{"id":3199,"date":"2022-02-27T13:14:08","date_gmt":"2022-02-27T05:14:08","guid":{"rendered":"https:\/\/egonlin.com\/?p=3199"},"modified":"2022-02-27T13:14:08","modified_gmt":"2022-02-27T05:14:08","slug":"%e7%ac%ac%e4%ba%94%e8%8a%82%ef%bc%9a%e4%b8%bb%e6%88%90%e5%88%86%e5%88%86%e6%9e%90%e4%bb%a3%e7%a0%81%e6%89%8b%e5%86%99%e6%95%b0%e5%ad%97%e8%af%86%e5%88%ab","status":"publish","type":"post","link":"https:\/\/egonlin.com\/?p=3199","title":{"rendered":"\u7b2c\u4e94\u8282\uff1a\u4e3b\u6210\u5206\u5206\u6790\u4ee3\u7801(\u624b\u5199\u6570\u5b57\u8bc6\u522b)"},"content":{"rendered":"

\u4e3b\u6210\u5206\u5206\u6790\u4ee3\u7801(\u624b\u5199\u6570\u5b57\u8bc6\u522b)<\/h1>\n

\"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\"<\/div><\/p>\n

\u5bfc\u5165\u6a21\u5757<\/h1>\n
import time\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom matplotlib.font_manager import FontProperties\nfrom sklearn import datasets\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.decomposition import PCA\nfrom sklearn.neighbors import KNeighborsClassifier\n%matplotlib inline\nfont = FontProperties(fname='\/Library\/Fonts\/Heiti.ttc')<\/code><\/pre>\n
\u6570\u636e\u9884\u5904\u7406<\/h1>\n
# \u5bfc\u5165\u624b\u5199\u8bc6\u522b\u6570\u5b57\u6570\u636e\u96c6\ndigits = datasets.load_digits()\nX = digits.data\ny = digits.target\n\nX_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1)<\/code><\/pre>\n
KNN\u8bad\u7ec3\u6570\u636e<\/h1>\n
knn = KNeighborsClassifier()\nknn.fit(X_train, y_train)<\/code><\/pre>\n
KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n           metric_params=None, n_jobs=None, n_neighbors=5, p=2,\n           weights='uniform')<\/code><\/pre>\n
\u51c6\u786e\u5ea6<\/h2>\n
knn.score(X_train, y_train)<\/code><\/pre>\n
0.9866369710467706<\/code><\/pre>\n
\u964d\u7ef4(2\u7ef4)<\/h1>\n
pca = PCA(n_components=2)\n\npca.fit(X_train)\nX_train_reduction = pca.transform(X_train)\nX_test_reduction = pca.transform(X_test)<\/code><\/pre>\n
KNN\u8bad\u7ec3\u6570\u636e<\/h2>\n
<\/p>
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\u67e5\u770b\u66f4\u591a<\/h2>
\u8054\u7cfb\u7ba1\u7406\u5458\u5fae\u4fe1tutu19192010\uff0c\u6ce8\u518c\u8d26\u53f7<\/p>\n<\/div>
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\u4e3b\u6210\u5206\u5206\u6790\u4ee3\u7801(\u624b\u5199\u6570\u5b57\u8bc6\u522b) \u5bfc\u5165\u6a21\u5757 import time import numpy as np imp […]<\/p>\n","protected":false},"author":3,"featured_media":3189,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":[],"categories":[276,299],"tags":[],"_links":{"self":[{"href":"https:\/\/egonlin.com\/index.php?rest_route=\/wp\/v2\/posts\/3199"}],"collection":[{"href":"https:\/\/egonlin.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/egonlin.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/egonlin.com\/index.php?rest_route=\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/egonlin.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=3199"}],"version-history":[{"count":0,"href":"https:\/\/egonlin.com\/index.php?rest_route=\/wp\/v2\/posts\/3199\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/egonlin.com\/index.php?rest_route=\/wp\/v2\/media\/3189"}],"wp:attachment":[{"href":"https:\/\/egonlin.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=3199"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/egonlin.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=3199"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/egonlin.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=3199"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}