def display_space():
domain_mapping, data, dim_names = xml_to_dict("Dataset\\prototypes.xml")
domain_mapping, data, dim_names = xml_to_dict(
"Dataset\\exemplars.xml",
domain_mapping=domain_mapping,
data=data,
dimension_names=dim_names)
domain_mapping, data, dim_names = less_dimensions(
domain_mapping=domain_mapping, data=data, dim_names=dim_names)
space.init(len(dim_names), domain_mapping, dim_names)
concepts = form_supercategories(data)
for name, values in zip(concepts, concepts.values()):
if not name == 'mammal':
family_into_space(name, values, add=True)
ci.init(dims=[6, 7, 8])
''
for category in ordered_dict:
print('putting',category,'into space')
cuboids=[]
domains = {}
for example in ordered_dict[category]:
if not is_consistent(concepts[example]):
print(example,' is inconsistent')
else:
subdomains={domain:space._domains[domain] for domain in concepts[example] if not domain == 'supercategory'}
point=[concepts[example].get(domain,{str(key):float("inf") for key in range(len(space._domains[domain]))}).values() for domain in space._domains]
point=[p for dom in point for p in dom]
#print(example)
#print('point: ',point)
cuboids.append(Cuboid([dim if not dim==float('inf') else -dim for dim in point], point, subdomains))
domains.update(subdomains)
#print(cuboids[-1]._p_max)
#for cuboid in cuboids:
#print('cuboid:',cuboid)
core=from_cuboids(cuboids,domains)
weights=Weights(space._def_dom_weights,space._def_dim_weights)
concept=Concept(core, 1.0, 0.5, weights)
space.add_concept(category,concept,'orange')
#print(space._concepts[concept].membership_of([p if not p==float('-inf') else 0 for p in p_min]))
#print(space._concepts[concept].membership_of(p_max))
#print(bear.subset_of(space._concepts[concept]))
'''print('amphibian:')
print(space._concepts['amphibian'])'''
ci.init()
def generate(target='bird', dims=[6, 7, 8], n=None):
#read concepts from prototypes and exemplars
domain_mapping, data, dim_names = xml_to_dict("Dataset\\prototypes.xml")
domain_mapping, data, dim_names = xml_to_dict(
"Dataset\\exemplars.xml",
domain_mapping=domain_mapping,
data=data,
dimension_names=dim_names)
domain_mapping, data, dim_names = less_dimensions(
domain_mapping=domain_mapping, data=data, dim_names=dim_names)
space.init(len(dim_names), domain_mapping, dim_names)
concepts = form_supercategories(data)
testdata = deserialize_testdata('testdata.pkl')
if not n:
n = random.choice(list(range(len(testdata[target]))))
ex = {'target': target, 'point': testdata[target][n], 'n': n}
sibblings = [family for family in concepts if not family == target]
print(ex)
centers = np.array(concepts[target]).T
means = []
for dimension in range(len(centers)):
means.append(
np.average([
value for value in centers[dimension]
if not value == float('inf')
]))
means = [mean if not np.isnan(mean) else float('inf') for mean in means]
aps = evaluate_example(ex,
data,
concepts,
domain_mapping,
dim_names,
method=two_cubs_PCA,
testdata=testdata,
criterion=ap_balanced)
aps = aps[list(aps.keys())[0]]
space.init(len(dim_names), domain_mapping, dim_names)
for name, values in zip(concepts, concepts.values()):
family_into_space(name, values, add=True)
space.add_concept(
'learnt_' + target,
one_shot(ex['point'],
method=two_cubs_PCA,
sibblings=sibblings,
name="learnt_" + target,
learn_weights=False))
space.add_concept(
'cheated_' + target,
one_shot(means,
method=two_cubs_PCA,
sibblings=sibblings,
name=target + 'cheated_' + target,
learn_weights=False))
del (space._concepts['mammal'])
ci.init(dims=dims)
#ap_learnt, ap_naive, ap_known, ap_cheated, ap_supercheated
print('learnt', aps[0])
print('small', aps[1])
print('big', aps[2])
print('known', aps[3])
print('cheated', aps[4])