Update documentation

src/methods/local_coordinate_coding/local_coordinate_coding.pl

+
 :- module(local_coordinate_coding, [    initModelWithTrain/8,
                                         initModelNoTrain/5,
                                         encode/6,
@@ -20,35 +21,41 @@
 
 %% definitions for the connected function
 
-%% TODO: 
+
 %% --Input--
 %%              mat     data,
-%%              bool    normalize       => (1)true / (0)false,
-%%              int     atoms,
-%%              float32 lambda,
+%%              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 and trains it so encode/6 can be called after
+%%
 foreign(initModelWithTrain, c, initModelWithTrain(      +pointer(float_array), +integer, +integer, 
-                                                        +integer, +integer, +float32, +integer, +float32)).
+                                                        +integer, 
+                                                        +integer, +float32, +integer, +float32)).
+
 
-%% TODO: 
 %% --Input--
-%%              bool    normalize       => (1)true / (0)false,
-%%              int     atoms,
-%%              float32 lambda,
+%%              bool    normalize       => (1)true / (0)false => false,
+%%              int     atoms           => 0,
+%%              float32 lambda          => 0.0,
 %%              int     maxIterations   => 0,
 %%              float32 tolerance       => 0.01
 %%
 %% --Output--
 %%
 %% --Description--
-foreign(initModelNoTrain, c, initModelNoTrain(+integer, +integer, +float32, +integer, +float32)).
+%%              Initializes the model but doesnt train it so train/4 has to be called befor encode/6 can be used.
+%%
+foreign(initModelNoTrain, c, initModelNoTrain(  +integer, 
+                                                +integer, +float32, +integer, +float32)).
+
 
-%% TODO: 
 %% --Input--
 %%              mat     data
 %%
@@ -56,9 +63,12 @@ foreign(initModelNoTrain, c, initModelNoTrain(+integer, +integer, +float32, +int
 %%              mat     codes
 %%
 %% --Description--
-foreign(encode, c, encode(+pointer(float_array), +integer, +integer, -pointer(float_array), -integer, -integer)).
+%%              Code each point via distance-weighted LARS.
+%%
+foreign(encode, c, encode(      +pointer(float_array), +integer, +integer, 
+                                -pointer(float_array), -integer, -integer)).
+
 
-%% TODO: 
 %% --Input--
 %%              mat     data
 %%
@@ -67,9 +77,13 @@ foreign(encode, c, encode(+pointer(float_array), +integer, +integer, -pointer(fl
 %%              vec     adjacencies
 %%
 %% --Description--
-foreign(objective, c, objective(+pointer(float_array), +integer, +integer, -pointer(float_array), -integer, -integer, -pointer(float_array), -integer)).
+%%              Compute objective function given the list of adjacencies.
+%%
+foreign(objective, c, objective(+pointer(float_array), +integer, +integer, 
+                                -pointer(float_array), -integer, -integer, 
+                                -pointer(float_array), -integer)).
+
 
-%% TODO: 
 %% --Input--
 %%              mat     data
 %%
@@ -78,9 +92,13 @@ foreign(objective, c, objective(+pointer(float_array), +integer, +integer, -poin
 %%              vec     adjacencies
 %%
 %% --Description--
-foreign(optimizeDictionary, c, optimizeDictionary(+pointer(float_array), +integer, +integer, -pointer(float_array), -integer, -integer, -pointer(float_array), -integer)).
+%%              Learn dictionary by solving linear system.
+%%
+foreign(optimizeDictionary, c, optimizeDictionary(      +pointer(float_array), +integer, +integer, 
+                                                        -pointer(float_array), -integer, -integer, 
+                                                        -pointer(float_array), -integer)).
+
 
-%% TODO: 
 %% --Input--
 %%              mat     data
 %%
@@ -88,7 +106,10 @@ foreign(optimizeDictionary, c, optimizeDictionary(+pointer(float_array), +intege
 %%              float32 final objective value
 %%
 %% --Description--
-foreign(train, c, train(+pointer(float_array), +integer, +integer, [-float32])).
+%%              Run local coordinate coding and train the model.
+%%
+foreign(train, c, train(+pointer(float_array), +integer, +integer, 
+                        [-float32])).
 
 
 %% Defines the functions that get connected from main.cpp

src/methods/lsh/lsh.pl

+
 :- module(lsh, [initModel/8,
                 computeRecall/7,
                 searchWithQuery/12,
@@ -19,11 +20,11 @@
 
 %% definitions for the connected function
 
-%% TODO: 
+
 %% --Input--
 %%              mat     referenceSet,
-%%              int     numProj,
-%%              int     numTables,
+%%              int     numProj         => 10-50,
+%%              int     numTables       => 10-20,
 %%              float32 hashWidth       => 0.0,
 %%              int     secondHashSize  => 99901,
 %%              int     bucketSize      => 500
@@ -31,9 +32,11 @@
 %% --Output--
 %%
 %% --Description--
+%%              Initiatzes the model and trains it.
+%%
 foreign(initModel, c, initModel(+pointer(float_array), +integer, +integer, +integer, +integer, +float32, +integer, +integer)).
 
-%% TODO: 
+
 %% --Input--
 %%              mat     foundNeighbors,
 %%              mat     realNeighbors
@@ -42,12 +45,14 @@ foreign(initModel, c, initModel(+pointer(float_array), +integer, +integer, +inte
 %%              float32 recall percentage       => values in between [0,1]
 %%
 %% --Description--
+%%              Compute the recall (% of neighbors found) given the neighbors returned by searchWithQuery/12 or searchNoQuery/9 and a "ground truth" set of neighbors.
+%%
 foreign(computeRecall, c, computeRecall(+pointer(float_array), +integer, +integer, +pointer(float_array), +integer, +integer, [-float32])).
 
-%% TODO: 
+
 %% --Input--
 %%              mat     querySet,
-%%              int     k,
+%%              int     k                       => 0,
 %%              int     numTablesToSearch       => 0,
 %%              int     T                       => 0
 %%
@@ -56,11 +61,14 @@ foreign(computeRecall, c, computeRecall(+pointer(float_array), +integer, +intege
 %%              mat     distances
 %%
 %% --Description--
+%%              Compute the nearest neighbors of the points in the given query set and store the output in the given matrices.
+%%              The matrices will be set to the size of n columns by k rows, where n is the number of points in the query dataset and k is the number of neighbors being searched for.
+%%
 foreign(searchWithQuery, c, searchWithQuery(+pointer(float_array), +integer, +integer, +integer, -pointer(float_array), -integer, -integer, -pointer(float_array), -integer, -integer, +integer, +integer)).
 
-%% TODO: 
+
 %% --Input--
-%%              int     k,
+%%              int     k                       => 0,
 %%              int     numTablesToSearch       => 0,
 %%              int     T                       => 0
 %%
@@ -69,13 +77,16 @@ foreign(searchWithQuery, c, searchWithQuery(+pointer(float_array), +integer, +in
 %%              mat     distances
 %%
 %% --Description--
+%%              Compute the nearest neighbors and store the output in the given matrices.
+%%              The matrices will be set to the size of n columns by k rows, where n is the number of points in the query dataset and k is the number of neighbors being searched for.
+%%
 foreign(searchNoQuery, c, searchNoQuery(+integer, -pointer(float_array), -integer, -integer, -pointer(float_array), -integer, -integer, +integer, +integer)).
 
-%% TODO: 
+
 %% --Input--
 %%              mat     referenceSet,
-%%              int     numProj,
-%%              int     numTables,
+%%              int     numProj         => 10-50,
+%%              int     numTables       => 10-20,
 %%              float32 hashWidth       => 0.0,
 %%              int     secondHashSize  => 99901,
 %%              int     bucketSize      => 500
@@ -83,6 +94,8 @@ foreign(searchNoQuery, c, searchNoQuery(+integer, -pointer(float_array), -intege
 %% --Output--
 %%
 %% --Description--
+%%              Train the LSH model on the given dataset.
+%%
 foreign(train, c, train(+pointer(float_array), +integer, +integer, +integer, +integer, +float32, +integer, +integer)).
 
 

src/methods/mean_shift/mean_shift.pl

+
 :- module(mean_shift, [meanShift/12]).
 
 %% requirements of library(struct)
@@ -17,19 +18,24 @@
 
 
 %% --Input--
-%%              double  radius                  => 0
-%%              int     maxIterations
-%%              mat     data                    => data(float_array), dataSize, dataRowNum
-%%              bool    forceConvergence        => (1)true / (0)false
-%%              bool    useSeeds                => (1)true / (0)false
+%%              flaot32 radius                  => 0,
+%%              int     maxIterations           => 1000,
+%%              mat     data,
+%%              bool    forceConvergence        => (1)true / (0)false => true,
+%%              bool    useSeeds                => (1)true / (0)false => true
 %%
 %% --Output--
-%%              vec     assignments             => assign(float_array), assignSize
-%%              mat     centroids               => cent(float_array), centColNum, centRowNum
+%%              vec     assignments,
+%%              mat     centroids
 %%
 %% --Description--
-foreign(meanShift, c, meanShift(+float32, +integer, +pointer(float_array), +integer, +integer, -pointer(float_array), -integer,
-                                -pointer(float_array), -integer, -integer, +integer, +integer)).
+%%              Perform mean shift clustering on the data, returning a list of cluster assignments and centroids.
+%%
+foreign(meanShift, c, meanShift(+float32, +integer, 
+                                +pointer(float_array), +integer, +integer, 
+                                -pointer(float_array), -integer,
+                                -pointer(float_array), -integer, -integer, 
+                                +integer, +integer)).
 
 
 %% Defines the functions that get connected from main.cpp

src/methods/naive_bayes_classifier/naive_bayes_classifier.pl

+
 :- module(naive_bayes_classifier, [     initModelWithTrain/8,
                                         initModelNoTrain/3,
                                           
@@ -23,77 +24,121 @@
 
 %% definitions for the connected function
 
-%% TODO: 
+
 %% --Input--
+%%              mat     data,
+%%              vec     labels,
+%%              int     numClasses              => 0,
+%%              bool    incrementalVariance     => (1)true / (0)false => false,
+%%              float32 epsilon                 => 1e-10
 %%
 %% --Output--
 %%
 %% --Description--
+%%              Initializes the classifier as per the input and then trains it by calculating the sample mean and variances.
+%%
 foreign(initModelWithTrain, c, initModelWithTrain(+pointer(float_array), +integer, +integer, +pointer(float_array), +integer, +integer, +integer, +float32)).
 
 
-%% TODO: 
 %% --Input--
+%%              int     numClasses              => 0,
+%%              bool    incrementalVariance     => (1)true / (0)false => false,
+%%              float32 epsilon                 => 1e-10
 %%
 %% --Output--
 %%
 %% --Description--
+%%              Initialize the Naive Bayes classifier without performing training.
+%%              All of the parameters of the model will be initialized to zero. Be sure to use train before calling classify, otherwise the results may be meaningless.
+%%
 foreign(initModelNoTrain, c, initModelNoTrain(+integer, +integer, +float32)).
 
-%% TODO: 
+
 %% --Input--
+%%              mat     data
 %%
 %% --Output--
+%%              vec     predictions,
+%%              mat     probabilities
 %%
 %% --Description--
+%%              Classify the given points using the trained NaiveBayesClassifier model and also return estimates of the probabilities for each class in the given matrix.
+%%              The predicted labels for each point are stored in the given vector.
+%%
 %%foreign(classifyPoint, c, classifyPoint(+pointer(float_array), +integer, -integer, -pointer(float_array), -integer)).
 
-%% TODO: 
+
 %% --Input--
+%%              mat     data
 %%
 %% --Output--
+%%              vec     predictions,
+%%              mat     probabilities
 %%
 %% --Description--
+%%              Classify the given points using the trained NaiveBayesClassifier model and also return estimates of the probabilities for each class in the given matrix.
+%%              The predicted labels for each point are stored in the given vector.
+%%
 foreign(classifyMatrix, c, classifyMatrix(+pointer(float_array), +integer, +integer, -pointer(float_array), -integer, -pointer(float_array), -integer, -integer)).
 
-%% TODO: 
+
 %% --Input--
 %%
 %% --Output--
+%%              mat     means
 %%
 %% --Description--
+%%              Get the sample means for each class.
+%%
 foreign(means, c, means(-pointer(float_array), -integer, -integer)).
 
-%% TODO: 
+
 %% --Input--
 %%
 %% --Output--
+%%              mat     probabilities
 %%
 %% --Description--
+%%              Get the sample probabilities for each class.
+%%
 foreign(probabilities, c, probabilities(-pointer(float_array), -integer, -integer)).
 
-%% TODO: 
+
 %% --Input--
+%%              mat     data,
+%%              vec     labels,
+%%              int     numClasses              => 0,
+%%              bool    incrementalVariance     => (1)true / (0)false => false
 %%
 %% --Output--
 %%
 %% --Description--
+%%              Train the Naive Bayes classifier on the given dataset. The data must be the same dimensionality as the existing model parameters.
+%%
 foreign(trainMatrix, c, trainMatrix(+pointer(float_array), +integer, +integer, +pointer(float_array), +integer, +integer, +integer)).
 
-%% TODO: 
+
 %% --Input--
+%%              vec     point,
+%%              int     label
 %%
 %% --Output--
 %%
 %% --Description--
+%%              Train the Naive Bayes classifier on the given point.
+%%              This will use the incremental algorithm for updating the model parameters. The data must be the same dimensionality as the existing model parameters.
+%%
 foreign(trainPoint, c, trainPoint(+pointer(float_array), +integer, +integer)).
 
-%% TODO: 
+
 %% --Input--
 %%
 %% --Output--
+%%              mat     variances
 %%
 %% --Description--
+%%              Get the sample variances for each class.
+%%
 foreign(variances, c, variances(-pointer(float_array), -integer, -integer)).
 
 

src/methods/nca/nca.pl

@@ -15,20 +15,20 @@
 
 %% definitions for the connected function
 
-%% TODO: 
+
 %% --Input--
 %%              string  optimizerType           => "sgd", "lbfgs",
-%%              float32 stepSize                used in "sgd",
-%%              int     maxIterations           used in "sgd, lbfgs",
-%%              float32 tolerance               used in "sgd",
-%%              bool    shuffle                 => (1)true / (0)false, used in "sgd",
-%%              int     numBasis                used in "lbfgs",
-%%              float32 armijoConstant          used in "lbfgs",
-%%              float32 wolfe                   used in "lbfgs",
-%%              int     maxLineSearchTrials     used in "lbfgs",
-%%              float32 minStep                 used in "lbfgs",
-%%              float32 maxStep                 used in "lbfgs",
-%%              int     batchSize               used in "sgd",
+%%              float32 stepSize                => 0.01 , used in "sgd",
+%%              int     maxIterations           => 500000 , used in "sgd, lbfgs",
+%%              float32 tolerance               => 1e-7 , used in "sgd",
+%%              bool    shuffle                 => (1)true / (0)false, => 0.01 , used in "sgd",
+%%              int     numBasis                => 5 , used in "lbfgs",
+%%              float32 armijoConstant          => 0.0001 , used in "lbfgs",
+%%              float32 wolfe                   => 0.9 , used in "lbfgs",
+%%              int     maxLineSearchTrials     => 50 , used in "lbfgs",
+%%              float32 minStep                 => 1e-20 , used in "lbfgs",
+%%              float32 maxStep                 => 1e+20 , used in "lbfgs",
+%%              int     batchSize               => 50 , used in "sgd",
 %%              mat     data,
 %%              vec     labels
 %%
@@ -36,6 +36,10 @@
 %%              mat     distance
 %%
 %% --Description--
+%%              Initialize the nca model and the selected optimizer.
+%%
+%%              Then perform nca on the given data and return the learned distance.
+%%
 foreign(nca, c, nca(   +string,
                         +float32, +integer, +float32,
                         +integer,