BP神经网络研究

时间：2018-08-17 19:59:03 阅读：269 评论：0 收藏：0 [点我收藏+]

标签：之间 htm bool inpu list 方差 make open inf

本随笔参考文章：《BP神经网络详解与实例》（链接: https://pan.baidu.com/s/1e2niIvD9KtLXEqwXtgdXxw 密码: vb8d）

本随笔原创，转发请注明原处：https://www.cnblogs.com/nanyunan/p/9494946.html

1 神经网络单元示例

2 神经网络示例

3 几个重要公式

3.1 单元前向公式

y=f[sum(wi*xi)-θ]

其中，wi是输入权重，xi是输入值，sum是求和函数，θ是阈(yu)值，f是激发函数

其中，f激发函数主要有：sgn，Sigmoid

其中，sgn是符号函数（https://baike.baidu.com/item/sign/115763）

其次，Sigmoid是S型函数（https://baike.baidu.com/item/Sigmoid%E5%87%BD%E6%95%B0/7981407?fr=aladdin）

3.2 单元后向公式

权重迭代公式

假设有n个样本训练网络，p表示当前训练样本，p-1表示上一个训练样本。

ω_l^p(i,j)=ω_l^p-1(i,j)+ηδ_l^pα_l-1^p(j)

其中，ω_l^p(i,j)表示第l层第i个神经单元的第j个输入权重

其中，ω_l^p(i,j)表示新的权重

其中，ω_l^p-1(i,j)表示旧的权重

其中，η是学习效率，取值(0,1)之间，一般默认取值0.1。η过小，收敛过慢，精度高。η过大，收敛快，优解容易丢失。

其中，δ_l^p(i)=f’Sum[δ_l+1^p(j)ω_l+1^p-1(j,i)]

其中，f’是激发函数f的导函数

其中，δ_l+1^p(j)表示下一层第j个神经单元的δ值

其中，ω_l+1^p-1(j,i)表示下一层第j个神经单元第i个权重的旧值

其次，δ_L^p(i)=f’(t^p(i)-a_L^p(i))

其中，L表示最后一层

其中，t^p(i)表示最后一层第i个输出神经单元的真值

其中，a_L^p(i)表示最后一层第i个输出神经单元的输出值

4 编程实例

使用语言：C#

下面并不是所有代码。

源代码（链接: https://pan.baidu.com/s/1nsh8T2VCISZFQb0fU_wBXg 密码: y8pi）

实例（包含程序，说明书，样本）（链接: https://pan.baidu.com/s/1sWkEOGqKXC6uMCGzD9eg_Q 密码: sjfh）

样本（链接: https://pan.baidu.com/s/1O9XlLOGXRbC0PfAVo9LULA 密码: b9st）

4.1 神经网络配置类

public class NerveConfig
    {
        public static double Func(double Value)
        {
            double myResult = 0;
            //-----
            double Key = Math.Pow(Math.E,-Value);
            myResult = 1.0 / (1+Key);
            //-----
            return myResult;
        }
        public static double Func_OrderOne(double Value)
        {
            double myResult = 0;
            //-----
            double Key = Math.Pow(Math.E, -Value);
            double Key2 = Math.Pow(1+Key,2);
            myResult = Key / (Key2);
            //-----
            return myResult;
        }
        public static double Eta = 0.1;
        public static Random R = new Random();
    }

神经网络配置类

4.2 神经单元类

public class NerveUnit
    {
        public NerveUnit(NerveList nerveList)
        {
            Input = new UnitInputAdmin(this);
            Output = new UnitOutput(this,0);
            Parent = nerveList;
            for(int i = 0; i < nerveList.Count; i++)
            {
                Bound(nerveList[i]);
            }
        }
        public NerveUnit(ValueList valueList)
        {
            Input = new UnitInputAdmin(this);
            Output = new UnitOutput(this, 0);
            for (int i = 0; i < valueList.Count; i++)
            {
                Bound((UnitOutput)valueList[i]);
            }
        }
        public class UnitInput
        {
            public UnitInput(UnitOutput Output,NerveUnit Parent)
            {
                this.Bound = Output;
                Output.Bound(this);
                this.Parent = Parent;
            }
            public UnitOmega Omega = new UnitOmega();
            public UnitOutput Bound=null;
            public NerveUnit Parent = null;
            public double Value
            {
                get
                {
                    double myResult = 0;
                    //-----
                    myResult = Omega.Value * Bound.Value;
                    //-----
                    return myResult;
                }
            }
            public int Location = 0;
            public void Back(double Delta)
            {
                double OriValue = Omega.Value;
                Omega.Value = Omega.Value + NerveConfig.Eta * Delta * Bound.Value;
                Log.Adds("Location",Location,"Ori Omega=",OriValue ,"Now Omega=",Omega.Value,"Last layer output=",Bound.Value);
            }
        }
        public class UnitInputAdmin
        {
            public UnitInputAdmin(NerveUnit Parent)
            {
                this.Parent = Parent;
            }
            public List<UnitInput> Data = new List<UnitInput>();
            public NerveUnit Parent;
            public void Bound(UnitOutput Output)
            {
                UnitInput unit = new UnitInput(Output,Parent);
                unit.Location = Data.Count;
                Data.Add(unit);
            }
            public double Value
            {
                get
                {
                    double myResult = 0;
                    //-----
                    for (int i = 0; i < Data.Count; i++)
                    {
                        myResult += Data[i].Value;
                    }
                    //-----
                    return myResult;
                }

            }
            public double Omega(int Index)
            {
                double myResult = 0;
                if (Index >= 0 && Index < Data.Count)
                {
                    myResult = Data[Index].Omega.Value;
                }
                return myResult;
            }
            public void SetIniOmega(int Index,double Value)
            {
                if (Index >= 0 && Index < Data.Count)
                {
                    Data[Index].Omega.Ini(Value);
                }
                else
                {
                    throw new Exception("Index="+Index.ToString()+",Count="+Data.Count.ToString());
                }
            }
            public void SetIniOmegas(OmegaNet.OmegaList.OmegaUnit Value)
            {
                for(int i = 0; i < Data.Count && i < Value.Count; i++)
                {
                    Data[i].Omega.Ini(Value[i]);
                }
            }
            public UnitInput this[int Index]
            {
                get
                {
                    UnitInput myResult;
                    //-----
                    if (Index >= 0 && Index < Data.Count)
                    {
                        myResult = Data[Index];
                    }
                    else
                    {
                        throw new Exception("Index=" + Index.ToString() + ",Count=" + Data.Count.ToString());
                    }
                    //-----
                    return myResult;
                }
            }
            public int Count
            {
                get
                {
                    return Data.Count;
                }
            }
        }
        public class UnitInputList
        {
            public List<UnitInput> Data = new List<UnitInput>();
            public void Bound(UnitInput Input)
            {
                Data.Add(Input);
            }
            public double Omega(int Index)
            {
                double myResult = 0;
                if (Index >= 0 && Index < Data.Count)
                {
                    myResult = Data[Index].Omega.Value;
                }
                return myResult;
            }
            public UnitInput this[int Index]
            {
                get
                {
                    UnitInput myResult;
                    //-----
                    if (Index >= 0 && Index < Data.Count)
                    {
                        myResult = Data[Index];
                    }
                    else
                    {
                        throw new Exception("Index=" + Index.ToString() + ",Count=" + Data.Count.ToString());
                    }
                    //-----
                    return myResult;
                }
            }
            public int Count
            {
                get
                {
                    return Data.Count;
                }
            }
        }
        public class UnitOmega
        {
            public UnitOmega()
            {
                Now=NerveConfig.R.NextDouble();
                Last = Now;
            }
            public double Now = 1;
            public double Last = 1;
            public double Value
            {
                get
                {
                    return Now;
                }
                set
                {
                    Last = Now;
                    Now = value;
                }
            }
            public void Ini(double Value)
            {
                this.Now = Value;
                this.Last = Value;
            }
        }
        public Point Location = new Point(0,0);

        public class UnitOutput
        {
            public UnitOutput(NerveUnit Parent,double Value)
            {
                this.Value = Value;
                this.Parent = Parent;
            }
            public UnitOutput(double Value)
            {
                this.Value = Value;
            }
            public UnitOutput(NerveUnit Parent)
            {
                this.Parent = Parent;
            }
            public UnitOutput()
            {

            }
            public UnitInputList InputList = new UnitInputList();
            public void Bound(UnitInput Input)
            {
                InputList.Bound(Input);
            }
            public NerveUnit Parent;
            public double Truth = 0;
            public double Value = 0;
            public void FigureDelta()
            {
                if (Parent.Parent.Layer == NerveList.LayerType.End)
                {
                    Delta = (Truth-Value) * NerveConfig.Func_OrderOne(Parent.Input.Value);
                }
                else
                {
                    double DeltaChild = 0;
                    for (int i = 0; i < InputList.Count; i++)
                    {
                        DeltaChild += InputList[i].Parent.Output.Delta * InputList[i].Omega.Last;
                    }
                    Delta = NerveConfig.Func_OrderOne(Parent.Input.Value) * DeltaChild;
                }
            }
            public double Delta = 0;
            public double TipSD = 0;
            public double SD
            {
                get
                {
                    return Math.Pow(Truth-Value,2);
                }
            }
            public override string ToString()
            {
                string myResult = string.Empty;
                //-----
                myResult = Value.ToString() + "," + Truth.ToString();
                //-----
                return myResult;
            }
        }
        public class UnitThreshold
        {
            public UnitThreshold(double Value)
            {
                this.Value = Value;
            }
            public double Value = 0;
        }

        public UnitThreshold Threshold = new UnitThreshold(1);
        public UnitInputAdmin Input;
        public UnitOutput Output;
        public NerveList Parent;
        public void Bound(UnitOutput Output)
        {
            Input.Bound(Output);
        }
        public void Bound(NerveUnit Unit)
        {
            Input.Bound(Unit.Output);
        }
        public void Forward()
        {
            double InValue = Input.Value;
            double ThValue = Threshold.Value;
            double delta = InValue - ThValue;
            Output.Value = NerveConfig.Func(delta);
            Log.Adds("[Forward]","Location:",Location.X,Location.Y ,"Sum=",InValue,"Threshold=",ThValue ,"delta=",
                delta,"Output=",Output.Value);
        }
        public void Back()
        {
            Output.FigureDelta();
            Log.Adds("[Back]","Location:",Location.X ,Location.Y ,"Delta=",Output.Delta);
            for(int i = 0; i < Input.Count; i++)
            {
                Input[i].Back(Output.Delta);
            }
        }
        public OmegaNet.OmegaList.OmegaUnit GetOmegaUnit()
        {
            OmegaNet.OmegaList.OmegaUnit myResult = new OmegaNet.OmegaList.OmegaUnit();
            myResult.Index = Location.Y;
            //-----
            for(int i = 0; i < Input.Count; i++)
            {
                myResult.Add(Input.Omega(i));
            }
            //-----
            return myResult;
        }
        public void SetOmegas(OmegaNet.OmegaList.OmegaUnit Value)
        {
            Input.SetIniOmegas(Value);
        }

    }

神经单元类

4.3 神经层类

    public class NerveList
    {

        public NerveList(NerveList Last,int Count)
        {
            this.Last = Last;
            Last.Next = this;
            //
            for(int i = 0; i < Count; i++)
            {
                NerveUnit Item = new NerveUnit(Last);
                Item.Parent = this;
                Data.Add(Item);
            }
        }
        public NerveList(ValueList Last,int Count)
        {
            for (int i = 0; i < Count; i++)
            {
                NerveUnit Item = new NerveUnit(Last);
                Item.Parent = this;
                Data.Add(Item);
            }
        }
        private int _Location = 0;
        public int Location
        {
            get
            {
                return _Location;
            }
            set
            {
                _Location = value;
                for(int i = 0; i < Data.Count; i++)
                {
                    Data[i].Location = new Point(value,i);
                }
            }

        }
        public enum LayerType
        {
            Middle, End
        }
        public LayerType Layer = LayerType.Middle;
        public List<NerveUnit> Data = new List<NerveUnit>();
        public NerveUnit this[int Index]
        {
            get
            {
                NerveUnit myResult;
                //-----
                if (Index >= 0 && Index < Data.Count)
                {
                    myResult = Data[Index];
                }
                else
                {
                    throw new Exception("Index="+Index.ToString()+",Count="+Data.Count.ToString());
                }
                //-----
                return myResult;
            }
        }
        public int Count
        {
            get
            {
                return Data.Count;
            }
        }
        public NerveList Last=null;
        public NerveList Next=null;
        public void Forward()
        {
            for(int i = 0; i < Data.Count; i++)
            {
                Data[i].Forward();
            }
        }
        public void Back()
        {
            for(int i = 0; i < Data.Count; i++)
            {
                Data[i].Back();
            }
        }
        public List<string> Report(bool NeedTruth=true)
        {
            List<string> myResult = new List<string>();
            //------
            if (NeedTruth)
            {
                for (int i = 0; i < Data.Count; i++)
                {
                    myResult.Add(Data[i].Output.ToString());
                }
            }
            else
            {
                for (int i = 0; i < Data.Count; i++)
                {
                    myResult.Add(Data[i].Output.Value.ToString());
                }
            }

            //------
            return myResult;
        }
        public void ApplyTruth(double[] Truth)
        {
            ApplyZero();
            for(int i=0;i<Data.Count && i < Truth.Length; i++)
            {
                Data[i].Output.Truth = Truth[i];
            }
        }
        public void ApplyTruth(List<double> Truth)
        {
            ApplyZero();
            for (int i = 0; i < Data.Count && i < Truth.Count; i++)
            {
                Data[i].Output.Truth = Truth[i];
            }
        }
        public void ApplyZero()
        {
            ApplyTruth(0);
        }
        public void ApplyTruth(double Value)
        {
            for (int i = 0; i < Data.Count; i++)
            {
                Data[i].Output.Value = Value;
            }
        }
        /// <summary>
        /// Square difference,平方差
        /// </summary>
        public double SD
        {
            get
            {
                double myResult = 0;
                //-----
                if(Layer== LayerType.End)
                {
                    for(int i = 0; i < Data.Count; i++)
                    {
                        myResult += Data[i].Output.SD;
                    }
                }
                else
                {
                    throw new Exception("Layer="+Layer.ToString());
                }
                //-----
                TipSD = myResult;
                return myResult;
            }
        }
        public double TipSD = 0;
        public OmegaNet.OmegaList GetOmegaList()
        {
            OmegaNet.OmegaList myResult = new OmegaNet.OmegaList();
            //-----
            for(int i = 0; i < Data.Count; i++)
            {
                myResult.Add(Data[i].GetOmegaUnit());
            }
            myResult.Index = _Location;
            //-----
            return myResult;
        }
        public void SetOmegas(OmegaNet.OmegaList Value)
        {
            for(int i = 0; i < Data.Count&&i<Value.Count; i++)
            {
                Data[i].SetOmegas(Value[i]);
            }
        }
    }

神经显层类

    public class ValueList
    {
        public ValueList(int Count)
        {
            for(int i = 0; i < Count; i++)
            {
                NerveUnit.UnitOutput unitValue = new NerveUnit.UnitOutput();
                Data.Add(unitValue);
            }
        }
        public List<NerveUnit.UnitOutput> Data = new List<NerveUnit.UnitOutput>();
        public NerveUnit.UnitOutput this[int Index]
        {
            get
            {
                NerveUnit.UnitOutput myResult;
                //-----
                if (Index >= 0 && Index < Data.Count)
                {
                    myResult = Data[Index];
                }
                else
                {
                    throw new Exception("Index=" + Index.ToString() + ",Count=" + Data.Count.ToString());
                }
                //-----
                return myResult;
            }
        }
        public int Count
        {
            get
            {
                return Data.Count;
            }
        }
        public void Apply(List<double> vs)
        {
            ApplyZero();
            for (int i = 0; i < vs.Count&&i<Data.Count; i++)
            {
                Data[i].Value = vs[i];
            }
        }
        public void Apply(double[] vs)
        {
            ApplyZero();
            for (int i = 0; i < vs.Length && i < Data.Count; i++)
            {
                Data[i].Value = vs[i];
            }
        }
        public void ApplyZero()
        {
            for (int i = 0; i < Data.Count; i++)
            {
                Data[i].Value = 0;
            }
        }
    }

神经隐层类

4.4 神经网络类

    public class NerveNetUnit
    {
        public NerveNetUnit(int Rows,int Count, int CountOfOutput)
        {
            Create(Rows,Count,CountOfOutput);
        }
        public NerveNetUnit(int[] RowsCounts,int CountOfOutput)
        {
            Create(RowsCounts,CountOfOutput);
        }
        public NerveNetUnit(OmegaNet Net)
        {
            SetOmegas(Net);
        }
        public void Create(int Rows, int Count, int CountOfOutput)
        {
            //初始化 输入
            Input = new ValueList(Count);
            NerveList Last = null;
            Data.Clear();
            //开始创建网络
            for (int i = 1; i < Rows; i++)
            {
                if (i == 1)
                {
                    NerveList Item = new NerveList(Input, Count);
                    Item.Location = i;
                    Data.Add(Item);
                    Last = Item;
                }
                else
                {
                    NerveList Item = new NerveList(Last, Count);
                    Item.Location = i;
                    Data.Add(Item);
                    Last = Item;
                }
            }
            Output = new NerveList(Last, CountOfOutput);
            Output.Location = Rows;
            Output.Layer = NerveList.LayerType.End;
        }
        public void Create(int[] RowsCounts, int CountOfOutput)
        {
            //初始化 输入
            Input = new ValueList(RowsCounts[0]);
            NerveList Last = null;
            Data.Clear();
            //开始创建网络
            for (int i = 1; i < RowsCounts.Length; i++)
            {
                if (i == 1)
                {
                    NerveList Item = new NerveList(Input, RowsCounts[i]);
                    Item.Location = i;
                    Data.Add(Item);
                    Last = Item;
                }
                else
                {
                    NerveList Item = new NerveList(Last, RowsCounts[i]);
                    Item.Location = i;
                    Data.Add(Item);
                    Last = Item;
                }
            }
            if(Last != null)
            {
                Output = new NerveList(Last, CountOfOutput);
            }
            else
            {
                Output = new NerveList(Input, CountOfOutput);
            }

            Output.Location = RowsCounts.Length;
            Output.Layer = NerveList.LayerType.End;
        }
        public List<NerveList> Data = new List<NerveList>();
        public ValueList Input;
        public NerveList Output;
        public NerveUnit this[int Row,int Index]
        {
            get
            {
                NerveUnit myResult=null;
                //
                if (Row >= 0 && Row < Data.Count)
                {
                    myResult = Data[Row][Index];
                }
                else
                {
                    throw new Exception("Row=" + Row.ToString() + ",Count=" + Data.Count.ToString());
                }
                //
                return myResult;
            }
        }
        public void Forward()
        {
            for(int i = 0; i < Data.Count; i++)
            {
                Data[i].Forward();
            }
            Output.Forward();
        }
        public void Back()
        {
            Output.Back();
            for (int i = Data.Count -1; i >=0; i--)
            {
                Data[i].Back();
            }
        }
        /// <summary>
        /// 此处的Input数目已经固定。
        /// </summary>
        /// <param name="Input">输入的值</param>
        /// <param name="Output">真值</param>
        public void SetSample(double[] Input,double[] Output)
        {
            this.Input.Apply(Input);
            this.Output.ApplyTruth(Output);
        }
        /// <summary>
        /// 此处的Input数目已经固定。
        /// </summary>
        /// <param name="Input">输入的值</param>
        /// <param name="Output">真值</param>
        public void SetSample(List<double> Input, List<double> Output)
        {
            this.Input.Apply(Input);
            this.Output.ApplyTruth(Output);
        }
        /// <summary>
        /// Square difference,平方差
        /// </summary>
        public double SD
        {
            get
            {
                double myResult = 0;
                //-----
                myResult = Output.SD;
                //-----
                return myResult;
            }
        }
        public List<string> Figure(double[] Input)
        {
            this.Input.Apply(Input);
            Forward();
            return Output.Report(false);
        }
        public string GetNetInfo()
        {
            StringBuilder myResult = new StringBuilder();
            //-----
            myResult.Append(Input.Count.ToString());
            for(int i = 0; i < Data.Count; i++)
            {
                myResult.Append(""+Data[i].Count .ToString());
            }
            myResult.Append(""+Output.Count .ToString());
            //-----
            return myResult.ToString();
        }
        public OmegaNet GetOmegaNet()
        {
            OmegaNet myResult = new OmegaNet();
            //-----
            for(int i = 0; i < Data.Count; i++)
            {
                myResult.Add(Data[i].GetOmegaList());
            }
            myResult.Add(Output.GetOmegaList());
            myResult.CountOfInput = Input.Count;
            //-----
            return myResult;
        }
        public void SetOmegas(OmegaNet Value)
        {
            //先创建网络
            List<int> Rows = Value.GetRows();
            int CountOfOutput = Value.GetCountOfOutput();
            Create(Rows.ToArray(),CountOfOutput);
            //
            for(int i = 0;i<Data.Count; i++)
            {
                Data[i].SetOmegas(Value[i]);
            }
            Output.SetOmegas(Value.End);
        }
    }

神经网络类

4.5 样本单元类

    public class SampleUnit
    {
        //样本单元
        public List<double> Input=new List<double>();
        public List<double> Output=new List<double>();
        public string Report()
        {
            StringBuilder myResult = new StringBuilder();
            //-----
            myResult.Append("Input="+Log.ToString(Input));
            myResult.Append(" Output=" + Log.ToString(Output));
            //-----
            return myResult.ToString();
        }
    }

样本单元类

4.6 样本容器类

    public class SampleContainer
    {
        //样本容器
        public List<SampleUnit> Data = new List<SampleUnit>();
        public void Add(double[] Input,double[] Output)
        {
            SampleUnit unit = new SampleUnit();
            unit.Input = Input.ToList();
            unit.Output = Output.ToList();
            Data.Add(unit);
        }
        public void Add(List<double> Input, List<double> Output)
        {
            SampleUnit unit = new SampleUnit
            {
                Input = Input,
                Output = Output
            };
            Data.Add(unit);
        }
        public void Add(SampleUnit unit)
        {
            Data.Add(unit);
        }
        public int Count
        {
            get
            {
                return Data.Count;
            }
        }
        public SampleUnit this[int Index]
        {
            get
            {
                SampleUnit myResult;
                //-----
                if (Index >= 0 && Index < Data.Count)
                {
                    myResult = Data[Index];
                }
                else
                {
                    throw new Exception("Index=" + Index.ToString() + ",Count=" + Data.Count.ToString());
                }
                //-----
                return myResult;
            }
        }
        public string Report()
        {
            StringBuilder myResult = new StringBuilder();
            //-----
            for(int i = 0; i < Data.Count; i++)
            {
                if (i == 0)
                {
                    myResult.Append(Data[i].Report());
                }
                else
                {
                    myResult.Append("\r\n"+Data[i].Report());
                }
            }
            //-----
            return myResult.ToString();
        }
    }

样本容器类

4.7 Omega网络类（用于提取和设置神经网络权重）

   public class OmegaNet
    {
        public class OmegaList
        {

            public class OmegaUnit
            {
                public OmegaUnit(List<double> Value)
                {
                    Data = Value;
                }
                public OmegaUnit()
                {

                }
                public List<double> Data = new List<double>();
                public void Add(double Value)
                {
                    Data.Add(Value);
                }
                public int Count
                {
                    get
                    {
                        return Data.Count;
                    }
                }
                public double this[int Index]
                {
                    get
                    {
                        if (Index >= 0 && Index < Data.Count)
                        {
                            return Data[Index];
                        }
                        else
                        {
                            throw new Exception("Index=" + Index.ToString() + ",Count=" + Data.Count.ToString());
                        }
                    }
                }
                public int Index = 0;
                public string Report(string Span="")
                {
                    StringBuilder myResult = new StringBuilder();
                    //-----
                    myResult.Append(Span+"."+Index.ToString()+" =");
                    for (int i = 0; i < Data.Count; i++)
                    {
                        if (i == 0)
                        {
                            myResult.Append(Data[i].ToString());
                        }
                        else
                        {
                            myResult.Append(","+Data[i].ToString());
                        }

                    }
                    //-----
                    return myResult.ToString();
                }
                public UConfig.Page GetPage()
                {
                    UConfig.Page myResult = new UConfig.Page();
                    myResult.Name = Index.ToString();
                    myResult.MakeSureCountOfVarsByCount(Data.Count);
                    for(int i = 0; i < Data.Count; i++)
                    {
                        myResult.Vars[i].Value = Data[i].ToString();
                    }
                    return myResult;
                }
                public void SetByPage(UConfig.Page Page)
                {
                    for(int i = 0; i < Page.Vars.Count; i++)
                    {
                        Data.Add(double.Parse(Page.Vars[i].Value));
                    }
                    Index = int.Parse(Page.Name);
                }
            }
            public List<OmegaUnit> Data = new List<OmegaUnit>();
            public void Add(OmegaUnit Value)
            {
                Data.Add(Value);
            }
            public int Count
            {
                get
                {
                    return Data.Count;
                }
            }
            public OmegaUnit this[int Index]
            {
                get
                {
                    if (Index >= 0 && Index < Data.Count)
                    {
                        return Data[Index];
                    }
                    else
                    {
                        throw new Exception("Index=" + Index.ToString() + ",Count=" + Data.Count.ToString());
                    }
                }
            }
            public int Index = 0;
            public string Report(string Span="")
            {
                StringBuilder myResult = new StringBuilder();
                //-----
                string INSpan = Span + "";
                myResult.Append(Span +Index.ToString());
                myResult.Append("\r\n");
                for(int i = 0; i < Data.Count; i++)
                {
                    if (i == 0)
                    {
                        myResult.Append(Span+Data[i].Report(INSpan));
                    }
                    else
                    {
                        myResult.Append("\r\n"+Span+Data[i].Report(INSpan));
                    }
                }
                //-----
                return myResult.ToString();
            }
            public UConfig.Page GetPage()
            {
                UConfig.Page myResult = new UConfig.Page();
                myResult.Name = Index.ToString();
                for (int i = 0; i < Data.Count; i++)
                {
                    myResult.Pages.Add(Data[i].GetPage());
                }
                return myResult;
            }
            public void SetByPage(UConfig.Page Page)
            {
                for (int i = 0; i < Page.Pages.Count; i++)
                {
                    OmegaUnit Item = new OmegaUnit();
                    Item.SetByPage(Page.Pages[i]);
                    Data.Add(Item);
                }
                Index = int.Parse(Page.Name);
            }
        }
        public List<OmegaList> Data = new List<OmegaList>();
        public void Add(OmegaList Value)
        {
            Data.Add(Value);
        }
        public int Count
        {
            get
            {
                return Data.Count;
            }
        }
        public OmegaList this[int Index]
        {
            get
            {
                if (Index >= 0 && Index < Data.Count)
                {
                    return Data[Index];
                }
                else
                {
                    throw new Exception("Index=" + Index.ToString() + ",Count=" + Data.Count.ToString());
                }
            }
        }
        public OmegaList End
        {
            get
            {
                return Data[Data.Count - 1];
            }
        }
        public string Report(string Span="")
        {
            StringBuilder myResult = new StringBuilder();
            //-----
            for(int i = 0; i < Data.Count; i++)
            {
                if (i == 0)
                {
                    myResult.Append(Data[i].Report(""));
                }
                else
                {
                    myResult.Append("\r\n***");
                    myResult.Append("\r\n"+Data[i].Report(""));
                }

            }
            //-----
            return myResult.ToString();
        }
        public int Index = 0;
        public UConfig.Page GetPage()
        {
            UConfig.Page myResult = new UConfig.Page();
            myResult.Name = Index.ToString();
            for (int i = 0; i < Data.Count; i++)
            {
                myResult.Pages.Add(Data[i].GetPage());
            }
            UConfig.UVariable Input = new UConfig.UVariable();
            Input.Name = "CountOfInput";
            Input.Value = CountOfInput.ToString();
            Input.Type = UConfig.VariableType.Digit;
            myResult.Vars.Add(Input);
            return myResult;
        }
        public void SetByPage(UConfig.Page Page)
        {
            Data.Clear();
            for (int i = 0; i < Page.Pages.Count; i++)
            {
                OmegaList Item = new OmegaList();
                Item.SetByPage(Page.Pages[i]);
                Data.Add(Item);
            }
            Index = int.Parse(Page.Name);
            CountOfInput = int.Parse(Page.Vars[0].Value);
        }
        public List<int> GetRows()
        {
            List<int> myResult = new List<int>();
            //-----
            myResult.Add(CountOfInput);
            for(int i=0;i<Data.Count - 1; i++)
            {
                myResult.Add(Data[i].Count);
            }
            //-----
            return myResult;
        }
        public int GetCountOfOutput()
        {
            return End.Count;
        }
        public int CountOfInput = 0;
    }

Omega网络类

5 具体研究

未完待续

BP神经网络研究

标签：之间 htm bool inpu list 方差 make open inf

原文地址：https://www.cnblogs.com/nanyunan/p/9494946.html

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