本文整理汇总了C#中Matrix.SetColumnVector方法的典型用法代码示例。如果您正苦于以下问题：C# Matrix.SetColumnVector方法的具体用法？C# Matrix.SetColumnVector怎么用？C# Matrix.SetColumnVector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类Matrix的用法示例。
在下文中一共展示了Matrix.SetColumnVector方法的8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: Regression
public Regression(double[] xdata, double[] ydata, int degree)
{
int numberOfSamples = xdata.Length;
//Define the results
coefficients = new double[degree + 1];
predicted = new double[numberOfSamples];
//Set up X and Y Matricies
Matrix YMatrix = new Matrix(ydata, numberOfSamples);
Matrix XMatrix = new Matrix(numberOfSamples, degree + 1);
XMatrix.SetColumnVector(new Vector(numberOfSamples, 1.0), 0);
for (int i = 0; i < degree; i++)
{
XMatrix.SetColumnVector(new Vector(RaisePower(xdata, i + 1)), i + 1);
}
//Do the calcs
Matrix XTranspose = Matrix.Transpose(XMatrix);
Matrix Temp = (XTranspose * XMatrix).Inverse();
Matrix hat = XMatrix.Multiply(Temp).Multiply(XTranspose);
Matrix result = Temp.Multiply(XTranspose * YMatrix);
//Get the coefficients
coefficients = result.GetColumnVector(0);
//Calculate the predicted values
Matrix Y = new Matrix(ydata, ydata.Count());
predicted = hat.Multiply(Y).GetColumnVector(0);
}示例2: ToMatrixByColumns
public static Matrix ToMatrixByColumns(this Vector input, int columnSize)
{
int matrixSize = (int)Math.Sqrt(input.Length);
Matrix columnsMatrix = new Matrix(m: matrixSize, n: matrixSize);
for (int i = 0; i < matrixSize; ++i)
{
double[] column = input.Skip(i * matrixSize).Take(matrixSize).ToArray();
columnsMatrix.SetColumnVector(new Vector(column), i);
}
return columnsMatrix;
}示例3: start
public void start(Analysis parent, HttpResponse response, System.Web.SessionState.HttpSessionState session)
{
ParameterStream stream = ParameterStream.getStream(session);
String[] features = (String[]) stream.get("selectedFeatures");
int PCs = (int) stream.get("numberOfPCs");
Registry.Registry registry = Registry.Registry.getRegistry(session);
System.Data.DataSet ds = (System.Data.DataSet) registry.GetDataset((string) stream.get("dataSetName"));
//retrieve dataset table (assume one for now)
System.Data.DataTable dt = ds.Tables[0];
//raw data
double[,] rawData = new double[dt.Rows.Count, features.Count()];
for (int i = 0; i < dt.Rows.Count; i++)
{
for (int j = 0; j < features.Count(); j++)
rawData[i, j] = (double)dt.Rows[i].ItemArray.ElementAt(dt.Columns[features[j]].Ordinal);
}
//Create matrix to hold data for PCA
Matrix X = new Matrix(rawData);
//Remove mean
Vector columnVector;
for (int i = 0; i < X.ColumnCount; i++)
{
columnVector = X.GetColumnVector(i);
X.SetColumnVector(columnVector.Subtract(columnVector.Average()),i);
}
Matrix PCmatrix = new Matrix(X.ColumnCount, PCs, 0);
Vector Weights = new Vector(PCs);
System.Diagnostics.Stopwatch watch = new Stopwatch();
//Run algorithm and time it
watch.Start();
NIPALS(X, PCs, PCmatrix, Weights);
watch.Stop();
stream.set("algRunTime", watch.ElapsedMilliseconds);
/*
response.Buffer = true;
response.Write(PCmatrix.ToString() + "\n");
response.Write(Weights.ToString() + "\n");
response.Flush();
*/
Debug.WriteLine("Done with PCA");
stream.set("PCmatrix", PCmatrix);
stream.set("Weights", Weights);
parent.next(response, session);
}示例4: NIPALS
//.........这里部分代码省略.........
//E is the residual error after i itereations
int i;
//PCmatrix = new Matrix(PCs, X.ColumnCount, 0);
//EigenValues = new Vector(PCs);
Matrix E = X.Clone();
Vector u = new Vector(E.RowCount);
Vector v = new Vector(E.ColumnCount);
Matrix E_transposed;
int initialVector = 0;
//convergence threshold
const double threshold = 0.0000001;
//from http://www.vias.org/tmdatanaleng/dd_nipals_algo.html
for (i = 0; i < PCs; i++)
{
//printMatrix(E, "E");
//1. u := x(i) Select a column vector xi of the matrix X and copy it to the vector u
//The vector must be such that the self-inner product is not zero
double ut_u = 0;
Boolean valid = false;
while (!valid && initialVector < E.ColumnCount)
{
u = E.GetColumnVector(initialVector);
initialVector++;
if (u.ScalarMultiply(u) != 0)
valid = true;
}
if (!valid)
throw new Exception("Could not find " + PCs + " principal components");
E_transposed = E.Clone();
E_transposed.Transpose();
//printMatrix(E_transposed, "E transposed");
//int step = 1;
double error = 1;
while (error > threshold)
{
// Console.Out.WriteLine("PC " + (i+1) + " Step : " + step++);
//2. v := (X'u)/(u'u) Project the matrix X onto u in order to find the corresponding loading vs
//Console.Out.WriteLine("u: " + u.ToColumnMatrix().ToString());
ut_u = u.ScalarMultiply(u);
if (ut_u == 0)
throw new Exception("Principal component results in complex answer");
//Console.Out.WriteLine("u'u: " + ut_u);
Matrix v_prime = E_transposed.Multiply(u.ToColumnMatrix());
//printMatrix(v_prime, "v prime");
v = v_prime.GetColumnVector(0) / ut_u;
//Console.Out.WriteLine("v: " + v.ToString());
//3. v := v/|v| Normalize the loading vector v to length 1
v = v.Normalize();
//v = v / v.Norm();
//Console.Out.WriteLine("v after normalization: " + v.ToString());
//4.1 u_old := u Store the score vector u into uold
Vector u_old = u.Clone();
//Console.Out.WriteLine("u old: " + u_old.ToString());
//4.2 u := (Xp)/(v'v) and project the matrix X onto v in order to find corresponding score vector u
Matrix u_prime = E.Multiply(v.ToColumnMatrix());
//Console.Out.WriteLine("u_prime: ");
//printMatrix(u_prime);
Vector u_primeColumn = u_prime.GetColumnVector(0);
//Console.Out.WriteLine("u_primeColumn: " + u_primeColumn.ToString());
double v_v = v.ScalarMultiply(v);
//Console.Out.WriteLine("v_v: " + v_v);
u = u_primeColumn / v_v;
//Console.Out.WriteLine("new u: " + u.ToString());
//5. d := uold-u In order to check for the convergence of the process
//calculate the difference vector d as the difference between the previous scores
//and the current scores. If the difference |d| is larger than a pre-defined threshold,
//then return to step 2.
Vector d = u_old.Subtract(u);
//Console.Out.WriteLine("d: " + d.ToString());
error = d.Norm();
//Console.Out.WriteLine("Error: " + error.ToString());
}
//6. E := X - tp' Remove the estimated PCA component (the product of the scores and the loadings) from X
Matrix tp = u.ToColumnMatrix().Multiply(v.ToRowMatrix());
//printMatrix(tp, "tp'");
E.Subtract(tp);
PCmatrix.SetColumnVector(v, i);
EigenValues[i] = u.Norm();
//7. X := E In order to estimate the other PCA components repeat this procedure from step 1 using the matrix E as the new X
}
}示例5: irisPCA
public static void irisPCA()
{
//setup to read from CSV
String CSVfilePath = "C:\\dataminingproject";
String connectionString = "Provider=Microsoft.Jet.OLEDB.4.0;Data Source=" + CSVfilePath + ";Extended Properties='text;HDR=Yes;FMT=Delimited'";
//Setup connection
OleDbConnection connection = new OleDbConnection(connectionString);
//read everything
OleDbCommand cmd = new OleDbCommand("SELECT * FROM " + "iris.csv", connection);
//table to hold the data
System.Data.DataTable dt = new System.Data.DataTable();
//adapter to read the data
OleDbDataAdapter da = new OleDbDataAdapter(cmd);
connection.Open();
da.Fill(dt);
//data should be in the table now
// create a matrix to hold the data, ignore species
// Matrix iris = new Matrix (dt.Rows.Count, dt.Columns.Count -1);
//select columns
dt.Columns.Remove(dt.Columns[4]);
double[,] dataArray = new double[dt.Rows.Count,dt.Columns.Count];
// double sample = Array.ConvertAll<System.Data.DataRow, double[]>(dt.Select(),;
for(int i = 0; i<dt.Rows.Count;i++){
Console.Out.Write ("[ ");
for(int j = 0; j<dt.Columns.Count;j++){
dataArray[i, j] = (double)dt.Rows[i].ItemArray.ElementAt(j);
Console.Out.Write (dt.Rows[i].ItemArray.ElementAt(j).ToString() + ", ");
}
Console.Out.WriteLine (" ]");
}
//dt.Rows.CopyTo(dataArray, 0);
//double[] dataArray = Array.ConvertAll(
Matrix iris = new Matrix(dataArray);
printMatrix(iris, "iris");
//remove mean
for (int i = 0; i < iris.ColumnCount; i++)
iris.SetColumnVector(iris.GetColumnVector(i).Subtract(iris.GetColumnVector(i).Average()), i);
int PCs = 2;
Matrix PCmatrix = new Matrix(iris.ColumnCount, PCs, 0);
Vector EigenValues = new Vector(PCs);
System.Diagnostics.Stopwatch timer = new System.Diagnostics.Stopwatch();
try
{
timer.Start();
NIPALS(iris, PCs, PCmatrix, EigenValues);
timer.Stop();
}
catch (Exception e)
{
Console.Out.WriteLine(e.ToString());
Console.In.ReadLine();
return;
}
Console.Out.WriteLine("NIPALS Time: " + timer.ElapsedMilliseconds);
printMatrix(PCmatrix, "Principal Components");
printMatrix(EigenValues.ToRowMatrix(), "Weights");
Console.Out.WriteLine("SVD:");
timer.Reset();
timer.Start();
SingularValueDecomposition svd = iris.SingularValueDecomposition;
timer.Stop();
Console.Out.WriteLine("SVD Time: " + timer.ElapsedMilliseconds);
//Console.Out.WriteLine("LSV: ");
//printMatrix(svd.LeftSingularVectors);
Console.Out.WriteLine("RSV: ");
printMatrix(svd.RightSingularVectors);
Console.Out.WriteLine("S: ");
printMatrix(svd.S);
Console.Out.WriteLine("Singular Values: ");
printMatrix(svd.SingularValues.ToRowMatrix());
Console.In.ReadLine();
// Console.Out.WriteLine(dt.Rows[0].ItemArray.Take(4).ToArray<double>());
/*
for (int i=0;i<iris.ColumnCount;i++){
iris.SetColumnVector(dt.Columns[i].Container.Components.GetEnumerator.
}
*/
}示例6: Main
//private static MathNet.Numerics.Algorithms.LinearAlgebra.Atlas.AtlasLinearAlgebraProvider provider;
static void Main(string[] args)
{
Console.Out.WriteLine ("Hello...");
irisPCA();
//provider = new MathNet.Numerics.Algorithms.LinearAlgebra.Atlas.AtlasLinearAlgebraProvider();
/*
double[,] A = new double[2,2];
A[0, 0] = 5;
A[0, 1] = 0;
A[1, 0] = 0;
A[1, 1] = 10;
/*
double[,] A = new double[3, 2];
A[0, 0] = 5;
A[0, 1] = 0;
A[1, 0] = 0;
A[1, 1] = 10;
A[2, 0] = 5;
A[2, 1] = 15;
*/
double[,] A = new double[4, 3];
A[0, 0] = 1;
A[0, 1] = 2;
A[0, 2] = 3;
A[1, 0] = 4;
A[1, 1] = 5;
A[1, 2] = 6;
A[2, 0] = 6;
A[2, 1] = 5;
A[2, 2] = 4;
A[3, 0] = 3;
A[3, 1] = 2;
A[3, 2] = 1;
Matrix X = new Matrix(A);
//remove mean
for (int i=0;i<X.ColumnCount;i++)
X.SetColumnVector(X.GetColumnVector(i).Subtract(X.GetColumnVector(i).Average()),i);
printMatrix(X, "X");
int PCs = 2;
Matrix PCmatrix = new Matrix(X.ColumnCount, PCs, 0);
Vector EigenValues = new Vector(PCs);
try
{
NIPALS(X, PCs, PCmatrix, EigenValues);
}
catch (Exception e)
{
Console.Out.WriteLine(e.ToString());
Console.In.ReadLine();
return;
}
printMatrix(PCmatrix, "Principal Components");
double projection = PCmatrix.GetColumnVector(0).ScalarMultiply(PCmatrix.GetColumnVector(1));
Console.Out.WriteLine("projection: " + projection);
printMatrix(EigenValues.ToRowMatrix(), "Weights");
Console.Out.WriteLine("SVD:");
SingularValueDecomposition svd = X.SingularValueDecomposition;
Console.Out.WriteLine("LSV: ");
printMatrix(svd.LeftSingularVectors);
Console.Out.WriteLine("RSV: ");
printMatrix(svd.RightSingularVectors);
Console.Out.WriteLine("S: ");
printMatrix(svd.S);
Console.Out.WriteLine("Singular Values: ");
printMatrix(svd.SingularValues.ToRowMatrix());
Console.In.ReadLine();
}示例7: Page_Load
protected void Page_Load(object sender, EventArgs e)
{
Matrix PCmatrix = (Matrix)stream.get("PCmatrix");
String[] features = (String[])stream.get("selectedFeatures");
System.Data.DataSet ds = (System.Data.DataSet)registry.GetDataset((string)stream.get("dataSetName"));
//retrieve dataset table (assume one for now)
System.Data.DataTable dt = ds.Tables[0];
//raw data
double[,] rawData = new double[dt.Rows.Count, features.Count()];
for (int i = 0; i < dt.Rows.Count; i++)
{
for (int j = 0; j < features.Count(); j++)
rawData[i, j] = (double)dt.Rows[i].ItemArray.ElementAt(dt.Columns[features[j]].Ordinal);
}
//Create matrix to hold data for PCA
Matrix X = new Matrix(rawData);
//Remove mean
Vector columnVector;
for (int i = 0; i < X.ColumnCount; i++)
{
columnVector = X.GetColumnVector(i);
X.SetColumnVector(columnVector.Subtract(columnVector.Average()), i);
}
//get first two PCs
Matrix xy = new Matrix(PCmatrix.RowCount,2);
xy.SetColumnVector(PCmatrix.GetColumnVector(0),0);
xy.SetColumnVector(PCmatrix.GetColumnVector(1),1);
//project
Matrix projected = X.Multiply(xy);
DataPoint point;
Projection.Series.Clear();
Projection.Legends.Clear();
//if a label column is selected
String LabelColumnName = LabelColumn.Text;
if (!LabelColumnName.Equals(""))
{
//get labels
int labelColumnIndex = dt.Columns[LabelColumnName].Ordinal;
List<String> labels = new List<String>();
String item;
for (int i = 0; i < dt.Rows.Count; i++)
{
item = (String)dt.Rows[i].ItemArray.ElementAt(labelColumnIndex);
if (!labels.Contains(item))
labels.Add(item);
}
Projection.Legends.Add(LabelColumnName);
System.Drawing.Font font = Projection.Legends[LabelColumnName].Font = new System.Drawing.Font(Projection.Legends[LabelColumnName].Font.Name, 14);
//Configure series
foreach (String label in labels)
{
Projection.Series.Add(label);
Projection.Series[label].LegendText = label;
Projection.Series[label].IsXValueIndexed = false;
Projection.Series[label].ChartType = SeriesChartType.Point;
Projection.Series[label].MarkerSize = 8;
}
//Add points
for (int i = 0; i < projected.RowCount; i++)
{
point = new DataPoint(projected[i, 0], projected[i, 1]);
String label = dt.Rows[i].ItemArray[labelColumnIndex].ToString();
Projection.Series[label].Points.Add(point);
}
}
else
{
//Single plot graph
Projection.Series.Add("series1");
Projection.Series[0].IsXValueIndexed = false;
Projection.Series[0].ChartType = SeriesChartType.Point;
Projection.Series[0].MarkerSize = 8;
for (int i = 0; i < projected.RowCount; i++)
{
point = new DataPoint(projected[i, 0], projected[i, 1]);
Projection.Series[0].Points.Add(point);
}
}
}示例8: addcontrol
//.........这里部分代码省略.........
Projection.Width = Unit.Pixel(Convert.ToInt16(cell.Width.Substring(0, cell.Width.Length - 2)) * cell.ColSpan - 2 * (layouttable.Border + layouttable.CellPadding));
Projection.Height = Unit.Pixel(Convert.ToInt16(row.Height.Substring(0, row.Height.Length - 2)) * cell.RowSpan - 2 * (layouttable.Border + layouttable.CellPadding));
DataMiningApp.Analysis.ParameterStream stream;
Registry.Registry registry;
stream = DataMiningApp.Analysis.ParameterStream.getStream(Session);
registry = Registry.Registry.getRegistry(Session);
Matrix PCmatrix = (Matrix)stream.get("PCmatrix");
String[] features = (String[])stream.get("selectedFeatures");
System.Data.DataSet ds = (System.Data.DataSet)registry.GetDataset((string)stream.get("dataSetName"));
//retrieve dataset table (assume one for now)
System.Data.DataTable dt = ds.Tables[0];
//raw data
double[,] rawData = new double[dt.Rows.Count, features.Count()];
for (int i = 0; i < dt.Rows.Count; i++)
{
for (int j = 0; j < features.Count(); j++)
rawData[i, j] = (double)dt.Rows[i].ItemArray.ElementAt(dt.Columns[features[j]].Ordinal);
}
//Create matrix to hold data for PCA
Matrix X = new Matrix(rawData);
//Remove mean
Vector columnVector;
for (int i = 0; i < X.ColumnCount; i++)
{
columnVector = X.GetColumnVector(i);
X.SetColumnVector(columnVector.Subtract(columnVector.Average()), i);
}
//get first two PCs
Matrix xy = new Matrix(PCmatrix.RowCount, 2);
xy.SetColumnVector(PCmatrix.GetColumnVector(0), 0);
xy.SetColumnVector(PCmatrix.GetColumnVector(1), 1);
//project
Matrix projected = X.Multiply(xy);
DataPoint point;
Projection.Series.Clear();
Projection.Legends.Clear();
//if a label column is selected
String LabelColumnName = "Species";
if (!LabelColumnName.Equals(""))
{
//get labels
int labelColumnIndex = dt.Columns[LabelColumnName].Ordinal;
List<String> labels = new List<String>();
String item;
for (int i = 0; i < dt.Rows.Count; i++)
{
item = (String)dt.Rows[i].ItemArray.ElementAt(labelColumnIndex);
if (!labels.Contains(item))
labels.Add(item);
}