Encog.ML.Data.Basic.BasicMLData.Clear C# (CSharp) Метод

        public void Clear()
        {
            EngineArray.Fill(_data, 0);
        }
    }

 public virtual IMLData Compute(IMLData input)
 {
     int num;
     int num2;
     int num3;
     int num4;
     double num5;
     double weight;
     double output;
     IMLData data = new BasicMLData(this._outputCount);
     goto Label_0271;
     Label_001B:
     num4++;
     Label_0021:
     if (num4 < this._neurons.Count)
     {
         NEATNeuron neuron = this._neurons[num4];
         num5 = 0.0;
         foreach (NEATLink link in neuron.InboundLinks)
         {
             weight = link.Weight;
             do
             {
                 output = link.FromNeuron.Output;
             }
             while ((((uint) weight) - ((uint) num2)) < 0);
             num5 += weight * output;
         }
         double[] d = new double[] { num5 / neuron.ActivationResponse };
         this._activationFunction.ActivationFunction(d, 0, d.Length);
         this._neurons[num4].Output = d[0];
         if (neuron.NeuronType == NEATNeuronType.Output)
         {
             data[num3++] = neuron.Output;
             if ((((uint) num2) - ((uint) num4)) < 0)
             {
                 goto Label_0206;
             }
             if (-1 == 0)
             {
                 goto Label_025D;
             }
         }
         goto Label_001B;
     }
     num2++;
     Label_0037:
     if (num2 < num)
     {
         num3 = 0;
         if ((((uint) weight) - ((uint) weight)) >= 0)
         {
             if (1 != 0)
             {
                 num4 = 0;
                 data.Clear();
                 while (this._neurons[num4].NeuronType == NEATNeuronType.Input)
                 {
                     this._neurons[num4].Output = input[num4];
                     num4++;
                 }
                 goto Label_01BB;
             }
             goto Label_001B;
         }
         if (((uint) output) <= uint.MaxValue)
         {
             goto Label_0271;
         }
         goto Label_0239;
     }
     Label_003E:
     this._outputActivationFunction.ActivationFunction(data.Data, 0, data.Count);
     return data;
     Label_01BB:
     this._neurons[num4++].Output = 1.0;
     if (((uint) output) <= uint.MaxValue)
     {
         goto Label_0021;
     }
     Label_0206:
     num2 = 0;
     goto Label_0037;
     Label_0239:
     num = this._networkDepth;
     if ((((uint) num5) | 0x7fffffff) == 0)
     {
         goto Label_003E;
     }
     goto Label_0206;
     Label_025D:
     if (this._neurons.Count == 0)
     {
         throw new NeuralNetworkError("This network has not been evolved yet, it has no neurons in the NEAT synapse.");
     }
     num = 1;
     if (this._snapshot)
     {
         goto Label_0239;
     }
     goto Label_0206;
     Label_0271:
     if (1 == 0)
     {
         goto Label_001B;
     }
     if (0 != 0)
     {
         goto Label_01BB;
     }
     goto Label_025D;
 }