public void Load(GraphicsDeviceManager graphics, ContentManager Content)
{
// TODO: use this.Content to load your game content here
tank = Content.Load <Texture2D>("Tank");
position_tank = new Vector2(30 * 20f, 0 * 20f);
origin = new Vector2(tank.Width / 2f, tank.Height / 2f);
pixel_black = Content.Load <Texture2D>("Pixel_2_black");
pixel_red = Content.Load <Texture2D>("Pixel_2_red");
font_wheels = Content.Load <SpriteFont>("Game");
r_pos = new List <Vector2>();
b_pos = new List <Vector2>();
lines = new List <Vector2>();
line_closest = new List <Vector2>();
r_pos = g.RedPositions;
b_pos = g.BluePositions;
vec_closest_r = ClosestDot(DOTS.RED);
AngleDist(vec_closest_r, out dir_closest_r, out dist_r);
vec_closest_b = ClosestDot(DOTS.BLUE);
AngleDist(vec_closest_b, out dir_closest_b, out dist_b);
net = new BNet(neurons, 1, .25f, .9f);//neurons, slope?, learningrate, momentum
net.Randomize();
}
public void ProcessBeliefNetwork()
{
m_net = new BNet();
m_query = new ArrayList();
m_net.Build(fileName);
m_net.PrintNet("BNet_layout.txt");
infer = new BElim(m_net);
_nodes.Clear();
foreach (BNode node in m_net.Nodes)
_nodes.Add(node.Name);
Console.WriteLine("For example: " + _nodes[0] + "=1; " + _nodes[1] + "=0");
Console.WriteLine("For example: " + _nodes[2] + "=1");
string evidence = "cloudy=1; sprinkler=0".TrimEnd(';'); // cloudy=1; sprinkler=0
string query = "wetgrass=1".TrimEnd(';'); //wetgrass=1
string obs = evidence.Trim();
string x = query.Trim();
if (IsValid(x, obs))
{
double pr = infer.GetBelief(x, obs);
string out1 = " P( " + x;
out1 += (obs.Length > 0) ? " | " + obs + " )" : " )";
string out2 = " = " + pr.ToString("F4");
string result = "";
result += out1;
result += out2;
}
}
/// <summary>
/// Loads the network by instantiating the Net property of the DAGLoader
/// </summary>
/// <param name="path">relative path from BaseDirectory</param>
public BNet LoadBNet(string path)
{
Debug.WriteLine($"Loading Bayesian Network from {path}...");
_app.Visible = false;
string net_file_name = AppDomain.CurrentDomain.BaseDirectory + path;
Streamer file = _app.NewStream(net_file_name, null);
BNet net = _app.ReadBNet(file);
net.Compile();
return(net);
}
public void Update()
{
// TODO: Add your update logic here
r_pos = new List <Vector2>();
b_pos = new List <Vector2>();
lines = new List <Vector2>();
line_closest = new List <Vector2>();
r_pos = g.RedPositions;
b_pos = g.BluePositions;
currentState = Keyboard.GetState();
if (currentState.IsKeyDown(Keys.R))
{
position_tank = new Vector2(30 * 20f, 0 * 20f);
rotation = 90f;
wheels[0] = .5f;
wheels[1] = .5f;
r_pos = new List <Vector2>();
lines = new List <Vector2>();
line_closest = new List <Vector2>();
r_pos = g.RedPositions;
vec_closest_r = ClosestDot(DOTS.RED);
AngleDist(vec_closest_r, out dir_closest_r, out dist_r);
net = new BNet(neurons, 1, .25f, .9f);//neurons, slope?, learningrate, momentum
net.Randomize();
}
if (draw)
{
draw = false;
}
CalcRotation();
CalcPosition();
DrawLines(DOTS.RED);
g.Collision((int)position_tank.X, (int)position_tank.Y, vec_closest_r);
if (currentState.IsKeyDown(Keys.N) || g.Reset)
{
float dist;
vec_closest_r = ClosestDot(DOTS.RED);
g.Reset = false;
}
else
{
AngleDot(vec_closest_r, out dir_closest_r);
}
Train();
AdjustWheels();
}
static void Main(string[] args)
{
try
{
Console.WriteLine("Welcome to Netica API for C# !");
Netica.Application app = new Netica.Application();
app.Visible = true;
string net_file_name = AppDomain.CurrentDomain.BaseDirectory + "..\\..\\..\\ChestClinic.dne";
Streamer file = app.NewStream(net_file_name, null);
BNet net = app.ReadBNet(file, "");
net.Compile();
BNode TB = net.Node("Tuberculosis");
double bel = TB.GetBelief("present");
Console.WriteLine("The probability of tuberculosis is " + bel.ToString("G4"));
BNode XRay = net.Node("XRay");
XRay.EnterFinding("abnormal");
bel = TB.GetBelief("present");
Console.WriteLine("Given an abnormal X-Ray, the probability of tuberculosis is " + bel.ToString("G4"));
net.Node("VisitAsia").EnterFinding("visit");
bel = TB.GetBelief("present");
Console.WriteLine("Given abnormal X-Ray and visit to Asia, the probability of TB is " + bel.ToString("G4"));
net.Node("Cancer").EnterFinding("present");
bel = TB.GetBelief("present");
Console.WriteLine("Given abnormal X-Ray, Asia visit, and lung cancer, the probability of TB is " + bel.ToString("G4"));
net.Delete();
if (!app.UserControl)
{
app.Quit();
}
}
catch (System.Runtime.InteropServices.COMException e)
{
Console.WriteLine("Netica Demo: Error " + (e.ErrorCode & 0x7FFF) + ": " + e.Message);
}
Console.WriteLine("Press <enter> to quit.");
Console.ReadLine();
}
public void ProcessBeliefNetwork()
{
m_net = new BNet();
m_query = new ArrayList();
m_net.Build(fileName);
m_net.PrintNet("BNet_layout.txt");
infer = new BElim(m_net);
_nodes.Clear();
foreach (BNode node in m_net.Nodes)
{
_nodes.Add(node.Name);
}
Console.WriteLine("For example: " + _nodes[0] + "=1; " + _nodes[1] + "=0");
Console.WriteLine("For example: " + _nodes[2] + "=1");
string evidence = "cloudy=1; sprinkler=0".TrimEnd(';'); // cloudy=1; sprinkler=0
string query = "wetgrass=1".TrimEnd(';'); //wetgrass=1
string obs = evidence.Trim();
string x = query.Trim();
if (IsValid(x, obs))
{
double pr = infer.GetBelief(x, obs);
string out1 = " P( " + x;
out1 += (obs.Length > 0) ? " | " + obs + " )" : " )";
string out2 = " = " + pr.ToString("F4");
string result = "";
result += out1;
result += out2;
}
}
private void MainForm_Load(object sender, System.EventArgs e)
{
m_net = new BNet();
m_query = new ArrayList();
}
public SpaceModel(DAGLoader loader)
{
net = loader.Net;
}
public DAGLoader(string netPath, Application context)
{
_app = context;
Net = LoadBNet(netPath);
}