public void Reset()
{
BeginDate = DateTime.Parse("20.12.2016");
EndDate = DateTime.Now;
Plotnost.Clear();
General.Clear();
AllLayer.Clear();
General.Clear();
Layer1.Clear();
Layer2.Clear();
Layer3.Clear();
InfoVision = false;
ButtonVisibility = false;
RegulationAll = false;
RegulationCoun = false;
RegulationGen = false;
RegulationLayer1 = false;
RegulationLayer2 = false;
RegulationLayer3 = false;
RegulationPlotn = false;
var tempTime = string.Format("{0} {1}:{2}:{3}", DateTime.Now.ToShortDateString(), DateTime.Now.Hour,
DateTime.Now.Minute, DateTime.Now.Second);
var startstop = new ReadStartStop("20.12.2016 18:00:00", tempTime);
_startStops = startstop.GetMassurm3();
Get();
}
protected override void WriteData(ESPWriter writer)
{
Layer0.WriteBinary(writer);
Layer1.WriteBinary(writer);
Layer2.WriteBinary(writer);
Layer3.WriteBinary(writer);
}
public void BackPropagationIsCorrect()
{
NetworkMatrix weights = new NetworkMatrix(new double[, ] {
{ 1, 2 }, { 3, 5 }
});
Layer2 layer = Layer2.CreateLinearLayer(weights);
NetworkVector layerinput = new NetworkVector(new double[] { 1, -1 });
layer.Run(layerinput);
NetworkVector outputgradient = new NetworkVector(new double[] { 7, 11 });
NetworkMatrix weightsGradientCheck = new NetworkMatrix(new double[, ] {
{ 7, -7 }, { 11, -11 }
});
Assert.AreEqual(weightsGradientCheck, layer.WeightsGradient(outputgradient));
NetworkVector biasesGradientCheck = new NetworkVector(new double[] { 7, 11 });
Assert.AreEqual(biasesGradientCheck, layer.BiasesGradient(outputgradient));
NetworkVector inputGradientCheck = new NetworkVector(new double[] { 40, 69 });
Assert.AreEqual(inputGradientCheck, layer.InputGradient(outputgradient));
}
public void CanUseBigSigmoidLayer()
{
double[,] weights = new double[2000, 1000];
double[] input = new double[1000];
for (int i = 0; i < 1000; i++)
{
weights[i, i] = 1.0;
input[i] = (double)i;
}
NetworkVector inputvector = new NetworkVector(input);
Layer2 layer = Layer2.CreateLogisticLayer(new NetworkMatrix(weights));
layer.Run(inputvector);
double[] result = layer.Output.ToArray();
double sig0 = logistic(0.0);
for (int i = 0, j = 1000; i < 1000; i++, j++)
{
Assert.AreEqual(logistic((double)i), result[i], "Failed for i = " + i);
Assert.AreEqual(sig0, result[j], "Failed for j = " + j);
}
}
public void CanMakeSigmoidLayer()
{
NetworkMatrix weights = new NetworkMatrix(new double[, ] {
{ 1, 2 }, { 3, 4 }
});
Layer2 layer = Layer2.CreateLogisticLayer(weights);
Assert.IsNotNull(layer);
}
static void DecodeLayer2()
{
var payload = GetPayload(Layer2Data);
var decoded = Ascii85.Decode(payload);
decoded = Layer2.ParityFilter(decoded);
var output = Encoding.ASCII.GetString(decoded, 0, decoded.Length);
File.WriteAllText($"{DataDirectory}{Layer3Data}", output);
}
public void ClearInterface()
{
ClearChildren();
Layer1.ClearChildren();
Layer2.ClearChildren();
Layer3.ClearChildren();
AddChild(Layer1);
AddChild(Layer2);
AddChild(Layer3);
}
public void InputGradientRuns()
{
NetworkMatrix weights = new NetworkMatrix(new double[, ] {
{ 1 }
});
NetworkVector outputgradient = new NetworkVector(new double[] { 1 });
Layer2 layer = Layer2.CreateLogisticLayer(weights);
NetworkVector inputGradientCheck = new NetworkVector(new double[] { 0 });
Assert.AreEqual(inputGradientCheck, layer.InputGradient(outputgradient));
}
public void SigmoidLayerHasRightRun()
{
NetworkMatrix weights = new NetworkMatrix(new double[, ] {
{ 1, 0, 1 }, { 1, 1, 0 }
});
NetworkVector inputvector = new NetworkVector(new double[] { 1, 2, 3 });
Layer2 layer = Layer2.CreateLogisticLayer(weights);
layer.Run(inputvector);
NetworkVector expectedResult = new NetworkVector(new double[] { logistic(4), logistic(3) });
Assert.AreEqual(expectedResult, layer.Output);
}
public void LinearLayerWithBiasesHasRightRun()
{
NetworkMatrix weights = new NetworkMatrix(new double[, ] {
{ 1, 0, 1 }, { 1, 1, 0 }
});
NetworkVector biases = new NetworkVector(new double[] { 4, 3 });
NetworkVector inputvector = new NetworkVector(new double[] { 1, 2, 3 });
Layer2 layer = Layer2.CreateLinearLayer(weights, biases);
layer.Run(inputvector);
NetworkVector expectedResult = new NetworkVector(new double[] { 8, 6 });
Assert.AreEqual(expectedResult, layer.Output);
}
// If Equals() returns true for a pair of objects
// then GetHashCode() must return the same value for these objects.
public override int GetHashCode()
{
//Get hash code for the Name field if it is not null.
int hashIzolName = Type == null ? 0 : Type.GetHashCode();
//Get hash code for the Code field.
int hashTube = Tube.GetHashCode();
int hashLayer1 = Layer1.GetHashCode();
int hashLayer2 = Layer2.GetHashCode();
int hashLayer3 = Layer3.GetHashCode();
//Calculate the hash code for the product.
//return hashProductName ^ hashProductCode;
return(hashIzolName ^ hashTube ^ hashLayer1 ^ hashLayer2 ^ hashLayer3);
}
protected override void WriteDataXML(XElement ele, ElderScrollsPlugin master)
{
XElement subEle;
ele.TryPathTo("Layer0", true, out subEle);
Layer0.WriteXML(subEle, master);
ele.TryPathTo("Layer1", true, out subEle);
Layer1.WriteXML(subEle, master);
ele.TryPathTo("Layer2", true, out subEle);
Layer2.WriteXML(subEle, master);
ele.TryPathTo("Layer3", true, out subEle);
Layer3.WriteXML(subEle, master);
}
protected override void WriteDataXML(XElement ele, ElderScrollsPlugin master)
{
XElement subEle;
ele.TryPathTo("Layer0", true, out subEle);
subEle.Value = Layer0.ToString();
ele.TryPathTo("Layer1", true, out subEle);
subEle.Value = Layer1.ToString();
ele.TryPathTo("Layer2", true, out subEle);
subEle.Value = Layer2.ToString();
ele.TryPathTo("Layer3", true, out subEle);
subEle.Value = Layer3.ToString();
}
public void BackpropagateRunsWithNonzeroLayerInput()
{
NetworkMatrix weights = new NetworkMatrix(new double[, ] {
{ 1 }
});
Layer2 layer = Layer2.CreateLinearLayer(weights);
NetworkVector layerinput = new NetworkVector(new double[] { 2 });
layer.Run(layerinput);
NetworkVector outputgradient = new NetworkVector(new double[] { 1 });
NetworkVector inputGradientCheck = new NetworkVector(new double[] { 1 });
Assert.AreEqual(inputGradientCheck, layer.InputGradient(outputgradient));
}
public void InputGradientRunsTwoByThree()
{
NetworkMatrix weights = new NetworkMatrix(new double[, ] {
{ 1, 2, 3 }, { 2, 3, 4 }
});
Layer2 layer = Layer2.CreateLinearLayer(weights);
NetworkVector layerinput = new NetworkVector(new double[] { 1, 0, -1 });
layer.Run(layerinput);
NetworkVector outputgradient = new NetworkVector(new double[] { 1, 1 });
NetworkVector inputGradientCheck = new NetworkVector(new double[] { 3, 5, 7 });
Assert.AreEqual(inputGradientCheck, layer.InputGradient(outputgradient));
}
public bool Equals(Izolation other)
{
//Check whether the compared object is null.
if (Object.ReferenceEquals(other, null))
{
return(false);
}
//Check whether the compared object references the same data.
if (Object.ReferenceEquals(this, other))
{
return(true);
}
//Check whether the products' properties are equal.
return(Type.Equals(other.Type) && Tube.Equals(other.Tube) && Layer1.Equals(other.Layer1) &&
Layer2.Equals(other.Layer2) && Layer3.Equals(other.Layer3));
}
protected override void ReadData(ESPReader reader)
{
using (MemoryStream stream = new MemoryStream(reader.ReadBytes(size)))
using (ESPReader subReader = new ESPReader(stream, reader.Plugin))
{
try
{
Layer0.ReadBinary(subReader);
Layer1.ReadBinary(subReader);
Layer2.ReadBinary(subReader);
Layer3.ReadBinary(subReader);
}
catch
{
return;
}
}
}
public void InputGradientRunsTwoByThree()
{
NetworkMatrix weights = new NetworkMatrix(new double[, ] {
{ 1, 2, 3 }, { 2, 3, 4 }
});
Layer2 layer = Layer2.CreateLogisticLayer(weights);
NetworkVector layerinput = new NetworkVector(new double[] { 1, 0, -1 });
layer.Run(layerinput);
NetworkVector outputgradient = new NetworkVector(new double[] { 1, 1 });
NetworkVector inputGradientCheck = new NetworkVector(
new double[] { 0.31498075621051952, 0.52496792701753248, 0.7349550978245456 }
);
Assert.AreEqual(inputGradientCheck, layer.InputGradient(outputgradient));
}
/// <summary>
/// Кнопка сброса графиков
/// </summary>
public void Vision()
{
Plotnost.Clear();
General.Clear();
AllLayer.Clear();
General.Clear();
Layer1.Clear();
Layer2.Clear();
Layer3.Clear();
InfoVision = false;
ButtonVisibility = false;
RegulationAll = false;
RegulationCoun = false;
RegulationGen = false;
RegulationLayer1 = false;
RegulationLayer2 = false;
RegulationLayer3 = false;
RegulationPlotn = false;
}
protected override void ReadDataXML(XElement ele, ElderScrollsPlugin master)
{
XElement subEle;
if (ele.TryPathTo("Layer0", false, out subEle))
{
Layer0.ReadXML(subEle, master);
}
if (ele.TryPathTo("Layer1", false, out subEle))
{
Layer1.ReadXML(subEle, master);
}
if (ele.TryPathTo("Layer2", false, out subEle))
{
Layer2.ReadXML(subEle, master);
}
if (ele.TryPathTo("Layer3", false, out subEle))
{
Layer3.ReadXML(subEle, master);
}
}
public void CanUseBigLinearLayer()
{
double[,] matrix = new double[2000, 1000];
double[] input = new double[1000];
for (int i = 0; i < 1000; i++)
{
matrix[i, i] = 1.0;
input[i] = (double)i;
}
NetworkMatrix weights = new NetworkMatrix(matrix);
NetworkVector inputvector = new NetworkVector(input);
Layer2 layer = Layer2.CreateLinearLayer(weights);
layer.Run(inputvector);
double[] result = layer.Output.ToArray();
for (int i = 0, j = 1000; i < 1000; i++, j++)
{
Assert.AreEqual((double)i, result[i], "Failed for i = " + i);
Assert.AreEqual(0.0, result[j], "Failed for j = " + j);
}
}
public T GetOrGen(ushort x, ushort y, ushort z)
{
var indx = (x & 192) >> 6 | (y & 192) >> 4 | (z & 192) >> 2;
return((layers[indx] ?? (layers[indx] = new Layer2())).GetOrGen(x, y, z));
}
public void Gen(ushort x, ushort y, ushort z)
{
var indx = (x & 192) >> 6 | (y & 192) >> 4 | (z & 192) >> 2;
(layers[indx] ?? (layers[indx] = new Layer2())).Gen(x, y, z);
}
private void btnAlert_Click(object sender, EventArgs e)
{
Layer2.Alert(pnlContent, "发生错误", new Size(300, 100), 2000);
}
