from shogun.Distribution import Gaussian, GMM
from shogun.Features import RealFeatures
import util
util.set_title('SMEM for 2d GMM example')
max_iter = 100
max_cand = 5
min_cov = 1e-9
max_em_iter = 1000
min_change = 1e-9
cov_type = 0
real_gmm = GMM(3)
real_gmm.set_nth_mean(array([2.0, 2.0]), 0)
real_gmm.set_nth_mean(array([-2.0, -2.0]), 1)
real_gmm.set_nth_mean(array([2.0, -2.0]), 2)
real_gmm.set_nth_cov(array([[1.0, 0.2], [0.2, 0.5]]), 0)
real_gmm.set_nth_cov(array([[0.2, 0.1], [0.1, 0.5]]), 1)
real_gmm.set_nth_cov(array([[0.3, -0.2], [-0.2, 0.8]]), 2)
real_gmm.set_coef(array([0.3, 0.4, 0.3]))
generated = array([real_gmm.sample()])
for i in range(199):
generated = append(generated, array([real_gmm.sample()]), axis=0)
generated = generated.transpose()
feat_train = RealFeatures(generated)
from pylab import figure,show,connect,hist,plot,legend
from numpy import array, append, arange, empty
from shogun.Distribution import Gaussian, GMM
from shogun.Features import RealFeatures
import util
util.set_title('EM for 1d GMM example')
min_cov=1e-9
max_iter=1000
min_change=1e-9
real_gmm=GMM(3)
real_gmm.set_nth_mean(array([-2.0]), 0)
real_gmm.set_nth_mean(array([0.0]), 1)
real_gmm.set_nth_mean(array([2.0]), 2)
real_gmm.set_nth_cov(array([[0.3]]), 0)
real_gmm.set_nth_cov(array([[0.1]]), 1)
real_gmm.set_nth_cov(array([[0.2]]), 2)
real_gmm.set_coef(array([0.3, 0.5, 0.2]))
generated=array([real_gmm.sample()])
for i in range(199):
generated=append(generated, array([real_gmm.sample()]), axis=1)
feat_train=RealFeatures(generated)
est_gmm=GMM(3)
est_gmm.train(feat_train)
import util
util.set_title('SMEM for 2d GMM example')
#set the parameters
max_iter=100
max_cand=5
min_cov=1e-9
max_em_iter=1000
min_change=1e-9
cov_type=0
#setup the real GMM
real_gmm=GMM(3)
real_gmm.set_nth_mean(array([2.0, 2.0]), 0)
real_gmm.set_nth_mean(array([-2.0, -2.0]), 1)
real_gmm.set_nth_mean(array([2.0, -2.0]), 2)
real_gmm.set_nth_cov(array([[1.0, 0.2],[0.2, 0.5]]), 0)
real_gmm.set_nth_cov(array([[0.2, 0.1],[0.1, 0.5]]), 1)
real_gmm.set_nth_cov(array([[0.3, -0.2],[-0.2, 0.8]]), 2)
real_gmm.set_coef(array([0.3, 0.4, 0.3]))
#generate training set from real GMM
generated=array([real_gmm.sample()])
for i in range(199):
generated=append(generated, array([real_gmm.sample()]), axis=0)
generated=generated.transpose()
from pylab import figure, scatter, contour, show, legend, connect
from numpy import array, append, arange, reshape, empty
from shogun.Distribution import Gaussian, GMM
from shogun.Features import RealFeatures
import util
util.set_title('EM for 2d GMM example')
min_cov = 1e-9
max_iter = 1000
min_change = 1e-9
cov_type = 0
real_gmm = GMM(2)
real_gmm.set_nth_mean(array([1.0, 1.0]), 0)
real_gmm.set_nth_mean(array([-1.0, -1.0]), 1)
real_gmm.set_nth_cov(array([[1.0, 0.2], [0.2, 0.1]]), 0)
real_gmm.set_nth_cov(array([[0.3, 0.1], [0.1, 1.0]]), 1)
real_gmm.set_coef(array([0.3, 0.7]))
generated = array([real_gmm.sample()])
for i in range(199):
generated = append(generated, array([real_gmm.sample()]), axis=0)
generated = generated.transpose()
feat_train = RealFeatures(generated)
est_gmm = GMM(2, cov_type)
est_gmm.train(feat_train)
# likely cluster to have generated the observation.
#
##!/usr/bin/env python
# """
# Explicit examples on how to use clustering
# """
from numpy import array, append
from shogun.Distribution import GMM
from shogun.Library import Math_init_random
Math_init_random(5)
real_gmm = GMM(2, 0)
real_gmm.set_nth_mean(array([1.0, 1.0]), 0)
real_gmm.set_nth_mean(array([-1.0, -1.0]), 1)
real_gmm.set_nth_cov(array([[1.0, 0.2], [0.2, 0.1]]), 0)
real_gmm.set_nth_cov(array([[0.3, 0.1], [0.1, 1.0]]), 1)
real_gmm.set_coef(array([0.3, 0.7]))
generated = array([real_gmm.sample()])
for i in range(199):
generated = append(generated, array([real_gmm.sample()]), axis=0)
generated = generated.transpose()
parameter_list = [[generated, 2, 1e-9, 1000, 1e-9, 0]]
