python——算法快速理解

风中的人36048821 2018-08-12 14:11:36

一、朴素贝叶斯（分类算法）

1.1概述：

朴素贝叶斯法是基于贝叶斯定理与特征条件独立假设的分类方法。 它多用于文本分类（比如垃圾邮件过滤、判断某篇文章的类型）。

分类是将一个未知样本分到几个预先已知类的过程。数据分类问题的解决是一个两步过程：第一步,建立一个模型，描述预先的数据集或概念集。通过分析由属性描述的样本（或实例，对象等）来构造模型。假定每一个样本都有一个预先定义的类，由一个被称为类标签的属性确定。为建立模型而被分析的数据元组形成训练数据集，该步也称作有指导的学习。

1.2朴素贝叶斯分类算--使用说明

1.2.1 步骤

（1）建立训练数据集

（2）建立训练结果集

（3）使用训练数据集及训练结果集构造模型

（4）使用模型对样本数据进行分类

1.3 示例

示例一：根据"谷丙转氨酶指标"判断肝病严重程度（正常、微高、偏高、严重）

· 属性：

["测量值","测量值持续天数"]

· 训练数据集：

[[0,1],[0,3],[0,13],[0,15],[0,30],[0,100],[3,1],[3,7],[3,9],[3,45],[3,33],[3,400],[20,5],[20,11],[20,16],[20,45],[20,333],[20,400],[36,2],[36,9],[36,14],[36,15],[36,333],[36,290],[40,3],[40,13],[40,15],[40,45],[40,233],[40,200],[45,3],[45,13],[45,15],[45,45],[45,233],[45,200],[56,3],[56,12],[56,17],[56,75],[56,233],[56,289],[77,3],[77,12],[77,17],[77,75],[77,233],[77,289],[83,3],[84,12],[85,17],[88,75],[89,233],[87,289],[96,3],[96,12],[96,17],[96,75],[96,233],[96,289],[155,3],[155,12],[155,17],[155,75],[155,233],[155,289],[300,1],[155,3],[155,5],[155,10],[155,15]]

· 目标值：

["正常","正常","正常","正常","正常","正常","正常","正常","正常","正常","正常","正常","正常","正常","正常","正常","正常","正常","正常","正常","正常","正常","正常","正常","正常","正常","正常","正常","正常","正常","微高","微高","微高","偏高","偏高","偏高","微高","微高","偏高","偏高","偏高","偏高","微高","微高","偏高","偏高","偏高","偏高","偏高","偏高","严重","严重","严重","严重","偏高","偏高","严重","严重","严重","严重","严重","严重","严重","严重","严重","严重","偏高","严重","严重","严重","严重"]

Python API 实现代码：

from sklearn.naive_bayes import GaussianNB

data=np.array([[0,1],[0,3],[0,13],[0,15],[0,30],[0,100],[3,1],[3,7],[3,9],[3,45],[3,33],[3,400],[20,5],[20,11],[20,16],[20,45],[20,333],[20,400],[36,2],[36,9],[36,14],[36,15],[36,333],[36,290],[40,3],[40,13],[40,15],[40,45],[40,233],[40,200],[45,3],[45,13],[45,15],[45,45],[45,233],[45,200],[56,3],[56,12],[56,17],[56,75],[56,233],[56,289],[77,3],[77,12],[77,17],[77,75],[77,233],[77,289],[83,3],[84,12],[85,17],[88,75],[89,233],[87,289],[96,3],[96,12],[96,17],[96,75],[96,233],[96,289],[155,3],[155,12],[155,17],[155,75],[155,233],[155,289],[300,1],[155,3],[155,5],[155,10],[155,15]])

target=np.array(["正常","正常","正常","正常","正常","正常","正常","正常","正常","正常","正常","正常","正常","正常","正常","正常","正常","正常","正常","正常","正常","正常","正常","正常","正常","正常","正常","正常","正常","正常","微高","微高","微高","偏高","偏高","偏高","微高","微高","偏高","偏高","偏高","偏高","微高","微高","偏高","偏高","偏高","偏高","偏高","偏高","严重","严重","严重","严重","偏高","偏高","严重","严重","严重","严重","严重","严重","严重","严重","严重","严重","偏高","严重","严重","严重","严重"])

GaussianNB.fit(data, target)

GaussianNB.predict(np.array([121,11]).reshape(1,-1))

输出结果为：["严重"]

GaussianNB.predict(np.array([55,55]).reshape(1,-1))

输出结果为：["偏高"]

GaussianNB.predict(np.array([35,55]).reshape(1,-1))

输出结果为：["正常"]

说明：当然该例子中的训练数据集肯定是不严谨的，应该给出更大体量更多情况的训练数据

二、K-Means（聚类算法）

1.1 概述：

K-MEANS算法是输入个数k，以及包含 n个的数据集，输出满足方差最小标准k个聚类的一种算法。k-means 算法接受输入量 k ；然后将n个划分为 k个以便使得所获得的聚类满足：同一聚类中的对象相似度较高；而不同聚类中的对象相似度较小。

1.2 K-Means聚类算法—使用说明

1.2.1步骤

（1）数据准备（待聚类的数据集）

（2）设定聚类个数k

（3） 其他相关的模型数据（如渲染图时的图例样式）

1.3 示例

示例一：根据城市（区域）的经纬度信息，将城市或区域聚类

· 数据集

data=["上海上海,121.48,31.22", "上海嘉定,121.24,31.4", "上海宝山,121.48,31.41", "上海川沙,121.7,31.19", "上海南汇,121.76,31.05", "上海奉贤,121.46,30.92", "上海松江,121.24,31", "上海金山,121.16,30.89", "上海青浦,121.1,31.15", "上海崇明,121.4,31.73", "云南昆明,102.73,25.04", "云南富民,102.48,25.21", "云南晋宁,102.58,24.68", "云南呈贡,102.79,24.9", "云南安宁,102.44,24.95", "云南昭通,103.7,29.32", "云南永善,103.63,28.22", "云南大关,103.91,27.74", "云南彝良,104.06,27.61", "云南鲁甸,103.54,27.21", "云南绥江,103.97,28.58", "云南盐津,104.28,28.08", "云南威信,105.05,27.85", "云南镇雄,104.86,27.42", "云南巧家,102.92,26.9", "云南永富,104.38,28.62", "云南曲靖,103.79,25.51", "云南宣威,104.09,26.24", "云南富源,104.24,25.67", "云南师宗,103.97,24.85", "云南嵩明,103.03,25.35", "云南会泽,103.27,26.41", "云南沽益,103.82,25.62", "云南罗平,104.3,24.88", "云南陆良,104.64,25.04", "云南宜良,103.12,24.9", "云南马龙,103.61,25.41", "云南路南,103.24,24.77", "云南寻甸,103.25,25.56", "云南玉溪,102.52,24.35", "云南华宁,102.93,24.26", "云南通海,102.75,24.09", "云南澄江,102.91,24.68", "云南江川,102.73,24.27", "云南易门,102.15,24.67", "云南元江,102,23.59", "云南新平,101.98,24.06", "云南峨山,102.38,24.16", "云南思茅,101,22.79", "云南普洱,101.03,23.07", "云南镇沅,100.88,23.9", "云南景东,100.82,24.42", "云南景谷,100.71,23.5", "云南黑江,101.71,23.4", "云南澜沦,99.97,22.55", "云南西盟,99.47,22.73", "云南江城,101.88,22.58", "云南孟连,99.55,22.32", "云南临沦,100.09,23.88", "云南云县,100.12,24.44", "云南镇康,99.02,23.92", "云南永德,99.25,24.03", "云南凤庆,99.92,24.58", "云南双江,99.85,23.45", "云南沧源,99.24,23.15", "云南耿马,99.41,23.56", "云南保由,99.18,25.12", "云南施甸,99.15,24.69", "云南腾冲,98.51,25.01", "云南昌宁,99.61,24.82", "云南龙陵,98.7,24.58", "云南丽江,100.25,26.86", "云南华坪,101.24,26.63", "云南永胜,100.76,26.71", "云南宁蒗,100.82,27.29", "云南文山,104.24,23.37", "云南广南,105.09,24.05", "云南西畴,104.68,23.42", "云南麻栗坡,104.71,23.12", "云南马关,104.4,23.01", "云南丘北,104.19,24.03", "云南砚山,104.35,23.62", "云南富宁,105.6,23.62", "云南个旧,102.43,23.35", "云南弥勒,103.43,24.41", "云南蒙自,103.41,23.36", "云南元阳,102.81,23.17", "云南红河,102.42,23.35", "云南石屏,102.48,23.73", "云南泸西,103.76,24.52", "云南金平,103.24,22.77", "云南开远,103.23,23.7", "云南绿春,102.42,23.01", "云南建水,102.79,23.64", "云南河口,103.98,22.52", "云南屏边,103.67,22.68", "云南景淇,100.79,22", "云南勐海,100.5,21.95", "云南勐腊,101.56,21.48", "云南楚雄,101.54,25.01", "云南元谋,101.85,25.7", "云南武定,102.36,25.55", "云南禄丰,102.08,25.15", "云南南华,101.26,25.21", "云南大姚,101.34,25.73", "云南永仁,101.7,26.07", "云南禄劝,102.45,25.58", "云南牟定,101.58,25.32", "云南双柏,101.67,24.68", "云南姚安,101.24,25.4", "云南下关,100.24,25.45", "云南剑川,99.88,26.53", "云南洱源,99.94,26.1", "云南宾川,100.55,25.82", "云南弥渡,100.52,25.34", "云南永平,99.52,25.45", "云南鹤庆,100.18,26.55", "云南大理,100.19,25.69", "云南漾濞,99.98,25.68", "云南云龙,99.39,25.9", "云南祥云,100.56,25.48", "云南巍山,100.33,25.23", "云南南涧,100.51,25.04", "云南潞西,98.6,24.41", "云南陇川,97.96,24.33", "云南盈江,97.93,24.69", "云南畹町,98.08,24.08", "云南瑞丽,97.83,24", "云南梁河,98.3,24.78", "云南泸水,98.82,25.97", "云南碧江,98.95,26.55", "云南福贡,98.92,26.89", "云南兰坪,99.29,26.49", "云南贡山,98.65,27.73", "云南中甸,99.72,27.78", "云南德钦,98.93,28.49", "云南维西,99.27,27.15", "北京北京,116.46,39.92", "北京平谷,117.1,40.13", "北京密云,116.85,40.37", "北京顺义,116.65,40.13", "北京通县,116.67,39.92", "北京怀柔,116.62,40.32", "北京大兴,116.33,39.73", "北京房山,115.98,39.72", "吉林长春,125.35,43.88", "吉林吉林,126.57,43.87", "吉林农安,125.15,44.45", "吉林德惠,125.68,44.52", "吉林榆树,126.55,44.83", "吉林九台,126.83,44.15", "吉林双阳,125.68,43.53", "吉林永吉,126.57,43.87", "吉林舒兰,126.97,44.4", "吉林蛟河,127.33,43.75", "吉林桦甸,126.72,42.97", "吉林磐石,126.03,42.93", "吉林延吉,129.52,42.93", "吉林汪清,129.75,43.32", "吉林珲春,130.35,42.85", "吉林图们,129.83,42.98", "吉林和龙,129,42.52", "吉林安图,128.3,42.58", "吉林敦化,128.18,43.35", "吉林通化,125.92,41.49", "吉林柳河,125.7,40.88", "吉林海龙,125.65,42.53", "吉林辉南,126.03,42.68", "吉林靖宇,126.8,42.38", "吉林浑江,126.4,41.97", "吉林抚松,127.27,42.33", "吉林集安,126.17,41.15", "吉林长白,128.17,41.43", "吉林四平,124.37,43.17", "吉林梨树,124.33,43.32", "吉林怀德,124.82,43.5", "吉林伊通,125.32,43.33", "吉林辽源,125.15,42.97", "吉林东丰,125.5,42.68", "吉林双辽,123.5,43.52", "吉林白城,122.82,45.63", "吉林大安,124.18,45.5", "吉林扶余,124.82,45.2", "吉林乾安,124.02,45", "吉林长岭,123.97,44.3", "吉林通榆,123.13,44.82", "吉林洮安,122.75,45.35", "四川成都,104.06,30.67", "四川金堂,104.32,30.88", "四川双流,104.94,30.57", "四川蒲江,103.29,30.2", "四川郫县,103.86,30.8", "四川新都,104.13,30.82", "四川来易,102.15,26.9", "四川盐边,101.56,26.9", "四川温江,103.81,30.97", "四川灌县,103.61,31.04", "四川彭县,103.94,30.99", "四川什邡,104.16,31.13", "四川广汉,104.25,30.99", "四川新津,103.78,30.42", "四川邛崃,103.47,30.42", "四川大邑,103.53,30.58", "四川崇庆,103.69,30.63", "四川绵阳,104.73,31.48", "四川江油,104.7,31.8", "四川青川,105.21,32.59", "四川平武,104.52,32.42", "四川光元,105.86,32.44", "四川旺苍,106.33,32.25", "四川剑阁,105.45,32.03", "四川梓潼,105.16,31.64", "四川三台,105.06,31.1", "四川盐亭,105.35,31.23", "四川射洪,105.31,30.9", "四川遂宁,105.58,30.52", "四川蓬溪,105.74,30.78", "四川中江,104.68,31.06", "四川德阳,104.37,31.13", "四川绵竹,104.19,31.32", "四川安县,104.41,31.64", "四川北川,104.44,31.89", "四川内江,105.04,29.59", "四川乐至,105.02,30.3", "四川安岳,105.3,30.12", "四川威远,104.7,29.57", "四川资中,104.85,29.81", "四川资阳,104.6,30.19", "四川简阳,104.53,30.38", "四川隆昌,105.25,29.64", "四川宜宾,104.56,29.77", "四川富顺,104.97,29.24", "四川南溪,104.96,28.87", "四川江安,105.06,28.71", "四川纳溪,105.38,28.77", "四川泸县,105.46,28.96", "四川合江,105.78,28.79", "四川泸州,105.39,28.91", "四川古蔺,105.79,28.03", "四川叙水,105.44,28.19", "四川长宁,104.91,28.6", "四川兴文,105.06,28.36", "四川琪县,104.81,28.38", "四川高县,104.52,28.4", "四川筠连,104.53,28.16", "四川屏由,104.15,28.68", "四川乐由,103.73,29.59", "四川夹江,103.59,29.75", "四川洪雅,103.38,29.95", "四川丹棱,103.53,30.04", "四川青神,103.81,29.86", "四川眉由,103.81,30.05", "四川彭由,103.83,30.22", "四川井研,104.06,29.67", "四川仁寿,104.09,30", "四川犍为,103.93,29.21", "四川沐川,103.98,28.96", "四川娥眉,103.5,29.62", "四川马边,103.53,28.87", "四川峨边,103.25,29.23", "四川金口,103.13,29.24", "四川涪陵,107.36,29.7", "四川垫江,107.34,30.36", "四川丰都,107.7,29.89", "四川石柱,108.13,29.98", "四川秀山,108.97,28.47", "四川西阳,108.75,28.85", "四川黔江,108.81,29.53", "四川彭水,108.19,29.29", "四川武隆,108.72,29.29", "四川南川,107.13,29.15", "四川万县,108.35,30.83", "四川开县,108.39,31.23", "四川城口,108.67,31.98", "四川巫溪,109.6,31.42", "四川巫山,109.86,31.1", "四川奉节,109.52,31.06", "四川云阳,108.89,30.99", "四川忠县,108.03,30.33", "四川梁平,107.78,30.66", "四川南允,106.06,30.8", "四川苍溪,105.96,31.75", "四川阆中,105.97,31.75", "四川仪陇,106.38,31.52", "四川南部,106.03,31.34", "四川西允,105.84,31.01", "四川营山,106.57,31.07", "四川蓬安,106.44,31.04", "四川广安,106.61,30.48", "四川岳池,106.43,30.55", "四川武胜,106.3,30.38", "四川华云,106.74,30.41", "四川达县,107.49,31.23", "四川万源,108.06,32.07", "四川宜汉,107.71,31.39", "四川开江,107.87,31.1", "四川邻水,106.91,30.36", "四川大竹,107.21,30.75", "四川渠县,106.94,30.85", "四川南江,106.83,32.36", "四川巴中,106.73,31.86", "四川平昌,107.11,31.59", "四川通江,108.24,31.95", "四川百沙,108.18,32", "四川雅安,102.97,29.97", "四川芦山,102.91,30.17", "四川名山,103.06,30.09", "四川荣经,102.81,29.79", "四川汉源,102.66,29.4", "四川石棉,102.38,29.21", "四川天全,102.78,30.09", "四川宝兴,102.84,30.36", "四川马尔康,102.22,31.92", "四川红原,102.55,31.79", "四川阿坝,101.72,31.93", "四川若尔盖,102.94,33.62", "四川黑水,102.95,32.06", "四川松潘,103.61,32.64", "四川南坪,104.19,33.23", "四川汶川,103.61,31.46", "四川理县,103.16,31.42", "四川小金,102.34,30.97", "四川金川,102.03,31.48", "四川壤塘,100.97,32.3", "四川茂汶,103.89,31.67", "四川康定,101.95,30.04", "四川炉霍,100.65,31.38", "四川甘孜,99.96,31.64", "四川新龙,100.28,30.96", "四川白玉,98.83,32.23", "四川德格,98.57,31.81", "四川石渠,98.06,33.01", "四川色达,100.35,32.3", "四川泸定,102.25,29.92", "四川丹巴,101.87,30.85", "四川九龙,101.53,29.01", "四川雅江,101,30.03", "四川道孚,101.14,30.99", "四川理塘,100.28,30.03", "四川乡城,99.78,28.93", "四川稻城,100.31,29.04", "四川巴塘,99,30", "四川得荣,99.25,28.71", "四川西昌,102.29,27.92", "四川昭觉,102.83,28.03", "四川甘洛,102.74,28.96", "四川雷波,103.62,28.21", "四川宁南,102.76,27.07", "四川会东,102.55,26.74", "四川会理,102.21,26.67", "四川德昌,102.15,27.4", "四川美姑,103.14,28.33", "四川金阳,103.22,27.73", "四川布拖,102.8,27.7", "四川普格,102.52,27.38", "四川喜德,102.42,28.33", "四川越西,102.49,28.66", "四川盐源,101.51,27.42", "四川冕宁,102.15,28.58", "四川木里,101.25,27.9", "天津天津,117.2,39.13", "天津宁河,117.83,39.33", "天津静海,116.92,38.93", "天津蓟县,117.4,40.05", "天津宝坻,117.3,39.75", "天津武清,117.05,39.4", "宁夏回族自治区银川,106.27,38.47", "宁夏回族自治区永宁,106.24,38.28", "宁夏回族自治区贺兰,106.35,38.55", "宁夏回族自治区石嘴山,106.39,39.04", "宁夏回族自治区平罗,106.54,38.91", "宁夏回族自治区陶乐,106.69,38.82", "宁夏回族自治区吴忠,106.21,37.99", "宁夏回族自治区同心,105.94,36.97", "宁夏回族自治区灵武,106.34,38.1", "宁夏回族自治区中宁,105.66,37.48", "宁夏回族自治区盐池,107.41,37.78", "宁夏回族自治区中卫,105.18,37.51", "宁夏回族自治区青铜峡,106.07,38.02", "宁夏回族自治区固原,106.28,36.01", "宁夏回族自治区西吉,105.7,35.97", "宁夏回族自治区泾源,106.33,35.5", "宁夏回族自治区海原,105.64,36.56", "宁夏回族自治区隆德,106.11,35.63", "安徽合肥,117.27,31.86", "安徽长丰,117.16,32.47", "安徽淮南,116.98,32.62", "安徽凤台,116.71,32.68", "安徽淮北,116.77,33.97", "安徽濉溪,116.76,33.92", "安徽芜湖,118.38,31.33", "安徽铜陵,117.82,30.93", "安徽蚌埠,117.34,32.93", "安徽马鞍山,118.48,31.56", "安徽安庆,117.03,30.52", "安徽宿州,116.97,33.63", "安徽宿县,116.97,33.63", "安徽砀山,116.34,34.42", "安徽萧县,116.93,34.19", "安徽吴壁,117.55,33.55", "安徽泗县,117.89,33.49", "安徽五河,117.87,33.14", "安徽固镇,117.32,33.33", "安徽怀远,117.19,32.95", "安徽滁州,118.31,32.33", "安徽嘉山,117.98,32.78", "安徽天长,119,32.68", "安徽来安,118.44,32.44", "安徽全椒,118.27,32.1", "安徽定远,117.68,32.52", "安徽凤阳,117.4,32.86", "安徽巢湖,117.87,31.62", "安徽巢县,117.87,31.62", "安徽肥东,117.47,31.89", "安徽含山,118.11,31.7", "安徽和县,118.37,31.7", "安徽无为,117.75,31.3", "安徽卢江,117.29,31.23", "安徽宣城,118.73,31.95", "安徽当涂,118.49,31.55", "安徽郎溪,119.17,31.14", "安徽广德,119.41,30.89", "安徽泾县,118.41,30.68", "安徽南陵,118.32,30.91", "安徽繁昌,118.21,31.07", "安徽宁国,118.95,30.62", "安徽青阳,117.84,30.64", "安徽屯溪,118.31,29.72", "安徽休宁,118.19,29.81", "安徽旌得,118.53,30.28", "安徽绩溪,118.57,30.07", "安徽歙县,118.44,29.88", "安徽祁门,117.7,29.86", "安徽黟县,117.92,29.93", "安徽太平,118.13,30.28", "安徽石台,117.48,30.19", "安徽桐城,116.94,31.04", "安徽纵阳,117.21,30.69", "安徽怀宁,116.63,30.41", "安徽望江,116.69,30.12", "安徽宿松,116.13,30.15", "安徽太湖,116.27,30.42", "安徽岳西,116.36,30.84", "安徽潜山,116.53,30.62", "安徽东至,116.99,30.08", "安徽贵池,117.48,30.66", "安徽六安,116.49,31.73", "安徽霍丘,116.27,32.32", "安徽寿县,116.78,32.57", "安徽肥西,117.15,31.7", "安徽舒城,116.94,31.45", "安徽霍山,116.32,31.38", "安徽金寨,115.87,31.67", "安徽阜阳,115.81,32.89", "安徽毫县,116.76,33.86", "安徽涡阳,116.21,33.49", "安徽蒙城,116.55,33.25", "安徽利辛,116.19,33.12", "安徽颖上,116.26,32.62", "安徽阜南,115.6,32.63", "安徽临泉,115.24,33.06", "安徽界首,115.34,33.24", "安徽太和,115.61,33.16", "山东济南,117,36.65", "山东历城,117.07,36.69", "山东长清,116.73,36.55", "山东章丘,117.53,36.72", "山东青岛,120.33,36.07", "山东崂山,120.42,36.15", "山东胶南,119.97,35.88", "山东即墨,120.45,36.38", "山东胶县,120,36.28", "山东淄博,118.05,36.78", "山东枣庄,117.57,34.86", "山东滕县,117.17,35.09", "山东东营,118.49,37.46", "山东垦利,118.54,37.59", "山东利津,118.25,37.49", "山东德州,116.29,37.45", "山东宁津,116.8,37.64", "山东乐陵,117.22,37.74", "山东商河,117.15,37.31", "山东济阳,117.2,36.97", "山东禹城,116.66,36.95", "山东夏津,116,36.95", "山东陵县,116.58,37.34", "山东庆云,117.37,37.37", "山东临邑,116.86,37.2", "山东齐河,116.76,36.79", "山东平原,116.44,37.16", "山东武城,116.08,37.2", "山东滨州,118.03,37.36", "山东滨县,117.97,37.47", "山东广饶,118.41,37.04", "山东桓台,118.12,36.95", "山东邹平,117.75,36.89", "山东阳信,117.58,37.65", "山东沾化,118.14,37.7", "山东博兴,118.12,37.12", "山东高青,117.66,37.18", "山东惠民,117.51,17.49", "山东无棣,117.58,37.73", "山东潍坊,119.1,36.62", "山东潍县,119.22,36.77", "山东平度,119.97,36.77", "山东诸城,119.42,35.99", "山东安丘,119.2,36.42", "山东临朐,118.53,36.5", "山东寿光,118.73,36.86", "山东昌邑,119.41,36.86", "山东高密,119.75,36.38", "山东五莲,119.2,35.74", "山东昌乐,118.83,36.69", "山东高都,118.47,36.69", "山东烟台,121.39,37.52", "山东牟平,121.59,37.38", "山东文登,122.05,37.2", "山东海阳,121.17,36.76", "山东莱阳,120.71,36.97", "山东栖霞,120.83,37.28", "山东掖县,119.93,37.18", "山东长岛,120.73,37.91", "山东威海,122.1,37.5", "山东福山,121.27,37.49", "山东荣成,122.41,37.16", "山东乳山,121.52,36.89", "山东莱西,120.53,36.86", "山东招远,120.38,37.35", "山东黄县,120.51,37.64", "山东蓬莱,120.75,37.8", "山东临沂,118.35,35.05", "山东沂水,118.64,35.78", "山东日照,119.46,35.42", "山东临沭,118.73,34.89", "山东仓山,118.03,34.84", "山东平邑,117.63,35.49", "山东沂源,118.17,36.18", "山东沂南,118.47,35.54", "山东营县,118.83,35.57", "山东莒南,118.83,35.17", "山东郯城,118.35,34.61", "山东费县,117.97,35.26", "山东蒙阴,117.95,35.7", "山东泰安,117.13,36.18", "山东莱芜,117.67,36.19", "山东肥城,116.76,36.24", "山东平阴,116.46,36.29", "山东新汶,117.67,35.86", "山东新泰,117.76,35.91", "山东宁阳,116.8,35.76", "山东东平,116.3,35.91", "山东济宁,116.59,35.38", "山东兖州,116.83,35.54", "山东泗水,117.27,35.65", "山东鱼台,116.65,35", "山东嘉祥,116.34,35.41", "山东汶上,116.49,35.71", "山东曲阜,116.98,35.59", "山东邹县,116.97,35.39", "山东微山,117.12,34.8", "山东金乡,116.32,35.07", "山东荷泽,115.43,35.24", "山东郓城,115.94,35.59", "山东巨野,116.08,35.38", "山东单县,116.07,34.82", "山东曹县,115.53,34.83", "山东鄄城,115.5,35.57", "山东梁山,116.1,35.8", "山东成武,115.88,34.97", "山东定陶,115.57,35.07", "山东东明,115.08,35.31", "山东聊城,115.97,36.45", "山东高唐,116.23,36.86", "山东东阿,116.23,36.32", "山东莘县,115.67,36.24", "山东临清,115.72,36.68", "山东茌平,116.27,36.58", "山东阳谷,115.78,36.11", "山东冠县,115.45,35.47"]

· n_clusters=8

Python API 实现代码：

import numpy as np

import matplotlib.pyplot as plt

from sklearn.cluster import KMeans

cls = KMeans(n_clusters).fit(data)

markers = ['^','v','o','*','+','.','s','|']

colors=["b","r","g","y","k","m","c","r"]

for i in range(n_clusters):

members = cls.labels_ == i

plt.scatter(X[members,0],X[members,1],s=60,marker=markers[i],c=colors[i],alpha=0.5)

plt.title(' city-of-china ')

plt.show()

运行结果：