clear; clc; close all;

load('params/materials_names')
load('params/names')

%% This script has to be ONLY used to test the generalization abilities
%% So, use this script only if you plan to train on one or more materials and test on DIFFERENT materials.
%% If you need to train and test on the same materials, use the other script: Prediction.m
%% Params
nTrees = 5000;
categories = [1,2,4,5];
%% Database to Train
materials = [1,3];
[DataTrain, ~, labelTrain, ~] = dataBaseLoader(categories, materials);
%% Database to Test
materials = [2];

[~, DataTest, ~, labelTest] = dataBaseLoader(categories, materials);
%% Training
disp('I am Training')
B = TreeBagger(nTrees,DataTrain,labelTrain, 'Method', 'classification'); 
disp('Training finished')
%% Testing
disp('I am testing')
predChar1 = B.predict(DataTest);  % Predictions is a char though. We want it to be a number.
predictedClass = str2double(predChar1);
accuracy=sum(predictedClass==labelTest)/length(labelTest);
disp("Accuracy: ")
disp(accuracy)

%%
cm = confusionchart(labelTest,predictedClass);

cm.NormalizedValues

fig = figure;
cm = confusionchart(labelTest,predictedClass,'RowSummary','row-normalized','ColumnSummary','column-normalized');
%Resize the container of the confusion chart so percentages appear in the row summary.

fig_Position = fig.Position;
fig_Position(3) = fig_Position(3)*1.5;
fig.Position = fig_Position;