/// <summary>
/// Con este metodos almacenamos un nuevo simbolo en la base de
/// datos.
///
/// Lanza <c>DuplicateSymbolException</c> si ya existe un simbolo
/// con la misma etiqueta y caracteristicas binarias en la base de datos.
/// </summary>
/// <param name="bitmap">
/// La imagen cuyas caracteristicas aprenderemos.
/// </param>
/// <param name="symbol">
/// El simbolo que representa a la imagen.
/// </param>
public override bool Learn(MathTextBitmap bitmap, MathSymbol symbol)
{
if (characteristics == null)
{
characteristics = CharacteristicFactory.CreateCharacteristicList();
}
FloatBitmap processedBitmap = bitmap.LastProcessedImage;
CheckVector vector = CreateVector(processedBitmap);
int position = symbolsDict.IndexOf(vector);
if (position >= 0)
{
// The vector exists in the list
List <MathSymbol> symbols = symbolsDict[position].Symbols;
if (symbols.Contains(symbol))
{
return(false);
}
else
{
symbols.Add(symbol);
}
}
else
{
vector.Symbols.Add(symbol);
symbolsDict.Add(vector);
}
return(true);
}
/// <summary>
/// Este metodo intenta recuperar los simbolos de la base de datos
/// correspondiente a una imagen.
/// </summary>
/// <param name="image">
/// La imagen cuyos simbolos asociados queremos encontrar.
/// </param>
/// <returns>
/// Los simbolos que se puedieron asociar con la imagen.
/// </returns>
public override List <MathSymbol> Match(MathTextBitmap image)
{
if (characteristics == null)
{
characteristics = CharacteristicFactory.CreateCharacteristicList();
}
List <MathSymbol> res = new List <MathSymbol>();
FloatBitmap processedImage = image.LastProcessedImage;
CheckVector vector = CreateVector(processedImage);
// We have this image vector, now we will compare with the stored ones.
// We consider a threshold.
int threshold = (int)(characteristics.Count * epsilon);
foreach (CheckVector storedVector in symbolsDict)
{
// If the distance is below the threshold, we consider it valid.
if (vector.Distance(storedVector) <= threshold)
{
foreach (MathSymbol symbol in storedVector.Symbols)
{
// We don't want duplicated symbols.
if (!res.Contains(symbol))
{
res.Add(symbol);
}
}
string msg =
String.Format("Distancia({0}, {1}) <= {2}",
vector.ToString(),
storedVector.ToString(),
threshold);
msg += "\nSe añadieron los símbolos almacenados.";
StepDoneInvoker(new StepDoneArgs(msg));
}
else
{
string msg = String.Format("Distancia({0}, {1}) > {2}",
vector.ToString(),
storedVector.ToString(),
threshold);
StepDoneInvoker(new StepDoneArgs(msg));
}
}
return(res);
}
/// <summary>
/// Crea una lista con todas las caracteristicas disponibles en
/// el sistema.
/// </summary>
/// <returns>Lista de todas las clases que implementan
/// <c>IBinaryCharacteristic</c></returns>
public static List <BinaryCharacteristic> CreateCharacteristicList()
{
List <BinaryCharacteristic> characteristics = new List <BinaryCharacteristic>();
Assembly a = Assembly.GetAssembly(typeof(BinaryCharacteristic));
foreach (Type t in a.GetTypes())
{
if (t.BaseType == typeof(BinaryCharacteristic))
{
characteristics.Add(CharacteristicFactory.CreateCharacteristic(t));
}
}
characteristics.Sort();
return(characteristics);
}
/// <summary>
/// Con este metodos almacenamos un nuevo simbolo en la base de
/// datos.
///
/// Lanza <c>DuplicateSymbolException</c> si ya existe un simbolo
/// con la misma etiqueta y caracteristicas binarias en la base de datos.
/// </summary>
/// <param name="bitmap">
/// La imagen cuyas caracteristicas aprenderemos.
/// </param>
/// <param name="symbol">
/// El simbolo que representa a la imagen.
///</param>
public override bool Learn(MathTextBitmap bitmap, MathSymbol symbol)
{
if (characteristics == null)
{
characteristics = CharacteristicFactory.CreateCharacteristicList();
}
CharacteristicNode node = rootNode;
bool characteristicValue;
FloatBitmap processedBitmap = bitmap.LastProcessedImage;
// Recorremos las caracteristicas, y vamos creando el arbol segun
// vamos necesitando nodos.
foreach (BinaryCharacteristic bc in characteristics)
{
if (characteristicValue = bc.Apply(processedBitmap))
{
if (node.TrueTree == null)
{
node.TrueTree = new CharacteristicNode();
}
node = node.TrueTree;
}
else
{
if (node.FalseTree == null)
{
node.FalseTree = new CharacteristicNode();
}
node = node.FalseTree;
}
StepDoneArgs a =
CreateStepDoneArgs(bc, characteristicValue, null);
this.StepDoneInvoker(a);
}
return(node.AddSymbol(symbol));
}
/// <summary>
/// Este metodo intenta recuperar los simbolos de la base de datos
/// correspondiente a una imagen.
/// </summary>
/// <param name="image">
/// La imagen cuyos simbolos asociados queremos encontrar.
/// </param>
/// <returns>
/// Los simbolos que se puedieron asociar con la imagen.
/// </returns>
public override List <MathSymbol> Match(MathTextBitmap image)
{
if (characteristics == null)
{
characteristics = CharacteristicFactory.CreateCharacteristicList();
}
List <MathSymbol> res = new List <MathSymbol>();
CharacteristicNode nodo = rootNode;
BinaryCharacteristic bc;
bool exists = true;
bool characteristicValue;
List <bool> vector = new List <bool>();
FloatBitmap processedImage = image.LastProcessedImage;
for (int i = 0; i < characteristics.Count && exists; i++)
{
bc = (BinaryCharacteristic)(characteristics[i]);
if (characteristicValue = bc.Apply(processedImage))
{
if (nodo.TrueTree == null)
{
exists = false;
}
nodo = nodo.TrueTree;
}
else
{
if (nodo.FalseTree == null)
{
exists = false;
}
nodo = nodo.FalseTree;
}
StepDoneArgs args;
//Avisamos de que hemos dado un paso
if (nodo != null)
{
args = CreateStepDoneArgs(bc,
characteristicValue,
nodo.ChildrenSymbols);
}
else
{
args = CreateStepDoneArgs(bc,
characteristicValue,
null);
}
StepDoneInvoker(args);
}
if (exists)
{
res = nodo.Symbols;
}
return(res);
}