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Commit 484dd9d0 authored by Dean Samuel Schmitz's avatar Dean Samuel Schmitz
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Update documentation

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src/methods/linear_regression/linear_regression.cpp
+ 5
− 36
View file @ 484dd9d0
......@@ -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)
{
......
src/methods/linear_regression/linear_regression.pl
+ 91
− 48
View file @ 484dd9d0
:- 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)]).
......@@ -12,108 +20,143 @@
float32 = float_32,
float_array = array(float32).
%% TODO: update Comment docs
%% 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).
src/methods/logistic_regression/logistic_regression.pl
+ 77
− 18
View file @ 484dd9d0
:- module(logistic_regression, [ initModelNoOptimizer/6,
initModelWithOptimizer/7,
classifyPoint/4,
......@@ -22,69 +23,127 @@
%% 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
......
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