public static void ExecutesScreenShotActionOnAgent(Agent agent, Act act) { NewPayLoad PL = new NewPayLoad("ScreenshotAction"); List <NewPayLoad> PLParams = new List <NewPayLoad>(); NewPayLoad AIVPL = new NewPayLoad("AIV", "WindowsToCapture", act.WindowsToCapture.ToString()); PLParams.Add(AIVPL); PL.AddListPayLoad(PLParams); // Get the action payload PL.ClosePackage(); // Send the payload to the service NewPayLoad RC = agent.GingerNodeProxy.RunAction(PL); if (RC.Name == "ScreenShots") { List <NewPayLoad> FieldsandParams = RC.GetListPayLoad(); foreach (NewPayLoad Np in FieldsandParams) { string Name = Np.GetValueString(); //string base64string = Np.GetValueString(); act.AddScreenShot(Name); } } else { // The RC is not OK when we faced some unexpected exception //TODO: string Err = RC.GetValueString(); act.Error += Err; } }
/// <summary> /// This lists the price changing method. /// </summary> /// <param name="shopLogic">Logic for Orders repository and SkiEqupments repository.</param> public static void ChangePrice(ShopLogic shopLogic) { int id = IntParse(Enter.ToString()); bool valid = false; try { shopLogic?.GetSkiEquipmentsById(id); valid = true; } catch (ArgumentException) { Console.WriteLine(Other.ToString()); } if (valid) { Console.WriteLine(Selected.ToString()); Console.WriteLine(shopLogic?.GetSkiEquipmentsById(id).ToString()); const string Np = "Enter new price here: "; int price = IntParse(Np.ToString()); shopLogic?.ChangePrice(id, price); Console.WriteLine(Saved.ToString()); } Console.ReadLine(); }
private NewPayLoad StartDriver(NewPayLoad pl) { try { List <NewPayLoad> FieldsandParams = pl.GetListPayLoad(); Dictionary <string, string> InputParams = new Dictionary <string, string>(); foreach (NewPayLoad Np in FieldsandParams) { string Name = Np.GetValueString(); string Value = Np.GetValueString(); if (!InputParams.ContainsKey(Name)) { InputParams.Add(Name, Value); } } ConfigureServiceParams(mService, InputParams); Console.WriteLine("Payload - Start Session"); ((IServiceSession)mService).StartSession(); NewPayLoad PLRC = new NewPayLoad("OK"); PLRC.ClosePackage(); return(PLRC); } catch (Exception ex) { return(NewPayLoad.Error(ex.Message)); } }
public string Create([Bind(Exclude = "Id")] Np Model) { ApplicationDbContext db = new ApplicationDbContext(); string msg; try { if (ModelState.IsValid) { db.Nps.Add(Model); db.SaveChanges(); msg = "Сохранено успешно"; } else { msg = "Данные не прошли проверку ввода"; } } catch (Exception ex) { msg = "Произошла ошибка:" + ex.Message; } return(msg); }
public static void ConvertPyPlot() { var x = Np.ARange(0, 5, 0.1); var y = Np.Sin(x); PyPlot.Plot(x, y); }
public string Delete(int Id) { ApplicationDbContext db = new ApplicationDbContext(); Np nps = db.Nps.Find(Id); db.Nps.Remove(nps); db.SaveChanges(); return("Удалено успешно"); }
// Update is called once per frame void Update() { if (!SetScore && Np) { Np.SetNum(optionManager.GetScore(number)); result.SetRankToStageSelect(optionManager.GetScore(number), number); SetScore = true; } }
private NewPayLoad TakeScreenot(NewPayLoad ActionPayload) { if (mService is IScreenShotService ScreenshotService) { Dictionary <string, string> InputParams = new Dictionary <string, string>(); List <NewPayLoad> FieldsandParams = ActionPayload.GetListPayLoad(); foreach (NewPayLoad Np in FieldsandParams) { string Name = Np.GetValueString(); string Value = Np.GetValueString(); if (!InputParams.ContainsKey(Name)) { InputParams.Add(Name, Value); } } NewPayLoad ResponsePL = new NewPayLoad("ScreenShots"); string WindowsToCapture = InputParams["WindowsToCapture"]; List <NewPayLoad> ScreenShots = new List <NewPayLoad>(); switch (WindowsToCapture) { case "OnlyActiveWindow": ScreenShots.Add(BitmapToPayload(ScreenshotService.GetActiveScreenImage())); break; case "AllAvailableWindows": foreach (Bitmap bmp in ScreenshotService.GetAllScreensImages()) { ScreenShots.Add(BitmapToPayload(bmp)); } break; default: return(NewPayLoad.Error("Service is not supporting IScreenShotService cannot delegate to take screen shot")); } Bitmap img = ScreenshotService.GetActiveScreenImage(); ResponsePL.AddListPayLoad(ScreenShots); ResponsePL.ClosePackage(); return(ResponsePL); } NewPayLoad err2 = NewPayLoad.Error("Service is not supporting IScreenShotService cannot delegate to take screen shot"); return(err2); }
public static void ConvertArange() { var int1 = Np.ARange(5); var int2 = Np.ARange(5, 10); var int3 = Np.ARange(5, 10, 3); var double1 = Np.ARange(5.1); var double2 = Np.ARange(5.1, 10); var double3 = Np.ARange(5.1, 10, 3); }
/// <summary> /// Compute sample weights such that the class distribution of y becomes /// balanced. /// </summary> /// <param name="?"></param> private static double[] BalanceWeights(int[] y) { var encoder = new LabelEncoder <int>(); y = encoder.FitTransform(y); var bins = Np.BinCount(y); var weights = bins.ElementsAt(y).Select(v => 1.0 / v * bins.Min()).ToArray(); return(weights); }
/// <summary> /// Returns true if Profitability instances are equal /// </summary> /// <param name="other">Instance of Profitability to be compared</param> /// <returns>Boolean</returns> public bool Equals(Profitability other) { if (other is null) { return(false); } if (ReferenceEquals(this, other)) { return(true); } return (( Roe == other.Roe || Roe != null && Roe.Equals(other.Roe) ) && ( Npm == other.Npm || Npm != null && Npm.Equals(other.Npm) ) && ( Gpm == other.Gpm || Gpm != null && Gpm.Equals(other.Gpm) ) && ( Np == other.Np || Np != null && Np.Equals(other.Np) ) && ( Eps == other.Eps || Eps != null && Eps.Equals(other.Eps) ) && ( Mbr == other.Mbr || Mbr != null && Mbr.Equals(other.Mbr) ) && ( Ts == other.Ts || Ts != null && Ts.Equals(other.Ts) ) && ( Cs == other.Cs || Cs != null && Cs.Equals(other.Cs) )); }
public void NdArrayDemo() { var a = new[] { new[] { 1, 2, 3 }, new[] { 4, 5, 6 } }; NdArray2DInt array2 = Np.Array2(a); // regular NdArray1D <int[]> array1 = Np.Array1(a); // not regular var tmp_sin = Np.Sin(array2); // var t1_sin = Np.Sin(array1); }
/// <summary> /// Gets the hash code /// </summary> /// <returns>Hash code</returns> public override int GetHashCode() { unchecked // Overflow is fine, just wrap { int hashCode = 41; // Suitable nullity checks etc, of course :) if (Roe != null) { hashCode = hashCode * 59 + Roe.GetHashCode(); } if (Npm != null) { hashCode = hashCode * 59 + Npm.GetHashCode(); } if (Gpm != null) { hashCode = hashCode * 59 + Gpm.GetHashCode(); } if (Np != null) { hashCode = hashCode * 59 + Np.GetHashCode(); } if (Eps != null) { hashCode = hashCode * 59 + Eps.GetHashCode(); } if (Mbr != null) { hashCode = hashCode * 59 + Mbr.GetHashCode(); } if (Ts != null) { hashCode = hashCode * 59 + Ts.GetHashCode(); } if (Cs != null) { hashCode = hashCode * 59 + Cs.GetHashCode(); } return(hashCode); } }
public string Edit(Np Model) { ApplicationDbContext db = new ApplicationDbContext(); string msg; try { if (ModelState.IsValid) { db.Entry(Model).State = EntityState.Modified; db.SaveChanges(); msg = "Сохранено успешно"; } else { msg = "Данные не прошли проверку ввода"; } } catch (Exception ex) { msg = "Произошла ошибка:" + ex.Message; } return(msg); }
public void TestMinSamplesLeaf() { foreach (var name in CLF_TREES) { var est = CreateClassifier <double>(name, min_samples_leaf: 5, random: new Random(0)); est.Fit(X, y); var @out = est.Tree.apply(X.ToDenseMatrix()); var node_counts = Np.BinCount(@out.Select(v => (int)v).ToArray()); var leaf_count = node_counts.Where(v => v != 0).ToList(); // drop inner nodes Assert.IsTrue(leaf_count.Min() > 4, "Failed with {0}".Frmt(name)); } foreach (var name in RegTrees) { var est = CreateRegressor(name, min_samples_leaf: 5, random: new Random(0)); est.Fit(X, y); var @out = est.Tree.apply(X.ToDenseMatrix()); var nodeCounts = Np.BinCount(@out.Select(v => (int)v).ToArray()); var leafCount = nodeCounts.Where(v => v != 0).ToList(); // drop inner nodes Assert.IsTrue(leafCount.Min() > 4, "Failed with {0}".Frmt(name)); } }
public static void CreateNumpyArrays() { var list = new[] { 1, 2, 3 }; var tmp = Np.Array1(new[] { 1.0, 2, 3 }); var tmp2 = Np.Array1(new[] { 1.0, 2, 3 }, order: NumpyArrayOrder.C); }
// Use this for initialization void Start() { m_root = this.transform.parent.GetComponent <Np>(); }
// Start is called before the first frame update void Start() { rootObj = transform.root.GetComponent <Np>(); PV = rootObj.PV; }
public void T01() { var a = Np.Array1(new [] { 2, 4, 6 }); Assert.Equal(4.0, a.Mean()); }
public void TestSampleWeight() { // Test that zero-weighted samples are not taken into account var X = Enumerable.Range(0, 100).ToColumnMatrix(); var y = Enumerable.Repeat(1, 100).ToArray(); Array.Clear(y, 0, 50); var sampleWeight = Enumerable.Repeat(1, 100).ToVector(); sampleWeight.SetSubVector(0, 50, Enumerable.Repeat(0, 50).ToVector()); var clf = new DecisionTreeClassifier <int>(random: new Random(0)); clf.Fit(X, y, sampleWeight: sampleWeight); AssertExt.ArrayEqual(clf.Predict(X), Enumerable.Repeat(1, 100).ToArray()); // Test that low weighted samples are not taken into account at low depth X = Enumerable.Range(0, 200).ToColumnMatrix(); y = new int[200]; Array.Copy(Enumerable.Repeat(1, 50).ToArray(), 0, y, 50, 50); Array.Copy(Enumerable.Repeat(2, 100).ToArray(), 0, y, 100, 100); X.SetSubMatrix(100, 100, 0, 1, Enumerable.Repeat(200, 100).ToColumnMatrix()); sampleWeight = Enumerable.Repeat(1, 200).ToVector(); sampleWeight.SetSubVector(100, 100, Enumerable.Repeat(0.51, 100).ToVector()); // Samples of class '2' are still weightier clf = new DecisionTreeClassifier <int>(maxDepth: 1, random: new Random(0)); clf.Fit(X, y, sampleWeight: sampleWeight); Assert.AreEqual(149.5, clf.Tree.Threshold[0]); sampleWeight.SetSubVector(100, 100, Enumerable.Repeat(0.50, 100).ToVector()); // Samples of class '2' are no longer weightier clf = new DecisionTreeClassifier <int>(maxDepth: 1, random: new Random(0)); clf.Fit(X, y, sampleWeight: sampleWeight); Assert.AreEqual(49.5, clf.Tree.Threshold[0]); // Threshold should have moved // Test that sample weighting is the same as having duplicates X = iris.Data; y = iris.Target; var random = new Random(0); var duplicates = new int[200]; for (int i = 0; i < duplicates.Length; i++) { duplicates[i] = random.Next(X.RowCount); } clf = new DecisionTreeClassifier <int>(random: new Random(1)); clf.Fit(X.RowsAt(duplicates), y.ElementsAt(duplicates)); sampleWeight = Np.BinCount(duplicates, minLength: X.RowCount).ToVector(); var clf2 = new DecisionTreeClassifier <int>(random: new Random(1)); clf2.Fit(X, y, sampleWeight: sampleWeight); var @internal = clf.Tree.ChildrenLeft.Indices(v => v != Tree._TREE_LEAF); AssertExt.AlmostEqual(clf.Tree.Threshold.ElementsAt(@internal), clf2.Tree.Threshold.ElementsAt(@internal)); }