code stringlengths 3 6.57k |
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enumerate(idxs1_ctxs[ctx]) |
enumerate(idxs2_ctxs[ctx]) |
len(temp1_ctxs) |
np.mean(temp1_ctxs, axis=0) |
len(temp2_ctxs) |
np.mean(temp2_ctxs, axis=0) |
np.concatenate(avg_hidden_ctxs, axis=1) |
analyze_accs(args, test_data, cortical_result, dist_results) |
analyze_credit_assignment(args, test_data, cortical_result, dist_results) |
proportions(args, test_data, cortical_result, dist_results) |
np.concatenate(h, axis=0) |
contexts (diagonals) |
p_pies.append(np.any(h>0, axis=0) |
ps.append(np.mean(h>0, axis=0) |
np.asarray(ps) |
np.sum(ps, axis=0, keepdims=True) |
trials (regardless of the context) |
calc_dist_ctx(args, test_data, cortical_result, dist_results) |
loc2idx.items() |
range(n_states) |
range(N_contexts) |
range(N_contexts) |
combinations(idxs, 2) |
samples.append((idx1, idx2) |
np.sqrt((x1-x2) |
grid_dists.append(grid_dist) |
range(N_contexts) |
np.zeros([2]) |
np.linalg.norm(hidd1 - hidd2) |
np.linalg.norm(hidd1 - hidd2) |
np.linalg.norm(hidd1 - hidd2) |
append(hidd_dist) |
WineGrid(N_responses, N_contexts) |
winegrid.ctx_to_r(ctx, loc1, loc2) |
append(np.abs(r1-r2) |
np.arctan2((y2-y1) |
grid_angles.append(grid_angle) |
np.array(grid_dists) |
np.array(grid_angles) |
np.array(samples) |
np.array(hidd_dists_ctxs) |
np.sin(2*grid_angles) |
np.sign(phi) |
enumerate(phi) |
np.abs(p) |
calc_dist(args, test_data, cortical_result, dist_results=None) |
loc2idx.items() |
range(n_states) |
combinations(idxs, 2) |
samples.append((idx1, idx2) |
np.sqrt((x1-x2) |
grid_dists.append(grid_dist) |
np.linalg.norm(emb1 - emb2) |
embed_dists.append(embed_dist) |
np.zeros([2]) |
np.linalg.norm(hidd1 - hidd2) |
np.linalg.norm(hidd1 - hidd2) |
np.linalg.norm(hidd1 - hidd2) |
hidd_dists.append(hidd_dist) |
np.arctan2((y2-y1) |
grid_angles.append(grid_angle) |
np.sin(2*grid_angle) |
np.abs(phi) |
np.sign(phi) |
cong_hidd_dists.append(hidd_dist) |
cong_grid_dists.append(grid_dist) |
cong_embed_dists.append(embed_dist) |
cong_grid_angles.append(grid_angle) |
incong_hidd_dists.append(hidd_dist) |
incong_grid_dists.append(grid_dist) |
incong_embed_dists.append(embed_dist) |
incong_grid_angles.append(grid_angle) |
np.array(grid_dists) |
np.array(embed_dists) |
np.array(hidd_dists) |
np.array(cong_grid_dists) |
np.array(incong_grid_dists) |
np.array(cong_hidd_dists) |
np.array(incong_hidd_dists) |
np.array(cong_embed_dists) |
np.array(incong_embed_dists) |
np.array(grid_angles) |
np.array(cong_grid_angles) |
np.array(incong_grid_angles) |
np.array(samples) |
np.sin(2*grid_angles) |
np.sign(phi) |
enumerate(phi) |
np.abs(p) |
analyze_dim_red(args, test_data, cortical_result, dist_results, n_components=2) |
loc2idx.items() |
range(n_states) |
np.asarray(hiddens_ctxs) |
np.concatenate(hiddens_ctxs, axis=0) |
squeeze() |
if ((args.cortical_model == 'rnn') |
or (args.cortical_model == 'rnncell') |
np.concatenate(avg_hidden_ctxs, axis=0) |
PCA(n_components=n_components) |
pca.fit_transform(embeddings) |
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