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Commit d79fa368 authored by Dean Samuel Schmitz's avatar Dean Samuel Schmitz
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Updating documentation

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src/methods/random_forest/random_forest.pl
+ 95
− 14
View file @ d79fa368
:- module(random_forest, [ initModelNoTrain/1,
initModelWithTrainNoWeights/10,
initModelWithTrainWithWeights/12,
......@@ -22,69 +23,149 @@
%% definitions for the connected function
%% TODO:
%% --Input--
%% int notUsedParam
%%
%% --Output--
%%
%% --Description--
%% Initilizes the model without training it.
%%
foreign(initModelNoTrain, c, initModelNoTrain(+integer)).
%% TODO:
%% --Input--
%% mat data,
%% vec labels,
%% int numClasses => 0,
%% int numTrees => 20,
%% int minimumLeafSize => 1,
%% float32 minimumGainSplit => 1e-7,
%% int maximumDepth => 0
%%
%% --Output--
%%
%% --Description--
foreign(initModelWithTrainNoWeights, c, initModelWithTrainNoWeights(+pointer(float_array), +integer, +integer, +pointer(float_array), +integer, +integer, +integer, +integer, +float32, +integer)).
%% Initilizes the model and trains it but does not apply weights to it.
%%
foreign(initModelWithTrainNoWeights, c, initModelWithTrainNoWeights(
+pointer(float_array), +integer, +integer,
+pointer(float_array), +integer,
+integer, +integer, +integer, +float32, +integer)).
%% TODO:
%% --Input--
%% mat data,
%% vec labels,
%% int numClasses => 0,
%% vec weights,
%% int numTrees => 20,
%% int minimumLeafSize => 1,
%% float32 minimumGainSplit => 1e-7,
%% int maximumDepth => 0
%%
%% --Output--
%%
%% --Description--
foreign(initModelWithTrainWithWeights, c, initModelWithTrainWithWeights(+pointer(float_array), +integer, +integer, +pointer(float_array), +integer, +integer, +pointer(float_array), +integer, +integer , +integer, +float32, +integer)).
%% Initilizes the model, trains it and applies weights to it.
%%
foreign(initModelWithTrainWithWeights, c, initModelWithTrainWithWeights(
+pointer(float_array), +integer, +integer,
+pointer(float_array), +integer,
+integer,
+pointer(float_array), +integer,
+integer , +integer, +float32, +integer)).
%% TODO:
%% --Input--
%% vec point
%%
%% --Output--
%% int prediction,
%% vec probabilities
%%
%% --Description--
foreign(classifyPoint, c, classifyPoint(+pointer(float_array), +integer, -integer, -pointer(float_array), -integer)).
%% Predict the class of the given point and return the predicted class probabilities for each class.
%% Random forest has to be train before using this.
%%
foreign(classifyPoint, c, classifyPoint(+pointer(float_array), +integer,
-integer,
-pointer(float_array), -integer)).
%% TODO:
%% --Input--
%% mat data
%%
%% --Output--
%% vec prediction,
%% mat probabilities
%%
%% --Description--
foreign(classifyMatrix, c, classifyMatrix(+pointer(float_array), +integer, +integer, -pointer(float_array), -integer, -pointer(float_array), -integer, -integer)).
%% Predict the classes of each point in the given dataset, also returning the predicted class probabilities for each point.
%% Random forest has to be train before using this.
%%
foreign(classifyMatrix, c, classifyMatrix( +pointer(float_array), +integer, +integer,
-pointer(float_array), -integer,
-pointer(float_array), -integer, -integer)).
%% TODO:
%% --Input--
%%
%% --Output--
%% int numTrees
%%
%% --Description--
%% Get the number of trees in the forest.
%%
foreign(numTrees, c, numTrees([-integer])).
%% TODO:
%% --Input--
%% mat data,
%% vec labels,
%% int numClasses => 0,
%% int numTrees => 20,
%% int minimumLeafSize => 1,
%% float32 minimumGainSplit => 1e-7,
%% int maximumDepth => 0
%%
%% --Output--
%% float32 average entropy of all the decision trees
%%
%% --Description--
foreign(trainNoWeights, c, trainNoWeights(+pointer(float_array), +integer, +integer, +pointer(float_array), +integer, +integer, +integer, +integer, +float32, +integer, [-float32])).
%% Train the random forest on the given labeled training data with the given number of trees.
%% The minimumLeafSize and minimumGainSplit parameters are given to each individual decision tree during tree building.
%%
foreign(trainNoWeights, c, trainNoWeights( +pointer(float_array), +integer, +integer,
+pointer(float_array), +integer,
+integer, +integer, +integer, +float32, +integer,
[-float32])).
%% TODO:
%% --Input--
%% mat data,
%% vec labels,
%% int numClasses => 0,
%% vec weights,
%% int numTrees => 20,
%% int minimumLeafSize => 1,
%% float32 minimumGainSplit => 1e-7,
%% int maximumDepth => 0
%%
%% --Output--
%% float32 average entropy of all the decision trees
%%
%% --Description--
foreign(trainWithWeights, c, trainWithWeights(+pointer(float_array), +integer, +integer, +pointer(float_array), +integer, +integer, +pointer(float_array), +integer, +integer, +integer, +float32, +integer, [-float32])).
%% Train the random forest on the given weighted labeled training data with the given number of trees.
%% The minimumLeafSize and minimumGainSplit parameters are given to each individual decision tree during tree building.
%%
foreign(trainWithWeights, c, trainWithWeights( +pointer(float_array), +integer, +integer,
+pointer(float_array), +integer,
+integer,
+pointer(float_array), +integer,
+integer, +integer, +float32, +integer,
[-float32])).
%% Defines the functions that get connected from main.cpp
......
src/methods/softmax_regression/softmax_regression.pl
+ 49
− 11
View file @ d79fa368
:- module(softmax_regression, [ initModelNoTrain/3,
initModelWithTrain/8,
classifyPoint/3,
......@@ -22,27 +23,39 @@
%% definitions for the connected function
%% --Input--
%% int inputSize => 0,
%% int numClasses => 0,
%% bool fitIntercept => (1)true / (0)false
%% bool fitIntercept => (1)true / (0)false => false
%%
%% --Output--
%%
%% --Description--
foreign(initModelNoTrain, c, initModelNoTrain(+integer, +integer, +integer)).
%% Initializes the softmax_regression model without training.
%% Be sure to use Train before calling Classif or ComputeAccuracy, otherwise the results may be meaningless.
%%
foreign(initModelNoTrain, c, initModelNoTrain( +integer, +integer,
+integer)).
%% --Input--
%% mat data,
%% vec labels,
%% int numClasses,
%% int numClasses => 0,
%% float32 lambda => 0.0001,
%% bool fitIntercept => (1)true / (0)false
%% bool fitIntercept => (1)true / (0)false => false
%%
%% --Output--
%%
%% --Description--
foreign(initModelWithTrain, c, initModelWithTrain(+pointer(float_array), +integer, +integer, +pointer(float_array), +integer, +integer, +float32, +integer)).
%% Initializes the softmax_regression model and trains it.
%%
foreign(initModelWithTrain, c, initModelWithTrain( +pointer(float_array), +integer, +integer,
+pointer(float_array), +integer,
+integer, +float32,
+integer)).
%% --Input--
%% vec point
......@@ -51,7 +64,11 @@ foreign(initModelWithTrain, c, initModelWithTrain(+pointer(float_array), +intege
%% int predicted label
%%
%% --Description--
foreign(classifyPoint, c, classifyPoint(+pointer(float_array), +integer, [-integer])).
%% Classify the given point.
%%
foreign(classifyPoint, c, classifyPoint(+pointer(float_array), +integer,
[-integer])).
%% --Input--
%% mat data
......@@ -61,7 +78,12 @@ foreign(classifyPoint, c, classifyPoint(+pointer(float_array), +integer, [-integ
%% mat probabilities
%%
%% --Description--
foreign(classifyMatrix, c, classifyMatrix(+pointer(float_array), +integer, +integer, -pointer(float_array), -integer, -pointer(float_array), -integer, -integer)).
%% Classify the given points, returning class probabilities and predicted class label for each point.
%%
foreign(classifyMatrix, c, classifyMatrix( +pointer(float_array), +integer, +integer,
-pointer(float_array), -integer,
-pointer(float_array), -integer, -integer)).
%% --Input--
%% mat data,
......@@ -71,7 +93,13 @@ foreign(classifyMatrix, c, classifyMatrix(+pointer(float_array), +integer, +inte
%% float32 accuracy
%%
%% --Description--
foreign(computeAccuracy, c, computeAccuracy(+pointer(float_array), +integer, +integer, +pointer(float_array), +integer, [-float32])).
%% Computes accuracy of the learned model given the feature data and the labels associated with each data point.
%% Predictions are made using the provided data and are compared with the actual labels.
%%
foreign(computeAccuracy, c, computeAccuracy( +pointer(float_array), +integer, +integer,
+pointer(float_array), +integer,
[-float32])).
%% --Input--
%%
......@@ -79,6 +107,8 @@ foreign(computeAccuracy, c, computeAccuracy(+pointer(float_array), +integer, +in
%% int size of the features
%%
%% --Description--
%% Gets the features size of the training data.
%%
foreign(featureSize, c, featureSize([-integer])).
%% --Input--
......@@ -87,18 +117,26 @@ foreign(featureSize, c, featureSize([-integer])).
%% mat parameters
%%
%% --Description--
%% Get the model parameters.
%%
foreign(parameters, c, parameters(-pointer(float_array), -integer, -integer)).
%% --Input--
%% mat data,
%% vec labels,
%% int numClasses
%% int numClasses => 0
%%
%% --Output--
%% float32 objective value of final point
%%
%% --Description--
foreign(train, c, train(+pointer(float_array), +integer, +integer, +pointer(float_array), +integer, +integer, [-float32])).
%% Trains the softmax regression model with the given training data.
%%
foreign(train, c, train(+pointer(float_array), +integer, +integer,
+pointer(float_array), +integer,
+integer,
[-float32])).
%% Defines the functions that get connected from main.cpp
......
src/methods/sparse_coding/sparse_coding.pl
+ 22
− 11
View file @ d79fa368
:- module(sparse_coding, [ initModelWithTrain/9,
initModelNoTrain/6,
encode/6,
......@@ -18,11 +19,11 @@
%% definitions for the connected function
%% TODO:
%% --Input--
%% mat data,
%% int atoms,
%% float32 lambda1,
%% int atoms => 15,
%% float32 lambda1 => 0,
%% float32 lambda2 => 0,
%% int maxIterations => 0,
%% float32 objTolerance => 0.01,
......@@ -31,13 +32,15 @@
%% --Output--
%%
%% --Description--
%% Initializes sparse_coding model and trains it.
%%
foreign(initModelWithTrain, c, initModelWithTrain( +pointer(float_array), +integer, +integer,
+integer, +float32, +float32, +integer, +float32, +float32)).
%% TODO:
%% --Input--
%% int atoms,
%% float32 lambda1,
%% int atoms => 15,
%% float32 lambda1 => 0,
%% float32 lambda2 => 0,
%% int maxIterations => 0,
%% float32 objTolerance => 0.01,
......@@ -46,9 +49,11 @@ foreign(initModelWithTrain, c, initModelWithTrain(+pointer(float_array), +intege
%% --Output--
%%
%% --Description--
%% Initializes sparse_coding model but will not train the model, and a subsequent call to Train will be required before the model can encode points with Encode.
%%
foreign(initModelNoTrain, c, initModelNoTrain(+integer, +float32, +float32, +integer, +float32, +float32)).
%% TODO:
%% --Input--
%% mat data
%%
......@@ -56,9 +61,12 @@ foreign(initModelNoTrain, c, initModelNoTrain(+integer, +float32, +float32, +int
%% mat codes
%%
%% --Description--
foreign(encode, c, encode(+pointer(float_array), +integer, +integer, -pointer(float_array), -integer, -integer)).
%% Sparse code each point in the given dataset via LARS, using the current dictionary and store the encoded data in the codes matrix.
%%
foreign(encode, c, encode( +pointer(float_array), +integer, +integer,
-pointer(float_array), -integer, -integer)).
%% TODO:
%% --Input--
%% mat data
%%
......@@ -66,7 +74,10 @@ foreign(encode, c, encode(+pointer(float_array), +integer, +integer, -pointer(fl
%% float32 final objective value
%%
%% --Description--
foreign(train, c, train(+pointer(float_array), +integer, +integer, [-float32])).
%% Train the sparse coding model on the given dataset.
%%
foreign(train, c, train(+pointer(float_array), +integer, +integer,
[-float32])).
%% Defines the functions that get connected from main.cpp
......
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