[mlpack-svn] r16700 - mlpack/trunk/src/mlpack/methods/decision_stump
fastlab-svn at coffeetalk-1.cc.gatech.edu
fastlab-svn at coffeetalk-1.cc.gatech.edu
Mon Jun 23 20:03:25 EDT 2014
Author: rcurtin
Date: Mon Jun 23 20:03:24 2014
New Revision: 16700
Log:
First pass: comment standardization, fix header guard names, move .cpp to .hpp
because it's all templated functions.
Added:
mlpack/trunk/src/mlpack/methods/decision_stump/decision_stump_impl.hpp
- copied, changed from r16694, /mlpack/trunk/src/mlpack/methods/decision_stump/decision_stump_impl.cpp
Removed:
mlpack/trunk/src/mlpack/methods/decision_stump/decision_stump_impl.cpp
Modified:
mlpack/trunk/src/mlpack/methods/decision_stump/CMakeLists.txt
mlpack/trunk/src/mlpack/methods/decision_stump/decision_stump.hpp
mlpack/trunk/src/mlpack/methods/decision_stump/decision_stump_main.cpp
Modified: mlpack/trunk/src/mlpack/methods/decision_stump/CMakeLists.txt
==============================================================================
--- mlpack/trunk/src/mlpack/methods/decision_stump/CMakeLists.txt (original)
+++ mlpack/trunk/src/mlpack/methods/decision_stump/CMakeLists.txt Mon Jun 23 20:03:24 2014
@@ -4,7 +4,7 @@
# Anything not in this list will not be compiled into MLPACK.
set(SOURCES
decision_stump.hpp
- decision_stump_impl.cpp
+ decision_stump_impl.hpp
)
# Add directory name to sources.
Modified: mlpack/trunk/src/mlpack/methods/decision_stump/decision_stump.hpp
==============================================================================
--- mlpack/trunk/src/mlpack/methods/decision_stump/decision_stump.hpp (original)
+++ mlpack/trunk/src/mlpack/methods/decision_stump/decision_stump.hpp Mon Jun 23 20:03:24 2014
@@ -1,127 +1,124 @@
/**
* @file decision_stump.hpp
* @author Udit Saxena
- *
- * Defintion of decision stumps.
+ *
+ * Definition of decision stumps.
*/
-
-#ifndef _MLPACK_METHODS_DECISION_STUMP_HPP
-#define _MLPACK_METHODS_DECISION_STUMP_HPP
+#ifndef __MLPACK_METHODS_DECISION_STUMP_DECISION_STUMP_HPP
+#define __MLPACK_METHODS_DECISION_STUMP_DECISION_STUMP_HPP
#include <mlpack/core.hpp>
namespace mlpack {
namespace decision_stump {
-/*
+
+/**
* This class implements a decision stump. It constructs a single level
- * decision tree, i.e. a decision stump. It uses entropy to decided splitting
+ * decision tree, i.e., a decision stump. It uses entropy to decide splitting
* ranges.
- *
*/
template <typename MatType = arma::mat>
class DecisionStump
{
public:
- /*
- Constructor. Train on the provided data. Generate a decision stump
- from data.
-
- @param: data - Input, training data.
- @param: labels - Labels of data.
- @param: classes - number of distinct classes in labels.
- @param: inpBucketSize - minimum size of bucket when splitting.
+ /**
+ * Constructor. Train on the provided data. Generate a decision stump from
+ * data.
+ *
+ * @param data Input, training data.
+ * @param labels Labels of training data.
+ * @param classes Number of distinct classes in labels.
+ * @param inpBucketSize Minimum size of bucket when splitting.
*/
DecisionStump(const MatType& data,
const arma::Row<size_t>& labels,
- const size_t classes,
+ const size_t classes,
size_t inpBucketSize);
- /*
- Classification function. After training, classify test, and put the
- predicted classes in predictedLabels.
-
- @param: test - testing data or data to classify.
- @param: predictedLabels - vector to store the predicted classes after
- classifying test
+ /**
+ * Classification function. After training, classify test, and put the
+ * predicted classes in predictedLabels.
+ *
+ * @param test Testing data or data to classify.
+ * @param predictedLabels Vector to store the predicted classes after
+ * classifying test data.
*/
void Classify(const MatType& test, arma::Row<size_t>& predictedLabels);
private:
- /* Stores the number of classes.*/
- size_t numClass;
-
- /* Stores the default class. Provided for handling missing attribute values.*/
+ //! Stores the number of classes.
+ size_t numClass;
+
+ //! Stores the default class. Provided for handling missing attribute values.
size_t defaultClass;
-
- /* Stores the value of the attribute on which to split.*/
+
+ //! Stores the value of the attribute on which to split.
int splitCol;
-
- /* Flag value for distinct input class labels.*/
- int oneClass;
-
- /* Size of bucket while determining splitting criterion.*/
+
+ //! Flag value for distinct input class labels.
+ int oneClass;
+
+ //! Size of bucket while determining splitting criterion.
size_t bucketSize;
-
- /* Stores the class labels for the input data*/
+
+ //! Stores the class labels for the input data.
arma::Row<size_t> classLabels;
-
- /* Stores the splitting criterion after training.*/
+
+ //! Stores the splitting criterion after training.
arma::mat split;
-
- /*
- Sets up attribute as if it were splitting on it and
- finds entropy when splitting on attribute.
-
- @param: attribute - a row from the training data, which might be a
- candidate for the splitting attribute.
- */
- double SetupSplitAttribute(const arma::rowvec& attribute);
- /*
- After having decided the attribute on which to split,
- train on that attribute.
+ /**
+ * Sets up attribute as if it were splitting on it and finds entropy when
+ * splitting on attribute.
+ *
+ * @param attribute A row from the training data, which might be a
+ * candidate for the splitting attribute.
+ */
+ double SetupSplitAttribute(const arma::rowvec& attribute);
- @param: attribute - attribute is the attribute decided by the constructor
- on which we now train the decision stump.
+ /**
+ * After having decided the attribute on which to split, train on that
+ * attribute.
+ *
+ * @param attribute attribute is the attribute decided by the constructor
+ * on which we now train the decision stump.
*/
template <typename rType> void TrainOnAtt(const arma::rowvec& attribute);
- /* After the "split" matrix has been set up,
- merging ranges with identical class labels.
+ /**
+ * After the "split" matrix has been set up, merge ranges with identical class
+ * labels.
*/
void MergeRanges();
- /*
- Used to count the most frequently occurring element in subCols.
-
- @param: subCols - the vector in which to find the most frequently
- occurring element.
+ /**
+ * Count the most frequently occurring element in subCols.
+ *
+ * @param subCols The vector in which to find the most frequently
+ * occurring element.
*/
template <typename rType> rType CountMostFreq(const arma::Row<rType>& subCols);
-
- /*
- Returns 1 if all the values of featureRow are not same.
-
- @param: featureRow - the attribute which is checked so that it
- does not have identical values.
- */
- template <typename rType> int isDistinct(const arma::Row<rType>& featureRow);
- /*
- Calculating Entropy of attribute.
+ /**
+ * Returns 1 if all the values of featureRow are not same.
+ *
+ * @param featureRow The attribute which is checked for identical values.
+ */
+ template <typename rType> int isDistinct(const arma::Row<rType>& featureRow);
- @param: attribute - the attribute of which we calculate the entropy.
- @param: labels - corresponding labels of the attribute.
- */
- double CalculateEntropy(const arma::rowvec& attribute,
+ /**
+ * Calculate the entropy of the given attribute.
+ *
+ * @param attribute The attribute of which we calculate the entropy.
+ * @param labels Corresponding labels of the attribute.
+ */
+ double CalculateEntropy(const arma::rowvec& attribute,
const arma::rowvec& labels);
-
-
};
-}; //namespace decision_stump
-}; //namespace mlpack
+}; // namespace decision_stump
+}; // namespace mlpack
-#include "decision_stump_impl.cpp"
+#include "decision_stump_impl.hpp"
-#endif
\ No newline at end of file
+#endif
Copied: mlpack/trunk/src/mlpack/methods/decision_stump/decision_stump_impl.hpp (from r16694, /mlpack/trunk/src/mlpack/methods/decision_stump/decision_stump_impl.cpp)
==============================================================================
--- /mlpack/trunk/src/mlpack/methods/decision_stump/decision_stump_impl.cpp (original)
+++ mlpack/trunk/src/mlpack/methods/decision_stump/decision_stump_impl.hpp Mon Jun 23 20:03:24 2014
@@ -1,11 +1,14 @@
/**
* @file decision_stump_impl.hpp
* @author Udit Saxena
-**/
+ *
+ * Implementation of DecisionStump class.
+ */
-#ifndef _MLPACK_METHODS_DECISION_STUMP_IMPL_HPP
-#define _MLPACK_METHODS_DECISION_STUMP_IMPL_HPP
+#ifndef __MLPACK_METHODS_DECISION_STUMP_DECISION_STUMP_IMPL_HPP
+#define __MLPACK_METHODS_DECISION_STUMP_DECISION_STUMP_IMPL_HPP
+// In case it hasn't been included yet.
#include "decision_stump.hpp"
#include <set>
@@ -13,14 +16,14 @@
namespace mlpack {
namespace decision_stump {
-/*
- Constructor. Train on the provided data. Generate a decision stump
- from data.
-
- @param: data - Input, training data.
- @param: labels - Labels of data.
- @param: classes - number of distinct classes in labels.
- @param: inpBucketSize - minimum size of bucket when splitting.
+
+/**
+ * Constructor. Train on the provided data. Generate a decision stump from data.
+ *
+ * @param data Input, training data.
+ * @param labels Labels of data.
+ * @param classes Number of distinct classes in labels.
+ * @param inpBucketSize Minimum size of bucket when splitting.
*/
template<typename MatType>
DecisionStump<MatType>::DecisionStump(const MatType& data,
@@ -31,99 +34,95 @@
arma::Row<size_t> zLabels(labels.n_elem);
zLabels.fill(0);
classLabels = labels + zLabels;
-
+
numClass = classes;
bucketSize = inpBucketSize;
/* Check whether the input labels are not all identical. */
- if ( !isDistinct<size_t>(classLabels) )
+ if (!isDistinct<size_t>(classLabels))
{
- // If the classLabels are all identical,
- // the default class is the only class set.
+ // If the classLabels are all identical, the default class is the only
+ // class.
oneClass = 1;
- defaultClass = classLabels(0);
+ defaultClass = classLabels(0);
}
else
{
- // If classLabels are not all identical
- // proceed for training
-
+ // If classLabels are not all identical, proceed with training.
oneClass = 0;
- int bestAtt=-1,i;
- double entropy,bestEntropy=DBL_MAX;
+ int bestAtt = -1;
+ double entropy;
+ double bestEntropy = DBL_MAX;
- // Set the default class to handle attribute values which are
- // not present in the training data.
+ // Set the default class to handle attribute values which are not present in
+ // the training data.
defaultClass = CountMostFreq<size_t>(classLabels);
- for (i = 0;i < data.n_rows; i++)
+ for (int i = 0; i < data.n_rows; i++)
{
- // going through each attribute of data.
+ // Go through each attribute of the data.
if (isDistinct<double>(data.row(i)))
{
- // for each attribute with non-identical values,
- // treat it as a potential splitting attribute
- // and calculate entropy if split on it.
- entropy=SetupSplitAttribute(data.row(i));
-
- // finding the attribute with the bestEntropy
- // so that the gain is max.
+ // For each attribute with non-identical values, treat it as a potential
+ // splitting attribute and calculate entropy if split on it.
+ entropy = SetupSplitAttribute(data.row(i));
+
+ // Find the attribute with the bestEntropy so that the gain is
+ // maximized.
if (entropy < bestEntropy)
{
bestAtt = i;
bestEntropy = entropy;
}
-
}
}
splitCol = bestAtt;
- // once the splitting column/attribute has been decided,
- // train on it.
+ // Once the splitting column/attribute has been decided, train on it.
TrainOnAtt<double>(data.row(splitCol));
}
}
-/*
- Classification function. After training, classify test, and put the
- predicted classes in predictedLabels.
-
- @param: test - testing data or data to classify.
- @param: predictedLabels - vector to store the predicted classes after
- classifying test
+/**
+ * Classification function. After training, classify test, and put the predicted
+ * classes in predictedLabels.
+ *
+ * @param test Testing data or data to classify.
+ * @param predictedLabels Vector to store the predicted classes after
+ * classifying test
*/
template<typename MatType>
void DecisionStump<MatType>::Classify(const MatType& test,
arma::Row<size_t>& predictedLabels)
{
- int i,j,flag;
+ int flag;
double val;
- if ( !oneClass )
+ if (!oneClass)
{
- for (i = 0; i < test.n_cols; i++)
+ for (int i = 0; i < test.n_cols; i++)
{
- j = 0;
+ int j = 0;
flag = 0;
val = test(splitCol,i);
while ((j < split.n_rows) && (!flag))
{
- if(val < split(j,0) && (!j))
+ if (val < split(j, 0) && (!j))
{
- predictedLabels(i) = split(0,1);
+ predictedLabels(i) = split(0, 1);
flag = 1;
}
- else if (val >= split(j,0))
+ else if (val >= split(j, 0))
{
- if(j == split.n_rows - 1)
+ if (j == split.n_rows - 1)
{
predictedLabels(i) = split(split.n_rows - 1, 1);
flag = 1;
}
- else if (val < split(j+1,0))
+ else if (val < split(j + 1, 0))
{
- predictedLabels(i) = split(j,1);
+ predictedLabels(i) = split(j, 1);
flag = 1;
}
}
@@ -133,18 +132,17 @@
}
else
{
- for (i = 0;i < test.n_cols;i++)
- predictedLabels(i)=defaultClass;
+ for (int i = 0; i < test.n_cols; i++)
+ predictedLabels(i) = defaultClass;
}
-
}
-/*
- Sets up attribute as if it were splitting on it and
- finds entropy when splitting on attribute.
-
- @param: attribute - a row from the training data, which might be a
- candidate for the splitting attribute.
+/**
+ * Sets up attribute as if it were splitting on it and finds entropy when
+ * splitting on attribute.
+ *
+ * @param attribute A row from the training data, which might be a candidate for
+ * the splitting attribute.
*/
template <typename MatType>
double DecisionStump<MatType>::SetupSplitAttribute(const arma::rowvec& attribute)
@@ -152,18 +150,16 @@
int i, count, begin, end;
double entropy = 0.0;
- // sorting the attribute, for calculating splitting ranges
+ // Sort the attribute in order to calculate splitting ranges.
arma::rowvec sortedAtt = arma::sort(attribute);
- // storing the indexes of the sorted attribute to build
- // a vector of sorted labels.
- // this sort is stable.
+ // Store the indices of the sorted attribute to build a vector of sorted
+ // labels. This sort is stable.
arma::uvec sortedIndexAtt = arma::stable_sort_index(attribute.t());
- // vector of sorted labels
arma::Row<size_t> sortedLabels(attribute.n_elem);
sortedLabels.fill(0);
-
+
for (i = 0; i < attribute.n_elem; i++)
sortedLabels(i) = classLabels(sortedIndexAtt(i));
@@ -173,7 +169,8 @@
i = 0;
count = 0;
- // this splits the sorted into buckets of size >= inpBucketSize
+ // This splits the sorted into buckets of size greater than or equal to
+ // inpBucketSize.
while (i < sortedLabels.n_elem)
{
count++;
@@ -195,14 +192,14 @@
entropy += CalculateEntropy(subColAtts, subColLabels);
i++;
}
- else if( sortedLabels(i) != sortedLabels(i + 1) )
+ else if (sortedLabels(i) != sortedLabels(i + 1))
{
- if (count < bucketSize)
+ if (count < bucketSize)
{
begin = i - count + 1;
end = begin + bucketSize - 1;
-
- if ( end > sortedLabels.n_elem - 1)
+
+ if (end > sortedLabels.n_elem - 1)
end = sortedLabels.n_elem - 1;
}
else
@@ -221,12 +218,11 @@
subColAtts = sortedAtt.cols(begin, end) + zSubColAtts;
- // now using subColLabels and subColAtts to calculate entropuy
+ // Now use subColLabels and subColAtts to calculate entropy.
entropy += CalculateEntropy(subColAtts, subColLabels);
i = end + 1;
count = 0;
-
}
else
i++;
@@ -234,12 +230,12 @@
return entropy;
}
-/*
- After having decided the attribute on which to split,
- train on that attribute.
-
- @param: attribute - attribute is the attribute decided by the constructor
- on which we now train the decision stump.
+/**
+ * After having decided the attribute on which to split, train on that
+ * attribute.
+ *
+ * @param attribute Attribute is the attribute decided by the constructor on
+ * which we now train the decision stump.
*/
template <typename MatType>
template <typename rType>
@@ -255,7 +251,7 @@
for (i = 0; i < attribute.n_elem; i++)
sortedLabels(i) = classLabels(sortedSplitIndexAtt(i));
-
+
arma::rowvec subCols;
rType mostFreq;
i = 0;
@@ -280,14 +276,15 @@
i++;
}
- else if( sortedLabels(i) != sortedLabels(i + 1) )
+ else if (sortedLabels(i) != sortedLabels(i + 1))
{
- if (count < bucketSize) // test for differevalues of bucketSize, especially extreme cases.
+ if (count < bucketSize)
{
+ // Test for different values of bucketSize, especially extreme cases.
begin = i - count + 1;
end = begin + bucketSize - 1;
-
- if ( end > sortedLabels.n_elem - 1)
+
+ if (end > sortedLabels.n_elem - 1)
end = sortedLabels.n_elem - 1;
}
else
@@ -300,12 +297,11 @@
subCols = sortedLabels.cols(begin, end) + zSubCols;
- // finding the most freq element in subCols so as to assign a label to the
- // bucket of subCols
-
+ // Find the most frequent element in subCols so as to assign a label to
+ // the bucket of subCols.
mostFreq = CountMostFreq<double>(subCols);
- tempSplit << sortedSplitAtt(begin)<< mostFreq << arma::endr;
+ tempSplit << sortedSplitAtt(begin) << mostFreq << arma::endr;
split = arma::join_cols(split, tempSplit);
i = end + 1;
@@ -315,26 +311,25 @@
i++;
}
- // now trimming the split matrix so that buckets one after the after
- // which point to the same classLabel are merged as one big bucket.
+ // Now trim the split matrix so that buckets one after the after which point
+ // to the same classLabel are merged as one big bucket.
MergeRanges();
}
-/* After the "split" matrix has been set up,
- merging ranges with identical class labels.
+/**
+ * After the "split" matrix has been set up, merge ranges with identical class
+ * labels.
*/
template <typename MatType>
void DecisionStump<MatType>::MergeRanges()
{
- int i;
- for (i = 1;i < split.n_rows; i++)
+ for (int i = 1; i < split.n_rows; i++)
{
- if (split(i,1) == split(i-1,1))
+ if (split(i, 1) == split(i - 1, 1))
{
- // remove this row, as it has the same label as
- // the previous bucket.
+ // Remove this row, as it has the same label as the previous bucket.
split.shed_row(i);
- // go back to previous row.
+ // Go back to previous row.
i--;
}
}
@@ -344,26 +339,25 @@
template <typename rType>
rType DecisionStump<MatType>::CountMostFreq(const arma::Row<rType>& subCols)
{
- // sort subCols for easier processing.
+ // Sort subCols for easier processing.
arma::Row<rType> sortCounts = arma::sort(subCols);
rType element;
- int count = 0, localCount = 0,i;
+ int count = 0, localCount = 0;
- // an O(n) loop which counts the most frequent element in sortCounts
- for (i = 0; i < sortCounts.n_elem ; ++i)
+ // An O(n) loop which counts the most frequent element in sortCounts
+ for (int i = 0; i < sortCounts.n_elem; ++i)
{
if (i == sortCounts.n_elem - 1)
{
- if (sortCounts(i-1) == sortCounts(i))
+ if (sortCounts(i - 1) == sortCounts(i))
{
- // element = sortCounts(i-1);
+ // element = sortCounts(i - 1);
localCount++;
}
- else
- if (localCount > count)
+ else if (localCount > count)
count = localCount;
}
- else if (sortCounts(i) != sortCounts(i+1))
+ else if (sortCounts(i) != sortCounts(i + 1))
{
localCount = 0;
count++;
@@ -374,7 +368,7 @@
if (localCount > count)
{
count = localCount;
- if(localCount == 1)
+ if (localCount == 1)
element = sortCounts(i);
}
}
@@ -382,34 +376,32 @@
return element;
}
-/*
- Returns 1 if all the values of featureRow are not same.
-
- @param: featureRow - the attribute which is checked so that it
- does not have identical values.
+/**
+ * Returns 1 if all the values of featureRow are not same.
+ *
+ * @param featureRow The attribute which is checked for identical values.
*/
template <typename MatType>
template <typename rType>
int DecisionStump<MatType>::isDistinct(const arma::Row<rType>& featureRow)
{
- if (featureRow.max()-featureRow.min() > 0)
+ if (featureRow.max() - featureRow.min() > 0)
return 1;
else
return 0;
}
-/*
- Calculating Entropy of attribute.
-
- @param: attribute - the attribute of which we calculate the entropy.
- @param: labels - corresponding labels of the attribute.
+/**
+ * Calculating Entropy of attribute.
+ *
+ * @param attribute The attribute for which we calculate the entropy.
+ * @param labels Corresponding labels of the attribute.
*/
template<typename MatType>
double DecisionStump<MatType>::CalculateEntropy(const arma::rowvec& attribute,
const arma::rowvec& labels)
{
- int i,j;
- double entropy=0.0;
+ double entropy = 0.0;
arma::rowvec uniqueAtt = arma::unique(attribute);
arma::rowvec uniqueLabel = arma::unique(labels);
@@ -417,12 +409,12 @@
numElem.fill(0);
arma::Mat<size_t> entropyArray(uniqueAtt.n_elem,numClass);
entropyArray.fill(0);
-
- // populating entropyArray and numElem, they are to be used as
- // helpers to calculate entropy
- for (j = 0;j < uniqueAtt.n_elem; j++)
+
+ // Populate entropyArray and numElem; they are used as helpers to calculate
+ // entropy.
+ for (int j = 0; j < uniqueAtt.n_elem; j++)
{
- for (i = 0; i < attribute.n_elem; i++)
+ for (int i = 0; i < attribute.n_elem; i++)
{
if (uniqueAtt[j] == attribute[i])
{
@@ -432,29 +424,23 @@
}
}
- double p1, p2, p3;
- for ( j = 0; j < uniqueAtt.size(); j++ )
+ for (int j = 0; j < uniqueAtt.size(); j++)
{
- p1 = ((double)numElem(j) / attribute.n_elem);
+ const double p1 = ((double) numElem(j) / attribute.n_elem);
- for ( i = 0; i < numClass; i++)
+ for (int i = 0; i < numClass; i++)
{
- p2 = ((double)entropyArray(j,i) / numElem(j));
-
- if(p2 == 0)
- p3 = 0;
- else
- p3 = ( p2 * log2(p2) );
+ const double p2 = ((double) entropyArray(j, i) / numElem(j));
+ const double p3 = (p2 == 0) ? 0 : p2 * log2(p2);
- entropy+=( p1 * p3 );
+ entropy += p1 * p3;
}
}
return entropy;
}
-
}; // namespace decision_stump
}; // namespace mlpack
-#endif
\ No newline at end of file
+#endif
Modified: mlpack/trunk/src/mlpack/methods/decision_stump/decision_stump_main.cpp
==============================================================================
--- mlpack/trunk/src/mlpack/methods/decision_stump/decision_stump_main.cpp (original)
+++ mlpack/trunk/src/mlpack/methods/decision_stump/decision_stump_main.cpp Mon Jun 23 20:03:24 2014
@@ -2,7 +2,7 @@
* @author: Udit Saxena
* @file: decision_stump_main.cpp
*
- *
+ * Main executable for the decision stump.
*/
#include <mlpack/core.hpp>
@@ -52,13 +52,13 @@
labelsIn = labelsIn.t();
size_t inpBucketSize = CLI::GetParam<int>("bucket_size");
-
+
// normalize the labels
data::NormalizeLabels(labelsIn.unsafe_col(0), labels, mappings);
const size_t num_classes = CLI::GetParam<size_t>("num_classes");
/*
- Should number of classes be input or should it be
+ Should number of classes be input or should it be
derived from the labels row ?
*/
const string testingDataFilename = CLI::GetParam<std::string>("test_file");
@@ -87,4 +87,4 @@
// saving the predictedLabels in the transposed manner in output
return 0;
-}
\ No newline at end of file
+}
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