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
