MATLAB: I have N samples of training data and M samples of test data, how i combine it together to make it MxN samples.

cell combine

testdata 40X1 cell

traindat 80X1 cell

train_label 80X1 double

I have N samples of training data and M samples of test data, how i combine it together to make it MxN samples. The rows, here, represent each sample and the columns the different types of features detected from a sample. also i want to add an extra column at LAST of the data (preferably): This column should represent the desired labels for the data.

Best Answer

Following code shows how to convert the cell array from your code to the table as generated by fishertable = readtable('fisheriris.csv')

load('db3.mat');
testdata = cell2mat(reduced_testdata')';
testdata_table = array2table(testdata, ...
    'VariableNames', ...
    {'SepalLength', 'SepalWidth', 'PetalLength', 'PetalWidth'});
sample_labels = repmat("abcd", 40, 1); % creating random data to fill last column
                                       % you should use your testlabels
testdata_table = addvars(testdata_table, sample_labels, ...
    'NewVariableNames', 'Species');

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MATLAB: How do you update the legend of Figure 2 instead of Figure 1

Hello,

In the code of figure 2, you should change a bit like this

figure
Class1_Synthetic = scatter(SLClass1_Normalized,PWClass1_Normalized,'b')
hold on;
set(Class1_Synthetic,{'DisplayName'},{'Synthetic Iris Data (blue)'})

MATLAB: How do you update the legend of Figure 2 and Figure 3 instead of Figure 1

When using the set function for Figure 2 and Figure 3,I had accidentally kept referring to the same plot name for Figure 1. Here is my corrected code.

%% The column vector, |species|, consists of iris flowers of three
% different species, setosa, versicolor, virginica. The double matrix
% |features| consists of four types of measurements on the flowers, the length
% and width of sepals and petals in centimeters, respectively.
features = xlsread("iris.xls", 2); %sepal length, sepal width, petal length, petal width
labels = xlsread("iris.xls", 3); % flower type classes
species = cell(size(labels));
species(labels==1)={'Setosa'};
species(labels==2)={'Versicolor'};
species(labels==3)={'Virginica'};
% The names of features are stored in an array.
feature_names = ["Sepal Length", "Sepal Width", "Petal Length", "Petal Width"];
%% Features
% Use petal length (third column in |features| ) and petal width (fourth column
% in |features| ) measurements. Save these as variables PL and PW,
% respectively.
% 4 features & 3 classes
SL = features(:,1);
SW = features(:,2);
PL = features(:,3);
PW = features(:,4);
%% Recall Original Iris Data 
% Recall Iris Setosa
Class1 = features(1:50,:);  
SLClass1 = features(1:50,1);  
SWClass1 = features(1:50,2);  
PLClass1 = features(1:50,3);  
PWClass1 = features(1:50,4);  
% Recall Iris Versicolor
Class2 = features(51:100,:);
SLClass2 = features(51:100,1);
SWClass2 = features(51:100,2);
PLClass2 = features(51:100,3);
PWClass2 = features(51:100,4);
% Recall Iris Virginica
Class3 = features(101:150,:);
SLClass3= features(101:150,1);
SWClass3= features(101:150,2);
PLClass3= features(101:150,3);
PWClass3= features(101:150,4);
%% Synthetic Data
%% Class 1 - SL vs. PW
% Plot Original Class 1 - Iris SL vs. PW.
figure
plotIris1 = scatter(SLClass1,PWClass1,'r')
hold on;
set(plotIris1,{'DisplayName'},{'Iris Data (red)'})
legend show;
title('Original Iris Data - Class 1 - Sepal Length vs. Petal Width');
xlabel('Sepal Length');
ylabel('Petal Width');
hold off;
%% Generate Random Data
% Create Random Data with 100 Additional Observations for Each Class
% Class 1 Randoms. Generate 100 extra observations. Random Data must be 100 x 4 and normally distributed.
Class1_r = randn(100,4); 
%Class1_r = randi([0 1],100,4); % Randi did not create a truly random data.
 SLClass1_r = Class1_r(:,1);
 SWClass1_r = Class1_r(:,2);
 PLClass1_r = Class1_r(:,3);
 PWClass1_r = Class1_r(:,4);
 
a = min(SLClass1_r(:));
b = max(SLClass1_r(:));
ra = 0.0886;
rb = 0.4684;
SLClass1_Normalized = (((ra-rb) * (SLClass1_r - a)) / (b - a)) + rb;
 
a = min(PWClass1_r(:));
b = max(PWClass1_r(:));
ra = -0.9200;
rb = -0.5200;
PWClass1_Normalized = (((ra-rb) * (PWClass1_r - a)) / (b - a)) + rb;
 
 
% Plot Synthetic Data - Class 1 - SL vs. PW
figure(2)
Class1_Syntheticslpw = scatter(SLClass1_Normalized,PWClass1_Normalized,'b')
hold on;
set(Class1_Syntheticslpw,{'DisplayName'},{'Synthetic Iris Data (blue)'})
legend show
title('Synthetic Iris Data - Class 1 - Sepal Length vs. Petal Width');
xlabel('Sepal Length');
ylabel('Petal Width');
% Plot Combined Graphs -  Class 1 - SL vs. PW
figure;
plotIris1 = scatter(SLClass1,PWClass1,'r')
set(plotIris1,{'DisplayName'},{'Iris Data (red)'})
hold on;
legend show
title('Class 1 - SL vs. PW - Original & Synthetic Data');
xlabel('Sepal Length');
ylabel('Petal Width');
Class1_Synthetic_slpw = scatter(SLClass1_Normalized,PWClass1_Normalized,'b')
set(Class1_Synthetic_slpw,{'DisplayName'},{'Synthetic Iris Data (blue)'})
legend show
hold off;

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MATLAB: How do you update the legend of Figure 2 instead of Figure 1

MATLAB: How do you update the legend of Figure 2 and Figure 3 instead of Figure 1

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