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Commit 7913021e authored by Jakhes's avatar Jakhes
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Finishing lcc

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src/methods/local_coordinate_coding/local_coordinate_coding.cpp
+ 156
− 18
View file @ 7913021e
......@@ -13,32 +13,170 @@
using namespace arma;
using namespace mlpack;
using namespace std;
using namespace mlpack::lcc;
// Global Variable of the GlobalMethodObject object so it can be accessed from all functions
GlobalMethodObject globalMethodObject;
// Global Variable of the LocalCoordinateCoding object so it can be accessed from all functions
LocalCoordinateCoding lccGlobObj;
bool normalizeData = false;
// TODO:
// input:
// input: const arma::mat & data,
// const size_t atoms,
// const double lambda,
// const size_t maxIterations = 0,
// const double tolerance = 0.01,
// const DictionaryInitializer & initializer = DictionaryInitializer()
// output:
// description:
void sampleFunction()
void initModelWithTrain(float *dataMatArr, SP_integer dataMatSize, SP_integer dataMatRowNum,
SP_integer normalize, SP_integer atoms, double lambda, SP_integer maxIterations, double tolerance)
{
normalizeData = normalize == 1;
// convert the Prolog arrays to arma::mat
mat data = convertArrayToMat(dataMatArr, dataMatSize, dataMatRowNum);
if (normalizeData)
{
Log::Info << "Normalizing data before coding..." << endl;
for (size_t i = 0; i < data.n_cols; ++i)
data.col(i) /= arma::norm(data.col(i), 2);
}
lccGlobObj = LocalCoordinateCoding(data, atoms, lambda, maxIterations, tolerance);
}
// TODO:
// input: const size_t atoms = 0,
// const double lambda = 0.0,
// const size_t maxIterations = 0,
// const double tolerance = 0.01
// output:
// description:
void initModelNoTrain(SP_integer normalize, SP_integer atoms, double lambda, SP_integer maxIterations, double tolerance)
{
normalizeData = normalize == 1;
lccGlobObj = LocalCoordinateCoding(atoms, lambda, maxIterations, tolerance);
}
// TODO:
// input: const arma::mat & data,
// arma::mat & codes <-
// output:
// description:
void encode(float *dataMatArr, SP_integer dataMatSize, SP_integer dataMatRowNum,
float **codesMatArr, SP_integer *codesMatColNum, SP_integer *codesMatRowNum)
{
// convert the Prolog arrays to arma::mat
mat data = convertArrayToMat(dataMatArr, dataMatSize, dataMatRowNum);
// create the ReturnMat
mat codesReturnMat;
// Normalize each point if the user asked for it.
if (IO::HasParam("normalize"))
{
Log::Info << "Normalizing test data before coding..." << endl;
for (size_t i = 0; i < data.n_cols; ++i)
data.col(i) /= arma::norm(data.col(i), 2);
}
lccGlobObj.Encode(data, codesReturnMat);
// return the Matrix
returnMatrixInformation(codesReturnMat, codesMatArr, codesMatColNum, codesMatRowNum);
}
// TODO:
// input: const arma::mat & data,
// const arma::mat & codes,
// const arma::uvec & adjacencies
// output:
// description:
void objective(float *dataMatArr, SP_integer dataMatSize, SP_integer dataMatRowNum,
float **codesMatArr, SP_integer *codesMatColNum, SP_integer *codesMatRowNum,
float **adjacenciesArr, SP_integer *adjacenciesArrSize)
{
// convert the Prolog arrays to arma::mat
mat data = convertArrayToMat(dataMatArr, dataMatSize, dataMatRowNum);
// create the ReturnMat
mat codesReturnMat;
// create the ReturnVector
uvec adjacenciesReturnVector;
// Normalize each point if the user asked for it.
if (IO::HasParam("normalize"))
{
Log::Info << "Normalizing test data before coding..." << endl;
for (size_t i = 0; i < data.n_cols; ++i)
data.col(i) /= arma::norm(data.col(i), 2);
}
lccGlobObj.Objective(data, codesReturnMat, adjacenciesReturnVector);
// return the Matrix
returnMatrixInformation(codesReturnMat, codesMatArr, codesMatColNum, codesMatRowNum);
// return the Vector
returnVectorInformation(arma::conv_to<vec>::from(adjacenciesReturnVector), adjacenciesArr, adjacenciesArrSize);
}
// TODO:
// input: const arma::mat & data,
// const arma::mat & codes,
// const arma::uvec & adjacencies
// output:
// description:
void optimizeDictionary(float *dataMatArr, SP_integer dataMatSize, SP_integer dataMatRowNum,
float **codesMatArr, SP_integer *codesMatColNum, SP_integer *codesMatRowNum,
float **adjacenciesArr, SP_integer *adjacenciesArrSize)
{
// convert the Prolog arrays to arma::mat
mat data = convertArrayToMat(dataMatArr, dataMatSize, dataMatRowNum);
// create the ReturnMat
mat codesReturnMat;
// create the ReturnVector
uvec adjacenciesReturnVector;
// Normalize each point if the user asked for it.
if (IO::HasParam("normalize"))
{
Log::Info << "Normalizing test data before coding..." << endl;
for (size_t i = 0; i < data.n_cols; ++i)
data.col(i) /= arma::norm(data.col(i), 2);
}
lccGlobObj.Objective(data, codesReturnMat, adjacenciesReturnVector);
// return the Matrix
returnMatrixInformation(codesReturnMat, codesMatArr, codesMatColNum, codesMatRowNum);
// return the Vector
returnVectorInformation(arma::conv_to<vec>::from(adjacenciesReturnVector), adjacenciesArr, adjacenciesArrSize);
}
// TODO:
// input: const arma::mat & data,
// const DictionaryInitializer & initializer = DictionaryInitializer()
// output: double final objective value
// description:
double train(float *dataMatArr, SP_integer dataMatSize, SP_integer dataMatRowNum)
{
// convert the Prolog arrays to arma::mat
mat data = convertArrayToMat(dataMatArr, dataMatSize, dataMatRowNum);
if (normalizeData)
{
Log::Info << "Normalizing data before coding..." << endl;
for (size_t i = 0; i < matX.n_cols; ++i)
matX.col(i) /= norm(matX.col(i), 2);
}
}
// Basic input parameters
//
// SP_integer integerNumber, double doubleNumber, char const *string
//
// Snippets
//
// Take Matrix: mat-take, take-mat
// Take Array: arr-take, take-arr
//
// Return Matrix: mat-return, return-mat
// Return Array: arr-return, return-arr
for (size_t i = 0; i < data.n_cols; ++i)
data.col(i) /= arma::norm(data.col(i), 2);
}
return lccGlobObj.Train(data);
}
\ No newline at end of file
src/methods/local_coordinate_coding/local_coordinate_coding.pl
+ 70
− 16
View file @ 7913021e
:- module(local_coordinate_coding, [function/1]).
:- module(local_coordinate_coding, [ initModelWithTrain/8,
initModelNoTrain/5,
encode/6,
objective/8,
optimizeDictionary/8,
train/4]).
%% requirements of library(struct)
:- load_files(library(str_decl),
......@@ -17,32 +22,81 @@
%% TODO:
%% --Input--
%% mat data,
%% bool normalize => (1)true / (0)false,
%% int atoms,
%% float32 lambda,
%% int maxIterations => 0,
%% float32 tolerance => 0.01
%%
%% --Output--
%%
%% --Description--
foreign(function, c, function(arguments)).
foreign(initModelWithTrain, c, initModelWithTrain(+pointer(float_array), +integer, +integer,
+integer, +integer, +float32, +integer, +float32)).
%% TODO:
%% --Input--
%% bool normalize => (1)true / (0)false,
%% int atoms,
%% float32 lambda,
%% int maxIterations => 0,
%% float32 tolerance => 0.01
%%
%% --Output--
%%
%% --Description--
foreign(initModelNoTrain, c, initModelNoTrain(+integer, +integer, +float32, +integer, +float32)).
%% TODO:
%% --Input--
%% mat data
%%
%% --Output--
%% mat codes
%%
%% --Description--
foreign(encode, c, encode(+pointer(float_array), +integer, +integer, -pointer(float_array), -integer, -integer)).
%% +integer , +float32, +string
%% [-integer] , [-float32], [-string]
%% matrix input
%% +pointer(float_array), +integer, +integer
%% array input
%% +pointer(float_array), +integer
%% TODO:
%% --Input--
%% mat data
%%
%% --Output--
%% mat codes,
%% vec adjacencies
%%
%% --Description--
foreign(objective, c, objective(+pointer(float_array), +integer, +integer, -pointer(float_array), -integer, -integer, -pointer(float_array), -integer)).
%% matrix return
%% -pointer(float_array), -integer, -integer
%% TODO:
%% --Input--
%% mat data
%%
%% --Output--
%% mat codes,
%% vec adjacencies
%%
%% --Description--
foreign(optimizeDictionary, c, optimizeDictionary(+pointer(float_array), +integer, +integer, -pointer(float_array), -integer, -integer, -pointer(float_array), -integer)).
%% array return
%% -pointer(float_array), -integer
%% TODO:
%% --Input--
%% mat data
%%
%% --Output--
%% float32 final objective value
%%
%% --Description--
foreign(train, c, train(+pointer(float_array), +integer, +integer, [-float32])).
%% bool name => (1)true / (0)false
%% Defines the functions that get connected from main.cpp
foreign_resource(local_coordinate_coding, [function]).
foreign_resource(local_coordinate_coding, [ initModelWithTrain,
initModelNoTrain,
encode,
objective,
optimizeDictionary,
train]).
:- load_foreign_resource(local_coordinate_coding).
\ No newline at end of file
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