/// <summary>
/// 线性拟合(根据point 返回灵敏度相应参数)
/// </summary>
/// <param name="parray"></param>
private SenParas LinearRegression(List <Point> parray)
{
SenParas para = new SenParas();
if (parray.Count < 2)
{
System.Console.WriteLine("点个数不能少于2");
return(null);
}
//求 X,Y平均值
double averagex = 0; double averagey = 0;
foreach (Point item in parray)
{
averagex += item.X;
averagey += item.Y;
}
averagex /= parray.Count;
averagey /= parray.Count;
//经验回归系数的分子与分母
double numerator = 0;
double denominator = 0;
foreach (Point p in parray)
{
numerator += (p.X - averagex) * (p.Y - averagey);
denominator += (p.X - averagex) * (p.X - averagex);
}
//回归系数b（Regression Coefficient）
para.RCB = numerator / denominator;
//回归系数a
para.RCA = averagey - para.RCB * averagex;
//剩余平方和与回归平方和
double residualSS = 0; //（Residual Sum of Squares）
double regressionSS = 0; //（Regression Sum of Squares）
foreach (Point p in parray)
{
residualSS +=
(p.Y - para.RCA - para.RCB * p.X) *
(p.Y - para.RCA - para.RCB * p.X);
regressionSS +=
(para.RCA + para.RCB * p.X - averagey) *
(para.RCA + para.RCB * p.X - averagey);
}
//计算R^2的值
numerator = 0; denominator = 0;
foreach (var item in parray)
{
denominator += Math.Pow((item.Y - averagey), 2.0);
numerator += Math.Pow((item.Y - (item.X * para.RCB + para.RCA)), 2.0);
}
para.ResidualSS = residualSS;
para.RegressionSS = regressionSS;
para.RSquare = 1 - numerator / denominator;
//拟合误差越接近1则表示越准确
return(para);
}
/// <summary>
/// 计算灵敏度
/// </summary>
double GetSensitivity()
{
StringBuilder sb = new StringBuilder();
List <Point> listPoint = new List <Point>();
try
{
//测量数量 衰减初始值 步进
int testCount = 12;
//灵敏度
double initialValue = IniAtt - 2;
double interval = 0.4;
Point point = new Point();
for (int i = 0; i < testCount; i++)
{
Hp8156A.SetAtt(initialValue.ToString());
point.X = initialValue;
initialValue += interval;
//sb.Append(point.X.ToString());
Thread.Sleep(1000);
double result = Mp2100A.GetErrorRate();
//Error rate
if (result >= 0)
{
double s = Math.Log10(result);
//判定当结果不为0时
if (s > -9)
{
//取对数
point.Y = s;
listPoint.Add(point);
}
}
}
//噪声过滤
while (IsCountChanged && listPoint.Count > 5)
{
listPoint = PointFilter(listPoint);
}
//计算灵敏度
SenPara = this.LinearRegression(listPoint);
//灵敏度计算条件：Error rate@E-3
SenPara.Sensitive = (ErrorRateInSensitivity - SenPara.RCA) / SenPara.RCB;
return(SenPara.Sensitive);
}
catch
{
return(0);
}
}
