def main(): # load matlab data mat = scipy.io.loadmat('../data/COIL20.mat') X = mat['X'] # data y = mat['Y'] # label y = y[:, 0] X = X.astype(float) n_samples, n_features = X.shape # construct affinity matrix kwargs_W = {"metric": "euclidean", "neighbor_mode": "knn", "weight_mode": "heat_kernel", "k": 5, 't': 1} W = construct_W.construct_W(X, **kwargs_W) # feature selection score = lap_score.lap_score(X, W = W) idx = lap_score.feature_ranking(score) # evaluation num_fea = 100 selected_features = X[:, idx[0:num_fea]] ari, nmi, acc = unsupervised_evaluation.evaluation(X_selected=selected_features, n_clusters=20, y=y) print 'ARI:', ari print 'NMI:', nmi print 'ACC:', acc
def main(): # load data mat = scipy.io.loadmat('../data/gisette.mat') X = mat['X'] y = mat['Y'] y = y[:, 0] X = X.astype(float) # feature selection kwargs = {'style': 0} score = SPEC.spec(X, **kwargs) idx = SPEC.feature_ranking(score, **kwargs) # evaluation num_fea = 100 selected_features = X[:, idx[0:num_fea]] ari, nmi, acc = unsupervised_evaluation.evaluation(selected_features=selected_features, n_clusters=2, y=y) print 'ARI:', ari print 'NMI:', nmi print 'ACC:', acc
def main(): # load matlab data mat = scipy.io.loadmat('../data/COIL20.mat') X = mat['X'] X = X.astype(float) y = mat['Y'] y = y[:, 0] # construct W kwargs = {"metric": "euclidean", "neighborMode": "knn", "weightMode": "heatKernel", "k": 5, 't': 1} W = construct_W.construct_W(X, **kwargs) # mcfs feature selection n_selected_features = 100 S = MCFS.mcfs(X, n_selected_features, W=W, n_clusters=20) idx = MCFS.feature_ranking(S) # evaluation X_selected = X[:, idx[0:n_selected_features]] ari, nmi, acc = unsupervised_evaluation.evaluation(X_selected=X_selected, n_clusters=20, y=y) print 'ARI:', ari print 'NMI:', nmi print 'ACC:', acc
def main(): # load data mat = scipy.io.loadmat('../data/COIL20.mat') X = mat['X'] # data X = X.astype(float) y = mat['Y'] # label y = y[:, 0] kwargs = {"metric": "euclidean", "neighbor_mode": "knn", "weight_mode": "heat_kernel", "k": 5, 't': 1} W = construct_W.construct_W(X, **kwargs) # NDFS feature selection W = NDFS.ndfs(X, W=W, n_clusters=20, verbose=False) idx = feature_ranking(W) # evaluation n_selected_features = 100 X_selected = X[:, idx[0:n_selected_features]] ari, nmi, acc = evaluation(X_selected=X_selected, n_clusters=20, y=y) print 'ARI:', ari print 'NMI:', nmi print 'ACC:', acc