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));
}
