main.py

import pandas as pd
import numpy as np
dataset = pd.read_csv('/home/parker/watermelonData/watermelon_3.csv', delimiter=",")

#according to P54--3.2
#process the dataset
attributeMap={}
attributeMap['浅白']=0
attributeMap['青绿']=0.5
attributeMap['乌黑']=1
attributeMap['蜷缩']=0
attributeMap['稍蜷']=0.5
attributeMap['硬挺']=1
attributeMap['沉闷']=0
attributeMap['浊响']=0.5
attributeMap['清脆']=1
attributeMap['模糊']=0
attributeMap['稍糊']=0.5
attributeMap['清晰']=1
attributeMap['凹陷']=0
attributeMap['稍凹']=0.5
attributeMap['平坦']=1
attributeMap['硬滑']=0
attributeMap['软粘']=1
attributeMap['否']=0
attributeMap['是']=1
del dataset['编号']
dataset=np.array(dataset)
m,n=np.shape(dataset)
for i in range(m):
    for j in range(n):
        if dataset[i,j] in attributeMap:
            dataset[i,j]=attributeMap[dataset[i,j]]
        dataset[i,j]=round(dataset[i,j],3)

trueY=dataset[:,n-1]
X=dataset[:,:n-1]
m,n=np.shape(X)

#according to P101, init the parameters
import random
d=n   #the dimension of the input vector
l=1   #the dimension of the  output vector
q=d+1   #the number of the hide nodes
theta=[random.random() for i in range(l)]   #the threshold of the output nodes
gamma=[random.random() for i in range(q)]   #the threshold of the hide nodes
# v size= d*q .the connection weight between input and hide nodes
v=[[random.random() for i in range(q)] for j in range(d)]
# w size= q*l .the connection weight between hide and output nodes
w=[[random.random() for i in range(l)] for j in range(q)]
eta=0.2    #the training speed
maxIter=5000 #the max training times

import math
def sigmoid(iX,dimension):#iX is a matrix with a dimension
    if dimension==1:
        for i in range(len(iX)):
            iX[i] = 1 / (1 + math.exp(-iX[i]))
    else:
        for i in range(len(iX)):
            iX[i] = sigmoid(iX[i],dimension-1)
    return iX


# do the repeat----standard BP
while(maxIter>0):
    maxIter-=1
    sumE=0
    for i in range(m):
        alpha=np.dot(X[i],v)#p101 line 2 from bottom, shape=1*q
        b=sigmoid(alpha-gamma,1)#b=f(alpha-gamma), shape=1*q
        beta=np.dot(b,w)#shape=(1*q)*(q*l)=1*l
        predictY=sigmoid(beta-theta,1)   #shape=1*l ,p102--5.3
        E = sum((predictY-trueY[i])*(predictY-trueY[i]))/2    #5.4
        sumE+=E#5.16
        #p104
        g=predictY*(1-predictY)*(trueY[i]-predictY)#shape=1*l p103--5.10
        e=b*(1-b)*((np.dot(w,g.T)).T) #shape=1*q , p104--5.15
        w+=eta*np.dot(b.reshape((q,1)),g.reshape((1,l)))#5.11
        theta-=eta*g#5.12
        v+=eta*np.dot(X[i].reshape((d,1)),e.reshape((1,q)))#5.13
        gamma-=eta*e#5.14
    # print(sumE)

# #accumulated BP
# trueY=trueY.reshape((m,l))
# while(maxIter>0):
#     maxIter-=1
#     sumE=0
#     alpha = np.dot(X, v)#p101 line 2 from bottom, shape=m*q
#     b = sigmoid(alpha - gamma,2)  # b=f(alpha-gamma), shape=m*q
#     beta = np.dot(b, w)  # shape=(m*q)*(q*l)=m*l
#     predictY = sigmoid(beta - theta,2)  # shape=m*l ,p102--5.3
#
#     E = sum(sum((predictY - trueY) * (predictY - trueY))) / 2  # 5.4
#     # print(round(E,5))
#     g = predictY * (1 - predictY) * (trueY - predictY)  # shape=m*l p103--5.10
#     e = b * (1 - b) * ((np.dot(w, g.T)).T)  # shape=m*q , p104--5.15
#     w += eta * np.dot(b.T, g)  # 5.11 shape (q*l)=(q*m) * (m*l)
#     theta -= eta * g  # 5.12
#     v += eta * np.dot(X.T, e)  # 5.13 (d,q)=(d,m)*(m,q)
#     gamma -= eta * e  # 5.14


def predict(iX):
    alpha = np.dot(iX, v)  # p101 line 2 from bottom, shape=m*q
    b=sigmoid(alpha-gamma,2)#b=f(alpha-gamma), shape=m*q
    beta = np.dot(b, w)  # shape=(m*q)*(q*l)=m*l
    predictY=sigmoid(beta - theta,2)  # shape=m*l ,p102--5.3
    return predictY

print(predict(X))