how to predict test data from trained model

Question

Hira Abid 2018-7-10

0
链接

此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/409557-how-to-predict-test-data-from-trained-model

评论： Warid Islam 2020-6-20

i'm making a project in which i have to classify hand gestures. i have a feature file(made in excel). i have trained a model using classification learner app. now prediction is supposed to be done by getting a new image of hand gesture and calculating all those features in an array. now i want that array as testing data. i am unable to predict that testing data from already trained model.i have changed array to matrix as well as table but failed. please help me out.

2 个评论
显示无隐藏无

Sammit Jain 2018-7-10

Hi, I think it'd help a lot if you could share some information about the dimensions of your dataset. Given that, the usual way to go about it would be to feed your features into the predict function in the same way as you put your training data.

Hira Abid 2018-7-10

编辑：Hira Abid 2018-7-10

在 MATLAB Online 中打开

this is auto generated code for training

    function [trainedClassifier, resubstitutionAccuracy] = trainClassifier(trainingData)
% trainClassifier(trainingData)
%  returns a trained classifier and its accuracy.
%  This code recreates the classification model trained in
%  Classification Learner app.
%
%   Input:
%       trainingData: the training data of same data type as imported
%        in the app (table or matrix).
%
%   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. It takes an input of the same form as this training
%        code (table or matrix) and returns predictions for the response.
%        If you supply a matrix, include only the predictors columns (or
%        rows).
%
%       resubstitutionAccuracy: 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, resubstitutionAccuracy] = trainClassifier(T)
%
%  To make predictions with the returned 'trainedClassifier' on new data T,
%  use
%    yfit = trainedClassifier.predictFcn(T)
%
%  To automate training the same classifier with new data, or to learn how
%  to programmatically train classifiers, examine the generated code.
% Auto-generated by MATLAB on 10-Jul-2018 09:47:17
% Extract predictors and response
% This code processes the data into the right shape for training the
% classifier.
 inputTable = trainingData;
 predictorNames = {'acpect_ratio', 'rectangularity', 'convex_area_ratio', 'eccenticity', 'diameter', 'form_factor', 'narrow_factor', 'perimeter_ratio_length_width', 'solidity', 'circularity', 'irregularity', 'm1', 'a1', 'm2', 'a2', 'm3', 'a3', 'm4', 'a4', 'm5', 'a5', 'm6', 'a6', 'm7', 'a7', 'm8', 'a8', 'm9', 'a9', 'cm1', 'cm2', 'cm3', 'cm4', 'cm5', 'cm6', 'cm7'};
predictors = inputTable(:, predictorNames);
response = inputTable.CLASS;
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];
% Train a classifier
% This code specifies all the classifier options and trains the classifier.
template = templateSVM(...
    'KernelFunction', 'gaussian', ...
    'PolynomialOrder', [], ...
    'KernelScale', 1.5, ...
    'BoxConstraint', 1, ...
    'Standardize', true);
classificationSVM = fitcecoc(...
    predictors, ...
    response, ...
    'Learners', template, ...
    'Coding', 'onevsone', ...
    'ClassNames', [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24]);
% Create the result struct with predict function
predictorExtractionFcn = @(t) t(:, predictorNames);
svmPredictFcn = @(x) predict(classificationSVM, x);
trainedClassifier.predictFcn = @(x) svmPredictFcn(predictorExtractionFcn(x));
% Add additional fields to the result struct
trainedClassifier.RequiredVariables = {'acpect_ratio', 'rectangularity', 'convex_area_ratio', 'eccenticity', 'diameter', 'form_factor', 'narrow_factor', 'perimeter_ratio_length_width', 'solidity', 'circularity', 'irregularity', 'm1', 'a1', 'm2', 'a2', 'm3', 'a3', 'm4', 'a4', 'm5', 'a5', 'm6', 'a6', 'm7', 'a7', 'm8', 'a8', 'm9', 'a9', 'cm1', 'cm2', 'cm3', 'cm4', 'cm5', 'cm6', 'cm7'};
trainedClassifier.ClassificationSVM = classificationSVM;
trainedClassifier.About = 'This struct is a trained classifier exported from Classification Learner R2016a.';
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. ''trainedClassifier''. \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
% classifier.
inputTable = trainingData;
predictorNames = {'acpect_ratio', 'rectangularity', 'convex_area_ratio', 'eccenticity', 'diameter', 'form_factor', 'narrow_factor', 'perimeter_ratio_length_width', 'solidity', 'circularity', 'irregularity', 'm1', 'a1', 'm2', 'a2', 'm3', 'a3', 'm4', 'a4', 'm5', 'a5', 'm6', 'a6', 'm7', 'a7', 'm8', 'a8', 'm9', 'a9', 'cm1', 'cm2', 'cm3', 'cm4', 'cm5', 'cm6', 'cm7'};
predictors = inputTable(:, predictorNames);
response = inputTable.CLASS;
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];
% Compute resubstitution accuracy
resubstitutionAccuracy = 1 - resubLoss(trainedClassifier.ClassificationSVM, 'LossFun', 'ClassifError');
% Compute resubstitution predictions and scores
[resubstitutionPredictions, resubstitutionScores] = predict(trainedClassifier.ClassificationSVM, predictors);

and calling this fucnction as

    T = xlsread('traindata.xlsx', 1,'B1:AK385');
[trainedClassifier, resubstitutionAccuracy] = trainClassifier(T)

where the first row of excel contains name of feature. and getting this error Function 'subsindex' is not defined for values of class 'cell'. in line: predictors = inputTable(:, predictorNames);

请先登录，再进行评论。

请先登录，再回答此问题。

Answer 1

Rahul Madan Raju 2019-1-9

0
链接

此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/409557-how-to-predict-test-data-from-trained-model#answer_355676

predictors = inputTable(:, predictorNames{:})

You can try this code at the place of 'predictors = inputTable(:, predictorNames)'

2 个评论
显示无隐藏无

ferkous ahmed 2019-1-17

Try to make sure that all the data contains a comma instead of a dot

Thanks

Warid Islam 2020-6-20

在 MATLAB Online 中打开

Hi guys,

I am having a smimilar problem where I want to test the model on a test image. I used the following line of code.

signalTemp2 = trainedModel.predictFcn(statsArray1);

But I get the following error.

Unable to use a value of type 'cell' as an index.
Error in mlearnapp.internal.model.DatasetSpecification>@(t)t(:,predictorNames) (line 156)
                extractPredictorsFromTableFcn = @(t) t(:,predictorNames);
Error in mlearnapp.internal.model.DatasetSpecification>@(x)extractPredictorsFromTableFcn(x) (line 161)
                    predictorExtractionFcn = @(x) extractPredictorsFromTableFcn(x);
Error in mlearnapp.internal.model.DatasetSpecification>@(x)exportableModel.predictFcn(predictorExtractionFcn(x)) (line 165)
            newExportableModel.predictFcn = @(x) exportableModel.predictFcn(predictorExtractionFcn(x));

请先登录，再进行评论。

how to predict test data from trained model

2 个评论
显示无隐藏无

回答（1 个）

2 个评论
显示无隐藏无

另请参阅

类别

标签

Community Treasure Hunt

how to predict test data from trained model

2 个评论 显示 无隐藏 无

回答（1 个）

2 个评论 显示 无隐藏 无

另请参阅

类别

标签

Community Treasure Hunt

2 个评论
显示无隐藏无

2 个评论
显示无隐藏无