from matplotlib import pyplot as plt
plt.interactive(True)
from pybasicbayes import models, distributions
GENERATE_DATA = True
###########################
# generate or load data #
###########################
alpha_0 = 5.0
obs_hypparams = dict(mu_0=np.zeros(2), sigma_0=np.eye(2), kappa_0=0.05, nu_0=5)
priormodel = models.Mixture(
alpha_0=alpha_0,
components=[distributions.Gaussian(**obs_hypparams) for itr in range(30)])
data, _ = priormodel.generate(100)
del priormodel
plt.figure()
plt.plot(data[:, 0], data[:, 1], 'kx')
plt.title('data')
raw_input() # pause for effect
###############
# inference #
###############
posteriormodel = models.Mixture(
from __future__ import division
import numpy as np
from pybasicbayes import models, distributions
data = np.loadtxt('data.txt')
alpha_0 = 3.
obs_hypparams = dict(mu_0=np.zeros(2), sigma_0=np.eye(2), kappa_0=0.05, nu_0=5)
model = models.Mixture(
alpha_0=alpha_0,
components=[distributions.Gaussian(**obs_hypparams) for _ in range(10)])
model.add_data(data)
for _ in xrange(100):
model.resample_model()
# import itertools, sys, json
# for i in itertools.count():
# model.resample_model()
# if i % 3 == 0:
# print json.dumps(model.to_json_dict())
# sys.stdout.flush()
np.seterr(invalid='raise')
from matplotlib import pyplot as plt
import copy
from pybasicbayes import models, distributions
from pybasicbayes.util.text import progprint_xrange
# EM is really terrible! Here's a demo of how to do it on really easy data
### generate and plot the data
alpha_0 = 100.
obs_hypparams = dict(mu_0=np.zeros(2), sigma_0=np.eye(2), kappa_0=0.05, nu_0=5)
priormodel = models.Mixture(
alpha_0=alpha_0,
components=[distributions.Gaussian(**obs_hypparams) for itr in range(6)])
data = priormodel.rvs(200)
del priormodel
plt.figure()
plt.plot(data[:, 0], data[:, 1], 'kx')
plt.title('data')
min_num_components, max_num_components = (1, 12)
num_tries_each = 5
### search over models using BIC as a model selection criterion
from __future__ import division
import numpy as np
np.seterr(invalid='raise')
from matplotlib import pyplot as plt
import copy
from pybasicbayes import models, distributions
from pybasicbayes.util.text import progprint_xrange
data = np.array([[12, 20], [12, 20], [12, 20], [12, 20], [12, 20], [22, 5],
[22, 5], [22, 5], [22, 5], [22, 5], [8, 0], [8, 0], [8, 0],
[8, 0], [8, 0], [1, 12], [1, 12], [1, 12], [1, 12], [1, 12]])
posteriormodel = models.Mixture(alpha_0=2.,
components=[
distributions.Multinomial(K=2, alpha_0=2.)
for itr in range(10)
])
posteriormodel.add_data(data)
for itr in progprint_xrange(50):
posteriormodel.resample_model()