kaggle gradient_descent

1.描述

自写梯度下降

2.代码

import numpy as np
import matplotlib.pyplot as plt# train_X = np.array([[1,2,3,4,5,6,7,8,9,10],[1,2,3,4,5,6,7,8,9,10]]).T
# train_y = np.array([2,4,6,8,10,12,14,16,18,20]).T
# test_X = np.array([[2,4,12,11],[3,6,3,9]]).T # 5 10 15 20train_X = np.random.randn(1000,10)
train_y = np.random.randn(1000,1)
test_X =  np.random.randn(1000,10)step_len = 0.1
max_iterations = 100000
epsilon = 1e-7def ComputeCost(X,y,theta):tmp = X.dot(theta)-y.reshape(y.shape[0],1)return 1/(2*len(y))*sum((tmp*tmp))def GradientDescent(X,y,step_len,max_iterations):X = np.array(X)y = np.array(y)X = np.column_stack( (np.ones((len(y),1)),X))theta = np.zeros((X.shape[1],1))m = len(y)J_his = []for i in range(0,max_iterations):tmp = X.dot(theta)-y.reshape(y.shape[0],1)theta = theta - step_len / m * X.T.dot(tmp)J_his.append(ComputeCost(X,y,theta))#print(J_his[-1])if(len(J_his)>=2 and J_his[-2] - J_his[-1] >= 0 and J_his[-2] - J_his[-1] <= epsilon):print('已收敛')breakif(len(J_his)>=2 and J_his[-1] - J_his[-2] >= 0):print('步长过大')breakreturn theta,J_hisdef Predict(X,theta):one = np.ones((X.shape[0],1))X = np.column_stack(( one,X ))return X.dot(theta)def Normalizetion(x):sum_tmp = np.sum(x,axis=0)max_tmp = np.max(x,axis=0)min_tmp = np.min(x,axis=0)ave_tmp = np.average(x,axis=0)return (x - ave_tmp)/(max_tmp-min_tmp)
#############################################################################train_X = Normalizetion(train_X)
theta,J_his = GradientDescent(train_X,train_y,step_len,max_iterations)
# print('theta =',theta,'\n')# print(Predict(test_X,theta))train_time = range(0,len(J_his))
plt.plot(train_time, J_his)
plt.xlabel('train_time')
plt.ylabel('cost_fun_J')
plt.show()