%% Train Pairwise Similarity Model Using Tabular Data
% Load training data.

load('pairwiseTrainTables.mat')

% The training data is a cell array of tables. Each table is a degradation feature profile for a component. Each profile consists of life time measurements in the "Time" variable and corresponding degradation feature measurements in the "Condition" variable.
%
% Create a pairwise similarity model that computes distance using dynamic time warping with an absolute distance metric.

mdl = pairwiseSimilarityModel('Method',"dtw",'Distance',"absolute");

%
% Train the similarity model using the training data. Specify the names of the life time and data variables.

fit(mdl,pairwiseTrainTables,"Time","Condition")

% Copyright 2012 The MathWorks, Inc.