def create_component_model(column_metadata, column_hypers, suffstats):
modeltype = column_metadata['modeltype']
if modeltype == 'normal_inverse_gamma':
component_model = CCM.p_ContinuousComponentModel(
column_hypers,
count=suffstats.get(b'N', 0),
sum_x=suffstats.get(b'sum_x', None),
sum_x_squared=suffstats.get(b'sum_x_squared', None))
elif modeltype == 'symmetric_dirichlet_discrete':
# TODO Can we change the suffstats data structure not to
# include the total count in the dictionary of per-item
# counts, please?
suffstats = copy.copy(suffstats)
count = suffstats.pop(b'N', 0)
component_model = MCM.p_MultinomialComponentModel(column_hypers,
count=count,
counts=suffstats)
elif modeltype == 'vonmises':
component_model = CYCM.p_CyclicComponentModel(
column_hypers,
count=suffstats.get(b'N', 0),
sum_sin_x=suffstats.get(b'sum_sin_x', None),
sum_cos_x=suffstats.get(b'sum_cos_x', None))
else:
raise ValueError('unknown modeltype: %r' % (modeltype, ))
return component_model
def create_component_model(column_metadata, column_hypers, suffstats):
modeltype = column_metadata['modeltype']
if modeltype == 'normal_inverse_gamma':
component_model = CCM.p_ContinuousComponentModel(
column_hypers,
count=suffstats.get(b'N', 0),
sum_x=suffstats.get(b'sum_x', None),
sum_x_squared=suffstats.get(b'sum_x_squared', None))
elif modeltype == 'symmetric_dirichlet_discrete':
# TODO Can we change the suffstats data structure not to
# include the total count in the dictionary of per-item
# counts, please?
suffstats = copy.copy(suffstats)
count = suffstats.pop(b'N', 0)
component_model = MCM.p_MultinomialComponentModel(
column_hypers, count=count, counts=suffstats)
elif modeltype == 'vonmises':
component_model = CYCM.p_CyclicComponentModel(
column_hypers,
count=suffstats.get(b'N', 0),
sum_sin_x=suffstats.get(b'sum_sin_x', None),
sum_cos_x=suffstats.get(b'sum_cos_x', None))
else:
raise ValueError('unknown modeltype: %r' % (modeltype,))
return component_model
def plot(results, filename=None):
n_samples = results['config']['n_samples']
samples = sorted(results['samples'])
conf = results['conf']
X_L = results['X_L_list'][0]
X_D = results['X_D_list'][0]
hgrm, _ = np.histogram(X_D[0], len(set(X_D[0])))
max_mass_mode = np.argmax(hgrm)
suffstats = X_L['view_state'][0]['column_component_suffstats'][0][max_mass_mode]
counts = suffstats['N']
sum_x = suffstats['sum_x']
sum_x_sq = suffstats['sum_x_squared']
scale = counts/results['config']['n_samples']
component_model = ccm.p_ContinuousComponentModel(
X_L['column_hypers'][0], counts, sum_x, sum_x_sq)
plt.figure(facecolor='white')
ax = plt.subplot(1, 2, 1)
ax.hist(samples, min(31, int(n_samples/10)), normed=True, label='Samples',
ec='none', fc='gray')
T = [[x] for x in samples]
M_c = du.gen_M_c_from_T(T, cctypes=['continuous'])
xvals = np.linspace(np.min(samples), np.max(samples), 300)
Q = [(n_samples, 0, x) for i, x in enumerate(xvals)]
p = [su.simple_predictive_probability(M_c, X_L, X_D, [], [q]) for q in Q]
p = np.array(p)
ax.plot(xvals, np.exp(p), c='#bbbbbb',
label='Predicitive probability', lw=3)
p = [component_model.calc_element_predictive_logp(x) for x in xvals]
ax.plot(xvals, np.exp(p)*scale, c='#222222', label='Summary mode',
lw=3)
plt.xlabel('Samples')
plt.legend(loc=0)
ax = plt.subplot(1, 2, 2)
ax.bar([0, 1], [conf, 1.0-conf], fc='#333333', ec='none')
ax.set_ylim([0, 1])
ax.set_xlim([-.25, 2])
ax.set_xticks([.5, 1.5])
plt.ylabel('Probability mass')
ax.set_xticklabels(['Summary mode', 'All other modes'])
if filename is None:
plt.show()
else:
plt.savefig(filename)
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
from __future__ import print_function
import numpy
import crosscat.cython_code.ContinuousComponentModel as CCM
import crosscat.cython_code.MultinomialComponentModel as MCM
import crosscat.cython_code.State as State
c_hypers = dict(r=10, nu=10, s=10, mu=10)
ccm = CCM.p_ContinuousComponentModel(c_hypers)
print("empty component model")
print(ccm)
#
for element in [numpy.nan, 0, 1, numpy.nan, 2]:
print()
ccm.insert_element(element)
print("inserted %s" % element)
print(ccm)
m_hypers = dict(dirichlet_alpha=10, K=3)
mcm = MCM.p_MultinomialComponentModel(m_hypers)
print("empty component model")
print(mcm)
for element in [numpy.nan, 0, 1, numpy.nan, 2]:
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
import numpy
import crosscat.cython_code.ContinuousComponentModel as CCM
import crosscat.cython_code.MultinomialComponentModel as MCM
import crosscat.cython_code.State as State
c_hypers = dict(r=10,nu=10,s=10,mu=10)
ccm = CCM.p_ContinuousComponentModel(c_hypers)
print "empty component model"
print ccm
#
for element in [numpy.nan, 0, 1, numpy.nan, 2]:
print
ccm.insert_element(element)
print "inserted %s" % element
print ccm
m_hypers = dict(dirichlet_alpha=10,K=3)
mcm = MCM.p_MultinomialComponentModel(m_hypers)
print "empty component model"
print mcm
for element in [numpy.nan, 0, 1, numpy.nan, 2]: