public static Results Calcu(List <PValue>[] inputs, CalcuInfo calcuinfo)
{
//公用变量
bool _errorFlag = false;
string _errorInfo = "";
bool _warningFlag = false;
string _warningInfo = "";
bool _fatalFlag = false;
string _fatalInfo = "";
//0输出初始化：该算法如果没有有效输入值（inputs为null）或者输入值得有效值为null，给出的计算结果。值为0，计算标志位为StatusConst.InputIsNull
List <PValue>[] results = new List <PValue> [16]; //平均值;最小值;最小值所在的点号;最大值;最大值所在的点号;最均差;最大差(最大值与最小值得差);和;绝对值和
for (int i = 0; i < results.Length; i++)
{
results[i] = new List <PValue>();
results[i].Add(new PValue(0, calcuinfo.fstarttime, calcuinfo.fendtime, (long)StatusConst.InputIsNull));
}
try
{
//0.1、输入处理：输入长度。当输入为空时，则输出项也为空.
if (inputs == null || inputs.Length == 0 || inputs[0] == null)
{
return(new Results(results, _errorFlag, _errorInfo, _warningFlag, _warningInfo, _fatalFlag, _fatalInfo));
}
//1 参数处理
string[] paras = calcuinfo.fparas.Split(';');
string[] vibstr = paras[0].Split(',');
double[] vib = new double[vibstr.Length];
for (int i = 0; i < vibstr.Length - 1; i++)
{
vib[i] = Convert.ToDouble(vibstr[i]);
}
string[] visstr = paras[1].Split(',');
double[] vis = new double[visstr.Length];
for (int i = 0; i < visstr.Length - 1; i++)
{
vis[i] = Convert.ToDouble(visstr[i]);
}
List <XFClass> XF = new List <XFClass>();
string[] XFStr = paras[2].Split(',');
foreach (string item in XFStr)
{
XFClass xfClass = new XFClass();
xfClass.A = Convert.ToInt32(item.Split(':')[0]);
List <double> bList = new List <double>();
foreach (string bItem in item.Split(':')[1].Split('*'))
{
bList.Add(Convert.ToDouble(bItem));
}
xfClass.B = bList;
}
double[] Y = new double[inputs[0].Count];
double[][] X = new double[inputs[1].Count][];
for (int i = 0; i < inputs[0].Count - 1; i++)
{
Y[i] = inputs[0][i].Value;
}
for (int i = 1; i < inputs.Length - 1; i++)
{
double[] xList = new double[inputs[i].Count];
for (int l = 0; l < inputs[i].Count - 1; l++)
{
xList[l] = inputs[i][l].Value;
}
X[i] = xList;
}
int k = 0;
double p = 0;
double[] mlist = new double[] { };
double[] resultlist = new double[] { };
string errmsg = string.Empty;
MultipleRegressionAlgorithmCaculate mm = new MultipleRegressionAlgorithmCaculate();
int s = mm.Regression(0, vib, vis, X, Y, XF, ref k, ref p, ref mlist, ref resultlist, ref errmsg);
if (s != 0)
{
_fatalFlag = true;
_errorInfo = errmsg;
return(new Results(results, _errorFlag, _errorInfo, _warningFlag, _warningInfo, _fatalFlag, _fatalInfo));
}
PValue kValue = new PValue(k, calcuinfo.fstarttime, calcuinfo.fendtime, 0);
PValue pValue = new PValue(p, calcuinfo.fstarttime, calcuinfo.fendtime, 0);
results[0] = new List <PValue>();
results[0].Add(kValue);
results[1] = new List <PValue>();
results[1].Add(pValue);
for (int i = 0; i < mlist.Length - 1; i++)
{
PValue mValue = new PValue(mlist[i], calcuinfo.fstarttime, calcuinfo.fendtime, 0);
results[i + 2] = new List <PValue>();
results[i + 2].Add(mValue);
}
//分别对应R2; sey; F;df;SSR;SSE;
for (int i = 0; i < resultlist.Length - 1; i++)
{
PValue rValue = new PValue(resultlist[i], calcuinfo.fstarttime, calcuinfo.fendtime, 0);
results[i + 2 + mlist.Length] = new List <PValue>();
results[i + 2 + mlist.Length].Add(rValue);
}
//不够16位的0补齐
if (results.Length < 16)
{
int l = results.Length;
for (int i = 0; i < 16 - results.Length; i++)
{
PValue rValue = new PValue(0, calcuinfo.fstarttime, calcuinfo.fendtime, 0);
results[i + l] = new List <PValue>();
results[i + l].Add(rValue);
}
}
//计算结果初始化
return(new Results(results, _errorFlag, _errorInfo, _warningFlag, _warningInfo, _fatalFlag, _fatalInfo));
}
catch (Exception ex)
{
_fatalFlag = true;
_fatalInfo = ex.ToString();
return(new Results(null, _errorFlag, _errorInfo, _warningFlag, _warningInfo, _fatalFlag, _fatalInfo));
}
}
public static Results Calcu(List <PValue>[] inputs, CalcuInfo calcuinfo)
{
//公用变量
bool _errorFlag = false;
string _errorInfo = "";
bool _warningFlag = false;
string _warningInfo = "";
bool _fatalFlag = false;
string _fatalInfo = "";
//0输出初始化：该算法如果没有有效输入值（inputs为null）或者输入值得有效值为null，给出的计算结果。值为0，计算标志位为StatusConst.InputIsNull
List <PValue>[] results = new List <PValue> [18]; //平均值;最小值;最小值所在的点号;最大值;最大值所在的点号;最均差;最大差(最大值与最小值得差);和;绝对值和
for (int i = 0; i < results.Length; i++)
{
results[i] = new List <PValue>();
results[i].Add(new PValue(0, calcuinfo.fstarttime, calcuinfo.fendtime, (long)StatusConst.InputIsNull));
}
try
{
//0.1、输入处理：输入长度。当输入为空时，则输出项也为空.
if (inputs == null || inputs.Length == 0 || inputs[0] == null)
{
return(new Results(results, _errorFlag, _errorInfo, _warningFlag, _warningInfo, _fatalFlag, _fatalInfo));
}
//1 参数处理
string[] paras = calcuinfo.fparas.Split(';');
string[] vibstr = paras[0].Split(',');
double[] vib = new double[vibstr.Length];
for (int i = 0; i < vibstr.Length - 1; i++)
{
vib[i] = Convert.ToDouble(vibstr[i]);
}
string[] visstr = paras[1].Split(',');
double[] vis = new double[visstr.Length];
for (int i = 0; i < visstr.Length - 1; i++)
{
vis[i] = Convert.ToDouble(visstr[i]);
}
List <XFClass> XF = new List <XFClass>();
string[] XFStr = paras[2].Split(',');
foreach (string item in XFStr)
{
XFClass xfClass = new XFClass();
xfClass.A = Convert.ToInt32(item.Split(':')[0]);
List <double> bList = new List <double>();
foreach (string bItem in item.Split(':')[1].Split('*'))
{
bList.Add(Convert.ToDouble(bItem));
}
xfClass.B = bList;
}
double[] Y = new double[inputs[0].Count];
double[][] X = new double[inputs[1].Count][];
for (int i = 0; i < inputs[0].Count - 1; i++)
{
Y[i] = inputs[0][i].Value;
}
for (int i = 1; i < inputs.Length - 1; i++)
{
double[] xList = new double[inputs[i].Count];
for (int l = 0; l < inputs[i].Count - 1; l++)
{
xList[l] = inputs[i][l].Value;
}
X[i] = xList;
}
int k = 0;
double p = 0;
double[] mlist = new double[] { };
double[] resultlist = new double[] { };
string errmsg = string.Empty;
MultipleRegressionAlgorithmCaculate mm = new MultipleRegressionAlgorithmCaculate();
int s = mm.Regression(0, vib, vis, X, Y, XF, ref k, ref p, ref mlist, ref resultlist, ref errmsg);
if (s != 0)
{
_fatalFlag = true;
_errorInfo = errmsg;
return(new Results(results, _errorFlag, _errorInfo, _warningFlag, _warningInfo, _fatalFlag, _fatalInfo));
}
PValue kValue = new PValue(k, calcuinfo.fstarttime, calcuinfo.fendtime, 0);
PValue pValue = new PValue(p, calcuinfo.fstarttime, calcuinfo.fendtime, 0);
results[0] = new List <PValue>();
results[0].Add(kValue);
results[1] = new List <PValue>();
results[1].Add(pValue);
//分别对应R2; sey; F;df;SSR;SSE;
for (int i = 0; i < resultlist.Length - 1; i++)
{
PValue rValue = new PValue(resultlist[i], calcuinfo.fstarttime, calcuinfo.fendtime, 0);
results[i + 2 + mlist.Length] = new List <PValue>();
results[i + 2 + mlist.Length].Add(rValue);
}
for (int i = 0; i < mlist.Length - 1; i++)
{
PValue mValue = new PValue(mlist[i], calcuinfo.fstarttime, calcuinfo.fendtime, 0);
results[i + 2] = new List <PValue>();
results[i + 2].Add(mValue);
}
//不够16位的0补齐
if (results.Length < 18)
{
int l = results.Length;
for (int i = 0; i < 18 - results.Length; i++)
{
PValue rValue = new PValue(0, calcuinfo.fstarttime, calcuinfo.fendtime, 0);
results[i + l] = new List <PValue>();
results[i + l].Add(rValue);
}
}
string year = string.Empty;
string month = string.Empty;
string day = string.Empty;
string hour = string.Empty;
DateTime dt = Convert.ToDateTime(inputs[0][0].Timestamp);
year = dt.Year.ToString();
month = dt.Month.ToString();
day = dt.Day.ToString();
hour = dt.Hour.ToString();
string type = calcuInfo.fsourtagids[0].ToString();
MultipleRegressionAlgorithmOutClass messageIn = new MultipleRegressionAlgorithmOutClass();
messageIn.k = results[0][0].Value.ToString();
messageIn.b = results[1][0].Value.ToString();
messageIn.r2 = results[2][0].Value.ToString();
messageIn.sey = results[3][0].Value.ToString();
messageIn.F = results[4][0].Value.ToString();
messageIn.df = results[5][0].Value.ToString();
messageIn.ssreg = results[6][0].Value.ToString();
messageIn.ssresid = results[7][0].Value.ToString();
messageIn.m1 = results[8][0].Value.ToString();
messageIn.m2 = results[9][0].Value.ToString();
messageIn.m3 = results[10][0].Value.ToString();
messageIn.m4 = results[11][0].Value.ToString();
messageIn.m5 = results[12][0].Value.ToString();
messageIn.m6 = results[13][0].Value.ToString();
messageIn.m7 = results[14][0].Value.ToString();
messageIn.m8 = results[15][0].Value.ToString();
messageIn.m9 = results[16][0].Value.ToString();
messageIn.m10 = results[17][0].Value.ToString();
//计算结果存入数据库
bool isok = BLL.AlgorithmBLL.InsertMultipleRegressionAlgorithm(messageIn, type, year, month, day, hour);
//计算结果初始化
return(new Results(results, _errorFlag, _errorInfo, _warningFlag, _warningInfo, _fatalFlag, _fatalInfo));
}
catch (Exception ex)
{
_fatalFlag = true;
_fatalInfo = ex.ToString();
return(new Results(null, _errorFlag, _errorInfo, _warningFlag, _warningInfo, _fatalFlag, _fatalInfo));
}
}
