Remove Audio Toolbox dependency from model function made in Classification Learner

Question

Milly 2024-6-19

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https://ww2.mathworks.cn/matlabcentral/answers/2130056-remove-audio-toolbox-dependency-from-model-function-made-in-classification-learner

回答： Milly 2024-6-19

采纳的回答： Milly

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I have the Statistics and Machine Learning Toolbox™ installed.

I am using the the "Classification Learner" app and the "Generate Function" option to export and use my model.

However, when I try to run the function in my code, it comes up with the following error:

"trainClassifier requires Audio Toolbox."

The code is for a narrow neural network using all the default settings.

It seems silly that this capability is not self contained in the original toolbox!

Please can anyone advise on how to remove this dependency from the code? I do not want to have to buy another toolbox to make this one work.

function [trainedClassifier, validationAccuracy] = trainClassifier(trainingData, responseData)
    % ....(comments removed) ...
    % Auto-generated by MATLAB on 19-Jun-2024 12:26:11
    % Extract predictors and response
    % This code processes the data into the right shape for training the
    % model.
    % Convert input to table
    inputTable = array2table(trainingData, 'VariableNames', {'column_1', 'column_2', 'column_3', 'column_4', 'column_5', 'column_6', 'column_7', 'column_8', 'column_9', 'column_10', 'column_11', 'column_12', 'column_13', 'column_14', 'column_15', 'column_16', 'column_17', 'column_18', 'column_19', 'column_20', 'column_21', 'column_22', 'column_23', 'column_24', 'column_25', 'column_26', 'column_27', 'column_28', 'column_29', 'column_30', 'column_31', 'column_32', 'column_33', 'column_34', 'column_35', 'column_36', 'column_37', 'column_38', 'column_39', 'column_40', 'column_41', 'column_42', 'column_43', 'column_44', 'column_45', 'column_46', 'column_47', 'column_48', 'column_49', 'column_50', 'column_51', 'column_52', 'column_53', 'column_54', 'column_55', 'column_56', 'column_57', 'column_58', 'column_59', 'column_60', 'column_61', 'column_62', 'column_63', 'column_64', 'column_65', 'column_66', 'column_67', 'column_68', 'column_69', 'column_70', 'column_71', 'column_72', 'column_73', 'column_74', 'column_75', 'column_76', 'column_77', 'column_78', 'column_79', 'column_80', 'column_81', 'column_82', 'column_83', 'column_84', 'column_85', 'column_86', 'column_87', 'column_88', 'column_89', 'column_90', 'column_91', 'column_92', 'column_93', 'column_94', 'column_95', 'column_96'});
    predictorNames = {'column_1', 'column_2', 'column_3', 'column_4', 'column_5', 'column_6', 'column_7', 'column_8', 'column_9', 'column_10', 'column_11', 'column_12', 'column_13', 'column_14', 'column_15', 'column_16', 'column_17', 'column_18', 'column_19', 'column_20', 'column_21', 'column_22', 'column_23', 'column_24', 'column_25', 'column_26', 'column_27', 'column_28', 'column_29', 'column_30', 'column_31', 'column_32', 'column_33', 'column_34', 'column_35', 'column_36', 'column_37', 'column_38', 'column_39', 'column_40', 'column_41', 'column_42', 'column_43', 'column_44', 'column_45', 'column_46', 'column_47', 'column_48', 'column_49', 'column_50', 'column_51', 'column_52', 'column_53', 'column_54', 'column_55', 'column_56', 'column_57', 'column_58', 'column_59', 'column_60', 'column_61', 'column_62', 'column_63', 'column_64', 'column_65', 'column_66', 'column_67', 'column_68', 'column_69', 'column_70', 'column_71', 'column_72', 'column_73', 'column_74', 'column_75', 'column_76', 'column_77', 'column_78', 'column_79', 'column_80', 'column_81', 'column_82', 'column_83', 'column_84', 'column_85', 'column_86', 'column_87', 'column_88', 'column_89', 'column_90', 'column_91', 'column_92', 'column_93', 'column_94', 'column_95', 'column_96'};
    predictors = inputTable(:, predictorNames);
    response = responseData;
    isCategoricalPredictor = [false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false];
    classNames = [1; 2; 3];
    % Train a classifier
    % This code specifies all the classifier options and trains the classifier.
    classificationNeuralNetwork = fitcnet(...
        predictors, ...
        response, ...
        'LayerSizes', 10, ...
        'Activations', 'relu', ...
        'Lambda', 0, ...
        'IterationLimit', 1000, ...
        'Standardize', true, ...
        'ClassNames', classNames);
    % Create the result struct with predict function
    predictorExtractionFcn = @(x) array2table(x, 'VariableNames', predictorNames);
    neuralNetworkPredictFcn = @(x) predict(classificationNeuralNetwork, x);
    trainedClassifier.predictFcn = @(x) neuralNetworkPredictFcn(predictorExtractionFcn(x));
    % Add additional fields to the result struct
    trainedClassifier.ClassificationNeuralNetwork = classificationNeuralNetwork;
    trainedClassifier.About = 'This struct is a trained model exported from Classification Learner R2023b.';
    trainedClassifier.HowToPredict = sprintf('To make predictions on a new predictor column matrix, X, use: \n  [yfit,scores] = c.predictFcn(X) \nreplacing ''c'' with the name of the variable that is this struct, e.g. ''trainedModel''. \n \nX must contain exactly 96 columns because this model was trained using 96 predictors. \nX must contain only predictor columns in exactly the same order and format as your training \ndata. Do not include the response column or any columns you did not import into the app. \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.
    % Convert input to table
    inputTable = array2table(trainingData, 'VariableNames', {'column_1', 'column_2', 'column_3', 'column_4', 'column_5', 'column_6', 'column_7', 'column_8', 'column_9', 'column_10', 'column_11', 'column_12', 'column_13', 'column_14', 'column_15', 'column_16', 'column_17', 'column_18', 'column_19', 'column_20', 'column_21', 'column_22', 'column_23', 'column_24', 'column_25', 'column_26', 'column_27', 'column_28', 'column_29', 'column_30', 'column_31', 'column_32', 'column_33', 'column_34', 'column_35', 'column_36', 'column_37', 'column_38', 'column_39', 'column_40', 'column_41', 'column_42', 'column_43', 'column_44', 'column_45', 'column_46', 'column_47', 'column_48', 'column_49', 'column_50', 'column_51', 'column_52', 'column_53', 'column_54', 'column_55', 'column_56', 'column_57', 'column_58', 'column_59', 'column_60', 'column_61', 'column_62', 'column_63', 'column_64', 'column_65', 'column_66', 'column_67', 'column_68', 'column_69', 'column_70', 'column_71', 'column_72', 'column_73', 'column_74', 'column_75', 'column_76', 'column_77', 'column_78', 'column_79', 'column_80', 'column_81', 'column_82', 'column_83', 'column_84', 'column_85', 'column_86', 'column_87', 'column_88', 'column_89', 'column_90', 'column_91', 'column_92', 'column_93', 'column_94', 'column_95', 'column_96'});
    predictorNames = {'column_1', 'column_2', 'column_3', 'column_4', 'column_5', 'column_6', 'column_7', 'column_8', 'column_9', 'column_10', 'column_11', 'column_12', 'column_13', 'column_14', 'column_15', 'column_16', 'column_17', 'column_18', 'column_19', 'column_20', 'column_21', 'column_22', 'column_23', 'column_24', 'column_25', 'column_26', 'column_27', 'column_28', 'column_29', 'column_30', 'column_31', 'column_32', 'column_33', 'column_34', 'column_35', 'column_36', 'column_37', 'column_38', 'column_39', 'column_40', 'column_41', 'column_42', 'column_43', 'column_44', 'column_45', 'column_46', 'column_47', 'column_48', 'column_49', 'column_50', 'column_51', 'column_52', 'column_53', 'column_54', 'column_55', 'column_56', 'column_57', 'column_58', 'column_59', 'column_60', 'column_61', 'column_62', 'column_63', 'column_64', 'column_65', 'column_66', 'column_67', 'column_68', 'column_69', 'column_70', 'column_71', 'column_72', 'column_73', 'column_74', 'column_75', 'column_76', 'column_77', 'column_78', 'column_79', 'column_80', 'column_81', 'column_82', 'column_83', 'column_84', 'column_85', 'column_86', 'column_87', 'column_88', 'column_89', 'column_90', 'column_91', 'column_92', 'column_93', 'column_94', 'column_95', 'column_96'};
    predictors = inputTable(:, predictorNames);
    response = responseData;
    isCategoricalPredictor = [false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false];
    classNames = [1; 2; 3];
    % Perform cross-validation
    partitionedModel = crossval(trainedClassifier.ClassificationNeuralNetwork, 'KFold', 5);
    % Compute validation predictions
    [validationPredictions, validationScores] = kfoldPredict(partitionedModel);
    % Compute validation accuracy
    validationAccuracy = 1 - kfoldLoss(partitionedModel, 'LossFun', 'ClassifError');