[mlpack-svn] r16843 - mlpack/trunk/src/mlpack/methods/adaboost
fastlab-svn at coffeetalk-1.cc.gatech.edu
fastlab-svn at coffeetalk-1.cc.gatech.edu
Sun Jul 20 14:02:45 EDT 2014
Author: saxena.udit
Date: Sun Jul 20 14:02:44 2014
New Revision: 16843
Log:
Adaboost.mh implemented
Modified:
mlpack/trunk/src/mlpack/methods/adaboost/CMakeLists.txt
mlpack/trunk/src/mlpack/methods/adaboost/adaboost.hpp
mlpack/trunk/src/mlpack/methods/adaboost/adaboost_impl.hpp
Modified: mlpack/trunk/src/mlpack/methods/adaboost/CMakeLists.txt
==============================================================================
--- mlpack/trunk/src/mlpack/methods/adaboost/CMakeLists.txt (original)
+++ mlpack/trunk/src/mlpack/methods/adaboost/CMakeLists.txt Sun Jul 20 14:02:44 2014
@@ -16,6 +16,7 @@
# the parent scope).
set(MLPACK_SRCS ${MLPACK_SRCS} ${DIR_SRCS} PARENT_SCOPE)
+
add_executable(adaboost
adaboost_main.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 Sun Jul 20 14:02:44 2014
@@ -21,6 +21,11 @@
public:
Adaboost(const MatType& data, const arma::Row<size_t>& labels,
int iterations, size_t classes, const WeakLearner& other);
+
+ void buildClassificationMatrix(arma::mat& t, const arma::Row<size_t>& l);
+
+ void buildWeightMatrix(const arma::mat& D, arma::rowvec& weights);
+
}; // class Adaboost
} // namespace adaboost
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 Sun Jul 20 14:02:44 2014
@@ -2,7 +2,7 @@
* @file adaboost_impl.hpp
* @author Udit Saxena
*
- * Implementation of the AdaBoost class
+ * Implementation of the Adaboost class
*
*/
@@ -13,115 +13,181 @@
namespace mlpack {
namespace adaboost {
-
-//
-// Currently this is an implementation of adaboost.m1
-// which will be templatized later and adaboost.mh and
-// adaboost.samme will be added.
-//
-
+/**
+ * Constructor. Currently runs the Adaboost.mh algorithm
+ *
+ * @param data Input data
+ * @param labels Corresponding labels
+ * @param iterations Number of boosting rounds
+ * @param classes Number of classes in labels
+ * @param other Weak Learner, which has been initialized already
+ */
template<typename MatType, typename WeakLearner>
-Adaboost<MatType, WeakLearner>::Adaboost(const MatType& data, const arma::Row<size_t>& labels,
- int iterations, size_t classes, const WeakLearner& other)
+Adaboost<MatType, WeakLearner>::Adaboost(const MatType& data,
+ const arma::Row<size_t>& labels, int iterations,
+ size_t classes, const WeakLearner& other)
{
- int j, i;
+ // note: put a fail safe for the variable 'classes' or
+ // remove it entirely by using unique function.
+ int i, j, k;
+ double rt, alphat = 0.0, zt;
+
+ // To be used for prediction by the Weak Learner for prediction.
+ arma::Row<size_t> predictedLabels(labels.n_cols);
+
+ // Use tempData to modify input Data for incorporating weights.
+ MatType tempData(data);
- // note: put a fail safe for classes or remove it entirely
- // by using unique function.
+ // Build the classification Matrix yt from labels
+ arma::mat yt(predictedLabels.n_cols, classes);
+
+ // Build a classification matrix of the form D(i,l)
+ // where i is the ith instance
+ // l is the lth class.
+ buildClassificationMatrix(yt, labels);
+
+ // ht(x), to be loaded after a round of prediction every time the weak
+ // learner is run, by using the buildClassificationMatrix function
+ arma::mat ht(predictedLabels.n_cols, classes);
- // load the initial weights
+ // This matrix is a helper matrix used to calculate the final hypothesis.
+ arma::mat sumFinalH(predictedLabels.n_cols, classes);
+ sumFinalH.fill(0.0);
+ // load the initial weights into a 2-D matrix
const double initWeight = 1 / (data.n_cols * classes);
- arma::rowvec D(data.n_cols);
+ arma::mat D(data.n_cols, classes);
D.fill(initWeight);
- size_t countMP; // for counting mispredictions.
- double rt, alphat = 0.0, zt, et;
- arma::Row<size_t> predictedLabels(labels.n_cols);
- MatType tempData(data);
-
- // This behaves as ht(x)
- arma::rowvec mispredict(predictedLabels.n_cols);
+ // Weights are to be compressed into this rowvector
+ // for focussing on the perceptron weights.
+ arma::rowvec weights(predictedLabels.n_cols);
- arma::mat sumFinalH(data.n_cols, classes);
- sumFinalH.fill(0.0);
+ // This is the final hypothesis.
+ arma::rowvec finalH(predictedLabels.n_cols);
- arma::rowvec finalH(labels.n_cols);
// now start the boosting rounds
for (i = 0; i < iterations; i++)
{
- countMP = 0;
- rt = 0.0;
+ // Initialized to zero in every round.
+ rt = 0.0;
zt = 0.0;
+ // Build the weight vectors
+ buildWeightMatrix(D, weights);
+
// call the other weak learner and train the labels.
- WeakLearner w(other, tempData, D, labels);
+ WeakLearner w(other, tempData, weights, labels);
w.Classify(tempData, predictedLabels);
- // Now, start calculation of alpha(t)
+ //Now from predictedLabels, build ht, the weak hypothesis
+ buildClassificationMatrix(ht, predictedLabels);
- // building a helper rowvector, mispredict to help in calculations.
- // this stores the value of Yi(l)*ht(xi,l)
+ // Now, start calculation of alpha(t) using ht
- // first calculate error:
- for(j = 0;j < predictedLabels.n_cols; j++)
- {
- if (predictedLabels(j) != labels(j))
- {
- mispredict(j) = -predictedLabels(j);
- countMP++;
- }
- else
- mispredict(j) = predictedLabels(j);
- }
- et = ((double) countMP / predictedLabels.n_cols);
+ // begin calculation of rt
- if (et < 0.5)
+ for (j = 0;j < ht.n_rows; j++)
{
- // begin calculation of rt
-
- // for (j = 0;j < predictedLabels.n_cols; j++)
- // rt +=(D(j) * mispredict(j));
-
- // end calculation of rt
+ for (k = 0;k < ht.n_cols; k++)
+ rt += (D(j,k) * yt(j,k) * ht(j,k));
+ }
- // alphat = 0.5 * log((1 + rt) / (1 - rt));
+ // end calculation of rt
- alphat = 0.5 * log((1 - et) / et);
+ alphat = 0.5 * log((1 + rt) / (1 - rt));
- // end calculation of alphat
-
- // now start modifying weights
+ // end calculation of alphat
+
+ // now start modifying weights
- for (j = 0;j < mispredict.n_cols; j++)
- {
+ for (j = 0;j < D.n_rows; j++)
+ {
+ for (k = 0;k < D.n_cols; k++)
+ {
// we calculate zt, the normalization constant
- zt += D(j) * exp(-1 * alphat * (mispredict(j) / predictedLabels(j)));
- D(j) = D(j) * exp(-1 * alphat * (mispredict(j) / predictedLabels(j)));
+ zt += D(j,k) * exp(-1 * alphat * yt(j,k) * ht(j,k));
+ D(j,k) = D(j,k) * exp(-1 * alphat * yt(j,k) * ht(j,k));
// adding to the matrix of FinalHypothesis
- if (mispredict(j) == predictedLabels(j)) // if correct prediction
- sumFinalH(j, mispredict(j)) += alphat;
+ sumFinalH(j,k) += (alphat * ht(j,k));
}
- // normalization of D
-
- D = D / zt;
}
+
+ // normalization of D
+
+ D = D / zt;
+
}
- // build a strong hypothesis from a weighted combination of these weak hypotheses.
+ // Iterations are over, now build a strong hypothesis
+ // from a weighted combination of these weak hypotheses.
- // This step of storing it in a temporary row vector can be improved upon.
+ // This step of storing it in a temporary row vector can be improved upon ?
arma::rowvec tempSumFinalH;
-
+ arma::uword max_index;
for (i = 0;i < sumFinalH.n_rows; i++)
{
tempSumFinalH = sumFinalH.row(i);
tempSumFinalH.max(max_index);
finalH(i) = max_index;
}
+
+ //finalH is the final hypothesis.
+}
+
+/**
+ * This function helps in building a classification Matrix which is of
+ * form:
+ * -1 if l is not the correct label
+ * 1 if l is the correct label
+ *
+ * @param t The classification matrix to be built
+ * @param l The labels from which the classification matrix is to be built.
+ */
+template <typename MatType, typename WeakLearner>
+void Adaboost<MatType, WeakLearner>::buildClassificationMatrix(
+ arma::mat& t, const arma::Row<size_t>& l)
+{
+ int i, j;
+
+ for (i = 0;i < t.n_rows; i++)
+ {
+ for (j = 0;j < t.n_cols; j++)
+ {
+ if (j == l(i))
+ t(i,j) = 1.0;
+ else
+ t(i,j) = -1.0;
+ }
+ }
+}
+
+/**
+ * This function helps in building the Weight Distribution matrix
+ * which is updated during every iteration. It calculates the
+ * "difficulty" in classifying a point by adding the weights for all
+ * instances, using D.
+ *
+ * @param D The 2 Dimensional weight matrix from which the weights are
+ * to be calculated.
+ * @param weights The output weight vector.
+ */
+template <typename MatType, typename WeakLearner>
+void Adaboost<MatType, WeakLearner>::buildWeightMatrix(
+ const arma::mat& D, arma::rowvec& weights)
+{
+ int i, j;
+ weights.fill(0.0);
+
+ for (i = 0;i < D.n_rows; i++)
+ {
+ for (j = 0;j < D.n_cols; j++)
+ weights(i) += D(i,j);
+ }
}
} // namespace adaboost
} // namespace mlpack
+
#endif
\ No newline at end of file
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