public static void Write(this BinaryWriter writer, Poly val)
{
writer.Write(val.Degrees);
writer.Write(val.InChannels);
writer.Write(val.OutChannels);
writer.Write(val.Ceofs.GetLength(1));
for (int c = 0; c < val.Ceofs.GetLength(0); ++c)
{
for (int i = 0; i < val.Ceofs.GetLength(1); ++i)
{
writer.Write(val.Ceofs[c, i]);
}
}
writer.Write(val.MaxError);
writer.Write(val.MeanError);
}
public static Poly Fit(int degrees, int channels, Matrix <float> x, Matrix <float> y)
{
var nItems = GetItemCount(degrees, channels);
Debug.Assert(x.NRows == y.NRows && x.NRows >= GetItemCount(degrees, channels));
var nSamples = x.NRows;
/* ********【预声明phy超定矩阵】************************/
/* 多项式拟合的函数为多项幂函数
* f(x)=a0*x^2 + a1*y^2 + a2*xy + a3*x + a4*y + a5
*
* 超定矩阵phy=x1^2 y1^2 x1y1 x1 y1 1
* x2^2 y2^2 x2y2 x2 y2 1
* ... ... ... ...
* ... ... ... ...
* xn^2 yn^2 xnyn xn yn 1
*
* *************************************************/
var phy = new Matrix <float> (nSamples, nItems);
for (var i = 0; i < nSamples; i++)
{
var items = GetItems(degrees, x.GetRow(i));
for (var j = 0; j < items.Length; ++j)
{
phy[i, j] = items[j];
}
}
var a = new float[channels, nItems];
for (var c = 0; c < channels; ++c)
{
var yc = y.GetColumn(c);
var ac = new Matrix <float> (nItems, 1);
Solver.SolveLinear(phy, yc, ac, (int)Solver.DecompTypes.DECOMP_NORMAL);
for (var i = 0; i < nItems; ++i)
{
a[c, i] = ac[i, 0];
}
}
// 构造PolyFunc对象
var f = new Poly(degrees, channels, a, 0.0f, 0.0f);
// 计算Max Error和RMSE
for (var i = 0; i < nSamples; ++i)
{
var evalValue = f.Evaluate(x.GetRow(i));
var sqrErr = 0.0f;
for (var j = 0; j < evalValue.Length; ++j)
{
sqrErr = (evalValue[j] - y[i, j]) * (evalValue[j] - y[i, j]);
}
var err = Mathf.Sqrt(sqrErr);
f.MaxError = Mathf.Max(err, f.MaxError);
f.MeanError += sqrErr;
}
f.MeanError = Mathf.Sqrt(f.MeanError / nSamples);
return(f);
}