{"id":1136194,"date":"2025-01-08T21:33:15","date_gmt":"2025-01-08T13:33:15","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1136194.html"},"modified":"2025-01-08T21:33:17","modified_gmt":"2025-01-08T13:33:17","slug":"%e5%a6%82%e4%bd%95%e7%94%a8python%e8%af%ad%e8%a8%80%e7%bc%96%e5%86%99%e4%b8%a4%e7%82%b9%e4%b9%8b%e9%97%b4%e7%9a%84%e8%b7%9d%e7%a6%bb","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1136194.html","title":{"rendered":"\u5982\u4f55\u7528Python\u8bed\u8a00\u7f16\u5199\u4e24\u70b9\u4e4b\u95f4\u7684\u8ddd\u79bb"},"content":{"rendered":"

\"\u5982\u4f55\u7528Python\u8bed\u8a00\u7f16\u5199\u4e24\u70b9\u4e4b\u95f4\u7684\u8ddd\u79bb\"<\/p>\n

\u5982\u4f55\u7528Python\u8bed\u8a00\u7f16\u5199\u4e24\u70b9\u4e4b\u95f4\u7684\u8ddd\u79bb<\/strong><\/p>\n<\/p>\n

\u4f7f\u7528Python\u8bed\u8a00\u7f16\u5199\u4e24\u70b9\u4e4b\u95f4\u7684\u8ddd\u79bb\u53ef\u4ee5\u901a\u8fc7\u6b27\u51e0\u91cc\u5f97\u8ddd\u79bb\u516c\u5f0f\u3001\u66fc\u54c8\u987f\u8ddd\u79bb\u516c\u5f0f\u3001Scipy\u5e93\u3001Numpy\u5e93<\/strong>\u7b49\u65b9\u6cd5\u6765\u5b9e\u73b0\u3002\u672c\u6587\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u8fd9\u51e0\u79cd\u65b9\u6cd5\uff0c\u5e76\u63d0\u4f9b\u793a\u4f8b\u4ee3\u7801\u3002\u6b27\u51e0\u91cc\u5f97\u8ddd\u79bb\u516c\u5f0f<\/strong>\u662f\u6700\u5e38\u7528\u7684\u4e00\u79cd\u65b9\u6cd5\uff0c\u5b83\u8ba1\u7b97\u4e24\u4e2a\u70b9\u5728\u4e8c\u7ef4\u5e73\u9762\u4e0a\u7684\u76f4\u7ebf\u8ddd\u79bb\u3002\u4ee5\u4e0b\u662f\u8be6\u7ec6\u63cf\u8ff0\uff1a<\/p>\n<\/p>\n

\u6b27\u51e0\u91cc\u5f97\u8ddd\u79bb\u516c\u5f0f<\/h3>\n<\/p>\n

\u6b27\u51e0\u91cc\u5f97\u8ddd\u79bb\u662f\u6700\u76f4\u89c2\u7684\u8ddd\u79bb\u5ea6\u91cf\u65b9\u6cd5\uff0c\u901a\u8fc7\u4e24\u70b9\u7684\u5750\u6807\u5dee\u7684\u5e73\u65b9\u548c\u518d\u5f00\u5e73\u65b9\u6839\u5f97\u5230\u3002\u5176\u516c\u5f0f\u4e3a\uff1a<\/p>\n<\/p>\n

[ d = \\sqrt{(x_2 – x_1)^2 + (y_2 – y_1)^2} ]<\/p>\n<\/p>\n

\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u901a\u8fc7\u57fa\u672c\u7684\u6570\u5b66\u8fd0\u7b97\u6765\u5b9e\u73b0\u8fd9\u4e00\u516c\u5f0f\u3002\u4e0b\u9762\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n

import math<\/p>\n
def euclidean_distance(point1, point2):<\/p>\n
    return math.sqrt((point2[0] - point1[0])<strong>2 + (point2[1] - point1[1])<\/strong>2)<\/p>\n
point1 = (1, 2)<\/p>\n
point2 = (4, 6)<\/p>\n
distance = euclidean_distance(point1, point2)<\/p>\n
print(f"Euclidean Distance: {distance}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e00\u3001\u6b27\u51e0\u91cc\u5f97\u8ddd\u79bb\u516c\u5f0f<\/h3>\n<\/p>\n
1\u3001\u57fa\u672c\u6570\u5b66\u8ba1\u7b97<\/h4>\n<\/p>\n
\u901a\u8fc7\u57fa\u672c\u7684\u6570\u5b66\u8fd0\u7b97\u6765\u8ba1\u7b97\u6b27\u51e0\u91cc\u5f97\u8ddd\u79bb\uff0c\u5982\u524d\u6587\u6240\u793a\uff0c\u5229\u7528Python\u7684math\u5e93\u5b9e\u73b0\u5e73\u65b9\u548c\u5f00\u5e73\u65b9\u64cd\u4f5c\u3002\u8fd9\u4e2a\u65b9\u6cd5\u975e\u5e38\u76f4\u63a5\u4e14\u6613\u4e8e\u7406\u89e3\uff0c\u4f46\u5f53\u5904\u7406\u9ad8\u7ef4\u7a7a\u95f4\u65f6\uff0c\u4ee3\u7801\u4f1a\u53d8\u5f97\u590d\u6742\u3002\u56e0\u6b64\uff0c\u4f7f\u7528Numpy\u5e93\u4f1a\u66f4\u65b9\u4fbf\u3002<\/p>\n<\/p>\n
import math<\/p>\n
def euclidean_distance(point1, point2):<\/p>\n
    return math.sqrt((point2[0] - point1[0])<strong>2 + (point2[1] - point1[1])<\/strong>2)<\/p>\n
point1 = (1, 2)<\/p>\n
point2 = (4, 6)<\/p>\n
distance = euclidean_distance(point1, point2)<\/p>\n
print(f"Euclidean Distance: {distance}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u4f7f\u7528Numpy\u5e93<\/h4>\n<\/p>\n
Numpy\u5e93\u662fPython\u4e2d\u5904\u7406\u6570\u7ec4\u548c\u77e9\u9635\u7684\u6807\u51c6\u5e93\uff0c\u5176\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u6570\u5b66\u51fd\u6570\uff0c\u53ef\u4ee5\u5927\u5927\u7b80\u5316\u8ba1\u7b97\u3002\u4f7f\u7528Numpy\u5e93\u8ba1\u7b97\u6b27\u51e0\u91cc\u5f97\u8ddd\u79bb\u53ef\u4ee5\u5904\u7406\u66f4\u9ad8\u7ef4\u5ea6\u7684\u6570\u636e\uff0c\u5e76\u4e14\u4ee3\u7801\u66f4\u52a0\u7b80\u6d01\u3002<\/p>\n<\/p>\n
import numpy as np<\/p>\n
def euclidean_distance(point1, point2):<\/p>\n
    point1 = np.array(point1)<\/p>\n
    point2 = np.array(point2)<\/p>\n
    return np.linalg.norm(point1 - point2)<\/p>\n
point1 = (1, 2)<\/p>\n
point2 = (4, 6)<\/p>\n
distance = euclidean_distance(point1, point2)<\/p>\n
print(f"Euclidean Distance using Numpy: {distance}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001\u66fc\u54c8\u987f\u8ddd\u79bb\u516c\u5f0f<\/h3>\n<\/p>\n
\u66fc\u54c8\u987f\u8ddd\u79bb\u662f\u53e6\u4e00\u79cd\u5e38\u7528\u7684\u8ddd\u79bb\u5ea6\u91cf\u65b9\u6cd5\uff0c\u5b83\u8ba1\u7b97\u4e24\u4e2a\u70b9\u5728\u5750\u6807\u7cfb\u4e0a\u7684\u7edd\u5bf9\u8ddd\u79bb\u3002\u5176\u516c\u5f0f\u4e3a\uff1a<\/p>\n<\/p>\n
[ d = |x_2 – x_1| + |y_2 – y_1| ]<\/p>\n<\/p>\n
\u8fd9\u79cd\u8ddd\u79bb\u5ea6\u91cf\u5728\u5f88\u591a\u5b9e\u9645\u573a\u666f\u4e2d\uff0c\u6bd4\u5982\u57ce\u5e02\u8857\u533a\u7684\u8ddd\u79bb\u8ba1\u7b97\u4e2d\uff0c\u6bd4\u6b27\u51e0\u91cc\u5f97\u8ddd\u79bb\u66f4\u9002\u7528\u3002\u4ee5\u4e0b\u662f\u5176\u5b9e\u73b0\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
1\u3001\u57fa\u672c\u6570\u5b66\u8ba1\u7b97<\/h4>\n<\/p>\n
def manhattan_distance(point1, point2):<\/p>\n
    return abs(point2[0] - point1[0]) + abs(point2[1] - point1[1])<\/p>\n
point1 = (1, 2)<\/p>\n
point2 = (4, 6)<\/p>\n
distance = manhattan_distance(point1, point2)<\/p>\n
print(f"Manhattan Distance: {distance}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u4f7f\u7528Numpy\u5e93<\/h4>\n<\/p>\n
\u540c\u6837\u5730\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528Numpy\u5e93\u6765\u7b80\u5316\u66fc\u54c8\u987f\u8ddd\u79bb\u7684\u8ba1\u7b97\uff1a<\/p>\n<\/p>\n
import numpy as np<\/p>\n
def manhattan_distance(point1, point2):<\/p>\n
    point1 = np.array(point1)<\/p>\n
    point2 = np.array(point2)<\/p>\n
    return np.sum(np.abs(point1 - point2))<\/p>\n
point1 = (1, 2)<\/p>\n
point2 = (4, 6)<\/p>\n
distance = manhattan_distance(point1, point2)<\/p>\n
print(f"Manhattan Distance using Numpy: {distance}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001\u4f7f\u7528Scipy\u5e93\u8ba1\u7b97\u8ddd\u79bb<\/h3>\n<\/p>\n
Scipy\u5e93\u63d0\u4f9b\u4e86\u66f4\u4e30\u5bcc\u7684\u8ddd\u79bb\u8ba1\u7b97\u65b9\u6cd5\uff0c\u5305\u62ec\u6b27\u51e0\u91cc\u5f97\u8ddd\u79bb\u548c\u66fc\u54c8\u987f\u8ddd\u79bb\uff0c\u4ee5\u53ca\u5176\u4ed6\u9ad8\u7ef4\u7a7a\u95f4\u7684\u8ddd\u79bb\u5ea6\u91cf\u3002\u4f7f\u7528Scipy\u5e93\u53ef\u4ee5\u5927\u5927\u7b80\u5316\u4ee3\u7801\uff0c\u5e76\u4e14\u63d0\u4f9b\u4e86\u66f4\u9ad8\u7684\u7075\u6d3b\u6027\u3002<\/p>\n<\/p>\n
1\u3001\u8ba1\u7b97\u6b27\u51e0\u91cc\u5f97\u8ddd\u79bb<\/h4>\n<\/p>\n
Scipy\u5e93\u7684spatial.distance<\/code>\u6a21\u5757\u63d0\u4f9b\u4e86\u76f4\u63a5\u8ba1\u7b97\u6b27\u51e0\u91cc\u5f97\u8ddd\u79bb\u7684\u51fd\u6570\uff1a<\/p>\n<\/p>\n
from scipy.spatial import distance<\/p>\n
point1 = (1, 2)<\/p>\n
point2 = (4, 6)<\/p>\n
distance = distance.euclidean(point1, point2)<\/p>\n
print(f"Euclidean Distance using Scipy: {distance}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u8ba1\u7b97\u66fc\u54c8\u987f\u8ddd\u79bb<\/h4>\n<\/p>\n
\u540c\u6837\u5730\uff0cScipy\u5e93\u4e5f\u63d0\u4f9b\u4e86\u8ba1\u7b97\u66fc\u54c8\u987f\u8ddd\u79bb\u7684\u51fd\u6570\uff1a<\/p>\n<\/p>\n
from scipy.spatial import distance<\/p>\n
point1 = (1, 2)<\/p>\n
point2 = (4, 6)<\/p>\n
distance = distance.cityblock(point1, point2)<\/p>\n
print(f"Manhattan Distance using Scipy: {distance}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001\u5176\u4ed6\u8ddd\u79bb\u8ba1\u7b97\u65b9\u6cd5<\/h3>\n<\/p>\n
\u9664\u4e86\u6b27\u51e0\u91cc\u5f97\u8ddd\u79bb\u548c\u66fc\u54c8\u987f\u8ddd\u79bb\uff0cScipy\u5e93\u8fd8\u63d0\u4f9b\u4e86\u5176\u4ed6\u8ddd\u79bb\u8ba1\u7b97\u65b9\u6cd5\uff0c\u5982\u5207\u6bd4\u96ea\u592b\u8ddd\u79bb\u3001\u95f5\u53ef\u592b\u65af\u57fa\u8ddd\u79bb\u7b49\u3002\u8fd9\u4e9b\u65b9\u6cd5\u5728\u4e0d\u540c\u7684\u5e94\u7528\u573a\u666f\u4e2d\u6709\u4e0d\u540c\u7684\u9002\u7528\u6027\u3002<\/p>\n<\/p>\n
1\u3001\u5207\u6bd4\u96ea\u592b\u8ddd\u79bb<\/h4>\n<\/p>\n
\u5207\u6bd4\u96ea\u592b\u8ddd\u79bb\u7528\u4e8e\u8ba1\u7b97\u4e24\u4e2a\u70b9\u4e4b\u95f4\u7684\u6700\u5927\u5750\u6807\u5dee\u3002\u5176\u516c\u5f0f\u4e3a\uff1a<\/p>\n<\/p>\n
[ d = \\max(|x_2 – x_1|, |y_2 – y_1|) ]<\/p>\n<\/p>\n
Scipy\u5e93\u4e2d\u7684\u5b9e\u73b0\u5982\u4e0b\uff1a<\/p>\n<\/p>\n
from scipy.spatial import distance<\/p>\n
point1 = (1, 2)<\/p>\n
point2 = (4, 6)<\/p>\n
distance = distance.chebyshev(point1, point2)<\/p>\n
print(f"Chebyshev Distance using Scipy: {distance}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u95f5\u53ef\u592b\u65af\u57fa\u8ddd\u79bb<\/h4>\n<\/p>\n
\u95f5\u53ef\u592b\u65af\u57fa\u8ddd\u79bb\u662f\u6b27\u51e0\u91cc\u5f97\u8ddd\u79bb\u548c\u66fc\u54c8\u987f\u8ddd\u79bb\u7684\u5e7f\u4e49\u5f62\u5f0f\uff0c\u53ef\u4ee5\u901a\u8fc7\u8c03\u8282\u53c2\u6570p\u6765\u6539\u53d8\u8ddd\u79bb\u5ea6\u91cf\u7684\u65b9\u5f0f\u3002\u5176\u516c\u5f0f\u4e3a\uff1a<\/p>\n<\/p>\n
[ d = \\left( \\sum_{i=1}^{n} |x_i – y_i|^p \\right)^{1\/p} ]<\/p>\n<\/p>\n
Scipy\u5e93\u4e2d\u7684\u5b9e\u73b0\u5982\u4e0b\uff1a<\/p>\n<\/p>\n
from scipy.spatial import distance<\/p>\n
point1 = (1, 2)<\/p>\n
point2 = (4, 6)<\/p>\n
distance = distance.minkowski(point1, point2, p=3)<\/p>\n
print(f"Minkowski Distance using Scipy with p=3: {distance}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u603b\u7ed3<\/h3>\n<\/p>\n
\u901a\u8fc7\u4e0a\u8ff0\u65b9\u6cd5\uff0c\u6211\u4eec\u53ef\u4ee5\u5728Python\u4e2d\u8f7b\u677e\u5b9e\u73b0\u4e24\u70b9\u4e4b\u95f4\u7684\u8ddd\u79bb\u8ba1\u7b97\u3002\u6b27\u51e0\u91cc\u5f97\u8ddd\u79bb<\/strong>\u9002\u7528\u4e8e\u5927\u591a\u6570\u60c5\u51b5\uff0c\u66fc\u54c8\u987f\u8ddd\u79bb<\/strong>\u9002\u7528\u4e8e\u57ce\u5e02\u8857\u533a\u7b49\u7279\u5b9a\u573a\u666f\uff0cScipy\u5e93<\/strong>\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u8ddd\u79bb\u8ba1\u7b97\u65b9\u6cd5\uff0c\u9002\u7528\u4e8e\u66f4\u590d\u6742\u7684\u9ad8\u7ef4\u7a7a\u95f4\u8ba1\u7b97\u3002\u9009\u62e9\u5408\u9002\u7684\u65b9\u6cd5\u53ef\u4ee5\u63d0\u9ad8\u8ba1\u7b97\u6548\u7387\u548c\u4ee3\u7801\u7b80\u6d01\u6027\u3002\u5e0c\u671b\u672c\u6587\u5bf9\u60a8\u7406\u89e3\u548c\u5b9e\u73b0\u4e24\u70b9\u4e4b\u95f4\u7684\u8ddd\u79bb\u8ba1\u7b97\u6709\u6240\u5e2e\u52a9\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u8ba1\u7b97\u4e24\u70b9\u4e4b\u95f4\u7684\u8ddd\u79bb\uff1f<\/strong>
\u8ba1\u7b97\u4e24\u70b9\u4e4b\u95f4\u7684\u8ddd\u79bb\u53ef\u4ee5\u4f7f\u7528\u6b27\u51e0\u91cc\u5f97\u8ddd\u79bb\u516c\u5f0f\u3002\u5177\u4f53\u6765\u8bf4\uff0c\u5982\u679c\u6709\u4e24\u70b9A(x1, y1)\u548cB(x2, y2)\uff0c\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u516c\u5f0f\u8ba1\u7b97\u8ddd\u79bb\uff1a
[ \\text{\u8ddd\u79bb} = \\sqrt{(x2 – x1)^2 + (y2 – y1)^2} ]
\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528math<\/code>\u5e93\u4e2d\u7684sqrt<\/code>\u51fd\u6570\u6765\u5b9e\u73b0\u8fd9\u4e2a\u8ba1\u7b97\u3002<\/p>\n
\u5728Python\u4e2d\u5982\u4f55\u5b9e\u73b0\u8fd9\u4e2a\u8ba1\u7b97\uff1f<\/strong>
\u53ef\u4ee5\u901a\u8fc7\u5b9a\u4e49\u4e00\u4e2a\u51fd\u6570\u6765\u5b9e\u73b0\u4e24\u70b9\u8ddd\u79bb\u7684\u8ba1\u7b97\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u793a\u4f8b\u4ee3\u7801\uff1a  <\/p>\n
import math\n\ndef calculate_distance(point1, point2):\n    return math.sqrt((point2[0] - point1[0])<strong>2 + (point2[1] - point1[1])<\/strong>2)\n\n# \u793a\u4f8b\ndistance = calculate_distance((1, 2), (4, 6))\nprint(distance)\n<\/code><\/pre>\n
\u8fd9\u79cd\u65b9\u6cd5\u5f88\u65b9\u4fbf\uff0c\u80fd\u591f\u5feb\u901f\u8ba1\u7b97\u4efb\u610f\u4e24\u4e2a\u70b9\u4e4b\u95f4\u7684\u8ddd\u79bb\u3002<\/p>\n
\u662f\u5426\u53ef\u4ee5\u4f7f\u7528\u5176\u4ed6\u5e93\u6765\u8ba1\u7b97\u8ddd\u79bb\uff1f<\/strong>
\u662f\u7684\uff0cPython\u4e2d\u6709\u591a\u4e2a\u5e93\u53ef\u4ee5\u7b80\u5316\u8ddd\u79bb\u8ba1\u7b97\uff0c\u6bd4\u5982NumPy\u548cSciPy\u3002\u4f7f\u7528NumPy\u7684numpy.linalg.norm<\/code>\u65b9\u6cd5\u53ef\u4ee5\u66f4\u9ad8\u6548\u5730\u8ba1\u7b97\u8ddd\u79bb\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u793a\u4f8b\uff1a  <\/p>\n
import numpy as np\n\npoint1 = np.array([1, 2])\npoint2 = np.array([4, 6])\ndistance = np.linalg.norm(point2 - point1)\nprint(distance)\n<\/code><\/pre>\n
\u8fd9\u79cd\u65b9\u5f0f\u7279\u522b\u9002\u5408\u9700\u8981\u5904\u7406\u5927\u91cf\u6570\u636e\u7684\u60c5\u51b5\uff0c\u6027\u80fd\u66f4\u4f18\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u5982\u4f55\u7528Python\u8bed\u8a00\u7f16\u5199\u4e24\u70b9\u4e4b\u95f4\u7684\u8ddd\u79bb \u4f7f\u7528Python\u8bed\u8a00\u7f16\u5199\u4e24\u70b9\u4e4b\u95f4\u7684\u8ddd\u79bb\u53ef\u4ee5\u901a\u8fc7\u6b27\u51e0\u91cc\u5f97\u8ddd\u79bb\u516c\u5f0f\u3001\u66fc\u54c8 […]","protected":false},"author":3,"featured_media":1136197,"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\/1136194"}],"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=1136194"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1136194\/revisions"}],"predecessor-version":[{"id":1136198,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1136194\/revisions\/1136198"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1136197"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1136194"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1136194"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1136194"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}