/// <summary>
/// Loads the standard data from file.
/// </summary>
/// <returns>Collection with datas</returns>
public static TrainingCollection LoadStandardData()
{
const int HOW_MANY_NUMBERS_TO_LEARN_FROM_FILE = 1000;
TrainingCollection collection = new TrainingCollection(HOW_MANY_NUMBERS_TO_LEARN_FROM_FILE);
GiveItemsFromFile itemReturner = new GiveItemsFromFile();
sbyte[,] expectedResultFromFile = itemReturner.GiveArrayOfExpectedResults();
sbyte[,] trainDataFromFile = itemReturner.GiveArrayOfTrainData();
const int NUMBER_OF_RESULTS = 10;
const int SIZE_OF_IMAGE = 28 * 28;
for (int numberCounter = 0; numberCounter < HOW_MANY_NUMBERS_TO_LEARN_FROM_FILE; numberCounter++)
{
double[] expectedResult = new double[NUMBER_OF_RESULTS];
int whatNumber = -1;
for (int i = 0; i < NUMBER_OF_RESULTS; i++)
{
expectedResult[i] = expectedResultFromFile[numberCounter, i];
if (expectedResult[i] == 1)
whatNumber = i;
}
double[] trainData = new double[SIZE_OF_IMAGE];
for (int i = 0; i < SIZE_OF_IMAGE; i++)
{
trainData[i] = trainDataFromFile[numberCounter, i];
}
TrainingSet trainingSet = new TrainingSet();
trainingSet.Input = trainData;
trainingSet.Response = expectedResult;
trainingSet.Value = whatNumber;
collection.Values.Add(trainingSet);
}
return collection;
}
/// <summary>
/// Teaches the network or recognize number.
/// </summary>
/// <param name="sender">The sender.</param>
/// <param name="args">The arguments.</param>
private void TeachNetworkOrRecognizeNumber(object sender, AddNumberOrRecognizeArgs args)
{
Rectangle boundsOfLetter = _model.FindBoundsOfPictureOnImageAndReturnRectangle(_neuralNetworkGUI.GiveImageFromDrawingArea(), _pen.Color);
Bitmap bitmapForFirstLayer = new Bitmap(WIDTH_FOR_BITMAP, HEIGHTH_FOR_BITMAP);
Graphics g = Graphics.FromImage(bitmapForFirstLayer);
g.DrawImage(_neuralNetworkGUI.GiveImageFromDrawingArea(),
new Rectangle(LEFT_MARGIN, UPPER_MARGIN, WIDTH_FOR_BITMAP - 2 * LEFT_MARGIN, HEIGHTH_FOR_BITMAP - 2 * UPPER_MARGIN),
boundsOfLetter,
GraphicsUnit.Pixel);
double[] arrayForFirstLayerOfNetwork = _model.GiveArrayOfIntegersFromImage(bitmapForFirstLayer, _pen.Color);
Pen newPen = new Pen(Color.Red);
_graphics.DrawRectangle(newPen, boundsOfLetter);
_neuralNetworkGUI.RefreshDrawingArea();
// ------------------------------------------------------------------------
//TestCuttingImageOfNumberAndTestExchangePixelsForNumber(arrayForFirstLayerOfNetwork);
// ------------------------------------------------------------------------
// ------------------------------------------------------------------------
// TestRecognizingNumbersFromFile();
// ------------------------------------------------------------------------
if (args.IsNeededAddingNumber)
{
if (args.WhatNumber == -1)
{
MessageBox.Show("Nie zaznaczono liczby do nauki");
return;
}
double[] expectedResults = new double[NUMBER_OF_RESULTS];
int whatNumber = 0;
for (int i = 0; i < NUMBER_OF_RESULTS; i++)
{
if (i != args.WhatNumber)
{
expectedResults[i] = 0;
}
else
{
expectedResults[i] = 1;
whatNumber = i;
}
}
TrainingCollection userTrainingData = TrainingCollection.LoadUserData(_path);
if (userTrainingData == null)
return;
TrainingSet trainingSet = new TrainingSet();
trainingSet.Input = arrayForFirstLayerOfNetwork;
trainingSet.Response = expectedResults;
trainingSet.Value = whatNumber;
userTrainingData.Values.Add(trainingSet);
userTrainingData.SaveUserData(_path);
}
else
{
// ------------------------------------------------------------------
TestNetworkUsingTestingDataFromFile();
// ------------------------------------------------------------------
_network.ComputeNetworkResponse(arrayForFirstLayerOfNetwork);
double[] resultFromNetwork = _network.GetResponse();
double max = -1;
int index = 0;
for (int i = 0; i < resultFromNetwork.Length; i++)
{
if (resultFromNetwork[i] > max)
{
max = resultFromNetwork[i];
index = i;
}
}
_neuralNetworkGUI.SelectNumber(index);
}
}