PrologMethods/Transformation/pca.md

@@ -2,11 +2,19 @@
 
 An implementation of several strategies for principal components analysis (PCA), a common preprocessing step. Given a dataset and a desired new dimensionality, this can reduce the dimensionality of the data using the linear transformation determined by PCA.
 
+```prolog
+:- use_module('path/to/.../src/methods/pca/pca.pl').
+
+%% usage example
+TrainData = [5.1,3.5,1.4, 4.9,3.0,1.4, 4.7,3.2,1.3, 4.6,3.1,1.5],
+pca(0, randomized, TrainData, 3, TDataList, _, EigValList, EigVecList, _).
+```
+
 # Available Predicates
 
-* [pca/13](/PrologMethods/Transformation/pca#pca13)
-* [pcaDimReduction/10](/PrologMethods/Transformation/pca#pcadimreduction10)
-* [pcaVarianceDimReduction/10](/PrologMethods/Transformation/pca#pcavariancedimreduction10)
+* [pca/9](/PrologMethods/Transformation/pca#pca9)
+* [pcaDimReduction/8](/PrologMethods/Transformation/pca#pcadimreduction8)
+* [pcaVarianceDimReduction/8](/PrologMethods/Transformation/pca#pcavariancedimreduction8)
 
 ---
 
@@ -17,12 +25,21 @@ An implementation of several strategies for principal components analysis (PCA),
 Apply Principal Component Analysis to the provided data set.
 
 ```prolog
-%% part of the predicate definition
-pca(  +integer, +string,
+%% predicate definition 
+pca(ScaleData, DecompositionPolicy, DataList, DataRows, TransformedList, TDataCols, EigValList, EigVecList, ZCols) :-
+        convert_list_to_float_array(DataList, DataRows, array(Xsize, Xrows, X)),
+        pcaI(ScaleData, DecompositionPolicy, X, Xsize, Xrows, TData, TDataCols, TDataRows, Y, Ysize, Z, ZCols, ZRows),
+        convert_float_array_to_2d_list(TData, TDataCols, TDataRows, TransformedList),
+        convert_float_array_to_list(Y, Ysize, EigValList),
+        convert_float_array_to_2d_list(Z, ZCols, ZRows, EigVecList).
+
+%% foreign c++ predicate definition 
+foreign(pca, c, pcaI( +integer, 
+                      +string,
                       +pointer(float_array), +integer, +integer,
                       -pointer(float_array), -integer, -integer,
                       -pointer(float_array), -integer,
-      -pointer(float_array), -integer, -integer).
+                      -pointer(float_array), -integer, -integer)).
 ```
 
 ### Parameters
@@ -45,12 +62,20 @@ Use PCA for dimensionality reduction on the given dataset.
 This will save the newDimension largest principal components of the data and remove the rest. The parameter returned is the amount of variance of the data that is retained; this is a value between 0 and 1. For instance, a value of 0.9 indicates that 90% of the variance present in the data was retained.
 
 ```prolog
-%% part of the predicate definition
-pcaDimReduction(  +integer, +string,
+%% predicate definition 
+pcaDimReduction(ScaleData, DecompositionPolicy, DataList, DataRows, NewDim, TransformedList, TDataCols, Variance) :-
+        NewDim > 0,
+        convert_list_to_float_array(DataList, DataRows, array(Xsize, Xrows, X)),
+        pcaDimReductionI(ScaleData, DecompositionPolicy, X, Xsize, Xrows, NewDim, TData, TDataCols, TDataRows, Variance),
+        convert_float_array_to_2d_list(TData, TDataCols, TDataRows, TransformedList).
+
+%% foreign c++ predicate definition 
+foreign(pcaDimReduction, c, pcaDimReductionI( +integer, 
+                                              +string,
                                               +pointer(float_array), +integer, +integer,
                                               +integer,
                                               -pointer(float_array), -integer, -integer,
-                  [-float32]).
+                                              [-float32])).
 ```
 
 ### Parameters
@@ -75,12 +100,21 @@ This will save as many dimensions as necessary to retain at least the given amou
 The method returns the actual amount of variance retained, which will always be greater than or equal to the varRetained parameter.
 
 ```prolog
-%% part of the predicate definition
-pcaVarianceDimReduction(  +integer, +string,
+%% predicate definition 
+pcaVarianceDimReduction(ScaleData, DecompositionPolicy, DataList, DataRows, VarRetained, TransformedList, TDataCols, Variance) :-
+        VarRetained >= 0.0,
+        VarRetained =< 1.0,
+        convert_list_to_float_array(DataList, DataRows, array(Xsize, Xrows, X)),
+        pcaVarianceDimReductionI(ScaleData, DecompositionPolicy, X, Xsize, Xrows, VarRetained, TData, TDataCols, TDataRows, Variance),
+        convert_float_array_to_2d_list(TData, TDataCols, TDataRows, TransformedList).
+
+%% foreign c++ predicate definition 
+foreign(pcaVarianceDimReduction, c, pcaVarianceDimReductionI( +integer, 
+                                                              +string,
                                                                 +pointer(float_array), +integer, +integer,
                                                               +float32,
                                                               -pointer(float_array), -integer, -integer,
-                          [-float32]).
+                                                              [-float32])).
 ```
 
 ### Parameters
@@ -100,8 +134,8 @@ pcaVarianceDimReduction(  +integer, +string,
 
 If you want a more detailed explanation, then go to the python documentation. There is most of the time a good explanation on how the methods work and what the parameters do.
 
-* [**MLpack::pca_C++\_documentation**](https://www.mlpack.org/doc/stable/doxygen/classmlpack_1_1pca_1_1PCA.html)
-* [**MLpack::pca_Python_documentation**](https://www.mlpack.org/doc/stable/python_documentation.html#pca)
+* [**MLpack::pca_C++\_documentation**](https://www.mlpack.org/doc/mlpack-3.4.2/doxygen/classmlpack_1_1pca_1_1PCA.html)
+* [**MLpack::pca_Python_documentation**](https://www.mlpack.org/doc/mlpack-3.4.2/python_documentation.html#pca)
 
 added some of the links from the python documentation