public Kanne(Functional.Answersheet AnswerSheet)
{
InitializeComponent();
StringBuilder sb = new StringBuilder();
sb.AppendLine("Richtige Antworten:\n");
foreach (var answer in AnswerSheet.SelectedQuestion.Answers)
if (answer.Second)
sb.AppendLine("- " + answer.First);
rightAnswers.Text = sb.ToString();
}
static void Main()
{
Functional functional = new Functional(MethodF);
Delegate3 delegate3 = functional.Invoke(new Delegate1(Method1), new Delegate2(Method2));
Console.WriteLine(delegate3.Invoke());
// Delay.
Console.ReadKey();
}
/// <summary> /// Forcefully casts one to ComplexNumber with downcasting /// </summary> /// <returns></returns> public ComplexNumber AsComplexNumber() => new ComplexNumber(Functional.Downcast(this));
public BookingController()
{
_functional = new Functional();
_carService = new CarService();
}
protected override void OnSelectConfirm()
{
if (!finished || ending)
{
foreach (var c in choices)
{
c.indicator.StopAllCoroutines();
c.indicator.Fade(false);
}
NetworkController.Instance.SelectCar(currentChoice);
AudioController.Instance.PlayClip(onConfirm);
finished = true;
AppController.Instance.currentSessionSettings.selectedCar = choices[currentChoice].car;
AppController.Instance.currentSessionSettings.selectedCarShortname = choices[currentChoice].shortName;
gasTex.SetTarget(0f);
turnTex.SetTarget(0f);
var selectedCar = choices[currentChoice].displayObject;
selectedCar.GetComponent <Rigidbody>().constraints = RigidbodyConstraints.None;
selectedCar.GetComponent <VehicleAudioController>().PlayIgnition();
//OLD SOUND CODE
/*var sound = selectedCar.GetComponent<CarSound>();
* sound.SelectSceneInit();
* sound.selectAudio.PlayOneShot(sound.startup, 0.7f * AudioController.Instance.VehicleVolume);
*
* StartCoroutine(Functional.DoAfter(sound.startup.length, () =>
* {
* sound.selectAudio.clip = sound.selectIdle;
* sound.selectAudio.Play();
* }));
*/
var vehicleController = selectedCar.GetComponent <VehicleController>();
//Camera follow
StartCoroutine(Functional.DoAfter(4f, () => {
var camTarget = vehicleController.cameraPosition;
camMove.SetFollowTarget(camTarget);
var driverCam = camMove.GetComponent <DriverCamera>();
StartCoroutine(Functional.DoTimed(2f, f =>
{
driverCam.SetFoV(Mathf.Lerp(AdminSettings.Instance.selectFov, AdminSettings.Instance.fov, f));
}));
StartCoroutine(Functional.DoAfter(1f, () => {
camMove.updateRotation = true;
}));
StartCoroutine(Functional.WatchFor(() => Vector3.Distance(camMove.transform.position, camTarget.position) < 1.5f, () =>
{
camMove.GetComponent <DriverCamera>().SetCulingMask(~(1 << LayerMask.NameToLayer("PlayerCar")));
}));
}));
//Car Pathing
StartCoroutine(Functional.DoAfter(0.5f, () =>
{
carInitialPos = selectedCar.transform.position;
carInitialRot = selectedCar.transform.rotation;
selectedCar.GetComponent <VehicleController>().enabled = true;
var careip = selectedCar.GetComponent <VehiclePathController>();
careip.enabled = true;
careip.onStop = () =>
{
StartCoroutine(Functional.DoAfter(1.5f, () => base.OnSelectConfirm()));
RemoteAdminController.Instance.SendMessage(RemoteAdminController.SendMessageType.SELECT_CAR, currentChoice);
Quaternion initalRot = selectedCar.transform.localRotation;
Quaternion targetRot = Quaternion.Euler(initalRot.eulerAngles.x, 90f, initalRot.eulerAngles.z);
StartCoroutine(Functional.DoTimed(1.5f, (f) => {
selectedCar.transform.localRotation = Quaternion.Lerp(initalRot, targetRot, f);
selectedCar.GetComponent <Rigidbody>().angularVelocity = Vector3.zero;
}));
};
}));
//cam.MoveToFar(() =>
// {
// base.OnSelectConfirm();
// });
}
}
public void UpVal()
{
double rr = 1;
Curve c = new Curve(0, 2 * Math.PI, (double t, double r) => r * Math.Cos(t), (double t, double r) => r * Math.Sin(t), rr);
c.S = (double tx, double ty, double r) => tx * ty * r; c.End = (double r) => 2 * Math.PI;
//Functional mp = (Point g) => 1;
//DoubleIntegral(mp, c, c.S, parallel: true, cy: 300, M: Method.GaussKronrod61).Show();
Func <Point, int, double> ro = (Point p, int k) => {
var cc = new Complex(p.x, p.y);
return(Math.Pow(cc.Abs, k) * Math.Cos(k * cc.Arg));
};
Func <Point, Point, double> E = (Point x, Point y) => Math.Log(Point.Eudistance(x, y));
int xc = 100, yc = 100;
double x0 = -8, X = 8, y0 = -8, Y = 8;
double hx = (X - x0) / (xc - 1), hy = (Y - y0) / (yc - 1);
double[] xa = new double[xc], ya = new double[yc];
for (int i = 0; i < xc; i++)
{
xa[i] = x0 + hx * i;
}
for (int i = 0; i < yc; i++)
{
ya[i] = y0 + hy * i;
}
int[] kk = new int[] { 3, 5, 25, 50, 200, 500 };
double[][,] val = new double[kk.Length][, ];
for (int i = 0; i < kk.Length; i++)
{
val[i] = new double[xc, yc];
Functional fl = (Point x) =>
{
Functional tmp = (Point y) => ro(y, kk[i]) * E(x, y);
return(DoubleIntegral(tmp, c, c.S, parallel: true, cy: 200, M: Method.GaussKronrod61));
};
Functional fl2 = (Point x) =>
{
Complex z = new Complex(x.x, x.y);
Functional tmp = (Point y) => {
Complex w = new Complex(y.x, y.y);
return(Math.Pow(w.Abs, kk[i]) * Math.Cos(kk[i] * (w.Arg)) * Math.Log(z.Abs.Sqr() + w.Abs.Sqr() - 2 * z.Abs * w.Abs * Math.Cos(w.Arg)));
};
return(DoubleIntegral(tmp, c, c.S, parallel: true, cy: 200, M: Method.GaussKronrod61) / 2 * Math.Cos(kk[i] * z.Arg));
};
Functional fr = (Point x) =>
{
Complex z = new Complex(x.x, x.y);
return(Math.PI * Math.Cos(kk[i] * z.Arg) / (kk[i] + 2) * Math.Max(Math.Log((z.Abs - 1).Sqr()).Abs(), Math.Log((z.Abs + 1).Sqr()).Abs()));
};
for (int ix = 0; ix < xc; ix++)
{
for (int iy = 0; iy < yc; iy++)
{
Point t = new Point(xa[ix], ya[iy]), o = new Point(0);
double rad = Point.Eudistance(t, o);
// $"t = {t} fr = {fr(t)} fl = {fl(t)}".Show();
//$"{fl(t)} {fl2(t)}".Show();
//Assert.IsTrue((fl(t) - fl2(t)).Abs() < 1e-7);
if (rad > 1.0)
{
val[i][ix, iy] = fr(t).Abs() - fl(t).Abs();
}
else
{
val[i][ix, iy] = Double.NaN;
}
}
}
}
StreamWriter args = new StreamWriter("arg.txt");
StreamWriter vals = new StreamWriter("vals.txt");
for (int i = 0; i < xc; i++)
{
args.WriteLine($"{xa[i]} {ya[i]}");
}
for (int i = 0; i < xc; i++)
{
for (int j = 0; j < yc; j++)
{
string st = "";
for (int s = 0; s < kk.Length; s++)
{
st += (val[s][i, j] + " ");
}
vals.WriteLine(st.Replace("NaN", "NA"));
}
}
args.Close();
vals.Close();
}
/// <summary>
/// Ультра-гибридный метод суперского решения по последней координате
/// </summary>
/// <param name="t"></param>
public void UltraHybridLast(int t) //гибридный с координатной минимизацией по последней координате
{
double[] c = new double[t];
for (int i = 0; i < t - 1; i++)
{
c[i] = x[i];
}
Vectors mk1 = new Vectors(c), mk2 = new Vectors(c);
bool existres = true;
double sum = 0;
GaussSpeedy(t);
double tmp = Error(t);
if (VALUE_FOR_ULTRA < tmp) //если погрешность выросла - исправить это, потому что новое решение не годится
{
$"{VALUE_FOR_ULTRA} < {tmp} при t = {t} (до покоординатной минимизации результата СПИДГАУССА)".Show();
//покоординатная минимизация результата СПИДГАУССА
for (int k = 0; k <= t - 1; k++)
{
for (int j = 0; j < k; j++)
{
sum += x[j] * A[k, j];
}
for (int j = k + 1; j < t - 1; j++)
{
sum += x[j] * A[k, j];
}
x[k] = (b[k] - sum) / A[k, k];
sum = 0;
}
tmp = Error(t);
if (VALUE_FOR_ULTRA < tmp)
{
$"{VALUE_FOR_ULTRA} < {tmp} при t = {t} (до полно покоординатной минимизации вектора с1 с2 ... 0)".Show();
for (int i = 0; i < t; i++)//исправили, теперь пробуем новый метод
{
x[i] = c[i];
}
//покоординатная минимизация
for (int k = 0; k <= t - 1; k++)
{
for (int j = 0; j < k; j++)
{
sum += x[j] * A[k, j];
}
for (int j = k + 1; j < t - 1; j++)
{
sum += x[j] * A[k, j];
}
x[k] = (b[k] - sum) / A[k, k];
sum = 0;
}
double tmp1 = Error(t);
if (VALUE_FOR_ULTRA < tmp1) //погрешность опять выросла - тогда просто оставляем 0 на конце
{
$"{VALUE_FOR_ULTRA} < {tmp1} при t = {t} (до полной покоординатной минимизации на конце)".Show();
for (int i = 0; i < t; i++)//исправили, теперь пробуем новый метод
{
x[i] = c[i];
}
for (int j = 0; j < t - 1; j++)
{
sum += x[j] * A[t - 1, j];
}
x[t - 1] = (b[t - 1] - sum) / A[t - 1, t - 1];
sum = 0;
tmp = Error(t);
if (VALUE_FOR_ULTRA < tmp)
{
for (int i = 0; i < t - 1; i++)
{
x[i] = c[i];
}
x[t - 1] = 0;
existres = false;
}
else
{
$"Погрешность уменьшена МИНИМАКОЙ НА КОНЦЕ на {(VALUE_FOR_ULTRA - tmp) / VALUE_FOR_ULTRA * 100} % (now {tmp})".Show();
VALUE_FOR_ULTRA = tmp;
}
}
else
{
$"Погрешность уменьшена ПОЛНОЙ МИНИМАКОЙ на {(VALUE_FOR_ULTRA - tmp1) / VALUE_FOR_ULTRA * 100}% (now {tmp1})".Show();
VALUE_FOR_ULTRA = tmp1;
}
}
else
{
$"Погрешность уменьшена МИНИМАКОЙ СПИДГАУССА на {(VALUE_FOR_ULTRA - tmp) / VALUE_FOR_ULTRA * 100} % (now {tmp})".Show();
VALUE_FOR_ULTRA = tmp;
}
}
else
{
$"Погрешность уменьшена СПИДГАУССОМ на {(VALUE_FOR_ULTRA - tmp) / VALUE_FOR_ULTRA * 100} % (now {tmp})".Show();
VALUE_FOR_ULTRA = tmp;
}
ErrorsMas[t - 1] = VALUE_FOR_ULTRA;
Functional f = (Point x) =>
{
sum = 0;
for (int ii = 1; ii <= t; ii++)
{
sum += this.x[ii - 1] * KursMethods.masPoints[ii - 1].PotentialF(x);
}
double s = sum - KursMethods.fig(x);
return(s * s);
};
ErrorMasP[t - 1] = Math.Sqrt(DoubleIntegral(f, curve, curve.S, FuncMethods.DefInteg.Method.GaussKronrod15, 0.001, FuncMethods.DefInteg.countY));
//UltraCount = t;
NEVA = Nev(A, x, b, t);
Functional fM1 = (Point x) =>
{
// Functional M1ApproxD = (Point u) =>
//{
// Functional ro = (Point y) =>
// {
// double sss = 0; for (int i = 0; i < t; i++) sss += this.x[i] * KursMethods.masPoints[i].PotentialF(u);
// return sss * KursMethods.Exy(u, y);
// };
// //return DoubleIntegral(ro, curve, curve.S, FuncMethods.DefInteg.Method.GaussKronrod15, 0.001, FuncMethods.DefInteg.countY) /*IntegralClass.Integral(ro, KursMethods.CIRCLE - 1)*/ /*- right(x)*/;
// return IntegralClass.Integral(ro, KursMethods.CIRCLE - 1);
//};
double su = KursMethods.M1Approx(x);
double s = su - KursMethods.U(x);
return(s * s);
};
if (existres)
{
$"Считается погрешность метода 1".Show();
ErrorsM1[t - 1] = Math.Sqrt(IntegralClass.Integral2(fM1, KursMethods.CIRCLE - 1));
}
else
{
ErrorsM1[t - 1] = ErrorsM1[t - 2];
}
$"{t} = {ErrorsM1[t - 1]}".Show();
}
[Test] public void Comparisons()
{
int x = 29, y = -17;
string s1 = "hello", s2 = "world";
Assert.IsTrue(Functional.LessThan(y, x));
Assert.IsFalse(Functional.LessThan(x, y));
Assert.IsTrue(Functional.GreaterThan(x, y));
Assert.IsFalse(Functional.GreaterThan(y, x));
Assert.IsTrue(Functional.LessThanOrEqual(y, x));
Assert.IsFalse(Functional.LessThanOrEqual(x, y));
Assert.IsTrue(Functional.GreaterThanOrEqual(x, y));
Assert.IsFalse(Functional.GreaterThanOrEqual(y, x));
y = 29;
Assert.IsFalse(Functional.LessThan(y, x));
Assert.IsFalse(Functional.LessThan(x, y));
Assert.IsFalse(Functional.GreaterThan(x, y));
Assert.IsFalse(Functional.GreaterThan(y, x));
Assert.IsTrue(Functional.LessThanOrEqual(y, x));
Assert.IsTrue(Functional.LessThanOrEqual(x, y));
Assert.IsTrue(Functional.GreaterThanOrEqual(x, y));
Assert.IsTrue(Functional.GreaterThanOrEqual(y, x));
Assert.IsTrue(Functional.LessThan(s1, s2));
Assert.IsFalse(Functional.LessThan(s2, s1));
Assert.IsTrue(Functional.GreaterThan(s2, s1));
Assert.IsFalse(Functional.GreaterThan(s1, s2));
Assert.IsTrue(Functional.LessThanOrEqual(s1, s2));
Assert.IsFalse(Functional.LessThanOrEqual(s2, s1));
Assert.IsTrue(Functional.GreaterThanOrEqual(s2, s1));
Assert.IsFalse(Functional.GreaterThanOrEqual(s1, s2));
s2 = "hello";
Assert.IsFalse(Functional.LessThan(s1, s2));
Assert.IsFalse(Functional.LessThan(s2, s1));
Assert.IsFalse(Functional.GreaterThan(s2, s1));
Assert.IsFalse(Functional.GreaterThan(s1, s2));
Assert.IsTrue(Functional.LessThanOrEqual(s1, s2));
Assert.IsTrue(Functional.LessThanOrEqual(s2, s1));
Assert.IsTrue(Functional.GreaterThanOrEqual(s2, s1));
Assert.IsTrue(Functional.GreaterThanOrEqual(s1, s2));
s1 = null;
Assert.IsTrue(Functional.LessThan(s1, s2));
Assert.IsFalse(Functional.LessThan(s2, s1));
Assert.IsTrue(Functional.GreaterThan(s2, s1));
Assert.IsFalse(Functional.GreaterThan(s1, s2));
Assert.IsTrue(Functional.LessThanOrEqual(s1, s2));
Assert.IsFalse(Functional.LessThanOrEqual(s2, s1));
Assert.IsTrue(Functional.GreaterThanOrEqual(s2, s1));
Assert.IsFalse(Functional.GreaterThanOrEqual(s1, s2));
s2 = null;
Assert.IsFalse(Functional.LessThan(s1, s2));
Assert.IsFalse(Functional.LessThan(s2, s1));
Assert.IsFalse(Functional.GreaterThan(s2, s1));
Assert.IsFalse(Functional.GreaterThan(s1, s2));
Assert.IsTrue(Functional.LessThanOrEqual(s1, s2));
Assert.IsTrue(Functional.LessThanOrEqual(s2, s1));
Assert.IsTrue(Functional.GreaterThanOrEqual(s2, s1));
Assert.IsTrue(Functional.GreaterThanOrEqual(s1, s2));
}
/// <summary>
/// Перевести функционал в функцию комплесного переменного
/// </summary>
/// <param name="f"></param>
/// <returns></returns>
public static ComplexFunc ToCompFunc(Functional f)
{
return((Complex z) => f(new Point(z.Re, z.Im)));
}
public void ProcessMessage(string json_message, MonoBehaviour owner, Action <string> response)
{
Command msgObj = JsonUtility.FromJson <Command> (json_message);
try
{
switch (msgObj.objectType)
{
case "Optimizer":
{
if (msgObj.functionCall == "create")
{
string optimizer_type = msgObj.tensorIndexParams[0];
// Extract parameters
List <int> p = new List <int>();
for (int i = 1; i < msgObj.tensorIndexParams.Length; i++)
{
p.Add(int.Parse(msgObj.tensorIndexParams[i]));
}
List <float> hp = new List <float>();
for (int i = 0; i < msgObj.hyperParams.Length; i++)
{
hp.Add(float.Parse(msgObj.hyperParams[i]));
}
Optimizer optim = null;
if (optimizer_type == "sgd")
{
optim = new SGD(this, p, hp[0], hp[1], hp[2]);
}
else if (optimizer_type == "rmsprop")
{
optim = new RMSProp(this, p, hp[0], hp[1], hp[2], hp[3]);
}
else if (optimizer_type == "adam")
{
optim = new Adam(this, p, hp[0], hp[1], hp[2], hp[3], hp[4]);
}
response(optim.Id.ToString());
return;
}
else
{
Optimizer optim = this.GetOptimizer(msgObj.objectIndex);
response(optim.ProcessMessage(msgObj, this));
return;
}
}
case "FloatTensor":
{
if (msgObj.objectIndex == 0 && msgObj.functionCall == "create")
{
FloatTensor tensor = floatTensorFactory.Create(_shape: msgObj.shape, _data: msgObj.data, _shader: this.Shader);
response(tensor.Id.ToString());
return;
}
else
{
FloatTensor tensor = floatTensorFactory.Get(msgObj.objectIndex);
// Process message's function
response(tensor.ProcessMessage(msgObj, this));
return;
}
}
case "IntTensor":
{
if (msgObj.objectIndex == 0 && msgObj.functionCall == "create")
{
int[] data = new int[msgObj.data.Length];
for (int i = 0; i < msgObj.data.Length; i++)
{
data[i] = (int)msgObj.data[i];
}
IntTensor tensor = intTensorFactory.Create(_shape: msgObj.shape, _data: data);
response(tensor.Id.ToString());
return;
}
else
{
IntTensor tensor = intTensorFactory.Get(msgObj.objectIndex);
// Process message's function
response(tensor.ProcessMessage(msgObj, this));
return;
}
}
case "agent":
{
if (msgObj.functionCall == "create")
{
Layer model = (Layer)GetModel(int.Parse(msgObj.tensorIndexParams[0]));
Optimizer optimizer = optimizers[int.Parse(msgObj.tensorIndexParams[1])];
response(new Syft.NN.RL.Agent(this, model, optimizer).Id.ToString());
return;
}
//Debug.Log("Getting Model:" + msgObj.objectIndex);
Syft.NN.RL.Agent agent = this.GetAgent(msgObj.objectIndex);
response(agent.ProcessMessageLocal(msgObj, this));
return;
}
case "model":
{
if (msgObj.functionCall == "create")
{
string model_type = msgObj.tensorIndexParams[0];
Debug.LogFormat("<color=magenta>createModel:</color> {0}", model_type);
if (model_type == "linear")
{
response(this.BuildLinear(msgObj.tensorIndexParams).Id.ToString());
return;
}
else if (model_type == "relu")
{
response(this.BuildReLU().Id.ToString());
return;
}
else if (model_type == "log")
{
response(this.BuildLog().Id.ToString());
return;
}
else if (model_type == "dropout")
{
response(this.BuildDropout(msgObj.tensorIndexParams).Id.ToString());
return;
}
else if (model_type == "sigmoid")
{
response(this.BuildSigmoid().Id.ToString());
return;
}
else if (model_type == "sequential")
{
response(this.BuildSequential().Id.ToString());
return;
}
else if (model_type == "softmax")
{
response(this.BuildSoftmax(msgObj.tensorIndexParams).Id.ToString());
return;
}
else if (model_type == "logsoftmax")
{
response(this.BuildLogSoftmax(msgObj.tensorIndexParams).Id.ToString());
return;
}
else if (model_type == "tanh")
{
response(new Tanh(this).Id.ToString());
return;
}
else if (model_type == "crossentropyloss")
{
response(new CrossEntropyLoss(this, int.Parse(msgObj.tensorIndexParams[1])).Id.ToString());
return;
}
else if (model_type == "categorical_crossentropy")
{
response(new CategoricalCrossEntropyLoss(this).Id.ToString());
return;
}
else if (model_type == "nllloss")
{
response(new NLLLoss(this).Id.ToString());
return;
}
else if (model_type == "mseloss")
{
response(new MSELoss(this).Id.ToString());
return;
}
else if (model_type == "embedding")
{
response(new Embedding(this, int.Parse(msgObj.tensorIndexParams[1]), int.Parse(msgObj.tensorIndexParams[2])).Id.ToString());
return;
}
else
{
Debug.LogFormat("<color=red>Model Type Not Found:</color> {0}", model_type);
}
}
else
{
//Debug.Log("Getting Model:" + msgObj.objectIndex);
Model model = this.GetModel(msgObj.objectIndex);
response(model.ProcessMessage(msgObj, this));
return;
}
response("Unity Error: SyftController.processMessage: Command not found:" + msgObj.objectType + ":" + msgObj.functionCall);
return;
}
case "controller":
{
if (msgObj.functionCall == "num_tensors")
{
response(floatTensorFactory.Count() + "");
return;
}
else if (msgObj.functionCall == "num_models")
{
response(models.Count + "");
return;
}
else if (msgObj.functionCall == "new_tensors_allowed")
{
Debug.LogFormat("New Tensors Allowed:{0}", msgObj.tensorIndexParams[0]);
if (msgObj.tensorIndexParams[0] == "True")
{
allow_new_tensors = true;
}
else if (msgObj.tensorIndexParams[0] == "False")
{
allow_new_tensors = false;
}
else
{
throw new Exception("Invalid parameter for new_tensors_allowed. Did you mean true or false?");
}
response(allow_new_tensors + "");
return;
}
else if (msgObj.functionCall == "load_floattensor")
{
FloatTensor tensor = floatTensorFactory.Create(filepath: msgObj.tensorIndexParams[0], _shader: this.Shader);
response(tensor.Id.ToString());
return;
}
else if (msgObj.functionCall == "set_seed")
{
Random.InitState(int.Parse(msgObj.tensorIndexParams[0]));
response("Random seed set!");
return;
}
else if (msgObj.functionCall == "concatenate")
{
List <int> tensor_ids = new List <int>();
for (int i = 1; i < msgObj.tensorIndexParams.Length; i++)
{
tensor_ids.Add(int.Parse(msgObj.tensorIndexParams[i]));
}
FloatTensor result = Functional.Concatenate(floatTensorFactory, tensor_ids, int.Parse(msgObj.tensorIndexParams[0]));
response(result.Id.ToString());
return;
}
else if (msgObj.functionCall == "ones")
{
int[] dims = new int[msgObj.tensorIndexParams.Length];
for (int i = 0; i < msgObj.tensorIndexParams.Length; i++)
{
dims[i] = int.Parse(msgObj.tensorIndexParams[i]);
}
FloatTensor result = Functional.Ones(floatTensorFactory, dims);
response(result.Id.ToString());
return;
}
else if (msgObj.functionCall == "randn")
{
int[] dims = new int[msgObj.tensorIndexParams.Length];
for (int i = 0; i < msgObj.tensorIndexParams.Length; i++)
{
dims[i] = int.Parse(msgObj.tensorIndexParams[i]);
}
FloatTensor result = Functional.Randn(floatTensorFactory, dims);
response(result.Id.ToString());
return;
}
else if (msgObj.functionCall == "random")
{
int[] dims = new int[msgObj.tensorIndexParams.Length];
for (int i = 0; i < msgObj.tensorIndexParams.Length; i++)
{
dims[i] = int.Parse(msgObj.tensorIndexParams[i]);
}
FloatTensor result = Functional.Random(floatTensorFactory, dims);
response(result.Id.ToString());
return;
}
else if (msgObj.functionCall == "zeros")
{
int[] dims = new int[msgObj.tensorIndexParams.Length];
for (int i = 0; i < msgObj.tensorIndexParams.Length; i++)
{
dims[i] = int.Parse(msgObj.tensorIndexParams[i]);
}
FloatTensor result = Functional.Zeros(floatTensorFactory, dims);
response(result.Id.ToString());
return;
}
else if (msgObj.functionCall == "model_from_json")
{
Debug.Log("Loading Model from JSON:");
var json_str = msgObj.tensorIndexParams[0];
var config = JObject.Parse(json_str);
Sequential model;
if ((string)config["class_name"] == "Sequential")
{
model = this.BuildSequential();
}
else
{
response("Unity Error: SyftController.processMessage: while Loading model, Class :" + config["class_name"] + " is not implemented");
return;
}
for (int i = 0; i < config["config"].ToList().Count; i++)
{
var layer_desc = config["config"][i];
var layer_config_desc = layer_desc["config"];
if ((string)layer_desc["class_name"] == "Linear")
{
int previous_output_dim;
if (i == 0)
{
previous_output_dim = (int)layer_config_desc["batch_input_shape"][layer_config_desc["batch_input_shape"].ToList().Count - 1];
}
else
{
previous_output_dim = (int)layer_config_desc["units"];
}
string[] parameters = { "linear", previous_output_dim.ToString(), layer_config_desc["units"].ToString(), "Xavier" };
Layer layer = this.BuildLinear(parameters);
model.AddLayer(layer);
string activation_name = layer_config_desc["activation"].ToString();
if (activation_name != "linear")
{
Layer activation;
if (activation_name == "softmax")
{
parameters = new string[] { activation_name, "1" };
activation = this.BuildSoftmax(parameters);
}
else if (activation_name == "relu")
{
activation = this.BuildReLU();
}
else
{
response("Unity Error: SyftController.processMessage: while Loading activations, Activation :" + activation_name + " is not implemented");
return;
}
model.AddLayer(activation);
}
}
else
{
response("Unity Error: SyftController.processMessage: while Loading layers, Layer :" + layer_desc["class_name"] + " is not implemented");
return;
}
}
response(model.Id.ToString());
return;
}
else if (msgObj.functionCall == "from_proto")
{
Debug.Log("Loading Model from ONNX:");
var filename = msgObj.tensorIndexParams[0];
var input = File.OpenRead(filename);
ModelProto modelProto = ModelProto.Parser.ParseFrom(input);
Sequential model = this.BuildSequential();
foreach (NodeProto node in modelProto.Graph.Node)
{
Layer layer;
GraphProto g = ONNXTools.GetSubGraphFromNodeAndMainGraph(node, modelProto.Graph);
if (node.OpType == "Gemm")
{
layer = new Linear(this, g);
}
else if (node.OpType == "Dropout")
{
layer = new Dropout(this, g);
}
else if (node.OpType == "Relu")
{
layer = new ReLU(this, g);
}
else if (node.OpType == "Softmax")
{
layer = new Softmax(this, g);
}
else
{
response("Unity Error: SyftController.processMessage: Layer not yet implemented for deserialization:");
return;
}
model.AddLayer(layer);
}
response(model.Id.ToString());
return;
}
else if (msgObj.functionCall == "to_proto")
{
ModelProto model = this.ToProto(msgObj.tensorIndexParams);
string filename = msgObj.tensorIndexParams[2];
string type = msgObj.tensorIndexParams[3];
if (type == "json")
{
response(model.ToString());
}
else
{
using (var output = File.Create(filename))
{
model.WriteTo(output);
}
response(new FileInfo(filename).FullName);
}
return;
}
response("Unity Error: SyftController.processMessage: Command not found:" + msgObj.objectType + ":" + msgObj.functionCall);
return;
}
case "Grid":
if (msgObj.functionCall == "learn")
{
var inputId = int.Parse(msgObj.tensorIndexParams[0]);
var targetId = int.Parse(msgObj.tensorIndexParams[1]);
response(this.grid.Run(inputId, targetId, msgObj.configurations, owner));
return;
}
if (msgObj.functionCall == "getResults")
{
this.grid.GetResults(msgObj.experimentId, response);
return;
}
// like getResults but doesn't pause to wait for results
// this function will return right away telling you if
// it knows whether or not it is done
if (msgObj.functionCall == "checkStatus")
{
this.grid.CheckStatus(msgObj.experimentId, response);
return;
}
break;
default:
break;
}
}
catch (Exception e)
{
Debug.LogFormat("<color=red>{0}</color>", e.ToString());
response("Unity Error: " + e.ToString());
return;
}
// If not executing createTensor or tensor function, return default error.
response("Unity Error: SyftController.processMessage: Command not found:" + msgObj.objectType + ":" + msgObj.functionCall);
return;
}
/// <summary>
/// Вычисление криволинейного интеграла первого рода по этой кривой от функции f методом Гаусса
/// </summary>
/// <param name="f"></param>
/// <returns></returns>
public double Firstkind(Functional f)
{
Func <double, double> h = (double t) => f(this.Transfer(t));
return(FuncMethods.DefInteg.GaussKronrod.Integral(h, this.a, this.b));
}
public ContractController(IOptions <StateConfigs> config)
{
_func = new Functional();
_info = new Information(config);
_reqModel = new RequestDataModel();
}
public static double Integral2(Functional f, int Curve) => DoubleIntegral(f, KursMethods.myCurve, KursMethods.myCurve.S, FuncMethods.DefInteg.Method.GaussKronrod15, 0.001, FuncMethods.DefInteg.countY);
public FunctionalAuthorizeFilter(Functional functional)
{
_functional = functional;
}
public void Graf3Dtest()
{
Functional f = (Point a) => Math.Cos(a.Abs);
ForScripts.MakeFilesForSurfaces(-2, 2, -4, 5, 100, "", new Functional[] { f }, (Point p) => true);
}
/// <summary>
/// Calculates the exact value of arccotangent of num
/// </summary>
/// <param name="num"></param>
/// <returns></returns>
public static ComplexNumber Arccotan(Number num)
=> (num as ComplexNumber).IsDefinite()
? Functional.Downcast(Complex.Atan(1 / num.AsComplex())) as ComplexNumber
: RealNumber.NaN();
public void ExistingDestinationComplex()
{
Mapper.Register <ItemModel, ItemModelViewModel>();
Mapper.Register <SubItem, SubItemViewModel>();
Mapper.Register <Unit, UnitViewModel>();
Mapper.Register <SubUnit, SubUnitViewModel>();
Mapper.Compile();
var sizeResult = Functional.ExistingDestinationComplex();
var itemModelHash = sizeResult.Value.GetHashCode();
var itemModelSubItemsHash = sizeResult.Value.SubItems.GetHashCode();
var subItemsHashes = new List <int>(sizeResult.Value.SubItems.Length);
var subItemUnitsCollHashes = new Dictionary <int, int>();
var subItemUnitsHashes = new Dictionary <int, List <int> >();
var subItemUnitSubUnitCollHashes = new Dictionary <int, List <int> >();
var subItemUnitSubUnitsHashes = new Dictionary <int, Dictionary <int, List <int> > >();
foreach (var subItem in sizeResult.Value.SubItems)
{
var sbHash = subItem.GetHashCode();
subItemsHashes.Add(sbHash);
subItemUnitsCollHashes.Add(sbHash, subItem.Units.GetHashCode());
subItemUnitsHashes.Add(sbHash, new List <int>());
subItemUnitSubUnitCollHashes.Add(sbHash, new List <int>());
subItemUnitSubUnitsHashes.Add(sbHash, new Dictionary <int, List <int> >());
foreach (var unit in subItem.Units.Skip(1))
{
subItemUnitsHashes[sbHash].Add(unit.GetHashCode());
subItemUnitSubUnitCollHashes[sbHash].Add(unit.SubUnits.GetHashCode());
subItemUnitSubUnitsHashes[sbHash][unit.GetHashCode()] = new List <int>();
foreach (var subUnit in unit.SubUnits)
{
subItemUnitSubUnitsHashes[sbHash][unit.GetHashCode()].Add(subUnit.GetHashCode());
}
}
}
var result = Mapper.Map(sizeResult.Key, sizeResult.Value);
Assert.AreEqual(result, sizeResult.Value);
Assert.AreEqual(result.GetHashCode(), itemModelHash);
Assert.AreEqual(result.SubItems.GetHashCode(), itemModelSubItemsHash);
for (var i = 0; i < result.SubItems.Length; i++)
{
Assert.AreEqual(result.SubItems[i].GetHashCode(), subItemsHashes[i]);
Assert.AreEqual(result.SubItems[i].Units.GetHashCode(), subItemUnitsCollHashes[result.SubItems[i].GetHashCode()]);
for (var j = 0; j < 4; j++)
{
Assert.AreEqual(result.SubItems[i].Units[j].GetHashCode(), subItemUnitsHashes[result.SubItems[i].GetHashCode()][j]);
Assert.AreEqual(result.SubItems[i].Units[j].SubUnits.GetHashCode(), subItemUnitSubUnitCollHashes[result.SubItems[i].GetHashCode()][j]);
for (var k = 0; k < 3; k++)
{
Assert.AreEqual(result.SubItems[i].Units[j].SubUnits[k].GetHashCode(), subItemUnitSubUnitsHashes[result.SubItems[i].GetHashCode()][result.SubItems[i].Units[j].GetHashCode()][k]);
}
}
}
}
public FileController(IOptions <StateConfigs> config)
{
_state = config.Value;
_func = new Functional();
_file = new FileManagement(config);
}
public LockService(Func <String, Tuple <Action, CancellationToken> > acquire, TimeSpan delay)
: this((key, token) => Functional.Lock(() => acquire(key), delay, token))
{
}
public string processMessage(string json_message)
{
//Debug.LogFormat("<color=green>SyftController.processMessage {0}</color>", json_message);
Command msgObj = JsonUtility.FromJson <Command> (json_message);
try
{
switch (msgObj.objectType)
{
case "Optimizer":
{
if (msgObj.functionCall == "create")
{
string optimizer_type = msgObj.tensorIndexParams[0];
// Extract parameters
List <int> p = new List <int>();
for (int i = 1; i < msgObj.tensorIndexParams.Length; i++)
{
p.Add(int.Parse(msgObj.tensorIndexParams[i]));
}
List <float> hp = new List <float>();
for (int i = 0; i < msgObj.hyperParams.Length; i++)
{
hp.Add(float.Parse(msgObj.hyperParams[i]));
}
Optimizer optim = null;
if (optimizer_type == "sgd")
{
optim = new SGD(this, p, hp[0], hp[1], hp[2]);
}
else if (optimizer_type == "rmsprop")
{
optim = new RMSProp(this, p, hp[0], hp[1], hp[2], hp[3]);
}
else if (optimizer_type == "adam")
{
optim = new Adam(this, p, hp[0], hp[1], hp[2], hp[3], hp[4]);
}
return(optim.Id.ToString());
}
else
{
Optimizer optim = this.getOptimizer(msgObj.objectIndex);
return(optim.ProcessMessage(msgObj, this));
}
}
case "FloatTensor":
{
if (msgObj.objectIndex == 0 && msgObj.functionCall == "create")
{
FloatTensor tensor = floatTensorFactory.Create(_shape: msgObj.shape, _data: msgObj.data, _shader: this.Shader);
return(tensor.Id.ToString());
}
else
{
FloatTensor tensor = floatTensorFactory.Get(msgObj.objectIndex);
// Process message's function
return(tensor.ProcessMessage(msgObj, this));
}
}
case "IntTensor":
{
if (msgObj.objectIndex == 0 && msgObj.functionCall == "create")
{
int[] data = new int[msgObj.data.Length];
for (int i = 0; i < msgObj.data.Length; i++)
{
data[i] = (int)msgObj.data[i];
}
IntTensor tensor = intTensorFactory.Create(_shape: msgObj.shape, _data: data, _shader: this.Shader);
return(tensor.Id.ToString());
}
else
{
IntTensor tensor = intTensorFactory.Get(msgObj.objectIndex);
// Process message's function
return(tensor.ProcessMessage(msgObj, this));
}
}
case "agent":
{
if (msgObj.functionCall == "create")
{
Layer model = (Layer)getModel(int.Parse(msgObj.tensorIndexParams[0]));
Optimizer optimizer = optimizers[int.Parse(msgObj.tensorIndexParams[1])];
return(new Syft.NN.RL.Agent(this, model, optimizer).Id.ToString());
}
//Debug.Log("Getting Model:" + msgObj.objectIndex);
Syft.NN.RL.Agent agent = this.getAgent(msgObj.objectIndex);
return(agent.ProcessMessageLocal(msgObj, this));
}
case "model":
{
if (msgObj.functionCall == "create")
{
string model_type = msgObj.tensorIndexParams[0];
Debug.LogFormat("<color=magenta>createModel:</color> {0}", model_type);
if (model_type == "linear")
{
return(new Linear(this, int.Parse(msgObj.tensorIndexParams[1]),
int.Parse(msgObj.tensorIndexParams[2]),
msgObj.tensorIndexParams[3]).Id.ToString());
}
else if (model_type == "relu")
{
return(new ReLU(this).Id.ToString());
}
else if (model_type == "log")
{
return(new Log(this).Id.ToString());
}
else if (model_type == "dropout")
{
return(new Dropout(this, float.Parse(msgObj.tensorIndexParams[1])).Id.ToString());
}
else if (model_type == "sigmoid")
{
return(new Sigmoid(this).Id.ToString());
}
else if (model_type == "sequential")
{
return(new Sequential(this).Id.ToString());
}
else if (model_type == "softmax")
{
return(new Softmax(this, int.Parse(msgObj.tensorIndexParams[1])).Id.ToString());
}
else if (model_type == "logsoftmax")
{
return(new LogSoftmax(this, int.Parse(msgObj.tensorIndexParams[1])).Id.ToString());
}
else if (model_type == "tanh")
{
return(new Tanh(this).Id.ToString());
}
else if (model_type == "crossentropyloss")
{
return(new CrossEntropyLoss(this, int.Parse(msgObj.tensorIndexParams[1])).Id.ToString());
}
else if (model_type == "categorical_crossentropy")
{
return(new CategoricalCrossEntropyLoss(this).Id.ToString());
}
else if (model_type == "nllloss")
{
return(new NLLLoss(this).Id.ToString());
}
else if (model_type == "mseloss")
{
return(new MSELoss(this).Id.ToString());
}
else if (model_type == "embedding")
{
return(new Embedding(this, int.Parse(msgObj.tensorIndexParams[1]), int.Parse(msgObj.tensorIndexParams[2])).Id.ToString());
}
else
{
Debug.LogFormat("<color=red>Model Type Not Found:</color> {0}", model_type);
}
}
else
{
//Debug.Log("Getting Model:" + msgObj.objectIndex);
Model model = this.getModel(msgObj.objectIndex);
return(model.ProcessMessage(msgObj, this));
}
return("Unity Error: SyftController.processMessage: Command not found:" + msgObj.objectType + ":" + msgObj.functionCall);
}
case "controller":
{
if (msgObj.functionCall == "num_tensors")
{
return(floatTensorFactory.Count() + "");
}
else if (msgObj.functionCall == "num_models")
{
return(models.Count + "");
}
else if (msgObj.functionCall == "new_tensors_allowed")
{
Debug.LogFormat("New Tensors Allowed:{0}", msgObj.tensorIndexParams[0]);
if (msgObj.tensorIndexParams[0] == "True")
{
allow_new_tensors = true;
}
else if (msgObj.tensorIndexParams[0] == "False")
{
allow_new_tensors = false;
}
else
{
throw new Exception("Invalid parameter for new_tensors_allowed. Did you mean true or false?");
}
return(allow_new_tensors + "");
}
else if (msgObj.functionCall == "load_floattensor")
{
FloatTensor tensor = floatTensorFactory.Create(filepath: msgObj.tensorIndexParams[0], _shader: this.Shader);
return(tensor.Id.ToString());
}
else if (msgObj.functionCall == "set_seed")
{
Random.InitState(int.Parse(msgObj.tensorIndexParams[0]));
return("Random seed set!");
}
else if (msgObj.functionCall == "concatenate")
{
List <int> tensor_ids = new List <int>();
for (int i = 1; i < msgObj.tensorIndexParams.Length; i++)
{
tensor_ids.Add(int.Parse(msgObj.tensorIndexParams[i]));
}
FloatTensor result = Functional.Concatenate(floatTensorFactory, tensor_ids, int.Parse(msgObj.tensorIndexParams[0]));
return(result.Id.ToString());
}
else if (msgObj.functionCall == "ones")
{
int[] dims = new int[msgObj.tensorIndexParams.Length];
for (int i = 0; i < msgObj.tensorIndexParams.Length; i++)
{
dims[i] = int.Parse(msgObj.tensorIndexParams[i]);
}
FloatTensor result = Functional.Ones(floatTensorFactory, dims);
return(result.Id.ToString());
}
else if (msgObj.functionCall == "randn")
{
int[] dims = new int[msgObj.tensorIndexParams.Length];
for (int i = 0; i < msgObj.tensorIndexParams.Length; i++)
{
dims[i] = int.Parse(msgObj.tensorIndexParams[i]);
}
FloatTensor result = Functional.Randn(floatTensorFactory, dims);
return(result.Id.ToString());
}
else if (msgObj.functionCall == "random")
{
int[] dims = new int[msgObj.tensorIndexParams.Length];
for (int i = 0; i < msgObj.tensorIndexParams.Length; i++)
{
dims[i] = int.Parse(msgObj.tensorIndexParams[i]);
}
FloatTensor result = Functional.Random(floatTensorFactory, dims);
return(result.Id.ToString());
}
else if (msgObj.functionCall == "zeros")
{
int[] dims = new int[msgObj.tensorIndexParams.Length];
for (int i = 0; i < msgObj.tensorIndexParams.Length; i++)
{
dims[i] = int.Parse(msgObj.tensorIndexParams[i]);
}
FloatTensor result = Functional.Zeros(floatTensorFactory, dims);
return(result.Id.ToString());
}
return("Unity Error: SyftController.processMessage: Command not found:" + msgObj.objectType + ":" + msgObj.functionCall);
}
default:
break;
}
}
catch (Exception e)
{
Debug.LogFormat("<color=red>{0}</color>", e.ToString());
return("Unity Error: " + e.ToString());
}
// If not executing createTensor or tensor function, return default error.
return("Unity Error: SyftController.processMessage: Command not found:" + msgObj.objectType + ":" + msgObj.functionCall);
}
public static T Execute <T>(this IReliable reliable, Func <T> func, TimeSpan wait, CancellationToken?token = null)
{
return(Functional.Wait(cancel => reliable.Execute(func, cancel), wait, token ?? new CancellationToken()));
}
private void OnGUI()
{
GUILayout.Label("Members");
if (GUILayout.Button("Get TestClass field name"))
{
string memberName;
if (StaticReflectionHelper.GetMemberName <TestClass>(c => c.testField).TryGet(out memberName))
{
Debug.Log(memberName);
}
}
if (GUILayout.Button("Get TestClass property name"))
{
string memberName;
if (StaticReflectionHelper.GetMemberName <TestClass>(c => c.testProperty).TryGet(out memberName))
{
Debug.Log(memberName);
}
}
if (GUILayout.Button("Get TestClass static field name"))
{
string memberName;
if (StaticReflectionHelper.GetMemberName(() => TestClass.staticTestField).TryGet(out memberName))
{
Debug.Log(memberName);
}
}
if (GUILayout.Button("Get TestClass static property name"))
{
string memberName;
if (StaticReflectionHelper.GetMemberName(() => TestClass.staticTestProperty).TryGet(out memberName))
{
Debug.Log(memberName);
}
}
GUILayout.Label("Methods");
if (GUILayout.Button("Get TestClass method name"))
{
Functional.Ignore =
from memberName in StaticReflectionHelper.GetMemberName <TestClass>(c => c.TestMethod())
from methodInfo in StaticReflectionHelper.GetMethod <TestClass>(c => c.TestMethod())
select Functional.Do(() => Debug.Log(memberName + ", MethodInfo.Name: " + methodInfo.Name));
}
if (GUILayout.Button("Get TestClass no return method name"))
{
Functional.Ignore =
from memberName in StaticReflectionHelper.GetMemberName <TestClass>(c => c.TestMethodNoReturn())
from methodInfo in StaticReflectionHelper.GetMethod <TestClass>(c => c.TestMethodNoReturn())
select Functional.Do(() => Debug.Log(memberName + ", MethodInfo.Name: " + methodInfo.Name));
}
if (GUILayout.Button("Get TestClass static methods name"))
{
Functional.Ignore =
from memberName in StaticReflectionHelper.GetMemberName(() => TestClass.StaticTestMethod())
from methodInfo in StaticReflectionHelper.GetMethod(() => TestClass.StaticTestMethod())
select Functional.Do(() => Debug.Log(memberName + ", MethodInfo.Name: " + methodInfo.Name));
}
if (GUILayout.Button("Get TestClass no return static methods name"))
{
Functional.Ignore =
from memberName in StaticReflectionHelper.GetMemberName(() => TestClass.StaticTestMethodNoReturn())
from methodInfo in StaticReflectionHelper.GetMethod(() => TestClass.StaticTestMethodNoReturn())
select Functional.Do(() => Debug.Log(memberName + ", MethodInfo.Name: " + methodInfo.Name));
}
}
public static void Execute(this IReliable reliable, Action action, TimeSpan wait, CancellationToken?token = null)
{
reliable.Execute(Functional.ToFunc <Boolean>(action), wait, token);
}
public string processMessage(string json_message, MonoBehaviour owner)
{
//Debug.LogFormat("<color=green>SyftController.processMessage {0}</color>", json_message);
Command msgObj = JsonUtility.FromJson <Command> (json_message);
try
{
switch (msgObj.objectType)
{
case "Optimizer":
{
if (msgObj.functionCall == "create")
{
string optimizer_type = msgObj.tensorIndexParams[0];
// Extract parameters
List <int> p = new List <int>();
for (int i = 1; i < msgObj.tensorIndexParams.Length; i++)
{
p.Add(int.Parse(msgObj.tensorIndexParams[i]));
}
List <float> hp = new List <float>();
for (int i = 0; i < msgObj.hyperParams.Length; i++)
{
hp.Add(float.Parse(msgObj.hyperParams[i]));
}
Optimizer optim = null;
if (optimizer_type == "sgd")
{
optim = new SGD(this, p, hp[0], hp[1], hp[2]);
}
else if (optimizer_type == "rmsprop")
{
optim = new RMSProp(this, p, hp[0], hp[1], hp[2], hp[3]);
}
else if (optimizer_type == "adam")
{
optim = new Adam(this, p, hp[0], hp[1], hp[2], hp[3], hp[4]);
}
return(optim.Id.ToString());
}
else
{
Optimizer optim = this.getOptimizer(msgObj.objectIndex);
return(optim.ProcessMessage(msgObj, this));
}
}
case "FloatTensor":
{
if (msgObj.objectIndex == 0 && msgObj.functionCall == "create")
{
FloatTensor tensor = floatTensorFactory.Create(_shape: msgObj.shape, _data: msgObj.data, _shader: this.Shader);
return(tensor.Id.ToString());
}
else
{
FloatTensor tensor = floatTensorFactory.Get(msgObj.objectIndex);
// Process message's function
return(tensor.ProcessMessage(msgObj, this));
}
}
case "IntTensor":
{
if (msgObj.objectIndex == 0 && msgObj.functionCall == "create")
{
int[] data = new int[msgObj.data.Length];
for (int i = 0; i < msgObj.data.Length; i++)
{
data[i] = (int)msgObj.data[i];
}
IntTensor tensor = intTensorFactory.Create(_shape: msgObj.shape, _data: data, _shader: this.Shader);
return(tensor.Id.ToString());
}
else
{
IntTensor tensor = intTensorFactory.Get(msgObj.objectIndex);
// Process message's function
return(tensor.ProcessMessage(msgObj, this));
}
}
case "agent":
{
if (msgObj.functionCall == "create")
{
Layer model = (Layer)getModel(int.Parse(msgObj.tensorIndexParams[0]));
Optimizer optimizer = optimizers[int.Parse(msgObj.tensorIndexParams[1])];
return(new Syft.NN.RL.Agent(this, model, optimizer).Id.ToString());
}
//Debug.Log("Getting Model:" + msgObj.objectIndex);
Syft.NN.RL.Agent agent = this.getAgent(msgObj.objectIndex);
return(agent.ProcessMessageLocal(msgObj, this));
}
case "model":
{
if (msgObj.functionCall == "create")
{
string model_type = msgObj.tensorIndexParams[0];
Debug.LogFormat("<color=magenta>createModel:</color> {0}", model_type);
if (model_type == "linear")
{
return(this.BuildLinear(msgObj.tensorIndexParams).Id.ToString());
}
else if (model_type == "relu")
{
return(this.BuildReLU().Id.ToString());
}
else if (model_type == "log")
{
return(this.BuildLog().Id.ToString());
}
else if (model_type == "dropout")
{
return(this.BuildDropout(msgObj.tensorIndexParams).Id.ToString());
}
else if (model_type == "sigmoid")
{
return(this.BuildSigmoid().Id.ToString());
}
else if (model_type == "sequential")
{
return(this.BuildSequential().Id.ToString());
}
else if (model_type == "softmax")
{
return(this.BuildSoftmax(msgObj.tensorIndexParams).Id.ToString());
}
else if (model_type == "logsoftmax")
{
return(this.BuildLogSoftmax(msgObj.tensorIndexParams).Id.ToString());
}
else if (model_type == "tanh")
{
return(new Tanh(this).Id.ToString());
}
else if (model_type == "crossentropyloss")
{
return(new CrossEntropyLoss(this, int.Parse(msgObj.tensorIndexParams[1])).Id.ToString());
}
else if (model_type == "categorical_crossentropy")
{
return(new CategoricalCrossEntropyLoss(this).Id.ToString());
}
else if (model_type == "nllloss")
{
return(new NLLLoss(this).Id.ToString());
}
else if (model_type == "mseloss")
{
return(new MSELoss(this).Id.ToString());
}
else if (model_type == "embedding")
{
return(new Embedding(this, int.Parse(msgObj.tensorIndexParams[1]), int.Parse(msgObj.tensorIndexParams[2])).Id.ToString());
}
else
{
Debug.LogFormat("<color=red>Model Type Not Found:</color> {0}", model_type);
}
}
else
{
//Debug.Log("Getting Model:" + msgObj.objectIndex);
Model model = this.getModel(msgObj.objectIndex);
return(model.ProcessMessage(msgObj, this));
}
return("Unity Error: SyftController.processMessage: Command not found:" + msgObj.objectType + ":" + msgObj.functionCall);
}
case "controller":
{
if (msgObj.functionCall == "num_tensors")
{
return(floatTensorFactory.Count() + "");
}
else if (msgObj.functionCall == "num_models")
{
return(models.Count + "");
}
else if (msgObj.functionCall == "new_tensors_allowed")
{
Debug.LogFormat("New Tensors Allowed:{0}", msgObj.tensorIndexParams[0]);
if (msgObj.tensorIndexParams[0] == "True")
{
allow_new_tensors = true;
}
else if (msgObj.tensorIndexParams[0] == "False")
{
allow_new_tensors = false;
}
else
{
throw new Exception("Invalid parameter for new_tensors_allowed. Did you mean true or false?");
}
return(allow_new_tensors + "");
}
else if (msgObj.functionCall == "load_floattensor")
{
FloatTensor tensor = floatTensorFactory.Create(filepath: msgObj.tensorIndexParams[0], _shader: this.Shader);
return(tensor.Id.ToString());
}
else if (msgObj.functionCall == "set_seed")
{
Random.InitState(int.Parse(msgObj.tensorIndexParams[0]));
return("Random seed set!");
}
else if (msgObj.functionCall == "concatenate")
{
List <int> tensor_ids = new List <int>();
for (int i = 1; i < msgObj.tensorIndexParams.Length; i++)
{
tensor_ids.Add(int.Parse(msgObj.tensorIndexParams[i]));
}
FloatTensor result = Functional.Concatenate(floatTensorFactory, tensor_ids, int.Parse(msgObj.tensorIndexParams[0]));
return(result.Id.ToString());
}
else if (msgObj.functionCall == "ones")
{
int[] dims = new int[msgObj.tensorIndexParams.Length];
for (int i = 0; i < msgObj.tensorIndexParams.Length; i++)
{
dims[i] = int.Parse(msgObj.tensorIndexParams[i]);
}
FloatTensor result = Functional.Ones(floatTensorFactory, dims);
return(result.Id.ToString());
}
else if (msgObj.functionCall == "randn")
{
int[] dims = new int[msgObj.tensorIndexParams.Length];
for (int i = 0; i < msgObj.tensorIndexParams.Length; i++)
{
dims[i] = int.Parse(msgObj.tensorIndexParams[i]);
}
FloatTensor result = Functional.Randn(floatTensorFactory, dims);
return(result.Id.ToString());
}
else if (msgObj.functionCall == "random")
{
int[] dims = new int[msgObj.tensorIndexParams.Length];
for (int i = 0; i < msgObj.tensorIndexParams.Length; i++)
{
dims[i] = int.Parse(msgObj.tensorIndexParams[i]);
}
FloatTensor result = Functional.Random(floatTensorFactory, dims);
return(result.Id.ToString());
}
else if (msgObj.functionCall == "zeros")
{
int[] dims = new int[msgObj.tensorIndexParams.Length];
for (int i = 0; i < msgObj.tensorIndexParams.Length; i++)
{
dims[i] = int.Parse(msgObj.tensorIndexParams[i]);
}
FloatTensor result = Functional.Zeros(floatTensorFactory, dims);
return(result.Id.ToString());
}
else if (msgObj.functionCall == "model_from_json")
{
Debug.Log("Loading Model from JSON:");
var json_str = msgObj.tensorIndexParams[0];
var config = JObject.Parse(json_str);
Sequential model;
if ((string)config["class_name"] == "Sequential")
{
model = this.BuildSequential();
}
else
{
return("Unity Error: SyftController.processMessage: while Loading model, Class :" + config["class_name"] + " is not implemented");
}
for (int i = 0; i < config["config"].ToList().Count; i++)
{
var layer_desc = config["config"][i];
var layer_config_desc = layer_desc["config"];
if ((string)layer_desc["class_name"] == "Linear")
{
int previous_output_dim;
if (i == 0)
{
previous_output_dim = (int)layer_config_desc["batch_input_shape"][layer_config_desc["batch_input_shape"].ToList().Count - 1];
}
else
{
previous_output_dim = (int)layer_config_desc["units"];
}
string[] parameters = new string[] { "linear", previous_output_dim.ToString(), layer_config_desc["units"].ToString(), "Xavier" };
Layer layer = this.BuildLinear(parameters);
model.AddLayer(layer);
string activation_name = layer_config_desc["activation"].ToString();
if (activation_name != "linear")
{
Layer activation;
if (activation_name == "softmax")
{
parameters = new string[] { activation_name, "1" };
activation = this.BuildSoftmax(parameters);
}
else if (activation_name == "relu")
{
activation = this.BuildReLU();
}
else
{
return("Unity Error: SyftController.processMessage: while Loading activations, Activation :" + activation_name + " is not implemented");
}
model.AddLayer(activation);
}
}
else
{
return("Unity Error: SyftController.processMessage: while Loading layers, Layer :" + layer_desc["class_name"] + " is not implemented");
}
}
return(model.Id.ToString());
}
return("Unity Error: SyftController.processMessage: Command not found:" + msgObj.objectType + ":" + msgObj.functionCall);
}
case "Grid":
if (msgObj.functionCall == "learn")
{
var inputId = int.Parse(msgObj.tensorIndexParams[0]);
var targetId = int.Parse(msgObj.tensorIndexParams[1]);
var g = new Grid(this);
g.Run(inputId, targetId, msgObj.configurations, owner);
return("");
}
break;
default:
break;
}
}
catch (Exception e)
{
Debug.LogFormat("<color=red>{0}</color>", e.ToString());
return("Unity Error: " + e.ToString());
}
// If not executing createTensor or tensor function, return default error.
return("Unity Error: SyftController.processMessage: Command not found:" + msgObj.objectType + ":" + msgObj.functionCall);
}
public FullApiController()
{
Functional = new Functional(pathToConfigs);
Utills = new Utills(pathToConfigs);
}
/// <summary> /// Forcefully casts one to RealNumber with downcasting /// </summary> /// <returns></returns> public RealNumber AsRealNumber() => new RealNumber(Functional.Downcast(this));
public AuthorizeController(IOptions <StateConfigs> configs)
{
func = new Functional();
auth = new AuthorizeModel(configs);
acc = new AccountModel(configs);
}
/// <summary> /// Forcefully casts one to IntegerNumber with downcasting /// </summary> /// <returns></returns> public IntegerNumber AsIntegerNumber() => new IntegerNumber(Functional.Downcast(this));
private IEnumerable <Args> GetFinalCommandLines(string baseCommandLine)
{
var exitCodeTest =
_testCasesToRun.SingleOrDefault(tc => tc.IsExitCodeTestCase);
if (exitCodeTest != null)
{
_testCasesToRun.Remove(exitCodeTest);
if (!_testCasesToRun.Any())
{
return(CreateDummyCommandLineArgs(baseCommandLine, exitCodeTest).Yield());
}
}
var commandLines = new List <Args>();
string userParam = GetAdditionalUserParameter();
if (AllTestCasesOfExecutableAreRun())
{
commandLines.Add(new Args(_testCasesToRun, baseCommandLine + userParam));
return(commandLines);
}
List <string> suitesRunningAllTests = GetSuitesRunningAllTests();
int maxSuiteLength = MaxCommandLength - _lengthOfExecutableString - userParam.Length - 1;
List <List <string> > suiteLists = GetSuiteListsForCommandLines(suitesRunningAllTests, maxSuiteLength);
// lambda to return the base commandline string (including suite filters) and the list of testcases to execute
var getFilterAndTestCasesForSuites = Functional.ToFunc((List <string> suites) =>
{
string suiteNamesFilter = GetFilterForSuitesRunningAllTests(suites);
System.Diagnostics.Debug.Assert(suiteNamesFilter.Length < maxSuiteLength);
string baseCommandLineWithFilter = baseCommandLine + GoogleTestConstants.FilterOption + suiteNamesFilter;
List <TestCase> testCases = suites.Select(GetTestCasesRunBySuite).SelectMany(testCase => testCase).ToList();
return(new { baseCommandLineWithFilter, testCases });
});
// process all but the last suites-list
suiteLists.Take(suiteLists.Count - 1)
.Select(getFilterAndTestCasesForSuites).ToList()
.ForEach(suitesFilterAndTests
=> commandLines.Add(new Args(suitesFilterAndTests.testCases, suitesFilterAndTests.baseCommandLineWithFilter + userParam)));
// process the last suite-list and all test-cases which belong to test-suites that are not fully executed
var filterAndTestsForLastSuites = getFilterAndTestCasesForSuites(suiteLists.Last());
string baseCommandLineForLastSuites = filterAndTestsForLastSuites.baseCommandLineWithFilter;
List <TestCase> testCasesNotRunBySuite = GetTestCasesNotRunBySuite(suitesRunningAllTests);
int remainingLength = MaxCommandLength - baseCommandLineForLastSuites.Length - _lengthOfExecutableString - userParam.Length - 1;
string commandLine = baseCommandLineForLastSuites + JoinTestsUpToMaxLength(testCasesNotRunBySuite, remainingLength, out var includedTestCases);
includedTestCases.AddRange(filterAndTestsForLastSuites.testCases);
// a single command line holding both suite- and single-tests-filters
commandLines.Add(new Args(includedTestCases, commandLine + userParam));
// command lines holding only single-test- but no suite-filters
string baseCommandLineWithoutSuites = baseCommandLine + GoogleTestConstants.FilterOption;
while (testCasesNotRunBySuite.Count > 0)
{
remainingLength = MaxCommandLength - baseCommandLineWithoutSuites.Length - _lengthOfExecutableString - userParam.Length - 1;
commandLine = baseCommandLineWithoutSuites + JoinTestsUpToMaxLength(testCasesNotRunBySuite, remainingLength, out includedTestCases);
commandLines.Add(new Args(includedTestCases, commandLine + userParam));
}
return(commandLines);
}
/// <summary>
/// Calculates the exact value of cosine of num
/// </summary>
/// <param name="num"></param>
/// <returns></returns>
public static ComplexNumber Cos(Number num)
=> (num as ComplexNumber).IsDefinite()
? Functional.Downcast(Complex.Cos(num.AsComplex())) as ComplexNumber
: RealNumber.NaN();
public HomeController()
{
Functional = new Functional(pathToConfigs);
Utills = new Utills(pathToConfigs);
}
