数据挖掘 - Python 函数返回一个 4x4 矩阵，而不是像线性回归问题中的等效 Octave 函数那样的浮点数 - 吾爱随笔录

我正在尝试将代码从 Octave 转换为 Python，但我被卡住了。我知道它们是那里的库，例如 scikit-learn 等，但是对于我自己的学习，我希望能够从头开始实现成本函数。此外，我设法通过查看 numpy 文档解决了一些问题，但我仍然坚持最后一个。该函数不返回预期结果。我提供了您在下面复制所需的一切。不需要更多了。python函数返回一个4x4矩阵而不是像Octave中那样的浮点数，我得到了这个：

实际结果：一个 numpy.ndarray

预期结果：一个浮点数

Octave中computeCost(X, y, theta)中的函数：

function J = computeCost(X, y, theta)
    m = length(y);
    J = (((X*theta)-y)'*((X*theta)-y))/(2*m);
end

Python中的函数compute_cost(X, y, theta)：

from numpy import dot


def compute_cost(X, y, theta):
    m = len(y)
    ans = (X * theta) - y
    product = dot((1/(2*m)), dot(ans, ans.T))
    return product

测试：

import unittest
import numpy as np
from ml.ml_utils import compute_cost


class TestComputeCost(unittest.TestCase):
    data = np.loadtxt('../data/data1.txt', delimiter=',')
    x = data[:, 0]
    y = data[:, 1]
    m = len(y)
    X = np.vstack((np.ones(m), x))

    def test_compute_cost_with_theta_zeros(self):
        theta = np.zeros((2, 1), dtype=int)
        result = compute_cost(self.X, self.y, theta)
        self.assertEqual(round(result, 2), 32.07,
                         'Result is wrong!')

    def test_compute_cost_with_theta_values(self):
        theta = np.array([[-1], [2]])
        result = compute_cost(self.X, self.y, theta)
        self.assertEqual(round(result, 2), 54.24,
                         'Result is wrong!')


if __name__ == '__main__':
    unittest.main()

数据1.txt

6.1101,17.592
5.5277,9.1302
8.5186,13.662
7.0032,11.854
5.8598,6.8233
8.3829,11.886
7.4764,4.3483
8.5781,12
6.4862,6.5987
5.0546,3.8166
5.7107,3.2522
14.164,15.505
5.734,3.1551
8.4084,7.2258
5.6407,0.71618
5.3794,3.5129
6.3654,5.3048
5.1301,0.56077
6.4296,3.6518
7.0708,5.3893
6.1891,3.1386
20.27,21.767
5.4901,4.263
6.3261,5.1875
5.5649,3.0825
18.945,22.638
12.828,13.501
10.957,7.0467
13.176,14.692
22.203,24.147
5.2524,-1.22
6.5894,5.9966
9.2482,12.134
5.8918,1.8495
8.2111,6.5426
7.9334,4.5623
8.0959,4.1164
5.6063,3.3928
12.836,10.117
6.3534,5.4974
5.4069,0.55657
6.8825,3.9115
11.708,5.3854
5.7737,2.4406
7.8247,6.7318
7.0931,1.0463
5.0702,5.1337
5.8014,1.844
11.7,8.0043
5.5416,1.0179
7.5402,6.7504
5.3077,1.8396
7.4239,4.2885
7.6031,4.9981
6.3328,1.4233
6.3589,-1.4211
6.2742,2.4756
5.6397,4.6042
9.3102,3.9624
9.4536,5.4141
8.8254,5.1694
5.1793,-0.74279
21.279,17.929
14.908,12.054
18.959,17.054
7.2182,4.8852
8.2951,5.7442
10.236,7.7754
5.4994,1.0173
20.341,20.992
10.136,6.6799
7.3345,4.0259
6.0062,1.2784
7.2259,3.3411
5.0269,-2.6807
6.5479,0.29678
7.5386,3.8845
5.0365,5.7014
10.274,6.7526
5.1077,2.0576
5.7292,0.47953
5.1884,0.20421
6.3557,0.67861
9.7687,7.5435
6.5159,5.3436
8.5172,4.2415
9.1802,6.7981
6.002,0.92695
5.5204,0.152
5.0594,2.8214
5.7077,1.8451
7.6366,4.2959
5.8707,7.2029
5.3054,1.9869
8.2934,0.14454
13.394,9.0551
5.4369,0.61705