How do I predict from a linear regression function in matlab?

So im asked to predict how much energy consumed by light. I have variables in my file and had done a linear regression on Matlab and generated a function. So i tried calling that function, then inputed the output in another table, then did a predict(). but it is not working :'(

I need help lol

The function:

function [trainedModel, validationRMSE] = Model1Light(trainingData)

% [trainedModel, validationRMSE] = trainRegressionModel(trainingData)

% Returns a trained regression model and its RMSE. This code recreates the

% model trained in Regression 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:

% trainedModel: A struct containing the trained regression model. The

% struct contains various fields with information about the trained

% model.

%

% trainedModel.predictFcn: A function to make predictions on new data.

%

% validationRMSE: A double containing the RMSE. In the app, the

% History list displays the RMSE for each model.

%

% Use the code to train the model with new data. To retrain your model,

% call the function from the command line with your original data or new

% data as the input argument trainingData.

%

% For example, to retrain a regression model trained with the original data

% set T, enter:

% [trainedModel, validationRMSE] = trainRegressionModel(T)

%

% To make predictions with the returned 'trainedModel' on new data T2, use

% yfit = trainedModel.predictFcn(T2)

%

% T2 must be a table containing at least the same predictor columns as used

% during training. For details, enter:

% trainedModel.HowToPredict

% Auto-generated by MATLAB on 03-Nov-2020 22:03:24

% Extract predictors and response

% This code processes the data into the right shape for training the

% model.

inputTable = trainingData;

predictorNames = {'Morning', 'Evening', 'T1', 'T4', 'RH_4', 'RH_7', 'T8', 'T9'};

predictors = inputTable(:, predictorNames);

response = inputTable.lights;

isCategoricalPredictor = [false, false, false, false, false, false, false, false];

% Train a regression model

% This code specifies all the model options and trains the model.

concatenatedPredictorsAndResponse = predictors;

concatenatedPredictorsAndResponse.lights = response;

linearModel = fitlm(...

concatenatedPredictorsAndResponse, ...

'linear', ...

'RobustOpts', 'off');

% Create the result struct with predict function

predictorExtractionFcn = @(t) t(:, predictorNames);

linearModelPredictFcn = @(x) predict(linearModel, x);

trainedModel.predictFcn = @(x) linearModelPredictFcn(predictorExtractionFcn(x));

% Add additional fields to the result struct

trainedModel.RequiredVariables = {'Evening', 'Morning', 'RH_4', 'RH_7', 'T1', 'T4', 'T8', 'T9'};

trainedModel.LinearModel = linearModel;

trainedModel.About = 'This struct is a trained model exported from Regression Learner R2020a.';

trainedModel.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''), ''appregression_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 = {'Morning', 'Evening', 'T1', 'T4', 'RH_4', 'RH_7', 'T8', 'T9'};

predictors = inputTable(:, predictorNames);

response = inputTable.lights;

isCategoricalPredictor = [false, false, false, false, false, false, false, false];

validationPredictFcn = @(x) linearModelPredictFcn(x);

% Compute resubstitution predictions

validationPredictions = validationPredictFcn(predictors);

% Compute validation RMSE

isNotMissing = ~isnan(validationPredictions) & ~isnan(response);

validationRMSE = sqrt(nansum(( validationPredictions - response ).^2) / numel(response(isNotMissing) ));