Use linalg in GMM (shogun-toolbox#2747)

src/shogun/clustering/GMM.cpp

@@ -9,15 +9,12 @@
  */
 #include <shogun/lib/config.h>
 
-#ifdef HAVE_LAPACK
-
 #include <shogun/base/Parameter.h>
 #include <shogun/clustering/GMM.h>
 #include <shogun/clustering/KMeans.h>
 #include <shogun/distance/EuclideanDistance.h>
 #include <shogun/labels/MulticlassLabels.h>
 #include <shogun/mathematics/Math.h>
-#include <shogun/mathematics/lapack.h>
 #include <shogun/mathematics/linalg/LinalgNamespace.h>
 #include <shogun/multiclass/KNN.h>
 #include <vector>
@@ -585,11 +582,8 @@ void CGMM::max_likelihood(SGMatrix<float64_t> alpha, float64_t min_cov)
 			switch (cov_type)
 			{
 				case FULL:
-				    cblas_dger(
-				        CblasRowMajor, num_dim, num_dim,
-				        alpha.matrix[j * alpha.num_cols + i], v.vector, 1,
-				        v.vector, 1, (double*)cov_sum.matrix, num_dim);
-
+				    linalg::dger(
+				        alpha.matrix[j * alpha.num_cols + i], v, v, cov_sum);
 				    break;
 			    case DIAG:
 			    {
@@ -827,4 +821,3 @@ void CGMM::register_params()
 	m_parameters->add(&m_coefficients, "m_coefficients", "Mixture coefficients.");
 }
 
-#endif

src/shogun/clustering/GMM.h

@@ -12,8 +12,6 @@
 
 #include <shogun/lib/config.h>
 
-#ifdef HAVE_LAPACK
-
 #include <shogun/distributions/Distribution.h>
 #include <shogun/distributions/Gaussian.h>
 #include <shogun/lib/common.h>
@@ -249,5 +247,4 @@ class CGMM : public CDistribution
 		SGVector<float64_t> m_coefficients;
 };
 }
-#endif //HAVE_LAPACK
 #endif //_GMM_H__

tests/unit/clustering/gmm_unittest.cc

@@ -5,55 +5,60 @@
  * Redistribution and use in source and binary forms, with or without
  * modification, are permitted provided that the following conditions are met:
  *
- * 1. Redistributions of source code must retain the above copyright notice, this
+ * 1. Redistributions of source code must retain the above copyright notice,
+ * this
  *    list of conditions and the following disclaimer.
  * 2. Redistributions in binary form must reproduce the above copyright notice,
  *    this list of conditions and the following disclaimer in the documentation
  *    and/or other materials provided with the distribution.
  *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+ * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+ * AND
  * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
  * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
- * ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+ * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE
+ * FOR
+ * ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+ * DAMAGES
  * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
  * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
  * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
  * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
  * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
  *
- * The views and conclusions contained in the software and documentation are those
- * of the authors and should not be interpreted as representing official policies,
+ * The views and conclusions contained in the software and documentation are
+ * those
+ * of the authors and should not be interpreted as representing official
+ * policies,
  * either expressed or implied, of the Shogun Development Team.
  *
- * Authors: 2018 Wuwei Lin
+ * Written (W) 2018 Wuwei Lin
  */
 
-#include <shogun/lib/common.h>
-#include <shogun/features/DenseFeatures.h>
-#include <shogun/clustering/GMM.h>
 #include <gtest/gtest.h>
+#include <shogun/base/some.h>
+#include <shogun/clustering/GMM.h>
+#include <shogun/features/DenseFeatures.h>
+#include <shogun/lib/common.h>
 
 using namespace shogun;
 
 TEST(GMM, train_em_full_cov)
 {
 	/*create a rectangle with four points as (0,0) (1,7) (10,6) (4,4) */
 	SGMatrix<float64_t> rect(2, 4);
-	rect(0,0)=0;
-	rect(0,1)=1;
-	rect(0,2)=10;
-	rect(0,3)=4;
-	rect(1,0)=0;
-	rect(1,1)=7;
-	rect(1,2)=6;
-	rect(1,3)=4;
+	rect(0, 0) = 0;
+	rect(0, 1) = 1;
+	rect(0, 2) = 10;
+	rect(0, 3) = 4;
+	rect(1, 0) = 0;
+	rect(1, 1) = 7;
+	rect(1, 2) = 6;
+	rect(1, 3) = 4;
 
 	const int num_components = 2;
-	CGMM *clustering = new CGMM(num_components, FULL);
-	CDenseFeatures<float64_t>* features=new CDenseFeatures<float64_t>(rect);
-	SG_REF(clustering);
-	SG_REF(features);
+	auto clustering = some<CGMM>(num_components, FULL);
+	auto features = some<CDenseFeatures<float64_t> >(rect);
 
 	clustering->train(features);
 	clustering->train_em();
@@ -72,14 +77,11 @@ TEST(GMM, train_em_full_cov)
 	EXPECT_NEAR(mean[1], 7.0, 1e-10);
 
 	SGVector<float64_t> cov = clustering->get_nth_cov(0);
-	EXPECT_NEAR(cov[0], 16.88888888888, 1e-10);	
-	EXPECT_NEAR(cov[1], 9.77777777777, 1e-10);	
+	EXPECT_NEAR(cov[0], 16.88888888888, 1e-10);
+	EXPECT_NEAR(cov[1], 9.77777777777, 1e-10);
 
 	cov = clustering->get_nth_cov(1);
-	EXPECT_NEAR(cov[0], 0.000000001, 1e-10);	
-	EXPECT_NEAR(cov[1], 0.0, 1e-10);	
+	EXPECT_NEAR(cov[0], 0.000000001, 1e-10);
+	EXPECT_NEAR(cov[1], 0.0, 1e-10);
 
-	SG_UNREF(clustering);
-	SG_UNREF(features);
 }
-