C#常用算法.doc

C#常用预测算法C#常用预测算法using System;using System.Collections.Generic;using System.Text;using System.Data;namespace DataProcess.Forecast class Forecast /data有二列，第一列表示x值，第二列表示y值 /一元线性回归分析预测,返回相关系数 public static double LinearRegression(double, data,out double A,out double B) double, derivedData = new doubledata.GetLength(0) + 1, data.GetLength(1) + 3; for (int i = 0; i data.GetLength(0); i+) derivedDatai, 0 = datai, 0; /X derivedDatai, 1 = datai, 1; /Y derivedDatai, 2 = datai, 0 * datai, 1; /XY derivedDatai, 3 = datai, 0 * datai, 0; /XX derivedDatai, 4 = datai, 1 * datai, 1; /YY derivedDataderivedData.GetLength(0) - 1, 0 += derivedDatai, 0; /X的累加 derivedDataderivedData.GetLength(0) - 1, 1 += derivedDatai, 1; /Y的累加 derivedDataderivedData.GetLength(0) - 1, 2 += derivedDatai, 2; /XY的累加 derivedDataderivedData.GetLength(0) - 1, 3 += derivedDatai, 3; /XX的累加 derivedDataderivedData.GetLength(0) - 1, 4 += derivedDatai, 4; /YY的累加 double xba = derivedDataderivedData.GetLength(0) - 1, 0 / data.GetLength(0); double yba = derivedDataderivedData.GetLength(0) - 1, 1 / data.GetLength(0); double Lxx = derivedDataderivedData.GetLength(0) - 1, 3 - Math.Pow(derivedDataderivedData.GetLength(0) - 1, 0, 2) / data.GetLength(0); double Lyy = derivedDataderivedData.GetLength(0) - 1, 4 - Math.Pow(derivedDataderivedData.GetLength(0) - 1, 1, 2) / data.GetLength(0); double Lxy = derivedDataderivedData.GetLength(0) - 1, 2 - derivedDataderivedData.GetLength(0) - 1, 0 * derivedDataderivedData.GetLength(0) - 1, 1 / data.GetLength(0); double b = Lxy / Lxx; double a = yba - b * xba; A = a; B = b; return Lxy / Math.Sqrt(Lxx * Lyy); public static double LinearRegression(double, data, double x, out double r) double a; double b; r = LinearRegression(data, out a, out b); return a + b * x; /幂回归分析预测 public static double PowerRegression(double, data, out double A, out double B) double, derivedData = (double,)data.Clone(); for (int i = 0; i derivedData.GetLength(0); i+) for (int j = 0; j derivedData.GetLength(1); j+) derivedDatai, j = Math.Log(derivedDatai, j); double a; double b; double r; r = LinearRegression(derivedData, out a, out b); A = Math.Exp(a); B = b; return r; public static double PowerRegression(double, data, double x, out double r) double a; double b; r = PowerRegression(data, out a, out b); return a * Math.Pow(x, b); /灰色模型法 public static double GM(double data, int x, out double p, out double c) int rowNum = data.GetLength(0); double x0 = new doublerowNum; double x1 = new doublerowNum; x00 = data0; x10 = data0; for (int i = 1; i rowNum; i+) x0i = datai; x1i = x1i - 1 + x0i; double, derivedData = new doublerowNum - 1, 2; for (int i = 0; i rowNum - 1; i+) derivedDatai, 0 = -0.5 * (x1i + x1i + 1); derivedDatai, 1 = x0i + 1; double a; double u; LinearRegression(derivedData, out u, out a); double result; result = (x00 - u / a) * (1 - Math.Exp(a) * Math.Exp(-a * (x - 1); /模型精度检验 double q = new doublerowNum; double epsilon = new doublerowNum; double epsilonSum = 0; for (int i = 0; i rowNum; i+) double x0i = x0i; double xxi = (x00 - u / a) * (1 - Math.Exp(a) * Math.Exp(-a * i); qi = x0i - xxi; epsiloni = qi / x0i; epsilonSum += Math.Abs(epsiloni); double epsilonAverage = epsilonSum / rowNum; p = 1 - epsilonAverage; /后验差比值 double x0Average; double qAverage; double x0Sum = 0; double qSum = 0; for (int i = 0; i rowNum; i+) x0Sum += x0i; qSum += qi; x0Average = x0Sum / rowNum; qAverage = qSum / rowNum; double s1; double s2; double s1Sum = 0; double s2Sum = 0; for (int i = 0; i rowNum; i+) s1Sum += Math.Pow(x0i - x0Average, 2); s2Sum += Math.Pow(qi - qAverage, 2); s1 = s1Sum / rowNum; s2 = s2Sum / rowNum; c = s2 / s1; return result; /加权移动平均法 public static double WeightedMovingAverage(double data, int x) int count = data.GetLength(0); double s = 0.0; for (int i = 0; i x; i+) s = 0.0; for (int j = 0; j co