DL之perceptron：利用perceptron感知机对股票实现预测

DL之perceptron：利用perceptron感知机对股票实现预测

 

 

目录

输出结果

实现代码

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输出结果

更新……

 

 

实现代码

import numpy as np
import operator
import os

# create a dataset which contains 3 samples with 2 classes
def createDataSet():
    # create a matrix: each row as a sample
    group = np.array([[20,2], [50,1], [10,3],[60,0.5]])
    labels = [1, -1, 1,-1] # four samples and two classes
    return group, labels

#classify using perceptron
def perceptronClassify(trainGroup,trainLabels):
    global w, b
    isFind = False  #the flag of find the best w and b
    numSamples = trainGroup.shape[0]    #计算矩阵的行数
    mLenth = trainGroup.shape[1]    #计算矩阵的列数
    w = [0]*mLenth  #初始化w
    b = 0   #初始化b
    while(not isFind):  #定义迭代计算w和b的循环
        for i in range(numSamples):
            if cal(trainGroup[i],trainLabels[i]) <= 0: #计算损失函数，y（wx+b）<=0时更新参数
                print (w,b)
                update(trainGroup[i],trainLabels[i]) #更新计算w和b
                break    #end for loop
            elif i == numSamples-1:
                print (w,b)
        isFind = True   #end while loop
        
def cal(row,trainLabel): #定义损失函数
    global w, b
    res = 0
    for i in range(len(row)):
        res += row[i] * w[i]
        res += b
        res *= trainLabel
    return res
def update(row,trainLabel): #学习率为1的更新计算
    global w, b
    for i in range(len(row)):
        w[i] += trainLabel * row[i]
        b += trainLabel

g,l =createDataSet()        #生成数据集
perceptronClassify(g,l)     #训练分类器