[mlpack-svn] r17096 - in mlpack/trunk/src/mlpack: methods/adaboost tests

fastlab-svn at coffeetalk-1.cc.gatech.edu fastlab-svn at coffeetalk-1.cc.gatech.edu
Fri Aug 22 00:53:14 EDT 2014


Author: saxena.udit
Date: Fri Aug 22 00:53:14 2014
New Revision: 17096

Log:
Style changes. No code changes

Modified:
   mlpack/trunk/src/mlpack/methods/adaboost/adaboost.hpp
   mlpack/trunk/src/mlpack/methods/adaboost/adaboost_impl.hpp
   mlpack/trunk/src/mlpack/methods/adaboost/adaboost_main.cpp
   mlpack/trunk/src/mlpack/tests/adaboost_test.cpp

Modified: mlpack/trunk/src/mlpack/methods/adaboost/adaboost.hpp
==============================================================================
--- mlpack/trunk/src/mlpack/methods/adaboost/adaboost.hpp	(original)
+++ mlpack/trunk/src/mlpack/methods/adaboost/adaboost.hpp	Fri Aug 22 00:53:14 2014
@@ -59,6 +59,12 @@
   // The tolerance for change in rt and when to stop.
   double tolerance;
 
+  /**
+   * Classification Function. 
+   * @param test Testing data.
+   * @param predictedLabels Vector to store the predicted labels of the 
+   *                         test set.
+   */
   void Classify(const MatType& test, arma::Row<size_t>& predictedLabels);
 
 private:

Modified: mlpack/trunk/src/mlpack/methods/adaboost/adaboost_impl.hpp
==============================================================================
--- mlpack/trunk/src/mlpack/methods/adaboost/adaboost_impl.hpp	(original)
+++ mlpack/trunk/src/mlpack/methods/adaboost/adaboost_impl.hpp	Fri Aug 22 00:53:14 2014
@@ -47,8 +47,7 @@
   tolerance = tol;
 
   double rt, crt, alphat = 0.0, zt;
-  // double tolerance = 1e-8;
-  // std::cout<<"Tolerance is "<<tolerance<<"\n";
+  
   // crt is for stopping the iterations when rt
   // stops changing by less than a tolerant value.
 
@@ -56,7 +55,6 @@
   // stops changing by less than a tolerant value.
 
   ztProduct = 1.0;
-  // ztAccumulator is
 
   // To be used for prediction by the Weak Learner for prediction.
   arma::Row<size_t> predictedLabels(labels.n_cols);
@@ -83,7 +81,6 @@
   // now start the boosting rounds
   for (int i = 0; i < iterations; i++)
   {
-    // std::cout<<"Run "<<i<<" times.\n";
     // Initialized to zero in every round.
     // rt is used for calculation of alphat, is the weighted error
     // rt = (sum)D(i)y(i)ht(xi)
@@ -95,12 +92,11 @@
     // Build the weight vectors
     BuildWeightMatrix(D, weights);
 
-    // std::cout<<"Just about to call the weak leaerner. \n";
     // call the other weak learner and train the labels.
     WeakLearner w(other, tempData, weights, labels);
     w.Classify(tempData, predictedLabels);
 
-    //Now from predictedLabels, build ht, the weak hypothesis
+    // Now from predictedLabels, build ht, the weak hypothesis
     // buildClassificationMatrix(ht, predictedLabels);
 
     // Now, start calculation of alpha(t) using ht
@@ -122,8 +118,6 @@
       }
     }
     // end calculation of rt
-    // std::cout<<"Value of rt is: "<<rt<<"\n";
-
 
     if (i > 0)
     {
@@ -187,7 +181,6 @@
   // Iterations are over, now build a strong hypothesis
   // from a weighted combination of these weak hypotheses.
 
-  // std::cout<<"Just about to look at the final hypo.\n";
   arma::colvec tempSumFinalH;
   arma::uword max_index;
   arma::mat sfh = sumFinalH.t();
@@ -202,7 +195,10 @@
 }
 
 /**
- *
+ * Classification Function. 
+ * @param test Testing data.
+ * @param predictedLabels Vector to store the predicted labels of the 
+ *                         test set.
  */
 template <typename MatType, typename WeakLearner>
 void AdaBoost<MatType, WeakLearner>::Classify(
@@ -222,7 +218,7 @@
     for (int j = 0; j < tempPredictedLabels.n_cols; j++)
       cMatrix(tempPredictedLabels(j), j) += (alpha[i] * tempPredictedLabels(j));
   }
-  // std::cout<<"Not working here ?\n";
+  
   arma::colvec cMRow;
   arma::uword max_index;
 
@@ -258,15 +254,6 @@
   }
 }
 
-/*/**
- * Return the value of ztProduct
- */
- /*
-template <typename MatType, typename WeakLearner>
-double GetztProduct()
-{
-  return ztProduct;
-}*/
 } // namespace adaboost
 } // namespace mlpack
 

Modified: mlpack/trunk/src/mlpack/methods/adaboost/adaboost_main.cpp
==============================================================================
--- mlpack/trunk/src/mlpack/methods/adaboost/adaboost_main.cpp	(original)
+++ mlpack/trunk/src/mlpack/methods/adaboost/adaboost_main.cpp	Fri Aug 22 00:53:14 2014
@@ -64,7 +64,6 @@
     " will be written.", "o", "output.csv");
 PARAM_INT("iterations","The maximum number of boosting iterations "
   "to be run", "i", 1000);
-// PARAM_INT("classes","The number of classes in the input label set.","c");
 PARAM_DOUBLE("tolerance","The tolerance for change in values of rt","e",1e-10);
 
 int main(int argc, char *argv[])

Modified: mlpack/trunk/src/mlpack/tests/adaboost_test.cpp
==============================================================================
--- mlpack/trunk/src/mlpack/tests/adaboost_test.cpp	(original)
+++ mlpack/trunk/src/mlpack/tests/adaboost_test.cpp	Fri Aug 22 00:53:14 2014
@@ -573,9 +573,7 @@
 /**
  *  This test case runs the AdaBoost.mh algorithm on the UCI Vertebral 
  *  Column dataset.
- *  It tests the Classify function and checks if the error returned by 
- *  running the boosted weak learner using adaboost is comparable to the 
- *  error returned by running Classify() on the same function.
+ *  It tests the Classify function and checks for a satisfiable error rate.
  */
 BOOST_AUTO_TEST_CASE(ClassifyTest_VERTEBRALCOL)
 {
@@ -620,9 +618,7 @@
 /**
  *  This test case runs the AdaBoost.mh algorithm on a non linearly 
  *  separable dataset.
- *  It tests the Classify function and checks if the error returned by 
- *  running the boosted weak learner using adaboost is comparable to the 
- *  error returned by running Classify() on the same function.
+ *  It tests the Classify function and checks for a satisfiable error rate.
  */
 BOOST_AUTO_TEST_CASE(ClassifyTest_NONLINSEP)
 {
@@ -667,6 +663,12 @@
   BOOST_REQUIRE(lError <= 0.30);
 }
 
+/**
+ *  This test case runs the AdaBoost.mh algorithm on the UCI Iris Dataset.
+ *  It trains it on two thirds of the Iris dataset (iris_train.csv), 
+ *  and tests on the remaining third of the dataset (iris_test.csv). 
+ *  It tests the Classify function and checks for a satisfiable error rate.
+ */
 BOOST_AUTO_TEST_CASE(ClassifyTest_IRIS)
 {
   arma::mat inputData;



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