static void Real()
{
var inputLayer = new InputLayer3D(1, 1, 1);
var outputLayer = new OutputLayer(1)
{
ActivationFunction = new ConstOutputArrayFunction()
};
var dataProvider = new FunctionProvider();
InitializeTrainingData(dataProvider.TrainData);
var perceptron1 = new PerceptronLayer(10, 2)
{
ActivationFunction = new SigmoidFunction()
};
var perceptron2 = new PerceptronLayer(10, 10)
{
ActivationFunction = new SigmoidFunction()
};
var perceptron3 = new PerceptronLayer(8, 10)
{
ActivationFunction = new SigmoidFunction()
};
var perceptron4 = new PerceptronLayer(6, 8)
{
ActivationFunction = new SigmoidFunction()
};
var perceptron5 = new PerceptronLayer(2, 6)
{
ActivationFunction = new SigmoidFunction()
};
MultiLayerPerceptron network = new MultiLayerPerceptron
{
InputLayer = inputLayer,
OutputLayer = outputLayer,
DataProvider = dataProvider
};
network.HiddenLayers.Add(perceptron1);
network.HiddenLayers.Add(perceptron2);
network.HiddenLayers.Add(perceptron3);
network.HiddenLayers.Add(perceptron4);
network.HiddenLayers.Add(perceptron5);
var trainer = new FCTrainer(network, 10, 1, dataProvider);
trainer.Train(1);
var error = network.Test(1);
}
static void OneTrainingData()
{
var inputLayer = new InputLayer3D(1, 1, 3);
var outputLayer = new OutputLayer(2)
{
ActivationFunction = new ConstOutputArrayFunction()
};
var weight1 = new List <Array3D> {
new Array3D(0.1, 0.3), new Array3D(0.3, 0.1)
};
var weight2 = new List <Array3D> {
new Array3D(0.4, 0.5), new Array3D(0.3, 0.5)
};
var perceptron1 = new PerceptronLayer(weight1)
{
ActivationFunction = new SigmoidFunction()
};
var perceptron2 = new PerceptronLayer(weight2)
{
ActivationFunction = new SigmoidFunction()
};
var dataProvider = new FunctionProvider
{
TrainData =
{
new TrainingData <Array3D, Array3D> {
Input = new Array3D(0.3, 0.4, 0.5), Expected = new Array3D(0.2, 0.6)
},
new TrainingData <Array3D, Array3D> {
Input = new Array3D(0.2, 0.4, 0.7), Expected = new Array3D(0.1, 0.8)
}
}
};
var network = new MultiLayerPerceptron
{
InputLayer = inputLayer,
OutputLayer = outputLayer,
DataProvider = dataProvider
};
network.HiddenLayers.Add(perceptron1);
network.HiddenLayers.Add(perceptron2);
var trainer = new FCTrainer(network, 2, 1, dataProvider);
trainer.Train(100);
}
/// <summary>
/// Contains方法解析
/// </summary>
/// <param name="fieldType"></param>
/// <param name="fieldName"></param>
/// <param name="paramType"></param>
/// <param name="paramName"></param>
protected virtual void VisitMethodContains(MethodCallExpression m, Type fieldType, string fieldName, Type paramType, string paramName)
{
// 非List<>形式
if (paramType != null && !IsGenericOrArray(paramType))
{
#region 搜索值串的处理
var param = ParamList.Find(o => o.ParameterName == paramName);
if (param != null && Regex.IsMatch(param.Value.ToString(), @"[\d]+") && (Type.GetTypeCode(fieldType) == TypeCode.Int16 || Type.GetTypeCode(fieldType) == TypeCode.Int32 || Type.GetTypeCode(fieldType) == TypeCode.Decimal || Type.GetTypeCode(fieldType) == TypeCode.Double || Type.GetTypeCode(fieldType) == TypeCode.Int64 || Type.GetTypeCode(fieldType) == TypeCode.UInt16 || Type.GetTypeCode(fieldType) == TypeCode.UInt32 || Type.GetTypeCode(fieldType) == TypeCode.UInt64))
{
param.Value = "," + param.Value + ",";
param.DbType = DbType.String;
if (DbProvider.KeywordAegis("").Length > 0)
{
fieldName = "','+" + fieldName.Substring(1, fieldName.Length - 2) + "+','";
}
else
{
fieldName = "','+" + fieldName + "+','";
}
}
#endregion
// 判断是不是字段调用Contains
var isFieldCall = m.Object != null && m.Object.NodeType == ExpressionType.MemberAccess && ((MemberExpression)m.Object).Expression != null && ((MemberExpression)m.Object).Expression.NodeType == ExpressionType.Parameter;
SqlList.Push(isFieldCall ? FunctionProvider.CharIndex(fieldName, paramName, IsNot) : FunctionProvider.CharIndex(paramName, fieldName, IsNot));
}
else
{
// 删除参数化，后面需要改成多参数
var paramValue = CurrentDbParameter.Value.ToString();
ParamList.RemoveAt(ParamList.Count - 1);
// 参数类型，转换成多参数
var lstParamName = new List <string>();
var index = 0;
foreach (var val in paramValue.Split(','))
{
var param = DbProvider.CreateDbParam(CurrentDbParameter.ParameterName.Substring(1) + "_" + index++, val, fieldType);
lstParamName.Add(param.ParameterName);
ParamList.Add(param);
}
SqlList.Push(FunctionProvider.In(fieldName, lstParamName, IsNot));
}
}
/// <summary>
/// 将属性变量转换成T-SQL字段名
/// </summary>
protected override Expression VisitMemberAccess(MemberExpression m)
{
if (m == null)
{
return(null);
}
//if (m.NodeType == ExpressionType.Constant) { return base.VisitMemberAccess(m); }
CurrentField = SetMap.PhysicsMap.GetState(m.Member.Name);
// 解析带SQL函数的字段
if (CurrentField.Key == null)
{
switch (m.Member.Name)
{
case "Count":
case "Length":
{
VisitMemberAccess((MemberExpression)m.Expression);
SqlList.Push(FunctionProvider.Len(SqlList.Pop()));
return(m);
}
}
if (m.Expression != null && m.Expression.NodeType == ExpressionType.MemberAccess)
{
VisitMemberAccess((MemberExpression)m.Expression);
return(m);
}
// 加入Sql队列
CurrentFieldName = m.Member.Name;
SqlList.Push(DbProvider.KeywordAegis(CurrentFieldName));
return(m);
}
// 加入Sql队列
CurrentFieldName = CurrentField.Value.Field.Name;
VisitMemberAccess(CurrentField);
return(m);
}
static void Xor()
{
const int batchSize = 4;
const int epochSize = 16;
var inputLayer = new InputLayer3D(1, 1, 1);
var outputLayer = new OutputLayer(1)
{
ActivationFunction = new ConstOutputArrayFunction()
};
var dataProvider = new FunctionProvider
{
TrainData =
{
new TrainingData <Array3D, Array3D>
{
Input = new Array3D(0, 0), Expected = new Array3D(0.0)
},
new TrainingData <Array3D, Array3D>
{
Input = new Array3D(0, 1), Expected = new Array3D(1.0)
},
new TrainingData <Array3D, Array3D>
{
Input = new Array3D(1, 0), Expected = new Array3D(1.0)
},
new TrainingData <Array3D, Array3D>
{
Input = new Array3D(1, 1), Expected = new Array3D(0.0)
}
},
TestData = { new TrainingData <Array3D, Array3D>
{
Input = new Array3D(0, 0), Expected = new Array3D(0)
},
new TrainingData <Array3D, Array3D>
{
Input = new Array3D(0, 1), Expected = new Array3D(1)
},
new TrainingData <Array3D, Array3D>
{
Input = new Array3D(1, 0), Expected = new Array3D(1)
},
new TrainingData <Array3D, Array3D>
{
Input = new Array3D(1, 1), Expected = new Array3D(0)
} },
IsQueue = false
};
var oneData = new FunctionProvider {
TrainData =
{
new TrainingData <Array3D, Array3D>
{
Input = new Array3D(0, 0), Expected = new Array3D(0.0)
},
new TrainingData <Array3D, Array3D>
{
Input = new Array3D(1, 1), Expected = new Array3D(0.0)
}
}, IsQueue = false
};
var function = new FunctionProvider(x => Math.Pow(x, 2));
var weight1 = new List <Array3D> {
new Array3D(0.1, 0.3), new Array3D(0.3, 0.1)
};
var weight2 = new List <Array3D> {
new Array3D(0.4, 0.5)
};
var perceptron1 = new PerceptronLayer(5, 2)
{
ActivationFunction = new TanhActivationFunction()
};
perceptron1.Trainer = new MiniBatchPerceptronTrainer(perceptron1.Neurals, false)
{
BatchSize = batchSize, ActivationFunction = new TanhActivationFunction(), LearningRate = 0.1, Momentum = 0.1
};
var perceptron2 = new PerceptronLayer(1, 5)
{
ActivationFunction = new TanhActivationFunction()
};
perceptron2.Trainer = new MiniBatchPerceptronTrainer(perceptron2.Neurals, true)
{
BatchSize = batchSize, ActivationFunction = new TanhActivationFunction(), LearningRate = 0.1, Momentum = 0.1
};
var network = new MultiLayerPerceptron
{
InputLayer = inputLayer,
OutputLayer = outputLayer,
DataProvider = dataProvider
};
network.HiddenLayers.Add(perceptron1);
network.HiddenLayers.Add(perceptron2);
var trainer = new FCTrainer(network, epochSize, batchSize, dataProvider);
trainer.Train(200);
}
/// <summary>
/// ToShortDate方法解析
/// </summary>
/// <param name="fieldType"></param>
/// <param name="fieldName"></param>
protected virtual void VisitMethodToShortDate(Type fieldType, string fieldName)
{
SqlList.Push(FunctionProvider.ToShortDate(fieldName));
}
/// <summary>
/// IsEquals方法解析
/// </summary>
/// <param name="fieldType"></param>
/// <param name="fieldName"></param>
/// <param name="paramType"></param>
/// <param name="paramName"></param>
protected virtual void VisitMethodEquals(Type fieldType, string fieldName, Type paramType, string paramName)
{
SqlList.Push(FunctionProvider.IsEquals(fieldName, paramName, IsNot));
}
/// <summary>
/// EndSwith方法解析
/// </summary>
/// <param name="fieldType"></param>
/// <param name="fieldName"></param>
/// <param name="paramType"></param>
/// <param name="paramName"></param>
protected virtual void VisitMethodEndswith(Type fieldType, string fieldName, Type paramType, string paramName)
{
SqlList.Push(FunctionProvider.EndsWith(fieldName, paramName, IsNot));
CurrentDbParameter.Value = $"%{CurrentDbParameter.Value}";
}
/// <summary>
/// StartSwith方法解析
/// </summary>
/// <param name="fieldType"></param>
/// <param name="fieldName"></param>
/// <param name="paramType"></param>
/// <param name="paramName"></param>
protected virtual void VisitMethodStartswith(Type fieldType, string fieldName, Type paramType, string paramName)
{
SqlList.Push(FunctionProvider.CharIndex(fieldName, paramName, IsNot));
}
private void InitializeNetwork()
{
var inputLayer = new InputLayer3D(1, 1, 1);
var outputLayer = new OutputLayer(1)
{
ActivationFunction = new ConstOutputArrayFunction()
};
var dataProvider = new FunctionProvider
{
TrainData =
{
new TrainingData <Array3D, Array3D>
{
Input = new Array3D(0, 0), Expected = new Array3D(0.0)
},
new TrainingData <Array3D, Array3D>
{
Input = new Array3D(0, 1), Expected = new Array3D(1.0)
},
new TrainingData <Array3D, Array3D>
{
Input = new Array3D(1, 0), Expected = new Array3D(1.0)
},
new TrainingData <Array3D, Array3D>
{
Input = new Array3D(1, 1), Expected = new Array3D(0.0)
}
},
TestData = { new TrainingData <Array3D, Array3D>
{
Input = new Array3D(0, 0), Expected = new Array3D(0)
},
new TrainingData <Array3D, Array3D>
{
Input = new Array3D(0, 1), Expected = new Array3D(1)
},
new TrainingData <Array3D, Array3D>
{
Input = new Array3D(1, 0), Expected = new Array3D(1)
},
new TrainingData <Array3D, Array3D>
{
Input = new Array3D(1, 1), Expected = new Array3D(0)
} },
IsQueue = false
};
var oneData = new FunctionProvider
{
TrainData =
{
new TrainingData <Array3D, Array3D>
{
Input = new Array3D(0, 0), Expected = new Array3D(0.0)
},
new TrainingData <Array3D, Array3D>
{
Input = new Array3D(1, 1), Expected = new Array3D(0.0)
}
},
IsQueue = false
};
var function = new FunctionProvider(x => Math.Pow(x, 2));
var perceptron1 = new PerceptronLayer(10, 1)
{
ActivationFunction = new TanhActivationFunction()
};
perceptron1.Trainer = new MiniBatchPerceptronTrainer(perceptron1.Neurals, false)
{
BatchSize = BatchSize, ActivationFunction = new TanhActivationFunction(), LearningRate = 0.1, Momentum = 0.1
};
var perceptron2 = new PerceptronLayer(1, 10)
{
ActivationFunction = new TanhActivationFunction()
};
perceptron2.Trainer = new MiniBatchPerceptronTrainer(perceptron2.Neurals, true)
{
BatchSize = BatchSize, ActivationFunction = new TanhActivationFunction(), LearningRate = 0.1, Momentum = 0.1
};
Network = new MultiLayerPerceptron
{
InputLayer = inputLayer,
OutputLayer = outputLayer,
DataProvider = function
};
Network.HiddenLayers.Add(perceptron1);
Network.HiddenLayers.Add(perceptron2);
Trainer = new FCTrainer(Network, EpochSize, BatchSize, function);
}
