機械学習練習----ニューラルネットワークの標準BPアルゴリズム(誤差逆伝播アルゴリズム)

このアルゴリズムの実現は以下の理論のそこのスイカの本の偽のコードに基づいて、データの部分を読むのは直接大神の1段のコードを使って、ソースの住所https://blog.csdn.net/qdbszsj/article/details/79110888
同時にいくつかの理論を与えます.https://blog.csdn.net/aaalswaaa1/article/details/83046813

import pandas as pd
import numpy as np
import random
import math

#     
def sigmoid(X, d):
    if d == 1:
        for i in range(len(X)):
            X[i] = 1 / (1 + math.exp(-X[i]))
    else:
        for i in range(len(X)):
            X[i] = sigmoid(X[i], d-1)
    return X


#     
dataset = pd.read_csv('watermelon3.0.csv', delimiter=",",
                      header=None)  #          ，    ，  header        
#      
attributeMap = {}
attributeMap['  '] = 0
attributeMap['  '] = 0.5
attributeMap['  '] = 1
attributeMap['  '] = 0
attributeMap['  '] = 0.5
attributeMap['  '] = 1
attributeMap['  '] = 0
attributeMap['  '] = 0.5
attributeMap['  '] = 1
attributeMap['  '] = 0
attributeMap['  '] = 0.5
attributeMap['  '] = 1
attributeMap['  '] = 0
attributeMap['  '] = 0.5
attributeMap['  '] = 1
attributeMap['  '] = 0
attributeMap['  '] = 1
attributeMap[' '] = 0
attributeMap[' '] = 1
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)

#      
X = dataset[:, :n-1]  #       
Y = dataset[:, -1]  #     
m, n = np.shape(X)
eta = 0.2  #     
q = 10  #       
l = 1  #      
w = [[random.random() for i in range(l)] for j in range(q)]  #             
v = [[random.random() for i in range(q)] for j in range(n)]  #             
theta = [random.random() for i in range(l)]  #      
gamma = [random.random() for i in range(q)]  #     
Ek = 4  #     
time = 0  #       

#     
while(time < 5000 and Ek > 0.1):
    Ek = 0
    time += 1
    for k in range(m):
        alpha = np.dot(X[k], v)  #     
        b = sigmoid(alpha-gamma, 1)  #     
        beta = np.dot(b, w)  #      
        y = sigmoid(beta-theta, 1)  #      
        g = y*(1-y)*(Y[k]-y)
        e = b*(1-b)*((np.dot(w, g)).T)
        w += eta*np.dot(b.reshape((q, 1)), g.reshape((1, l)))
        theta -= eta*g
        v += eta*np.dot(X[k].reshape((n, 1)), e.reshape((1, q)))
        gamma -= eta*e
        E = sum((y-Y[k])*(y-Y[k]))/2
        Ek += E

alpha = np.dot(X, v)
b = sigmoid(alpha-gamma, 2)
beta = np.dot(b, w)
y = sigmoid(beta - theta, 2)
print(y)
print(Ek)
print(time)