backtrace

#0  __GI_raise (sig=sig@entry=6) at ../sysdeps/unix/sysv/linux/raise.c:51
#1  0x00007ffff0ea13fa in __GI_abort () at abort.c:89
#2  0x00007ffff0eddbd0 in __libc_message (do_abort=do_abort@entry=2, fmt=fmt@entry=0x7ffff0fd2dd0 "*** Error in `%s': %s: 0x%s ***\n")
    at ../sysdeps/posix/libc_fatal.c:175
#3  0x00007ffff0ee3f96 in malloc_printerr (action=3, str=0x7ffff0fd31d8 "malloc(): memory corruption (fast)", ptr=<optimized out>,
    ar_ptr=<optimized out>) at malloc.c:5049
#4  0x00007ffff0ee6461 in _int_malloc (av=av@entry=0x7ffff1206b00 <main_arena>, bytes=bytes@entry=7) at malloc.c:3424
#5  0x00007ffff0ee7f34 in __GI___libc_malloc (bytes=7) at malloc.c:2928
#6  0x00007ffff5df91da in (anonymous namespace)::sg_malloc (size=7) at /home/geektoni/shogun/src/shogun/lib/memory.cpp:205
#7  0x00007ffff584fe19 in (anonymous namespace)::sg_generic_malloc<unsigned char> (len=7) at /home/geektoni/shogun/src/shogun/lib/memory.h:91
#8  0x00007ffff5e032ca in (anonymous namespace)::get_copy (src=0x7ffff6c15de6, len=7) at /home/geektoni/shogun/src/shogun/lib/memory.cpp:446
#9  0x00007ffff5e0333c in (anonymous namespace)::get_strdup (str=0x7ffff6c15de6 "subset")
    at /home/geektoni/shogun/src/shogun/lib/memory.cpp:456
#10 0x00007ffff58471ca in (anonymous namespace)::TParameter::TParameter (this=0x5555557fb9c0, datatype=0x7fffffffd5e0,
    parameter=0x5555557fa410, name=0x7ffff6c15de6 "subset", description=0x7ffff6c15dcd "Vector of subset indices")
    at /home/geektoni/shogun/src/shogun/base/Parameter.cpp:1788
#11 0x00007ffff584a70e in (anonymous namespace)::Parameter::add_type (this=0x5555557f96e0, type=0x7fffffffd5e0, param=0x5555557fa410,
    name=0x7ffff6c15de6 "subset", description=0x7ffff6c15dcd "Vector of subset indices")
    at /home/geektoni/shogun/src/shogun/base/Parameter.cpp:2758
#12 0x00007ffff5843369 in (anonymous namespace)::Parameter::add (this=0x5555557f96e0, param=0x5555557fa3f8, name=0x7ffff6c15de6 "subset",
    description=0x7ffff6c15dcd "Vector of subset indices") at /home/geektoni/shogun/src/shogun/base/Parameter.cpp:692
#13 0x00007ffff5b63d08 in (anonymous namespace)::CSubset::init (this=0x5555557fa380) at /home/geektoni/shogun/src/shogun/features/Subset.cpp:34
#14 0x00007ffff5b63c23 in (anonymous namespace)::CSubset::CSubset (this=0x5555557fa380, subset_idx=<incomplete type>)
    at /home/geektoni/shogun/src/shogun/features/Subset.cpp:23
#15 0x00007ffff5b64446 in (anonymous namespace)::CSubsetStack::add_subset (this=0x5555557ab500, subset=<incomplete type>)
    at /home/geektoni/shogun/src/shogun/features/SubsetStack.cpp:140
#16 0x00007ffff5cb1d36 in (anonymous namespace)::CCustomKernel::add_col_subset (this=0x5555557a9420, subset=<incomplete type>)
    at /home/geektoni/shogun/src/shogun/kernel/CustomKernel.cpp:416
#17 0x00007ffff5ee904a in (anonymous namespace)::CKernelMachine::train_locked (this=0x5555557a23e0, indices=<incomplete type>)
    at /home/geektoni/shogun/src/shogun/machine/KernelMachine.cpp:451
#18 0x00007ffff5a31685 in (anonymous namespace)::CCrossValidation::evaluate_one_run (this=0x5555557a8140, index=0, storage=0x7fffffffdf20)
    at /home/geektoni/shogun/src/shogun/evaluation/CrossValidation.cpp:192
#19 0x00007ffff5a31006 in (anonymous namespace)::CCrossValidation::evaluate (this=0x5555557a8140)
    at /home/geektoni/shogun/src/shogun/evaluation/CrossValidation.cpp:124
#20 0x000055555555a6f6 in test_cross_validation () at evaluation.cpp:102
#21 0x000055555555ab10 in main (argc=1, argv=0x7fffffffe4c8) at evaluation.cpp:133

evaluation.cpp

/*
 * 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 (W) 2012 Heiko Strathmann
 * Copyright (C) 2012 Berlin Institute of Technology and Max-Planck-Society
 */

#include <shogun/base/init.h>
#include <shogun/features/DenseFeatures.h>
#include <shogun/labels/RegressionLabels.h>
#include <shogun/kernel/LinearKernel.h>
#include <shogun/regression/KernelRidgeRegression.h>
#include <shogun/evaluation/CrossValidation.h>
#include <shogun/evaluation/CrossValidationSplitting.h>
#include <shogun/evaluation/MeanSquaredError.h>
#include <shogun/lib/parameter_observers/ParameterObserverCV.h>

using namespace shogun;

void print_message(FILE* target, const char* str)
{
	fprintf(target, "%s", str);
}

void test_cross_validation()
{
#ifdef HAVE_LAPACK
	/* data matrix dimensions */
	index_t num_vectors=100;
	index_t num_features=1;

	/* training label data */
	SGVector<float64_t> lab(num_vectors);

	/* fill data matrix and labels */
	SGMatrix<float64_t> train_dat(num_features, num_vectors);
	SGVector<float64_t>::range_fill_vector(train_dat.matrix, num_vectors);
	for (index_t i=0; i<num_vectors; ++i)
	{
		/* labels are linear plus noise */
		lab.vector[i]=i+CMath::normal_random(0, 1.0);

	}

	/* training features */
	CDenseFeatures<float64_t>* features=
			new CDenseFeatures<float64_t>(train_dat);
	SG_REF(features);

	/* training labels */
	CRegressionLabels* labels=new CRegressionLabels(lab);

	/* kernel */
	CLinearKernel* kernel=new CLinearKernel();
	kernel->init(features, features);

	/* kernel ridge regression*/
	float64_t tau=0.0001;
	CKernelRidgeRegression* krr=new CKernelRidgeRegression(tau, kernel, labels);

	/* evaluation criterion */
	CMeanSquaredError* eval_crit=
			new CMeanSquaredError();

	/* train and output */
	krr->train(features);
	CRegressionLabels* output= CLabelsFactory::to_regression(krr->apply());
	for (index_t i=0; i<num_vectors; ++i)
	{
		SG_SPRINT("x=%f, train=%f, predict=%f\n", train_dat.matrix[i],
				labels->get_label(i), output->get_label(i));
	}

	/* evaluate training error */
	float64_t eval_result=eval_crit->evaluate(output, labels);
	SG_SPRINT("training error: %f\n", eval_result);
	SG_UNREF(output);

	/* assert that regression "works". this is not guaranteed to always work
	 * but should be a really coarse check to see if everything is going
	 * approx. right */
	ASSERT(eval_result<2);

	/* splitting strategy */
	index_t n_folds=5;
	CCrossValidationSplitting* splitting=
			new CCrossValidationSplitting(labels, n_folds);

	/* cross validation instance, 100 runs, 95% confidence interval */
	CCrossValidation* cross=new CCrossValidation(krr, features, labels,
			splitting, eval_crit);
	cross->set_num_runs(100);

	/* Create the parameter observer */
	ParameterObserverCV par; 
	cross->subscribe_to_parameters(&par);

	/* actual evaluation */
	CCrossValidationResult* result=(CCrossValidationResult*)cross->evaluate();

	auto obs = par.get_observations();
	for (auto i : obs)
	{
		//i.get_train_indices().display_vector();
	}

	if (result->get_result_type() != CROSSVALIDATION_RESULT)
		SG_SERROR("Evaluation result is not of type CCrossValidationResult!");

	SG_SPRINT("cross_validation estimate:\n");
	result->print_result();

	cross->list_observable_parameters();

	/* same crude assertion as for above evaluation */
	ASSERT(result->mean<2);

	/* clean up */
	SG_UNREF(result);
	SG_UNREF(cross);
	SG_UNREF(features);
#endif /* HAVE_LAPACK */
}

int main(int argc, char **argv)
{
	init_shogun(&print_message, &print_message, &print_message);

	test_cross_validation();

	exit_shogun();

	return 0;
}