Python GMM Examples

Example #1

File: clustering_gmm_modular.py Project: behollis/muViewBranch

def clustering_gmm_modular(fm_train=generated, n=2, min_cov=1e-9, max_iter=1000, min_change=1e-9, cov_type=0):

    from shogun.Distribution import GMM
    from shogun.Features import RealFeatures
    from shogun.Library import Math_init_random

    Math_init_random(5)

    feat_train = RealFeatures(generated)

    est_gmm = GMM(n, cov_type)
    est_gmm.train(feat_train)
    est_gmm.train_em(min_cov, max_iter, min_change)

    return est_gmm

Example #2

File: clustering_gmm_modular.py Project: vinodrajendran001/ASP

def clustering_gmm_modular (fm_train=generated,n=2,min_cov=1e-9,max_iter=1000,min_change=1e-9,cov_type=0):

	from shogun.Distribution import GMM
	from shogun.Features import RealFeatures
	from shogun.Library import Math_init_random

	Math_init_random(5)

	feat_train=RealFeatures(generated)

	est_gmm=GMM(n, cov_type)
	est_gmm.train(feat_train)
	est_gmm.train_em(min_cov, max_iter, min_change)

	return est_gmm

Example #3

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('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)

Example #4

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)

Example #5

File: smem_1d_gmm.py Project: sploving/shogun

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('SMEM for 1d GMM example')

max_iter=100
max_cand=5
min_cov=1e-9
max_em_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)

Example #6

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)

Example #7

File: smem_2d_gmm.py Project: AlexBinder/shogun

from shogun.Distribution import Gaussian, GMM
from shogun.Features import RealFeatures
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)

Example #8

File: clustering_gmm_modular.py Project: sploving/shogun

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]]


def clustering_gmm_modular(fm_train=generated,
                           n=2,
                           min_cov=1e-9,
                           max_iter=1000,
                           min_change=1e-9,
                           cov_type=0):

Example #9

File: clustering_gmm_modular.py Project: behollis/muViewBranch

# minimum covariance, maximum iterations and minimum log-likelihood change as
# input. After training one can cluster observations by selecting the most
# 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()

Example #10

File: em_2d_gmm.py Project: AlexBinder/shogun

from pylab import figure,scatter,contour,show,legend,connect
from numpy import array, append, arange, reshape, empty, exp
from shogun.Distribution import Gaussian, GMM
from shogun.Features import RealFeatures
import util

util.set_title('EM for 2d GMM example')

#set the parameters
min_cov=1e-9
max_iter=1000
min_change=1e-9
cov_type=0

#setup the real GMM
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]))

#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()

Example #11

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('SMEM for 1d GMM example')

max_iter = 100
max_cand = 5
min_cov = 1e-9
max_em_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)

Example #12

import numpy as np
import os
from shogun.Features import RealFeatures
from shogun.Distribution import GMM
from shogun.Library import Math_init_random

# Load the data.
f = open(os.path.dirname(__file__) + '../data/mvnrnd.data')
data = np.fromfile(f, dtype=np.float64, sep=' ')
data = data.reshape(-1, 2)
f.close()

Math_init_random(5)
feat = RealFeatures(data.T)

# Calculate mixture of Gaussians.
gmm = GMM(2, 0)
gmm.set_features(feat)
gmm.train_em()

# Vector of covariances; one for each Gaussian.
print gmm.get_nth_cov(0)
print gmm.get_nth_cov(1)
# The vector of means.
print gmm.get_nth_mean(0)
print gmm.get_nth_mean(1)
# The a priori weights of each Gaussian.
print gmm.get_coef()