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Function_trainClassifier.m
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Function_trainClassifier.m
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function [trainedClassifier, validationAccuracy] = trainClassifier(trainingData)
% [trainedClassifier, validationAccuracy] = trainClassifier(trainingData)
% Returns a trained classifier and its accuracy. This code recreates the
% classification model trained in Classification Learner app. Use the
% generated code to automate training the same model with new data, or to
% learn how to programmatically train models.
%
% Input:
% trainingData: A table containing the same predictor and response
% columns as those imported into the app.
%
% Output:
% trainedClassifier: A struct containing the trained classifier. The
% struct contains various fields with information about the trained
% classifier.
%
% trainedClassifier.predictFcn: A function to make predictions on new
% data.
%
% validationAccuracy: A double containing the accuracy in percent. In
% the app, the History list displays this overall accuracy score for
% each model.
%
% Use the code to train the model with new data. To retrain your
% classifier, call the function from the command line with your original
% data or new data as the input argument trainingData.
%
% For example, to retrain a classifier trained with the original data set
% T, enter:
% [trainedClassifier, validationAccuracy] = trainClassifier(T)
%
% To make predictions with the returned 'trainedClassifier' on new data T2,
% use
% yfit = trainedClassifier.predictFcn(T2)
%
% T2 must be a table containing at least the same predictor columns as used
% during training. For details, enter:
% trainedClassifier.HowToPredict
% % Importar para o Workspace os vectores APL a treinar
PS_APL_Treinar = readtable("E:\BkSctt_Research\1_MatLab_MLClassif\2_DadosAPL\ASCII_Files\DadosDeTreino\PS_APLData_2TrainClassf.txt");
% SB_APL_Treinar = readtable("E:\BkSctt_Research\1_MatLab_MLClassif\2_DadosAPL\ASCII_Files\DadosDeTreino\SB_APLData_2TrainClassf.txt");
%
% % Importar para o Workspace os vectores BS a testar
Proc_BS_Testar = readtable("E:\BkSctt_Research\1_MatLab_MLClassif\1_ML_BkSctt Tests\#39 Processed BkSctt_ARC_Parameters.txt");
% Extract predictors and response
% This code processes the data into the right shape for training the
% model.
inputTable = PS_APL_Treinar;
predictorNames = {'BS_MEDIO', 'INTERC1', 'INTERC2', 'SLOPE', 'ANGMEDIO'};
predictors = inputTable(:, predictorNames);
response = inputTable.CLASSE;
isCategoricalPredictor = [false, false, false, false, false];
% Train a classifier
% This code specifies all the classifier options and trains the classifier.
classificationKNN = fitcknn(...
predictors, ...
response, ...
'Distance', 'Euclidean', ...
'Exponent', [], ...
'NumNeighbors', 16, ...
'DistanceWeight', 'Inverse', ...
'Standardize', true, ...
'ClassNames', {'AF'; 'AG'; 'AM'; 'AMF'; 'AMG'; 'S'});
% Create the result struct with predict function
predictorExtractionFcn = @(t) t(:, predictorNames);
knnPredictFcn = @(x) predict(classificationKNN, x);
trainedClassifier.predictFcn = @(x) knnPredictFcn(predictorExtractionFcn(x));
% Add additional fields to the result struct
trainedClassifier.RequiredVariables = {'ANGMEDIO', 'BS_MEDIO', 'INTERC1', 'INTERC2', 'SLOPE'};
trainedClassifier.ClassificationKNN = classificationKNN;
trainedClassifier.About = 'This struct is a trained model exported from Classification Learner R2020b.';
trainedClassifier.HowToPredict = sprintf('To make predictions on a new table, T, use: \n yfit = c.predictFcn(T) \nreplacing ''c'' with the name of the variable that is this struct, e.g. ''trainedModel''. \n \nThe table, T, must contain the variables returned by: \n c.RequiredVariables \nVariable formats (e.g. matrix/vector, datatype) must match the original training data. \nAdditional variables are ignored. \n \nFor more information, see <a href="matlab:helpview(fullfile(docroot, ''stats'', ''stats.map''), ''appclassification_exportmodeltoworkspace'')">How to predict using an exported model</a>.');
% Extract predictors and response
% This code processes the data into the right shape for training the
% model.
inputTable = PS_APL_Treinar;
predictorNames = {'BS_MEDIO', 'INTERC1', 'INTERC2', 'SLOPE', 'ANGMEDIO'};
predictors = inputTable(:, predictorNames);
response = inputTable.CLASSE;
isCategoricalPredictor = [false, false, false, false, false];
% Perform cross-validation
partitionedModel = crossval(trainedClassifier.ClassificationKNN, 'KFold', 5);
% Compute validation predictions
[validationPredictions, validationScores] = kfoldPredict(partitionedModel);
% Compute validation accuracy
validationAccuracy = 1 - kfoldLoss(partitionedModel, 'LossFun', 'ClassifError');