#MPI.Abort(MPI.COMM_WORLD)
break
comm.send(None, dest=0, tag=EXIT)
if __name__ == "__main__":
comm=MPI.COMM_WORLD
size=comm.size
rank=comm.rank
status=MPI.Status()
READY, START, DONE, EXIT = 0, 1, 2, 3
smile_old=zinc_data_with_bracket_original()
val,smile=zinc_processed_with_bracket(smile_old)
print val
val2=['C', '(', ')', 'c', '1', '2', 'o', '=', 'O', 'N', '3', 'F', '[C@@H]', 'n', '-', '#', 'S', 'Cl', '[O-]', '[C@H]', '[NH+]', '[C@]', 's', 'Br', '/', '[nH]', '[NH3+]', '4', '[NH2+]', '[C@@]', '[N+]', '[nH+]', '\\', '[S@]', '5', '[N-]', '[n+]', '[S@@]', '[S-]', '6', '7', 'I', '[n-]', 'P', '[OH+]', '[NH-]', '[P@@H]', '[P@@]', '[PH2]', '[P@]', '[P+]', '[S+]', '[o+]', '[CH2-]', '[CH-]', '[SH+]', '[O+]', '[s+]', '[PH+]', '[PH]', '8', '[S@@+]']
logP_values = np.loadtxt('logP_values.txt')
SA_scores = np.loadtxt('SA_scores.txt')
cycle_scores = np.loadtxt('cycle_scores.txt')
SA_mean = np.mean(SA_scores)
#print len(SA_scores)
SA_std=np.std(SA_scores)
logP_mean = np.mean(logP_values)
logP_std= np.std(logP_values)
cycle_mean = np.mean(cycle_scores)
cycle_std=np.std(cycle_scores)
#return new_sentence
def save_model(model):
# serialize model to JSON
model_json = model.to_json()
with open("model.json", "w") as json_file:
json_file.write(model_json)
# serialize weights to HDF5
model.save_weights("model.h5")
print("Saved model to disk")
if __name__ == "__main__":
smile=zinc_data_with_bracket_original()
valcabulary,all_smile=zinc_processed_with_bracket(smile)
print(valcabulary)
print(len(all_smile))
X_train,y_train=prepare_data(valcabulary,all_smile)
maxlen=81
X= sequence.pad_sequences(X_train, maxlen=81, dtype='int32',
padding='post', truncating='pre', value=0.)
y = sequence.pad_sequences(y_train, maxlen=81, dtype='int32',
padding='post', truncating='pre', value=0.)
y_train_one_hot = np.array([to_categorical(sent_label, num_classes=len(valcabulary)) for sent_label in y])
print (y_train_one_hot.shape)