使用组的回归格式： [英] Regression format by using group:

问题描述

大家好，
 
 
 
i有一些疑问 回归格式通过使用组：
 
 
假设：如果我有< span class =code-digit> 3 组 0 ， 1 ， 2  
 
  group   0  ：代表整场比赛
 
 group1：第一个表达式分组
 
 group2：第二个表达式分组
 
请给我By 使用任何链接或网址  此 
，

我的尝试：



< pre lang =C＃>如何做 此 in 我的文本框。



请 此

请使用任意链接或网址 code-keyword> for   this  

解决方案

Got你好

我认为你需要和SQL一起做这个以及



但如果它有帮助请看这个链接



多元回归 - 免费统计和预测软件（计算器）v.1.1.23-r7 [ ^ ]



但如果没有，请看下面的代码



你试过Matrix逻辑 -

矩阵y = 新矩阵（
 < span class =code-keyword> new   double  [，] {{ 745 } ，
 { 895 }，
 { 442 }，
 {  440 }，
 { 1598 }}）; 
 
矩阵x = 新矩阵（
 新  double  [，] {{ 1 ， 36 ，< span class =code-digit> 66 }，
 { 1 ， 37 ， 68 }，
 { 1 ， 47 ， 64 }，
 { 1 ， 32 ， 53 }，
 { 1 ，< span class =code-digit> 1 ， 101 }}）; 
 
 Matrix b =（x.Transpose（）* x）.Inverse（）* x.Transpose（）* y; 
 
  for （ int  i =  0 ; i <  b.Rows; i ++）
 {
 Trace.WriteLine（  INFO： + b [i， 0 ]。ToDouble（） ）; 
} 
 
 
 _StatConn.EvaluateNoReturn（ string  .Format（   output<  -  lm（{0}），equation））; 
  object  intercept = _StatConn.Evaluate（ 系数（输出）[ '（截距）']）; 
参数[ 0 ] =（ double ）拦截; 
  for （ int  i =  0 ; i <  xColCount; i ++）
 {
  object  parameter = _StatConn.Evaluate（ string  .Format（  systems（输出） ）['x {0}']，i））; 
参数[i +  1 ] =（ double ）参数; 
} 
 
  public   void  Foo（List< Sample> samples ）
 {
  int  nAttributes =  3 ;  //   3分钟，2分钟，1分钟 
  int  nSamples = samples.Count; 
  double  [，] tsData =  new   double  [nSamples，nAttributes]; 
  double  [] resultData =  new   double < /跨度> [NSAMPLES]; 
 
  for （ int  i =  0 ; i <  samples.Count; i ++）
 {
 tsData [i， 0 ] = samples [i] .Tminus1min; 
 tsData [i， 1 ] = samples [i] .Tminus2min; 
 tsData [i， 2 ] = samples [i] .Tminus3min; 
 
 resultData [i] = samples [i] .Final; 
} 
 
  double  [] weights =  null ; 
  int  fitResult =  0 ; 
 alglib.lsfit.lsfitreport rep =  new  alglib.lsfit.lsfitreport（）; 
 alglib.lsfit.lsfitlinear（resultData，tsData，nSamples，nAttributes， ref  fitResult， ref 权重，代表; 
 
字典<字符串，双> labelsAndWeights =  new  Dictionary< string，double>（）; 
 labelsAndWeights.Add（  1min，权重[ 0 ]）; 
 labelsAndWeights.Add（  2min，权重[ 1 ]）; 
 labelsAndWeights.Add（  3min，权重[ 2 ]）; 
} 

hi guys,
 
 
 
       i have some doubts in Regression format by using Group:
 
 
   Assume:if i have  3 groups 0,1,2
 
  group 0:Represent the Whole match
 
 group1 : first expression grouping
 
 group2 : second expression grouping
 
 Please give me the By using any link or url for this
 , 

What I have tried:

How to do this in my text box .
 
 
 
Please help for this

Please give me the By using any link or url for this

解决方案

Got you
I think you need to do this in combination with SQL as well

but see this link if it helps

Multiple Regression - Free Statistics and Forecasting Software (Calculators) v.1.1.23-r7[^]

but if it doesn't then, see the code below

Have you tried Matrix logic -

Matrix y = new Matrix(
    new double[,]{{745},
                  {895},
                  {442},
                  {440},
                  {1598}});

Matrix x = new Matrix(
     new double[,]{{1, 36, 66},
                 {1, 37, 68},
                 {1, 47, 64},
                 {1, 32, 53},
                 {1, 1, 101}});

Matrix b = (x.Transpose() * x).Inverse() * x.Transpose() * y;

for (int i = 0; i < b.Rows; i++)
{
  Trace.WriteLine("INFO: " + b[i, 0].ToDouble());
}


_StatConn.EvaluateNoReturn(string.Format("output <- lm({0})", equation));
object intercept = _StatConn.Evaluate("coefficients(output)['(Intercept)']");
parameters[0] = (double)intercept;
for (int i = 0; i < xColCount; i++)
{
  object parameter = _StatConn.Evaluate(string.Format("coefficients(output)['x{0}']", i));
  parameters[i + 1] = (double)parameter;
}

public void Foo(List<Sample> samples)
{
  int nAttributes = 3; // 3min, 2min, 1min
  int nSamples = samples.Count;
  double[,] tsData = new double[nSamples, nAttributes];
  double[] resultData = new double[nSamples];

  for (int i = 0; i < samples.Count; i++)
  {
    tsData[i, 0] = samples[i].Tminus1min;
    tsData[i, 1] = samples[i].Tminus2min;
    tsData[i, 2] = samples[i].Tminus3min;

    resultData[i] = samples[i].Final;
  }

  double[] weights = null;
  int fitResult = 0;
  alglib.lsfit.lsfitreport rep = new alglib.lsfit.lsfitreport();
  alglib.lsfit.lsfitlinear(resultData, tsData, nSamples, nAttributes, ref fitResult, ref weights, rep);

  Dictionary<string, double> labelsAndWeights = new Dictionary<string, double>();
  labelsAndWeights.Add("1min", weights[0]);
  labelsAndWeights.Add("2min", weights[1]);
  labelsAndWeights.Add("3min", weights[2]);
}

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