public FittingResult Predict(Spectrum specInput, bool needFilter = true, int numOfId = 5, int topK = 1)
{
var spec = specInput.Clone();
var y = new MWNumericArray(specInput.Data.Lenght, 1, specInput.Data.Y);
//进行预处理
if (needFilter && this._filters != null)
{
y = Preprocesser.ProcessForPredict(this._filters, y);
spec.Data.Y = (double[])y.ToVector(MWArrayComponent.Real);
}
var handler = Tools.ModelHandler;
var rlst = handler.FitPredictor(5, this._lib.X, y, this._wind, (MWNumericArray)this._region.VarIndex, topK);
var tq = (MWNumericArray)rlst[0];
var sq = (MWNumericArray)rlst[1];
var ratio = (MWNumericArray)rlst[2];
var idx = (MWNumericArray)rlst[3];
var fit = (MWNumericArray)rlst[4];
var fitspec = spec.Clone();
fitspec.Components = this._lib.Components.Clone();
fitspec.Data.Y = (double[])fit.ToVector(MWArrayComponent.Real);
var r = new FittingResult()
{
SpecOriginal = spec,
SQ = sq.ToScalarDouble(),
TQ = tq.ToScalarDouble(),
FitSpec = fitspec,
MinSQ = this._minSQ,
MinTQ = this._TQ,
Wind = this._wind,
VarIndex = this._region.VarIndex
};
var flst = new List <FittingSpec>();
for (int i = 1; i <= ratio.NumberOfElements; i++)
{
if ((double)ratio[i] <= double.Epsilon)
{
break;
}
flst.Add(new FittingSpec()
{
Spec = this._lib[(int)idx[i] - 1],
Rate = (double)ratio[i]
});
}
r.Specs = flst.ToArray();
this.getResult(ref r);
r.Result = r.TQ >= this._TQ && r.SQ >= this._minSQ;
return(r);
}
private static SpecBase mix(Spectrum s, double[] v1, double[] v2, double[] v3, string[] names)
{
var lib = new SpecBase();
lib.Comp_Add(new Component()
{
Name = names[0], Eps = 2
});
lib.Comp_Add(new Component()
{
Name = names[1], Eps = 2
});
lib.Comp_Add(new Component()
{
Name = names[2], Eps = 2
});
var rlst = Tools.ToolHandler.Mix3(4, (MWNumericArray)v1, (MWNumericArray)v2, (MWNumericArray)v3);
var x = (MWNumericArray)rlst[0];
var c1 = (double[])((MWNumericArray)rlst[1]).ToVector(MWArrayComponent.Real);
var c2 = (double[])((MWNumericArray)rlst[2]).ToVector(MWArrayComponent.Real);
var c3 = (double[])((MWNumericArray)rlst[3]).ToVector(MWArrayComponent.Real);
for (int i = 0; i < x.Dimensions[1]; i++)
{
var st = s.Clone();
st.Usage = UsageTypeEnum.Calibrate;
st.Components.Clear();
st.Components.Add(new Component()
{
Name = names[0],
ActualValue = c1[i]
});
st.Components.Add(new Component()
{
Name = names[1],
ActualValue = c2[i]
});
st.Components.Add(new Component()
{
Name = names[2],
ActualValue = c3[i]
});
st.Name = (i + 1).ToString();
st.Color = Spectrum.RandomColor();
st.Data.Y = (double[])Tools.SelectColumn(x, i + 1).ToVector(MWArrayComponent.Real);
lib.Add(st);
}
return(lib);
}
public IdentifyResult Predict(Spectrum speciii, bool needFilter = true, int numOfId = 5, int topK = 1)
{
if (this._lib == null || speciii == null)
{
throw new ArgumentNullException("");
}
var spec = speciii.Clone();
var y = new MWNumericArray(speciii.Data.Lenght, 1, speciii.Data.Y);
//进行预处理
if (needFilter && this._filters != null)
{
y = Preprocesser.ProcessForPredict(this._filters, y);
spec.Data.Y = (double[])y.ToVector(MWArrayComponent.Real);
}
var handler = Tools.ModelHandler;
//Tools.ToolHandler.Save(this._lib.X, "x", "x");
//Tools.ToolHandler.Save(y, "y", "y");
//Tools.ToolHandler.Save(this._baseLib.X, "xa", "xa");
//this.LibBase.FullPath = "aaaa.lib";
//this.LibBase.Save();
this._maxNum = Math.Min(this._maxNum, this._lib.X.Dimensions[1]);
var rlst = handler.IdentifyPredictor(3, this._lib.X, y, this._wind, this._maxNum, this._p, this._w, this._t, topK);
var minSQ = (MWNumericArray)rlst[0];
var minTQ = (MWNumericArray)rlst[1];
var ad = (MWNumericArray)rlst[2];
var r = new IdentifyResult()
{
Spec = spec,
MinSQ = this.MinSQ,
MinTQ = this.TQ
};
//设置结果
var itemlst = new List <IdentifyItem>();
// r.Items = new IdentifyItem[ad.NumberOfElements];
for (int i = 0; i < this._maxNum; i++)
{
var adidx = (int)ad[i + 1] - 1;
if (adidx >= this._lib.Count || adidx < 0)
{
break;
}
itemlst.Add(new IdentifyItem()
{
Parent = r,
TQ = (double)minTQ[i + 1],
SQ = (double)minSQ[i + 1],
Spec = this._lib[adidx],
Wind = this._wind,
SpecOriginal = r.Spec
});
}
r.Items = itemlst.ToArray();
r = GetPredictValue(r, r.Items.Length, numOfId);
r.IsId = r.Items.Where(d => d.Result).Count() > 0;
return(r);
}
private void btnCompute_Click(object sender, EventArgs e)
{
if (spec1 == null || spec2 == null)
{
MessageBox.Show(string.Format("第{0}条光谱为空，请先选择。",
spec1 == null ? 1 : 2), "信息提示");
return;
}
int mwin = 0;
if (!int.TryParse(txbmwin.Text, out mwin))
{
MessageBox.Show("移动窗口大小必须为整数", "信息提示");
return;
}
IList <IFilter> filters = new List <IFilter>();
if (this._model != null)
{
if (this._model is IdentifyModel)
{
filters = ((IdentifyModel)this._model).Filters;
}
else if (this._model is FittingModel)
{
filters = ((FittingModel)this._model).Filters;
}
}
var s1 = spec1.Clone();
var s2 = spec2.Clone();
//对光谱预处理
if (filters != null)
{
var ya = new MWNumericArray(s1.Data.Lenght, 1, s1.Data.Y);
var yb = new MWNumericArray(s2.Data.Lenght, 1, s2.Data.Y);
bool splitadd = false;
var y1 = (MWNumericArray)ya.Clone();
var y2 = (MWNumericArray)yb.Clone();
var x1 = Serialize.DeepClone <double[]>(s1.Data.X);
var x2 = Serialize.DeepClone <double[]>(s2.Data.X);
var y1lst = new List <MWNumericArray>();
var x1lst = new List <double[]>();
var y2lst = new List <MWNumericArray>();
var x2lst = new List <double[]>();
foreach (var tf in filters)
{
var f = RIPP.Lib.Serialize.DeepClone <RIPP.NIR.Data.Filter.IFilter>(tf);
if (f is Spliter)
{
if (splitadd)
{
x1lst.Add(x1);
y1lst.Add(y1);
x2lst.Add(x2);
y2lst.Add(y2);
}
splitadd = false;
y1 = (MWNumericArray)ya.Clone();
y2 = (MWNumericArray)yb.Clone();
x1 = Serialize.DeepClone <double[]>(s1.Data.X);
x2 = Serialize.DeepClone <double[]>(s2.Data.X);
}
else
{
splitadd = true;
}
y1 = f.Process(y1);
y2 = f.Process(y2);
if (f.FType == FilterType.VarFilter)
{
x1 = f.VarProcess(x1);
x2 = f.VarProcess(x2);
}
}
if (splitadd)
{
x1lst.Add(x1);
y1lst.Add(y1);
x2lst.Add(x2);
y2lst.Add(y2);
}
//合并
if (x1lst.Count > 0)
{
s1.Data.X = x1lst[0];
s1.Data.Y = (double[])y1lst[0].ToVector(MWArrayComponent.Real);
s2.Data.X = x2lst[0];
s2.Data.Y = (double[])y2lst[0].ToVector(MWArrayComponent.Real);
for (int i = 1; i < x1lst.Count; i++)
{
s1.Data.X = RIPP.NIR.Data.Tools.InsertColumn(s1.Data.X, x1lst[i], s1.Data.X.Length + 1);
s1.Data.Y = RIPP.NIR.Data.Tools.InsertColumn(s1.Data.Y, (double[])y1lst[i].ToVector(MWArrayComponent.Real), s1.Data.Y.Length + 1);
s2.Data.X = RIPP.NIR.Data.Tools.InsertColumn(s2.Data.X, x2lst[i], s2.Data.X.Length + 1);
s2.Data.Y = RIPP.NIR.Data.Tools.InsertColumn(s2.Data.Y, (double[])y2lst[i].ToVector(MWArrayComponent.Real), s2.Data.Y.Length + 1);
}
}
else
{
s1.Data.X = x1;
s1.Data.Y = (double[])y1.ToVector(MWArrayComponent.Real);
s2.Data.X = x2;
s2.Data.Y = (double[])y2.ToVector(MWArrayComponent.Real);
}
//s1.Data.Y = (double[])y1.ToVector(MWArrayComponent.Real);
//s2.Data.Y = (double[])y2.ToVector(MWArrayComponent.Real);
}
if (this._model is FittingModel)
{
var f = ((FittingModel)this._model).IdRegion;
s1.Data.X = f.VarProcess(s1.Data.X);
s2.Data.X = f.VarProcess(s2.Data.X);
s1.Data.Y = f.VarProcess(s1.Data.Y);
s2.Data.Y = f.VarProcess(s2.Data.Y);
}
//绘制
var lst = new List <Spectrum>();
lst.Add(s1);
lst.Add(s2);
this.specGraph3.DrawSpec(lst);
this.specGraph3.SetTitle("预处理后光谱");
double[] tSQ;
double tTQ;
RIPP.NIR.Data.Tools.MWCorr(s1.Data.Y, s2.Data.Y, mwin, out tTQ, out tSQ);
var spec = RIPP.Lib.Serialize.DeepClone <Spectrum>(s1);
spec.Data.Y = tSQ;
spec.Name = "SQ";
spec.Color = Color.Blue;
this.specGraph2.DrawSpec(new List <Spectrum>()
{
spec
});
this.txbTQ.Text = tTQ.ToString("f3");
this.txbSQ.Text = tSQ.Min().ToString("f3");
}
/// <summary>
/// 预测
/// </summary>
/// <param name="spec"></param>
/// <param name="needFilter">是否需要前处理</param>
/// <returns></returns>
public PLS1Result Predict(Spectrum spec, bool needFilter = true, int numOfId = 5, int topK = 1)
{
if (spec == null)
{
throw new ArgumentNullException("");
}
var s = spec.Clone();
if (s.Data == null || s.Data.Y == null || s.Data.Y.Length == 0)
{
return(null);
}
var x = new MWNumericArray(s.Data.Lenght, 1, s.Data.Y);
//进行预处理
if (needFilter && this._filters != null)
{
x = Preprocesser.ProcessForPredict(this._filters, x);
s.Data.Y = (double[])x.ToVector(MWArrayComponent.Real);
}
var c = this._comp.Clone();
//定义变量
double[] SR, MD, ND, Ylast;
var handler = Tools.ModelHandler;
if (this._annType != PLSAnnEnum.None)
{
var r = handler.PLS1Predictor(5, x, this._Scores, this._Loads, this._Weights, this._Bias, this._Score_Length, this._centerSpecData, this._centerCompValue, (int)this._method);
var toolHandler = Tools.ToolHandler;
// SR,MD,nd,XScores
Ylast = (double[])((MWNumericArray)r[0]).ToVector(MWArrayComponent.Real);
SR = (double[])((MWNumericArray)r[1]).ToVector(MWArrayComponent.Real);
MD = (double[])((MWNumericArray)r[2]).ToVector(MWArrayComponent.Real);
ND = (double[])((MWNumericArray)r[3]).ToVector(MWArrayComponent.Real);
var mscores = (MWNumericArray)toolHandler.Centring(1, r[4], this._ScoresMean)[0];
var annp = (MWNumericArray)handler.annp(1,
mscores,
this._annModel.w1,
this._annModel.b1,
this._annModel.w2,
this._annModel.b2,
this._annArgus.F1.GetDescription(),
this._annArgus.F2.GetDescription())[0];
Ylast[this._factor - 1] = annp.ToScalarDouble() + this._centerCompValue;
}
else
{
var r = handler.PLS1Predictor(5, x, this._Scores, this._Loads, this._Weights, this._Bias, this._Score_Length, this._centerSpecData, this._centerCompValue, (int)this._method);
Ylast = (double[])((MWNumericArray)r[0]).ToVector(MWArrayComponent.Real);
SR = (double[])((MWNumericArray)r[1]).ToVector(MWArrayComponent.Real);
MD = (double[])((MWNumericArray)r[2]).ToVector(MWArrayComponent.Real);
ND = (double[])((MWNumericArray)r[3]).ToVector(MWArrayComponent.Real);
}
c.PredictedValue = Ylast[this.Factor - 1];
// c.PredictedValue = this._Nonnegative && c.PredictedValue < 0 ? 0 : c.PredictedValue;
c.PredictedValue = this._Nonnegative && c.PredictedValue < 0 ? Math.Pow(10, -c.Eps)
: c.PredictedValue;
if (s.Components != null && s.Components.Contains(c.Name))
{
c.ActualValue = s.Components[c.Name].ActualValue;
c.Error = c.PredictedValue - c.ActualValue;
}
//加入判定条件，设置样式
int errorcount = 0;
if (MD[this._factor - 1] > this._MDMin)
{
errorcount++;
}
else if (SR[this._factor - 1] > this._SRMin)
{
errorcount++;
}
else if (ND[this._factor - 1] > this._NNDMin)
{
errorcount++;
}
switch (errorcount)
{
case 0:
c.State = ComponentStatu.Pass;
break;
case 1:
c.State = ComponentStatu.Green;
break;
case 2:
c.State = ComponentStatu.Blue;
break;
default:
c.State = ComponentStatu.Red;
break;
}
PLS1Result item = new PLS1Result()
{
Spec = s,
MDMin = this._MDMin,
NDMin = this._NNDMin,
SRMin = this._SRMin,
Comp = c,
MahDist = MD,
ND = ND,
SR = SR,
YLast = Ylast,
Factor = this.Factor
};
return(item);
}