'''
Created on Jun 14, 2013
MCMC Sampler script for AoMR Simple Shape Grammar
@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
@author: goker
"""
import cPickle as pickle
from mcmc_sampler import *
from vision_forward_model import VisionForwardModel
# 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()
Created on Jun 17, 2013
AoMRShapeGrammar class tests
@author: goker
'''
from treelib import Tree
from pcfg_tree import ParseNode
from aomr_grammar import AoMRShapeState, AoMRSpatialModel
from vision_forward_model import VisionForwardModel
from haptics_forward_model import HapticsForwardModel
import numpy as np
import pandas as pd
if __name__ == '__main__':
data = np.load('data/visual/2.npy')
params = {'b': 9000.0}
forward_model = VisionForwardModel()
#h_forward_model = HapticsForwardModel(body_fixed=False)
part1 = 'Bottom0'
part2 = 'Front0'
part3 = 'Top0'
part4 = 'Ear1'
# wrong parts for this object
wpart1 = 'Bottom1'
wpart2 = 'Front1'
wpart3 = 'Top1'
wpart4 = 'Ear0'
# ======================================================================
# TEST TREES: We look at the prior, likelihood and acceptance probabilities for
def __str__(self):
return "".join(self.tree[node].tag.symbol+repr(self.spatial_model.positions[node]) for node in self.tree.expand_tree(mode=Tree.DEPTH)
if self.tree[node].tag.symbol in self.grammar.terminals and
self.tree[node].tag.symbol is not 'Null')
if __name__ == '__main__':
# best b values: for visual 750, for haptics
# object 1: 5000
# object 2: 9000
# object 3: 5000 (maybe lower)
# object 4: 6000
data = np.load('data/visual/1.npy')
params = {'b': 750.0}
forward_model = VisionForwardModel()
#forward_model = HapticsForwardModel()
# TEST TREES: We look at the prior, likelihood and acceptance probabilities for
# empty tree, correct configuration (4 parts in correct positions) and tree with
# 1 part (ear) removed. Our purpose is to understand the b value we should set
# to make sure correct configuration has the highest posterior.
# Tree with no parts
t1 = Tree()
t1.create_node(ParseNode('S', 1), identifier='S')
t1.create_node(ParseNode('Null', ''), parent='S')
spatial_model1 = AoMRSimpleSpatialModel()
rrs = AoMRSimpleShapeState(forward_model=forward_model, data=data,
'''
Created on Jun 14, 2013
MCMC Sampler script for AoMR Simple Shape Grammar
@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
}
@author: goker
'''
import cPickle as pickle
from mcmc_sampler import *
from vision_forward_model import VisionForwardModel
# 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()
import numpy as np
from aomr_grammar import AoMRShapeState, AoMRSpatialModel
from vision_forward_model import VisionForwardModel
from mcmc_sampler import MCMCSampler
import sys
from haptics_forward_model import HapticsForwardModel
if __name__ == '__main__':
# AoMR Simple Shape Grammar, visual condition
obj_id = int(sys.argv[1])
b = float(sys.argv[2])
forward_model_type = sys.argv[3] #v, h or vh
spatial_model = AoMRSpatialModel()
if forward_model_type == 'v':
forward_model = VisionForwardModel(body_fixed=False)
npy_path = 'data/visual'
info = 'AoMRShapeGrammar Visual Obj{0:d}'.format(obj_id)
elif forward_model_type == 'h':
forward_model = HapticsForwardModel(body_fixed=False)
npy_path = 'data/haptic'
info = 'AoMRShapeGrammar Haptic Obj{0:d}'.format(obj_id)
elif forward_model_type == 'vh':
raise NotImplementedError()
else:
raise Exception('Forward model type can be v, h or vh')
data = np.load('{0:s}/{1:d}.npy'.format(npy_path, obj_id))
# visual condition
# b parameter for objects
