遗传算法的神经网络python实现代码，神经网络python,## {{{ http:
## {{{ http://code.activestate.com/recipes/578241/ (r1)from operator import itemgetter, attrgetterimport mathimport randomimport stringimport timeitfrom timeit import Timer as timport matplotlib.pyplot as pltimport numpy as npdef sigmoid (x): return math.tanh(x)def makeMatrix ( I, J, fill=0.0): m = [] for i in range(I): m.append([fill]*J) return mdef 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 samedef roulette (fitnessScores): cumalativeFitness = 0.0 r = random.random() for i in range(len(fitnessScores)): cumalativeFitness += fitnessScores[i] if cumalativeFitness > r: return idef 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 objectsdef 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 tupledef 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 newpopWgraphical_error_scale = 100max_iterations = 4000pop_size = 100mutation_rate = 0.1crossover_rate = 0.8ni, nh, no = 4,6,1def 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, nodef 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, output2def 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']]]## end of http://code.activestate.com/recipes/578241/ }}}
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