Skip to content
Snippets Groups Projects

Changes

Page history
  • New page
  • Templates
    • Update perceptron authored by Dean Samuel Schmitz's avatar Dean Samuel Schmitz
    Show whitespace changes
    Inline Side-by-side
    PrologMethods/Classification/perceptron.md
    View page @ 7c4fc073
    ......@@ -2,26 +2,43 @@
    An implementation of a perceptron -a single level neural network- for classification.
    ```prolog
    :- use_module('path/to/.../src/methods/perceptron/perceptron.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],
    TestData = [3,2,0, 5,1,4, 0,0,4, 3,3,5, 0,5,5, 2,5,5],
    perceptron_initModelWithTrain(TrainData, 3, [0,1,0,1], 2, 1000),
    perceptron_classify(TestData, 3, PredictList).
    ```
    # Available Predicates
    * [initModelNoTrain/3](/PrologMethods/Classification/perceptron#initmodelnotrain3)
    * [initModelWithTrain/7](/PrologMethods/Classification/perceptron#initmodelwithtrain7)
    * [biases/2](/PrologMethods/Classification/perceptron#biases2)
    * [classify/5](/PrologMethods/Classification/perceptron#classify5)
    * [train/8](/PrologMethods/Classification/perceptron#train8)
    * [weights/3](/PrologMethods/Classification/perceptron#weights3)
    * [perceptron_initModelNoTrain/3](/PrologMethods/Classification/perceptron#perceptron_initmodelnotrain3)
    * [perceptron_initModelWithTrain/5](/PrologMethods/Classification/perceptron#perceptron_initmodelwithtrain5)
    * [perceptron_biases/1](/PrologMethods/Classification/perceptron#perceptron_biases1)
    * [perceptron_classify/3](/PrologMethods/Classification/perceptron#perceptron_classify3)
    * [perceptron_train/5](/PrologMethods/Classification/perceptron#perceptron_train5)
    * [perceptron_weights/2](/PrologMethods/Classification/perceptron#perceptron_weights2)
    ---
    [links/resources](/PrologMethods/Classification/perceptron#connected-linksresources)
    ## **_initModelNoTrain/3_**
    ## **_perceptron_initModelNoTrain/3_**
    Initilizes the perceptron model and its weight matrix but doesn't train it.
    ```prolog
    %% part of the predicate definition
    initModelNoTrain(+integer, +integer, +integer).
    %% predicate definition
    perceptron_initModelNoTrain(NumClasses, Dimensionality, MaxIterations) :-
    NumClasses >= 0,
    Dimensionality > 0,
    MaxIterations >= 0,
    initModelNoTrainI(NumClasses, Dimensionality, MaxIterations).
    %% foreign c++ predicate definition
    foreign(initModelNoTrain, c, initModelNoTrainI(+integer, +integer, +integer)).
    ```
    ### Parameters
    ......@@ -33,15 +50,24 @@ initModelNoTrain(+integer, +integer, +integer).
    ---
    ## **_initModelWithTrain/7_**
    ## **_perceptron_initModelWithTrain/5_**
    Initilizes the perceptron model and its weight matrix and trains it with the given data.
    ```prolog
    %% part of the predicate definition
    initModelWithTrain( +pointer(float_array), +integer, +integer,
    %% predicate definition
    perceptron_initModelWithTrain(DataList, DataRows, LabelsList, NumClasses, MaxIterations) :-
    NumClasses >= 0,
    MaxIterations >= 0,
    convert_list_to_float_array(DataList, DataRows, array(Xsize, Xrownum, X)),
    convert_list_to_float_array(LabelsList, array(Ysize, Y)),
    initModelWithTrainI(X, Xsize, Xrownum, Y, Ysize, NumClasses, MaxIterations).
    %% foreign c++ predicate definition
    foreign(initModelWithTrain, c, initModelWithTrainI( +pointer(float_array), +integer, +integer,
    +pointer(float_array), +integer,
    +integer, +integer).
    +integer, +integer)).
    ```
    ### Parameters
    ......@@ -54,13 +80,18 @@ initModelWithTrain( +pointer(float_array), +integer, +integer,
    ---
    ## **_biases/2_**
    ## **_perceptron_biases/1_**
    Get the biases.
    ```prolog
    %% part of the predicate definition
    biases(-pointer(float_array), -integer).
    %% predicate definition
    perceptron_biases(BiasesList) :-
    biasesI(Y, Ysize),
    convert_float_array_to_list(Y, Ysize, BiasesList).
    %% foreign c++ predicate definition
    foreign(biases, c, biasesI(-pointer(float_array), -integer)).
    ```
    ### Parameters
    ......@@ -70,14 +101,20 @@ biases(-pointer(float_array), -integer).
    ---
    ## **_classify/5_**
    ## **_perceptron_classify/3_**
    After training, use the weights matrix to classify test, and put the predicted classes in predictedLabels.
    ```prolog
    %% part of the predicate definition
    classify( +pointer(float_array), +integer, +integer,
    -pointer(float_array), -integer).
    %% predicate definition
    perceptron_classify(TestList, TestRows, PredictLabelList) :-
    convert_list_to_float_array(TestList, TestRows, array(Xsize, Xrows, X)),
    classifyI(X, Xsize, Xrows, Y, Ysize),
    convert_float_array_to_list(Y, Ysize, PredictLabelList).
    %% foreign c++ predicate definition
    foreign(classify, c, classifyI( +pointer(float_array), +integer, +integer,
    -pointer(float_array), -integer)).
    ```
    ### Parameters
    ......@@ -88,16 +125,24 @@ classify( +pointer(float_array), +integer, +integer,
    ---
    ## **_train/8_**
    ## **_perceptron_train/5_**
    Train the perceptron on the given data for up to the maximum number of iterations. This training does not reset the model weights, so you can call train/8 on multiple datasets sequentially.
    ```prolog
    %% part of the predicate definition
    train( +pointer(float_array), +integer, +integer,
    %% predicate definition
    perceptron_train(DataList, DataRows, LabelsList, NumClasses, WeightsList) :-
    NumClasses >= 0,
    convert_list_to_float_array(DataList, DataRows, array(Xsize, Xrownum, X)),
    convert_list_to_float_array(LabelsList, array(Ysize, Y)),
    convert_list_to_float_array(WeightsList, array(Zsize, Z)),
    trainI(X, Xsize, Xrownum, Y, Ysize, NumClasses, Z, Zsize).
    %% foreign c++ predicate definition
    foreign(train, c, trainI( +pointer(float_array), +integer, +integer,
    +pointer(float_array), +integer,
    +integer,
    +pointer(float_array), +integer).
    +pointer(float_array), +integer)).
    ```
    ### Parameters
    ......@@ -110,13 +155,18 @@ train( +pointer(float_array), +integer, +integer,
    ---
    ## **_weights/3_**
    ## **_perceptron_weights/2_**
    Get the weight matrix.
    ```prolog
    %% part of the predicate definition
    weights(-pointer(float_array), -integer, -integer).
    %% predicate definition
    perceptron_weights(WeightsList, XCols) :-
    weightsI(X, XCols, XRows),
    convert_float_array_to_2d_list(X, XCols, XRows, WeightsList).
    %% foreign c++ predicate definition
    foreign(weights, c, weightsI(-pointer(float_array), -integer, -integer)).
    ```
    ### Parameters
    ......@@ -130,8 +180,8 @@ weights(-pointer(float_array), -integer, -integer).
    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::perceptron_C++\_documentation**](https://www.mlpack.org/doc/stable/doxygen/classmlpack_1_1perceptron_1_1Perceptron.html)
    * [**MLpack::perceptron_Python_documentation**](https://www.mlpack.org/doc/stable/python_documentation.html#perceptron)
    * [**MLpack::perceptron_C++\_documentation**](https://www.mlpack.org/doc/mlpack-3.4.2/doxygen/classmlpack_1_1perceptron_1_1Perceptron.html)
    * [**MLpack::perceptron_Python_documentation**](https://www.mlpack.org/doc/mlpack-3.4.2/python_documentation.html#perceptron)
    added some of the links from the python documentation
    ......