# load results
fname = "./results/AoMRShapeGrammar_Visual_Obj820130624004057.mcmc"
f = open(fname)
results = pickle.load(f)
# print results
best1 = results.best_samples[0][0].state
print(best1)
best1.tree.show()
forward_model = VisionForwardModel()
i = 0
for sample in results.best_samples[0]:
# sample.state.tree.show()
print(sample.posterior)
forward_model._view(sample.state)
i += 1
# load results
fname = "./results/AoMRShapeGrammar_Visual_Obj220130624001257.mcmc"
f = open(fname)
results = pickle.load(f)
# print results
best2 = results.best_samples[0][0].state
print(best2)
best2.tree.show()
# load results
fname = "./results/AoMRShapeGrammar_Visual_Obj1020130708020033.mcmc"
f = open(fname)
results = pickle.load(f)
@author: goker
'''
import numpy as np
from aomr_simple_grammar import AoMRSimpleShapeState, AoMRSimpleSpatialModel
from vision_forward_model import VisionForwardModel
from mcmc_sampler import MCMCSampler
if __name__ == '__main__':
# AoMR Simple Shape Grammar, visual condition
spatial_model = AoMRSimpleSpatialModel()
forward_model = VisionForwardModel()
data = np.load('data/visual/1.npy')
state_params = {'b': 750.0}
init_state = AoMRSimpleShapeState(forward_model, data, state_params, spatial_model)
sampler_params ={'info' : 'aoMR Simple Grammar - Visual Condition',
'runs' : 1,
'iters' : 5000,
'keep_top_n' : 20,
'burn_in' : 1000,
'thinning_period' : 400,
'random_move' : False,
'results_folder' : './',
'save_results' : True,
'verbose': True}
ms = MCMCSampler(sampler_params, init_state)
results = ms.run()
print results
forward_model._view(results.best_samples[0][0].state)
t2.create_node(ParseNode('P', 7), identifier='P4', parent='S4')
t2.create_node(ParseNode(part1, ''), parent='P1', identifier='B0')
t2.create_node(ParseNode(part2, ''), parent='P2', identifier='F0')
t2.create_node(ParseNode(part3, ''), parent='P3', identifier='T0')
t2.create_node(ParseNode(part4, ''), parent='P4', identifier='E0')
spatial_model2 = AoMRSpatialModel()
voxels2 = {'S' : [0,0,0], 'S1' : [0, 0, -1], 'S2' : [1, 0, 0], 'S3' : [-1, 0, 1],
'S4' : [1, 0, 1]}
spatial_model2.voxels = voxels2
spatial_model2._update_positions(t2)
rrs2 = AoMRShapeState(forward_model=forward_model, data=data, ll_params=params,
spatial_model=spatial_model2, initial_tree=t2)
forward_model._view(rrs2)
print rrs2
print ('Prior: %g' % rrs2.prior)
print ('Likelihood: %g' % rrs2.likelihood)
print ('Posterior: %g' % (rrs2.prior*rrs2.likelihood))
rrs2.tree.show()
# bottom, front, top
t3 = Tree()
t3.create_node(ParseNode('S', 3), identifier='S')
t3.create_node(ParseNode('S', 4), parent='S', identifier='S1')
t3.create_node(ParseNode('S', 4), parent='S', identifier='S2')
t3.create_node(ParseNode('S', 4), parent='S', identifier='S3')
# tree with only bottom and ear
t4 = Tree()
t4.create_node(ParseNode('S', 0), identifier='S')
t4.create_node(ParseNode('P', 0), parent='S', identifier='P1')
t4.create_node(ParseNode('S', 0), parent='S', identifier='S1')
t4.create_node(ParseNode('Bottom0', ''), parent='P1', identifier='B0')
t4.create_node(ParseNode('P', 2), parent='S1', identifier='P2')
t4.create_node(ParseNode('S', 1), parent='S1', identifier='S2')
t4.create_node(ParseNode('Ear0', ''), parent='P2', identifier='E0')
t4.create_node(ParseNode('Null', ''), parent='S2')
positions4 = {'B0' : actual_positions['Bottom0'],
'E0' : actual_positions['Ear0'],}
spatial_model4 = AoMRSimpleSpatialModel(positions4)
rrs4 = AoMRSimpleShapeState(forward_model=forward_model, data=data,
ll_params=params, spatial_model=spatial_model4, initial_tree=t4)
print rrs4
print ('Prior: %g' % rrs4.prior)
print ('Likelihood: %g' % rrs4.likelihood)
print ('Posterior: %g' % (rrs4.prior*rrs4.likelihood))
rrs4.tree.show()
print ('Acceptance Prob 1-4: %f' % rrs._subtree_acceptance_probability(rrs4))
print ('Acceptance Prob 2-4: %f' % rrs2._subtree_acceptance_probability(rrs4))
print ('Acceptance Prob 3-4: %f' % rrs3._subtree_acceptance_probability(rrs4))
forward_model._view(rrs2)
import numpy as np
from aomr_simple_grammar import AoMRSimpleShapeState, AoMRSimpleSpatialModel
from vision_forward_model import VisionForwardModel
from mcmc_sampler import MCMCSampler
if __name__ == '__main__':
# AoMR Simple Shape Grammar, visual condition
spatial_model = AoMRSimpleSpatialModel()
forward_model = VisionForwardModel()
data = np.load('data/visual/1.npy')
state_params = {'b': 750.0}
init_state = AoMRSimpleShapeState(forward_model, data, state_params,
spatial_model)
sampler_params = {
'info': 'aoMR Simple Grammar - Visual Condition',
'runs': 1,
'iters': 5000,
'keep_top_n': 20,
'burn_in': 1000,
'thinning_period': 400,
'random_move': False,
'results_folder': './',
'save_results': True,
'verbose': True
}
ms = MCMCSampler(sampler_params, init_state)
results = ms.run()
print results
forward_model._view(results.best_samples[0][0].state)
# load results
fname = './results/AoMRShapeGrammar_Visual_Obj820130624004057.mcmc'
f = open(fname)
results = pickle.load(f)
# print results
best1 = results.best_samples[0][0].state
print(best1)
best1.tree.show()
forward_model = VisionForwardModel()
i = 0
for sample in results.best_samples[0]:
#sample.state.tree.show()
print(sample.posterior)
forward_model._view(sample.state)
i += 1
# load results
fname = './results/AoMRShapeGrammar_Visual_Obj220130624001257.mcmc'
f = open(fname)
results = pickle.load(f)
# print results
best2 = results.best_samples[0][0].state
print(best2)
best2.tree.show()
# load results
fname = './results/AoMRShapeGrammar_Visual_Obj1020130708020033.mcmc'
f = open(fname)
results = pickle.load(f)