matrix 1x3 as input for testing data after train the model in classification learner

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Hussein Hasan Mohsen 2020-12-10

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此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/689324-matrix-1x3-as-input-for-testing-data-after-train-the-model-in-classification-learner

评论： Hussein Hasan Mohsen 2020-12-10

I have done with classification learner for training algorithm, i have explor to model to workspace for testing the data, my input data in classification learner are import from excel (table) but for testing i want to use user input, i come out with this code. (one more thing if i import data from excel file, the testing data can classifiy but i dont want this way)

H2_percentage=15.217
CH4_percentage=53.261
C2H6_percentage= 31.522
testing=[H2_percentage  CH4_percentage C2H6_percentage];
yfit = FineKNN.predictFcn('testing');

but when i run that code come out with this error....

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Answer 1

Hiro Yoshino 2020-12-10

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https://ww2.mathworks.cn/matlabcentral/answers/689324-matrix-1x3-as-input-for-testing-data-after-train-the-model-in-classification-learner#answer_571074

在 MATLAB Online 中打开

yfit = FineKNN.predictFcn(testing);

You should use this instead.

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Hussein Hasan Mohsen 2020-12-10

在 MATLAB Online 中打开

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
% Auto-generated by MATLAB on 10-Dec-2020 13:21:14
% Extract predictors and response
% This code processes the data into the right shape for training the
% model.
inputTable = trainingData;
predictorNames = {'H2_percentage', 'CH4_percentage', 'C2H6_percentage'};
predictors = inputTable(:, predictorNames);
response = inputTable.Classification;
isCategoricalPredictor = [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', 1, ...
    'DistanceWeight', 'Equal', ...
    'Standardize', true, ...
    'ClassNames', categorical({'Fault'; 'N/D'; 'Stray'}));
% 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 = {'C2H6_percentage', 'CH4_percentage', 'H2_percentage'};
trainedClassifier.ClassificationKNN = classificationKNN;
trainedClassifier.About = 'This struct is a trained model exported from Classification Learner R2020a.';
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 = trainingData;
predictorNames = {'H2_percentage', 'CH4_percentage', 'C2H6_percentage'};
predictors = inputTable(:, predictorNames);
response = inputTable.Classification;
isCategoricalPredictor = [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');

here is the code can someone help because i am not good in coding...

Hiro Yoshino 2020-12-10

How about convert test data into the right format?

Hussein Hasan Mohsen 2020-12-10

H2_percentage=15.217 CH4_percentage=53.261 C2H6_percentage= 31.522 testing=[H2_percentage CH4_percentage C2H6_percentage];

%the format of testing data will be like the above code which asking user to enter data for testing

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matrix 1x3 as input for testing data after train the model in classification learner

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matrix 1x3 as input for testing data after train the model in classification learner

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