From 3b3eba96fb1d9251f4d0cec9ca2d27ba7f66a65a Mon Sep 17 00:00:00 2001
From: Jakhes <dean.schmitz@schmitzbauer.de>
Date: Sat, 12 Nov 2022 00:15:17 +0100
Subject: [PATCH] Finishing lcc tests
---
.../local_coordinate_coding.cpp | 205 ++----------------
.../local_coordinate_coding.pl | 131 ++---------
.../local_coordinate_coding_test.pl | 44 ++--
3 files changed, 58 insertions(+), 322 deletions(-)
diff --git a/src/methods/local_coordinate_coding/local_coordinate_coding.cpp b/src/methods/local_coordinate_coding/local_coordinate_coding.cpp
index 8cd6474..231c4c0 100644
--- a/src/methods/local_coordinate_coding/local_coordinate_coding.cpp
+++ b/src/methods/local_coordinate_coding/local_coordinate_coding.cpp
@@ -15,11 +15,6 @@ using namespace mlpack;
using namespace std;
using namespace mlpack::lcc;
-// Global Variable of the LocalCoordinateCoding object so it can be accessed from all functions
-LocalCoordinateCoding lccGlobObj;
-
-bool normalizeData = false;
-
// input: const arma::mat & data,
// const size_t atoms,
@@ -27,219 +22,47 @@ bool normalizeData = false;
// const size_t maxIterations = 0,
// const double tolerance = 0.01,
// const DictionaryInitializer & initializer = DictionaryInitializer()
+// const arma::mat & toEncodedata,
+// arma::mat & codes <-
// output:
// description:
-// Initializes the model and trains it so encode/6 can be called after
-//
-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 array 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);
- }
-
-
- try
- {
- lccGlobObj = LocalCoordinateCoding(data, atoms, lambda, maxIterations, tolerance);lccGlobObj = LocalCoordinateCoding(atoms, lambda, maxIterations, tolerance);
- }
- catch(const std::exception& e)
- {
- raisePrologSystemExeption(e.what());
- }
-}
-
-
-// input: const size_t atoms = 0,
-// const double lambda = 0.0,
-// const size_t maxIterations = 0,
-// const double tolerance = 0.01
-// output:
-// description:
-// Initializes the model but doesnt train it so train/4 has to be called befor encode/6 can be used.
+// Initializes the model, trains it and encodes each point of the given data via distance-weighted LARS.
//
-void initModelNoTrain(SP_integer normalize, SP_integer atoms, double lambda, SP_integer maxIterations, double tolerance)
-{
- normalizeData = normalize == 1;
-
-
- try
- {
- lccGlobObj = LocalCoordinateCoding(atoms, lambda, maxIterations, tolerance);
- }
- catch(const std::exception& e)
- {
- raisePrologSystemExeption(e.what());
- }
-}
-
-
-// input: const arma::mat & data,
-// arma::mat & codes <-
-// output:
-// description:
-// Code each point via distance-weighted LARS.
-//
-void encode(float *dataMatArr, SP_integer dataMatSize, SP_integer dataMatRowNum,
+void lcc(float *dataMatArr, SP_integer dataMatSize, SP_integer dataMatRowNum,
+ SP_integer normalize, SP_integer atoms, double lambda, SP_integer maxIterations, double tolerance,
+ float *toEncodeDataMatArr, SP_integer toEncodeDataMatSize, SP_integer toEncodeDataMatRowNum,
float **codesMatArr, SP_integer *codesMatColNum, SP_integer *codesMatRowNum)
{
// convert the Prolog array to arma::mat
mat data = convertArrayToMat(dataMatArr, dataMatSize, dataMatRowNum);
+ mat toEncodeData = convertArrayToMat(toEncodeDataMatArr, toEncodeDataMatSize, toEncodeDataMatRowNum);
// create the ReturnMat
mat codesReturnMat;
- // Normalize each point if the user asked for it.
- if (IO::HasParam("normalize"))
+ if (normalize == 1)
{
- Log::Info << "Normalizing test data before coding..." << endl;
+ 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);
- }
-
-
- try
- {
- lccGlobObj.Encode(data, codesReturnMat);
- }
- catch(const std::exception& e)
- {
- raisePrologSystemExeption(e.what());
- }
-
-
- // return the Matrix
- returnMatrixInformation(codesReturnMat, codesMatArr, codesMatColNum, codesMatRowNum);
-}
-
-// TODO: wrong inputs
-// input: const arma::mat & data,
-// const arma::mat & codes,
-// const arma::uvec & adjacencies
-// output:
-// description:
-// Compute objective function given the list of adjacencies.
-//
-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);
+ for (size_t i = 0; i < toEncodeData.n_cols; ++i)
+ toEncodeData.col(i) /= arma::norm(toEncodeData.col(i), 2);
}
-
+
try
{
- lccGlobObj.Objective(data, codesReturnMat, adjacenciesReturnVector);
+ LocalCoordinateCoding(data, atoms, lambda, maxIterations, tolerance)
+ .Encode(toEncodeData, codesReturnMat);
}
catch(const std::exception& e)
{
raisePrologSystemExeption(e.what());
}
-
-
- // return the Matrix
- returnMatrixInformation(codesReturnMat, codesMatArr, codesMatColNum, codesMatRowNum);
-
- // return the Vector
- returnVectorInformation(arma::conv_to<vec>::from(adjacenciesReturnVector), adjacenciesArr, adjacenciesArrSize);
-}
-
-// TODO: wrong inputs
-// input: const arma::mat & data,
-// const arma::mat & codes,
-// const arma::uvec & adjacencies
-// output:
-// description:
-// Learn dictionary by solving linear system.
-//
-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);
- }
- try
- {
- lccGlobObj.OptimizeDictionary(data, codesReturnMat, adjacenciesReturnVector);
- }
- catch(const std::exception& e)
- {
- raisePrologSystemExeption(e.what());
- }
-
-
// return the Matrix
returnMatrixInformation(codesReturnMat, codesMatArr, codesMatColNum, codesMatRowNum);
-
- // return the Vector
- returnVectorInformation(arma::conv_to<vec>::from(adjacenciesReturnVector), adjacenciesArr, adjacenciesArrSize);
-}
-
-
-// input: const arma::mat & data,
-// const DictionaryInitializer & initializer = DictionaryInitializer()
-// output: double final objective value
-// description:
-// Run local coordinate coding and train the model.
-//
-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 < data.n_cols; ++i)
- data.col(i) /= arma::norm(data.col(i), 2);
- }
-
-
- try
- {
- return lccGlobObj.Train(data);
- }
- catch(const std::exception& e)
- {
- raisePrologSystemExeption(e.what());
- }
}
\ No newline at end of file
diff --git a/src/methods/local_coordinate_coding/local_coordinate_coding.pl b/src/methods/local_coordinate_coding/local_coordinate_coding.pl
index 97c4580..08a1dca 100644
--- a/src/methods/local_coordinate_coding/local_coordinate_coding.pl
+++ b/src/methods/local_coordinate_coding/local_coordinate_coding.pl
@@ -1,10 +1,5 @@
-:- module(local_coordinate_coding, [ lcc_initModelWithTrain/7,
- lcc_initModelNoTrain/5,
- lcc_encode/4,
- lcc_objective/5,
- lcc_optimizeDictionary/5,
- lcc_train/3]).
+:- module(local_coordinate_coding, [lcc/11]).
%% requirements of library(struct)
:- load_files(library(str_decl),
@@ -29,123 +24,33 @@
%% float32 lambda => 0.0,
%% int maxIterations => 0,
%% float32 tolerance => 0.01
-%%
-%% --Output--
-%%
-%% --Description--
-%% Initializes the model and trains it so encode/6 can be called after
-%%
-lcc_initModelWithTrain(DataList, DataRows, Normalize, Atoms, Lambda, MaxIterations, Tolerance) :-
- convert_list_to_float_array(DataList, DataRows, array(Xsize, Xrownum, X)),
- initModelWithTrainI(X, Xsize, Xrownum, Normalize, Atoms, Lambda, MaxIterations, Tolerance).
-
-foreign(initModelWithTrain, c, initModelWithTrainI( +pointer(float_array), +integer, +integer,
- +integer,
- +integer, +float32, +integer, +float32)).
-
-
-%% --Input--
-%% bool normalize => (1)true / (0)false => false,
-%% int atoms => 0,
-%% float32 lambda => 0.0,
-%% int maxIterations => 0,
-%% float32 tolerance => 0.01
-%%
-%% --Output--
-%%
-%% --Description--
-%% Initializes the model but doesnt train it so train/4 has to be called befor encode/6 can be used.
-%%
-lcc_initModelNoTrain(Normalize, Atoms, Lambda, MaxIterations, Tolerance) :-
- initModelNoTrainI(Normalize, Atoms, Lambda, MaxIterations, Tolerance).
-
-foreign(initModelNoTrain, c, initModelNoTrainI( +integer,
- +integer, +float32, +integer, +float32)).
-
-
-%% --Input--
%% mat data
%%
%% --Output--
%% mat codes
%%
%% --Description--
-%% Code each point via distance-weighted LARS.
-%%
-lcc_encode(DataList, DataRows, CodesList, YCols) :-
- convert_list_to_float_array(DataList, DataRows, array(Xsize, Xrows, X)),
- encodeI(X, Xsize, Xrows, Y, YCols, YRows),
- convert_float_array_to_2d_list(Y, YCols, YRows, CodesList).
-
-foreign(encode, c, encodeI( +pointer(float_array), +integer, +integer,
- -pointer(float_array), -integer, -integer)).
-
-
-%% --Input--
-%% mat data
-%%
-%% --Output--
-%% mat codes,
-%% vec adjacencies
-%%
-%% --Description--
-%% Compute objective function given the list of adjacencies.
-%%
-lcc_objective(DataList, DataRows, CodesList, YCols, AdjacenciesList) :-
- convert_list_to_float_array(DataList, DataRows, array(Xsize, Xrows, X)),
- objectiveI(X, Xsize, Xrows, Y, YCols, YRows, Z, Zsize),
- convert_float_array_to_2d_list(Y, YCols, YRows, CodesList),
- convert_float_array_to_list(Z, Zsize, AdjacenciesList).
-
-foreign(objective, c, objectiveI( +pointer(float_array), +integer, +integer,
- -pointer(float_array), -integer, -integer,
- -pointer(float_array), -integer)).
-
-
-%% --Input--
-%% mat data
-%%
-%% --Output--
-%% mat codes,
-%% vec adjacencies
-%%
-%% --Description--
-%% Learn dictionary by solving linear system.
-%%
-lcc_optimizeDictionary(DataList, DataRows, CodesList, YCols, AdjacenciesList) :-
- convert_list_to_float_array(DataList, DataRows, array(Xsize, Xrows, X)),
- optimizeDictionaryI(X, Xsize, Xrows, Y, YCols, YRows, Z, Zsize),
- convert_float_array_to_2d_list(Y, YCols, YRows, CodesList),
- convert_float_array_to_list(Z, Zsize, AdjacenciesList).
-
-foreign(optimizeDictionary, c, optimizeDictionaryI( +pointer(float_array), +integer, +integer,
- -pointer(float_array), -integer, -integer,
- -pointer(float_array), -integer)).
-
-
-%% --Input--
-%% mat data
-%%
-%% --Output--
-%% float32 final objective value
-%%
-%% --Description--
-%% Run local coordinate coding and train the model.
+%% Initializes the model and trains it so encode/6 can be called after
%%
-lcc_train(DataList, DataRows, ReturnValue) :-
- convert_list_to_float_array(DataList, DataRows, array(Xsize, Xrows, X)),
- trainI(X, Xsize, Xrows, ReturnValue).
+lcc(DataList, DataRows, Normalize, Atoms, Lambda, MaxIterations, Tolerance, TestDataList, TestDataRows, CodesList, ZRows) :-
+ Atoms > 0,
+ Lambda >= 0,
+ MaxIterations >= 0,
+ Tolerance > 0,
+ convert_list_to_float_array(DataList, DataRows, array(Xsize, Xrownum, X)),
+ Atoms < (Xsize / Xrownum),
+ convert_list_to_float_array(TestDataList, TestDataRows, array(Ysize, Yrownum, Y)),
+ lccI(X, Xsize, Xrownum, Normalize, Atoms, Lambda, MaxIterations, Tolerance, Y, Ysize, Yrownum, Z, ZCols, ZRows),
+ convert_float_array_to_2d_list(Z, ZCols, ZRows, CodesList).
-foreign(train, c, trainI( +pointer(float_array), +integer, +integer,
- [-float32])).
+foreign(lcc, c, lccI( +pointer(float_array), +integer, +integer,
+ +integer,
+ +integer, +float32, +integer, +float32,
+ +pointer(float_array), +integer, +integer,
+ -pointer(float_array), -integer, -integer)).
%% Defines the functions that get connected from main.cpp
-foreign_resource(local_coordinate_coding, [ initModelWithTrain,
- initModelNoTrain,
- encode,
- objective,
- optimizeDictionary,
- train]).
+foreign_resource(local_coordinate_coding, [lcc]).
:- load_foreign_resource(local_coordinate_coding).
diff --git a/src/methods/local_coordinate_coding/local_coordinate_coding_test.pl b/src/methods/local_coordinate_coding/local_coordinate_coding_test.pl
index 0bdb5de..d4682db 100644
--- a/src/methods/local_coordinate_coding/local_coordinate_coding_test.pl
+++ b/src/methods/local_coordinate_coding/local_coordinate_coding_test.pl
@@ -6,38 +6,46 @@
:- use_module(local_coordinate_coding).
:- use_module('../../helper_files/helper.pl').
-reset_Model :-
- lcc_initModel(1,0,50,0.0001).
%%
-%% TESTING predicate predicate/10
+%% TESTING predicate lcc/11
%%
-:- begin_tests(predicate).
+:- begin_tests(lcc).
%% Failure Tests
-test(testDescription, [error(domain_error('expectation' , culprit), _)]) :-
- reset_Model_No_Train(perceptron),
- train([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, [0,0,0,0], 2, culprit, 50, 0.0001, _).
+test(lcc_Bad_Atom_Input, fail) :-
+ lcc([5.1,3.5,1.4, 4.9,3.0,1.4, 4.7,3.2,1.3, 4.6,3.1,1.5], 3, 0, -2, 0.0, 0, 0.01, [5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, _, _),
+ lcc([5.1,3.5,1.4, 4.9,3.0,1.4, 4.7,3.2,1.3, 4.6,3.1,1.5], 3, 0, 4, 0.0, 0, 0.01, [5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, _, _).
+
+test(lcc_Negative_Lambda, fail) :-
+ lcc([5.1,3.5,1.4, 4.9,3.0,1.4, 4.7,3.2,1.3, 4.6,3.1,1.5], 3, 0, 2, -1.0, 0, 0.01, [5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, _, _).
+
+test(lcc_Negative_MaxIterations, fail) :-
+ lcc([5.1,3.5,1.4, 4.9,3.0,1.4, 4.7,3.2,1.3, 4.6,3.1,1.5], 3, 0, 2, 0.0, -1, 0.01, [5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, _, _).
+
+test(lcc_Negative_Tolerance, fail) :-
+ lcc([5.1,3.5,1.4, 4.9,3.0,1.4, 4.7,3.2,1.3, 4.6,3.1,1.5], 3, 0, 2, 0.0, 0, -0.01, [5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, _, _).
+
+test(lcc_Different_Dims, [error(_,system_error('matrix multiplication: incompatible matrix dimensions: 2x4 and 3x4'))]) :-
+ lcc([5.1,3.5,1.4, 4.9,3.0,1.4, 4.7,3.2,1.3, 4.6,3.1,1.5], 4, 0, 2, 0.0, 0, 0.01, [5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, _, _).
-test(testDescription2, [error(_,system_error('The values of the Label have to start at 0 and be >= 0 and < the given numClass!'))]) :-
- reset_Model_No_Train(perceptron),
- train([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, [0,1,0,2], 2, perceptron, 50, 0.0001, _).
-
%% Successful Tests
-test(testDescription3, [true(Error =:= 1)]) :-
- reset_Model_No_Train(perceptron),
- train([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, [0,0,0,0], 2, perceptron, 50, 0.0001, Error).
+test(lcc_Normal_Use) :-
+ lcc([5.1,3.5,1.4, 4.9,3.0,1.4, 4.7,3.2,1.3, 4.6,3.1,1.5], 3, 0, 2, 0.0, 0, 0.01, [5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, CodesList, _),
+ print('\nCodes: '),
+ print(CodesList).
-test(testDescription4, [true(Error =:= 0.9797958971132711)]) :-
- reset_Model_No_Train(perceptron),
+test(lcc_CSV_Input) :-
open('src/data_csv/iris2.csv', read, File),
take_csv_row(File, skipFirstRow,10, Data),
- train(Data, 4, [0,1,0,1,1,0,1,1,1,0], 2, perceptron, 50, 0.0001, Error).
+ lcc(Data, 4, 1, 5, 1.5, 50, 0.042, [5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 4, CodesList, _),
+ print('\nCodes: '),
+ print(CodesList).
-:- end_tests(predicate).
+:- end_tests(lcc).
run_local_coordinate_coding_tests :-
run_tests.
--
GitLab