ML之UliR：利用非線性回歸，梯度下降法(迭代十萬(wàn)次)求出學(xué)習(xí)參數(shù)θ，進(jìn)而求得Cost函數(shù)最優(yōu)值

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目錄

輸出結(jié)果

代碼設(shè)計(jì)

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輸出結(jié)果

更新……

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代碼設(shè)計(jì)

import numpy as np import random def genData(numPoints,bias,variance): x = np.zeros(shape=(numPoints,2)) y = np.zeros(shape=(numPoints)) for i in range(0,numPoints): x[i][0]=1 x[i][1]=i y[i]=(i+bias)+random.uniform(0,1)%variance return x,ydef gradientDescent(x,y,theta,alpha,m,numIterations): xTran = np.transpose(x) for i in range(numIterations):hypothesis = np.dot(x,theta) loss = hypothesis-y cost = np.sum(loss**2)/(2*m) gradient=np.dot(xTran,loss)/mtheta = theta-alpha*gradient print ("Iteration %d | cost :%f" %(i,cost))return thetax,y = genData(100, 25, 10) #100行， print ("x:") print (x) print ("y:") print (y)m,n = np.shape(x) n_y = np.shape(y) print("m:"+str(m)+" n:"+str(n)+" n_y:"+str(n_y))numIterations = 100000 alpha = 0.0005 theta = np.ones(n) theta= gradientDescent(x, y, theta, alpha, m, numIterations) print(theta)

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ML之UliR：利用非線性回歸，梯度下降法(迭代十萬(wàn)次)求出學(xué)習(xí)參數(shù)θ，進(jìn)而求得Cost函數(shù)最優(yōu)值

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