iris-virginica']] , 
  [[6.8, 3.2, 5.9, 2.3], [1], ['Iris-virginica']] , 
  [[6.7, 3.3, 5.7, 2.5], [1], ['Iris-virginica']] , 
  [[6.7, 3.0, 5.2, 2.3], [1], ['Iris-virginica']] , 
  [[6.3, 2.5, 5.0, 1.9], [1], ['Iris-virginica']] , 
  [[6.5, 3.0, 5.2, 2.0], [1], ['Iris-virginica']] , 
  [[6.2, 3.4, 5.4, 2.3], [1], ['Iris-virginica']] , 
  [[5.9, 3.0, 5.1, 1.8], [1], ['Iris-virginica']] 
]

原创文章，作者：奋斗，如若转载，请注明出处：https://blog.ytso.com/tech/pnotes/8029.html

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