/// <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())); }