#!/usr/bin/env python
#
# This program is free software you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation either version 3 of the License, or
# (at your option) any later version.
#
# Written (C) 2012-2013 <NAME>
#
from numpy import *
#from pylab import *

parameter_list = [[1000,10,5,3,pi/4, "opt"], [1000,10,5,3,pi/4, "max"], [1000,10,5,3,pi/4, "median"]]

def statistics_mmd_kernel_selection_single(m,distance,stretch,num_blobs,angle,selection_method):
	from modshogun import RealFeatures
	from modshogun import GaussianBlobsDataGenerator
	from modshogun import GaussianKernel, CombinedKernel
	from modshogun import LinearTimeMMD
	from modshogun import MMDKernelSelectionMedian
	from modshogun import MMDKernelSelectionMax
	from modshogun import MMDKernelSelectionOpt
	from modshogun import PERMUTATION, MMD1_GAUSSIAN
	from modshogun import EuclideanDistance
	from modshogun import Statistics, Math

	# init seed for reproducability
	Math.init_random(1)

	# note that the linear time statistic is designed for much larger datasets
	# results for this low number will be bad (unstable, type I error wrong)
	m=1000
	distance=10
	stretch=5
	num_blobs=3
	angle=pi/4

	# streaming data generator
	gen_p=GaussianBlobsDataGenerator(num_blobs, distance, 1, 0)
	gen_q=GaussianBlobsDataGenerator(num_blobs, distance, stretch, angle)

	# stream some data and plot
	num_plot=1000
	features=gen_p.get_streamed_features(num_plot)
	features=features.create_merged_copy(gen_q.get_streamed_features(num_plot))
	data=features.get_feature_matrix()

	#figure()
	#subplot(2,2,1)
	#grid(True)
	#plot(data[0][0:num_plot], data[1][0:num_plot], 'r.', label='$x$')
	#title('$X\sim p$')
	#subplot(2,2,2)
	#grid(True)
	#plot(data[0][num_plot+1:2*num_plot], data[1][num_plot+1:2*num_plot], 'b.', label='$x$', alpha=0.5)
	#title('$Y\sim q$')


	# create combined kernel with Gaussian kernels inside (shoguns Gaussian kernel is
	# different to the standard form, see documentation)
	sigmas=[2**x for x in range(-3,10)]
	widths=[x*x*2 for x in sigmas]
	combined=CombinedKernel()
	for i in range(len(sigmas)):
		combined.append_kernel(GaussianKernel(10, widths[i]))

	# mmd instance using streaming features, blocksize of 10000
	block_size=1000
	mmd=LinearTimeMMD(combined, gen_p, gen_q, m, block_size)

	# kernel selection instance (this can easily replaced by the other methods for selecting
	# single kernels
	if selection_method=="opt":
		selection=MMDKernelSelectionOpt(mmd)
	elif selection_method=="max":
		selection=MMDKernelSelectionMax(mmd)
	elif selection_method=="median":
		selection=MMDKernelSelectionMedian(mmd)

	# print measures (just for information)
	# in case Opt: ratios of MMD and standard deviation
	# in case Max: MMDs for each kernel
	# Does not work for median method
	if selection_method!="median":
		ratios=selection.compute_measures()
		#print "Measures:", ratios

	#subplot(2,2,3)
	#plot(ratios)
	#title('Measures')

	# perform kernel selection
	kernel=selection.select_kernel()
	kernel=GaussianKernel.obtain_from_generic(kernel)
	#print "selected kernel width:", kernel.get_width()

	# compute tpye I and II error (use many more trials). Type I error is only
	# estimated to check MMD1_GAUSSIAN method for estimating the null
	# distribution. Note that testing has to happen on difference data than
	# kernel selecting, but the linear time mmd does this implicitly
	mmd.set_kernel(kernel)
	mmd.set_null_approximation_method(MMD1_GAUSSIAN)

	# number of trials should be larger to compute tight confidence bounds
	num_trials=5;
	alpha=0.05 # test power
	typeIerrors=[0 for x in range(num_trials)]
	typeIIerrors=[0 for x in range(num_trials)]
	for i in range(num_trials):
		# this effectively means that p=q - rejecting is tpye I error
		mmd.set_simulate_h0(True)
		typeIerrors[i]=mmd.perform_test()>alpha
		mmd.set_simulate_h0(False)

		typeIIerrors[i]=mmd.perform_test()>alpha

	#print "type I error:", mean(typeIerrors), ", type II error:", mean(typeIIerrors)

	return kernel,typeIerrors,typeIIerrors

if __name__=='__main__':
	print('MMDKernelSelection')
	statistics_mmd_kernel_selection_single(*parameter_list[0])
	#show()

#!/usr/bin/env python
#
# This program is free software you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation either version 3 of the License, or
# (at your option) any later version.
#
# Written (C) 2012-2013 <NAME>
#
from numpy import *
#from pylab import *

parameter_list = [[1000,10,5,3,pi/4, "opt"], [1000,10,5,3,pi/4, "max"], [1000,10,5,3,pi/4, "median"]]

def statistics_mmd_kernel_selection_single(m,distance,stretch,num_blobs,angle,selection_method):
	from modshogun import RealFeatures
	from modshogun import GaussianBlobsDataGenerator
	from modshogun import GaussianKernel, CombinedKernel
	from modshogun import LinearTimeMMD
	from modshogun import MMDKernelSelectionMedian
	from modshogun import MMDKernelSelectionMax
	from modshogun import MMDKernelSelectionOpt
	from modshogun import PERMUTATION, MMD1_GAUSSIAN
	from modshogun import EuclideanDistance
	from modshogun import Statistics, Math

	# init seed for reproducability
	Math.init_random(1)

	# note that the linear time statistic is designed for much larger datasets
	# results for this low number will be bad (unstable, type I error wrong)
	m=1000
	distance=10
	stretch=5
	num_blobs=3
	angle=pi/4

	# streaming data generator
	gen_p=GaussianBlobsDataGenerator(num_blobs, distance, 1, 0)
	gen_q=GaussianBlobsDataGenerator(num_blobs, distance, stretch, angle)

	# stream some data and plot
	num_plot=1000
	features=gen_p.get_streamed_features(num_plot)
	features=features.create_merged_copy(gen_q.get_streamed_features(num_plot))
	data=features.get_feature_matrix()

	#figure()
	#subplot(2,2,1)
	#grid(True)
	#plot(data[0][0:num_plot], data[1][0:num_plot], 'r.', label='$x$')
	#title('$X\sim p$')
	#subplot(2,2,2)
	#grid(True)
	#plot(data[0][num_plot+1:2*num_plot], data[1][num_plot+1:2*num_plot], 'b.', label='$x$', alpha=0.5)
	#title('$Y\sim q$')


	# create combined kernel with Gaussian kernels inside (shoguns Gaussian kernel is
	# different to the standard form, see documentation)
	sigmas=[2**x for x in range(-3,10)]
	widths=[x*x*2 for x in sigmas]
	combined=CombinedKernel()
	for i in range(len(sigmas)):
		combined.append_kernel(GaussianKernel(10, widths[i]))

	# mmd instance using streaming features, blocksize of 10000
	block_size=1000
	mmd=LinearTimeMMD(combined, gen_p, gen_q, m, block_size)

	# kernel selection instance (this can easily replaced by the other methods for selecting
	# single kernels
	if selection_method=="opt":
		selection=MMDKernelSelectionOpt(mmd)
	elif selection_method=="max":
		selection=MMDKernelSelectionMax(mmd)
	elif selection_method=="median":
		selection=MMDKernelSelectionMedian(mmd)

	# print measures (just for information)
	# in case Opt: ratios of MMD and standard deviation
	# in case Max: MMDs for each kernel
	# Does not work for median method
	if selection_method!="median":
		ratios=selection.compute_measures()
		#print "Measures:", ratios

	#subplot(2,2,3)
	#plot(ratios)
	#title('Measures')

	# perform kernel selection
	kernel=selection.select_kernel()
	kernel=GaussianKernel.obtain_from_generic(kernel)
	#print "selected kernel width:", kernel.get_width()

	# compute tpye I and II error (use many more trials). Type I error is only
	# estimated to check MMD1_GAUSSIAN method for estimating the null
	# distribution. Note that testing has to happen on difference data than
	# kernel selecting, but the linear time mmd does this implicitly
	mmd.set_kernel(kernel)
	mmd.set_null_approximation_method(MMD1_GAUSSIAN)

	# number of trials should be larger to compute tight confidence bounds
	num_trials=5;
	alpha=0.05 # test power
	typeIerrors=[0 for x in range(num_trials)]
	typeIIerrors=[0 for x in range(num_trials)]
	for i in range(num_trials):
		# this effectively means that p=q - rejecting is tpye I error
		mmd.set_simulate_h0(True)
		typeIerrors[i]=mmd.perform_test()>alpha
		mmd.set_simulate_h0(False)

		typeIIerrors[i]=mmd.perform_test()>alpha

	#print "type I error:", mean(typeIerrors), ", type II error:", mean(typeIIerrors)

	return kernel,typeIerrors,typeIIerrors

if __name__=='__main__':
	print('MMDKernelSelection')
	statistics_mmd_kernel_selection_single(*parameter_list[0])
	#show()

#!/usr/bin/env python # # This program is free software you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation either version 3 of the License, or # (at your option) any later version. # # Written (C) 2012-2013 <NAME> # #from pylab import * # init seed for reproducability # note that the linear time statistic is designed for much larger datasets # results for this low number will be bad (unstable, type I error wrong) # streaming data generator # stream some data and plot #figure() #subplot(2,2,1) #grid(True) #plot(data[0][0:num_plot], data[1][0:num_plot], 'r.', label='$x$') #title('$X\sim p$') #subplot(2,2,2) #grid(True) #plot(data[0][num_plot+1:2*num_plot], data[1][num_plot+1:2*num_plot], 'b.', label='$x$', alpha=0.5) #title('$Y\sim q$') # create combined kernel with Gaussian kernels inside (shoguns Gaussian kernel is # different to the standard form, see documentation) # mmd instance using streaming features, blocksize of 10000 # kernel selection instance (this can easily replaced by the other methods for selecting # single kernels # print measures (just for information) # in case Opt: ratios of MMD and standard deviation # in case Max: MMDs for each kernel # Does not work for median method #print "Measures:", ratios #subplot(2,2,3) #plot(ratios) #title('Measures') # perform kernel selection #print "selected kernel width:", kernel.get_width() # compute tpye I and II error (use many more trials). Type I error is only # estimated to check MMD1_GAUSSIAN method for estimating the null # distribution. Note that testing has to happen on difference data than # kernel selecting, but the linear time mmd does this implicitly # number of trials should be larger to compute tight confidence bounds # test power # this effectively means that p=q - rejecting is tpye I error #print "type I error:", mean(typeIerrors), ", type II error:", mean(typeIIerrors) #show()