# im_, gt_ = 'Pavia', 'Pavia_gt'
# im_, gt_ = 'Botswana', 'Botswana_gt'
# 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
'''
# keys = ['Iris', 'Wine', 'wdbc', 'cotton', 'vowel', 'yeast', 'soybean']
keys = ['yeast', 'satellite', 'shuttle']
if __name__ == '__main__':
acc_final = {}
for key in keys:
print('processing ', key)
data = mat[key]
X, y = data[:, 1:].astype('float32'), data[:, 0].astype('int8')
classes = np.unique(y)
for c in classes:
if np.nonzero(y == c)[0].shape[0] < 10:
X = np.delete(X, np.nonzero(y == c), axis=0)
y = np.delete(y, np.nonzero(y == c))
p = Processor()
y = p.standardize_label(y)
X = minmax_scale(X)
print('num classes:', np.unique(y).__len__(), X.shape)
# # execute classification
# X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.6, stratify=y)
# he_elm = HE_ELM([[BaseELM(10, dropout_prob=0.9)]*5, ]*1, KELM(C=1e-5, kernel='rbf'), is_nonmlize=True)
accs_outer = []
n_learner = 30
n_hidden = 20
# range_ = range(10) if X.shape[0] >= 1000 else range(20)
for j in range(10) if X.shape[0] >= 2000 else range(20):
X_train, X_test, y_train, y_test = train_test_split(X,
y,
train_size=0.6,
stratify=y)
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))
