Merge pull request #618 from karlnapf/master

make examples run faster
shogun-toolbox · Jul 3, 2012 · 343650e · 343650e
2 parents 9bf4f00 + cf13270
commit 343650e
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Showing 4 changed files with 21 additions and 3 deletions.
diff --git a/examples/undocumented/libshogun/statistics_linear_time_mmd.cpp b/examples/undocumented/libshogun/statistics_linear_time_mmd.cpp
@@ -68,6 +68,7 @@ void test_linear_mmd_random()
 	float64_t sigma=2;
 
 	index_t num_runs=100;
+	num_runs=10; //speed up
 	SGVector<float64_t> mmds(num_runs);
 
 	SGMatrix<float64_t> data(dimension, 2*m);
@@ -108,6 +109,7 @@ void test_linear_mmd_variance_estimate()
 	float64_t sigma=2;
 
 	index_t num_runs=100;
+	num_runs=10; //speed up
 	SGVector<float64_t> vars(num_runs);
 
 	SGMatrix<float64_t> data(dimension, 2*m);
@@ -144,6 +146,7 @@ void test_linear_mmd_variance_estimate_vs_bootstrap()
 {
 	index_t dimension=3;
 	index_t m=50000;
+	m=1000; //speed up
 	float64_t difference=0.5;
 	float64_t sigma=2;
 
@@ -156,6 +159,9 @@ void test_linear_mmd_variance_estimate_vs_bootstrap()
 
 	CLinearTimeMMD* mmd=new CLinearTimeMMD(kernel, features, m);
 
+	/* for checking results, set to 100 */
+	mmd->set_bootstrap_iterations(100);
+	mmd->set_bootstrap_iterations(10); // speed up
 	SGVector<float64_t> null_samples=mmd->bootstrap_null();
 	float64_t bootstrap_variance=CStatistics::variance(null_samples);
 	float64_t estimated_variance=mmd->compute_variance_estimate();
@@ -181,6 +187,7 @@ void test_linear_mmd_type2_error()
 	float64_t sigma=2;
 
 	index_t num_runs=500;
+	num_runs=50; // speed up
 	index_t num_errors=0;
 
 	SGMatrix<float64_t> data(dimension, 2*m);
@@ -224,6 +231,10 @@ int main(int argc, char** argv)
 {
 	init_shogun_with_defaults();
 
+	/* all tests have been "speed up" by reducing the number of runs/samples.
+	 * If you have any doubts in the results, set all num_runs to original
+	 * numbers and activate asserts. If they fail, something is wrong.
+	 */
 	test_linear_mmd_fixed();
 	test_linear_mmd_random();
 	test_linear_mmd_variance_estimate();

diff --git a/examples/undocumented/libshogun/statistics_quadratic_time_mmd.cpp b/examples/undocumented/libshogun/statistics_quadratic_time_mmd.cpp
@@ -71,6 +71,7 @@ void test_quadratic_mmd_bootstrap()
 	float64_t difference=0.5;
 	float64_t sigma=2;
 	index_t num_iterations=1000;
+	num_iterations=10; //speed up
 
 	SGMatrix<float64_t> data(dimension, 2*m);
 	create_mean_data(data, difference);
@@ -121,6 +122,7 @@ void test_quadratic_mmd_spectrum()
 	CQuadraticTimeMMD* mmd=new CQuadraticTimeMMD(kernel, features, m);
 
 	mmd->set_num_samples_sepctrum(1000);
+	mmd->set_num_samples_sepctrum(10); //speed up
 	mmd->set_num_eigenvalues_spectrum(m);
 	mmd->set_null_approximation_method(MMD2_SPECTRUM);
 	mmd->set_statistic_type(BIASED);
@@ -179,6 +181,7 @@ void test_quadratic_mmd_random()
 	float64_t sigma=2;
 
 	index_t num_runs=100;
+	num_runs=10; //speed up
 	SGVector<float64_t> mmds(num_runs);
 
 	SGMatrix<float64_t> data(dimension, 2*m);
@@ -211,6 +214,10 @@ int main(int argc, char** argv)
 {
 	init_shogun_with_defaults();
 
+	/* all tests have been "speed up" by reducing the number of runs/samples.
+	 * If you have any doubts in the results, set all num_runs to original
+	 * numbers and activate asserts. If they fail, something is wrong. */
+
 	test_quadratic_mmd_fixed();
 	test_quadratic_mmd_random();
 	test_quadratic_mmd_bootstrap();

diff --git a/examples/undocumented/python_modular/statistics_linear_time_mmd.py b/examples/undocumented/python_modular/statistics_linear_time_mmd.py
@@ -64,7 +64,7 @@ def statistics_linear_time_mmd():
 
 	print "computing p-value using bootstrapping"
 	mmd.set_null_approximation_method(BOOTSTRAP)
-	mmd.set_bootstrap_iterations(500)
+	mmd.set_bootstrap_iterations(50) # normally, far more iterations are needed
 	p_value=mmd.compute_p_value(statistic)
 	print "p_value:", p_value
 	print "p_value <", alpha, ", i.e. test sais p!=q:", p_value<alpha
@@ -78,7 +78,7 @@ def statistics_linear_time_mmd():
 	# sample from null distribution (these may be plotted or whatsoever)
 	# mean should be close to zero, variance stronly depends on data/kernel
 	mmd.set_null_approximation_method(BOOTSTRAP)
-	mmd.set_bootstrap_iterations(100)
+	mmd.set_bootstrap_iterations(10) # normally, far more iterations are needed
 	null_samples=mmd.bootstrap_null()
 	print "null mean:", mean(null_samples)
 	print "null variance:", var(null_samples)

diff --git a/examples/undocumented/python_modular/statistics_quadratic_time_mmd.py b/examples/undocumented/python_modular/statistics_quadratic_time_mmd.py
@@ -96,7 +96,7 @@ def statistics_linear_time_mmd():
 	mmd.set_null_approximation_method(MMD2_SPECTRUM)
 	mmd.set_statistic_type(BIASED)
 	# 200 samples using 100 eigenvalues
-	null_samples=mmd.sample_null_spectrum(200,100)
+	null_samples=mmd.sample_null_spectrum(50,10)
 	print "null mean:", mean(null_samples)
 	print "null variance:", var(null_samples)