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C# DenseVector.MinimumIndex方法代码示例

本文整理汇总了C#中MathNet.Numerics.LinearAlgebra.Double.DenseVector.MinimumIndex方法的典型用法代码示例。如果您正苦于以下问题:C# DenseVector.MinimumIndex方法的具体用法?C# DenseVector.MinimumIndex怎么用?C# DenseVector.MinimumIndex使用的例子?那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在MathNet.Numerics.LinearAlgebra.Double.DenseVector的用法示例。


在下文中一共展示了DenseVector.MinimumIndex方法的2个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的C#代码示例。

示例1: ModificatedDualIteration


//.........这里部分代码省略.........
            var nk = kappaValue < _task.dLower[kappaIndex] ? 1 : -1;
            var deltaYT =
                nk * DenseVector.Create(_task.Jb.Count, i => i == _task.Jb.ToList().IndexOf(kappaIndex) ? 1 : 0)
                * DenseMatrix.OfColumnVectors(vectorCollection.ToArray()).Inverse();

            var nVector = new DenseVector(_task.A.ColumnCount);
            for (int i = 0; i < _task.A.ColumnCount; i++)
            {
                if (!_task.Jb.Contains(i))
                {
                    nVector[i] = (deltaYT * _task.A.Column(i));
                }
            }

            // Step6
            Vector<double> sigmaVector = new DenseVector(_task.A.ColumnCount);
            for (int i = 0; i < sigmaVector.Count; i++)
            {
                if (_task.dLower[i] == _task.dUpper[i])
                {
                    sigmaVector[i] = double.PositiveInfinity;
                }
                else if (_JNbUpper.Contains(i) && nVector[i] < Eps)
                {
                    sigmaVector[i] = -deltas[i] / nVector[i];
                }
                else if (_JNbLower.Contains(i) && nVector[i] > Eps)
                {
                    sigmaVector[i] = -deltas[i] / nVector[i];
                }
                else
                {
                    sigmaVector[i] = double.PositiveInfinity;
                }
            }

            var sigma0 = sigmaVector.Min();
            var sigma0Index = sigmaVector.MinimumIndex();

            // Step7
            if (sigma0 == double.PositiveInfinity)
            {
                //Logger.Log("Stopped at seventh step");
                _stopStep = 7;
                return false;
            }

            // Step8
            Vector<double> newDeltas = new DenseVector(_task.A.ColumnCount);
            for (int i = 0; i < newDeltas.Count; i++)
            {
                if (_task.Jb.Contains(i) && i != kappaIndex)
                {
                    newDeltas[i] = 0;
                }
                else if (i == kappaIndex)
                {
                    newDeltas[i] = sigma0 * nk;
                }
                else
                {
                    newDeltas[i] = deltas[i] + sigma0 * nVector[i];
                }
            }

            deltas = newDeltas.ToList();
            // Step9
            _task.Jb[_task.Jb.ToList().IndexOf(kappaIndex)] = sigma0Index;

            // Step10
            if (nk == 1.0)
            {
                if (_JNbUpper.Contains(sigma0Index))
                {
                    _JNbUpper[_JNbUpper.IndexOf(sigma0Index)] = kappaIndex;
                }
                else
                {
                    _JNbUpper.Add(kappaIndex);
                }
            }
            else if (nk == -1.0)
            {
                if (_JNbUpper.Contains(sigma0Index))
                {
                    _JNbUpper.Remove(sigma0Index);
                }
            }

            _JNbLower.Clear();
            for (int i = 0; i < _task.A.ColumnCount; i++)
            {
                if (!_JNbUpper.Contains(i) && !_task.Jb.Contains(i))
                {
                    _JNbLower.Add(i);
                }
            }

            return true;
        }
开发者ID:Kant8,项目名称:IOp,代码行数:101,代码来源:ModifiedDualSimplexMethod.cs

示例2: Train

        public void Train(DenseMatrix X, DenseVector d, DenseVector Kd)
        {
            int R = X.RowCount;
            int N = X.ColumnCount;
            int U = 0; //the number of neurons in the structure


            var c = new DenseMatrix(R, 1);
            var sigma = new DenseMatrix(R, 1);

            var Q = new DenseMatrix((R + 1), (R + 1));
            var O = new DenseMatrix(1, (R + 1));
            var pT_n = new DenseMatrix((R + 1), 1);

            double maxPhi = 0;
            int maxIndex;

            var Psi = new DenseMatrix(N, 1);

            Console.WriteLine("Running...");
            //for each observation n in X
            for (int i = 0; i < N; i++)
            {
                Console.WriteLine(100*(i/(double) N) + "%");

                var x = new DenseVector(R);
                X.Column(i, x);

                //if there are neurons in structure,
                //update structure recursively.
                if (U == 0)
                {
                    c = (DenseMatrix) x.ToColumnMatrix();
                    sigma = new DenseMatrix(R, 1, SigmaZero);
                    U = 1;
                    Psi = CalculatePsi(X, c, sigma);
                    UpdateStructure(X, Psi, d, ref Q, ref O);
                    pT_n =
                        (DenseMatrix)
                            (CalculateGreatPsi((DenseMatrix) x.ToColumnMatrix(), (DenseMatrix) Psi.Row(i).ToRowMatrix()))
                                .Transpose();
                }
                else
                {
                    StructureRecurse(X, Psi, d, i, ref Q, ref O, ref pT_n);
                }


                bool KeepSpinning = true;
                while (KeepSpinning)
                {
                    //Calculate the error and if-part criteria
                    double ee = pT_n.Multiply(O)[0, 0];

                    double approximationError = Math.Abs(d[i] - ee);

                    DenseVector Phi;
                    double SumPhi;
                    CalculatePhi(x, c, sigma, out Phi, out SumPhi);

                    maxPhi = Phi.Maximum();
                    maxIndex = Phi.MaximumIndex();

                    if (approximationError > delta)
                    {
                        if (maxPhi < threshold)
                        {
                            var tempSigma = new DenseVector(R);
                            sigma.Column(maxIndex, tempSigma);

                            double minSigma = tempSigma.Minimum();
                            int minIndex = tempSigma.MinimumIndex();
                            sigma[minIndex, maxIndex] = k_sigma*minSigma;
                            Psi = CalculatePsi(X, c, sigma);
                            UpdateStructure(X, Psi, d, ref Q, ref O);
                            var psi = new DenseVector(Psi.ColumnCount);
                            Psi.Row(i, psi);

                            pT_n =
                                (DenseMatrix)
                                    CalculateGreatPsi((DenseMatrix) x.ToColumnMatrix(), (DenseMatrix) psi.ToRowMatrix())
                                        .Transpose();
                        }
                        else
                        {
                            //add a new neuron and update strucutre

                            double distance = 0;
                            var cTemp = new DenseVector(R);
                            var sigmaTemp = new DenseVector(R);

                            //foreach input variable
                            for (int j = 0; j < R; j++)
                            {
                                distance = Math.Abs(x[j] - c[j, 0]);
                                int distanceIndex = 0;

                                //foreach neuron past 1
                                for (int k = 1; k < U; k++)
                                {
//.........这里部分代码省略.........
开发者ID:ifzz,项目名称:QuantSys,代码行数:101,代码来源:SOFNN.cs


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