C# DenseVector.SubVector方法代码示例

        public double[] transformMoments(double[] forces)
        {
            double[] fforce = new double[6];
            forces.CopyTo(fforce, 0);
            DenseVector forceV = new DenseVector(fforce);
            DenseVector tforce = (DenseVector)forceV.SubVector(0, 3);
            DenseVector moments = (DenseVector)forceV.SubVector(3, 3);

            DenseVectorCrossProduct crs = new DenseVectorCrossProduct(tforce);
            moments = (DenseVector)crs.crossProduct(directionalVector).Add(moments);

            forceV.SetSubVector(3, 3, moments);
            return forceV.Values;
        }

        public void VarianceRatioTest(double[] data, int lag = 1, TimeSeriesType cor = TimeSeriesType.HOM)
        {
            DenseVector x = new DenseVector(data);

            //Mean
            double mu = x.Mean();

            //Variance for 1st Order Difference
            double s1 = (x.SubVector(lag, x.Count - lag) - x.SubVector(0, x.Count - 2)).Variance();


            double dLag = lag;
            double varvrt = double.NaN;
            switch (cor)
            {
                case TimeSeriesType.HOM:
                {
                    varvrt = 2*(2*dLag - 1)*(dLag - 1)/(3*dLag*x.Count);
                    break;
                }
                case TimeSeriesType.HET:
                {
                    varvrt = 0;
                    double sum2 = 0;
                    for (int j = 0; j < lag; j++)
                    {
                        double sum1a = 0; //(x(j+2:n)-x(j+1:n-1)-mu).^2
                        double sum1b = 0; //(x(2:n-j)-x(1:n-j-1)-mu).^2;
                        double sum1 = sum1a*sum1b;
                        double delta = sum1/(Math.Pow(sum2, 2));
                        varvrt += 0;  //(2*(q(i)-j)/q(i))^2)*delta;
                    }
                    break;
                }
            }

            ZScore = (VRatio - 1)/Math.Sqrt(varvrt);
            PValue = NormCDF(ZScore);

        }

        /// <summary>
        /// Run example
        /// </summary>
        public void Run()
        {
            // Format vector output to console
            var formatProvider = (CultureInfo)CultureInfo.InvariantCulture.Clone();
            formatProvider.TextInfo.ListSeparator = " ";

            // Create new empty vector
            var vectorA = new DenseVector(10);
            Console.WriteLine(@"Empty vector A");
            Console.WriteLine(vectorA.ToString("#0.00\t", formatProvider));
            Console.WriteLine();
            
            // 1. Fill vector by data using indexer []
            for (var i = 0; i < vectorA.Count; i++)
            {
                vectorA[i] = i;
            }

            Console.WriteLine(@"1. Fill vector by data using indexer []");
            Console.WriteLine(vectorA.ToString("#0.00\t", formatProvider));
            Console.WriteLine();

            // 2. Fill vector by data using SetValues method
            vectorA.SetValues(new[] { 9.0, 8.0, 7.0, 6.0, 5.0, 4.0, 3.0, 2.0, 1.0, 0.0 });
            Console.WriteLine(@"2. Fill vector by data using SetValues method");
            Console.WriteLine(vectorA.ToString("#0.00\t", formatProvider));
            Console.WriteLine();

            // 3. Convert Vector to double[]
            var data = vectorA.ToArray();
            Console.WriteLine(@"3. Convert vector to double array");
            for (var i = 0; i < data.Length; i++)
            {
                Console.Write(data[i].ToString("#0.00\t", formatProvider) + @" ");
            }

            Console.WriteLine();
            Console.WriteLine();

            // 4. Convert Vector to column matrix. A matrix based on this vector in column form (one single column)
            var columnMatrix = vectorA.ToColumnMatrix();
            Console.WriteLine(@"4. Convert vector to column matrix");
            Console.WriteLine(columnMatrix.ToString("#0.00\t", formatProvider));
            Console.WriteLine();

            // 5. Convert Vector to row matrix. A matrix based on this vector in row form (one single row)
            var rowMatrix = vectorA.ToRowMatrix();
            Console.WriteLine(@"5. Convert vector to row matrix");
            Console.WriteLine(rowMatrix.ToString("#0.00\t", formatProvider));
            Console.WriteLine();

            // 6. Clone vector
            var cloneA = vectorA.Clone();
            Console.WriteLine(@"6. Clone vector");
            Console.WriteLine(cloneA.ToString("#0.00\t", formatProvider));
            Console.WriteLine();

            // 7. Clear vector
            cloneA.Clear();
            Console.WriteLine(@"7. Clear vector");
            Console.WriteLine(cloneA.ToString("#0.00\t", formatProvider));
            Console.WriteLine();

            // 8. Copy part of vector into another vector. If you need to copy all data then use CopoTy(vector) method.
            vectorA.CopySubVectorTo(cloneA, 3, 3, 4);
            Console.WriteLine(@"8. Copy part of vector into another vector");
            Console.WriteLine(cloneA.ToString("#0.00\t", formatProvider));
            Console.WriteLine();

            // 9. Get part of vector as another vector
            var subvector = vectorA.SubVector(0, 5);
            Console.WriteLine(@"9. Get subvector");
            Console.WriteLine(subvector.ToString("#0.00\t", formatProvider));
            Console.WriteLine();

           // 10. Enumerator usage
            Console.WriteLine(@"10. Enumerator usage");
            foreach (var value in vectorA)
            {
                Console.Write(value.ToString("#0.00\t", formatProvider) + @" ");
            }

            Console.WriteLine();
            Console.WriteLine();

            // 11. Indexed enumerator usage
            Console.WriteLine(@"11. Enumerator usage");
            foreach (var value in vectorA.GetIndexedEnumerator())
            {
                Console.WriteLine(@"Index = {0}; Value = {1}", value.Item1, value.Item2.ToString("#0.00\t", formatProvider));
            }

            Console.WriteLine();
        }

        // Computes d(ei)/d(P) for ith line numericaly
        public void ComputeJacobianForLine_Numerical(Matrix<double> J, int l, int p0)
        {
            Vector<double> error_n = new DenseVector(_currentErrorVector.Count);
            Vector<double> error_p = new DenseVector(_currentErrorVector.Count);
            for(int k = 0; k < DistortionModel.ParametersCount; ++k)
            {
                double oldK = DistortionModel.Parameters[k];
                double k_n = Math.Abs(oldK) > float.Epsilon ? oldK * (1 - NumericalDerivativeStep) : -NumericalDerivativeStep * 0.01;
                double k_p = Math.Abs(oldK) > float.Epsilon ? oldK * (1 + NumericalDerivativeStep) : NumericalDerivativeStep * 0.01;

                DistortionModel.Parameters[k] = k_n;
                UpdateAll(l);
                ComputeErrorVector(error_n);
                Vector<double> error_n_line = error_n.SubVector(p0, LinePoints[l].Count);

                DistortionModel.Parameters[k] = k_p;
                UpdateAll(l);
                ComputeErrorVector(error_p);
                Vector<double> error_p_line = error_p.SubVector(p0, LinePoints[l].Count);

                Vector<double> diff_e = (1.0 / (k_p - k_n)) * (error_p_line - error_n_line);
                J.SetColumn(k, p0, LinePoints[l].Count, diff_e);

                DistortionModel.Parameters[k] = oldK;

                // Throw if NaN found in jacobian
                bool nanInfFound = diff_e.Exists((e) => { return double.IsNaN(e) || double.IsInfinity(e); });
                if(nanInfFound)
                {
                    throw new DivideByZeroException("NaN or Infinity found on jacobian");
                }
            }

            UpdateAll(l);
        }