C# DoubleVector类代码示例

        /// <summary>
        /// Creates statistics for the c-chart
        /// </summary>
        /// <param name="defects">Count of defect / nonconformity per-sample period</param>
        /// <param name="Stds">Number of standard deviations, either 1, 2, or 3 to use for control limits</param>
        /// <param name="ChartTitle">Title of chart.</param>
        /// <param name="TimeStart">The start time of the data.</param>
        /// <param name="TimeInterval">Time interval between each sample group.</param>
        /// <param name="TimeAxisLabel">Horizontal axis label.</param>
        /// <param name="StatisticsLabel">Vertical axis label.</param>
        public Stats_c(DoubleVector Defects, int Stds, String ChartTitle, Double TimeStart, Double TimeInterval, String TimeAxisLabel, String StatisticsLabel)
        {
            if (Stds == 1 || Stds == 2 || Stds == 3)
            {
                this.CenterLine = StatsFunctions.Mean(Defects);

                this.ConstControlLimits = true;
                this.UCL = new DoubleVector(Defects.Length, this.CenterLine + Stds * Math.Sqrt(this.CenterLine));

            if(this.CenterLine - Stds * Math.Sqrt(this.CenterLine) > 0)
              this.LCL = new DoubleVector(Defects.Length, this.CenterLine - Stds * Math.Sqrt(this.CenterLine));
            else
              this.LCL = new DoubleVector(Defects.Length, 0);

                this.Statistic = Defects;

                this.TimeStart = TimeStart;
                this.TimeSampleInterval = TimeInterval;
                this.TimeLabel = TimeAxisLabel;
                this.DefectLabel = StatisticsLabel;

            this.ChartTitle = ChartTitle;

            }
            else
            {
                throw new ArgumentException("In stats_c, the number of standard deviations must be either 1, 2, or 3");
            }
        }

 public override void method(ref double[] vpre, DoubleVector vpost)
 {
   Assert(vpre[0], 1.0);
   vpre[0] = 2.0;
   Assert(vpost.Count, 2);
   vpost.Add(1.0);
 }

        /// <summary>
        /// Creates statistics for the np-chart.
        /// </summary>
        /// <param name="DefectCountInSample">Count of failed samples per-sample.</param>
        /// <param name="SampleSize">Size of each sample</param>
        /// <param name="Stds">Number of standard deviations, either 1, 2, or 3 to use for control limits</param>
        /// <param name="ChartTitle">Title of chart.</param>
        /// <param name="TimeStart">The start time of the data.</param>
        /// <param name="TimeInterval">Time interval between each sample group.</param>
        /// <param name="TimeAxisLabel">Horizontal axis label.</param>
        /// <param name="StatisticsLabel">Vertical axis label.</param>
        public Stats_np(DoubleVector DefectCountInSample, int SampleSize, Double Stds, String ChartTitle, Double TimeStart, Double TimeInterval, String TimeAxisLabel, String StatisticsLabel)
        {
            double pbar;

            if (Stds == 1 || Stds == 2 || Stds == 3)
            {

            pbar = StatsFunctions.Sum(DefectCountInSample) / (SampleSize * DefectCountInSample.Length);
            this.CenterLine = pbar * SampleSize;

                this.ConstControlLimits = true;
            this.UCL = new DoubleVector(DefectCountInSample.Length, this.CenterLine + Stds * StatsFunctions.Sqrt( SampleSize * pbar * (1 - pbar) ));
            this.LCL = new DoubleVector(DefectCountInSample.Length, this.CenterLine - Stds * StatsFunctions.Sqrt( SampleSize * pbar * (1 - pbar) ));

            for (int i = 0; i < this.LCL.Length; i++)
                {
                    if (this.LCL[i] < 0)
                        this.LCL[i] = 0;
                }

                this.Statistic = DefectCountInSample;

                this.TimeStart = TimeStart;
                this.TimeSampleInterval = TimeInterval;
                this.TimeLabel = TimeAxisLabel;
                this.DefectLabel = StatisticsLabel;

            this.ChartTitle = ChartTitle;

            }
            else
            {
                throw new ArgumentException("In Stats_np, the number of standard deviations must be either 1, 2, or 3");
            }
        }

        /// <summary>
        /// Adds a step line series to the chart
        /// </summary>
        /// <param name="chart"></param>
        /// <param name="name"></param>
        /// <param name="vector"></param>
        /// <param name="xstart"></param>
        /// <param name="xincrement"></param>
        void AddStepLineSeries(NChart chart, string name, DoubleVector vector, double xstart, double xincrement)
        {
            NStepLineSeries stepline = new NStepLineSeries();
            chart.Series.Add(stepline);

            // Name the line
            stepline.Name = name;

            // Tell the series to regard the X values
            stepline.UseXValues = true;

            // No data labels
            stepline.DataLabelStyle.Visible = false;

            // Set the line color
            stepline.BorderStyle = new NStrokeStyle(1.0f, Color.Gray, LinePattern.Dash);

            // Fill X values
            for (int i = 0; i < vector.Length; i++)
            {
                stepline.XValues.Add(xstart + xincrement * i);
            }

            // Fill Y values
            stepline.Values.AddRange(vector.DataBlock.Data);
        }

        /// <summary>
        /// returns a DoubleMatrix with the given band structure planarised in the transverse direction
        /// </summary>
        public static Band_Data Expand_BandStructure(DoubleVector structure, int ny)
        {
            DoubleMatrix result = new DoubleMatrix(ny, structure.Length);
            for (int i = 0; i < ny; i++)
                for (int j = 0; j < structure.Length; j++)
                    result[i, j] = structure[j];

            return new Band_Data(result);
        }

 static void Main(string[] args)
 {
     Console.Write("**************** INFORMATION ***************" + "\n");
     Console.Write("This example was auto-generated by the language-agnostic dev/scripts/example_generator.py script written by Ian Bell" + "\n");
     Console.Write("CoolProp version:" + " " + CoolProp.get_global_param_string("version") + "\n");
     Console.Write("CoolProp gitrevision:" + " " + CoolProp.get_global_param_string("gitrevision") + "\n");
     Console.Write("CoolProp Fluids:" + " " + CoolProp.get_global_param_string("FluidsList") + "\n");
     // See http://www.coolprop.org/coolprop/HighLevelAPI.html#table-of-string-inputs-to-propssi-function for a list of inputs to high-level interface;
     Console.Write("*********** HIGH LEVEL INTERFACE *****************" + "\n");
     Console.Write("Critical temperature of water:" + " " + CoolProp.Props1SI("Water", "Tcrit") + " " + "K" + "\n");
     Console.Write("Boiling temperature of water at 101325 Pa:" + " " + CoolProp.PropsSI("T", "P", 101325, "Q", 0, "Water") + " " + "K" + "\n");
     Console.Write("Phase of water at 101325 Pa and 300 K:" + " " + CoolProp.PhaseSI("P", 101325, "Q", 0, "Water") + "\n");
     Console.Write("c_p of water at 101325 Pa and 300 K:" + " " + CoolProp.PropsSI("C", "P", 101325, "T", 300, "Water") + " " + "J/kg/K" + "\n");
     Console.Write("c_p of water (using derivatives) at 101325 Pa and 300 K:" + " " + CoolProp.PropsSI("d(H)/d(T)|P", "P", 101325, "T", 300, "Water") + " " + "J/kg/K" + "\n");
     Console.Write("*********** HUMID AIR PROPERTIES *****************" + "\n");
     Console.Write("Humidity ratio of 50% rel. hum. air at 300 K, 101325 Pa:" + " " + CoolProp.HAPropsSI("W", "T", 300, "P", 101325, "R", 0.5) + " " + "kg_w/kg_da" + "\n");
     Console.Write("Relative humidity from last calculation:" + " " + CoolProp.HAPropsSI("R", "T", 300, "P", 101325, "W", CoolProp.HAPropsSI("W", "T", 300, "P", 101325, "R", 0.5)) + " " + "(fractional)" + "\n");
     Console.Write("*********** INCOMPRESSIBLE FLUID AND BRINES *****************" + "\n");
     Console.Write("Density of 50% (mass) ethylene glycol/water at 300 K, 101325 Pa:" + " " + CoolProp.PropsSI("D", "T", 300, "P", 101325, "INCOMP::MEG-50%") + " " + "kg/m^3" + "\n");
     Console.Write("Viscosity of Therminol D12 at 350 K, 101325 Pa:" + " " + CoolProp.PropsSI("V", "T", 350, "P", 101325, "INCOMP::TD12") + " " + "Pa-s" + "\n");
     // If you don't have REFPROP installed, disable the following lines;
     Console.Write("*********** REFPROP *****************" + "\n");
     Console.Write("Critical temperature of water:" + " " + CoolProp.Props1SI("REFPROP::Water", "Tcrit") + " " + "K" + "\n");
     Console.Write("Boiling temperature of water at 101325 Pa:" + " " + CoolProp.PropsSI("T", "P", 101325, "Q", 0, "REFPROP::Water") + " " + "K" + "\n");
     Console.Write("c_p of water at 101325 Pa and 300 K:" + " " + CoolProp.PropsSI("C", "P", 101325, "T", 300, "REFPROP::Water") + " " + "J/kg/K" + "\n");
     Console.Write("*********** TABULAR BACKENDS *****************" + "\n");
     AbstractState TAB = AbstractState.factory("BICUBIC&HEOS", "R245fa");
     TAB.update(input_pairs.PT_INPUTS, 101325, 300);
     Console.Write("Mass density of refrigerant R245fa at 300 K, 101325 Pa:" + " " + TAB.rhomass() + " " + "kg/m^3" + "\n");
     Console.Write("*********** SATURATION DERIVATIVES (LOW-LEVEL INTERFACE) ***************" + "\n");
     AbstractState AS_SAT = AbstractState.factory("HEOS", "R245fa");
     AS_SAT.update(input_pairs.PQ_INPUTS, 101325, 0);
     Console.Write("First saturation derivative:" + " " + AS_SAT.first_saturation_deriv(parameters.iP, parameters.iT) + " " + "Pa/K" + "\n");
     Console.Write("*********** LOW-LEVEL INTERFACE *****************" + "\n");
     AbstractState AS = AbstractState.factory("HEOS", "Water&Ethanol");
     DoubleVector z = new DoubleVector(new double[]{0.5, 0.5});
     AS.set_mole_fractions(z);
     AS.update(input_pairs.PQ_INPUTS, 101325, 1);
     Console.Write("Normal boiling point temperature of water and ethanol:" + " " + AS.T() + " " + "K" + "\n");
     // If you don't have REFPROP installed, disable the following block;
     Console.Write("*********** LOW-LEVEL INTERFACE (REFPROP) *****************" + "\n");
     AbstractState AS2 = AbstractState.factory("REFPROP", "Methane&Ethane");
     DoubleVector z2 = new DoubleVector(new double[]{0.2, 0.8});
     AS2.set_mole_fractions(z2);
     AS2.update(input_pairs.QT_INPUTS, 1, 120);
     Console.Write("Vapor molar density:" + " " + AS2.keyed_output(parameters.iDmolar) + " " + "mol/m^3" + "\n");
 }

        /// <summary>
        /// Example c-Chart
        /// </summary>
        private void nButtonCChart_Click(object sender, EventArgs e)
        {
            // c-Chart sample data
              // This data-set was copied from the 'pcmanufact' data set packaged with the R qcc package by Luca Scrucca
              //
              // Example Data Description
              // A personal computer manufacturer counts the number of nonconformities per unit on the final
              // assembly line. He collects data on 20 samples of 5 computers each.
              DoubleVector defects = new DoubleVector(10, 12,  8, 14, 10, 16, 11,  7, 10, 15,  9,  5,  7, 11, 12,  6,  8, 10,  7,  5);
              IAttributeChartStats stats_c = new Stats_c(defects, 3, "c-Chart pcmanufact dataset");

            // build the Nevron c-Chart visualization
            this.nQualityControlChart.AutoRefresh = true;
            this.nQualityControlChart.Clear();
            AttributeChart cChart = new AttributeChart(stats_c, this.nQualityControlChart);

              // Update description in UI
              this.nRichDescription.Text = "The c-Chart, or Count Chart, is an attribute control chart for monitoring the total number of nonconformities per subgroup over time.  The size of each measured subgroup must be constant.";
        }

        /// <summary>
        /// Creates statistics for the p-chart.
        /// </summary>
        /// <param name="DefectCountInSample">Count of failed samples per-sample.</param>
        /// <param name="SampleSizes">Size of each sample</param>
        /// <param name="Stds">Number of standard deviations, either 1, 2, or 3 to use for control limits</param>
        /// <param name="ChartTitle">Title of chart.</param>
        /// <param name="TimeStart">The start time of the data.</param>
        /// <param name="TimeInterval">Time interval between each sample group.</param>
        /// <param name="TimeAxisLabel">Horizontal axis label.</param>
        /// <param name="StatisticsLabel">Vertical axis label.</param>
        public Stats_p(DoubleVector DefectCountInSample, DoubleVector SampleSizes, Double Stds, String ChartTitle, Double TimeStart, Double TimeInterval, String TimeAxisLabel, String StatisticsLabel)
        {
            if (Stds == 1 || Stds == 2 || Stds == 3)
            {
                if (DefectCountInSample.Length == SampleSizes.Length)
                {

                    this.CenterLine = StatsFunctions.Sum(DefectCountInSample) / StatsFunctions.Sum(SampleSizes);

                    this.ConstControlLimits = false;
                    this.UCL = this.CenterLine + Stds * StatsFunctions.Sqrt( (new DoubleVector(SampleSizes.Length, this.CenterLine)) * (new DoubleVector(SampleSizes.Length, 1.0 - this.CenterLine)) ) / StatsFunctions.Sqrt(SampleSizes);
              this.LCL = this.CenterLine - Stds * StatsFunctions.Sqrt( (new DoubleVector(SampleSizes.Length, this.CenterLine)) * (new DoubleVector(SampleSizes.Length, 1.0 - this.CenterLine)) ) / StatsFunctions.Sqrt(SampleSizes);

              for (int i = 0; i < this.LCL.Length; i++)
                    {
                        if (this.LCL[i] < 0)
                            this.LCL[i] = 0;
                    }

                    this.Statistic = DefectCountInSample / SampleSizes;

                    this.TimeStart = TimeStart;
                    this.TimeSampleInterval = TimeInterval;
                    this.TimeLabel = TimeAxisLabel;
                    this.DefectLabel = StatisticsLabel;

              this.ChartTitle = ChartTitle;
                }
                else
                {
                    throw new ArgumentException("In Stats_p, the defect count and sample size vectors much have the same length");
                }
            }
            else
            {
                throw new ArgumentException("In Stats_p, the number of standard deviations must be either 1, 2, or 3");
            }
        }

        /// <summary>
        /// Create a new neural network.
        /// </summary>
        /// <param name="inputs">The number of inputs that this network will accept.</param>
        /// <param name="hiddenNeurons">The number of hidden neurons in this network.</param>
        /// <param name="outputNeurons">The number of outputs to expect from this network.</param>
        /// <param name="learningRate">The rate to train this network.</param>
        public SimpleNeuralNetwork(int inputs, int hiddenNeurons, int outputNeurons, double learningRate)
        {
            random = new Random();

            inputsCount = inputs;
            hiddenCount = hiddenNeurons;
            outputCount = outputNeurons;

            // The weights
            weights_IH = new double[inputsCount, hiddenCount];
            weights_HO = new double[hiddenCount, outputCount];
            InitializeWeightsRandomly();

            // The neuron activations
            neurons_H = new double[hiddenCount];
            neurons_O = new DoubleVector(outputCount);

            // The error arrays
            error_H = new double[hiddenCount];
            error_O = new double[outputCount];

            this.learningRate = learningRate;
        }

 public Polynomial Division(Polynomial DividedPolynome, Polynomial DividerPolynome)
 {
     Polynomial FractionPolynome = new Polynomial();
     string coefs = "";
     List<double> DividedPolynomeCoeffs = DividedPolynome.Coeff.ToList();
     List<double> DividerPolynomeCoeffs = DividerPolynome.Coeff.ToList();
     int FractionPolynomeDegree = DividedPolynome.Degree - DividerPolynome.Degree;
     Polynomial NewDividerPolynome = new Polynomial();
     Polynomial RestPolynome = new Polynomial();
     int j = 0;
     List<double> FractionPolynomeCoeffs = new List<double>();
     do
     {
         for (int k = 0; k < FractionPolynomeDegree; k++)
         {
             coefs += "0 ";
         }
         FractionPolynomeCoeffs.Insert(0, DividedPolynomeCoeffs[DividedPolynomeCoeffs.Count - 1] / DividerPolynomeCoeffs[DividerPolynomeCoeffs.Count - 1]);
         coefs += (DividedPolynomeCoeffs[DividedPolynomeCoeffs.Count - 1] / DividerPolynomeCoeffs[DividerPolynomeCoeffs.Count - 1]).ToString() + " ";
         FractionPolynome = new Polynomial(new DoubleVector(coefs));
         NewDividerPolynome = Polynomial.Multiply(FractionPolynome, DividerPolynome);
         RestPolynome = Polynomial.Subtract(DividedPolynome, NewDividerPolynome);
         DividedPolynome = RestPolynome;
         DividedPolynomeCoeffs = DividedPolynome.Coeff.ToList();
         FractionPolynomeDegree--;
         coefs = String.Empty;
         j++;
     } while ((RestPolynome.Degree != 0));
     FractionPolynomeCoeffs.Insert(0, DividedPolynomeCoeffs[DividedPolynomeCoeffs.Count - 1] / DividerPolynomeCoeffs[DividerPolynomeCoeffs.Count - 1]);
     string FractionPolynomeCoefficients = String.Empty;
     foreach (var coef in FractionPolynomeCoeffs)
     {
         FractionPolynomeCoefficients += Convert.ToString(coef) + " ";
     }
     DoubleVector FractionPolynomeVector = new DoubleVector(FractionPolynomeCoefficients);
     FractionPolynome = new Polynomial(FractionPolynomeVector);
     return FractionPolynome;
 }

 public DoubleVector forwards() {
   DoubleVector ret = new DoubleVector(NQuantLibcPINVOKE.ForwardCurve_forwards(swigCPtr), false);
   if (NQuantLibcPINVOKE.SWIGPendingException.Pending) throw NQuantLibcPINVOKE.SWIGPendingException.Retrieve();
   return ret;
 }

 public Band_Data(int nz, double val)
 {
     this.vec = new DoubleVector(nz, val);
     dim = 1;
 }

        public double Min()
        {
            if (dim == 1)
                return vec.Min();
            else if (dim == 2)
                return mat.Min();
            else if (dim == 3)
            {
                DoubleVector mat_min = new DoubleVector(vol.Length);
                for (int i = 0; i < vol.Length; i++)
                    mat_min[i] = vol[i].Min();

                return mat_min.Min();
            }
            else
                throw new NotImplementedException();
        }

        public Band_Data DeepenThisCopy()
        {
            Band_Data tmp_result = null;

            if (dim == 1)
            {
                DoubleVector result = new DoubleVector(Length);
                for (int i = 0; i < Length; i++)
                    result[i] = this[i];

                tmp_result = new Band_Data(result);
            }
            else if (dim == 2)
            {
                DoubleMatrix result = new DoubleMatrix(mat.Rows, mat.Cols);
                for (int i = 0; i < mat.Rows; i++)
                    for (int j = 0; j < mat.Cols; j++)
                        result[i, j] = mat[i, j];

                tmp_result = new Band_Data(result);
            }
            else if (dim == 3)
            {
                int nx = vol[0].Rows;
                int ny = vol[0].Cols;
                int nz = vol.Length;

                Band_Data result = new Band_Data(nx, ny, nz, 0.0);

                for (int k = 0; k < nz; k++)
                    for (int i = 0; i < nx; i++)
                        for (int j = 0; j < ny; j++)
                            result.vol[k][i, j] = this.vol[k][i, j];

                tmp_result = result;
            }
            else
                throw new NotImplementedException();

            if (this.Laplacian != null)
                tmp_result.Laplacian = this.Laplacian.DeepenThisCopy();

            return tmp_result;
        }

 public DiscountCurve(DateVector dates, DoubleVector discounts, DayCounter dayCounter) : this(NQuantLibcPINVOKE.new_DiscountCurve__SWIG_2(DateVector.getCPtr(dates), DoubleVector.getCPtr(discounts), DayCounter.getCPtr(dayCounter)), true) {
   if (NQuantLibcPINVOKE.SWIGPendingException.Pending) throw NQuantLibcPINVOKE.SWIGPendingException.Retrieve();
 }