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testball.m
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function [y1] = testball(x1)
%MYNEURALNETWORKFUNCTION neural network simulation function.
%
% Generated by Neural Network Toolbox function genFunction, 28-Apr-2016 18:08:26.
%
% [y1] = myNeuralNetworkFunction(x1) takes these arguments:
% x = Qx349 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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x1_step1_gain = 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x1_step1_ymin = -1;
% Layer 1
b1 = [1.3612215768095062;0.94369929257810814;0.60437891866296511;-0.47450873383855663;0.11435504802363132;0.13223426091816515;0.48270888985907545;0.62927800384169408;0.87499647086149779;1.1439125101425869];
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-0.079442756158024125 -0.06831968988748667 -0.0023320625247832505 -0.071140420667584622 0.019921816005268992 -0.035757043986022127 0.12602227407861036 -0.062572866152629858 0.13295035160195881 -0.0076308546410551892 -0.13962585235400415 0.026781308406641249 0.055715112451190388];
% Layer 2
b2 = [0.28335165424471487;-0.7754437961341617;-0.038493880588752902;-0.37485200974363875;-1.1549457366976141;-0.28396333013774772;0.037405759692655659];
LW2_1 = [-1.9535503085366241 0.7567129101935598 2.4377333983214995 -1.1935836462157516 1.280908074132731 2.8692086113601274 -0.88006181047410215 -1.4015523795074125 0.62707469681035599 -0.22672447622201644;-1.1226010537311584 -2.5707376317691004 0.80148811989857005 -0.80990228035293965 1.5437888539810449 -2.6296215095102045 0.34692437700529938 2.2525802012468157 1.7976395306216912 -1.2636953104910413;0.7313204510798692 -1.2507427059915637 -1.7998137632905784 -0.53543601227846138 -2.7143796128736555 2.6006394936451298 -0.32145116705173021 0.28436051106475069 0.99324625035815539 -2.3715004775385617;0.77160533582820612 2.1932776661605571 -1.4463041058494175 2.6834834492927762 1.0089808525263095 -0.48825918896477627 -1.1185353649151399 0.96055099207253525 2.2334886184990261 1.2025382163514631;0.90413859068603741 1.7828213623346707 2.5924571315913703 -0.94850903280726817 2.0222281989928437 -1.600221351002322 1.0713993727736335 0.71580012429297735 -2.5777443311367758 1.5819049942569254;0.28633338256021501 -2.339168781549132 -0.69835441312921609 0.81904060386042266 -1.0519823316720931 0.12761604043423788 2.0269695124874869 -2.5624370965803958 -2.0853619614865315 2.4584908066907176;-2.2121893359236551 1.5252378959340942 -0.95965270180062234 -1.1421586402150534 -2.9589936318637884 -1.3743112624852878 -1.5238303092244019 1.4950159944488823 -1.2973900189292278 0.55464667465005713];
% ===== 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