遺傳算法的神經(jīng)網(wǎng)絡(luò)python實(shí)現(xiàn)源碼

代碼過(guò)程中，把代碼過(guò)程較好的一些代碼段記錄起來(lái)涮较，下邊代碼是關(guān)于遺傳算法的神經(jīng)網(wǎng)絡(luò)python實(shí)現(xiàn)的代碼稠鼻，應(yīng)該對(duì)大伙有一些用處。

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):

? 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

? ? 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):

? ? for k in range(self.no):

? ? 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)):

? ? 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

? 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) 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]

? 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()

def crossover (m1, m2):

? 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']]

]

?著作權(quán)歸作者所有,轉(zhuǎn)載或內(nèi)容合作請(qǐng)聯(lián)系作者

人面猴
序言：七十年代末狂票，一起剝皮案震驚了整個(gè)濱河市候齿，隨后出現(xiàn)的幾起案子，更是在濱河造成了極大的恐慌闺属，老刑警劉巖慌盯，帶你破解...
沈念sama閱讀 219,589評(píng)論 6贊 508
死咒
序言：濱河連續(xù)發(fā)生了三起死亡事件，死亡現(xiàn)場(chǎng)離奇詭異掂器，居然都是意外死亡亚皂，警方通過(guò)查閱死者的電腦和手機(jī)，發(fā)現(xiàn)死者居然都...
沈念sama閱讀 93,615評(píng)論 3贊 396
救了他兩次的神仙讓他今天三更去死
文/潘曉璐我一進(jìn)店門国瓮，熙熙樓的掌柜王于貴愁眉苦臉地迎上來(lái)灭必，“玉大人，你說(shuō)我怎么就攤上這事乃摹〗欤” “怎么了？”我有些...
開(kāi)封第一講書(shū)人閱讀 165,933評(píng)論 0贊 356
道士緝兇錄：失蹤的賣姜人
文/不壞的土叔我叫張陵孵睬，是天一觀的道長(zhǎng)播歼。經(jīng)常有香客問(wèn)我，道長(zhǎng)掰读，這世上最難降的妖魔是什么秘狞？我笑而不...
開(kāi)封第一講書(shū)人閱讀 58,976評(píng)論 1贊 295
?港島之戀（遺憾婚禮）
正文為了忘掉前任，我火速辦了婚禮蹈集，結(jié)果婚禮上谒撼，老公的妹妹穿的比我還像新娘。我一直安慰自己雾狈，他們只是感情好廓潜，可當(dāng)我...
茶點(diǎn)故事閱讀 67,999評(píng)論 6贊 393
惡毒庶女頂嫁案：這布局不是一般人想出來(lái)的
文/花漫我一把揭開(kāi)白布。她就那樣靜靜地躺著，像睡著了一般辩蛋。火紅的嫁衣襯著肌膚如雪呻畸。梳的紋絲不亂的頭發(fā)上，一...
開(kāi)封第一講書(shū)人閱讀 51,775評(píng)論 1贊 307
城市分裂傳說(shuō)
那天悼院，我揣著相機(jī)與錄音伤为，去河邊找鬼。笑死据途，一個(gè)胖子當(dāng)著我的面吹牛绞愚，可吹牛的內(nèi)容都是我干的。我是一名探鬼主播颖医，決...
沈念sama閱讀 40,474評(píng)論 3贊 420
雙鴛鴦連環(huán)套：你想象不到人心有多黑
文/蒼蘭香墨我猛地睜開(kāi)眼位衩，長(zhǎng)吁一口氣：“原來(lái)是場(chǎng)噩夢(mèng)啊……” “哼！你這毒婦竟也來(lái)了熔萧？” 一聲冷哼從身側(cè)響起糖驴，我...
開(kāi)封第一講書(shū)人閱讀 39,359評(píng)論 0贊 276
萬(wàn)榮殺人案實(shí)錄
序言：老撾萬(wàn)榮一對(duì)情侶失蹤，失蹤者是張志新（化名）和其女友劉穎佛致，沒(méi)想到半個(gè)月后贮缕，有當(dāng)?shù)厝嗽跇?shù)林里發(fā)現(xiàn)了一具尸體，經(jīng)...
沈念sama閱讀 45,854評(píng)論 1贊 317
?護(hù)林員之死
正文獨(dú)居荒郊野嶺守林人離奇死亡俺榆，尸身上長(zhǎng)有42處帶血的膿包…… 初始之章·張勛以下內(nèi)容為張勛視角年9月15日...
茶點(diǎn)故事閱讀 38,007評(píng)論 3贊 338
?白月光啟示錄
正文我和宋清朗相戀三年感昼，在試婚紗的時(shí)候發(fā)現(xiàn)自己被綠了。大學(xué)時(shí)的朋友給我發(fā)了我未婚夫和他白月光在一起吃飯的照片罐脊。...
茶點(diǎn)故事閱讀 40,146評(píng)論 1贊 351
活死人
序言：一個(gè)原本活蹦亂跳的男人離奇死亡定嗓，死狀恐怖，靈堂內(nèi)的尸體忽然破棺而出爹殊，到底是詐尸還是另有隱情蜕乡，我是刑警寧澤奸绷，帶...
沈念sama閱讀 35,826評(píng)論 5贊 346
?日本核電站爆炸內(nèi)幕
正文年R本政府宣布梗夸，位于F島的核電站，受9級(jí)特大地震影響号醉，放射性物質(zhì)發(fā)生泄漏反症。R本人自食惡果不足惜，卻給世界環(huán)境...
茶點(diǎn)故事閱讀 41,484評(píng)論 3贊 331
男人毒藥：我在死后第九天來(lái)索命
文/蒙蒙一畔派、第九天我趴在偏房一處隱蔽的房頂上張望铅碍。院中可真熱鬧，春花似錦线椰、人聲如沸胞谈。這莊子的主人今日做“春日...
開(kāi)封第一講書(shū)人閱讀 32,029評(píng)論 0贊 22
一樁弒父案，背后竟有這般陰謀
文/蒼蘭香墨我抬頭看了看天上的太陽(yáng)烦绳。三九已至卿捎，卻和暖如春，著一層夾襖步出監(jiān)牢的瞬間径密，已是汗流浹背午阵。一陣腳步聲響...
開(kāi)封第一講書(shū)人閱讀 33,153評(píng)論 1贊 272
情欲美人皮
我被黑心中介騙來(lái)泰國(guó)打工，沒(méi)想到剛下飛機(jī)就差點(diǎn)兒被人妖公主榨干…… 1. 我叫王不留享扔，地道東北人底桂。一個(gè)月前我還...
沈念sama閱讀 48,420評(píng)論 3贊 373
代替公主和親
正文我出身青樓，卻偏偏與公主長(zhǎng)得像惧眠，于是被迫代替她去往敵國(guó)和親籽懦。傳聞我的和親對(duì)象是個(gè)殘疾皇子，可洞房花燭夜當(dāng)晚...
茶點(diǎn)故事閱讀 45,107評(píng)論 2贊 356

遺傳算法的神經(jīng)網(wǎng)絡(luò)python實(shí)現(xiàn)源碼

推薦閱讀更多精彩內(nèi)容