public static void UnregisterTextInput()
{
_variable = null;
KeyGrabber.InboundCharEvent -= ProcessInput;
KeyGrabber.UnregisterMessageFilter();
_recievingRawInput = false;
}
public MyVariable(Data.Variable var, Type type) : base(var.Type, var.Name, var.Relation)
{
this.type = type;
this.objectName = var.Type;
this.alias = string.Empty;
this.isList = false;
}
public static void UnregisterTextInput()
{
_variable = null;
KeyGrabber.InboundCharEvent -= ProcessInput;
KeyGrabber.UnregisterMessageFilter();
_recievingRawInput = false;
}
public static void RegisterTextInput(Data.Variable variable)
{
_variable = variable;
KeyGrabber.InboundCharEvent += ProcessInput;
KeyGrabber.RegisterMessageFilter();
_recievingRawInput = true;
}
public static void RegisterTextInput(Data.Variable variable)
{
_variable = variable;
KeyGrabber.InboundCharEvent += ProcessInput;
KeyGrabber.RegisterMessageFilter();
_recievingRawInput = true;
}
private static void ProcessInput(char input)
{
//Only append characters that exist in the spritefont.
if (input == 13)
{
try
{
var method =
EngineGlobals.GenerateMethodFromString(
_variable.AsString.Replace(' ',';'));
method.ExecuteMethod(null);
ConsoleWindow.WriteLine("Executing...");
}
catch(EngineException ex)
{
ConsoleWindow.WriteLine("Warning: {0}", ex.Message);
}
_variable.AsString = "";
return;
}
if (input < 32 && input > 9)
return;
if (input > 126)
return;
if (input == 8)
{
if (_variable.AsString.Length > 0)
_variable.AsString = _variable.AsString.Substring(0, _variable.AsString.Length - 1);
}
else
_variable += input;
}
/// <summary>
/// Connectionist Temporal Classification is a loss function useful for performing supervised learning on sequence data, without needing an alignment between input data and labels. For example, CTC can be used to train end-to-end systems for speech recognition
/// </summary>
protected static Data.Function ConnectionistTemporalClassificationFunction(Data.Variable labels, Data.Variable predictions)
{
return(CNTKLib.EditDistanceError(predictions, labels, 0, 1, 1, true,
new SizeTVector(1)
{
(uint)labels.Shape.TotalSize
}));
}
/// <summary>
/// Tops the k accuracy.
/// </summary>
private static Function TopKAccuracyFunction(Data.Variable labels, Data.Variable predictions, uint k)
{
return((Data.Constant) 1f - (Data.Function)CNTKLib.ClassificationError(predictions, labels, k));
}
public static void UnregisterTextInput()
{
_variable = null;
_recievingRawInput = false;
}
public static void RegisterTextInput(Data.Variable variable)
{
_variable = variable;
_recievingRawInput = true;
}
/// <summary>
/// Binaries the cross entropy.
/// </summary>
protected static Data.Function BinaryCrossEntropyFunction(Data.Variable labels, Data.Variable predictions)
{
return(CNTKLib.BinaryCrossEntropy(predictions, labels));
}
/// <summary>
/// Crosses the entropy.
/// </summary>
protected static Data.Function CrossEntropyFunction(Data.Variable labels, Data.Variable predictions)
{
return(CNTKLib.CrossEntropyWithSoftmax(predictions, labels));
}
public static void UnregisterTextInput()
{
_variable = null;
_recievingRawInput = false;
}
public static void RegisterTextInput(Data.Variable variable)
{
_variable = variable;
_recievingRawInput = true;
}
/// <summary>
/// Sparses the cross entropy.
/// </summary>
protected static Data.Function SparseCrossEntropyFunction(Data.Variable labels, Data.Variable predictions)
{
return(CNTKLib.CrossEntropyWithSoftmax(predictions,
CNTKLib.Reshape(labels, new[] { labels.Shape.TotalSize })));
}
