function evaluateFCNNwithTextInput() {
  if (fcnn == null) {
    document.getElementById("output-display-area").innerHTML = "Erreur, vous devez d'abord importer un RNCC valide.";
    return ;
  }
  var inputText = document.getElementById("importBox").value;
  var inputValues = document.getElementById("inputValues").value;
  inputValues = ''+inputValues.trim().replace(/ +(?= )/g,''); // remove multple spaces by a single one
  var input = null;
  if (inputValues.indexOf(",") != -1) {
    input = inputValues.split(",").map(function(x) {return parseFloat(x)});
  } else {
    input = inputValues.split(" ").map(function(x) {return parseFloat(x)});
  }

  fcnn.evaluateWithNewInput(input);

  outputValues = fcnn.getOutput();
  outputHTML = outputValues.map(function(valeur, index) {
    return "&emsp;Neuron " + (index+1) + " : " + valeur;
  }).join("<br>");
  document.getElementById("output-display-area").innerHTML = "Valeurs des neurones en sortie :<br>" +  outputHTML;

  fcnn.displaySelf();
}
/**
 * Load the neural network as described in the importBox text area and
 * evaluates it using the input values specified in the inputValues.
 */
Load the neural network as described in the importBox text area and
evaluates it using the input values specified in the inputValues.
Load the neural network as described in the importBox text area and evaluates it using the input values specified in the inputValues .
function evaluateFCNNwithTextInput() {
  if (fcnn == null) {
    document.getElementById("output-display-area").innerHTML = "Erreur, vous devez d'abord importer un RNCC valide.";
    return ;
  }
  var inputText = document.getElementById("importBox").value;
  var inputValues = document.getElementById("inputValues").value;
  inputValues = ''+inputValues.trim().replace(/ +(?= )/g,''); 
  var input = null;
  if (inputValues.indexOf(",") != -1) {
    input = inputValues.split(",").map(function(x) {return parseFloat(x)});
  } else {
    input = inputValues.split(" ").map(function(x) {return parseFloat(x)});
  }
  fcnn.evaluateWithNewInput(input);
  outputValues = fcnn.getOutput();
  outputHTML = outputValues.map(function(valeur, index) {
    return "&emsp;Neuron " + (index+1) + " : " + valeur;
  }).join("<br>");
  document.getElementById("output-display-area").innerHTML = "Valeurs des neurones en sortie :<br>" +  outputHTML;
  fcnn.displaySelf();
}
function evaluateFCNNwithTextInput ( ) { if ( fcnn == null ) { document . getElementById ( "output-display-area" ) . innerHTML = "Erreur, vous devez d'abord importer un RNCC valide." ; return ; } var inputText = document . getElementById ( "importBox" ) . value ; var inputValues = document . getElementById ( "inputValues" ) . value ; inputValues = '' + inputValues . trim ( ) . replace ( /  +(?= ) / g , '' ) ; var input = null ; if ( inputValues . indexOf ( "," ) != - 1 ) { input = inputValues . split ( "," ) . map ( function ( x ) { return parseFloat ( x ) } ) ; } else { input = inputValues . split ( " " ) . map ( function ( x ) { return parseFloat ( x ) } ) ; } fcnn . evaluateWithNewInput ( input ) ; outputValues = fcnn . getOutput ( ) ; outputHTML = outputValues . map ( function ( valeur , index ) { return "&emsp;Neuron " + ( index + 1 ) + " : " + valeur ; } ) . join ( "<br>" ) ; document . getElementById ( "output-display-area" ) . innerHTML = "Valeurs des neurones en sortie :<br>" + outputHTML ; fcnn . displaySelf ( ) ; }
Load the neural network as described in the importBox text area and
 evaluates it using the input values specified in the inputValues.
Load the neural network as described in the importBox text area and evaluates it using the input values specified in the inputValues .
// remove multple spaces by a single one