alpha1 = np.arange(0, 0.5, 0.05)
results = dict.fromkeys(alpha1)
for i, a2 in tqdm(enumerate(alpha1)):
model_output = opt.minimize(glm.gain_only_objective, x0, (rec, big_mask, small_mask, a2, nLV), options={'gtol':1e-6, 'disp': True})
weights = model_output.x
# get model output
w1 = weights.reshape((2+nLV), nCells)[0, :]
w2 = weights.reshape((2+nLV), nCells)[1:-1, :]
b = weights.reshape((2+nLV), nCells)[-1, :]
# compute model prediction
mse, pred, lv = glm.gain_only_objective(weights, rec, big_mask, small_mask, b1=a2, nLV=nLV, verbose=True)
# compute prediction correlation, save model prediction, weights, and latent variable
cc = glm.corrcoef_by_neuron(rec['resp']._data, pred)
mse = glm.mean_square_error(rec['resp']._data, pred)
results[a2] = {}
results[a2]['cc'] = cc
results[a2]['mse'] = mse
results[a2]['pred'] = pred
results[a2]['lv'] = lv
results[a2]['beta1'] = w1
results[a2]['beta2'] = w2
results[a2]['baseline'] = b
x0 = model_output.x
f2, ax2 = plt.subplots(1, 2)
best = 0
for k in results.keys():
x0, (rec),
options={
'gtol': 1e-6,
'disp': True
})
weights = model_output.x
g1 = weights.reshape(3, nCells)[0, :]
#w1 = w1 / np.linalg.norm(w1)
d1 = weights.reshape(3, nCells)[1, :]
b = weights.reshape(3, nCells)[-1, :]
# compute model prediction
mse, pred = glm.pupil_only_objective(weights, rec, verbose=True)
# compute prediction correlation, save model prediction, weights, and latent variable
cc = glm.corrcoef_by_neuron(rec['resp']._data, pred)
mse = glm.mean_square_error(rec['resp']._data, pred)
results = {}
results['cc'] = cc
results['mse'] = mse
results['pred'] = pred
results['gain'] = g1
results['dc'] = d1
results['baseline'] = b
# create plots to evaluate outcome of fits
plt.figure(figsize=(6, 4))
ax2 = plt.subplot2grid((1, 2), (0, 0), rowspan=1, colspan=1)
ax3 = plt.subplot2grid((1, 2), (0, 1), rowspan=1, colspan=1)