node.cs

股票预测的程序

using System;
using System.Collections.Generic;
using System.Text;

namespace NeuralNetCS
{
    class Node
    {
        private Node[] _inputs;
        private double[] _inputWeights;
        private double _value;
        private double _lastInput;

        /// <summary>
        /// Creates a new node with no inputs and the specified value.
        /// </summary>
        /// <param name="value">The initial value of the node.</param>
        /// <remarks>This constructor creates a node with no inputs, which can be used for nodes on the
        /// input layer, or for bias nodes.</remarks>
        public Node(double value)
        {
            _value = value;
        }

        /// <summary>
        /// Creates a node with the specified inputs.
        /// </summary>
        /// <param name="inputs">The inputs for this node.</param>
        /// <remarks>The weights for the inputs will be initialized to a random value. The value of the
        /// node is zero until Eval is called.</remarks>
        public Node(Node[] inputs)
        {
            if( inputs == null )
                throw new ArgumentNullException("inputs");
            if( inputs.Length == 0 )
                throw new ArgumentException("A node cannot have an empty input list.", "inputs");

            _inputs = inputs;
            _inputWeights = new double[inputs.Length];

            Random rnd = new Random();
            for( int x = 0; x < _inputWeights.Length; ++x )
                _inputWeights[x] = rnd.NextDouble();            
        }

        /// <summary>
        /// Evaluates the value of the node.
        /// </summary>
        /// <remarks>
        /// Computes the weighted sum of the inputs and uses the sigmoid activation function to
        /// determine the activation value of this node. After calling Eval, the activation value
        /// can be retrieved using the <see cref="Value" /> property.
        /// </remarks>
        public void Eval()
        {
            if( _inputs != null )
            {
                double weightedInput = 0.0f;
                int length = _inputs.Length;
                for( int x = 0; x < length; ++x )
                    weightedInput += _inputs[x].Value * _inputWeights[x];
                _lastInput = weightedInput;
                _value = Net.Sigmoid(weightedInput);
            }
        }

	    /// <summary>
	    /// Gets the weight for the specified input.
	    /// </summary>
	    /// <param name="input">The input whose weight to get.</param>
	    /// <returns>The weight for the specified input.</returns>
	    public double GetWeigth(int input)
	    {
		    return _inputWeights[input];
	    }

	    /// <summary>
	    /// Sets the weight for the specified input.
	    /// </summary>
	    /// <param name="input">The input whose weight to set.</param>
	    /// <param name="weight">The new weight value.</param>
	    public void SetWeight(int input, double weight)
	    {
		    _inputWeights[input] = weight;
	    }

        /// <summary>
        /// Gets or sets the current value of the node.
        /// </summary>
        public double Value
        {
            get
            {
                return _value;
            }
            set
            {
                _value = value;
            }
        }

        /// <summary>
        /// Gets or sets the weighted sum of the inputs that was used in the last call
        /// to <see cref="Eval" />.
        /// </summary>
        public double LastInput
        {
            get
            {
                return _lastInput;
            }
        }
    }
}