コード例 #1
0
		//~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~//   
	    /// <summary>
	    /// 
	    /// </summary>
	    /// <param name="Unit"></param>
	    /// <param name="LayerTotheRight"></param>
	    /// <returns></returns>
		private double Sigmoud(Node Unit , ArrayList LayerTotheRight)
		{	   
			if (LayerTotheRight == null) //Neuron in Output Layer
				return (Unit.Output * (Unit.Target - Unit.Output) * ( 1 - Unit.Output));
			else                         //Neuron in Hidden Layer  
				return (Unit.Output * ( 1 - Unit.Output) * SumOF(LayerTotheRight , Unit.Number) );
		
		}
コード例 #2
0
        //~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~//
        /// <summary>
        /// For each input vector presented, 
        /// the Euclidean distance to the output node is computed
        /// </summary>
        /// <param name="Input">input vector</param>
        /// <param name="Unit">the output</param>
        /// <returns>the Euclidean distance</returns>
        protected double Distance(ArrayList Input , Node Unit)
        {
            double Euclidean_distance = 0,Xi;

            for(int i = 0 ; i < Input.Count ; i++)
            {
                Xi = ((Node)Input[i]).Output;
                Euclidean_distance += ((Xi - Unit[i]) * (Xi - Unit[i])) ;
            }

               return Euclidean_distance;
        }
コード例 #3
0
		//~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~//   
		/// <summary>
		/// 
		/// </summary>
		/// <param name="CurrentUnit"></param>
		/// <param name="PreviousLayer"></param>
		/// <returns></returns>
		private double Net(Node CurrentUnit, ArrayList PreviousLayer )
		{
			double NET = 0;

			foreach(Node Unit in PreviousLayer )
			{
			   NET += Unit.Output * CurrentUnit.Weight(Unit.Number);
			}
										 		  
			return (NET + CurrentUnit.Bias);
		}