def func(crosscat):
synthesizer = GibbsCrossCat(crosscat, Cd=self.Cd, Ci=self.Ci)
synthesizer.transition_structure_cpp(N=N,
S=S,
outputs=outputs,
progress=progress)
return synthesizer.crosscat
def func_inference(crosscat):
synthesizer = GibbsCrossCat(crosscat)
n_step = 1000 if integration else 1
synthesizer = GibbsCrossCat(crosscat)
synthesizer.transition_structure_cpp(N=n_step)
synthesizer.transition_hypers_distributions()
synthesizer.transition_hypers_row_divide()
return synthesizer
def func_inference(crosscat):
synthesizer = GibbsCrossCat(crosscat)
n_step = 540 if integration else 1
for _step in xrange(n_step):
synthesizer.transition_row_assignments()
synthesizer.transition_hypers_row_divide()
synthesizer.transition_hypers_distributions()
synthesizer.transition_view_assignments()
return synthesizer
def test_dependencies_no_cpp():
prng = get_prng(2)
ensemble = CrossCatEnsemble(outputs=(0, 1),
inputs=[],
Ci=[(0, 1)],
distributions=[('normal', None)] * 2,
chains=5,
rng=prng)
ensemble.observe(0, {0: 0, 1: 1})
synthesizer = GibbsCrossCat(ensemble.cgpms[0], Ci=ensemble.Ci)
synthesizer.transition_view_assignments()
def custom_program(crosscat):
from cgpm2.transition_crosscat import GibbsCrossCat
synthesizer = GibbsCrossCat(crosscat)
synthesizer.transition(N=N,
kernels=[
'hypers_distributions',
'hypers_row_divide',
'hypers_column_divide',
'row_assignments',
],
progress=False)
return synthesizer.crosscat
def get_crosscat_synthesizer(prng):
view = FlexibleRowMixture(cgpm_row_divide=CRP([2], [], rng=prng),
cgpm_components_base=Product([
Normal([0], [], rng=prng),
Normal([1], [], rng=prng),
],
rng=prng),
rng=prng)
crosscat = Product(cgpms=[view], rng=prng)
data = make_bivariate_two_clusters(prng)
for rowid, row in enumerate(data):
crosscat.observe(rowid, {0: row[0], 1: row[1]})
return GibbsCrossCat(crosscat)
def test_crosscat_two_component_nominal__ci_():
prng = get_prng(10)
integration = pytest.config.getoption('--integration')
# Build CGPM with adversarial initialization.
crosscat = Product([
FlexibleRowMixture(
cgpm_row_divide=CRP([-1], [], rng=prng),
cgpm_components_base=Product([
Normal([0], [], rng=prng),
], rng=prng),
rng=prng),
FlexibleRowMixture(
cgpm_row_divide=CRP([-2], [], rng=prng),
cgpm_components_base=Product([
Normal([1], [], rng=prng),
Categorical([50], [], distargs={'k':4}, rng=prng),
], rng=prng),
rng=prng),
], rng=prng,)
# Fetch data and add a nominal variable.
data_xy = make_bivariate_two_clusters(prng)
data_z = np.zeros(len(data_xy))
data_z[:15] = 0
data_z[15:30] = 1
data_z[30:45] = 2
data_z[45:60] = 3
data = np.column_stack((data_xy, data_z))
# Observe.
for rowid, row in enumerate(data):
crosscat.observe(rowid, {0: row[0], 1: row[1], 50:row[2]})
# Run inference.
synthesizer = GibbsCrossCat(crosscat)
synthesizer.transition(N=(50 if integration else 1), progress=False)
synthesizer.transition(N=(100 if integration else 1),
kernels=['hypers_distributions','hypers_row_divide'],
progress=False)
# Assert views are merged into one.
assert not integration or len(synthesizer.crosscat.cgpms) == 1
crp_output = synthesizer.crosscat.cgpms[0].cgpm_row_divide.outputs[0]
# Check joint samples for all nominals.
samples = synthesizer.crosscat.simulate(None, [crp_output,0,1,50], N=250)
not integration or check_sampled_data(samples, [0, 7], 3, 110)
# Check joint samples for nominals [0, 2].
samples_a = [s for s in samples if s[50] in [0,2]]
not integration or check_sampled_data(samples_a, [0, 7], 3, 45)
# Check joint samples for nominals [1, 3].
samples_b = [s for s in samples if s[50] in [1,3]]
not integration or check_sampled_data(samples_b, [0, 7], 3, 45)
# Check conditional samples in correct quadrants.
means = {0:0, 1:0, 2:7, 3:7}
for z in [0, 1, 2, 3]:
samples = synthesizer.crosscat.simulate(None, [0, 1], {50:z}, N=100)
not integration or check_sampled_data(samples, [means[z]], 3, 90)
def custom_program(crosscat):
from cgpm2.transition_crosscat import GibbsCrossCat
synthesizer = GibbsCrossCat(crosscat)
synthesizer.transition(N=1, progress=False)
return synthesizer.crosscat