public static NeuralNetwork ChooseOne(Generation generation, NeuralNetwork other_network = null)
{
float r = RandomF.NextFloat();
NeuralNetwork network = null;
if (other_network != null)
{
r = RandomF.NextFloat(0f, 1f - other_network.fitness);
}
int index = 0;
for (int i = 0; i < generation.Population; i++)
{
if (other_network != null)
{
if (i == other_network.id)
{
continue;
}
}
r -= generation[i].fitness;
if (r < 0)
{
index = i;
break;
}
}
network = generation[index];
return(network);
}
public static NeuralNetwork Diseased(NeuralNetwork neuralNetwork, float rate)
{
for (int i = 0; i < neuralNetwork.Length; i++)
{
for (int j = 0; j < neuralNetwork[i].Length; j++)
{
for (int k = 0; k < neuralNetwork[i][j].Length; k++)
{
if (RandomF.NextFloat() < rate)
{
float value = neuralNetwork[i][j][k].x;
var offset = RandomF.NextGaussian() * 0.5f;
value += offset;
neuralNetwork[i][j][k].x = value;
}
}
}
if (!neuralNetwork[i].IsBiasNull())
{
for (int k = 0; k < neuralNetwork[i].GetBias().Length; k++)
{
if (RandomF.NextFloat() < rate)
{
float value = neuralNetwork[i].GetBias()[k].x;
var offset = RandomF.NextGaussian() * 0.5f;
value += offset;
neuralNetwork[i].GetBias()[k].x = value;
}
}
}
}
return(new NeuralNetwork(neuralNetwork));
}
private void PictureBoxPoster_MouseUp(object sender, MouseEventArgs e)
{
toggleMove = false;
PictureBox pictureBox = (PictureBox)sender;
bool train = false;
float value = 0f;
if (pictureBox.Location.X >= (this.Width - (this.Width * padding)))
{
value = 1f;
train = true;
existRight = true;
}
else if (pictureBox.Location.X <= (this.Width * padding - size.Width))
{
value = 0f;
train = true;
existLeft = true;
}
else if (trainingData.Length > 0 && (existLeft && existRight))
{
pictureBox.Location = new Point(lastPoint.X, pictureBox.Location.Y);
pictureBox.BringToFront();
}
if (train)
{
string[] split = linesList[int.Parse(pictureBox.Tag.ToString())].Split(' ');
int day = int.Parse(split[1]) - 1;
int month = months.IndexOf(split[2]);
float year = Map.Float(0f, float.Parse(split[3]), 9999f, 0f, 1f);
List <float> inputs = new List <float>();
for (int i = 0; i < 31; i++)
{
if (i == day)
{
inputs.Add(1f);
}
else
{
inputs.Add(0f);
}
}
for (int i = 0; i < 12; i++)
{
if (i == month)
{
inputs.Add(1f);
}
else
{
inputs.Add(0f);
}
}
inputs.Add(year);
string[] categoriesArr = split[4].Split(',');
for (int i = 0; i < categories.Count; i++)
{
bool exist = false;
for (int j = 0; j < categoriesArr.Length; j++)
{
if (categories[i] == categoriesArr[j])
{
exist = true;
break;
}
}
if (exist)
{
inputs.Add(1f);
}
else
{
inputs.Add(0f);
}
}
string[] directorsArr = split[5].Split(',');
for (int i = 0; i < directors.Count; i++)
{
bool exist = false;
for (int j = 0; j < directorsArr.Length; j++)
{
if (directors[i] == directorsArr[j])
{
exist = true;
break;
}
}
if (exist)
{
inputs.Add(1f);
}
else
{
inputs.Add(0f);
}
}
string[] starsArr = split[6].Split(',');
for (int i = 0; i < stars.Count; i++)
{
bool exist = false;
for (int j = 0; j < starsArr.Length; j++)
{
if (stars[i] == starsArr[j])
{
exist = true;
break;
}
}
if (exist)
{
inputs.Add(1f);
}
else
{
inputs.Add(0f);
}
}
trainingData.Add(inputs.ToArray(), new[] { value });
stopwatch.Start();
neuralNetwork.Training(trainingData);
stopwatch.Stop();
Console.WriteLine("Training : " + stopwatch.ElapsedMilliseconds);
stopwatch.Reset();
pictureBox.MouseDown -= PictureBoxPoster_MouseDown;
pictureBox.MouseUp -= PictureBoxPoster_MouseUp;
pictureBox.MouseMove -= PictureBoxPoster_MouseMove;
pictureBoxes.Remove(pictureBox);
if (existLeft && existRight)
{
for (int i = 0; i < pictureBoxes.Count; i++)
{
split = linesList[int.Parse(pictureBoxes[i].Tag.ToString())].Split(' ');
day = int.Parse(split[1]) - 1;
month = months.IndexOf(split[2]);
year = Map.Float(1f, float.Parse(split[3]), 9999f, 0f, 1f);
inputs = new List <float>();
for (int j = 0; j < 31; j++)
{
if (j == day)
{
inputs.Add(1f);
}
else
{
inputs.Add(0f);
}
}
for (int j = 0; j < 12; j++)
{
if (j == month)
{
inputs.Add(1f);
}
else
{
inputs.Add(0f);
}
}
inputs.Add(year);
categoriesArr = split[4].Split(',');
for (int j = 0; j < categories.Count; j++)
{
bool exist = false;
for (int z = 0; z < categoriesArr.Length; z++)
{
if (categories[j] == categoriesArr[z])
{
exist = true;
break;
}
}
if (exist)
{
inputs.Add(1f);
}
else
{
inputs.Add(0f);
}
}
directorsArr = split[5].Split(',');
for (int j = 0; j < directors.Count; j++)
{
bool exist = false;
for (int z = 0; z < directorsArr.Length; z++)
{
if (directors[j] == directorsArr[z])
{
exist = true;
break;
}
}
if (exist)
{
inputs.Add(1f);
}
else
{
inputs.Add(0f);
}
}
starsArr = split[6].Split(',');
for (int j = 0; j < stars.Count; j++)
{
bool exist = false;
for (int z = 0; z < starsArr.Length; z++)
{
if (stars[j] == starsArr[z])
{
exist = true;
break;
}
}
if (exist)
{
inputs.Add(1f);
}
else
{
inputs.Add(0f);
}
}
stopwatch.Start();
Result[] guesses = neuralNetwork.Predict(inputs.ToArray());
stopwatch.Stop();
Console.WriteLine("Predict:" + stopwatch.ElapsedMilliseconds);
stopwatch.Reset();
pictureBoxes[i].Location = new Point((int)Map.Float(0f,
guesses[0].value,
1f,
this.Width * padding,
this.Width - (this.Width * padding) - size.Width),
(int)RandomF.NextFloat(this.Height - size.Height));
}
}
}
}
private void formUI_Load(object sender, EventArgs e)
{
ToolTip toolTip = new ToolTip();
months.Add("January");
months.Add("February");
months.Add("March");
months.Add("April");
months.Add("May");
months.Add("June");
months.Add("July");
months.Add("August");
months.Add("September");
months.Add("October");
months.Add("November");
months.Add("December");
string path = @"images\";
string[] lines = File.ReadAllLines(path + "data.txt");
linesList.AddRange(lines);
for (int i = 0; i < lines.Length; i++)
{
string[] split = lines[i].Split(' ');
string name = split[0];
PictureBox pictureBoxPoster = new PictureBox()
{
Name = Path.GetFileName(name),
Location = new Point((int)Map.Float(0f,
RandomF.NextFloat(this.Width),
this.Width,
this.Width * padding,
this.Width - (this.Width * padding) - size.Width),
RandomF.Next(this.Height - size.Height)),
Image = Image.FromFile(path + name),
Size = size,
SizeMode = PictureBoxSizeMode.StretchImage,
Tag = i.ToString(),
BorderStyle = BorderStyle.FixedSingle
};
pictureBoxPoster.MouseDown += PictureBoxPoster_MouseDown;
pictureBoxPoster.MouseUp += PictureBoxPoster_MouseUp;
pictureBoxPoster.MouseMove += PictureBoxPoster_MouseMove;
toolTip.SetToolTip(pictureBoxPoster, split[0]);
pictureBoxes.Add(pictureBoxPoster);
this.Controls.Add(pictureBoxPoster);
// Categories
string[] categoriesArr = split[4].Split(',');
for (int j = 0; j < categoriesArr.Length; j++)
{
if (!categories.Contains(categoriesArr[j]))
{
categories.Add(categoriesArr[j]);
}
}
// Directors
string[] directorsArr = split[5].Split(',');
for (int j = 0; j < directorsArr.Length; j++)
{
if (!directors.Contains(directorsArr[j]))
{
directors.Add(directorsArr[j]);
}
}
// Stars
string[] starsArr = split[6].Split(',');
for (int j = 0; j < starsArr.Length; j++)
{
if (!stars.Contains(starsArr[j]))
{
stars.Add(starsArr[j]);
}
}
}
neuralNetwork = new NeuralNetwork(31 + 12 + 1 + categories.Count + directors.Count + stars.Count, 64, 1)
{
max_circle = 10000,
learning_rate = 0.5f,
momentum_rate = 0.96f
};
}
public Synapse()
{
this.x = RandomF.NextFloat(-1f, 1f);
this.error = 0f;
}
