def run(model, data):
num_subparts = data["data"].shape[
0] # mmm the first dimension of data represents each subpart?? interesting.
num_resources = len(model["learns"])
trans_softcounts = np.zeros((num_resources, 2, 2))
emission_softcounts = np.zeros((num_subparts, 2, 2))
init_softcounts = np.zeros((2, 1))
result = {}
result['all_trans_softcounts'] = trans_softcounts
result['all_emission_softcounts'] = emission_softcounts
result['all_initial_softcounts'] = init_softcounts
result = E_step.run(data, model, result['all_trans_softcounts'],
result['all_emission_softcounts'],
result['all_initial_softcounts'], 1)
for j in range(num_resources):
result['all_trans_softcounts'][j] = result['all_trans_softcounts'][
j].transpose()
for j in range(num_subparts):
result['all_emission_softcounts'][j] = result[
'all_emission_softcounts'][j].transpose()
state_predictions = predict_onestep_states.run(data, model,
result['alpha'])
correct_emission_predictions = []
for i in range(len(model["guesses"])):
correct_emission_predictions.append(
model["guesses"][i] * state_predictions[0, :] +
(1 - model["slips"][i]) * state_predictions[1, :])
return (correct_emission_predictions, state_predictions)
def run(model, data, parallel=True):
num_subparts = data["data"].shape[
0] # mmm the first dimension of data represents each subpart?? interesting.
num_resources = len(model["learns"])
result = E_step.run(data, model, 1, int(parallel))
for j in range(num_resources):
result['all_trans_softcounts'][j] = result['all_trans_softcounts'][
j].transpose()
for j in range(num_subparts):
result['all_emission_softcounts'][j] = result[
'all_emission_softcounts'][j].transpose()
state_predictions = predict_onestep_states.run(data, model,
result['alpha'],
int(parallel))
p = state_predictions.shape
state_predictions = state_predictions.flatten(order='C').reshape(p,
order='F')
# multiguess solution, should work
correct_emission_predictions = np.expand_dims(
model["guesses"], axis=1) @ np.expand_dims(
state_predictions[0, :], axis=0) + np.expand_dims(
1 - model["slips"], axis=1) @ np.expand_dims(
state_predictions[1, :], axis=0)
#correct_emission_predictions = model['guesses'] * np.asarray([state_predictions[0,:]]).T + (1 - model['slips']) * np.asarray([state_predictions[1,:]]).T
flattened_predictions = np.take_along_axis(
correct_emission_predictions,
(data['data'] != 0).argmax(axis=0)[:, None].T,
axis=0)
return (flattened_predictions.ravel(), state_predictions)
def run(model, data):
num_subparts = data["data"].shape[0] # mmm the first dimension of data represents each subpart?? interesting.
num_resources = len(model["learns"])
trans_softcounts = np.zeros((num_resources, 2, 2))
emission_softcounts = np.zeros((num_subparts, 2, 2))
init_softcounts = np.zeros((2, 1))
result = {}
result['all_trans_softcounts'] = trans_softcounts
result['all_emission_softcounts'] = emission_softcounts
result['all_initial_softcounts'] = init_softcounts
result = E_step.run(data, model, result['all_trans_softcounts'], result['all_emission_softcounts'], result['all_initial_softcounts'], 1)
for j in range(num_resources):
result['all_trans_softcounts'][j] = result['all_trans_softcounts'][j].transpose()
for j in range(num_subparts):
result['all_emission_softcounts'][j] = result['all_emission_softcounts'][j].transpose()
state_predictions = predict_onestep_states.run(data, model, result['alpha'])
p = state_predictions.shape
state_predictions = state_predictions.flatten(order = 'C').reshape(p, order = 'F')
# multiguess solution, should work
correct_emission_predictions = np.expand_dims(model["guesses"], axis = 1) @ np.expand_dims(state_predictions[0,:], axis = 0) + np.expand_dims(1-model["slips"], axis = 1) @ np.expand_dims(state_predictions[1,:], axis = 0)
#correct_emission_predictions = model['guesses'] * np.asarray([state_predictions[0,:]]).T + (1 - model['slips']) * np.asarray([state_predictions[1,:]]).T
flattened_predictions = np.zeros((len(correct_emission_predictions[0]),))
for i in range(len(correct_emission_predictions)):
for j in range(len(correct_emission_predictions[0])):
if data["data"][i][j] != 0:
flattened_predictions[j] = correct_emission_predictions[i][j]
return (flattened_predictions, state_predictions)