シーケンスを修正する方法C＃に要素エラーがありません？

Question

私は現在、魅力のためのコードを作成しています。私のコンセプトでは、Population、Green Spaces、Residential Spacesのような因子に細胞を割り当てています。私は人口が緑の空間と緑の空間への居住空間に惹かれているこれらの要因の間に魅力の関係をコード化したい。私はいくつかのC＃コンポーネントを持っていますが、これらのいくつかは、このシーケンスが要素エラーを含まないために動作していません。私はコード内でエラーを見つけることができないようだ、誰かがこれを修正する方法を知っていれば感謝します。ご協力ありがとうございました！

//====================================================== 
// ------- compute the attractivity landscape ---------- 
//====================================================== 
// -- find the max distance value - usually the diagonal from first to last: 
int nrLocations = Distances.BranchCount; 
int nrDists = Distances.Branch(0).Count; 
double maxDist = Distances.Branch(0)[nrDists - 1]; 

// -- the list store the attractivity values for population to green spaces and housing to green spaces 
List<double> attractPop = new List<double>(); 
List<double> attractSpaces = new List<double>(); 
List<double> attractResid = new List<double>(); 
double maxPop = PopulDistrib.Max(); 
double maxSpaces = GreenSDistrib.Max(); 
double maxResid = ResiDistrib.Max(); 

for(int i = 0; i < nrLocations; i++) 
{ 
    // -- reset lists in each loop: 
    double curPopAttract = 0; 
    double curSpacesAttract = 0; 
    double curResidAttract = 0; 
    for(int k = 0; k < nrDists; k++) 
    { 
    double curPop = PopulDistrib[k]/maxPop; // population of all cells 
    double curSpaces = GreenSDistrib[k]/maxPop; // Green Spaces of all cells 
    double curResid = ResiDistrib[k]/maxResid; // Residential Spaces of all cells 

    // -- distances to all cells: 
    // -- index i = from current cell // index k = to other cells 
    // -- division by maxDist gives normalized values in the range [0; 1] for the distance 
    double curDist = Distances.Branch(i)[k]/maxDist; 

    // ** Remember: the distances are normalized! Therefore: 
    // ** (1 - distance) :: makes close things more important than distant ones. 
    // ** (Math.Pow(1-distance, 6); :: decrease the effect of the distance exponentially. 
    // ** The perception of people concerning things in a certain distance can now be expressed by: 
    double distPerceptPop = Math.Pow(1 - curDist, 7); 
    //Print("distPercept: " + distPerceptPop.ToString()); 

    // -- in addition, we just cut the relevance of distant things completely: 
    if (distPerceptPop < 0.4) distPerceptPop = 0; 

    // -- corresponding "perception" of Green Spaces concerning things in a distance: 
    double distPerceptSpaces = Math.Pow(1 - curDist, 2); 

    // -- corresponding "perception" of Residential Space concerning things in a distance: 
    double distPerceptResid = Math.Pow(1 - curDist, 4); 

    // ----------- Attractivity Functions ---------------------- 
    // -- define the functions for the attractivity ------------ 
    // -- here we control, what is prefered by which land use -- 
    // -- linear attractivity function -> can become more complex functions!!! 

    // -- people like to be close to Green Spaces: 
    curPopAttract += ((distPerceptPop) * curSpaces); 
    //double curAttract = ((distPerceptPop) * curPop); 
    //curPopAttract += curAttract; 
    //Print("attractivityPop: " + curPopAttract.ToString()); 

    // -- people DON'T like to be close to other people: 
    //curPopAttract += 1- ((distPerceptPop) * curPop); 

    // -- people DON'T like to be close to other people, + but to workplaces: 
    //double weightImportWork = 0.3; // -- weighting factor to express the importancy to be close to workplaces in contrast to be away from other people 
    //curPopAttract += 1- ((distPerceptPop) * curPop)  + weightImportWork * Math.Pow(1 - curDist, 2) * curWork; 

    // -- Residential Spaces like to be close to Green Spaces 
    curResidAttract += (distPerceptResid) * curSpaces; 

    // *** EXPERIMENT WITH THE ATTRACTIVITY FUNCTIONS ABOVE !!! ********************************** 
    // *** You may also add additional relations similar to the other system dynamics examples *** 
    } 
    // -- collect the current values in the attractivity values lists: 
    attractPop.Add(curPopAttract); 
    attractSpaces.Add(curSpacesAttract); 
    attractResid.Add(curResidAttract); 
} 

//======================================================= 
// -- normalize the attractivity values for both lists -- 
double minPopAtt = attractPop.Min(); 
double maxPopAtt = attractPop.Max(); 
double minSpacesAtt = attractSpaces.Min(); 
double maxSpacesAtt = attractSpaces.Max(); 
double minResidAtt = attractResid.Min(); 
double maxResidAtt = attractResid.Max(); 

// -- formula for normalization to the range [0; 1]: 
// -- normalized valueOfList = (valueOfList - minValueOfList) * 1/maxValueOfList 
for(int i = 0; i < attractPop.Count; i++) 
{ 
    attractPop[i] = (attractPop[i] - minPopAtt) * (1/maxPopAtt); 
    attractSpaces[i] = (attractSpaces[i] - minSpacesAtt) * (1/maxSpacesAtt); 
    attractResid[i] = (attractResid[i] - minResidAtt) * (1/maxResidAtt); 
} 

//======================================================= 
// -- return the lists: 
AttractPop = attractPop; 
AttractSpaces = attractSpaces; 
AttractResid = attractResid; 

Answer 1

あなたは空のコレクションに.Max()または.Min()を呼んでいます。

空のコレクションを確認します。 if (!collection.Any()) ...を使用して停止し（入力エラーの場合）、アルゴリズムを調整してエラーを処理します。

Answer 2

Peterは、空のコレクションにMin()とMax()と呼んでいます。周り

作業は次のようになります。

double minPopAtt = attractPop.DefaultIfEmpty(-1).Min(); 

-1デフォルト値は、あなたのコレクションが空の場合になります。