机器学习基石第四次作业代码
花了一个多月总算把《机器学习基石》这门公开课学习完了，作业也做完了，老师讲的很棒，学到了不少东西:).
Q13
Q14
Q15
Q16
Q17
Q18
Q19
Q20
import sys
import urllib2
import numpy as np
#Download data
# url = 'https://d396qusza40orc.cloudfront.net/ntumlone%2Fhw4%2Fhw4_test.dat'
# f = urllib2.urlopen(url)
# with open("hw4_test.dat", "wb") as code:
# code.write(f.read())
#
# url = 'https://d396qusza40orc.cloudfront.net/ntumlone%2Fhw4%2Fhw4_train.dat'
# f = urllib2.urlopen(url)
# with open("hw4_train.dat", "wb") as code:
# code.write(f.read())
#load data
def load_data(filename):
code = open(filename, "r")
lines = code.readlines()
xn = np.zeros((len(lines), 3)).astype(np.float)
yn = np.zeros((len(lines),)).astype(np.int)
for i in range(0, len(lines)):
line = lines[i]
line = line.rstrip('\r\n').replace('\t', ' ').split(' ')
xn[i, 0] = 1
for j in range(1, len(xn[0])):
xn[i, j] = float(line[j-1])
yn[i] = int(line[len(xn[0]) - 1])
return xn, yn
def calculate_W_reg(x, y, lambda_value):
return np.dot(np.dot(np.linalg.inv(np.dot(x.transpose(), x)+lambda_value * np.eye(x.shape[1])), x.transpose()), y)
# test result
def calculate_Eout_or_Ein(w, x, y):
scores = np.dot(w, x.transpose())
predicts = np.where(scores>=0, 1.0, -1.0)
Eout = sum(predicts != y)
return (Eout*1.0) / predicts.shape[0]
if __name__ == '__main__':
# prepare train and test data
train_x, train_y = load_data("hw4_train.dat")
test_x,test_y = load_data("hw4_test.dat")
#Q13
lambda_value = 10
W = calculate_W_reg(train_x, train_y, lambda_value)
Ein = calculate_Eout_or_Ein(W, train_x, train_y)
Eout = calculate_Eout_or_Ein(W, test_x, test_y)
print 'Q13: Ein = ', Ein, ', Eout= ', Eout
#Q14-Q15
Ein_min = sys.maxint
optimal_Eout = 0
optimal_lambda_Ein = 0
Eout_min = sys.maxint
optimal_Ein = 0
optimal_lambda_Eout = 0
for lambda_value in range(2, -11, -1):
# calculate ridge regression W
w_reg = calculate_W_reg(train_x, train_y, pow(10,lambda_value))
Ein = calculate_Eout_or_Ein(w_reg, train_x, train_y)
Eout = calculate_Eout_or_Ein(w_reg, test_x, test_y)
# update Ein Eout lambda
if Ein_min > Ein:
Ein_min = Ein
optimal_lambda_Ein = lambda_value
optimal_Eout = Eout
if Eout_min > Eout:
Eout_min = Eout
optimal_lambda_Eout = lambda_value
optimal_Ein = Ein
#Q14
print 'Q14: log10 = ', optimal_lambda_Ein, ', Ein= ', Ein_min, ', Eout = ', optimal_Eout
#Q15
print 'Q15: log10 = ', optimal_lambda_Eout, ', Ein = ', optimal_Ein, ', Eout= ', Eout_min
# Q16-Q18
Etrain_min = sys.maxint
Eval_min = sys.maxint
Eout_Etrain_min = 0
Eout_Eval_min = 0
Eval_Etrain_min = 0
Etrain_Eval_min = 0
optimal_lambda_Etrain_min = 0
optimal_lambda_Eval_min = 0
split = 120
for lambda_value in range(2, -11, -1):
w_reg = calculate_W_reg(train_x[:split], train_y[:split], pow(10, lambda_value))
Etrain = calculate_Eout_or_Ein(w_reg, train_x[:split], train_y[:split])
Eval = calculate_Eout_or_Ein(w_reg, train_x[split:],train_y[split:])
Eout = calculate_Eout_or_Ein(w_reg, test_x, test_y)
if Etrain_min > Etrain:
optimal_lambda_Etrain_min = lambda_value
Etrain_min = Etrain
Eout_Etrain_min = Eout
Eval_Etrain_min = Eval
if Eval_min > Eval:
optimal_lambda_Eval_min = lambda_value
Eout_Eval_min = Eout
Eval_min = Eval
Etrain_Eval_min = Etrain
#Q16
print 'Q16: log10 = ', optimal_lambda_Etrain_min, ', Etrain= ', Etrain_min, ', Eval = ', Eval_Etrain_min, ', Eout = ', Eout_Etrain_min
#Q17
print 'Q17: log10 = ', optimal_lambda_Eval_min, ', Etrain= ', Etrain_Eval_min, ', Eval = ', Eval_min, ', Eout = ', Eout_Eval_min
#Q18
w_reg = calculate_W_reg(train_x, train_y, pow(10, optimal_lambda_Eval_min))
optimal_Ein = calculate_Eout_or_Ein(w_reg, train_x, train_y)
optimal_Eout = calculate_Eout_or_Ein(w_reg, test_x, test_y)
print 'Q18: Ein = ', optimal_Ein, ', Eout = ', optimal_Eout
#Q19
folder_num = 5
split_folder = 40
Ecv_min = sys.maxint
optimal_lambda = 0
for lambda_value in range(2, -11, -1):
total_cv = 0
for i in range(folder_num):
test_data_x = train_x[i * split_folder:(i + 1) * split_folder, :]
test_data_y = train_y[i * split_folder:(i + 1) * split_folder]
if i > 0 and i < (folder_num - 1):
train_data_x = np.concatenate((train_x[0:i * split_folder, :], train_x[(i + 1) * split_folder:, :]), axis=0)
train_data_y = np.concatenate((train_y[0:i * split_folder], train_y[(i + 1) * split_folder:]), axis=0)
elif i == 0:
train_data_x = train_x[split_folder:, :]
train_data_y = train_y[split_folder:]
else:
train_data_x = train_x[0:i * split_folder, :]
train_data_y = train_y[0:i * split_folder]
w_reg = calculate_W_reg(train_data_x, train_data_y, pow(10, lambda_value))
Ecv = calculate_Eout_or_Ein(w_reg, test_data_x, test_data_y)
total_cv += Ecv
total_cv = total_cv * 1. / folder_num
if Ecv_min > total_cv :
Ecv_min = total_cv
optimal_lambda = lambda_value
print 'Q19: log10=', optimal_lambda, ' Ecv=', Ecv_min
w_reg = calculate_W_reg(train_x, train_y, pow(10, optimal_lambda))
Ein = calculate_Eout_or_Ein(w_reg, train_x, train_y)
Eout = calculate_Eout_or_Ein(w_reg, test_x, test_y)
print 'Q20: Ein = ', Ein, 'Eout = ', EoutRun以上程序，得出的结果如下：
Q13: Ein = 0.05 , Eout= 0.045
Q14: log10 = -8 , Ein= 0.015 , Eout = 0.02
Q15: log10 = -7 , Ein = 0.03 , Eout= 0.015
Q16: log10 = -8 , Etrain= 0.0 , Eval = 0.05 , Eout = 0.025
Q17: log10 = 0 , Etrain= 0.0333333333333 , Eval = 0.0375 , Eout = 0.028
Q18: Ein = 0.035 , Eout = 0.02
Q19: log10= -8 Ecv= 0.03
Q20: Ein = 0.015 Eout = 0.02总体来说，机器学习基石这门课还是相对比较基础的，老师课件的内容组织的也很好，深入浅出。作业相对比较难，但是可以加深对知识的理解，同时也可以锻炼编码的能力:).