feedc0de/boost_algorithm
1
0
Fork 0
forked from boostorg/algorithm
Code Pull Requests Activity

Created detail subdir.

Browse Source 
Moved naive query function into its own header file in detail subdir.
Added header for Yuan Ming Chen's k_means algorithm implementation.



[SVN r45228]
This commit is contained in:
Jonathan Franklin
2008-05-08 20:36:49 +00:00
parent cdf58b4785
commit 64d219039e
3 changed files with 248 additions and 25 deletions
Download Patch File Download Diff File Expand all files Collapse all files

195
include/boost/algorithm/cluster/k_means.hpp Normal file
View File

@ -0,0 +1,195 @@
/*****
** References
** - J. MacQueen, "Some methods for classification and analysis
** of multivariate observations", Fifth Berkeley Symposium on
** Math Statistics and Probability, 281-297, 1967.
** - I.S. Dhillon and D.S. Modha, "A data-clustering algorithm
** on distributed memory multiprocessors",
** Large-Scale Parallel Data Mining, 245-260, 1999.
** Yuanming Chen, 2008-05-08
*/
#ifndef BOOST_ALGORITHM_CLUSTER_K_MEANS_HPP
#define BOOST_ALGORITHM_CLUSTER_K_MEANS_HPP
#include <cmath>
#include <float.h>
//#include "common.hpp"
#include <vector>
#include <list>
#include <cassert>
namespace boost {
namespace algorithm {
namespace cluster {
namespace detail {
template<typename AttributeType, typename differenceType>
//The original C function
int *k_means(AttributeType **data, int n, int m, int k, differenceType eps, AttributeType **centroids)
{
/* output cluster label for each data point */
int *labels = (int*)calloc(n, sizeof(int));
int h, i, j; /* loop counters, of course :) */
int *counts = (int*)calloc(k, sizeof(int)); /* size of each cluster */
AttributeType old_error, error = FLT_MAX; /* sum of squared euclidean distance */
AttributeType **c = centroids ? centroids : (AttributeType**)calloc(k, sizeof(AttributeType*));
AttributeType **c1 = (AttributeType**)calloc(k, sizeof(AttributeType*)); /* temp centroids */
//assert(data && k > 0 && k <= n && m > 0 && t >= 0); /* for debugging */
/****
** initialization */
for (h = i = 0; i < k; h += n / k, i++) {
c1[i] = (AttributeType*)calloc(m, sizeof(AttributeType));
if (!centroids) {
c[i] = (AttributeType*)calloc(m, sizeof(AttributeType));
}
/* pick k points as initial centroids */
for (j = m; j-- > 0; c[i][j] = data[h][j]);
}
/****
** main loop */
do {
/* save error from last step */
old_error = error, error = 0;
/* clear old counts and temp centroids */
for (i = 0; i < k; counts[i++] = 0) {
for (j = 0; j < m; c1[i][j++] = 0);
}
for (h = 0; h < n; h++) {
/* identify the closest cluster */
AttributeType min_distance = FLT_MAX;
for (i = 0; i < k; i++) {
AttributeType distance = 0;
for (j = m; j-- > 0; distance += pow(data[h][j] - c[i][j], 2));
if (distance < min_distance) {
labels[h] = i;
min_distance = distance;
}
}
/* update size and temp centroid of the destination cluster */
for (j = m; j-- > 0; c1[labels[h]][j] += data[h][j]);
counts[labels[h]]++;
/* update standard error */
error += min_distance;
}
for (i = 0; i < k; i++) { /* update all centroids */
for (j = 0; j < m; j++) {
c[i][j] = counts[i] ? c1[i][j] / counts[i] : c1[i][j];
}
}
} while (fabs(error - old_error) > eps);
/****
** housekeeping */
for (i = 0; i < k; i++) {
if (!centroids) {
free(c[i]);
}
free(c1[i]);
}
if (!centroids) {
free(c);
}
free(c1);
free(counts);
return labels;
}
} //End of details namespace
template<typename PointType>
struct KMeansCluster {
PointType centroid;
std::vector<int> points; //The indice of points are stored here
};
template <typename KMeansCluster>
struct KMeansClustering {
typedef std::vector< KMeansCluster > type;
type clusters;
};
/**
* @param first: the first data point's iterator
* @param last: the last data point's iterator
* @param k: the k value for the k-mean algorithm
* @return collections of clusters
*/
template <typename NTupleIter>
typename KMeansClustering< typename KMeansCluster<typename NTupleIter::value_type> >
k_means(NTupleIter first, NTupleIter last, unsigned k,
typename NTupleIter::difference_type const & eps)
{
typedef NTupleIter::difference_type DistanceType;
typedef NTupleIter::value_type PointType;
typedef PointType::value_type AttributeType; //For the c funtion test, it will be a double type
const DistanceType knumOfPoints = last - first; //The n variable in the C function
const size_t knDimension = PointType::size(); //The m variable in the C function
AttributeType** ppData = new AttributeType* [knumOfPoints];
AttributeType** centroids = new AttributeType* [k];
//Pre-allocate the result array
for(size_t nCentroid = 0; nCentroid < k; nCentroid++)
{
centroids[nCentroid] = new AttributeType[knDimension];
}
int nIndex = 0;
for(NTupleIter iter = first; iter != last; iter++, nIndex++)
{
PointType& pt= *iter; //A point
ppData[nIndex] = new AttributeType[knDimension];
for(unsigned int nAttribute = 0; nAttribute < knDimension; nAttribute++)
{
ppData[nIndex][nAttribute] = pt[nAttribute];
}
}
int* labels = detail::k_means(ppData, (int) knumOfPoints, (int) knDimension, k, eps, centroids);
typedef KMeansCluster<PointType> KMeansClusterType;
KMeansClustering< KMeansClusterType > clustering;
for(size_t nCentroid = 0; nCentroid < k; nCentroid++)
{
KMeansClusterType cluster;
PointType centroid;
for(unsigned int nAttribute = 0; nAttribute < knDimension; nAttribute++)
{
centroid[nAttribute] = centroids[nCentroid][nAttribute];
}
cluster.centroid = centroid;
clustering.clusters.push_back(cluster);
delete[] centroids[nCentroid];
}
for(int nPoint = 0; nPoint < knumOfPoints; nPoint++)
{
int nCentroidIndex = labels[nPoint];
clustering.clusters[nCentroidIndex].points.push_back(nPoint);
delete[] ppData[nPoint];
}
delete[] centroids;
delete[] ppData;
delete[] labels;
return clustering;
}
} //End of cluster namespace
} //End of algorithm namespace
} //End of boost namespace
#endif // BOOST_ALGORITHM_CLUSTER_K_MEANS_HPP