/// <summary>
/// AddWeightsPreTest is used to modify a Layer with a seires of inputs, training it to either be
/// biased towards inputs with similar values, or opposed to inputs with simialr values
/// </summary>
/// <param name="size">The size of the input arrays</param>
/// <param name="tag">The tag that <see cref="DebugWrite"/> method will use as it's 'tag'</param>
/// <param name="imageLocation">The File loation of the data to be checked (.png)</param>
/// <param name="isCorrect">Whether the Layer will be trained up to bias or anti-bias the inputs' values</param>
/// <param name="testLayer">The 'Layer' class that is being trained</param>
/// <param name="testAmmounts">How many itterations of training will be performed</param>
private static void AddWeightsPreTest(int size, String tag, String imageLocation, bool isCorrect, Layer testLayer, int testAmmounts)
{
Bitmap[] inputs = new Bitmap[size];
String[] inputstrings = new String[size];
for (int i = 0; i < inputs.Length; i++)
{
inputstrings[i] = imageLocation + "/Image" + (i + 1) + ".png";
DebugWrite(tag, " Loading Image " + inputstrings[i], true);// Confirm Image Loaded
inputs[i] = (Bitmap)Image.FromFile(inputstrings[i]);
}
imageBeingSearched = inputstrings[0];
DebugWrite(tag, " Image 0 height is " + inputs[0].Height, true); // Confirm Images are valid
testLayer.baseCell = new ImageCell(inputs[0]);
DebugWrite(tag, " Begining Loop to Test Cells", true); // Section Definition
for (int i = 0; i < inputs.Length; i++)
{
for (int r = 0; r < testAmmounts; r++)
{
DateTime nowTime = DateTime.Now;
imageBeingSearched = inputstrings[i];
DebugWrite(tag, " Testing & Rewarding Image " + inputstrings[i], true); // State Which image is being actioned
ImageCell.ChangeImage((ImageCell)testLayer.baseCell, inputs[i]);
DebugWrite(tag, " Rebuilding Image " + inputstrings[i], true); // State that image has changed correctly
testLayer.baseCell.ReBuild();
DebugWrite(tag, " Getting Call of Image " + inputstrings[i], true); // State that 'GetCall()' Method is running successfully
ValueClass debugValueClass = testLayer.baseCell.GetCall();
// DebugWrite(tag, " Image Call was " + debugValueClass.value + "," + debugValueClass.weight, false); // Get the value Class of the training
for (int xAxis = 0; xAxis < 2; xAxis++)
{
for (int yAxis = 0; yAxis < 2; yAxis++)
{
if (isCorrect)
{
testLayer.AddValue(testLayer.baseCell);
testLayer.baseCell.Reward();
}
else
{
testLayer.baseCell.TellOff();
}
}
}
averageCellTime += DateTime.Now.Subtract(nowTime);
double averageCellTimeMili = averageCellTime.TotalMilliseconds;
averageCellTime = TimeSpan.FromMilliseconds(averageCellTimeMili / 2);
}
}
DebugWrite(tag, " Average Time Per Cell is " + averageCellTime, true, true); //Optimize time of calculation
}
/// <summary>
/// CreateSubCell uses <see cref="Bitmap"/> functions to segregate the root image and fill the
/// 'subCell' array with he relevant subsections of the root image.
/// </summary>
/// <param name="i">The X co-ord of the subsection to create</param>
/// <param name="f">he Y co-ord of the subsection ot create</param>
/// <returns>The created subcell made from the specified sub-image of the root image</returns>
public override Cell CreateSubCell(int i, int f)
{
Bitmap subImage = new Bitmap(img.Width / 2, img.Height / 2);
NeuralNet.DebugWrite("ImageCell->Cell()->", String.Format("Image Width/Height is {0},{1}", img.Width, img.Height), false);
// Create new subImage
for (int z = 0; z < subImage.Height; z++)
{
for (int x = 0; x < subImage.Height; x++)
{
subImage.SetPixel(z, x, img.GetPixel((img.Width / 2 * i) + z, (img.Width / 2 * f) + x));
}
}
if (subCells[i, f] != null)
{
ImageCell.ChangeImage((ImageCell)subCells[i, f], subImage);
}
else
{
subCells[i, f] = new ImageCell(subImage);
}
return(subCells[i, f]);
}