diff --git a/src/methods/linear_regression/linear_regression.cpp b/src/methods/linear_regression/linear_regression.cpp
index 47b9ea2d533148c669e2e29a8872b21f0c9f8815..2fa7ec55068e97a9e0e702b45bd6db5d4d42a507 100644
--- a/src/methods/linear_regression/linear_regression.cpp
+++ b/src/methods/linear_regression/linear_regression.cpp
@@ -19,7 +19,7 @@ LinearRegression regressor;
// input: const arma::mat &data, const arma::rowvec &responses, const double lambda, const bool intercept
// output:
-void initModel(float *matrix, SP_integer matSize, SP_integer matRowNum, float *responses_arr, SP_integer vecSize, SP_integer lambda, SP_integer bool_intercept)
+void initModel(float *matrix, SP_integer matSize, SP_integer matRowNum, float *responses_arr, SP_integer vecSize, double lambda, SP_integer bool_intercept)
{
if(matSize / matRowNum != vecSize)
{
@@ -35,7 +35,7 @@ void initModel(float *matrix, SP_integer matSize, SP_integer matRowNum, float *r
// input: const arma::mat &data, const arma::rowvec &responses, const arma::rowvec &weights, const double lambda, const bool intercept
// output:
-void initModelWithWeights(float *matrix, SP_integer matSize, SP_integer matRowNum, float *responses_arr, SP_integer vecSize, float *weights_arr, SP_integer weightsSize, SP_integer lambda, SP_integer bool_intercept)
+void initModelWithWeights(float *matrix, SP_integer matSize, SP_integer matRowNum, float *responses_arr, SP_integer vecSize, float *weights_arr, SP_integer weightsSize, double lambda, SP_integer bool_intercept)
{
if(matSize / matRowNum != vecSize)
{
@@ -50,16 +50,9 @@ void initModelWithWeights(float *matrix, SP_integer matSize, SP_integer matRowNu
regressor = LinearRegression(data, responses, weights, lambda, bool_intercept);
}
-// input:
-// output:
-void initModelEmpty(SP_integer)
-{
- regressor = LinearRegression();
-}
-
// input: const arma::mat &points, const arma::rowvec &responses
// output: double L2SquardError
-SP_integer computeError(float *points_mat_arr, SP_integer matSize, SP_integer matRowNum, float *responses_arr, SP_integer vecSize)
+double computeError(float *points_mat_arr, SP_integer matSize, SP_integer matRowNum, float *responses_arr, SP_integer vecSize)
{
if(matSize / matRowNum != vecSize)
{
@@ -74,30 +67,6 @@ SP_integer computeError(float *points_mat_arr, SP_integer matSize, SP_integer ma
return regressor.ComputeError(data, responses);
}
-// input:
-// output: double
-SP_integer intercept()
-{
- if(regressor.Intercept()) {return 1;}
- else {return 0;}
-}
-
-// input:
-// output: double Tikhonov regularization parameter
-SP_integer lambda()
-{
- return regressor.Lambda();;
-}
-
-// input: double new Tikhonov regularization parameter
-// output:
-void modifyLambda(SP_integer new_lambda)
-{
- double& lambda_ref = regressor.Lambda();
- lambda_ref = new_lambda;
-}
-
-
// input:
// output: const arma::vec & parameters b vector
float * parameters(SP_integer *arraySize)
@@ -149,7 +118,7 @@ void predict(float *pointsArr, SP_integer pointsSize, SP_integer pointsRowNum, f
// input: const arma::mat &data, const arma::rowvec &responses, const bool intercept
// output:
-SP_integer train(float *matrix, SP_integer matSize, SP_integer matRowNum, float *responses_arr, SP_integer vecSize, SP_integer bool_intercept)
+double train(float *matrix, SP_integer matSize, SP_integer matRowNum, float *responses_arr, SP_integer vecSize, SP_integer bool_intercept)
{
if(matSize / matRowNum != vecSize)
{
@@ -166,7 +135,7 @@ SP_integer train(float *matrix, SP_integer matSize, SP_integer matRowNum, float
// input: const arma::mat &data, const arma::rowvec &responses, const arma::rowvec &weights, const bool intercept
// output:
-SP_integer trainWithWeights(float *matrix, SP_integer matSize, SP_integer matRowNum, float *responses_arr, SP_integer vecSize, float *weights_arr, SP_integer weightsSize, SP_integer bool_intercept)
+double trainWithWeights(float *matrix, SP_integer matSize, SP_integer matRowNum, float *responses_arr, SP_integer vecSize, float *weights_arr, SP_integer weightsSize, SP_integer bool_intercept)
{
if(matSize / matRowNum != vecSize)
{
diff --git a/src/methods/linear_regression/linear_regression.pl b/src/methods/linear_regression/linear_regression.pl
index 9e25233750c4671b44e4b0a30b74f6aec03a5bca..7bc477e97a3f51382b063d63a4292edc8e1bebcb 100644
--- a/src/methods/linear_regression/linear_regression.pl
+++ b/src/methods/linear_regression/linear_regression.pl
@@ -1,4 +1,12 @@
-:- module(linear_regression, [initModel/7, initModelWithWeights/9, initModelEmpty/1, computeError/6, intercept/1, lambda/1, modifyLambda/1, parameters/2, modifyParameters/2, predict/5, train/7, trainWithWeights/9]).
+
+:- module(linear_regression, [ initModel/7,
+ initModelWithWeights/9,
+ computeError/6,
+ parameters/2,
+ modifyParameters/2,
+ predict/5,
+ train/7,
+ trainWithWeights/9]).
:- load_files(library(str_decl),
[when(compile_time), if(changed)]).
@@ -9,111 +17,146 @@
%% type definitions for the float array
:- foreign_type
- float32 = float_32,
- float_array = array(float32).
-
-%% TODO: update Comment docs
+ float32 = float_32,
+ float_array = array(float32).
%% definitions for the connected function and what there inputs and output arguments are
-%% TODO:
-%% --Input--
-%%
-%% --Output--
-%%
-%% --Description--
-foreign(initModel, c, initModel(+pointer(float_array), +integer, +integer, +pointer(float_array), +integer, +integer, +integer)).
-%% TODO:
%% --Input--
+%% mat data,
+%% vec responses,
+%% float32 lambda => 0.0,
+%% bool intercept => (1)true / (0)false => true,
%%
%% --Output--
%%
%% --Description--
-foreign(initModelWithWeights, c, initModelWithWeights(+pointer(float_array), +integer, +integer, +pointer(float_array), +integer, +pointer(float_array), +integer, +integer, +integer)).
-
-%% TODO:
-%% --Input--
+%% Initializes the linear_regression model and trains it but doesnt include weights.
%%
-%% --Output--
-%%
-%% --Description--
-foreign(initModelEmpty, c, initModelEmpty(+integer)).
+foreign(initModel, c, initModel( +pointer(float_array), +integer, +integer,
+ +pointer(float_array), +integer,
+ +float32,
+ +integer)).
-%% TODO:
-%% --Input--
-%%
-%% --Output--
-%%
-%% --Description--
-foreign(computeError, c, computeError(+pointer(float_array), +integer, +integer, +pointer(float_array), +integer, [-integer])).
-%% TODO:
%% --Input--
+%% mat data,
+%% vec responses,
+%% vec weights,
+%% float32 lambda => 0.0,
+%% bool intercept => (1)true / (0)false => true,
%%
%% --Output--
%%
%% --Description--
-foreign(intercept, c, intercept([-integer])).
-
-%% TODO:
-%% --Input--
+%% Initializes the linear_regression model, trains it and adds weights to it.
%%
-%% --Output--
-%%
-%% --Description--
-foreign(lambda, c, lambda([-integer])).
+foreign(initModelWithWeights, c, initModelWithWeights( +pointer(float_array), +integer, +integer,
+ +pointer(float_array), +integer,
+ +pointer(float_array), +integer,
+ +float32,
+ +integer)).
+
-%% TODO:
%% --Input--
+%% mat points,
+%% vec responses
%%
%% --Output--
+%% float32 error
%%
%% --Description--
-foreign(modifyLambda, c, modifyLambda(+integer)).
+%% Calculate the L2 squared error on the given predictors and responses using this linear regression model.
+%%
+foreign(computeError, c, computeError( +pointer(float_array), +integer, +integer,
+ +pointer(float_array), +integer,
+ [-float32])).
+
-%% TODO:
%% --Input--
%%
%% --Output--
+%% vec parameters
%%
%% --Description--
+%% Get the parameters (the b vector).
+%%
foreign(parameters, c, parameters([-pointer(float_array)], -integer)).
-%% TODO:
+
%% --Input--
+%% vec parameters
%%
%% --Output--
%%
%% --Description--
+%% Modify the parameters (the b vector).
+%%
foreign(modifyParameters, c, modifyParameters(+pointer(float_array), +integer)).
-%% TODO:
+
%% --Input--
+%% mat points
%%
%% --Output--
+%% vec responses
%%
%% --Description--
-foreign(predict, c, predict(+pointer(float_array), +integer, +integer, -pointer(float_array), -integer)).
+%% Calculate y_i for each data point in points.
+%%
+foreign(predict, c, predict( +pointer(float_array), +integer, +integer,
+ -pointer(float_array), -integer)).
+
-%% TODO:
%% --Input--
+%% mat data,
+%% vec responses,
+%% bool intercept => (1)true / (0)false => true,
%%
%% --Output--
+%% float32 least quares error after training
%%
%% --Description--
-foreign(train, c, train(+pointer(float_array), +integer, +integer, +pointer(float_array), +integer, +integer, [-integer])).
+%% Train the linear_regression model on the given data.
+%% Careful!
+%% This will completely ignore and overwrite the existing model. This particular implementation does not have an incremental training algorithm.
+%%
+foreign(train, c, train( +pointer(float_array), +integer, +integer,
+ +pointer(float_array), +integer,
+ +integer,
+ [-float32])).
+
-%% TODO:
%% --Input--
+%% mat data,
+%% vec responses,
+%% vec weights,
+%% bool intercept => (1)true / (0)false => true,
%%
%% --Output--
+%% float32 least quares error after training
%%
%% --Description--
-foreign(trainWithWeights, c, trainWithWeights(+pointer(float_array), +integer, +integer, +pointer(float_array), +integer, +pointer(float_array), +integer, +integer, [-integer])).
+%% Train the linear_regression model on the given data and weights.
+%% Careful!
+%% This will completely ignore and overwrite the existing model. This particular implementation does not have an incremental training algorithm.
+%%
+foreign(trainWithWeights, c, trainWithWeights( +pointer(float_array), +integer, +integer,
+ +pointer(float_array), +integer,
+ +pointer(float_array), +integer,
+ +integer,
+ [-float32])).
%% Defines what functions should be connected from main.cpp
-foreign_resource(linear_regression, [initModel, initModelWithWeights, initModelEmpty, computeError, intercept, lambda, modifyLambda, parameters, modifyParameters, predict, train, trainWithWeights]).
+foreign_resource(linear_regression, [ initModel,
+ initModelWithWeights,
+ computeError,
+ parameters,
+ modifyParameters,
+ predict,
+ train,
+ trainWithWeights]).
:- load_foreign_resource(linear_regression).
diff --git a/src/methods/logistic_regression/logistic_regression.cpp b/src/methods/logistic_regression/logistic_regression.cpp
index 4f79bf1deeca6a16cda4f317d309e2dc0fd7f6fe..f206bda7e273146d487352d0d70307866f4fa45d 100644
--- a/src/methods/logistic_regression/logistic_regression.cpp
+++ b/src/methods/logistic_regression/logistic_regression.cpp
@@ -183,4 +183,4 @@ void train(float *predictorsMatArr, SP_integer predictorsMatSize, SP_integer pre
{
cout << "Wrong optimizer input" << endl;
}
-}
\ No newline at end of file
+}
diff --git a/src/methods/logistic_regression/logistic_regression.pl b/src/methods/logistic_regression/logistic_regression.pl
index 8a1ab26a4e3b148e11734e716a3ea9c5fea3522d..ea7b70ad67f5c64a5e755548e0d819f2e7dd79e3 100644
--- a/src/methods/logistic_regression/logistic_regression.pl
+++ b/src/methods/logistic_regression/logistic_regression.pl
@@ -1,3 +1,4 @@
+
:- module(logistic_regression, [ initModelNoOptimizer/6,
initModelWithOptimizer/7,
classifyPoint/4,
@@ -17,74 +18,132 @@
%% type definitions for the float array
:- foreign_type
- float32 = float_32,
- float_array = array(float32).
+ float32 = float_32,
+ float_array = array(float32).
%% definitions for the connected function
-%% TODO:
+
%% --Input--
+%% mat data,
+%% vec responses,
+%% float32 lambda => 0.0
%%
%% --Output--
%%
%% --Description--
-foreign(initModelNoOptimizer, c, initModelNoOptimizer(+pointer(float_array), +integer, +integer, +pointer(float_array), +integer, +float32)).
+%% Initialize the logistic_regression model without specifing a optimizer.
+%%
+foreign(initModelNoOptimizer, c, initModelNoOptimizer( +pointer(float_array), +integer, +integer,
+ +pointer(float_array), +integer,
+ +float32)).
+
-%% TODO:
%% --Input--
+%% mat data,
+%% vec responses,
+%% string optimizer => "lbfgs", "psgd" => lbfgs,
+%% float32 lambda => 0.0
%%
%% --Output--
%%
%% --Description--
-foreign(initModelWithOptimizer, c, initModelWithOptimizer(+pointer(float_array), +integer, +integer, +pointer(float_array), +integer, +string, +float32)).
+%% Initialize the logistic_regression model and specify the optimizer.
+%%
+foreign(initModelWithOptimizer, c, initModelWithOptimizer( +pointer(float_array), +integer, +integer,
+ +pointer(float_array), +integer,
+ +string,
+ +float32)).
+
-%% TODO:
%% --Input--
+%% vec point,
+%% float32 decisionBoundary => 0.5
%%
%% --Output--
+%% int predicLabel
%%
%% --Description--
-foreign(classifyPoint, c, classifyPoint(+pointer(float_array), +integer, +float32, [-integer])).
+%% Classify the given point.
+%%
+foreign(classifyPoint, c, classifyPoint(+pointer(float_array), +integer,
+ +float32,
+ [-integer])).
+
-%% TODO:
%% --Input--
+%% mat data,
+%% float32 decisionBoundary => 0.5
%%
%% --Output--
+%% vec predicLabels
%%
%% --Description--
-foreign(classifyLabels, c, classifyLabels(+pointer(float_array), +integer, +integer, -pointer(float_array), -integer, +float32)).
+%% Classify the given points, returning the predicted labels for each point.
+%%
+foreign(classifyLabels, c, classifyLabels( +pointer(float_array), +integer, +integer,
+ -pointer(float_array), -integer,
+ +float32)).
+
-%% TODO:
%% --Input--
+%% mat data
%%
%% --Output--
+%% mat probabilities
%%
%% --Description--
-foreign(classifyProbs, c, classifyProbs(+pointer(float_array), +integer, +integer, -pointer(float_array), -integer, -integer)).
+%% Classify the given points, returning class probabilities for each point.
+%%
+foreign(classifyProbs, c, classifyProbs(+pointer(float_array), +integer, +integer,
+ -pointer(float_array), -integer, -integer)).
+
-%% TODO:
%% --Input--
+%% mat data,
+%% vec responses,
+%% float32 decisionBoundary => 0.5
%%
%% --Output--
+%% float32 accuracy
%%
%% --Description--
-foreign(computeAccuracy, c, computeAccuracy(+pointer(float_array), +integer, +integer, +pointer(float_array), +integer, +float32, [-float32])).
+%% Compute the accuracy of the model on the given predictors and responses, using the given decision boundary.
+%%
+foreign(computeAccuracy, c, computeAccuracy( +pointer(float_array), +integer, +integer,
+ +pointer(float_array), +integer,
+ +float32,
+ [-float32])).
+
-%% TODO:
%% --Input--
+%% mat data,
+%% vec responses,
%%
%% --Output--
+%% float32 error
%%
%% --Description--
-foreign(computeError, c, computeError(+pointer(float_array), +integer, +integer, +pointer(float_array), +integer, [-float32])).
+%% Compute the error of the model.
+%%
+foreign(computeError, c, computeError( +pointer(float_array), +integer, +integer,
+ +pointer(float_array), +integer,
+ [-float32])).
+
-%% TODO:
%% --Input--
+%% mat data,
+%% vec responses,
+%% string optimizer => "lbfgs", "psgd" => lbfgs
%%
%% --Output--
%%
%% --Description--
-foreign(train, c, train(+pointer(float_array), +integer, +integer, +pointer(float_array), +integer, +string)).
+%% Train the logistic_regression model on the given input data.
+%%
+foreign(train, c, train(+pointer(float_array), +integer, +integer,
+ +pointer(float_array), +integer,
+ +string)).
%% Defines the functions that get connected from main.cpp