import numpy as np
import matplotlib as plt
import pandas as pd
digits_train=pd.read_csv('http://archive.ics.uci.edu/ml/machine-learning-databases/optdigits/optdigits.tra',header=None)
digits_test=pd.read_csv('http://archive.ics.uci.edu/ml/machine-learning-databases/optdigits/optdigits.tes',header=None)
X_train=digits_train[np.arange(64)]
y_train=digits_train[64]
X_test=digits_test[np.arange(64)]
y_test=digits_test[64]
from sklearn.cluster import KMeans
kmeans=KMeans(n_clusters=10)
kmeans.fit(X_train)
y_pred=kmeans.predict(X_test)
from sklearn import metrics
print(metrics.adjusted_rand_score(y_test,y_pred))
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