template MatLab script from nnstart, saved dataset to csv for possible py use

README.md

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+# Neural Network Training Coursework
+
+Evaluating a neural network using the MatLab `cancer_dataset`.
+
+1. Evaluate the network's tendency to overfit by varying the number of epochs and hidden layers being used
+2. Multiple classifier performance using majority vote
+3. Repeat 2 with two different optimisers (`trainlm`, `trainrp`)
+4. ***Extension***: Distinguish between two equi-probable classes of overlapping 2D Gaussians

features.csv

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matlab/data.m

@@ -0,0 +1,5 @@
+%% load and export dataset
+[cancerInputs, cancerTargets] = cancer_dataset;
+
+writematrix(cancerInputs, 'features.csv')
+writematrix(cancerTargets, 'targets.csv')

matlab/nn.m

@@ -0,0 +1,96 @@
+% Solve a Pattern Recognition Problem with a Neural Network
+% Script generated by Neural Pattern Recognition app
+% Created 02-Mar-2021 18:31:05
+%
+% This script assumes these variables are defined:
+%
+%   cancerInputs - input data.
+%   cancerTargets - target data.
+
+x = cancerInputs;
+t = cancerTargets;
+
+% Choose a Training Function
+% For a list of all training functions type: help nntrain
+% 'trainlm' is usually fastest.
+% 'trainbr' takes longer but may be better for challenging problems.
+% 'trainscg' uses less memory. Suitable in low memory situations.
+trainFcn = 'trainscg';  % Scaled conjugate gradient backpropagation.
+
+% Create a Pattern Recognition Network
+hiddenLayerSize = 10;
+net = patternnet(hiddenLayerSize, trainFcn);
+
+% Choose Input and Output Pre/Post-Processing Functions
+% For a list of all processing functions type: help nnprocess
+net.input.processFcns = {'removeconstantrows','mapminmax'};
+
+% Setup Division of Data for Training, Validation, Testing
+% For a list of all data division functions type: help nndivision
+net.divideFcn = 'dividerand';  % Divide data randomly
+net.divideMode = 'sample';  % Divide up every sample
+net.divideParam.trainRatio = 50/100;
+net.divideParam.valRatio = 0/100;
+net.divideParam.testRatio = 50/100;
+
+% Choose a Performance Function
+% For a list of all performance functions type: help nnperformance
+net.performFcn = 'crossentropy';  % Cross-Entropy
+
+% Choose Plot Functions
+% For a list of all plot functions type: help nnplot
+net.plotFcns = {'plotperform','plottrainstate','ploterrhist', ...
+    'plotconfusion', 'plotroc'};
+
+% Train the Network
+[net,tr] = train(net,x,t);
+
+% Test the Network
+y = net(x);
+e = gsubtract(t,y);
+performance = perform(net,t,y)
+tind = vec2ind(t);
+yind = vec2ind(y);
+percentErrors = sum(tind ~= yind)/numel(tind);
+
+% Recalculate Training, Validation and Test Performance
+trainTargets = t .* tr.trainMask{1};
+valTargets = t .* tr.valMask{1};
+testTargets = t .* tr.testMask{1};
+trainPerformance = perform(net,trainTargets,y)
+valPerformance = perform(net,valTargets,y)
+testPerformance = perform(net,testTargets,y)
+
+% View the Network
+view(net)
+
+% Plots
+% Uncomment these lines to enable various plots.
+figure, plotperform(tr)
+%figure, plottrainstate(tr)
+%figure, ploterrhist(e)
+figure, plotconfusion(t,y)
+%figure, plotroc(t,y)
+
+% Deployment
+% Change the (false) values to (true) to enable the following code blocks.
+% See the help for each generation function for more information.
+if (false)
+    % Generate MATLAB function for neural network for application
+    % deployment in MATLAB scripts or with MATLAB Compiler and Builder
+    % tools, or simply to examine the calculations your trained neural
+    % network performs.
+    genFunction(net,'myNeuralNetworkFunction');
+    y = myNeuralNetworkFunction(x);
+end
+if (false)
+    % Generate a matrix-only MATLAB function for neural network code
+    % generation with MATLAB Coder tools.
+    genFunction(net,'myNeuralNetworkFunction','MatrixOnly','yes');
+    y = myNeuralNetworkFunction(x);
+end
+if (false)
+    % Generate a Simulink diagram for simulation or deployment with.
+    % Simulink Coder tools.
+    gensim(net);
+end

targets.csv

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