/// <summary>
/// 训练方法
/// </summary>
/// <param name="weightMatrixList">权值矩阵列表，对样本进行线性变换</param>
/// <param name="matrix">输入矩阵，每一列就是一个样本的特征向量</param>
/// <param name="desiredMatrix">期望矩阵</param>
/// <returns>返回训练后的权值矩阵</returns>
public static List <decimal[, ]> Train(List <decimal[, ]> weightMatrixList, decimal[,] matrix, decimal[,] desiredMatrix)
{
decimal learnRate = new decimal(.11);
List <decimal[, ]> prevInput = new List <decimal[, ]>();
List <decimal[, ]> prevWeight = new List <decimal[, ]>();
List <decimal[, ]> prevOutput = new List <decimal[, ]>();
List <decimal[, ]> prevActive = new List <decimal[, ]>();
decimal[,] output;
foreach (decimal[,] weightMatrix in weightMatrixList)
{
prevWeight.Insert(0, weightMatrix);
prevInput.Insert(0, matrix);
matrix = DefaultActivateFunc.Active(output = weightMatrix.MatrixMultiMatrix(matrix));
prevOutput.Insert(0, output);
prevActive.Insert(0, matrix);
}
decimal[,] backPro = desiredMatrix.MatrixReduceMatrix(prevActive[0]).MatrixVec().VecDiag().MatrixMultiMatrix(
DefaultActivateFunc.Dactive(prevOutput[0]).MatrixVec().VecDiag().MatrixMultiScalar(-1));
List <decimal[, ]> partialWeight = new List <decimal[, ]>();
partialWeight.Insert(0, backPro.MatrixMultiMatrix(prevInput[0].Turn().MatrixKronecker(MatrixHelper.CreateIdentityMatrix(prevWeight[0].GetLength(0)))).Turn());
for (int i = 1, len = weightMatrixList.Count; i < len; i++)
{
backPro = backPro.MatrixMultiMatrix(MatrixHelper.CreateIdentityMatrix(prevActive[i].GetLength(1)).MatrixKronecker(weightMatrixList[i - 1]))
.MatrixMultiMatrix(DefaultActivateFunc.Dactive(prevActive[i]).MatrixVec().VecDiag());
partialWeight.Insert(0, backPro.MatrixMultiMatrix(prevInput[i].Turn().MatrixKronecker(MatrixHelper.CreateIdentityMatrix(weightMatrixList[i].GetLength(0)))));
}
for (int i = 0, len = weightMatrixList.Count; i < len; i++)
{
weightMatrixList[i] = weightMatrixList[i].MatrixAndMatrix(partialWeight[i].MatrixMultiScalar(learnRate));
}
return(weightMatrixList);
}
