コード例 #1
0
ファイル: CramersMethod.cs プロジェクト: Sequenta/MathMate
 public Vector<double> Solve(DenseMatrix systemMatrix, Vector<double> freeTerms)
 {
     var determinant = systemMatrix.Determinant();
     var determinants = new double[systemMatrix.ColumnCount];
     var results = new double[systemMatrix.ColumnCount];
     for (var i = 0; i < systemMatrix.ColumnCount; i++)
     {
         var matrix = systemMatrix.RemoveColumn(i).InsertColumn(i, freeTerms);
         determinants[i] = matrix.Determinant();
     }
     for (var i = 0; i < determinants.Length; i++)
     {
         results[i] = determinants[i]/determinant;
     }
     return Vector.Build.Dense(results);
 }
コード例 #2
0
ファイル: MainWindow.xaml.cs プロジェクト: osst/University
        private DenseVector NewtonMethod(ref Parser[] parsers)
        {
            double eps = 10e-3;

            //var helperMatrix = new DenseMatrix(equationsCount, equationsCount);
            var helperMatrixR = new double[equationsCount, equationsCount];
            for (int i = 0; i < equationsCount; i++)
            {
                for (int j = 0; j < equationsCount; j++)
                {
                    double p;
                    double p1;
                    try
                    {
                        parsers[i].Variables[variables.ElementAt(j).Key] = variables.ElementAt(j).Value + eps;
                        p1 = parsers[i].Calculate();
                        parsers[i].Variables[variables.ElementAt(j).Key] = variables.ElementAt(j).Value;
                        p = parsers[i].Calculate();
                    }
                    catch(Exception exception)
                    {
                        ErrorAllert(exception.Message);
                        return null;
                    }

                    helperMatrixR[i, j] = (p1 - p)/eps;
                }
            }
            var helperMatrix = new DenseMatrix(helperMatrixR);

            if (Math.Abs(helperMatrix.Determinant()) < eps)
            {
                ErrorAllert("Determinant of Jacobi matrix is 0. Can't calculate result.");
                return null;
            }
            var inverseMatrix = helperMatrix.Inverse();
            var initVars = new DenseVector(equationsCount);
            var modVars = new Dictionary<string, double>();

            for (int i = 0; i < variables.Count; i++)
            {
                initVars[i] = variables.ElementAt(i).Value;
                modVars[variables.ElementAt(i).Key] = variables.ElementAt(i).Value;
            }

            foreach (var parser in parsers)
            {
                parser.Variables = modVars;
            }

            bool diff = false;
            int itCount = 0;

            while (!diff && itCount < 10)
            {
                var f = new DenseVector(equationsCount);

                for (int j = 0; j < equationsCount; j++)
                {
                    f[j] = parsers[j].Calculate();
                }

                var result = new DenseVector((initVars - inverseMatrix*f).ToArray());

                for (int i = 0; i < equationsCount; i++)
                {
                    diff = diff && (Math.Abs(result[i] - initVars[i]) < eps);
                }

                initVars = result;

                for (int j = 0; j < equationsCount; j++)
                {
                    modVars[modVars.ElementAt(j).Key] = initVars[j];
                }
                ++itCount;
            }

            return initVars;
        }
コード例 #3
0
        /// <summary>
        /// Run example
        /// </summary>
        /// <seealso cref="http://en.wikipedia.org/wiki/Determinant">Determinant</seealso>
        /// <seealso cref="http://en.wikipedia.org/wiki/Rank_%28linear_algebra%29">Rank (linear algebra)</seealso>
        /// <seealso cref="http://en.wikipedia.org/wiki/Trace_%28linear_algebra%29">Trace (linear algebra)</seealso>
        /// <seealso cref="http://en.wikipedia.org/wiki/Condition_number">Condition number</seealso>
        public void Run()
        {
            // Format matrix output to console
            var formatProvider = (CultureInfo)CultureInfo.InvariantCulture.Clone();
            formatProvider.TextInfo.ListSeparator = " ";

            // Create random square matrix
            var matrix = new DenseMatrix(5);
            var rnd = new Random(1);
            for (var i = 0; i < matrix.RowCount; i++)
            {
                for (var j = 0; j < matrix.ColumnCount; j++)
                {
                    matrix[i, j] = rnd.NextDouble();
                }
            }

            Console.WriteLine(@"Initial matrix");
            Console.WriteLine(matrix.ToString("#0.00\t", formatProvider));
            Console.WriteLine();

            // 1. Determinant
            Console.WriteLine(@"1. Determinant");
            Console.WriteLine(matrix.Determinant());
            Console.WriteLine();

            // 2. Rank
            Console.WriteLine(@"2. Rank");
            Console.WriteLine(matrix.Rank());
            Console.WriteLine();

            // 3. Condition number
            Console.WriteLine(@"2. Condition number");
            Console.WriteLine(matrix.ConditionNumber());
            Console.WriteLine();

            // 4. Trace
            Console.WriteLine(@"4. Trace");
            Console.WriteLine(matrix.Trace());
            Console.WriteLine();
        }
コード例 #4
0
ファイル: CD-HMM.cs プロジェクト: Daniel-Nichol/sign-align
        //Given an observation, mu and sigma. What is N(O | mu, sigma)?
        public double queryGuassian(DenseVector observation, DenseVector mean, DenseMatrix covar)
        {
            double scaletemp, scale, exponent, prob;
            DenseMatrix v1, v2; //Temp matrices, for multiplying

            scaletemp = (Math.Pow(2 * Math.PI, mean.Count / 2));
            scale = (Math.Sqrt(covar.Determinant()));
            scale *= scaletemp;
            scale = 1 / scale;

            v1 = (DenseMatrix)(observation - mean).ToRowMatrix();
            v2 = (DenseMatrix)(observation - mean).ToColumnMatrix();
            v2 = (DenseMatrix)(covar.Inverse()) * v2;

            exponent = (-0.5) *  ((v1 * v2).ToArray()[0,0]);
            prob = scale * Math.Pow(Math.E, exponent);

            return prob;
        }
        private static double LinePlaneIntersection(MathPoint origin, MathVector axis0, MathVector axis1, MathPoint lineOrigin, MathVector lineVector)
        {
            double x1, y1, z1;
            double x2, y2, z2;
            double x3, y3, z3;
            double x4, y4, z4;
            double x5, y5, z5;
            double t;

            var p0 = (MathPoint) origin.AddVector(axis0);
            var p1 = (MathPoint) origin.AddVector(axis1);

            Destructure(origin, out x1, out y1, out z1);
            Destructure(p0, out x2, out y2, out z2);
            Destructure(p1, out x3, out y3, out z3);

            var p2 = (MathPoint) origin.AddVector(lineVector);
            Destructure(lineOrigin, out x4, out y4, out z4);
            Destructure(p2, out x5, out y5, out z5);

            Matrix a = new DenseMatrix(4, 4, new double[]
            {
                1, x1, y1, z1, 1, x2, y2, z2, 1, x3, y3, z3, 1, x4, y4, z4
            });

            Matrix b = new DenseMatrix(4, 4, new double[]
            {
                1, x1, y1, z1, 1, x2, y2, z2, 1, x3, y3, z3, 0, x5 - x4, y5 - y4, z5 - z4
            });

            var da = a.Determinant();
            var db = b.Determinant();
            t = da/db;

            return t;
        }