"""
Test Regression
"""

from sg import sg
from numpy import double
import util


def _set_regression (indata):
	prefix='regression_'
	sg('threads', indata[prefix+'num_threads'])
	sg('set_labels', 'TRAIN', double(indata[prefix+'labels']))

	rname=util.fix_regression_name_inconsistency(indata[prefix+'name'])
	print rname
	sg('new_regression', rname)


def _train (indata):
	if indata['regression_type']=='svm':
		sg('c', double(indata['regression_C']))
		sg('svm_epsilon', indata['regression_epsilon'])
		sg('svr_tube_epsilon', indata['regression_tube_epsilon'])
	elif indata['regression_type']=='kernelmachine':
		sg('krr_tau', indata['regression_tau'])
	else:
		raise StandardError, 'Incomplete regression data.'

	sg('train_regression')


def _evaluate (indata):
	alphas=0
	bias=0
	sv=0

	if indata.has_key('regression_bias'):
		[bias, weights]=sg('get_svm')
		weights=weights.T
		bias=abs(bias-indata['regression_bias'])
		for item in weights[0].tolist():
			alphas+=item
		alphas=abs(alphas-indata['regression_alpha_sum'])
		for item in weights[1].tolist():
			sv+=item
		sv=abs(sv-indata['regression_sv_sum'])

	classified=max(abs(sg('classify')-indata['regression_classified']))

	return util.check_accuracy(indata['regression_accuracy'],
		alphas=alphas, bias=bias, support_vectors=sv, classified=classified)


########################################################################
# public
########################################################################

def test (indata):
	try:
		util.set_features(indata, 'kernel_')
	except NotImplementedError, e:
		print e
		return True

	util.set_and_train_kernel(indata)

	try:
		_set_regression(indata)
	except RuntimeError, e:
		print "%s is disabled/unavailable!" % indata['name']
		return True

	try:
		_train(indata)
	except StandardError, e:
		print e
		return False

	return _evaluate(indata)


"""
Test Regression
"""

from sg import sg
from numpy import double
import util


def _set_regression (indata):
	prefix='regression_'
	sg('threads', indata[prefix+'num_threads'])
	sg('set_labels', 'TRAIN', double(indata[prefix+'labels']))

	rname=util.fix_regression_name_inconsistency(indata[prefix+'name'])
	print rname
	sg('new_regression', rname)


def _train (indata):
	if indata['regression_type']=='svm':
		sg('c', double(indata['regression_C']))
		sg('svm_epsilon', indata['regression_epsilon'])
		sg('svr_tube_epsilon', indata['regression_tube_epsilon'])
	elif indata['regression_type']=='kernelmachine':
		sg('krr_tau', indata['regression_tau'])
	else:
		raise StandardError, 'Incomplete regression data.'

	sg('train_regression')


def _evaluate (indata):
	alphas=0
	bias=0
	sv=0

	if indata.has_key('regression_bias'):
		[bias, weights]=sg('get_svm')
		weights=weights.T
		bias=abs(bias-indata['regression_bias'])
		for item in weights[0].tolist():
			alphas+=item
		alphas=abs(alphas-indata['regression_alpha_sum'])
		for item in weights[1].tolist():
			sv+=item
		sv=abs(sv-indata['regression_sv_sum'])

	classified=max(abs(sg('classify')-indata['regression_classified']))

	return util.check_accuracy(indata['regression_accuracy'],
		alphas=alphas, bias=bias, support_vectors=sv, classified=classified)


########################################################################
# public
########################################################################

def test (indata):
	try:
		util.set_features(indata, 'kernel_')
	except NotImplementedError, e:
		print e
		return True

	util.set_and_train_kernel(indata)

	try:
		_set_regression(indata)
	except RuntimeError, e:
		print "%s is disabled/unavailable!" % indata['name']
		return True

	try:
		_train(indata)
	except StandardError, e:
		print e
		return False

	return _evaluate(indata)


Test Regression ######################################################################## # public ########################################################################