import numpy as np
if __name__ == '__main__':
# root = '/Users/cengmeng/PycharmProjects/python/Deep-subspace-clustering-networks/Data/'
root = '/content/'
# im_, gt_ = 'SalinasA_corrected', 'SalinasA_gt'
im_, gt_ = 'Indian_pines_corrected', 'Indian_pines_gt'
# im_, gt_ = 'Pavia', 'Pavia_gt'
# im_, gt_ = 'KSC', 'KSC_gt'
img_path = root + im_ + '.mat'
gt_path = root + gt_ + '.mat'
p = Processor()
img, gt = p.prepare_data(img_path, gt_path)
n_row, n_column, n_band = img.shape
train_inx, test_idx = p.get_tr_tx_index(p.get_correct(img, gt)[1],
test_size=0.9)
img = minmax_scale(img.reshape(n_row * n_column, n_band)).reshape(
(n_row, n_column, n_band))
x_input = img.reshape(n_row * n_column, n_band)
num_class = 15
snmf = BandSelection_SNMF(num_class)
X_new = snmf.predict(x_input).reshape(n_row, n_column, num_class)
a, b = p.get_correct(X_new, gt)
b = p.standardize_label(b)
print(eval_band_cv(a, b, times=5))
# im_, gt_ = 'KSC', 'KSC_gt'
img_path = root + im_ + '.mat'
gt_path = root + gt_ + '.mat'
print(img_path)
p = Processor()
img, gt = p.prepare_data(img_path, gt_path)
# Img, Label = Img[:256, :, :], Label[:256, :]
n_row, n_column, n_band = img.shape
X_img = minmax_scale(img.reshape(n_row * n_column, n_band)).reshape(
(n_row, n_column, n_band))
img_correct, gt_correct = p.get_correct(X_img, gt)
gt_correct = p.standardize_label(gt_correct)
X_img_2D = X_img.reshape(n_row * n_column, n_band)
train_inx, test_idx = p.get_tr_tx_index(gt_correct, test_size=0.4)
n_input = [n_row, n_column]
kernel_size = [11]
n_hidden = [32]
batch_size = n_band
model_path = './pretrain-model-COIL20/model.ckpt'
ft_path = './pretrain-model-COIL20/model.ckpt'
logs_path = './pretrain-model-COIL20/logs'
batch_size_test = n_band
iter_ft = 0
display_step = 1
alpha = 0.04
learning_rate = 1e-3
X = np.delete(X, np.nonzero(y == c), axis=0)
y = np.delete(y, np.nonzero(y == c))
y = p.standardize_label(y)
X = MinMaxScaler().fit_transform(X)
print('size:', X.shape, 'n_classes:', np.unique(y).shape[0])
############################
# feature extraction
############################
'''
step 1: set common parameters
'''
n_hidden = 50
max_iter = 1000
train_index, test_index = p.get_tr_tx_index(y, test_size=0.4)
results = []
X_proj = []
for rho in np.arange(0.1, 1, 0.1):
start = time.clock()
instance_emo_elm = EMO_AE_ELM(n_hidden,
sparse_degree=rho,
max_iter=max_iter,
n_pop=100)
X_projection_emo_elm = instance_emo_elm.fit(X, X).predict(X)
# instance_emo_elm.save_evo_result('./experimental_results/EMO-ELM-AE-results-KSC-50hidden.npz')
time_emo_elmae = round(time.clock() - start, 3)
X_proj.append(X_projection_emo_elm)
# TODO: calculate accuracy
X_train, X_test = X_projection_emo_elm[train_index], X_projection_emo_elm[
test_index] # [index]
