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); }