Question

import numpy as np
import pandas as pd
import numpy as np
from matplotlib import pyplot as pt
def computeCost(X,y,theta):
    m=len(y)
    predictions= X*theta-y
    sqrerror=np.power(predictions,2)
    return 1/(2*m)*np.sum(sqrerror)

def gradientDescent(X, y, theta, alpha, num_iters):
    m = len(y)
    jhistory = np.zeros((num_iters,1))
    for i in range(num_iters):
        h = X * theta
        s = h - y
        theta = theta - (alpha / m) * (s.T*X).T
        jhistory_iter = computeCost(X, y, theta)
    return theta,jhistory_iter


data = open(r'C:\Users\Coding\Desktop\machine-learning-ex1\ex1\ex1data1.txt')
data1=np.array(pd.read_csv(r'C:\Users\Coding\Desktop\machine-learning-ex1\ex1\ex1data1.txt',header=None))
y =np.array(data1[:,1])
m=len(y)
y=np.asmatrix(y.reshape(m,1))
X = np.array([data1[:,0]]).reshape(m,1)
X = np.asmatrix(np.insert(X,0,1,axis=1))
theta=np.zeros((2,1))
iterations = 1500
alpha = 0.01;

print('Testing the cost function ...')
J = computeCost(X, y, theta)
print('With theta = [0 , 0]\nCost computed = ', J)
print('Expected cost value (approx) 32.07')
theta=np.asmatrix([[-1,0],[1,2]])
J = computeCost(X, y, theta)
print('With theta = [-1 , 2]\nCost computed =', J)
print('Expected cost value (approx) 54.24')

theta,JJ = gradientDescent(X, y, theta, alpha, iterations)
print('Theta found by gradient descent:')
print(theta)
print('Expected theta values (approx)')
print(' -3.6303\n  1.1664\n')
predict1 = [1, 3.5] *theta
print(predict1*10000)

结果：

测试成本函数...

theta = [0，0]

计算的费用= 32.072733877455676

预期成本值（大约）32.07

theta = [-1，2]

计算的费用= 69.84811062494227

预期成本值（大约）54.24

通过梯度下降发现的θ：

[[-3.70304726 -3.64357517]

[1.17367146 1.16769684]]

预期theta值（大约）

-3.6303

1.1664

[[4048.02858742 4433.63790186]]

有两个问题，第一个计算的成本是正确的，但是第二个是错误的。而且我的梯度下降中有4个元素（假设是2个）

Answer 1

当您提及“ With theta = [-1，2]”

然后输入

theta=np.asmatrix([[-1,0],[1,2]])

我认为这是不正确的。假设您具有单一功能，并添加了1列，则您尝试进行简单的线性回归

正确的方法应该是

np.array([-1,2])

也在哪里

predictions= X*theta-y

如果您这样做

np.dot(X,theta)-y

乘以时，它不是在做同一件事。

机器学习中的Python问题

1 个答案: