/// <summary>
/// Register data that corresponds to the gesture
/// </summary>
protected override void RegisterGesture()
{
//We only detectr frame with a minimum of velocity in the palm gesture
if (Math.Abs(this.SelectedHand.PalmVelocity.x) >= MIN_GESTURE_VELOCITY_X_FRAME_DETECTION)
{
//We keep the departure point for the gesture
if (_frameGestureCount == 0)
{
_gestureFirstPoint = this.SelectedHand.StabilizedPalmPosition;
}
_frameGestureCount++;
//Determine the direction of the initiated gesture
_currentGestureDirection = this.SelectedHand.PalmVelocity.x > 0 ? Side.Right : Side.Left;
_inputs.Add(this.SelectedHand.StabilizedPalmPosition.x);
_outputs.Add(this.SelectedHand.StabilizedPalmPosition.y);
//Use Ordinary Least Squares to learn the regression
try
{
_regression = _ols.Learn(_inputs.ToArray(), _outputs.ToArray());
//Gets the coefficient of determination, as known R-squared
_coefficientDetermination = _regression.CoefficientOfDetermination(_inputs.ToArray(), _outputs.ToArray());
//Checking max velocity on the gesture
//Abs use for compatibility for both gesture (right and left)
_xVelocityMax = Math.Max(Math.Abs(this.SelectedHand.PalmVelocity.x), _xVelocityMax);
_xVelocityMin = Math.Min(Math.Abs(this.SelectedHand.PalmVelocity.x), _xVelocityMin);
//Calc the distance from the first point of the gesture
//Do the hypothenus of the triangle given from the delta between the first point and the current point
_distance = Math.Sqrt(Math.Pow(this.SelectedHand.StabilizedPalmPosition.x - _gestureFirstPoint.x, 2) +
Math.Pow(this.SelectedHand.StabilizedPalmPosition.y - _gestureFirstPoint.y, 2));
}
catch (InvalidOperationException ex)
{
Console.WriteLine("Exception: {0}", ex.Message);
_regression = null;
_frameGestureCount = 0;
_xVelocityMax = 0;
_xVelocityMin = Double.MaxValue;
_distance = 0;
_inputs.Clear();
_outputs.Clear();
}
}
}
public static object TestRegression(double[] x, double[] y)
{
SimpleLinearRegression slr = new SimpleLinearRegression();
double err = slr.Regress(x, y);
double[] values = slr.Compute(x);
object[,] ret = new object[values.Length + 3, 2];
ret[0, 0] = "R^2";
ret[0, 1] = slr.CoefficientOfDetermination(x, y);
ret[1, 0] = "Slope";
ret[1, 1] = slr.Slope;
ret[2, 0] = "Error";
ret[2, 1] = err;
for (int i = 0; i < values.Length; ++i)
{
ret[i + 3, 0] = x[i];
ret[i + 3, 1] = values[i];
}
return(ret);
}
/// <summary>
/// 求出输入数据的一次模型 和 二次模型 输出顺序为 一次斜率k 截距b 模型拟合程度r 二次参数a 一次参数b 截距c 模型拟合程度r
/// </summary>
/// <param name="input"></param>
/// <returns></returns>
public static List <PValue> getModel(List <PValue> input)
{
double MTAEquRLK = 0; //k
double MTAEquRLB = 0; //b
double MTAEquRLR = 0; //r
double MTAEquRQA = 0; //a
double MTAEquRQB = 0; //b
double MTAEquRQC = 0; //c
double MTAEquRQR = 0; //r
int length = input.Count;
double[] X = new double[length];
double[] Y = new double[length];
for (int i = 0; i < length; i++)
{
X[i] = i + 1;
Y[i] = input[i].Value;
}
OrdinaryLeastSquares ols = new OrdinaryLeastSquares();
SimpleLinearRegression regression = ols.Learn(X, Y);
MTAEquRLK = regression.Slope; //一次斜率
MTAEquRLB = regression.Intercept; //截距
MTAEquRLR = regression.CoefficientOfDetermination(X, Y); //R^2代表模型拟合程度
double[][] MultiX = new double[X.Length][];
for (int i = 0; i < X.Length; i++)
{
double x = X[i];
MultiX[i] = new double[] { x *x, x };
}
double[][] MultiY = new double[X.Length][];
//MultiY =X'
for (int i = 0; i < X.Length; i++)
{
double y = Y[i];
MultiY[i] = new double[] { y };
}
OrdinaryLeastSquares olsMulti = new OrdinaryLeastSquares();
MultivariateLinearRegression regressionMulti = olsMulti.Learn(MultiX, MultiY);
double[][] weights = regressionMulti.Weights;
MTAEquRQA = weights[0][0]; //二次参数
MTAEquRQB = weights[1][0]; //一次参数
MTAEquRQC = regressionMulti.Intercepts[0]; //截距
MTAEquRQR = regressionMulti.CoefficientOfDetermination(MultiX, MultiY)[0]; //R^2代表模型拟合程度
PValue LK = new PValue(); LK.Value = MTAEquRLK;
PValue LB = new PValue(); LB.Value = MTAEquRLB;
PValue LR = new PValue(); LR.Value = MTAEquRLR;
PValue QA = new PValue(); QA.Value = MTAEquRQA;
PValue QB = new PValue(); QB.Value = MTAEquRQB;
PValue QC = new PValue(); QC.Value = MTAEquRQC;
PValue QR = new PValue(); QR.Value = MTAEquRQR;
List <PValue> list = new List <PValue>();
list.Add(LK);
list.Add(LB);
list.Add(LR);
list.Add(QA);
list.Add(QB);
list.Add(QC);
list.Add(QR);
return(list);
}
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 = "";
int i;
//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，则应当有输出，大部分的输出项为0。个别输出项为空
List <PValue>[] results = new List <PValue> [7];
for (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
{
//数据量分析
//——MMTAAnaBase对偏差的计算结果，有19个值，一个设备100个温度点，那么也就1900个点。readmulti读取速度不会太慢。
//——本计算仅用到19个计算结果中的均值，但是为了配置方便，仍然按MMTAAnaBase计算结果全部取得方式
int MMTAAnaBaseOutputNumber = 19; //本算法是直接读取MMTAAnaBase算法所有计算结果。MMTAAnaBase算法有19个计算结果。要靠次参数完成对计算结果的解析。
int validDataNumber = 0;
//0、输入：输入是同一设备上所有温度点的小时均值，或者天均值
//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)
{
return(new Results(results, _errorFlag, _errorInfo, _warningFlag, _warningInfo, _fatalFlag, _fatalInfo)); //不报错，直接返回默认值。整体为空计算引擎已经记录报警或错误。
}
//0.2、输入处理：截止时刻值。该算法不需要截止时刻点参与计算。
//输入的是该设备上所有温度测点每小时基本统计值，每个统计值也就是每个测点，仅有两个值，一个是小时值，一个是截止时刻值
string[] sourtagname = calcuinfo.sourcetagname.Split(';'); //本算法是做管子位置与温度的回归。管子的位置需要从标签名称中取
List <List <PValue> > AvgInputsList = new List <List <PValue> >(); //将MMTAAnaBase算法结果中的均值取出来
List <string> AvgtagnameList = new List <string>(); //将均值对应的标签名称取出来
for (i = 0; i < inputs.Length; i++)
{
//按MMTAAnaBase算法,将均值（MMTAAnaBaseOutputNumber==1 ）取出
//对于设备来说，某些单点状态位异常，直接过滤而不进行报警。只有这一类计算的结果全部异常才报警。
if (i % MMTAAnaBaseOutputNumber == 1 && inputs[i] != null && inputs[i].Count >= 2 && inputs[i][0].Status == 0)
{
//找到所有有效均值的标签名
AvgtagnameList.Add(sourtagname[i]);
//找到数据
//if (AvgInputsList[i / MMTAAnaBaseOutputNumber] == null)
AvgInputsList.Add(new List <PValue>());
//只取第一个值
AvgInputsList[validDataNumber].Add(new PValue(inputs[i][0].Value, inputs[i][0].Timestamp, inputs[i][0].Endtime, inputs[i][0].Status));
validDataNumber = validDataNumber + 1;
}
}//end for
string[] Avgtagname = AvgtagnameList.ToArray(); //有效点不同，标签数量不同，删除后面的空位。
List <PValue>[] AvgInputs = AvgInputsList.ToArray(); //与标签点对应添加的有效数据
if (validDataNumber < 2)
{
_warningFlag = true;
_warningInfo = "当前时间段过滤非正常状态点后，全部点的均值有效个数少于2个，无法进行回归计算！";
return(new Results(results, _errorFlag, _errorInfo, _warningFlag, _warningInfo, _fatalFlag, _fatalInfo));
}
//定义变量
double MTAEquRLK = 0;
double MTAEquRLB = 0;
double MTAEquRLR = 0;
double MTAEquRQA = 0;
double MTAEquRQB = 0;
double MTAEquRQC = 0;
double MTAEquRQR = 0;
//计算
//1、线性回归
//参考 http://accord-framework.net/docs/html/T_Accord_Statistics_Models_Regression_Linear_SimpleLinearRegression.htm
List <double> ArrayX = new List <double>(); //回归分析，自变量是管号
List <double> ArrayY = new List <double>(); //回归分析，应变量是温度
for (i = 0; i < AvgInputs.Length; i++)
{
double xposition = double.Parse(Avgtagname[i].Substring(7, 2));
for (int j = 0; j < AvgInputs[i].Count; j++)
{
ArrayX.Add(xposition);
ArrayY.Add(AvgInputs[i][j].Value);
}
}
/*
* //测试数据
* ArrayX = new List<double> { 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 };
* ArrayY = new List<double> { 2, 4, 6, 8, 10, 12, 14, 16, 18, 20 };
* //按上述测试数据，MTAEquRLK=2，MTAEquRLB=0，MTAEquRLRR=1
*
*
* ArrayX = new double[10] { 1.2, 1.9, 3.1, 3.7, 5, 6, 7.1, 7.9, 9, 10 };
* ArrayY = new double[10] { 2.1, 4.2, 6.8, 8.5, 10, 12, 14.6, 14, 18, 20 };
*/
//按上述测试数据，MTAEquRLK=1.911，MTAEquRLB=0.526，MTAEquRLRR=0.985
double[] X = ArrayX.ToArray();
double[] Y = ArrayY.ToArray();;
OrdinaryLeastSquares ols = new OrdinaryLeastSquares();
SimpleLinearRegression regression = ols.Learn(X, Y);
MTAEquRLK = regression.Slope; //一次斜率
MTAEquRLB = regression.Intercept; //截距
MTAEquRLR = regression.CoefficientOfDetermination(X, Y); //R^2代表模型拟合程度
//2、多项式回归
//参考 http://accord-framework.net/docs/html/T_Accord_Statistics_Models_Regression_Linear_MultivariateLinearRegression.htm
//MultiX =[X^2 ,X]
double[][] MultiX = new double[X.Length][];
for (i = 0; i < X.Length; i++)
{
double x = X[i];
MultiX[i] = new double[] { x *x, x };
}
double[][] MultiY = new double[X.Length][];
//MultiY =X'
for (i = 0; i < X.Length; i++)
{
double y = Y[i];
MultiY[i] = new double[] { y };
}
//测试数据
/*
* MultiX = new double[5][];
* MultiX[0] = new double[2] { 1, 1};
* MultiX[1] = new double[2] { 4, 2};
* MultiX[2] = new double[2] { 9, 3};
* MultiX[3] = new double[2] { 16, 4};
* MultiX[4] = new double[2] { 25, 5};
*
* MultiY = new double[5][];
* MultiY[0] = new double[1] { 7 };
* MultiY[1] = new double[1] { 17 };
* MultiY[2] = new double[1] { 31 };
* MultiY[3] = new double[1] { 49 };
* MultiY[4] = new double[1] { 71 };
*/
//按上述测试数据，MTAEquRQA=2，MTAEquRQB=4，MTAEquRQC=1，MTAEquRQR=1
/*
* MultiX = new double[5][];
* MultiX[0] = new double[2] { 1, 1 };
* MultiX[1] = new double[2] { 4, 2 };
* MultiX[2] = new double[2] { 9, 3 };
* MultiX[3] = new double[2] { 16, 4 };
* MultiX[4] = new double[2] { 25, 5 };
*
* MultiY = new double[5][];
* MultiY[0] = new double[1] { 7.1 };
* MultiY[1] = new double[1] { 16.9 };
* MultiY[2] = new double[1] { 32 };
* MultiY[3] = new double[1] { 51 };
* MultiY[4] = new double[1] { 69 };
*/
//按上述测试数据，MTAEquRQA=1.45，MTAEquRQB=7.08，MTAEquRQC=-2，MTAEquRQR=0.998
OrdinaryLeastSquares olsMulti = new OrdinaryLeastSquares();
MultivariateLinearRegression regressionMulti = olsMulti.Learn(MultiX, MultiY);
double[][] weights = regressionMulti.Weights;
MTAEquRQA = weights[0][0]; //二次参数
MTAEquRQB = weights[1][0]; //一次参数
MTAEquRQC = regressionMulti.Intercepts[0]; //截距
MTAEquRQR = regressionMulti.CoefficientOfDetermination(MultiX, MultiY)[0]; //R^2代表模型拟合程度
//组织计算结果
results[0] = new List <PValue>();
results[0].Add(new PValue(MTAEquRLK, calcuinfo.fstarttime, calcuinfo.fendtime, 0));
results[1] = new List <PValue>();
results[1].Add(new PValue(MTAEquRLB, calcuinfo.fstarttime, calcuinfo.fendtime, 0));
results[2] = new List <PValue>();
results[2].Add(new PValue(MTAEquRLR, calcuinfo.fstarttime, calcuinfo.fendtime, 0));
results[3] = new List <PValue>();
results[3].Add(new PValue(MTAEquRQA, calcuinfo.fstarttime, calcuinfo.fendtime, 0));
results[4] = new List <PValue>();
results[4].Add(new PValue(MTAEquRQB, calcuinfo.fstarttime, calcuinfo.fendtime, 0));
results[5] = new List <PValue>();
results[5].Add(new PValue(MTAEquRQC, calcuinfo.fstarttime, calcuinfo.fendtime, 0));
results[6] = new List <PValue>();
results[6].Add(new PValue(MTAEquRQR, calcuinfo.fstarttime, calcuinfo.fendtime, 0));
return(new Results(results, _errorFlag, _errorInfo, _warningFlag, _warningInfo, _fatalFlag, _fatalInfo));
}
catch (Exception ex)
{
//计算中出任何错误，则需要记录log
//LogHelper.Write(LogType.Error, "计算模块错误!");
//记录计算模块的名称、当前标签、起始时间、结束时间
//string moduleInfo = string.Format("——计算模块的名称是：{0}，当前计算源标签是：{1}，计算起始时间是：{2}，计算结束时间是：{3}。", calcuinfo.fmodulename, calcuInfo.sourcetagname, calcuinfo.fstarttime.ToString(), calcuinfo.fendtime.ToString());
//LogHelper.Write(LogType.Error, moduleInfo);
//计算引擎报错具体信息
//string errInfo = string.Format("——具体报错信息：{0}。", ex.ToString());
//LogHelper.Write(LogType.Error, errInfo);
//返回null供计算引擎处理
_fatalFlag = true;
_fatalInfo = ex.ToString();
return(new Results(results, _errorFlag, _errorInfo, _warningFlag, _warningInfo, _fatalFlag, _fatalInfo));
}
}
