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); }