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54.3588997.615773 7.81025 0 2 1 0 1 13.03842.828427 0 0 0 0 0 04.2426415.385165 1 0 0 0 0 1 19 0 0 0 0
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34.69041629.46184 0 04.242641 1 01.414214 22.828427 0 0 0 0 0 0 0 0 0 03.1622787.071068 0 2 22.24868.544004 45.656854 0
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111.414214 6 0 03.4641021.732051 01.73205115.26434 2 2 17.1414285.385165 1 0 1 5 0 0 0 0 11.4142142.236068 0 04.242641 0
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よろしくお願い致します。
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以下コードです。変更したのはPATHの部分と繰り返し回数の部分のみです。
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＊で上記エラーが出ています。
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import pandas as pd
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import numpy as np
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from sklearn.cluster import KMeans
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path1 = r'C:\Users\Road01\Desktop\test\kmn\test5\make_cluster.csv'
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path2 = r'C:\Users\Road01\Desktop\test\kmn\test5\vec.csv'
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path3 = r'C:\Users\Road01\Desktop\test\kmn\test5\verification.csv'
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path4 = r'C:\Users\Road01\Desktop\test\kmn\test5\file.txt'
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#データの読み込み，ファイル名はクラスタ作成用のcsvファイル
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df9 = pd.read_csv(path1, header = 0, index_col = 0, encoding='shift-JIS')
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#N/Aの削除
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df8 =df9.dropna()
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vec = KMeans(n_clusters = 3)
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#KMeansの実行
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vec.fit(df8)
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#クラスタ化したデータに0,1,2の仮ラベル付け，ファイルに書き込み
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with open (path4,'w') as f:
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print(vec.labels_, file=f)
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#重心ベクトルの表示
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print(vec.cluster_centers_)
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center = vec.cluster_centers_
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#csvファイルにいったん書き込み，ファイル名は任意
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np.savetxt(path2, center, delimiter=',')
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def cos_sim(v1, v2):
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return np.dot(v1, v2) / (np.linalg.norm(v1) * np.linalg.norm(v2)) ＊
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#先ほど書き込んだものを読み込む
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df = pd.read_csv(path2,header = None, index_col=None)
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115
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#検証用データを読み込む，ファイル名は検証用のcsvファイル
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df1 = pd.read_csv(path3, header = 0, index_col=0, encoding='shift-JIS')
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118
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119
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120
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#繰り返し回数は検証用のデータ数
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123
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i = 0
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124
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125
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while i < 51:
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127
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df3 = df1.iloc[i]
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128
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129
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j = 0
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mylist = []
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while j < 3:
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df2 = df.iloc[j]
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+
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137
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+
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138
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139
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mylist.append(cos_sim(df2, df3)) ＊
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+
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141
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j = j + 1
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142
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+
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else:
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+
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145
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with open (path4,'a') as f:
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147
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print(mylist.index(max(mylist)), file=f)
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148
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+
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149
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print(max(mylist)) #最大の類似度とそのラベルを表示
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152
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i = i + 1
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