code stringlengths 3 6.57k |
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np.sin(2*grid_angle) |
np.abs(phi) |
np.sign(phi) |
model(f1, f2, ctx) |
out1.cpu() |
numpy() |
out2.cpu() |
numpy() |
append(out1) |
append(out2) |
append(out1) |
append(out2) |
out.cpu() |
numpy() |
hiddens.append(out) |
append(out) |
cpu() |
numpy() |
idxs1.append(idx1) |
idxs2.append(idx2) |
append(idx1) |
append(idx2) |
append(batch) |
if ((cong==1) |
and ((ctx==0) |
or (ctx==1) |
append(out1) |
append(out2) |
hiddens_cong.append(out) |
samples_cong.append(batch) |
elif ((cong==-1) |
and ((ctx==0) |
or (ctx==1) |
append(out1) |
append(out2) |
hiddens_incong.append(out) |
samples_incong.append(batch) |
np.asarray(hiddens) |
squeeze() |
np.asarray(hiddens_incong) |
squeeze() |
np.asarray(hiddens_cong) |
squeeze() |
np.concatenate(np.asarray(hiddens_ctxs) |
squeeze() |
np.concatenate((hiddens_incong, hiddens_cong) |
np.concatenate(hiddens_ctxs, axis = 0) |
squeeze() |
np.concatenate((hiddens_incong, hiddens_cong) |
if ((args.cortical_model=='rnn') |
or (args.cortical_model=='rnncell') |
np.concatenate((samples_incong, samples_cong) |
np.zeros([2, n_states, hiddens.shape[-1]]) |
np.zeros([2, args.N_contexts, n_states, hiddens.shape[-1]]) |
np.zeros([n_states, hiddens.shape[-1]]) |
np.zeros([args.N_contexts, n_states, hiddens.shape[-1]]) |
if ((args.cortical_model=='rnn') |
or (args.cortical_model=='rnncell') |
np.asarray(hiddens_ctxs) |
squeeze() |
range(n_states) |
np.expand_dims(hiddens[i,f1_ind,:], axis=0) |
enumerate(idxs1) |
np.expand_dims(hiddens[i,f2_ind,:], axis=0) |
enumerate(idxs2) |
len(temp1 + temp2) |
np.concatenate(temp1 + temp2, axis=0) |
mean(axis=0) |
range(args.N_contexts) |
enumerate(idxs1_ctxs[ctx]) |
enumerate(idxs2_ctxs[ctx]) |
len(temp1_ctxs + temp2_ctxs) |
np.zeros([2,hiddens_ctxs.shape[-1]]) |
np.mean(np.asarray(temp1_ctxs) |
np.mean(np.asarray(temp2_ctxs) |
np.mean(m, axis=0) |
np.concatenate(temp1_ctxs + temp2_ctxs, axis=0) |
mean(axis=0) |
np.concatenate(avg_hidden_ctxs, axis=0) |
range(n_states) |
enumerate(zip(idxs1, idxs2) |
if ((idx1==f) |
len(temp) |
np.mean(temp, axis=0) |
range(args.N_contexts) |
enumerate(zip(idxs1_ctxs[ctx], idxs2_ctxs[ctx]) |
if ((idx1==f) |
len(temp_ctxs) |
np.mean(temp_ctxs, axis=0) |
np.concatenate(avg_hidden_ctxs, axis=0) |
np.asarray(hiddens_ctxs) |
squeeze() |
range(n_states) |
enumerate(idxs1) |
enumerate(idxs2) |
len(temp1) |
np.mean(temp1, axis=0) |
len(temp2) |
np.mean(temp2, axis=0) |
range(args.N_contexts) |
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