net_features_dir = os.path.join(features_dir, 'net%d' % i)
dump_features(net_features, net_features_dir)
features.append(net_features)
predictions = []
for i, net_features in enumerate(features):
print 'Using features from network %d...' % i
if stim_type == 'grating':
tr_features, te_features = train_test_split_grating(net_features,
grating_train_idxs)
else:
tr_features, te_features = train_test_split(net_features)
print 'Creating classifier...'
classifier = DTWClassifier(10)
classifier.fit(tr_features)
print 'Prediction from simulated responses...'
pred = classifier.predict(te_features)
cm = confusion_matrix(pred)
# cm = confusion_matrix_grating(pred, grating_train_idxs)
print cm, cm_goodness(cm, stim_type)
predictions.append(pred)
pred_outpath = os.path.join(predictions_dir, 'pred_%d.pickle' % i)
if not os.path.isdir(predictions_dir):
os.makedirs(predictions_dir)
with open(pred_outpath, 'w') as pred_out:
pickle.dump((pred, cm), pred_out)
for i, response in enumerate(responses):
name = response.name
print 'Mouse %s' % name
print 'Splitting out training and test data...'
tr_rsp, te_rsp, tr_mov, te_mov = train_test_splits[i]
# tr_rsp, te_rsp = train_test_splits[i]
print 'Fitting template-matching model...'
model = AverageTemplate()
model.fit(tr_rsp)
print 'Decoding movie indices from test responses...'
pred_movies = model.predict(te_rsp)
cm = confusion_matrix(pred_movies)
# cm = confusion_matrix_grating(pred_movies)
print cm, cm_goodness(cm, exp_type)
"""
print 'Fitting DTW model...'
model = DTWClassifier(3)
tr_data = [[tr_rsp.data[s,:,:,tr].T for tr in range(tr_rsp.data.shape[3])]
for s in range(tr_rsp.data.shape[0])]
model.fit(tr_data)
te_data = [[te_rsp.data[s,:,:,tr].T for tr in range(te_rsp.data.shape[3])]
for s in range(te_rsp.data.shape[0])]
pred_movies = model.predict(te_data)
cm = confusion_matrix(np.array(pred_movies))
# cm = confusion_matrix_grating(pred_movies)
print cm, cm_goodness(cm, exp_type)
"""
for i, response in enumerate(responses):
name = response.name
print 'Mouse %s' % name
print 'Splitting out training and test data...'
tr_rsp, te_rsp, tr_mov, te_mov = train_test_splits[i]
# tr_rsp, te_rsp = train_test_splits[i]
print 'Fitting template-matching model...'
model = AverageTemplate()
model.fit(tr_rsp)
print 'Decoding movie indices from test responses...'
pred_movies = model.predict(te_rsp)
cm = confusion_matrix(pred_movies)
# cm = confusion_matrix_grating(pred_movies)
print cm, cm_goodness(cm, exp_type)
"""
print 'Fitting DTW model...'
model = DTWClassifier(3)
tr_data = [[tr_rsp.data[s,:,:,tr].T for tr in range(tr_rsp.data.shape[3])]
for s in range(tr_rsp.data.shape[0])]
model.fit(tr_data)
te_data = [[te_rsp.data[s,:,:,tr].T for tr in range(te_rsp.data.shape[3])]
for s in range(te_rsp.data.shape[0])]
pred_movies = model.predict(te_data)
cm = confusion_matrix(np.array(pred_movies))
# cm = confusion_matrix_grating(pred_movies)
print cm, cm_goodness(cm, exp_type)
"""
