# 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)