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Commit f65f7180 authored by Jakhes's avatar Jakhes
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Adding DBScan to the project

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Makefile
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View file @ f65f7180
......@@ -7,6 +7,7 @@ all:
make -C src/methods/lars splfr=$(SPLFR_PATH)
make -C src/methods/ada_boost splfr=$(SPLFR_PATH)
make -C src/methods/kmeans splfr=$(SPLFR_PATH)
make -C src/methods/dbscan splfr=$(SPLFR_PATH)
clean:
make -C src/methods/bayesian_linear_regression clean
......@@ -14,3 +15,4 @@ clean:
make -C src/methods/lars clean
make -C src/methods/ada_boost clean
make -C src/methods/kmeans clean
make -C src/methods/dbscan clean
src/methods/dbscan/Makefile 0 → 100644
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splfr=/usr/local/sicstus4.7.1/bin/splfr
METHOD_NAME=dbscan
$(METHOD_NAME).so: $(METHOD_NAME).pl $(METHOD_NAME).cpp
$(splfr) -larmadillo -fopenmp -lmlpack -lstdc++ -cxx --struct $(METHOD_NAME).pl $(METHOD_NAME).cpp ../../helper_files/helper.cpp
clean:
rm $(METHOD_NAME).so
src/methods/dbscan/dbscan.cpp 0 → 100644
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#include <sicstus/sicstus.h>
/* ex_glue.h is generated by splfr from the foreign/[2,3] facts.
Always include the glue header in your foreign resource code.
*/
#include "dbscan_glue.h"
#include <mlpack/methods/dbscan/dbscan.hpp>
#include <mlpack/methods/range_search/rs_model.hpp>
#include <mlpack/core.hpp>
// including helper functions for converting between arma structures and arrays
#include "../../helper_files/helper.hpp"
// some of the most used namespaces
using namespace arma;
using namespace mlpack;
using namespace std;
using namespace mlpack::dbscan;
using namespace mlpack::range;
using namespace mlpack::metric;
template<typename RangeSearchType = range::RangeSearch<>, typename PointSelectionPolicy = OrderedPointSelection>
void initAndCluster(double epsilon, SP_integer minPoints, SP_integer batchMode, float *dataMatArr, SP_integer dataMatSize, SP_integer dataMatRowNum,
float **assignmentsArr, SP_integer *assignmentsArrSize, float **centroidsMatArr, SP_integer *centroidsMatColNum, SP_integer *centroidsMatRowNum)
{
// convert the Prolog arrays to arma::mat
mat data = convertArrayToMat(dataMatArr, dataMatSize, dataMatRowNum);
// get the ReturnVector
arma::Row<size_t> toReturnVector;
// get the ReturnMat
mat toReturnMat;
DBSCAN<RangeSearchType, PointSelectionPolicy>(epsilon, minPoints, (batchMode == 1)).Cluster(data, toReturnVector, toReturnMat);
// return the Vector lenght
*assignmentsArrSize = toReturnVector.n_elem;
// return the Vector as Array
*assignmentsArr = convertToArray(toReturnVector);
// return the Matrix dimensions
*centroidsMatColNum = toReturnMat.n_cols;
*centroidsMatRowNum = toReturnMat.n_rows;
// return the Matrix as one long Array
*centroidsMatArr = convertToArray(toReturnMat);
}
// TODO:
// input: const double epsilon,
// const size_t minPoints,
// const bool batchMode = true,
// RangeSearchType rangeSearch = RangeSearchType(),
// PointSelectionPolicy pointSelector = PointSelectionPolicy()
//
// const MatType & data,
// arma::Row< size_t > & assignments,
// arma::mat & centroids
// output:
// description:
void dbscan(double epsilon, SP_integer minPoints, SP_integer batchMode, char const *selectionType, char const *treeType, float *dataMatArr, SP_integer dataMatSize, SP_integer dataMatRowNum,
float **assignmentsArr, SP_integer *assignmentsArrSize, float **centroidsMatArr, SP_integer *centroidsMatColNum, SP_integer *centroidsMatRowNum)
{
if(strcmp(selectionType, "ordered") == 0)
{
cout << "ordered" << endl;
if (strcmp(treeType, "kd") == 0)
{
cout << "kd" << endl;
initAndCluster<RangeSearch<EuclideanDistance, mat, tree::KDTree>, OrderedPointSelection>(epsilon, minPoints, batchMode, dataMatArr, dataMatSize, dataMatRowNum, assignmentsArr, assignmentsArrSize,
centroidsMatArr, centroidsMatColNum, centroidsMatRowNum);
}
else if (strcmp(treeType, "r") == 0)
{
cout << "r" << endl;
initAndCluster<RangeSearch<EuclideanDistance, mat, tree::RTree>, OrderedPointSelection>(epsilon, minPoints, batchMode, dataMatArr, dataMatSize, dataMatRowNum, assignmentsArr, assignmentsArrSize,
centroidsMatArr, centroidsMatColNum, centroidsMatRowNum);
}
else if (strcmp(treeType, "r_star") == 0)
{
cout << "r_star" << endl;
initAndCluster<RangeSearch<EuclideanDistance, mat, tree::RStarTree>, OrderedPointSelection>(epsilon, minPoints, batchMode, dataMatArr, dataMatSize, dataMatRowNum, assignmentsArr, assignmentsArrSize,
centroidsMatArr, centroidsMatColNum, centroidsMatRowNum);
}
else if (strcmp(treeType, "x") == 0)
{
cout << "x" << endl;
initAndCluster<RangeSearch<EuclideanDistance, mat, tree::XTree>, OrderedPointSelection>(epsilon, minPoints, batchMode, dataMatArr, dataMatSize, dataMatRowNum, assignmentsArr, assignmentsArrSize,
centroidsMatArr, centroidsMatColNum, centroidsMatRowNum);
}
else if (strcmp(treeType, "hilbert_r") == 0)
{
cout << "hilbert_r" << endl;
initAndCluster<RangeSearch<EuclideanDistance, mat, tree::HilbertRTree>, OrderedPointSelection>(epsilon, minPoints, batchMode, dataMatArr, dataMatSize, dataMatRowNum, assignmentsArr, assignmentsArrSize,
centroidsMatArr, centroidsMatColNum, centroidsMatRowNum);
}
else if (strcmp(treeType, "r_plus") == 0)
{
cout << "r_plus" << endl;
initAndCluster<RangeSearch<EuclideanDistance, mat, tree::RPlusTree>, OrderedPointSelection>(epsilon, minPoints, batchMode, dataMatArr, dataMatSize, dataMatRowNum, assignmentsArr, assignmentsArrSize,
centroidsMatArr, centroidsMatColNum, centroidsMatRowNum);
}
else if (strcmp(treeType, "r_plus_plus") == 0)
{
cout << "r_plus_plus" << endl;
initAndCluster<RangeSearch<EuclideanDistance, mat, tree::RPlusPlusTree>, OrderedPointSelection>(epsilon, minPoints, batchMode, dataMatArr, dataMatSize, dataMatRowNum, assignmentsArr, assignmentsArrSize,
centroidsMatArr, centroidsMatColNum, centroidsMatRowNum);
}
else if (strcmp(treeType, "cover") == 0)
{
cout << "cover" << endl;
initAndCluster<RangeSearch<EuclideanDistance, mat, tree::CoverTree>, OrderedPointSelection>(epsilon, minPoints, batchMode, dataMatArr, dataMatSize, dataMatRowNum, assignmentsArr, assignmentsArrSize,
centroidsMatArr, centroidsMatColNum, centroidsMatRowNum);
}
else if (strcmp(treeType, "ball") == 0)
{
cout << "ball" << endl;
initAndCluster<RangeSearch<EuclideanDistance, mat, tree::BallTree>, OrderedPointSelection>(epsilon, minPoints, batchMode, dataMatArr, dataMatSize, dataMatRowNum, assignmentsArr, assignmentsArrSize,
centroidsMatArr, centroidsMatColNum, centroidsMatRowNum);
}
else
{
cout << "wrong treeType input" << endl;
}
}
else if (strcmp(selectionType, "random") == 0)
{
cout << "random" << endl;
if (strcmp(treeType, "kd") == 0)
{
cout << "kd" << endl;
initAndCluster<RangeSearch<EuclideanDistance, mat, tree::KDTree>, RandomPointSelection>(epsilon, minPoints, batchMode, dataMatArr, dataMatSize, dataMatRowNum, assignmentsArr, assignmentsArrSize,
centroidsMatArr, centroidsMatColNum, centroidsMatRowNum);
}
else if (strcmp(treeType, "r") == 0)
{
cout << "r" << endl;
initAndCluster<RangeSearch<EuclideanDistance, mat, tree::RTree>, RandomPointSelection>(epsilon, minPoints, batchMode, dataMatArr, dataMatSize, dataMatRowNum, assignmentsArr, assignmentsArrSize,
centroidsMatArr, centroidsMatColNum, centroidsMatRowNum);
}
else if (strcmp(treeType, "r_star") == 0)
{
cout << "r_star" << endl;
initAndCluster<RangeSearch<EuclideanDistance, mat, tree::RStarTree>, RandomPointSelection>(epsilon, minPoints, batchMode, dataMatArr, dataMatSize, dataMatRowNum, assignmentsArr, assignmentsArrSize,
centroidsMatArr, centroidsMatColNum, centroidsMatRowNum);
}
else if (strcmp(treeType, "x") == 0)
{
cout << "x" << endl;
initAndCluster<RangeSearch<EuclideanDistance, mat, tree::XTree>, RandomPointSelection>(epsilon, minPoints, batchMode, dataMatArr, dataMatSize, dataMatRowNum, assignmentsArr, assignmentsArrSize,
centroidsMatArr, centroidsMatColNum, centroidsMatRowNum);
}
else if (strcmp(treeType, "hilbert_r") == 0)
{
cout << "hilbert_r" << endl;
initAndCluster<RangeSearch<EuclideanDistance, mat, tree::HilbertRTree>, RandomPointSelection>(epsilon, minPoints, batchMode, dataMatArr, dataMatSize, dataMatRowNum, assignmentsArr, assignmentsArrSize,
centroidsMatArr, centroidsMatColNum, centroidsMatRowNum);
}
else if (strcmp(treeType, "r_plus") == 0)
{
cout << "r_plus" << endl;
initAndCluster<RangeSearch<EuclideanDistance, mat, tree::RPlusTree>, RandomPointSelection>(epsilon, minPoints, batchMode, dataMatArr, dataMatSize, dataMatRowNum, assignmentsArr, assignmentsArrSize,
centroidsMatArr, centroidsMatColNum, centroidsMatRowNum);
}
else if (strcmp(treeType, "r_plus_plus") == 0)
{
cout << "r_plus_plus" << endl;
initAndCluster<RangeSearch<EuclideanDistance, mat, tree::RPlusPlusTree>, RandomPointSelection>(epsilon, minPoints, batchMode, dataMatArr, dataMatSize, dataMatRowNum, assignmentsArr, assignmentsArrSize,
centroidsMatArr, centroidsMatColNum, centroidsMatRowNum);
}
else if (strcmp(treeType, "cover") == 0)
{
cout << "cover" << endl;
initAndCluster<RangeSearch<EuclideanDistance, mat, tree::CoverTree>, RandomPointSelection>(epsilon, minPoints, batchMode, dataMatArr, dataMatSize, dataMatRowNum, assignmentsArr, assignmentsArrSize,
centroidsMatArr, centroidsMatColNum, centroidsMatRowNum);
}
else if (strcmp(treeType, "ball") == 0)
{
cout << "ball" << endl;
initAndCluster<RangeSearch<EuclideanDistance, mat, tree::BallTree>, RandomPointSelection>(epsilon, minPoints, batchMode, dataMatArr, dataMatSize, dataMatRowNum, assignmentsArr, assignmentsArrSize,
centroidsMatArr, centroidsMatColNum, centroidsMatRowNum);
}
else
{
cout << "wrong treeType input" << endl;
}
}
else
{
cout << "wrong selectionType input" << endl;
}
}
void takeNumberFunction(SP_integer integerNumber, double doubleNumber, char const *string)
{
}
SP_integer returnNumberFunction()
{
return 7;
}
void takeMatrixFunction(float *givenMatArr, SP_integer givenMatSize, SP_integer givenMatRowNum)
{
// convert the Prolog arrays to arma::mat
mat data = convertArrayToMat(givenMatArr, givenMatSize, givenMatRowNum);
}
void takeArrayFunction(float *givenArr, SP_integer givenArrSize)
{
// convert the Prolog arrays to arma::rowvec
rowvec givenVector = convertArrayToRowvec(givenArr, givenArrSize);
}
void returnMatrixFunction(float **returnMatArr, SP_integer *returnMatColNum, SP_integer *returnMatRowNum)
{
// get the ReturnMat
mat toReturnMat = rowvec(3, 3, fill::ones);
// return the Matrix dimensions
*returnMatColNum = toReturnMat.n_cols;
*returnMatRowNum = toReturnMat.n_rows;
// return the Matrix as one long Array
*returnMatArr = convertToArray(toReturnMat);
}
void returnArrayFunction(float **returnArr, SP_integer *returnArrSize)
{
// get the ReturnVector
rowvec toReturnVector = rowvec(3, fill::ones);
// return the Vector lenght
*returnArrSize = toReturnVector.n_elem;
// return the Vector as Array
*returnArr = convertToArray(toReturnVector);
}
\ No newline at end of file
src/methods/dbscan/dbscan.pl 0 → 100644
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:- module(dbscan, [dbscan/13]).
%% requirements of library(struct)
:- load_files(library(str_decl),
[when(compile_time), if(changed)]).
%% needed for using the array type
:- use_module(library(structs)).
:- use_module('../../helper_files/helper.pl').
%% type definitions for the float array
:- foreign_type
float32 = float_32,
float_array = array(float32).
%% definitions for the connected function
%% input: double epsilon
%% int minPoints
%% bool batchMode (1)true / (0)false
%% str selectionType "orderd", "random"
%% str treeType "kd", "r", "r_star", "x", "hilbert_r", "r_plus", "r_plus_plus", "cover", "ball"
%% mat data data(float_array), dataSize, dataRowNum
%%
%% output: vec assignments assign(float_array), assignSize
%% mat centroids cent(float_array), centColNum, centRowNum
%%
%% description:
foreign(dbscan, c, dbscan(+float32, +integer, +integer, +string, +string, +pointer(float_array), +integer, +integer,
-pointer(float_array), -integer, -pointer(float_array), -integer, -integer)).
%% Defines the functions that get connected from main.cpp
foreign_resource(dbscan, [dbscan]).
:- load_foreign_resource(dbscan).
\ No newline at end of file
src/methods/dbscan/dbscan_test.pl 0 → 100644
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:- use_module(library(plunit)).
:- use_module(dbscan).
:- use_module('../../helper_files/helper.pl').
:- begin_tests(lists).
test(dbscanKDTree) :-
convert_list_to_float_array([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5, 1.4, 3.0, 2.1, 0.1],4, array(Xsize, Xrownum, X)),
dbscan(0.7, 1, 1, ordered, kd, X, Xsize, Xrownum, Assign, Assignsize, Centroids, Centroidscolnum, Centroidsrownum),
print(Assignsize),
convert_float_array_to_list(Assign, Assignsize, ResultsAssign),
print(ResultsAssign),
print(Centroidscolnum),
convert_float_array_to_2d_list(Centroids, Centroidscolnum, Centroidsrownum, Results),
print(Results).
test(dbscanRTree) :-
convert_list_to_float_array([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5, 1.4, 3.0, 2.1, 0.1],4, array(Xsize, Xrownum, X)),
dbscan(0.7, 1, 1, ordered, r, X, Xsize, Xrownum, Assign, Assignsize, Centroids, Centroidscolnum, Centroidsrownum),
print(Assignsize),
convert_float_array_to_list(Assign, Assignsize, ResultsAssign),
print(ResultsAssign),
print(Centroidscolnum),
convert_float_array_to_2d_list(Centroids, Centroidscolnum, Centroidsrownum, Results),
print(Results).
test(dbscanRStarTree) :-
convert_list_to_float_array([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5, 1.4, 3.0, 2.1, 0.1],4, array(Xsize, Xrownum, X)),
dbscan(0.7, 1, 1, ordered, r_star, X, Xsize, Xrownum, Assign, Assignsize, Centroids, Centroidscolnum, Centroidsrownum),
print(Assignsize),
convert_float_array_to_list(Assign, Assignsize, ResultsAssign),
print(ResultsAssign),
print(Centroidscolnum),
convert_float_array_to_2d_list(Centroids, Centroidscolnum, Centroidsrownum, Results),
print(Results).
test(dbscanrandomKDTree) :-
convert_list_to_float_array([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5, 1.4, 3.0, 2.1, 0.1],4, array(Xsize, Xrownum, X)),
dbscan(0.7, 1, 1, random, kd, X, Xsize, Xrownum, Assign, Assignsize, Centroids, Centroidscolnum, Centroidsrownum),
print(Assignsize),
convert_float_array_to_list(Assign, Assignsize, ResultsAssign),
print(ResultsAssign),
print(Centroidscolnum),
convert_float_array_to_2d_list(Centroids, Centroidscolnum, Centroidsrownum, Results),
print(Results).
:- end_tests(lists).
\ No newline at end of file
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