/// <summary>
/// Takes in a Hopfield pattern and returns the pattern it most closely matches.
/// </summary>
/// <param name="inputPattern"></param>
/// <returns></returns>
public HopfieldPattern Recognize(HopfieldPattern inputPattern)
{
var recognizedPattern = HopfieldPattern.CreateEmptyPattern(inputPattern.Length);
// Get value from each column and place it in an array.
for(var colIndex=0; colIndex < inputPattern.Length; colIndex++)
{
// We need to sum the totals for each column
var columnResult = 0.0;
for(var rowIndex=0; rowIndex < inputPattern.Length; rowIndex++)
{
columnResult += _WeightMatrix.GetValue(rowIndex, colIndex) * inputPattern[rowIndex];
}
// Then place each column's scalar result in the result vector.
recognizedPattern[colIndex] = columnResult;
}
// switch from bipolar form
for (var i = 0; i < recognizedPattern.Length; i++)
{
recognizedPattern[i] = recognizedPattern[i] < 0 ? 0 : 1;
}
// We have the result vector so return it as a pattern.
return recognizedPattern;
}
/// <summary>
/// Takes in a Hopfield pattern and returns the pattern it most closely matches.
/// </summary>
/// <param name="inputPattern"></param>
/// <returns></returns>
public HopfieldPattern Recognize(HopfieldPattern inputPattern)
{
var recognizedPattern = HopfieldPattern.CreateEmptyPattern(inputPattern.Length);
// Get value from each column and place it in an array.
for (var colIndex = 0; colIndex < inputPattern.Length; colIndex++)
{
// We need to sum the totals for each column
var columnResult = 0.0;
for (var rowIndex = 0; rowIndex < inputPattern.Length; rowIndex++)
{
columnResult += _WeightMatrix.GetValue(rowIndex, colIndex) * inputPattern[rowIndex];
}
// Then place each column's scalar result in the result vector.
recognizedPattern[colIndex] = columnResult;
}
// switch from bipolar form
for (var i = 0; i < recognizedPattern.Length; i++)
{
recognizedPattern[i] = recognizedPattern[i] < 0 ? 0 : 1;
}
// We have the result vector so return it as a pattern.
return(recognizedPattern);
}
public void Setup()
{
Pattern1 = new HopfieldPattern(0.0, 1.0, 0.0, 1.0);
Pattern2 = new HopfieldPattern(1.0, 0.0, 0.0, 1.0);
Network = new HopfieldNetwork(4);
Network.Train(Pattern1);
Network.Train(Pattern2);
}
public void Train(HopfieldPattern pattern)
{
_WeightMatrix = _WeightMatrix.Add(new Matrix(pattern.GetWeightMatrix()));
}
private void RecognizeButton_Click(object sender, RoutedEventArgs e)
{
var colCount = 5;
var rowCount = 5;
var pattern = new double[colCount * rowCount];
for (var row = 0; row < rowCount; row++)
{
for (var col = 0; col < colCount; col++)
{
pattern[row * colCount + col] = ((ToggleButton)_GetControlIn(DrawingGrid, new GridCoordinates(row, col))).IsChecked.Value
? 1.0
: 0.0;
}
}
var hopfieldPattern = new HopfieldPattern(pattern);
var recognizedPattern = hopfieldNetwork.Recognize(hopfieldPattern);
for (var row = 0; row < rowCount; row++)
{
for (var col = 0; col < colCount; col++)
{
((ToggleButton) _GetControlIn(DrawingGrid, new GridCoordinates(row, col))).IsChecked =
recognizedPattern[row*colCount + col] <= 0
? false
: true;
}
}
}
private void TrainButton_Click(object sender, RoutedEventArgs e)
{
var colCount = 5;
var rowCount = 5;
var pattern = new double[colCount * rowCount];
for(var row=0; row<rowCount; row++)
{
for(var col=0; col<colCount; col++)
{
pattern[row*colCount + col] = ((ToggleButton)_GetControlIn(DrawingGrid, new GridCoordinates(row, col))).IsChecked.Value
? 1.0
: 0.0;
}
}
var hopfieldPattern = new HopfieldPattern(pattern);
hopfieldNetwork = hopfieldNetwork ?? new HopfieldNetwork(25);
hopfieldNetwork.Train(hopfieldPattern);
}
public void Train(HopfieldPattern pattern)
{
_WeightMatrix = _WeightMatrix.Add(new Matrix(pattern.GetWeightMatrix()));
}
