[mlpack-svn] r13359 - mlpack/trunk/src/mlpack/tests

[fastlab-svn at coffeetalk-1.cc.gatech.edu]

Author: rmohan
Date: 2012-08-07 14:55:30 -0400 (Tue, 07 Aug 2012)
New Revision: 13359

Modified:
   mlpack/trunk/src/mlpack/tests/nmf_test.cpp
Log:
Fixed NMF Test

Modified: mlpack/trunk/src/mlpack/tests/nmf_test.cpp
===================================================================
--- mlpack/trunk/src/mlpack/tests/nmf_test.cpp	2012-08-07 13:53:53 UTC (rev 13358)
+++ mlpack/trunk/src/mlpack/tests/nmf_test.cpp	2012-08-07 18:55:30 UTC (rev 13359)
@@ -6,6 +6,9 @@
  */
 #include <mlpack/core.hpp>
 #include <mlpack/methods/nmf/nmf.hpp>
+#include <mlpack/methods/nmf/random_acol_init.hpp>
+#include <mlpack/methods/nmf/mult_div_update_rules.hpp>
+#include <mlpack/methods/nmf/als_update_rules.hpp>
 
 #include <boost/test/unit_test.hpp>
 #include "old_boost_test_definitions.hpp"
@@ -19,26 +22,90 @@
 
 /**
  * Check the if the product of the calculated factorization is close to the
- * input matrix.
+ * input matrix. Default case
  */
-BOOST_AUTO_TEST_CASE(NMFTest)
+BOOST_AUTO_TEST_CASE(NMFDefaultTest)
 {
-  mat v = randu<mat>(5, 5);
-  size_t r = 4;
-  mat w, h;
+  mat w = randu<mat>(20, 16);
+  mat h = randu<mat>(16, 20);
+  mat v = w * h;
+  size_t r = 16;
 
-  NMF<> nmf(0);
+  NMF<> nmf;
   nmf.Apply(v, r, w, h);
 
   mat wh = w * h;
 
-  v.print("v");
-  wh.print("wh");
+  for (size_t row = 0; row < 5; row++)
+    for (size_t col = 0; col < 5; col++)
+      BOOST_REQUIRE_CLOSE(v(row, col), wh(row, col), 10.0);
+}
 
-//  for (size_t row = 0; row < 5; row++)
-//    for (size_t col = 0; col < 5; col++)
-//      BOOST_REQUIRE_CLOSE(v(row, col), wh(row, col), 5.0);
+/**
+ * Check the if the product of the calculated factorization is close to the
+ * input matrix. Random Acol Initialization Distance Minimization Update
+ */
+BOOST_AUTO_TEST_CASE(NMFAcolDistTest)
+{
+  mat w = randu<mat>(20, 16);
+  mat h = randu<mat>(16, 20);
+  mat v = w * h;
+  size_t r = 16;
+
+  NMF<RandomAcolInitialization> nmf;
+  nmf.Apply(v, r, w, h);
+
+  mat wh = w * h;
+
+  for (size_t row = 0; row < 5; row++)
+    for (size_t col = 0; col < 5; col++)
+      BOOST_REQUIRE_CLOSE(v(row, col), wh(row, col), 10.0);
 }
 
+/**
+ * Check the if the product of the calculated factorization is close to the
+ * input matrix. Random Initialization Divergence Minimization Update
+ */
+BOOST_AUTO_TEST_CASE(NMFRandomDivTest)
+{
+  mat w = randu<mat>(20, 16);
+  mat h = randu<mat>(16, 20);
+  mat v = w * h;
+  size_t r = 16;
 
+  NMF<RandomInitialization, 
+      WMultiplicativeDivergenceRule,
+      HMultiplicativeDivergenceRule> nmf;
+  nmf.Apply(v, r, w, h);
+
+  mat wh = w * h;
+
+  for (size_t row = 0; row < 5; row++)
+    for (size_t col = 0; col < 5; col++)
+      BOOST_REQUIRE_CLOSE(v(row, col), wh(row, col), 10.0);
+}
+
+/**
+ * Check the if the product of the calculated factorization is close to the
+ * input matrix. Random Initialization Alternating Least Squares Update
+ */
+BOOST_AUTO_TEST_CASE(NMFALSTest)
+{
+  mat w = randu<mat>(20, 16);
+  mat h = randu<mat>(16, 20);
+  mat v = w * h;
+  size_t r = 16;
+
+  NMF<RandomInitialization, 
+      WAlternatingLeastSquaresRule,
+      HAlternatingLeastSquaresRule> nmf;
+  nmf.Apply(v, r, w, h);
+
+  mat wh = w * h;
+
+  for (size_t row = 0; row < 5; row++)
+    for (size_t col = 0; col < 5; col++)
+      BOOST_REQUIRE_CLOSE(v(row, col), wh(row, col), 10.0);
+}
+
 BOOST_AUTO_TEST_SUITE_END();