'normalize': False,
'resize_to': (128, 128)},
{'crop': None,
'dtype': 'float32',
'mask': None,
'mode': 'L',
'normalize': False,
'resize_to': (128, 128)}]
M = np.random.rand(20, 20)
ytest = [0, 0, 1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6, 7, 7, 8, 8, 9, 9]
mean_performance(M)
label_variance(M, ytest)
y = dataset.meta['synset']
# X = P[:] # may have to memmap this
splits = get_subset_splits(dataset.meta, npc_train=150, npc_tests=[50], num_splits=1, catfunc=lambda x: x['synset'])
split = splits[0][0]
#
#
# dtype='float32', mode='w+', shape=memmap_shape)
# return np.memmap(filename, 'float32', 'w+'))
for i, preproc in enumerate(preprocs):
P = dataset.get_pixel_features(preproc)
Xtrain = np.memmap(filename='train_memmap_'+str(i)+'.dat',
dtype='float32', mode='w+', shape=(len(split['train']), P.shape[1]))
Xtest = np.memmap(filename='test_memmap_'+str(i)+'.dat',
dtype='float32', mode='w+', shape=(len(split['test']), P.shape[1]))
Xtrain[:] = P[split['train']]
def get_subset_splits(self, *args, **kwargs):
return get_subset_splits(self.meta, *args, **kwargs)
