[mlpack-git] master: Update API, and add tests for retraining. (516ef59)

[gitdub at big.cc.gt.atl.ga.us]

Repository : https://github.com/mlpack/mlpack

On branch  : master
Link       : https://github.com/mlpack/mlpack/compare/5858f46c3113651f598596bade89c1b838410652...c829fc1a2415f3dddb672431bb51ff05cbc40a76

>---------------------------------------------------------------

commit 516ef599298675ec4932353090568de77605293b
Author: ryan <ryan at ratml.org>
Date:   Thu Dec 3 10:49:20 2015 -0500

    Update API, and add tests for retraining.


>---------------------------------------------------------------

516ef599298675ec4932353090568de77605293b
 src/mlpack/tests/lars_test.cpp | 70 +++++++++++++++++++++++++++++++++++-------
 1 file changed, 59 insertions(+), 11 deletions(-)

diff --git a/src/mlpack/tests/lars_test.cpp b/src/mlpack/tests/lars_test.cpp
index 99a7155..83bd0a8 100644
--- a/src/mlpack/tests/lars_test.cpp
+++ b/src/mlpack/tests/lars_test.cpp
@@ -24,11 +24,10 @@ void GenerateProblem(arma::mat& X, arma::vec& y, size_t nPoints, size_t nDims)
   y = trans(X) * beta;
 }
 
-
 void LARSVerifyCorrectness(arma::vec beta, arma::vec errCorr, double lambda)
 {
   size_t nDims = beta.n_elem;
-  const double tol = 1e-12;
+  const double tol = 1e-10;
   for(size_t j = 0; j < nDims; j++)
   {
     if (beta(j) == 0)
@@ -49,7 +48,6 @@ void LARSVerifyCorrectness(arma::vec beta, arma::vec errCorr, double lambda)
   }
 }
 
-
 void LassoTest(size_t nPoints, size_t nDims, bool elasticNet, bool useCholesky)
 {
   arma::mat X;
@@ -71,7 +69,7 @@ void LassoTest(size_t nPoints, size_t nDims, bool elasticNet, bool useCholesky)
 
     LARS lars(useCholesky, lambda1, lambda2);
     arma::vec betaOpt;
-    lars.Regress(X, y, betaOpt);
+    lars.Train(X, y, betaOpt);
 
     arma::vec errCorr = (X * trans(X) + lambda2 *
         arma::eye(nDims, nDims)) * betaOpt - X * y;
@@ -80,7 +78,6 @@ void LassoTest(size_t nPoints, size_t nDims, bool elasticNet, bool useCholesky)
   }
 }
 
-
 BOOST_AUTO_TEST_CASE(LARSTestLassoCholesky)
 {
   LassoTest(100, 10, false, true);
@@ -92,13 +89,11 @@ BOOST_AUTO_TEST_CASE(LARSTestLassoGram)
   LassoTest(100, 10, false, false);
 }
 
-
 BOOST_AUTO_TEST_CASE(LARSTestElasticNetCholesky)
 {
   LassoTest(100, 10, true, true);
 }
 
-
 BOOST_AUTO_TEST_CASE(LARSTestElasticNetGram)
 {
   LassoTest(100, 10, true, false);
@@ -122,7 +117,7 @@ BOOST_AUTO_TEST_CASE(CholeskySingularityTest)
   {
     LARS lars(true, lambda1, 0.0);
     arma::vec betaOpt;
-    lars.Regress(X, y, betaOpt);
+    lars.Train(X, y, betaOpt);
 
     arma::vec errCorr = (X * X.t()) * betaOpt - X * y;
 
@@ -146,7 +141,7 @@ BOOST_AUTO_TEST_CASE(NoCholeskySingularityTest)
   {
     LARS lars(false, lambda1, 0.0);
     arma::vec betaOpt;
-    lars.Regress(X, y, betaOpt);
+    lars.Train(X, y, betaOpt);
 
     arma::vec errCorr = (X * X.t()) * betaOpt - X * y;
 
@@ -174,7 +169,7 @@ BOOST_AUTO_TEST_CASE(PredictTest)
       {
         LARS lars(useCholesky, lambda1, lambda2);
         arma::vec betaOpt;
-        lars.Regress(X, y, betaOpt);
+        lars.Train(X, y, betaOpt);
 
         // Calculate what the actual error should be with these regression
         // parameters.
@@ -206,7 +201,7 @@ BOOST_AUTO_TEST_CASE(PredictRowMajorTest)
 
   LARS lars(false, 0, 0);
   arma::vec betaOpt;
-  lars.Regress(X, y, betaOpt);
+  lars.Train(X, y, betaOpt);
 
   // Get both row-major and column-major predictions.  Make sure they are the
   // same.
@@ -225,4 +220,57 @@ BOOST_AUTO_TEST_CASE(PredictRowMajorTest)
   }
 }
 
+/**
+ * Make sure that if we train twice, there is no issue.
+ */
+BOOST_AUTO_TEST_CASE(RetrainTest)
+{
+  arma::mat origX;
+  arma::vec origY;
+  GenerateProblem(origX, origY, 1000, 50);
+
+  arma::mat newX;
+  arma::vec newY;
+  GenerateProblem(newX, newY, 750, 75);
+
+  LARS lars(false, 0.1, 0.1);
+  arma::vec betaOpt;
+  lars.Train(origX, origY, betaOpt);
+
+  // Now train on new data.
+  lars.Train(newX, newY, betaOpt);
+
+  arma::vec errCorr = (newX * trans(newX) + 0.1 *
+        arma::eye(75, 75)) * betaOpt - newX * newY;
+
+  LARSVerifyCorrectness(betaOpt, errCorr, 0.1);
+}
+
+/**
+ * Make sure if we train twice using the Cholesky decomposition, there is no
+ * issue.
+ */
+BOOST_AUTO_TEST_CASE(RetrainCholeskyTest)
+{
+  arma::mat origX;
+  arma::vec origY;
+  GenerateProblem(origX, origY, 1000, 50);
+
+  arma::mat newX;
+  arma::vec newY;
+  GenerateProblem(newX, newY, 750, 75);
+
+  LARS lars(true, 0.1, 0.1);
+  arma::vec betaOpt;
+  lars.Train(origX, origY, betaOpt);
+
+  // Now train on new data.
+  lars.Train(newX, newY, betaOpt);
+
+  arma::vec errCorr = (newX * trans(newX) + 0.1 *
+        arma::eye(75, 75)) * betaOpt - newX * newY;
+
+  LARSVerifyCorrectness(betaOpt, errCorr, 0.1);
+}
+
 BOOST_AUTO_TEST_SUITE_END();