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Working on a method to sample part of the data set #18

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13 changes: 11 additions & 2 deletions Pilot2/P2B1/p2b1.py
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Original file line number Diff line number Diff line change
Expand Up @@ -254,6 +254,7 @@ def datagen(self, epoch=0, print_out=1, test=0):
if print_out:
print (files[f_ind], '\n')

print (files[f_ind], '\n')
(X, nbrs, resnums) = helper.get_data_arrays(files[f_ind])

# normalizing the location coordinates and bond lengths and scale type encoding
Expand All @@ -269,8 +270,13 @@ def datagen(self, epoch=0, print_out=1, test=0):

xt_all = np.array([])
yt_all = np.array([])
frames_all = np.array([])

for i in range(num_frames):
num_active_frames = random.sample(range(num_frames),
int(self.sampling_density*num_frames))
print ('Formating on the following frames', self.num_active_frames)
print ('Datagen on the following frames', num_active_frames)
for i in num_active_frames:

if self.conv_net:
xt = Xnorm[i]
Expand Down Expand Up @@ -325,7 +331,10 @@ def train_ac(self):
encoder_weight_file = '%s/%s.hdf5' % (current_path, 'encoder_weights')

for curr_file, xt_all, yt_all in self.datagen(i):
for frame in random.sample(range(len(xt_all)), int(self.sampling_density*len(xt_all))):
print ('Training on the following frames', xt_all)
for frame in self.num_active_frames:
# for frame in random.sample(range(len(xt_all)), int(self.sampling_density*len(xt_all))):
# for frame in range(xt_all):

history = self.molecular_model.fit(xt_all[frame], yt_all[frame], epochs=1,
batch_size=self.batch_size, callbacks=self.callbacks[:2],
Expand Down