public static double[][][][] ResetSize(this LMatrix source, int depth, int row, int column)
{
int sampleLen = source.Length;
double[][][][] res = new double[sampleLen][][][];
int total = depth * row * column;
int channelLen = row * column;
for (int index = 0; index < sampleLen; index++)
{
double[] value = source[index];
if (total != value.Length)
{
throw new Exception("值不正确");
}
//int singleDepth = value.Length / depth;
res[index] = new double[depth][][];
//还原单个样本数据
for (int sig = 0; sig < depth; sig++)
{
res[index][sig] = new double[row][];
double[] channel = value.Skip(channelLen * sig).Take(channelLen).ToArray();
for (int r = 0; r < row; r++)
{
res[index][sig][r] = channel.Skip(r * column).Take(column).ToArray();
}
}
}
return(res);
}
public void ChangeValue(LMatrix v, double value = 0)
{
for (int r = 0; r < Row; r++)
{
for (int i = 0; i < Column; i++)
{
Matrix[r][i] = v[r][i] > value ? Matrix[r][i] : value;
}
}
}
/// <summary>
/// 求矩阵最大值
/// </summary>
/// <param name="axis">轴值或者纬度值</param>
/// <returns></returns>
public static LMatrix Max(this LMatrix mat, int axis = -1)
{
double[,] res = null;
switch (axis)
{
case -1:
//所有元素的最大值
res = new double[1, 1];
for (int row = 0; row < mat.Row; row++)
{
for (int col = 0; col < mat.Column; col++)
{
if (res[0, 0] < mat.Matrix[row, col])
{
res[0, 0] = mat.Matrix[row, col];
}
}
}
break;
case 0:
//所有行的相同列最大值，返回相同列 【计算每一列的最大值】
res = new double[1, mat.Column];
for (int col = 0; col < mat.Column; col++)
{
for (int row = 0; row < mat.Row; row++)
{
if (res[0, col] < mat.Matrix[row, col])
{
res[0, col] = mat.Matrix[row, col];
}
}
}
break;
case 1:
//同一行所有列的最大值 【计算每一行的最大值】
res = new double[mat.Row, 1];
for (int row = 0; row < mat.Row; row++)
{
for (int col = 0; col < mat.Column; col++)
{
if (res[row, 0] < mat.Matrix[row, col])
{
res[row, 0] = mat.Matrix[row, col];
}
}
}
break;
default:
return(null);
}
return(res);
}
/// <summary>
/// 归一化
/// </summary>
public LMatrix NormalizationMaxMin()
{
int row = Matrix.Length;
LMatrix img = Matrix;
LMatrix maxMatrix = img.Max(0);
LMatrix minMatrix = img.Min(0);
LMatrix ranges = maxMatrix - minMatrix + 0.00001;
var val = (Matrix - minMatrix) / ranges;
return(val);
}
/// <summary>
/// 求矩阵均值
/// </summary>
/// <param name="axis">轴值或者纬度值</param>
/// <returns></returns>
public static LMatrix Mean(this LMatrix mat, int axis = -1)
{
double[,] res = null;
double value = 0;
switch (axis)
{
case -1:
//所有元素的平均值
res = new double[1, 1];
for (int row = 0; row < mat.Row; row++)
{
for (int col = 0; col < mat.Column; col++)
{
value += mat.Matrix[row, col];
}
}
res[0, 0] = value / mat.Total;
break;
case 0:
//所有行的相同列平均值，返回相同列 【计算每一列的平均值】
res = new double[1, mat.Column];
for (int col = 0; col < mat.Column; col++)
{
value = 0;
for (int row = 0; row < mat.Row; row++)
{
value += mat.Matrix[row, col];
}
res[0, col] = value / mat.Row;
}
break;
case 1:
//同一行所有列的平均值 【计算每一行的平均值】
res = new double[mat.Row, 1];
for (int row = 0; row < mat.Row; row++)
{
value = 0;
for (int col = 0; col < mat.Column; col++)
{
value += mat.Matrix[row, col];
}
res[row, 0] = value / mat.Column;
}
break;
default:
return(null);
}
return(res);
}
/// <summary>
/// 复制出来一个相同值的矩阵
/// </summary>
/// <param name="a"></param>
/// <returns></returns>
public static LMatrix Copy(this LMatrix a)
{
var res = new double[a.Row, a.Column];
for (int row = 0; row < a.Row; row++)
{
for (int col = 0; col < a.Column; col++)
{
res[row, col] = a.Matrix[row, col];
}
}
return(res);
}
/// <summary>
/// 展开二维数组
/// </summary>
/// <param name="a">二维数组</param>
/// <returns></returns>
public static double[] Flatten(this LMatrix a)
{
var flat = new double[a.Total];
for (var i = 0; i < a.Row; i++)
{
for (var j = 0; j < a.Column; j++)
{
flat[i * a.Column + j] = a.Matrix[i, j];
}
}
return(flat);
}
public static double Sum(this LMatrix mat)
{
double value = 0;
for (int row = 0; row < mat.Row; row++)
{
for (int col = 0; col < mat.Column; col++)
{
value += mat.Matrix[row, col];
}
}
return(value);
}
/// <summary>
/// 正则化数据
/// </summary>
public void Regularization()
{
int row = Matrix.Length;
LMatrix img = Matrix;
LMatrix avgMatrix = img.Mean(0);
LMatrix stdMatrix = img.Std(0);
int column = Matrix[0].Length;
for (int r = 0; r < row; r++)
{
for (int c = 0; c < column; c++)
{
if (stdMatrix[0][c] == 0)
{
MessageBox.Show("有空值");
}
double val = (Matrix[r][c] - avgMatrix[0][c]) / stdMatrix[0][c];
Matrix[r][c] = val;
}
}
}
/// <summary>
/// 二维数组重新设置大小
/// </summary>
/// <param name="matrix">矩阵</param>
/// <param name="row">矩阵的行</param>
/// <param name="column">矩阵的列</param>
/// <returns></returns>
public static double[,] ReShape(this LMatrix matrix, int row, int column)
{
if (matrix == null)
{
return(null);
}
if (matrix.Total % row != 0)
{
throw new Exception(string.Format("不能转换大小:{0},{1}", matrix.Total, row));
}
double[,] res = new double[row, column];
for (int r = 0; r < row; r++)
{
for (int c = 0; c < column; c++)
{
int _index = r * column + c;
res[r, c] = matrix.Matrix[_index / matrix.Column, _index % matrix.Column];
}
}
return(res);
}
/// <summary>
/// 求矩阵标准差
/// </summary>
/// <param name="axis"></param>
/// <returns></returns>
public LMatrix Std(int axis = -1)
{
switch (axis)
{
case -1:
//所有元素的标准差
LMatrix avg = Mean(-1);
double[] res = new double[1];
double sum = 0;
for (int r = 0; r < Row; r++)
{
for (int c = 0; c < Column; c++)
{
sum += Math.Pow(Matrix[r][c] - avg[0][0], 2);
}
}
res[0] = Math.Sqrt(sum / (Row * Column - 1));
return(res);
case 0:
//所有行的相同列平均值，返回相同列 【计算每一列的标准差】
LMatrix avg0 = Mean(0);
double[][] res0 = new double[1][];
res0[0] = new double[Column];
for (int c = 0; c < Column; c++)
{
double sum0 = 0;
for (int r = 0; r < Row; r++)
{
sum0 += Math.Pow(Matrix[r][c] - avg0[0][c], 2);
}
res0[0][c] = Math.Sqrt(sum0 / (Row));
}
return(res0);
case 1:
//同一行所有列的平均值 【计算每一行的标准差】
LMatrix avg1 = Mean(1);
double[][] res1 = new double[Row][];
for (int r = 0; r < Row; r++)
{
res1[r] = new double[1];
double sum1 = 0;
for (int c = 0; c < Column; c++)
{
sum1 += Math.Pow(Matrix[r][c] - avg1[r][0], 2);
}
if (sum1 == 0)
{
sum1 = 1e-7;
}
res1[r][0] = Math.Sqrt(sum1 / (Column));
if (double.IsNaN(res1[r][0]) || res1[r][0] == 0)
{
}
}
return(res1);
default:
return(null);
}
}
/// <summary>
/// 求矩阵标准差
/// </summary>
/// <param name="axis"></param>
/// <returns></returns>
public static LMatrix Std(this LMatrix mat, int axis = -1)
{
double[,] res = null;
LMatrix avg = null;
double sum = 0;
switch (axis)
{
case -1:
//所有元素的标准差
avg = mat.Mean(-1);
res = new double[1, 1];
for (int r = 0; r < mat.Row; r++)
{
for (int c = 0; c < mat.Column; c++)
{
sum += Math.Pow(mat.Matrix[r, c] - avg.Matrix[0, 0], 2);
}
}
if (sum == 0)
{
res[0, 0] = 0;
}
else
{
res[0, 0] = Math.Sqrt(sum / mat.Total);
}
break;
case 0:
//所有行的相同列平均值，返回相同列 【计算每一列的标准差】
avg = mat.Mean(0);
res = new double[1, mat.Column];
for (int c = 0; c < mat.Column; c++)
{
sum = 0;
for (int r = 0; r < mat.Row; r++)
{
sum += Math.Pow(mat.Matrix[r, c] - avg.Matrix[0, c], 2);
}
if (sum == 0)
{
res[0, c] = 0;
}
else
{
res[0, c] = Math.Sqrt(sum / mat.Row);
}
}
break;
case 1:
//同一行所有列的平均值 【计算每一行的标准差】
avg = mat.Mean(1);
res = new double[mat.Row, 1];
for (int r = 0; r < mat.Row; r++)
{
sum = 0;
for (int c = 0; c < mat.Column; c++)
{
sum += Math.Pow(mat.Matrix[r, c] - avg.Matrix[r, 0], 2);
}
if (sum == 0)
{
res[r, 0] = 0;
}
else
{
res[r, 0] = Math.Sqrt(sum / mat.Column);
}
}
break;
default:
return(null);
}
return(res);
}