import math
import random
import pandas as pd

random.seed(0)
def rand(a,b):
    return (b-a)*random.random()+a
def dsigmoid(x):
    return x*(1-x)
def sigmoid(gamma):
  if gamma < 0:
    return 1 - 1/(1 + math.exp(gamma))
  else:
    return 1/(1 + math.exp(-gamma))
def makematrix(I,J):
    m = []
    for i in range(I):
        m.append([0.0]*J)#一维构造二维
    return m


class BP:
    ''' 三层BP网络'''
    def __init__(self,ni,nh,no):
        self.ni=ni+1
        self.nh=nh+1
        self.no=no

        self.ai=[1.0]*self.ni
        self.ah=[1.0]*self.nh
        self.ao=[1.0]*self.no

        self.wi=makematrix(self.ni,self.nh)
        self.wo=makematrix(self.nh,self.no)

        for i in range(self.ni):
            for j in range(self.nh):
                self.wi[i][j]=rand(-2,2)

        for i in range(self.nh):
            for j in range(self.no):
                self.wo[i][j]=rand(-2,2)

    def Forward(self,input):
        for i in range(self.ni-1):
            self.ai=input
        for i in range(self.nh):
            sum=0.0
            for j in range(self.ni-1):
                #print(self.ai[j])
                sum+=self.wi[j][i]*self.ai[j]
            self.ah[i]=sigmoid(sum)
        for i in range(self.no):
            sum=0.0
            for j in range(self.no):
                sum+=self.wo[j][i]*self.ah[j]
            self.ao[i]=sigmoid(sum)
        return self.ao
    def backward(self,tartget,lv):
        rerror=0.0
        outerror=[1.0]*self.no
        for i in range(self.no):
            error=tartget[i]-self.ao[i]
            rerror=error
            outerror[i]=dsigmoid(self.ao[i])*error

        herror=[1.0]*self.nh
        for i in range(self.nh):
            error=0.0
            for j in range(self.no):
                error+=outerror[j]*self.wo[i][j]
            herror[i]=dsigmoid(self.ah[i])*error

        for i in range(self.nh):
            for j in range(self.no):
                change=outerror[j]*self.ah[i]
                self.wo[i][j]=self.wo[i][j]+lv*change

        for i in range(self.ni-1):
            for j in range(self.nh):
                change=herror[j]*self.ai[i]
                self.wi[i][j]=self.wi[i][j]+lv*change

        error = 0.0
        error += 0.5 * rerror ** 2
        return error

    def train(self, patterns, iterations, lr=0.009):
        # lr: 学习速率(learning rate)
        for i in range(iterations):
            error = 0.0
            for p in patterns:
                inputs = p[0]
                targets = p[1]
                #print(inputs, ",", p[1])
                self.Forward(inputs)
                error = error + self.backward(targets, lr)
            if i%100==0:
                print('error: %-.9f' % error)
    def test(self,patterns):
        count=0
        for p in patterns:
            target=p[1]
            #print(target)
            result=self.Forward(p[0])
            index = result.index(max(result))
            if index==0 and target==[1,0,0]:
                count=count+1
            elif index==1 and target==[0,1,0]:
                    count=count+1
            elif index==2 and target==[0,0,1]:
                count = count + 1
        accuracy = float(count / len(patterns))
        print('accuracy: %-.9f' % accuracy)
def iris():
    data=[]
    raw=pd.read_csv('iris.csv')
    raw_data=raw.values
    raw_feature=raw_data[:,1:6]
    print(raw_feature)
    for i in range(len(raw_feature)):
        ele=[]
        ele.append(raw_feature[i])
        if raw_data[i][5]=='setosa':
            ele.append([1,0,0])
        elif raw_data[i][5]=='versicolor':
            ele.append([0,1,0])
        else:
            ele.append([0,0,1])
        print(ele)
        data.append(ele)
    random.shuffle(data)
    trainning=data[0:100]
    test=data[101:]
    nn=BP(4,7,3)
    nn.train(data,10000)
    nn.test(test)
if __name__ == '__main__':
    iris()


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