try:
import matplotlib.pyplot as plt
except ImportError:
raise RuntimeError("This script needs the matplotlib library")
import nipy.algorithms.clustering.gmm as gmm
dim = 2
# 1. generate a 3-components mixture
x1 = np.random.randn(100, dim)
x2 = 3 + 2 * np.random.randn(50, dim)
x3 = np.repeat(np.array([- 2, 2], ndmin=2), 30, 0) \
+ 0.5 * np.random.randn(30, dim)
x = np.concatenate((x1, x2, x3))
# 2. fit the mixture with a bunch of possible models
krange = range(1, 5)
lgmm = gmm.best_fitting_GMM(x,
krange,
prec_type='diag',
niter=100,
delta=1.e-4,
ninit=1,
verbose=0)
# 3, plot the result
z = lgmm.map_label(x)
gmm.plot2D(x, lgmm, z, verbose=0)
plt.show()
Requires matplotlib
Author : Bertrand Thirion, 2008-2009
"""
print __doc__
import numpy as np
try:
import matplotlib.pyplot as plt
except ImportError:
raise RuntimeError("This script needs the matplotlib library")
import nipy.algorithms.clustering.gmm as gmm
dim = 2
# 1. generate a 3-components mixture
x1 = np.random.randn(100, dim)
x2 = 3 + 2 * np.random.randn(50, dim)
x3 = np.repeat(np.array([-2, 2], ndmin=2), 30, 0) + 0.5 * np.random.randn(30, dim)
x = np.concatenate((x1, x2, x3))
# 2. fit the mixture with a bunch of possible models
krange = range(1, 5)
lgmm = gmm.best_fitting_GMM(x, krange, prec_type="diag", niter=100, delta=1.0e-4, ninit=1, verbose=0)
# 3, plot the result
z = lgmm.map_label(x)
gmm.plot2D(x, lgmm, z, verbose=0)
plt.show()
be = -np.inf
for k in krange:
b = bgmm.VBGMM(k, dim)
b.guess_priors(x)
b.initialize(x)
b.estimate(x)
ek = float(b.evidence(x))
if ek > be:
be = ek
bestb = b
print k, 'classes, free energy:', ek
###############################################################################
# 3. plot the result
z = bestb.map_label(x)
plot2D(x, bestb, z, verbose=0)
plt.title('Variational Bayes')
###############################################################################
# 4. the same, with the Gibbs GMM algo
niter = 1000
krange = range(1, 6)
bbf = -np.inf
for k in krange:
b = bgmm.BGMM(k, dim)
b.guess_priors(x)
b.initialize(x)
b.sample(x, 100)
w, cent, prec, pz = b.sample(x, niter=niter, mem=1)
bplugin = bgmm.BGMM(k, dim, cent, prec, w)
bplugin.guess_priors(x)
be = - np.inf
for k in krange:
b = bgmm.VBGMM(k, dim)
b.guess_priors(x)
b.initialize(x)
b.estimate(x)
ek = float(b.evidence(x))
if ek > be:
be = ek
bestb = b
print k, 'classes, free energy:', ek
###############################################################################
# 3. plot the result
z = bestb.map_label(x)
plot2D(x, bestb, z, verbose=0)
plt.title('Variational Bayes')
###############################################################################
# 4. the same, with the Gibbs GMM algo
niter = 1000
krange = range(1, 6)
bbf = - np.inf
for k in krange:
b = bgmm.BGMM(k, dim)
b.guess_priors(x)
b.initialize(x)
b.sample(x, 100)
w, cent, prec, pz = b.sample(x, niter=niter, mem=1)
bplugin = bgmm.BGMM(k, dim, cent, prec, w)
bplugin.guess_priors(x)
