DL之perceptron：利用perceptron感知機對股票實現預測

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目錄

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實現代碼

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輸出結果

更新……

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實現代碼

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 samplegroup = np.array([[20,2], [50,1], [10,3],[60,0.5]])labels = [1, -1, 1,-1] # four samples and two classesreturn group, labels#classify using perceptron def perceptronClassify(trainGroup,trainLabels):global w, bisFind = False #the flag of find the best w and bnumSamples = trainGroup.shape[0] #計算矩陣的行數mLenth = trainGroup.shape[1] #計算矩陣的列數w = [0]*mLenth #初始化wb = 0 #初始化bwhile(not isFind): #定義迭代計算w和b的循環for i in range(numSamples):if cal(trainGroup[i],trainLabels[i]) <= 0: #計算損失函數，y（wx+b）<=0時更新參數print (w,b)update(trainGroup[i],trainLabels[i]) #更新計算w和bbreak #end for loopelif i == numSamples-1:print (w,b)isFind = True #end while loopdef cal(row,trainLabel): #定義損失函數global w, bres = 0for i in range(len(row)):res += row[i] * w[i]res += bres *= trainLabelreturn res def update(row,trainLabel): #學習率為1的更新計算global w, bfor i in range(len(row)):w[i] += trainLabel * row[i]b += trainLabelg,l =createDataSet() #生成數據集 perceptronClassify(g,l) #訓練分類器

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