玩一下kmeans簿晓，調(diào)戲以下國足，順便預(yù)測一下18世界杯冠軍千埃，18-7-15 23:00世界杯

物以類聚憔儿，人以群分，選亞洲15支球隊

data = {"zhongguo":[50,50,50,40],
            "riben":[28,9,29,12],
            "hanguo":[17,15,27,26],
            "yilang":[25,40,28,18],
            "shate":[28,40,50,25],
            "yilake":[50,50,40,40],
            "kataer":[50,40,40,40],
            "alianqiu":[50,40,50,40],
            "wuzibiekesitan":[40,40,40,40],
            "taiguo":[50,50,50,40],
            "yuenan":[50,50,50,50],
            "aman":[50,50,40,50],
            "balin":[40,40,50,50],
            "chaoxian":[40,32,50,50],
            "yinni":[50,50,50,50]}

依次選2006年放可，2010年谒臼，2014年朝刊，2018年世界杯的數(shù)據(jù)作為聚類樣本，打進(jìn)世界杯的得分用排名衡量蜈缤，預(yù)選賽小組未出線的給50拾氓，預(yù)選賽十強的給40，澳大利亞沒統(tǒng)計底哥，18年的排名是估計的咙鞍，雖然11點是冠亞軍決賽，理論上其他隊伍排名已經(jīng)定了趾徽，但是我不會续滋，這樣算，得分越多的越low孵奶。

k選3疲酌，初始中心選中國，日本了袁，沙特徐勃，先計算每一條數(shù)據(jù)到三個中心點的歐氏距離，并將其歸為最近點那一類早像，處理完所有數(shù)據(jù)后僻肖，計算每個類的中心點，更新聚類中心卢鹦，重新以上步驟臀脏，知道聚類中心不再變化，代碼：

import numpy as np


def name2indexf(names):
    name2index = {}
    for index, name in enumerate(names):
        name2index[name] = index
    return name2index
def cacul_eudist(vec1, vec2):
    assert len(vec1) == len(vec2)
    dist = np.linalg.norm(vec1 - vec2)
    return dist

if __name__ == "__main__":
    data = {"zhongguo":[50,50,50,40],
            "riben":[28,9,29,12],
            "hanguo":[17,15,27,26],
            "yilang":[25,40,28,18],
            "shate":[28,40,50,25],
            "yilake":[50,50,40,40],
            "kataer":[50,40,40,40],
            "alianqiu":[50,40,50,40],
            "wuzibiekesitan":[40,40,40,40],
            "taiguo":[50,50,50,40],
            "yuenan":[50,50,50,50],
            "aman":[50,50,40,50],
            "balin":[40,40,50,50],
            "chaoxian":[40,32,50,50],
            "yinni":[50,50,50,50]}
    data_array = np.zeros(shape=(len(data.keys()),4))
    name2index = name2indexf(data.keys())
    for name in data.keys():
        index = name2index[name]
        data_array[index] = np.array(data[name])
    k_center = np.array([data_array[0],data_array[1],data_array[4]])
    k_center_with_near = {}

    # ------cacul center and it near-------
    epoch = 0
    while True:

        for index, item in enumerate(k_center):
            k_center_with_near[index] = []
        # ----------choice nearest for each data---------
        for index in range(len(data.keys())):
            data_item = data_array[index]
            near = 0
            dist_min = 100000
            for i in range(len(k_center)):
                dist = cacul_eudist(data_item, k_center[i])
                if dist_min > dist:
                    dist_min = dist
                    near = i
            k_center_with_near[near].append(index)

        # ------recacul center---------------
        end_tag = True
        for center_near_index in k_center_with_near.keys():
            contry_index = k_center_with_near[center_near_index]
            center_near_data = []
            for item in contry_index:
                center_near_data.append(data_array[item])
            center_near_data = np.array(center_near_data)
            new_center = np.mean(center_near_data, axis=0)
            if not (k_center[center_near_index] == new_center).all():
                end_tag = False
            k_center[center_near_index] = new_center
        print("epoch:",epoch, "center:", k_center)
        epoch += 1
        test = data.keys()
        if end_tag:
            for item in k_center_with_near.keys():
                print("classv{} include".format(item), [list(data.keys())[n] for n in (k_center_with_near[item])])
            break

結(jié)果：

classv0 include ['zhongguo', 'yilake', 'kataer', 'alianqiu', 'wuzibiekesitan', 'taiguo', 'yuenan', 'aman', 'balin', 'chaoxian', 'yinni']
classv1 include ['riben', 'hanguo']
classv2 include ['yilang', 'shate']

這樣算冀自，中國隊在亞洲只能算3流球隊

預(yù)測克羅地亞冠軍揉稚，雖然實力比法國弱一些，但是不要低估對冠軍渴望的心