AdpKolos

#from sklearn.datasets.samples_generator import make_blobs
#x, y = make_blobs(n_samples=250, n_features=15, centers=5, cluster_std=1.0)
#
#from sklearn.cluster import SpectralClustering
#
#cluster = SpectralClustering(n_clusters=5)
#
#Y = cluster.fit_predict(x)
#
#from sklearn.model_selection import ShuffleSplit
#XY=np.stack((x,y))
#_,_,_,_, train_test_split(XY, split_ratio=0.7)
#_,_,_,_, train_test_split(_,_, split_ratio=0.5)
#for i in range(15):
#    pca = decomposition.PCA(i)
#    pca.fit(x)
#    total_variance = np.sum(pca.explained_variance_ratio_)
#    if total_variance > 0.95:
#        break