Exemplo n.º 1
0
def test_em_selection():
    # test that the basic GMM-based model selection tool returns
    # something sensible (i.e. the gmm used to represent the data has
    # indeed one or two classes)

    # generate some data
    dim = 2
    x = np.concatenate((nr.randn(100, dim), 3 + 2 * nr.randn(100, dim)))

    krange = range(1, 10)
    lgmm = gmm.best_fitting_GMM(x, krange, prec_type="full", niter=100, delta=1.0e-4, ninit=1, verbose=0)
    assert_true(lgmm.k < 4)
Exemplo n.º 2
0
The possible number of clusters is in the [1,10] range
The proposed algorithm correctly selects a solution with 2 or 3 classes

Author : Bertrand Thirion, 2008-2009
"""
print __doc__

import numpy as np

import nipy.neurospin.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, show=1, verbose=0)