{"id":1154748,"date":"2025-01-13T17:55:08","date_gmt":"2025-01-13T09:55:08","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1154748.html"},"modified":"2025-01-13T17:55:10","modified_gmt":"2025-01-13T09:55:10","slug":"%e5%a6%82%e4%bd%95%e7%94%a8python%e8%be%93%e5%85%a5%e7%9f%a9%e9%98%b5","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1154748.html","title":{"rendered":"\u5982\u4f55\u7528python\u8f93\u5165\u77e9\u9635"},"content":{"rendered":"

\"\u5982\u4f55\u7528python\u8f93\u5165\u77e9\u9635\"<\/p>\n

\u4f7f\u7528Python\u8f93\u5165\u77e9\u9635\u7684\u65b9\u5f0f\u6709\u591a\u79cd\uff0c\u5e38\u89c1\u7684\u65b9\u6cd5\u5305\u62ec\uff1a\u624b\u52a8\u8f93\u5165\u3001\u4ece\u6587\u4ef6\u8bfb\u53d6\u3001\u4f7f\u7528numpy\u5e93\u7b49\u3002<\/strong>\u5176\u4e2d\uff0c\u4f7f\u7528numpy\u5e93\u662f\u6700\u5e38\u89c1\u4e14\u9ad8\u6548\u7684\u65b9\u6cd5\uff0c\u56e0\u4e3anumpy\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u77e9\u9635\u64cd\u4f5c\u529f\u80fd\uff0c\u80fd\u5927\u5927\u7b80\u5316\u4ee3\u7801\u5b9e\u73b0\u3002\u63a5\u4e0b\u6765\uff0c\u6211\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528numpy\u5e93\u8f93\u5165\u77e9\u9635\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u624b\u52a8\u8f93\u5165\u77e9\u9635<\/h3>\n<\/p>\n

\u624b\u52a8\u8f93\u5165\u77e9\u9635\u901a\u5e38\u9002\u7528\u4e8e\u5c0f\u578b\u77e9\u9635\u6216\u7b80\u5355\u7684\u5e94\u7528\u573a\u666f\u3002\u53ef\u4ee5\u901a\u8fc7\u5d4c\u5957\u5217\u8868\u7684\u65b9\u5f0f\u6765\u5b9e\u73b0\u77e9\u9635\u7684\u8f93\u5165\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u793a\u4f8b\uff1a<\/p>\n<\/p>\n

# \u624b\u52a8\u8f93\u5165\u77e9\u9635<\/p>\n
matrix = [<\/p>\n
    [1, 2, 3],<\/p>\n
    [4, 5, 6],<\/p>\n
    [7, 8, 9]<\/p>\n
]<\/p>\n
print("\u8f93\u5165\u7684\u77e9\u9635\u4e3a\uff1a")<\/p>\n
for row in matrix:<\/p>\n
    print(row)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u8ff0\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u521b\u5efa\u4e86\u4e00\u4e2a3×3\u7684\u77e9\u9635\uff0c\u5e76\u901a\u8fc7\u904d\u5386\u5217\u8868\u7684\u65b9\u5f0f\u8f93\u51fa\u77e9\u9635\u5185\u5bb9\u3002\u8fd9\u79cd\u65b9\u6cd5\u867d\u7136\u7b80\u5355\u76f4\u63a5\uff0c\u4f46\u5bf9\u4e8e\u5927\u89c4\u6a21\u77e9\u9635\u7684\u8f93\u5165\u548c\u64cd\u4f5c\u5e76\u4e0d\u9ad8\u6548\u3002<\/p>\n<\/p>\n
\u4e8c\u3001\u4ece\u6587\u4ef6\u8bfb\u53d6\u77e9\u9635<\/h3>\n<\/p>\n
\u5bf9\u4e8e\u8f83\u5927\u7684\u77e9\u9635\uff0c\u6211\u4eec\u53ef\u4ee5\u5c06\u6570\u636e\u5b58\u50a8\u5728\u6587\u4ef6\u4e2d\uff0c\u5e76\u901a\u8fc7Python\u4ee3\u7801\u8bfb\u53d6\u6587\u4ef6\u5185\u5bb9\u6765\u6784\u5efa\u77e9\u9635\u3002\u5e38\u89c1\u7684\u6587\u4ef6\u683c\u5f0f\u5305\u62ecCSV\u3001TXT\u7b49\u3002\u4ee5\u4e0b\u662f\u4eceCSV\u6587\u4ef6\u8bfb\u53d6\u77e9\u9635\u7684\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
import csv<\/p>\n
\u4eceCSV\u6587\u4ef6\u8bfb\u53d6\u77e9\u9635<\/strong><\/h2>\n
def read_matrix_from_csv(file_path):<\/p>\n
    matrix = []<\/p>\n
    with open(file_path, 'r') as file:<\/p>\n
        reader = csv.reader(file)<\/p>\n
        for row in reader:<\/p>\n
            matrix.append([int(element) for element in row])<\/p>\n
    return matrix<\/p>\n
file_path = 'matrix.csv'<\/p>\n
matrix = read_matrix_from_csv(file_path)<\/p>\n
print("\u4ece\u6587\u4ef6\u8bfb\u53d6\u7684\u77e9\u9635\u4e3a\uff1a")<\/p>\n
for row in matrix:<\/p>\n
    print(row)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u8ff0\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u5b9a\u4e49\u4e86\u4e00\u4e2a\u51fd\u6570read_matrix_from_csv<\/code>\uff0c\u5b83\u63a5\u53d7\u6587\u4ef6\u8def\u5f84\u4f5c\u4e3a\u53c2\u6570\uff0c\u5e76\u8fd4\u56de\u8bfb\u53d6\u5230\u7684\u77e9\u9635\u3002\u901a\u8fc7\u4f7f\u7528csv\u6a21\u5757\uff0c\u6211\u4eec\u80fd\u591f\u65b9\u4fbf\u5730\u8bfb\u53d6CSV\u6587\u4ef6\u5185\u5bb9\uff0c\u5e76\u5c06\u5176\u8f6c\u6362\u4e3a\u77e9\u9635\u683c\u5f0f\u3002<\/p>\n<\/p>\n
\u4e09\u3001\u4f7f\u7528numpy\u5e93<\/h3>\n<\/p>\n
numpy\u5e93\u662fPython\u4e2d\u5904\u7406\u77e9\u9635\u548c\u6570\u7ec4\u7684\u5f3a\u5927\u5de5\u5177\u3002\u4f7f\u7528numpy\u5e93\uff0c\u6211\u4eec\u53ef\u4ee5\u65b9\u4fbf\u5730\u521b\u5efa\u3001\u8bfb\u53d6\u548c\u64cd\u4f5c\u77e9\u9635\u3002\u4ee5\u4e0b\u662f\u4f7f\u7528numpy\u5e93\u8f93\u5165\u77e9\u9635\u7684\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
import numpy as np<\/p>\n
\u624b\u52a8\u8f93\u5165\u77e9\u9635<\/strong><\/h2>\n
matrix = np.array([<\/p>\n
    [1, 2, 3],<\/p>\n
    [4, 5, 6],<\/p>\n
    [7, 8, 9]<\/p>\n
])<\/p>\n
print("\u8f93\u5165\u7684\u77e9\u9635\u4e3a\uff1a")<\/p>\n
print(matrix)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e0a\u8ff0\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528numpy\u7684array<\/code>\u51fd\u6570\u521b\u5efa\u4e86\u4e00\u4e2a3×3\u7684\u77e9\u9635\uff0c\u5e76\u901a\u8fc7print\u51fd\u6570\u8f93\u51fa\u77e9\u9635\u5185\u5bb9\u3002numpy\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u77e9\u9635\u64cd\u4f5c\u51fd\u6570\uff0c\u4f8b\u5982\u77e9\u9635\u8f6c\u7f6e\u3001\u77e9\u9635\u4e58\u6cd5\u7b49\uff0c\u80fd\u591f\u6781\u5927\u5730\u65b9\u4fbf\u77e9\u9635\u7684\u64cd\u4f5c\u3002<\/p>\n<\/p>\n
\u56db\u3001\u4ece\u6587\u4ef6\u8bfb\u53d6\u77e9\u9635\uff08\u4f7f\u7528numpy\uff09<\/h3>\n<\/p>\n
numpy\u5e93\u8fd8\u63d0\u4f9b\u4e86\u65b9\u4fbf\u7684\u51fd\u6570\u7528\u4e8e\u4ece\u6587\u4ef6\u8bfb\u53d6\u77e9\u9635\uff0c\u4f8b\u5982numpy.loadtxt<\/code>\u548cnumpy.genfromtxt<\/code>\u3002\u4ee5\u4e0b\u662f\u4eceTXT\u6587\u4ef6\u8bfb\u53d6\u77e9\u9635\u7684\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
import numpy as np<\/p>\n
\u4eceTXT\u6587\u4ef6\u8bfb\u53d6\u77e9\u9635<\/strong><\/h2>\n
file_path = 'matrix.txt'<\/p>\n
matrix = np.loadtxt(file_path, delimiter=',')<\/p>\n
print("\u4ece\u6587\u4ef6\u8bfb\u53d6\u7684\u77e9\u9635\u4e3a\uff1a")<\/p>\n
print(matrix)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u8ff0\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528numpy.loadtxt<\/code>\u51fd\u6570\u4eceTXT\u6587\u4ef6\u4e2d\u8bfb\u53d6\u77e9\u9635\uff0c\u5e76\u6307\u5b9a\u9017\u53f7\u4f5c\u4e3a\u5206\u9694\u7b26\u3002\u8fd9\u4e2a\u65b9\u6cd5\u540c\u6837\u9002\u7528\u4e8e\u5176\u4ed6\u6587\u4ef6\u683c\u5f0f\uff0c\u5982CSV\u6587\u4ef6\u3002<\/p>\n<\/p>\n
\u4e94\u3001\u4f7f\u7528numpy\u751f\u6210\u968f\u673a\u77e9\u9635<\/h3>\n<\/p>\n
\u9664\u4e86\u624b\u52a8\u8f93\u5165\u548c\u4ece\u6587\u4ef6\u8bfb\u53d6\u77e9\u9635\uff0cnumpy\u8fd8\u63d0\u4f9b\u4e86\u51fd\u6570\u7528\u4e8e\u751f\u6210\u968f\u673a\u77e9\u9635\uff0c\u4f8b\u5982numpy.random.rand<\/code>\u548cnumpy.random.randint<\/code>\u3002\u4ee5\u4e0b\u662f\u751f\u6210\u968f\u673a\u77e9\u9635\u7684\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
import numpy as np<\/p>\n
\u751f\u6210\u4e00\u4e2a3x3\u7684\u968f\u673a\u77e9\u9635\uff0c\u5143\u7d20\u4e3a[0, 1)\u4e4b\u95f4\u7684\u6d6e\u70b9\u6570<\/strong><\/h2>\n
random_matrix = np.random.rand(3, 3)<\/p>\n
print("\u751f\u6210\u7684\u968f\u673a\u77e9\u9635\u4e3a\uff1a")<\/p>\n
print(random_matrix)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u8ff0\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528numpy.random.rand<\/code>\u51fd\u6570\u751f\u6210\u4e86\u4e00\u4e2a3×3\u7684\u968f\u673a\u77e9\u9635\uff0c\u5176\u4e2d\u6bcf\u4e2a\u5143\u7d20\u90fd\u662f[0, 1)\u4e4b\u95f4\u7684\u6d6e\u70b9\u6570\u3002numpy\u8fd8\u63d0\u4f9b\u4e86\u5176\u4ed6\u751f\u6210\u968f\u673a\u77e9\u9635\u7684\u51fd\u6570\uff0c\u4f8b\u5982\u751f\u6210\u6574\u578b\u968f\u673a\u6570\u7684numpy.random.randint<\/code>\u51fd\u6570\u3002<\/p>\n<\/p>\n
\u516d\u3001\u77e9\u9635\u64cd\u4f5c<\/h3>\n<\/p>\n
\u6709\u4e86\u8f93\u5165\u7684\u77e9\u9635\u540e\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528numpy\u5e93\u8fdb\u884c\u5404\u79cd\u77e9\u9635\u64cd\u4f5c\uff0c\u4f8b\u5982\u77e9\u9635\u52a0\u6cd5\u3001\u51cf\u6cd5\u3001\u4e58\u6cd5\u3001\u8f6c\u7f6e\u7b49\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u5e38\u89c1\u7684\u77e9\u9635\u64cd\u4f5c\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
import numpy as np<\/p>\n
\u521b\u5efa\u4e24\u4e2a\u77e9\u9635<\/strong><\/h2>\n
matrix1 = np.array([<\/p>\n
    [1, 2, 3],<\/p>\n
    [4, 5, 6],<\/p>\n
    [7, 8, 9]<\/p>\n
])<\/p>\n
matrix2 = np.array([<\/p>\n
    [9, 8, 7],<\/p>\n
    [6, 5, 4],<\/p>\n
    [3, 2, 1]<\/p>\n
])<\/p>\n
\u77e9\u9635\u52a0\u6cd5<\/strong><\/h2>\n
matrix_add = matrix1 + matrix2<\/p>\n
print("\u77e9\u9635\u52a0\u6cd5\u7ed3\u679c\uff1a")<\/p>\n
print(matrix_add)<\/p>\n
\u77e9\u9635\u51cf\u6cd5<\/strong><\/h2>\n
matrix_sub = matrix1 - matrix2<\/p>\n
print("\u77e9\u9635\u51cf\u6cd5\u7ed3\u679c\uff1a")<\/p>\n
print(matrix_sub)<\/p>\n
\u77e9\u9635\u4e58\u6cd5<\/strong><\/h2>\n
matrix_mul = np.dot(matrix1, matrix2)<\/p>\n
print("\u77e9\u9635\u4e58\u6cd5\u7ed3\u679c\uff1a")<\/p>\n
print(matrix_mul)<\/p>\n
\u77e9\u9635\u8f6c\u7f6e<\/strong><\/h2>\n
matrix_transpose = np.transpose(matrix1)<\/p>\n
print("\u77e9\u9635\u8f6c\u7f6e\u7ed3\u679c\uff1a")<\/p>\n
print(matrix_transpose)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u8ff0\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u5c55\u793a\u4e86\u77e9\u9635\u7684\u52a0\u6cd5\u3001\u51cf\u6cd5\u3001\u4e58\u6cd5\u548c\u8f6c\u7f6e\u64cd\u4f5c\u3002numpy\u5e93\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u77e9\u9635\u64cd\u4f5c\u51fd\u6570\uff0c\u80fd\u591f\u6ee1\u8db3\u5927\u591a\u6570\u77e9\u9635\u8ba1\u7b97\u7684\u9700\u6c42\u3002<\/p>\n<\/p>\n
\u4e03\u3001\u77e9\u9635\u7684\u5e94\u7528<\/h3>\n<\/p>\n
\u77e9\u9635\u5728\u79d1\u5b66\u8ba1\u7b97\u3001\u56fe\u50cf\u5904\u7406\u3001\u673a\u5668\u5b66\u4e60<\/a>\u7b49\u9886\u57df\u6709\u7740\u5e7f\u6cdb\u7684\u5e94\u7528\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u5e38\u89c1\u7684\u5e94\u7528\u573a\u666f\uff1a<\/p>\n<\/p>\n
1. \u79d1\u5b66\u8ba1\u7b97<\/h4>\n<\/p>\n
\u5728\u79d1\u5b66\u8ba1\u7b97\u4e2d\uff0c\u77e9\u9635\u7528\u4e8e\u8868\u793a\u548c\u6c42\u89e3\u7ebf\u6027\u65b9\u7a0b\u7ec4\u3001\u7279\u5f81\u503c\u95ee\u9898\u7b49\u3002\u4f8b\u5982\uff0c\u4f7f\u7528\u77e9\u9635\u53ef\u4ee5\u6c42\u89e3\u7ebf\u6027\u65b9\u7a0b\u7ec4Ax = b\uff0c\u5176\u4e2dA\u662f\u7cfb\u6570\u77e9\u9635\uff0cx\u662f\u672a\u77e5\u6570\u5411\u91cf\uff0cb\u662f\u5e38\u6570\u5411\u91cf\u3002\u4ee5\u4e0b\u662f\u4f7f\u7528numpy\u6c42\u89e3\u7ebf\u6027\u65b9\u7a0b\u7ec4\u7684\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
import numpy as np<\/p>\n
\u7cfb\u6570\u77e9\u9635A<\/strong><\/h2>\n
A = np.array([<\/p>\n
    [3, 2, -1],<\/p>\n
    [2, -2, 4],<\/p>\n
    [-1, 0.5, -1]<\/p>\n
])<\/p>\n
\u5e38\u6570\u5411\u91cfb<\/strong><\/h2>\n
b = np.array([1, -2, 0])<\/p>\n
\u6c42\u89e3\u7ebf\u6027\u65b9\u7a0b\u7ec4Ax = b<\/strong><\/h2>\n
x = np.linalg.solve(A, b)<\/p>\n
print("\u7ebf\u6027\u65b9\u7a0b\u7ec4\u7684\u89e3\u4e3a\uff1a")<\/p>\n
print(x)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u8ff0\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528numpy.linalg.solve<\/code>\u51fd\u6570\u6c42\u89e3\u7ebf\u6027\u65b9\u7a0b\u7ec4Ax = b\uff0c\u5f97\u5230\u672a\u77e5\u6570\u5411\u91cfx\u7684\u503c\u3002<\/p>\n<\/p>\n
2. \u56fe\u50cf\u5904\u7406<\/h4>\n<\/p>\n
\u5728\u56fe\u50cf\u5904\u7406\u4e2d\uff0c\u77e9\u9635\u7528\u4e8e\u8868\u793a\u56fe\u50cf\u7684\u50cf\u7d20\u503c\u3002\u56fe\u50cf\u7684\u6bcf\u4e2a\u50cf\u7d20\u53ef\u4ee5\u7528\u4e00\u4e2a\u77e9\u9635\u5143\u7d20\u8868\u793a\uff0c\u77e9\u9635\u7684\u5927\u5c0f\u5bf9\u5e94\u56fe\u50cf\u7684\u5206\u8fa8\u7387\u3002\u4f8b\u5982\uff0c\u7070\u5ea6\u56fe\u50cf\u53ef\u4ee5\u7528\u4e8c\u7ef4\u77e9\u9635\u8868\u793a\uff0c\u5f69\u8272\u56fe\u50cf\u53ef\u4ee5\u7528\u4e09\u7ef4\u77e9\u9635\u8868\u793a\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u56fe\u50cf\u5904\u7406\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
import numpy as np<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
import cv2<\/p>\n
\u8bfb\u53d6\u56fe\u50cf\u5e76\u8f6c\u6362\u4e3a\u7070\u5ea6\u56fe\u50cf<\/strong><\/h2>\n
image = cv2.imread('image.jpg', cv2.IMREAD_GRAYSCALE)<\/p>\n
\u5c06\u56fe\u50cf\u8f6c\u6362\u4e3a\u77e9\u9635<\/strong><\/h2>\n
image_matrix = np.array(image)<\/p>\n
\u663e\u793a\u539f\u59cb\u56fe\u50cf<\/strong><\/h2>\n
plt.subplot(1, 2, 1)<\/p>\n
plt.title("Original Image")<\/p>\n
plt.imshow(image_matrix, cmap='gray')<\/p>\n
\u5bf9\u56fe\u50cf\u8fdb\u884c\u5904\u7406\uff0c\u4f8b\u5982\u5bf9\u6bd4\u5ea6\u589e\u5f3a<\/strong><\/h2>\n
enhanced_image = cv2.equalizeHist(image_matrix)<\/p>\n
\u663e\u793a\u5904\u7406\u540e\u7684\u56fe\u50cf<\/strong><\/h2>\n
plt.subplot(1, 2, 2)<\/p>\n
plt.title("Enhanced Image")<\/p>\n
plt.imshow(enhanced_image, cmap='gray')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u8ff0\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528OpenCV\u5e93\u8bfb\u53d6\u56fe\u50cf\uff0c\u5e76\u5c06\u5176\u8f6c\u6362\u4e3a\u7070\u5ea6\u56fe\u50cf\u77e9\u9635\u3002\u7136\u540e\uff0c\u6211\u4eec\u5bf9\u56fe\u50cf\u8fdb\u884c\u5bf9\u6bd4\u5ea6\u589e\u5f3a\u5904\u7406\uff0c\u5e76\u663e\u793a\u539f\u59cb\u56fe\u50cf\u548c\u5904\u7406\u540e\u7684\u56fe\u50cf\u3002<\/p>\n<\/p>\n
3. \u673a\u5668\u5b66\u4e60<\/h4>\n<\/p>\n
\u5728\u673a\u5668\u5b66\u4e60\u4e2d\uff0c\u77e9\u9635\u7528\u4e8e\u8868\u793a\u548c\u5904\u7406\u6570\u636e\u96c6\u3002\u6570\u636e\u96c6\u4e2d\u7684\u6bcf\u4e2a\u6837\u672c\u53ef\u4ee5\u7528\u4e00\u4e2a\u5411\u91cf\u8868\u793a\uff0c\u6574\u4e2a\u6570\u636e\u96c6\u53ef\u4ee5\u7528\u4e00\u4e2a\u77e9\u9635\u8868\u793a\u3002\u4f8b\u5982\uff0c\u5728\u76d1\u7763\u5b66\u4e60\u4e2d\uff0c\u8bad\u7ec3\u6570\u636e\u96c6\u53ef\u4ee5\u7528\u77e9\u9635X\u8868\u793a\uff0c\u6807\u7b7e\u53ef\u4ee5\u7528\u5411\u91cfy\u8868\u793a\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u7ebf\u6027\u56de\u5f52\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
import numpy as np<\/p>\n
from sklearn.linear_model import LinearRegression<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
\u751f\u6210\u8bad\u7ec3\u6570\u636e\u96c6<\/strong><\/h2>\n
X = np.array([[1], [2], [3], [4], [5]])<\/p>\n
y = np.array([1, 3, 2, 3, 5])<\/p>\n
\u521b\u5efa\u7ebf\u6027\u56de\u5f52\u6a21\u578b\u5e76\u8fdb\u884c\u8bad\u7ec3<\/strong><\/h2>\n
model = LinearRegression()<\/p>\n
model.fit(X, y)<\/p>\n
\u9884\u6d4b\u7ed3\u679c<\/strong><\/h2>\n
y_pred = model.predict(X)<\/p>\n
\u7ed8\u5236\u8bad\u7ec3\u6570\u636e\u548c\u56de\u5f52\u76f4\u7ebf<\/strong><\/h2>\n
plt.scatter(X, y, color='blue', label='TrAI<\/a>ning Data')<\/p>\n
plt.plot(X, y_pred, color='red', label='Regression Line')<\/p>\n
plt.xlabel('X')<\/p>\n
plt.ylabel('y')<\/p>\n
plt.title('Linear Regression')<\/p>\n
plt.legend()<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u8ff0\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528scikit-learn\u5e93\u521b\u5efa\u5e76\u8bad\u7ec3\u7ebf\u6027\u56de\u5f52\u6a21\u578b\uff0c\u7136\u540e\u7ed8\u5236\u8bad\u7ec3\u6570\u636e\u548c\u56de\u5f52\u76f4\u7ebf\u3002<\/p>\n<\/p>\n
\u516b\u3001\u77e9\u9635\u7684\u5b58\u50a8\u4e0e\u52a0\u8f7d<\/h3>\n<\/p>\n
\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u6211\u4eec\u53ef\u80fd\u9700\u8981\u5c06\u77e9\u9635\u5b58\u50a8\u5230\u6587\u4ef6\u4e2d\uff0c\u4ee5\u4fbf\u540e\u7eed\u52a0\u8f7d\u548c\u4f7f\u7528\u3002numpy\u5e93\u63d0\u4f9b\u4e86\u65b9\u4fbf\u7684\u51fd\u6570\u7528\u4e8e\u77e9\u9635\u7684\u5b58\u50a8\u548c\u52a0\u8f7d\uff0c\u4f8b\u5982numpy.save<\/code>\u548cnumpy.load<\/code>\u3002\u4ee5\u4e0b\u662f\u77e9\u9635\u7684\u5b58\u50a8\u4e0e\u52a0\u8f7d\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
import numpy as np<\/p>\n
\u521b\u5efa\u77e9\u9635<\/strong><\/h2>\n
matrix = np.array([<\/p>\n
    [1, 2, 3],<\/p>\n
    [4, 5, 6],<\/p>\n
    [7, 8, 9]<\/p>\n
])<\/p>\n
\u5c06\u77e9\u9635\u5b58\u50a8\u5230\u6587\u4ef6<\/strong><\/h2>\n
file_path = 'matrix.npy'<\/p>\n
np.save(file_path, matrix)<\/p>\n
\u4ece\u6587\u4ef6\u52a0\u8f7d\u77e9\u9635<\/strong><\/h2>\n
loaded_matrix = np.load(file_path)<\/p>\n
print("\u52a0\u8f7d\u7684\u77e9\u9635\u4e3a\uff1a")<\/p>\n
print(loaded_matrix)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u8ff0\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528numpy.save<\/code>\u51fd\u6570\u5c06\u77e9\u9635\u5b58\u50a8\u5230\u6587\u4ef6\u4e2d\uff0c\u7136\u540e\u4f7f\u7528numpy.load<\/code>\u51fd\u6570\u4ece\u6587\u4ef6\u4e2d\u52a0\u8f7d\u77e9\u9635\u3002<\/p>\n<\/p>\n
\u4e5d\u3001\u77e9\u9635\u7684\u9ad8\u6548\u64cd\u4f5c<\/h3>\n<\/p>\n
\u5728\u5904\u7406\u5927\u89c4\u6a21\u77e9\u9635\u65f6\uff0c\u6548\u7387\u662f\u4e00\u4e2a\u91cd\u8981\u7684\u95ee\u9898\u3002numpy\u5e93\u63d0\u4f9b\u4e86\u591a\u79cd\u9ad8\u6548\u7684\u77e9\u9635\u64cd\u4f5c\u51fd\u6570\uff0c\u80fd\u591f\u5229\u7528\u5e95\u5c42\u7684\u4f18\u5316\u7b97\u6cd5\u548c\u5e76\u884c\u8ba1\u7b97\u80fd\u529b\uff0c\u63d0\u9ad8\u77e9\u9635\u64cd\u4f5c\u7684\u6548\u7387\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u63d0\u9ad8\u77e9\u9635\u64cd\u4f5c\u6548\u7387\u7684\u6280\u5de7\uff1a<\/p>\n<\/p>\n
1. \u4f7f\u7528\u5e7f\u64ad\u673a\u5236<\/h4>\n<\/p>\n
\u5e7f\u64ad\u673a\u5236\u662fnumpy\u5e93\u7684\u4e00\u9879\u5f3a\u5927\u529f\u80fd\uff0c\u5b83\u5141\u8bb8\u5bf9\u4e0d\u540c\u5f62\u72b6\u7684\u6570\u7ec4\u8fdb\u884c\u5143\u7d20\u7ea7\u64cd\u4f5c\uff0c\u800c\u65e0\u9700\u663e\u5f0f\u5730\u8fdb\u884c\u6570\u7ec4\u6269\u5c55\u3002\u4ee5\u4e0b\u662f\u4f7f\u7528\u5e7f\u64ad\u673a\u5236\u8fdb\u884c\u77e9\u9635\u64cd\u4f5c\u7684\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
import numpy as np<\/p>\n
\u521b\u5efa\u77e9\u9635\u548c\u5411\u91cf<\/strong><\/h2>\n
matrix = np.array([<\/p>\n
    [1, 2, 3],<\/p>\n
    [4, 5, 6],<\/p>\n
    [7, 8, 9]<\/p>\n
])<\/p>\n
vector = np.array([1, 2, 3])<\/p>\n
\u4f7f\u7528\u5e7f\u64ad\u673a\u5236\u8fdb\u884c\u77e9\u9635\u548c\u5411\u91cf\u7684\u52a0\u6cd5\u64cd\u4f5c<\/strong><\/h2>\n
result = matrix + vector<\/p>\n
print("\u4f7f\u7528\u5e7f\u64ad\u673a\u5236\u7684\u52a0\u6cd5\u7ed3\u679c\uff1a")<\/p>\n
print(result)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u8ff0\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u5bf9\u77e9\u9635\u548c\u5411\u91cf\u8fdb\u884c\u4e86\u52a0\u6cd5\u64cd\u4f5c\uff0cnumpy\u81ea\u52a8\u8fdb\u884c\u4e86\u5e7f\u64ad\u64cd\u4f5c\uff0c\u4f7f\u5411\u91cf\u6269\u5c55\u4e3a\u4e0e\u77e9\u9635\u76f8\u540c\u7684\u5f62\u72b6\uff0c\u7136\u540e\u8fdb\u884c\u5143\u7d20\u7ea7\u52a0\u6cd5\u64cd\u4f5c\u3002<\/p>\n<\/p>\n
2. \u4f7f\u7528\u77e2\u91cf\u5316\u64cd\u4f5c<\/h4>\n<\/p>\n
\u77e2\u91cf\u5316\u64cd\u4f5c\u662f\u53e6\u4e00\u79cd\u63d0\u9ad8\u77e9\u9635\u64cd\u4f5c\u6548\u7387\u7684\u65b9\u6cd5\uff0c\u5b83\u901a\u8fc7\u5c06\u5faa\u73af\u64cd\u4f5c\u8f6c\u6362\u4e3a\u6570\u7ec4\u64cd\u4f5c\uff0c\u5229\u7528\u5e95\u5c42\u7684\u4f18\u5316\u7b97\u6cd5\u548c\u5e76\u884c\u8ba1\u7b97\u80fd\u529b\uff0c\u663e\u8457\u63d0\u9ad8\u8ba1\u7b97\u901f\u5ea6\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u77e2\u91cf\u5316\u64cd\u4f5c\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
import numpy as np<\/p>\n
\u521b\u5efa\u77e9\u9635<\/strong><\/h2>\n
matrix = np.array([<\/p>\n
    [1, 2, 3],<\/p>\n
    [4, 5, 6],<\/p>\n
    [7, 8, 9]<\/p>\n
])<\/p>\n
\u4f7f\u7528\u77e2\u91cf\u5316\u64cd\u4f5c\u8ba1\u7b97\u77e9\u9635\u7684\u5e73\u65b9<\/strong><\/h2>\n
result = np.square(matrix)<\/p>\n
print("\u4f7f\u7528\u77e2\u91cf\u5316\u64cd\u4f5c\u7684\u7ed3\u679c\uff1a")<\/p>\n
print(result)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u8ff0\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528numpy.square<\/code>\u51fd\u6570\u5bf9\u77e9\u9635\u8fdb\u884c\u4e86\u5143\u7d20\u7ea7\u5e73\u65b9\u64cd\u4f5c\uff0c\u8be5\u64cd\u4f5c\u662f\u77e2\u91cf\u5316\u7684\uff0c\u80fd\u591f\u6bd4\u663e\u5f0f\u5faa\u73af\u66f4\u9ad8\u6548\u5730\u8fdb\u884c\u8ba1\u7b97\u3002<\/p>\n<\/p>\n
\u5341\u3001\u77e9\u9635\u7684\u9ad8\u7ea7\u5e94\u7528<\/h3>\n<\/p>\n
\u9664\u4e86\u57fa\u672c\u7684\u77e9\u9635\u64cd\u4f5c\u548c\u5e94\u7528\uff0c\u77e9\u9635\u8fd8\u5728\u8bb8\u591a\u9ad8\u7ea7\u9886\u57df\u4e2d\u6709\u7740\u91cd\u8981\u7684\u5e94\u7528\uff0c\u4f8b\u5982\u7ebf\u6027\u4ee3\u6570\u3001\u6570\u636e\u5206\u6790\u3001\u4fe1\u53f7\u5904\u7406\u7b49\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u9ad8\u7ea7\u5e94\u7528\u7684\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
1. \u5947\u5f02\u503c\u5206\u89e3\uff08SVD\uff09<\/h4>\n<\/p>\n
\u5947\u5f02\u503c\u5206\u89e3\uff08SVD\uff09\u662f\u7ebf\u6027\u4ee3\u6570\u4e2d\u7684\u4e00\u79cd\u91cd\u8981\u5206\u89e3\u65b9\u6cd5\uff0c\u5b83\u5c06\u77e9\u9635\u5206\u89e3\u4e3a\u4e09\u4e2a\u77e9\u9635\u7684\u4e58\u79ef\uff0c\u7528\u4e8e\u964d\u7ef4\u3001\u6570\u636e\u538b\u7f29\u7b49\u5e94\u7528\u3002\u4ee5\u4e0b\u662f\u4f7f\u7528numpy\u8fdb\u884cSVD\u7684\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
import numpy as np<\/p>\n
\u521b\u5efa\u77e9\u9635<\/strong><\/h2>\n
matrix = np.array([<\/p>\n
    [1, 2, 3],<\/p>\n
    [4, 5, 6],<\/p>\n
    [7, 8, 9]<\/p>\n
])<\/p>\n
\u8fdb\u884c\u5947\u5f02\u503c\u5206\u89e3<\/strong><\/h2>\n
U, S, V = np.linalg.svd(matrix)<\/p>\n
print("\u77e9\u9635\u7684\u5947\u5f02\u503c\u5206\u89e3\u7ed3\u679c\uff1a")<\/p>\n
print("U\u77e9\u9635\uff1a")<\/p>\n
print(U)<\/p>\n
print("\u5947\u5f02\u503c\uff1a")<\/p>\n
print(S)<\/p>\n
print("V\u77e9\u9635\uff1a")<\/p>\n
print(V)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u8ff0\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528numpy.linalg.svd<\/code>\u51fd\u6570\u5bf9\u77e9\u9635\u8fdb\u884c\u4e86\u5947\u5f02\u503c\u5206\u89e3\uff0c\u5f97\u5230U\u77e9\u9635\u3001\u5947\u5f02\u503c\u548cV\u77e9\u9635\u3002<\/p>\n<\/p>\n
2. \u4e3b\u6210\u5206\u5206\u6790\uff08PCA\uff09<\/h4>\n<\/p>\n
\u4e3b\u6210\u5206\u5206\u6790\uff08PCA\uff09\u662f\u4e00\u79cd\u5e38\u7528\u7684\u6570\u636e\u964d\u7ef4\u6280\u672f\uff0c\u7528\u4e8e\u63d0\u53d6\u6570\u636e\u7684\u4e3b\u8981\u7279\u5f81\u3002\u4ee5\u4e0b\u662f\u4f7f\u7528numpy\u548cscikit-learn\u8fdb\u884cPCA\u7684\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
import numpy as np<\/p>\n
from sklearn.decomposition import PCA<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
\u521b\u5efa\u6570\u636e\u96c6<\/strong><\/h2>\n
data = np.array([<\/p>\n
    [2.5, 2.4],<\/p>\n
    [0.5, 0.7],<\/p>\n
    [2.2, 2.9],<\/p>\n
    [1.9, 2.2],<\/p>\n
    [3.1, 3.0],<\/p>\n
    [2.3, 2.7],<\/p>\n
    [2.0, 1.6],<\/p>\n
    [1.0, 1.1],<\/p>\n
    [1.5, 1.6],<\/p>\n
    [1.1, 0.9]<\/p>\n
])<\/p>\n
\u8fdb\u884c\u4e3b\u6210\u5206\u5206\u6790<\/strong><\/h2>\n
pca = PCA(n_components=2)<\/p>\n
pca_result = pca.fit_transform(data)<\/p>\n
\u7ed8\u5236\u539f\u59cb\u6570\u636e\u548cPCA\u7ed3\u679c<\/strong><\/h2>\n
plt.scatter(data[:, 0], data[:, 1], color='blue', label='Original Data')<\/p>\n
plt.scatter(pca_result[:, 0], pca_result[:, 1], color='red', label='PCA Result')<\/p>\n
plt.xlabel('X')<\/p>\n
plt.ylabel('Y')<\/p>\n
plt.title('Principal Component Analysis')<\/p>\n
plt.legend()<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u8ff0\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528scikit-learn\u5e93\u8fdb\u884c\u4e3b\u6210\u5206\u5206\u6790\uff0c\u5e76\u7ed8\u5236\u539f\u59cb\u6570\u636e\u548cPCA\u7ed3\u679c\u3002<\/p>\n<\/p>\n
\u7efc\u4e0a\u6240\u8ff0\uff0c\u4f7f\u7528Python\u8f93\u5165\u77e9\u9635\u7684\u65b9\u6cd5\u591a\u79cd\u591a\u6837\uff0c\u4ece\u624b\u52a8\u8f93\u5165\u3001\u4ece\u6587\u4ef6\u8bfb\u53d6\u5230\u4f7f\u7528numpy\u5e93\u751f\u6210\u968f\u673a\u77e9\u9635\u7b49\u3002numpy\u5e93\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u77e9\u9635\u64cd\u4f5c\u51fd\u6570\uff0c\u80fd\u591f\u6ee1\u8db3\u5927\u591a\u6570\u77e9\u9635\u8ba1\u7b97\u7684\u9700\u6c42\u3002\u5728\u79d1\u5b66\u8ba1\u7b97\u3001\u56fe\u50cf\u5904\u7406\u3001\u673a\u5668\u5b66\u4e60\u7b49\u9886\u57df\uff0c\u77e9\u9635\u6709\u7740\u5e7f\u6cdb\u7684\u5e94\u7528\uff0c\u638c\u63e1\u77e9\u9635\u7684\u8f93\u5165\u548c\u64cd\u4f5c\u65b9\u6cd5\u5bf9\u4e8e\u6570\u636e\u5206\u6790\u548c\u79d1\u5b66\u7814\u7a76\u81f3\u5173\u91cd\u8981\u3002\u5e0c\u671b\u672c\u6587\u5bf9\u4f60\u7406\u89e3\u548c\u4f7f\u7528Python\u8f93\u5165\u77e9\u9635\u6709\u6240\u5e2e\u52a9\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u5728Python\u4e2d\u8f93\u5165\u4e00\u4e2a\u77e9\u9635\uff1f<\/strong>
\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528\u5d4c\u5957\u5217\u8868\u7684\u65b9\u5f0f\u6765\u8f93\u5165\u77e9\u9635\u3002\u7528\u6237\u53ef\u4ee5\u901a\u8fc7\u624b\u52a8\u8f93\u5165\u6bcf\u4e2a\u5143\u7d20\uff0c\u6216\u8005\u5229\u7528\u5faa\u73af\u6765\u7b80\u5316\u8f93\u5165\u8fc7\u7a0b\u3002\u4f8b\u5982\uff0c\u53ef\u4ee5\u4f7f\u7528input()<\/code>\u51fd\u6570\u6765\u83b7\u53d6\u7528\u6237\u8f93\u5165\uff0c\u5e76\u5c06\u5176\u8f6c\u6362\u4e3a\u5217\u8868\u5f62\u5f0f\uff0c\u4ece\u800c\u5f62\u6210\u77e9\u9635\u3002<\/p>\n
\u6709\u54ea\u4e9b\u5e93\u53ef\u4ee5\u5e2e\u52a9\u6211\u66f4\u65b9\u4fbf\u5730\u8f93\u5165\u548c\u5904\u7406\u77e9\u9635\uff1f<\/strong>
\u5728Python\u4e2d\uff0cNumPy\u662f\u5904\u7406\u77e9\u9635\u548c\u6570\u7ec4\u7684\u5f3a\u5927\u5e93\u3002\u901a\u8fc7NumPy\uff0c\u7528\u6237\u53ef\u4ee5\u8f7b\u677e\u521b\u5efa\u77e9\u9635\uff0c\u751a\u81f3\u53ef\u4ee5\u4ece\u6587\u4ef6\u4e2d\u8bfb\u53d6\u6570\u636e\u3002\u4f7f\u7528numpy.array()<\/code>\u51fd\u6570\u53ef\u4ee5\u5c06\u5217\u8868\u8f6c\u6362\u4e3a\u77e9\u9635\u5f62\u5f0f\uff0c\u540c\u65f6\u8fd8\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u6570\u5b66\u8fd0\u7b97\u529f\u80fd\uff0c\u65b9\u4fbf\u7528\u6237\u8fdb\u884c\u5404\u79cd\u77e9\u9635\u64cd\u4f5c\u3002<\/p>\n
\u8f93\u5165\u77e9\u9635\u65f6\uff0c\u5982\u4f55\u786e\u4fdd\u6570\u636e\u683c\u5f0f\u7684\u6b63\u786e\u6027\uff1f<\/strong>
\u786e\u4fdd\u8f93\u5165\u6570\u636e\u7684\u683c\u5f0f\u6b63\u786e\u53ef\u4ee5\u901a\u8fc7\u8bbe\u7f6e\u9a8c\u8bc1\u673a\u5236\u5b9e\u73b0\u3002\u5728\u8f93\u5165\u77e9\u9635\u65f6\uff0c\u53ef\u4ee5\u4f7f\u7528try-except\u5757\u6765\u6355\u83b7\u8f93\u5165\u9519\u8bef\uff0c\u786e\u4fdd\u7528\u6237\u8f93\u5165\u7684\u662f\u6570\u5b57\u3002\u540c\u65f6\uff0c\u53ef\u4ee5\u5bf9\u8f93\u5165\u7684\u77e9\u9635\u7ef4\u5ea6\u8fdb\u884c\u68c0\u67e5\uff0c\u786e\u4fdd\u7528\u6237\u8f93\u5165\u7684\u6570\u636e\u7b26\u5408\u9884\u671f\u7684\u884c\u5217\u6570\uff0c\u8fd9\u6837\u53ef\u4ee5\u6709\u6548\u907f\u514d\u540e\u7eed\u64cd\u4f5c\u4e2d\u7684\u9519\u8bef\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u4f7f\u7528Python\u8f93\u5165\u77e9\u9635\u7684\u65b9\u5f0f\u6709\u591a\u79cd\uff0c\u5e38\u89c1\u7684\u65b9\u6cd5\u5305\u62ec\uff1a\u624b\u52a8\u8f93\u5165\u3001\u4ece\u6587\u4ef6\u8bfb\u53d6\u3001\u4f7f\u7528numpy\u5e93\u7b49\u3002\u5176\u4e2d\uff0c\u4f7f\u7528nu […]","protected":false},"author":3,"featured_media":1154758,"comment_status":"closed","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[37],"tags":[],"acf":[],"_links":{"self":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1154748"}],"collection":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/comments?post=1154748"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1154748\/revisions"}],"predecessor-version":[{"id":1154759,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1154748\/revisions\/1154759"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1154758"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1154748"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1154748"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1154748"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}