Python set_title Examples

Example #1

File: multiple_smvs.py Project: AlexBinder/shogun

#!/usr/bin/env python
# -*- coding: latin-1 -*-

from pylab import figure,pcolor,scatter,contour,colorbar,show,subplot,connect,axis
from numpy import concatenate
from numpy.random import randn
from shogun.Features import *
from shogun.Classifier import *
from shogun.Kernel import *
import util

util.set_title('Multiple SVMS')

num_svms=6
width=0.5

svmList = [None]*num_svms
trainfeatList = [None]*num_svms
traindatList = [None]*num_svms
trainlabList = [None]*num_svms
trainlabsList = [None]*num_svms
kernelList = [None]*num_svms

for i in range(num_svms):
	pos=util.get_realdata(True)
	neg=util.get_realdata(False)
	traindatList[i] = concatenate((pos, neg), axis=1)
	trainfeatList[i] = util.get_realfeatures(pos, neg)
	trainlabsList[i] = util.get_labels(True)
	trainlabList[i] = util.get_labels()
	kernelList[i] = GaussianKernel(trainfeatList[i], trainfeatList[i], width)

Example #2

File: prc.py Project: frx/shogun

from pylab import plot,grid,title,subplot,xlabel,ylabel,text,subplots_adjust,fill_between,mean,connect,show
from shogun.Kernel import GaussianKernel
from shogun.Classifier import LibSVM, LDA
from shogun.Evaluation import PRCEvaluation
import util

util.set_title('PRC example')
util.DISTANCE=0.5
subplots_adjust(hspace=0.3)

pos=util.get_realdata(True)
neg=util.get_realdata(False)
features=util.get_realfeatures(pos, neg)
labels=util.get_labels()

# classifiers
gk=GaussianKernel(features, features, 1.0)
svm = LibSVM(1000.0, gk, labels)
svm.train()
lda=LDA(1,features,labels)
lda.train()

## plot points
subplot(211)
plot(pos[0,:], pos[1,:], "r.")
plot(neg[0,:], neg[1,:], "b.")
grid(True)
title('Data',size=10)

# plot PRC for SVM
subplot(223)

Example #3

File: svr_sinc.py Project: joseph-chan/rqpersonalsvn

from pylab import figure, pcolor, scatter, contour, colorbar, show, subplot, plot, legend, connect
from shogun.Features import *
from shogun.Regression import *
from shogun.Kernel import *
import util

util.set_title("SVR on Sinus")

X, Y = util.get_sinedata()
C = 10
width = 0.5
epsilon = 0.01

feat = RealFeatures(X)
lab = RegressionLabels(Y.flatten())
gk = GaussianKernel(feat, feat, width)
# svr = SVRLight(C, epsilon, gk, lab)
svr = LibSVR(C, epsilon, gk, lab)
svr.train()

plot(X, Y, ".", label="train data")
plot(X[0], svr.apply().get_labels(), hold=True, label="train output")

XE, YE = util.compute_output_plot_isolines_sine(svr, gk, feat, regression=True)
plot(XE[0], YE, hold=True, label="test output")

connect("key_press_event", util.quit)
show()

Example #4

File: roc.py Project: DEVESHTARASIA/shogun

from pylab import plot,grid,title,subplot,xlabel,ylabel,text,subplots_adjust,fill_between,mean,connect,show
from shogun import GaussianKernel
from shogun import LibSVM, LDA
from shogun import ROCEvaluation
import util

util.set_title('ROC example')
util.DISTANCE=0.5
subplots_adjust(hspace=0.3)

pos=util.get_realdata(True)
neg=util.get_realdata(False)
features=util.get_realfeatures(pos, neg)
labels=util.get_labels()

# classifiers
gk=GaussianKernel(features, features, 1.0)
svm = LibSVM(1000.0, gk, labels)
svm.train()
lda=LDA(1,features,labels)
lda.train()

## plot points
subplot(211)
plot(pos[0,:], pos[1,:], "r.")
plot(neg[0,:], neg[1,:], "b.")
grid(True)
title('Data',size=10)

# plot ROC for SVM
subplot(223)

Example #5

File: svm.py Project: vishalbelsare/netpred

# Based on svm.py example from Shogun

import pylab
import numpy
import util
import latex_plot_inits

from shogun.Features import *
from shogun.Classifier import *
from shogun.Kernel import *

util.set_title('SVM')
util.NUM_EXAMPLES = 200

width = 5

# positive examples
pos = util.get_realdata(True)
pylab.plot(pos[0, :], pos[1, :], "rs")

# negative examples
neg = util.get_realdata(False)
pylab.plot(neg[0, :], neg[1, :], "bo")

# train svm
labels = util.get_labels()
train = util.get_realfeatures(pos, neg)
gk = GaussianKernel(train, train, width)
svm = LibSVM(10.0, gk, labels)
svm.train()

Example #6

File: kernel_ridge_regression_sinc.py Project: zhqanddcy/shogun

from pylab import figure,pcolor,scatter,contour,colorbar,show,subplot,plot,legend,connect
from modshogun import *
import util

util.set_title('KernelRidgeRegression on Sine')


X, Y=util.get_sinedata()
width=1

feat=RealFeatures(X)
lab=RegressionLabels(Y.flatten())
gk=GaussianKernel(feat, feat, width)
krr=KernelRidgeRegression()
krr.set_labels(lab)
krr.set_kernel(gk)
krr.set_tau(1e-6)
krr.train()

plot(X, Y, '.', label='train data')
plot(X[0], krr.apply().get_labels(), hold=True, label='train output')

XE, YE=util.compute_output_plot_isolines_sine(krr, gk, feat, regression=True)
YE200=krr.apply_one(200)

plot(XE[0], YE, hold=True, label='test output')
plot([XE[0,200]], [YE200], '+', hold=True)
#print YE[200], YE200

connect('key_press_event', util.quit)
show()

Example #7

File: vis.py Project: AlexBinder/shogun

from pylab import figure,pcolor,scatter,contour,colorbar,show,subplot,plot,connect,axis
from numpy.random import randn
from shogun.Features import *
from shogun.Classifier import *
from shogun.Kernel import *
import util

util.set_title('SVM')
util.NUM_EXAMPLES=200

width=5

# positive examples
pos=util.get_realdata(True)
plot(pos[0,:], pos[1,:], "r.")

# negative examples
neg=util.get_realdata(False)
plot(neg[0,:], neg[1,:], "b.")

# train svm
labels=util.get_labels()
train=util.get_realfeatures(pos, neg)
gk=GaussianKernel(train, train, width)
svm = LibSVM(10.0, gk, labels)
svm.train()

x, y, z=util.compute_output_plot_isolines(svm, gk, train)
pcolor(x, y, z, shading='interp')
contour(x, y, z, linewidths=1, colors='black', hold=True)
axis('tight')

Example #8

File: smem_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('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()])

Example #9

from sg import sg
from pylab import plot, show, connect
from numpy import array,transpose,sin,double

import util

util.set_title('SVR Regression')

sg('new_regression', 'LIBSVR')
features=array([range(0,100)],dtype=double)
features.resize(1,100)
labels=sin(features)[0]
sg('set_features', "TRAIN", features)
sg('set_labels', "TRAIN", labels)
sg('set_kernel', 'GAUSSIAN', 'REAL', 20, 10.)
sg('c', 1.)
sg('train_regression')
[bias, alphas]=sg('get_svm');
sg('set_features', "TEST", features)
out=sg('classify');

plot(features[0],labels,'b-')
plot(features[0],labels,'bo')
plot(features[0],out,'r-')
plot(features[0],out,'ro')
connect('key_press_event', util.quit)
show()

Example #10

from pylab import figure, pcolor, scatter, contour, colorbar, show, subplot, plot, connect
from numpy import array, meshgrid, reshape, linspace, min, max
from numpy import concatenate, transpose, ravel
from shogun.Features import *
from shogun.Regression import *
from shogun.Kernel import *
import util

util.set_title('KRR')

width = 20

# positive examples
pos = util.get_realdata(True)
plot(pos[0, :], pos[1, :], "r.")

# negative examples
neg = util.get_realdata(False)
plot(neg[0, :], neg[1, :], "b.")

# train svm
labels = util.get_labels()
train = util.get_realfeatures(pos, neg)
gk = GaussianKernel(train, train, width)
krr = KRR()
krr.set_labels(labels)
krr.set_kernel(gk)
krr.set_tau(1e-3)
krr.train()

# compute output plot iso-lines

Example #11

File: svm_classification.py Project: vinodrajendran001/ASP

from sg import sg
from pylab import pcolor, scatter, contour, colorbar, show, imshow, connect
from numpy import min, max, where
import util

util.set_title('SVM Classification')

#sg('loglevel', 'ALL')
traindata = util.get_traindata()
labels = util.get_labels()
width = 1.
size_cache = 10

sg('set_features', 'TRAIN', traindata)
sg('set_labels', 'TRAIN', labels)
sg('set_kernel', 'GAUSSIAN', 'REAL', size_cache, width)
sg('new_classifier', 'LIBSVM')
sg('c', 100.)
sg('train_classifier')
[bias, alphas] = sg('get_svm')
#print bias
#print alphas

#print "objective: %f" % sg('get_svm_objective')

x, y = util.get_meshgrid(traindata)
testdata = util.get_testdata(x, y)
sg('set_features', 'TEST', testdata)
z = sg('classify')

z.resize((50, 50))

Example #12

File: lda.py Project: 42MachineLearning/shogun

from pylab import figure,pcolor,scatter,contour,colorbar,show,subplot,plot,connect
from modshogun import *
from modshogun import *
import util

util.set_title('LDA')
util.DISTANCE=0.5

gamma=0.1

# positive examples
pos=util.get_realdata(True)
plot(pos[0,:], pos[1,:], "r.")

# negative examples
neg=util.get_realdata(False)
plot(neg[0,:], neg[1,:], "b.")

# train lda
labels=util.get_labels()
features=util.get_realfeatures(pos, neg)
lda=LDA(gamma, features, labels)
lda.train()

# compute output plot iso-lines
x, y, z=util.compute_output_plot_isolines(lda)

c=pcolor(x, y, z, shading='interp')
contour(x, y, z, linewidths=1, colors='black', hold=True)
colorbar(c)

Example #13

from pylab import figure, pcolor, scatter, contour, colorbar, show, subplot, plot, legend, connect
from shogun.Features import *
from shogun.Regression import *
from shogun.Kernel import *
import util

util.set_title('KRR on Sine')

X, Y = util.get_sinedata()
width = 1

feat = RealFeatures(X)
lab = Labels(Y.flatten())
gk = GaussianKernel(feat, feat, width)
krr = KRR()
krr.set_labels(lab)
krr.set_kernel(gk)
krr.set_tau(1e-6)
krr.train()

plot(X, Y, '.', label='train data')
plot(X[0], krr.classify().get_labels(), hold=True, label='train output')

XE, YE = util.compute_output_plot_isolines_sine(krr, gk, feat)
YE200 = krr.classify_example(200)

plot(XE[0], YE, hold=True, label='test output')
plot([XE[0, 200]], [YE200], '+', hold=True)
#print YE[200], YE200

connect('key_press_event', util.quit)

Example #14

File: em_1d_gmm.py Project: joseph-chan/rqpersonalsvn

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

util.set_title("EM for 1d GMM example")

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

# setup the real GMM
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]))

# generate training set from real GMM
generated = array([real_gmm.sample()])
for i in range(199):
    generated = append(generated, array([real_gmm.sample()]), axis=1)

Example #15

File: svm_classification.py Project: 42MachineLearning/shogun

from sg import sg
from pylab import pcolor, scatter, contour, colorbar, show, imshow, connect
from numpy import min, max, where
import util

util.set_title('SVM Classification')

#sg('loglevel', 'ALL')
traindata=util.get_traindata()
labels=util.get_labels()
width=1.
size_cache=10

sg('set_features', 'TRAIN', traindata)
sg('set_labels', 'TRAIN', labels)
sg('set_kernel', 'GAUSSIAN', 'REAL', size_cache, width)
sg('new_classifier', 'LIBSVM')
sg('c', 100.)
sg('train_classifier')
[bias, alphas]=sg('get_svm')
#print bias
#print alphas

#print "objective: %f" % sg('get_svm_objective')

x, y=util.get_meshgrid(traindata)
testdata=util.get_testdata(x, y)
sg('set_features', 'TEST', testdata)
z=sg('classify')

z.resize((50,50))

Example #16

File: svr_sinc.py Project: ashish-sadh/shogun

from pylab import figure,pcolor,scatter,contour,colorbar,show,subplot,plot,legend, connect
from shogun.Features import *
from shogun.Regression import *
from shogun.Kernel import *
import util

util.set_title('SVR on Sinus')

X, Y=util.get_sinedata()
C=10
width=0.5
epsilon=0.01

feat = RealFeatures(X)
lab = Labels(Y.flatten())
gk=GaussianKernel(feat,feat, width)
#svr = SVRLight(C, epsilon, gk, lab)
svr = LibSVR(C, epsilon, gk, lab)
svr.train()

plot(X, Y, '.', label='train data')
plot(X[0], svr.apply().get_labels(), hold=True, label='train output')

XE, YE=util.compute_output_plot_isolines_sine(svr, gk, feat)
plot(XE[0], YE, hold=True, label='test output')

connect('key_press_event', util.quit)
show()

Example #17

from pylab import plot, grid, title, subplot, xlabel, ylabel, text, subplots_adjust, fill_between, mean, connect, show
from shogun import GaussianKernel
from shogun import LibSVM, LDA
from shogun import ROCEvaluation
import util

util.set_title('ROC example')
util.DISTANCE = 0.5
subplots_adjust(hspace=0.3)

pos = util.get_realdata(True)
neg = util.get_realdata(False)
features = util.get_realfeatures(pos, neg)
labels = util.get_labels()

# classifiers
gk = GaussianKernel(features, features, 1.0)
svm = LibSVM(1000.0, gk, labels)
svm.train()
lda = LDA(1, features, labels)
lda.train()

## plot points
subplot(211)
plot(pos[0, :], pos[1, :], "r.")
plot(neg[0, :], neg[1, :], "b.")
grid(True)
title('Data', size=10)

# plot ROC for SVM
subplot(223)

Example #18

File: kernel_ridge_regression.py Project: 42MachineLearning/shogun

from pylab import figure,pcolor,scatter,contour,colorbar,show,subplot,plot,connect
from numpy import array,meshgrid,reshape,linspace,min,max
from numpy import concatenate,transpose,ravel
from modshogun import *
from modshogun import *
from modshogun import *
import util

util.set_title('KernelRidgeRegression')

width=20

# positive examples
pos=util.get_realdata(True)
plot(pos[0,:], pos[1,:], "r.")

# negative examples
neg=util.get_realdata(False)
plot(neg[0,:], neg[1,:], "b.")

# train svm
labels = util.get_labels(type='regression')
train = util.get_realfeatures(pos, neg)
gk=GaussianKernel(train, train, width)
krr = KernelRidgeRegression()
krr.set_labels(labels)
krr.set_kernel(gk)
krr.set_tau(1e-3)
krr.train()

# compute output plot iso-lines

Example #19

File: lda.py Project: zhqanddcy/shogun

from pylab import figure, pcolor, scatter, contour, colorbar, show, subplot, plot, connect
from modshogun import *
import util

util.set_title('LDA')
util.DISTANCE = 0.5

gamma = 0.1

# positive examples
pos = util.get_realdata(True)
plot(pos[0, :], pos[1, :], "r.")

# negative examples
neg = util.get_realdata(False)
plot(neg[0, :], neg[1, :], "b.")

# train lda
labels = util.get_labels()
features = util.get_realfeatures(pos, neg)
lda = LDA(gamma, features, labels)
lda.train()

# compute output plot iso-lines
x, y, z = util.compute_output_plot_isolines(lda)

c = pcolor(x, y, z)
contour(x, y, z, linewidths=1, colors='black', hold=True)
colorbar(c)

connect('key_press_event', util.quit)

Example #20

File: svr_sinc.py Project: pombredanne/rqpersonalsvn

from pylab import figure, pcolor, scatter, contour, colorbar, show, subplot, plot, legend, connect
from shogun.Features import *
from shogun.Regression import *
from shogun.Kernel import *
import util

util.set_title('SVR on Sinus')

X, Y = util.get_sinedata()
C = 10
width = 0.5
epsilon = 0.01

feat = RealFeatures(X)
lab = Labels(Y.flatten())
gk = GaussianKernel(feat, feat, width)
#svr = SVRLight(C, epsilon, gk, lab)
svr = LibSVR(C, epsilon, gk, lab)
svr.train()

plot(X, Y, '.', label='train data')
plot(X[0], svr.apply().get_labels(), hold=True, label='train output')

XE, YE = util.compute_output_plot_isolines_sine(svr, gk, feat)
plot(XE[0], YE, hold=True, label='test output')

connect('key_press_event', util.quit)
show()

Example #21

File: multiple_smvs.py Project: zhqanddcy/shogun

#!/usr/bin/env python
# -*- coding: latin-1 -*-

from pylab import figure, pcolor, scatter, contour, colorbar, show, subplot, connect, axis
from numpy import concatenate
from numpy.random import randn
from modshogun import *
import util

util.set_title('Multiple SVMS')

num_svms = 6
width = 0.5

svmList = [None] * num_svms
trainfeatList = [None] * num_svms
traindatList = [None] * num_svms
trainlabList = [None] * num_svms
trainlabsList = [None] * num_svms
kernelList = [None] * num_svms

for i in range(num_svms):
    pos = util.get_realdata(True)
    neg = util.get_realdata(False)
    traindatList[i] = concatenate((pos, neg), axis=1)
    trainfeatList[i] = util.get_realfeatures(pos, neg)
    trainlabsList[i] = util.get_labels(True)
    trainlabList[i] = util.get_labels()
    kernelList[i] = GaussianKernel(trainfeatList[i], trainfeatList[i], width)
    svmList[i] = LibSVM(10, kernelList[i], trainlabList[i])

Example #22

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 #23

File: svmlin.py Project: manantomar/test

from pylab import figure,pcolor,scatter,contour,colorbar,show,subplot,plot,axis, connect
from modshogun import *
from modshogun import *
from modshogun import *
import util

util.set_title('SVM Linear 1')
util.NUM_EXAMPLES=4000
C=1000

# positive examples
pos=util.get_realdata(True)
# negative examples
neg=util.get_realdata(False)

# train svm lin
labels=util.get_labels()
dense=util.get_realfeatures(pos, neg)
train=SparseRealFeatures()
train.obtain_from_simple(dense)
svm=SVMLin(C, train, labels)
svm.train()

lk=LinearKernel(dense, dense)
try:
	svmlight=LibSVM(C, lk, labels)
except NameError:
	print 'No SVMLight support available'
	import sys
	sys.exit(1)
svmlight.train()

Example #24

File: krr_sinc.py Project: AsherBond/shogun

from pylab import figure,pcolor,scatter,contour,colorbar,show,subplot,plot,legend,connect
from shogun.Features import *
from shogun.Regression import *
from shogun.Kernel import *
import util

util.set_title('KRR on Sine')


X, Y=util.get_sinedata()
width=1

feat=RealFeatures(X)
lab=Labels(Y.flatten())
gk=GaussianKernel(feat, feat, width)
krr=KRR()
krr.set_labels(lab)
krr.set_kernel(gk)
krr.set_tau(1e-6)
krr.train()

plot(X, Y, '.', label='train data')
plot(X[0], krr.classify().get_labels(), hold=True, label='train output')

XE, YE=util.compute_output_plot_isolines_sine(krr, gk, feat)
YE200=krr.classify_example(200)

plot(XE[0], YE, hold=True, label='test output')
plot([XE[0,200]], [YE200], '+', hold=True)
#print YE[200], YE200

Example #25

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 #26

File: kernel_ridge_regression.py Project: zym-wade/shogun

from pylab import figure, pcolor, scatter, contour, colorbar, show, subplot, plot, connect
from numpy import array, meshgrid, reshape, linspace, min, max
from numpy import concatenate, transpose, ravel
from shogun import *
import util

util.set_title('KernelRidgeRegression')

width = 20

# positive examples
pos = util.get_realdata(True)
plot(pos[0, :], pos[1, :], "r.")

# negative examples
neg = util.get_realdata(False)
plot(neg[0, :], neg[1, :], "b.")

# train krr
labels = util.get_labels(type='regression')
train = util.get_realfeatures(pos, neg)
gk = GaussianKernel(train, train, width)
krr = KernelRidgeRegression()
krr.set_labels(labels)
krr.set_kernel(gk)
krr.set_tau(1e-3)
krr.train()

# compute output plot iso-lines
x, y, z = util.compute_output_plot_isolines(krr, gk, train, regression=True)

Example #27

File: em_1d_gmm.py Project: 42MachineLearning/shogun

from pylab import figure,show,connect,hist,plot,legend
from numpy import array, append, arange, empty, exp
from modshogun import Gaussian, GMM
from modshogun import RealFeatures
import util

util.set_title('EM for 1d GMM example')

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

#setup the real GMM
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]))

#generate training set from real GMM
generated=array([real_gmm.sample()])
for i in range(199):
    generated=append(generated, array([real_gmm.sample()]), axis=1)

Example #28

File: krr.py Project: AsherBond/shogun

from pylab import figure,pcolor,scatter,contour,colorbar,show,subplot,plot,connect
from numpy import array,meshgrid,reshape,linspace,min,max
from numpy import concatenate,transpose,ravel
from shogun.Features import *
from shogun.Regression import *
from shogun.Kernel import *
import util

util.set_title('KRR')

width=20

# positive examples
pos=util.get_realdata(True)
plot(pos[0,:], pos[1,:], "r.")

# negative examples
neg=util.get_realdata(False)
plot(neg[0,:], neg[1,:], "b.")

# train svm
labels = util.get_labels()
train = util.get_realfeatures(pos, neg)
gk=GaussianKernel(train, train, width)
krr = KRR()
krr.set_labels(labels)
krr.set_kernel(gk)
krr.set_tau(1e-3)
krr.train()

# compute output plot iso-lines