def mcfs_ours(train, test, K, debug=True): W = mcfs(train[0], n_selected_features=K, verbose=debug) bindices = mcfs_ranking(W)[:K] if debug: print(bindices) return train[0][:, bindices], test[0][:, bindices]
def my_mcfs(X, y): result = mcfs(copy.deepcopy(X), X.shape[1]) new_result = result.max(1) return new_result
#!/usr/bin/env python2 # -*- coding: utf-8 -*- import numpy as np import scipy.io from ConstructPairwiseDistance import ConstructPairwiseDistance from sklearn.metrics.pairwise import pairwise_distances import datetime import construct_W from skfeature.function.sparse_learning_based.MCFS import mcfs mat = scipy.io.loadmat("COIL20.mat") X = mat['X'] kwrags_W = { "metric": "euclidean", "neighbor_mode": "knn", "weight_mode": "heat_kernel", "k": 5, "t": 1 } W = construct_W.construct_W(X, **kwrags_W) print W weightMat = mcfs(X, 10, **{"W": W, "n_clusters": 20}) print weightMat print weightMat.shape np.savetxt("a.txt", weightMat, fmt='%.5f')