LargestObject_NeuralNetworkFunction.m

function [y1] = myNeuralNetworkFunction(x1)
%MYNEURALNETWORKFUNCTION neural network simulation function.
%
% Generated by Neural Network Toolbox function genFunction, 21-Mar-2016 18:42:50.
%
% [y1] = myNeuralNetworkFunction(x1) takes these arguments:
%   x = Qx1024 matrix, input #1
% and returns:
%   y = Qx7 matrix, output #1
% where Q is the number of samples.

%#ok<*RPMT0>

% ===== NEURAL NETWORK CONSTANTS =====

% Input 1
x1_step1_xoffset = 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-0.0035369782649943638 -0.011650220515383662 0.10847244885662929 0.10333223299688576 -0.058945784107957692 -0.082358648262188472 0.082737039714241234 -0.0062949689654152241 -0.01235331401930518 -0.036099477717790297 -0.013928539752347466 -0.02210246871931594 -0.053170875461063354 0.081866717689666108 0.086411283255482529 0.055883079862689443 0.029950839078781183 -0.036673217627973841 -0.092739608521134442 0.11285408167989762 -0.083530671893752045 -0.00058074509061089999 0.094917759067474183 0.093789387081807216 0.0010089136305107297 0.091748460658556838 0.038263100386004667 0.011399945179135397 -0.094706485631418522 -0.0025857580528303656 0.0071100570038714374 -0.030038757225834323 0.014330419837407285 0.02107319042256009];

% Layer 2
b2 = [-0.53041300348039033;0.72823960764008266;-0.34904664120749934;-0.4505910553280153;0.80773236978924801;-0.7010222945669794;-0.7066133912539605];
LW2_1 = [-0.90537320433243595 0.48711810875095912 0.338503993018475 0.88654473715248849 0.91467924803338108 -0.57549463189286443 -0.24664734020215179 1.0910888304431194 1.1791932862083068 0.94628619275259562;0.24247954613949166 -1.7360446216834697 0.21744303887443744 -0.810115237939663 -0.74049172034801802 -0.8740801935420115 1.2555139603668888 -0.76789764583845477 -1.078432170230865 -0.78059309514238184;0.98000575568654524 0.82101774970270069 -0.53682272112797247 0.78960735473321875 -0.9070322436346604 -1.1343165659608612 1.3860529473577328 -0.9917040795656844 -0.12199569098864885 0.48773270194425544;-1.2882931018815238 0.6412841658729882 0.050452402880852426 -1.0803295035555729 1.0654762856311364 -0.22956882661147179 -0.35942721008650536 -1.9786011869179101 -0.1871014488896928 -1.2103688085907498;0.43226485382302215 0.84756592956499011 0.85289619604920552 1.0976581312660789 -1.0860319009759374 1.591995675325609 -0.86029794026690543 -0.68372135822538938 0.13824367478909838 -0.9962423257498989;-1.0962443629420195 0.58487392778893033 -0.27585084867250476 -1.0506914577748612 -0.99578742462079461 0.61080200365798332 -0.073026946440886739 0.70654052135636347 -1.531104673991043 1.2352560994665585;0.20758623761794917 -0.25519524691838252 0.14807467595694687 -1.2363602140467154 1.0054664509586229 -0.80665359507787482 -0.91023745139871037 1.6015562288093252 0.23479510300234097 -1.5909929440270179];

% ===== SIMULATION ========

% Dimensions
Q = size(x1,1); % samples

% Input 1
x1 = x1';
xp1 = mapminmax_apply(x1,x1_step1_gain,x1_step1_xoffset,x1_step1_ymin);

% Layer 1
a1 = tansig_apply(repmat(b1,1,Q) + IW1_1*xp1);

% Layer 2
a2 = softmax_apply(repmat(b2,1,Q) + LW2_1*a1);

% Output 1
y1 = a2;
y1 = y1';
end

% ===== MODULE FUNCTIONS ========

% Map Minimum and Maximum Input Processing Function
function y = mapminmax_apply(x,settings_gain,settings_xoffset,settings_ymin)
y = bsxfun(@minus,x,settings_xoffset);
y = bsxfun(@times,y,settings_gain);
y = bsxfun(@plus,y,settings_ymin);
end

% Competitive Soft Transfer Function
function a = softmax_apply(n)
nmax = max(n,[],1);
n = bsxfun(@minus,n,nmax);
numer = exp(n);
denom = sum(numer,1);
denom(denom == 0) = 1;
a = bsxfun(@rdivide,numer,denom);
end

% Sigmoid Symmetric Transfer Function
function a = tansig_apply(n)
a = 2 ./ (1 + exp(-2*n)) - 1;
end