How to Use Neural Network After Deployment?

I know that this is a fairly basic question, but after I have generated the code for my neural network how do I actually feed new data into it to predict outcomes? I have looked around but the tutorials out there seem to focus more on how to train the network than on how to use it after you have generated it. Below is the code for my algorithm:

function [Y,Xf,Af] = myNeuralNetworkFunction(X,~,~)
%MYNEURALNETWORKFUNCTION neural network simulation function.
%
% Generated by Neural Network Toolbox function genFunction, 25-Sep-2014 13:48:20.
% 
% [Y] = myNeuralNetworkFunction(X,~,~) takes these arguments:
% 
%   X = 1xTS cell, 1 inputs over TS timsteps
%   Each X{1,ts} = 24xQ matrix, input #1 at timestep ts.
% 
% and returns:
%   Y = 1xTS cell of 1 outputs over TS timesteps.
%   Each Y{1,ts} = 3xQ matrix, output #1 at timestep ts.
% 
% where Q is number of samples (or series) and TS is the number of timesteps.

%#ok<*RPMT0>

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

    % Input 1
    x1_step1_xoffset = [14.8911704312115;15.2767639519654;0.51064;1.90413;33.60375;15.43632;24.71805;0.01846;4.981;0.00033;0.22159;39.32377;19.36387;9.81086;0.22033;4.6786;14.9629;8.8028;4.74512;0.55702;660.25663;8.59155;0.37416;17.83039];
    x1_step1_gain = [0.027354428009736;0.0612618147797722;0.53764452556903;0.108070429498904;0.00273415535440648;0.0167784432574442;0.00143874231937304;0.483828047511914;0.499625281039221;0.652986597450087;1.59790355054169;0.00545387219881284;0.018425510635389;0.00896113957019686;0.837910418997105;0.00686875293360145;0.00614397382765453;0.0196487182502502;0.10665671204021;1.48306341579166;0.000773677797796089;0.0480697241735192;0.314333448065042;0.00523034792222075];
    x1_step1_ymin = -1;

    % Layer 1
    b1 = [1.55987050956456;1.3844896446184087;1.1858356678234501;-1.0437094397090567;-0.7695743943051927;0.7727728185085837;-0.49850097623420581;0.3360196027312633;-0.5052450230407648;0.12573629438984127;-0.36142377450191465;-0.26512162787695787;0.43422049198818885;-0.59484996326687367;-0.74347840297622803;-1.1241254208475855;1.0621269773342072;-1.1217914200306649;1.4625923192101142;-1.5876844882508814];
    IW1_1 = [-0.055982436209679104 -0.28944502014743512 0.38896483763687473 -0.17520091781902516 -0.36788963119290879 0.58680010891648471 0.38941048594029137 0.010330200046712359 -0.049196997026008424 -0.25965095783883646 -0.02330444839336468 0.48471956663410432 0.35540617499483801 -0.34500146111020641 -0.0306422432801076 -0.20558648541285637 -0.48852403830931596 0.58018036801883965 -0.21778584198672668 -0.17639473933123317 -0.12894993242501401 -0.32191664913965018 0.36502076112253562 -0.35423424394067804;-0.44842057227554855 0.39700388069390274 0.18239852514402061 0.33330369404033905 -0.33083266709586073 -0.42109550142439961 -0.37936412673907055 0.14092987565726023 0.27498893910037497 -0.037696461239827603 0.42058106837803533 -0.35405721057928519 -0.31200898537356103 0.3266151312604435 0.24758020461158411 -0.00028318649770493406 0.48622068190587153 -0.050159207180306711 0.038521280211102946 -0.43752851101801921 -0.40917699558077231 -0.31417436789272912 -0.30924992912408938 0.38587651437275444;-0.23169755933546252 -0.033754347614425945 -0.43966963319617031 -0.10362737153323315 -0.23869583433068692 -0.018842535943556636 -0.27934947951447109 -0.43974815194076045 -0.33977835068587897 0.17162613017120945 -0.36065553178060761 -0.39840592499124527 -0.10812891657493709 -0.39993101606248699 0.19264396978343784 -0.56134807445038559 -0.3646549207685566 0.03693663630665505 0.28992344266856157 -0.24197846571776097 -0.28421945026123852 0.2238898665802749 0.57957009993848951 0.52865696060643663;0.46775595437716078 -0.49759398923140102 -0.25543635820865557 0.18011414654693347 0.22839857302768798 -0.39817052105690232 0.42124548524474159 -0.38302040274848675 -0.45018774604344564 0.30776271594062077 -0.053687146243323117 0.21008425591895208 -0.25086014236085991 0.46442044353123357 0.37013016450855851 -0.35562597240295934 0.48246075163860352 -0.22143337282043704 -0.15354622986226024 -0.19520148498923329 0.28175659072346265 -0.19991745788738577 -0.265495614539076 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0.086756882167071767;0.20194393498084873 0.33601377745253913 -0.10774209305964288 -0.29722624992594709 0.30223692004740155 -0.41190860275852431 0.32459858890469206 -0.4103664016524477 0.1718744178031123 0.36646047872493631 0.24021043279112594 -0.47527338312608136 0.45522056842903807 0.4663571072074067 -0.15619524330182147 0.17042159594600717 -0.0094643117123553756 0.34972229572902347 -0.35841045332376159 0.44184179501847487 0.50657491877032257 -0.27251535338974842 0.053976124783220322 -0.45817262113177243;-0.48112733765313997 -0.15382945548644572 0.59568206101452548 0.071917432620535626 0.14546994145938053 -0.029054476752923268 -0.44239514130279495 -0.49288663170035307 0.61976876211435661 -0.35515864087981674 -0.26095866399195611 -0.49938787137002588 0.041803130505081311 0.35681709414517526 -0.24095955980424952 0.26090619437256735 0.27496224831511668 -0.10807493710234177 0.11792476953631001 0.12084195942242892 0.13552026682635013 0.26233519005540168 0.48436137135289897 0.41261415007384206;0.21895780734810372 -0.1511323910665277 -0.30822548003275396 -0.33710575421358474 0.034674652442276827 0.15101859650020807 -0.011485449807831588 -0.25985968754425742 -0.43197950537600155 0.43497607287821932 -0.064820281951947667 -0.073022254701932118 0.015714438133674614 -0.023776065260460819 0.34997606652439067 0.37469844465000585 -0.25689851488533372 0.51569778564256707 -0.44838098074602389 0.31305831108437643 0.51306142767010743 -0.40214657636097817 -0.41763716358690595 0.45699543228831752;-0.53017416936845407 0.42831417232332336 0.11786784798380144 0.039227806546526515 -0.29763012137133199 0.24637901223770131 0.29762857397361714 -0.013347397012488965 -0.4627281529891169 0.24554434668388281 0.39129352061411959 0.379553553064787 0.04415707626655066 -0.11971558212994232 -0.15276429992136409 0.45228689028401242 -0.50322528984760739 0.046552169551343309 -0.37788000442556102 -0.29612757986196442 0.33798412345818085 0.069089800822850531 -0.49046720164829072 0.26511335230057065;0.20196677020567266 0.095488430461431573 0.16399244062844004 0.52716962232017073 0.4348012802986464 0.018067285639911158 -0.26705074363994968 0.48825114495670008 0.14127835124956273 0.24629868830206755 0.54017198057440774 0.06480510843471024 -0.022685160228366745 -0.15438176399914305 -0.43132627576811389 -0.13129532598575516 0.096451476137207587 0.5926396743011304 0.091208771991736159 0.42720517218438908 -0.29783725969092761 0.65683408878263738 0.36153177278145338 -0.22919208447591802;-0.50452115900815619 0.0030003517081181488 0.46134789637779744 -0.60227711400545947 -0.48214549250915872 0.013063050810550585 0.15135559839444082 0.39143093064769241 -0.14086525474058859 -0.21731704108556071 -0.054170772583192923 -0.49764000322426905 -0.30456997680804121 0.18375313948369021 0.41927012683947518 0.19128196902407038 -0.37761853390034739 -0.26576194181536378 0.07726206216389829 -0.38520339916841162 0.025887458179402578 -0.11906795914922447 -0.56692586761027453 0.13538403175109057;0.35891662644586675 -0.37425630339293836 -0.13087333241744242 -0.16492608364523814 -0.47868126798293337 -0.29135042798915084 -0.32892700173155859 -0.41341689342409382 -0.47684656050809077 -0.40828991848978646 -0.22424380129271532 0.29329791200577621 0.52835706198039978 -0.24064601775448177 -0.078866115462040517 0.29104242992340346 -0.24155428609588137 -0.12412108758956597 -0.12661134901318469 0.53644335880766647 0.12353437383200713 0.044443354598736433 0.21915106707248028 0.45738952754175133;-0.4006229555345403 -0.38491870113799098 0.12684216755732858 0.23789761071382912 0.25780049722498549 0.12128670064482043 0.35455194739691609 -0.4402274458485313 0.26906181992343847 0.57958726634921054 -0.025706324845669152 0.16140130401483566 -0.060519286269974548 -0.49816799130907657 -0.32786559565987894 0.16032470166880464 0.47671706759696875 0.12126382162524643 0.11497301319122195 -0.060984633499597526 0.46572964627020685 0.53029114632588248 0.34913629799983792 0.3783603854876138;-0.23751836785709313 0.48943920219833192 -0.29803103360979344 0.51154553661021629 -0.10243898977969999 0.1738700091501662 0.40995996639230364 0.39877231971176491 0.019918105182817482 0.15560102498926082 -0.098114106905600795 -0.15612390887354927 0.17301979172967061 0.567425194056702 -0.53484903500060321 0.17128726452515966 -0.34133214953438257 0.36482792066829411 0.40500232676302061 -0.3046651610223628 0.14649536078138331 -0.23949813135658626 -0.36393981963734012 0.22581701137418828;-0.40046957858520676 0.6034617628482869 -0.48336085729216799 0.086551313937414401 0.11485328389518146 0.069661771564103803 0.047510680842533633 -0.14211127107043481 0.22762855836704607 -0.21910785086188639 -0.29622202947083798 0.010467063845525388 0.23958478560501784 0.47212874176804626 0.1021228950976064 -0.36988758340717282 0.43616237795047241 -0.34513066977461881 0.14396012746107209 0.17986038875407201 0.48818701767399414 0.28917556942311645 -0.10086763238985247 0.33793394200564547;0.46284253016065918 -0.13727771516669529 0.13577149568213143 0.58818192843346606 -0.20148925476427895 -0.46029269863375366 -0.11294539393881832 0.024584643894249029 0.096542815957358211 -0.47936958224293125 -0.38391418257758381 -0.0060708736228519012 0.31378810959162057 0.29455211667194553 -0.14010352441871604 -0.33539638866126609 -0.15418382335315545 -0.53468970341166444 -0.14260673859838446 -0.1588041819874888 -0.38130427331586136 -0.3152783892401666 -0.13797679272406654 0.41318952672339815;-0.0037580606519731774 0.25559446638500755 0.20805798628313835 -0.1197347663038016 0.25029241343311948 -0.19266109493430622 0.52557544297059011 -0.1619733303821082 -0.59587716408971514 -0.37733940188634407 -0.36931374243099563 0.035618524883801912 -0.36574413766651082 0.62552801072201847 -0.15345989677803071 -0.26277675374506049 -0.69325217331710443 -0.19889757010483353 0.080406773105486662 0.075152499178945523 -0.2528234547543543 0.47888754226850988 0.44361594565071144 0.13773610213966411;0.20986694884019669 0.5838069905096277 -0.24368635959982227 -0.31599013988815555 -0.18540921192951351 0.10603309773028717 -0.67295724327081385 -0.24897469750728726 0.19173947449901921 0.51005578175094068 -0.015723561815696684 0.1587217224633255 -0.29884049261948881 -0.0039568545329125258 -0.1061339521622824 0.16201250459705921 -0.28062245454944174 0.10496187687903319 -0.34786621560772413 0.32270413963798411 -0.054666413518356639 -0.31614441259179926 -0.25305596431344596 -0.62685043098789994;-0.44657252161722044 0.40823814755654514 0.064212018038187074 0.0066019305589507665 0.25098026347930169 -0.18569199430015434 -0.11776683585309426 0.46833540687344827 -0.19916527198638995 -0.2905264441725523 -0.45266095490372243 -0.34687790231020543 0.2229130626291119 -0.38366535966479787 0.017187383060134738 -0.29935260697370364 -0.38314823395341346 0.44303063064199694 0.2368545199511122 0.35728303698551611 -0.42103390461422724 0.37014001452647027 0.38735250852526876 -0.23260623539266509];

    % Layer 2
    b2 = [0.25980539367757699;0.58647489488472682;-1.065941024916949];
    LW2_1 = [0.070686644098666221 -0.62704849467284407 -0.26668403380813344 -0.60847661573672651 0.49634405645024138 0.96115472226825938 0.70161314683215703 -0.16244831393351064 -0.38778721201635435 -0.82737438162882337 -0.24871150318794885 0.33797064247996977 0.53933794530594548 -0.37673777430541439 0.66603786612798555 -0.86058356658893731 -0.1846439763729246 0.25597795984504707 0.88301766928111958 0.70154602431628132;-0.27224631592661641 -0.46534028660614779 0.31748339396483055 -0.77568458059698819 -0.19985189513930471 0.058899860449242475 -0.10649552828923813 0.59251644442143325 0.54884630250946731 -0.74722920996123465 0.78936347266716889 0.68795991441040816 -0.11649640733421024 0.88899475748526757 -0.27752223550178284 0.12836172010559904 0.34304188529668661 -0.3303334581681488 0.26083195268179116 0.28358224497140561;0.67722868060993668 -0.73839160801059034 -0.52668020024017737 -0.42037687153952519 0.42608979733760222 -0.085145700117314219 0.40224829869858036 -0.4380368280033714 0.22960637835244566 -0.61906073689858399 0.55422321506755923 -0.15626454164673309 0.58835836575191547 -0.89973441932108511 -0.77901845165683903 0.77329875011083038 -0.68252063535418506 -0.80613188132936098 0.85129342660612917 0.86638907626986761];

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

    % Format Input Arguments
    isCellX = iscell(X);
    if ~isCellX, X = {X}; end;

    % Dimensions
    TS = size(X,2); % timesteps
    if ~isempty(X)
      Q = size(X{1},2); % samples/series
    else
      Q = 0;
    end

    % Allocate Outputs
    Y = cell(1,TS);

    % Time loop
    for ts=1:TS

      % Input 1
      Xp1 = mapminmax_apply(X{1,ts},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
      Y{1,ts} = a2;
    end

    % Final Delay States
    Xf = cell(1,0);
    Af = cell(2,0);

    % Format Output Arguments
    if ~isCellX, Y = cell2mat(Y); end
  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

Answer 1

[Greg Heath]

See

     help gensim

     doc gensim

Hope this helps.

Thank you for formally accepting my answer

Greg

Answer 2

[KAE]

I have added some comments to an example in the sim documentation which does what you want (thanks StarStrider for the earlier suggestion to look there),

% Load the data
[x,t] =  bodyfat_dataset;
% In x there are 13 quantities measured and 
% we have 252 examples of these measurements, for example 252 patients
[nVar, nExamples] = size(x); 
% In t there is 1 quantity, body fat, which we want to predict

% Make a network with 1 hidden layer containing 10 neurons  
net = feedforwardnet(10);
% Train the network on the data
net = train(net,x,t);

% Apply the network to some new data to get a prediction
% We can have a different number of examples in the new dataset, but 
% must have the same number of variables for the model to work
xNew = rand(nVar, 100); % Fake new data for 100 new patients

y = net(xNew); % Body mass predicted from the new measurements
% Confusingly, we never call the sim function here:
% sim is called implicitly by calling the neural network as a function