/// <summary>Initializes a new instance of the <see cref="State"/> class.
/// </summary>
/// <param name="rank">The rank of the decomposed correlation matrix, i.e. of matrix B where B * B^t represents the correlation matrix.</param>
/// <param name="iterationsNeeded">The number of iterations needed by the algorithm to reach the desired accuracy.</param>
/// <param name="infoOutputDetailLevel">The info-output level of detail.</param>
protected internal State(int rank, int iterationsNeeded = Int32.MaxValue, InfoOutputDetailLevel infoOutputDetailLevel = InfoOutputDetailLevel.Low)
{
IterationsNeeded = iterationsNeeded;
Rank = rank;
InfoOutputDetailLevel = infoOutputDetailLevel;
var strBuilder = new StringBuilder();
strBuilder.AppendFormat("Rank: {0}", Rank);
if (iterationsNeeded < Int32.MaxValue)
{
strBuilder.AppendFormat("; Iterations needed: {0}", iterationsNeeded);
}
m_StringRepresentation = strBuilder.ToString();
m_FillInfoOutput = (infoOutput, categoryName) =>
{
var infoOutputPackage = infoOutput.AcquirePackage(categoryName);
infoOutputPackage.Add("Rank", Rank);
if (iterationsNeeded < Int32.MaxValue)
{
infoOutputPackage.Add("Iterations needed", IterationsNeeded);
}
};
}
/// <summary>Gets informations of the current object as a specific <see cref="InfoOutput" /> instance.
/// </summary>
/// <param name="infoOutput">The <see cref="InfoOutput" /> object which is to be filled with informations concering the current instance.</param>
/// <param name="categoryName">The name of the category, i.e. all informations will be added to these category.</param>
public void FillInfoOutput(InfoOutput infoOutput, string categoryName = InfoOutput.GeneralCategoryName)
{
var infoOutputPackage = infoOutput.AcquirePackage(categoryName);
infoOutputPackage.Add("Distribution", "Empirical");
infoOutputPackage.Add("Sample Size", SampleSize);
infoOutputPackage.Add("Mean", Mean);
infoOutputPackage.Add("Minimum", Minimum);
infoOutputPackage.Add("Maximum", Maximum);
infoOutputPackage.Add("Media", Median);
if (InfoOutputDetailLevel.IsAtLeastAsComprehensiveAs(InfoOutputDetailLevel.High) == true)
{
var sampleDataTable = new DataTable("Sample");
sampleDataTable.Columns.Add("Value", typeof(double));
foreach (var value in Sample)
{
var row = sampleDataTable.NewRow();
row[0] = value;
sampleDataTable.Rows.Add(row);
}
infoOutputPackage.Add(sampleDataTable);
}
m_MomentCalculator.Value.FillInfoOutput(infoOutput, categoryName + ".Moments");
m_DensityEstimator.FillInfoOutput(infoOutput, categoryName + ".Density");
}
/// <summary>Creates a 'n x r' matrix B such that B * B' is a correlation matrix and 'near' to the specified symmetric, normalized matrix of dimension n. A rank reduction will apply if r is strict less than n.
/// </summary>
/// <param name="rawCorrelationMatrix">The symmetric, normalized matrix where to find the 'nearest' correlation matrix.</param>
/// <param name="state">The state of the operation in its <see cref="PseudoSqrtMatrixDecomposer.State"/> representation (output).</param>
/// <param name="triangularMatrixType">A value indicating which part of <paramref name="rawCorrelationMatrix"/> to take into account.</param>
/// <param name="outputEntries">This argument will be used to store the matrix entries of the resulting matrix B, i.e. the return value array points to this array if != <c>null</c>; otherwise a memory allocation will be done.</param>
/// <param name="worksspaceContainer">A specific <see cref="PseudoSqrtMatrixDecomposer.WorkspaceContainer"/> object to reduce memory allocation; ignored if <c>null</c>.</param>
/// <returns>A <see cref="DenseMatrix"/> object that represents a matrix B such that B * B' is the 'nearest' correlation matrix with respect to <paramref name="rawCorrelationMatrix"/>.</returns>
/// <remarks>In general the return object does <b>not</b> represents the pseudo-root of <paramref name="rawCorrelationMatrix"/>, i.e. output of the Cholesky decomposition.
/// <para>The parameters <paramref name="outputEntries"/>, <paramref name="worksspaceContainer"/> allows to avoid memory allocation and to re-use arrays if the calculation of correlation matrices will be done often.</para></remarks>
public override DenseMatrix Create(DenseMatrix rawCorrelationMatrix, out State state, double[] outputEntries = null, PseudoSqrtMatrixDecomposer.WorkspaceContainer worksspaceContainer = null, BLAS.TriangularMatrixType triangularMatrixType = BLAS.TriangularMatrixType.LowerTriangularMatrix)
{
if (rawCorrelationMatrix.IsQuadratic == false)
{
throw new ArgumentException("rawCorrelationMatrix");
}
int n = rawCorrelationMatrix.RowCount;
if ((outputEntries == null) || (outputEntries.Length < n * n))
{
outputEntries = new double[n * n];
}
var ws = worksspaceContainer as Workspace;
if ((ws == null) || (ws.Dimension < n))
{
ws = new Workspace(n);
}
int m;
BLAS.Level1.dcopy(n * n, rawCorrelationMatrix.Data, ws.data);
LAPACK.EigenValues.Symmetric.driver_dsyevr(LapackEigenvalues.SymmetricGeneralJob.All, n, ws.data, out m, ws.eigenValues, outputEntries, ws.isuppz, ws.work, ws.iwork);
var originalEigenValueDataTable = InfoOutputDetailLevel.IsAtLeastAsComprehensiveAs(InfoOutputDetailLevel.High) ? CreateDataTableWithEigenvalues("Eigenvalues.Original", m, ws.eigenValues) : null;
int rank = n;
int minNumberOfEigenvaluesToSetZero = n - Math.Min(MaximalRank ?? n, n);
int i = 0;
while ((i < minNumberOfEigenvaluesToSetZero) || (ws.eigenValues[i] < 0.0))
{
ws.eigenValues[i] = 0.0;
i++;
rank--;
}
var adjustedEigenValueDataTable = InfoOutputDetailLevel.IsAtLeastAsComprehensiveAs(InfoOutputDetailLevel.High) ? CreateDataTableWithEigenvalues("Eigenvalues.Adjusted", m, ws.eigenValues) : null;
VectorUnit.Basics.Sqrt(n, ws.eigenValues); // calculate sqrt of eigenvalues only once, i.e. the array 'eigenValues' contains the sqrt of the eigenvalues!
for (i = 0; i < n; i++)
{
var t_i = 0.0;
for (int j = n - 1; j >= n - rank; j--)
{
t_i += outputEntries[i + j * n] * outputEntries[i + j * n] * ws.eigenValues[j] * ws.eigenValues[j];
outputEntries[i + j * n] *= ws.eigenValues[j];
}
BLAS.Level1.dscal(rank, 1.0 / Math.Sqrt(t_i), outputEntries, -n, i + (n - 1) * n); // [i+j*n] *= 1/Math.Sqrt(tempValue) for j = n-1, ..., n-rank
}
/* The eigenvalues are in ascending order. Thefore the first (and not last) 'rank' columns of the eigenvectors are not required. Therefore we swap the relevant part */
BLAS.Level1.dscal(n * (n - rank), 0.0, outputEntries);
BLAS.Level1.dswap(n * rank, outputEntries, 1, outputEntries, 1, n * (n - rank), 0);
state = State.Create(rank, detailProperties: new[] { InfoOutputProperty.Create("Eigenvalues set to 0.0", n - rank) }, detailDataTables: new[] { originalEigenValueDataTable, adjustedEigenValueDataTable }, iterationsNeeded: 1, infoOutputDetailLevel: InfoOutputDetailLevel);
return(new DenseMatrix(n, rank, outputEntries));
}
/// <summary>Initializes a new instance of the <see cref="State"/> class.
/// </summary>
/// <param name="classification">The classification of the result.</param>
/// <param name="infoOutputPackageAction">A method applied to fill a specific <see cref="InfoOutputPackage"/>.</param>
/// <param name="infoOutputDetailLevel">The info output detail level.</param>
protected internal State(NumericalIntegratorErrorClassification classification, Action <InfoOutputPackage> infoOutputPackageAction, InfoOutputDetailLevel infoOutputDetailLevel = InfoOutputDetailLevel.Full)
{
Classification = classification;
InfoOutputDetailLevel = infoOutputDetailLevel;
if (infoOutputPackageAction == null)
{
throw new ArgumentNullException(nameof(infoOutputPackageAction));
}
m_InfoOutputPackageAction = infoOutputPackageAction;
}
/// <summary>Creates a 'n x r' matrix B such that B * B' is a correlation matrix and 'near' to the specified symmetric, normalized matrix of dimension n. A rank reduction will apply if r is strict less than n.
/// </summary>
/// <param name="rawCorrelationMatrix">The symmetric, normalized matrix where to find the 'nearest' correlation matrix.</param>
/// <param name="state">The state of the operation in its <see cref="PseudoSqrtMatrixDecomposer.State"/> representation (output).</param>
/// <param name="triangularMatrixType">A value indicating which part of <paramref name="rawCorrelationMatrix"/> to take into account.</param>
/// <param name="outputEntries">This argument will be used to store the matrix entries of the resulting matrix B, i.e. the return value array points to this array if != <c>null</c>; otherwise a memory allocation will be done.</param>
/// <param name="worksspaceContainer">A specific <see cref="PseudoSqrtMatrixDecomposer.WorkspaceContainer"/> object to reduce memory allocation; ignored if <c>null</c>.</param>
/// <returns>A <see cref="DenseMatrix"/> object that represents a matrix B such that B * B' is the 'nearest' correlation matrix with respect to <paramref name="rawCorrelationMatrix"/>.</returns>
/// <remarks>In general the return object does <b>not</b> represents the pseudo-root of <paramref name="rawCorrelationMatrix"/>, i.e. output of the Cholesky decomposition.
/// <para>The parameters <paramref name="outputEntries"/>, <paramref name="worksspaceContainer"/> allows to avoid memory allocation and to re-use arrays if the calculation of correlation matrices will be done often.</para></remarks>
public override DenseMatrix Create(DenseMatrix rawCorrelationMatrix, out State state, double[] outputEntries = null, PseudoSqrtMatrixDecomposer.WorkspaceContainer worksspaceContainer = null, BLAS.TriangularMatrixType triangularMatrixType = BLAS.TriangularMatrixType.LowerTriangularMatrix)
{
if (rawCorrelationMatrix.IsQuadratic == false)
{
throw new ArgumentException("rawCorrelationMatrix");
}
int n = rawCorrelationMatrix.RowCount;
var ws = worksspaceContainer as Workspace;
if ((ws == null) || (ws.Dimension < n))
{
ws = new Workspace(n, this);
}
/* calculate an initial value for the optimizer:
* (i) Apply the EZN algorithm for the calculation of a matrix B_0 such that B_0 * B_0^t is near to the (raw) correlation matrix
* (ii) calculate angle parameters \theta such that B_0 = B(\theta).
* */
State initState;
var initialAngleParameterMatrix = GetAngleParameter(m_InitialDecomposer.Create(rawCorrelationMatrix, out initState, outputEntries, ws.InitalDecomposerWorkspace, triangularMatrixType), ws.ArgMinData);
/* prepare and apply optimization algorithm: */
int rank = initState.Rank;
if ((outputEntries == null) || (outputEntries.Length < n * n))
{
outputEntries = new double[n * rank];
}
var B = new DenseMatrix(n, rank, outputEntries, createDeepCopyOfArgument: false);
var C = new DenseMatrix(n, n, ws.CorrelationMatrixData, createDeepCopyOfArgument: false);
var optAlgorithm = m_MultiDimOptimizer.Create(n * (rank - 1));
optAlgorithm.SetFunction(theta =>
{
GetParametricMatrix(theta, B);
C.AddAssignment(B, B.T, beta: 0.0); // C = B * B^t
VectorUnit.Basics.Sub(n * n, C.Data, rawCorrelationMatrix.Data, ws.TempDifferences);
return(BLAS.Level1.dnrm2sq(n * n, ws.TempDifferences));
});
double minimum;
var optState = optAlgorithm.FindMinimum(ws.ArgMinData, out minimum);
state = State.Create(rank, optState.IterationsNeeded, InfoOutputDetailLevel,
Tuple.Create <string, IInfoOutputQueriable>("Initial.State", initState),
Tuple.Create <string, IInfoOutputQueriable>("Final.Optimizer", optState),
Tuple.Create <string, IInfoOutputQueriable>("Initial.Parameters", initialAngleParameterMatrix),
InfoOutputDetailLevel.IsAtLeastAsComprehensiveAs(InfoOutputDetailLevel.High) ? Tuple.Create <string, IInfoOutputQueriable>("Final.Parameters", new DenseMatrix(n, rank, ws.ArgMinData, createDeepCopyOfArgument: false)) : null
);
GetParametricMatrix(ws.ArgMinData, B); // B should be already set to B(\theta^*), we just want to be sure that B is correct on exit
return(B);
}
/// <summary>Gets the level of details, i.e. the value of the optional property 'Level of Details'.
/// </summary>
/// <param name="generalPropertyExcelDataQuery">A <see cref="IExcelDataQuery"/> object that contains general properties.</param>
/// <param name="propertyValueColumnIndex">The null-based index of the column which contains the value, the second column is standard.</param>
/// <returns>A value indicating the level of detail for the output, i.e. for the Pool inspector etc.</returns>
public static InfoOutputDetailLevel GetLevelOfDetails(this IExcelDataQuery generalPropertyExcelDataQuery, int propertyValueColumnIndex = 1)
{
if (generalPropertyExcelDataQuery == null)
{
throw new ArgumentNullException("generalPropertyExcelDataQuery");
}
InfoOutputDetailLevel levelOfDetails = InfoOutputDetailLevel.Full;
generalPropertyExcelDataQuery.TryGetOptionalPropertyValue <InfoOutputDetailLevel>("Level of Details", ref levelOfDetails, EnumStringRepresentationUsage.StringAttribute, propertyValueColumnIndex);
return(levelOfDetails);
}
/// <summary>Initializes a new instance of the <see cref="SapMatrixDecomposer" /> class.
/// </summary>
/// <param name="multiDimOptmizer">The multi-dimensional </param>
/// <param name="infoOutputDetailLevel">The info-output level of detail.</param>
public SapMatrixDecomposer(OrdinaryMultiDimOptimizer multiDimOptmizer, InfoOutputDetailLevel infoOutputDetailLevel = InfoOutputDetailLevel.Middle)
: base(infoOutputDetailLevel)
{
if (multiDimOptmizer == null)
{
throw new ArgumentNullException("multiDimOptmizer");
}
m_MultiDimOptimizer = multiDimOptmizer;
m_InitialDecomposer = new EznMatrixDecomposer();
m_Name = new IdentifierString("SAP Decomposer");
}
/// <summary>Initializes a new instance of the <see cref="State"/> class.
/// </summary>
/// <param name="rank">The rank of the decomposed correlation matrix, i.e. of matrix B where B * B^t represents the correlation matrix.</param>
/// <param name="detailProperties">Additional details in its <see cref="InfoOutputProperty"/> representation.</param>
/// <param name="detailDataTables">Additional details in its <see cref="DataTable"/> representation; <c>null</c> entries will be ignored.</param>
/// <param name="iterationsNeeded">The number of iterations needed by the algorithm to reach the desired accuracy.</param>
/// <param name="infoOutputDetailLevel">The info-output level of detail.</param>
protected internal State(int rank, IList<InfoOutputProperty> detailProperties, IList<DataTable> detailDataTables, int iterationsNeeded, InfoOutputDetailLevel infoOutputDetailLevel = InfoOutputDetailLevel.High)
{
IterationsNeeded = iterationsNeeded;
Rank = rank;
InfoOutputDetailLevel = infoOutputDetailLevel;
var strBuilder = new StringBuilder();
strBuilder.AppendFormat("Rank: {0}", Rank);
if (iterationsNeeded < Int32.MaxValue)
{
strBuilder.AppendFormat("; Iterations needed: {0}", iterationsNeeded);
}
if (detailProperties != null)
{
foreach (var property in detailProperties)
{
strBuilder.AppendFormat("; {0}: {1}", property.Name.String, property.Value);
}
}
m_StringRepresentation = strBuilder.ToString();
m_FillInfoOutput = (infoOutput, categoryName) =>
{
var infoOutputPackage = infoOutput.AcquirePackage(categoryName);
infoOutputPackage.Add("Rank", Rank);
if (iterationsNeeded < Int32.MaxValue)
{
infoOutputPackage.Add("Iterations needed", IterationsNeeded);
}
if (detailProperties != null)
{
foreach (var property in detailProperties)
{
infoOutputPackage.Add(property);
}
}
if (detailDataTables != null)
{
foreach (var dataTable in detailDataTables)
{
if (dataTable != null)
{
infoOutputPackage.Add(dataTable);
}
}
}
};
}
/// <summary>Initializes a new instance of the <see cref="State"/> class.
/// </summary>
/// <param name="rank">The rank of the decomposed correlation matrix, i.e. of matrix B where B * B^t represents the correlation matrix.</param>
/// <param name="details">Additional details in its <see cref="IInfoOutputQueriable"/> representation together with a specific name.</param>
/// <param name="iterationsNeeded">The number of iterations needed by the algorithm to reach the desired accuracy.</param>
/// <param name="infoOutputDetailLevel">The info-output level of detail.</param>
protected internal State(int rank, IEnumerable<Tuple<string, IInfoOutputQueriable>> details, int iterationsNeeded = Int32.MaxValue, InfoOutputDetailLevel infoOutputDetailLevel = InfoOutputDetailLevel.High)
{
IterationsNeeded = iterationsNeeded;
Rank = rank;
InfoOutputDetailLevel = infoOutputDetailLevel;
var strBuilder = new StringBuilder();
strBuilder.AppendFormat("Rank: {0}", Rank);
if (iterationsNeeded < Int32.MaxValue)
{
strBuilder.AppendFormat("; Iterations needed: {0}", iterationsNeeded);
}
if (details != null)
{
foreach (var item in details)
{
if (item != null)
{
strBuilder.AppendFormat("; <{0}: {1}>", item.Item1, item.Item2.ToString());
}
}
}
m_StringRepresentation = strBuilder.ToString();
m_FillInfoOutput = (infoOutput, categoryName) =>
{
var infoOutputPackage = infoOutput.AcquirePackage(categoryName);
infoOutputPackage.Add("Rank", Rank);
if (iterationsNeeded < Int32.MaxValue)
{
infoOutputPackage.Add("Iterations needed", IterationsNeeded);
}
if (details != null)
{
foreach (var item in details)
{
if (item != null)
{
item.Item2.FillInfoOutput(infoOutput, item.Item1);
}
}
}
};
}
/// <summary>Sets the <see cref="IInfoOutputQueriable.InfoOutputDetailLevel" /> property.
/// </summary>
/// <param name="infoOutputDetailLevel">The info-output level of detail.</param>
/// <returns>A value indicating whether the <see cref="IInfoOutputQueriable.InfoOutputDetailLevel" /> has been set to <paramref name="infoOutputDetailLevel" />.</returns>
public bool TrySetInfoOutputDetailLevel(InfoOutputDetailLevel infoOutputDetailLevel)
{
InfoOutputDetailLevel = infoOutputDetailLevel;
return(true);
}
/// <summary>Initializes a new instance of the <see cref="PseudoSqrtMatrixDecomposer"/> class.
/// </summary>
/// <param name="annotation">The annotation, i.e. short description, of the correlation matrix decomposer.</param>
/// <param name="infoOutputDetailLevel">The info-output level of detail.</param>
protected PseudoSqrtMatrixDecomposer(string annotation, InfoOutputDetailLevel infoOutputDetailLevel = InfoOutputDetailLevel.Middle)
{
Annotation = (annotation != null) ? annotation : String.Empty;
InfoOutputDetailLevel = infoOutputDetailLevel;
}
/// <summary>Initializes a new instance of the <see cref="PseudoSqrtMatrixDecomposer"/> class.
/// </summary>
/// <param name="infoOutputDetailLevel">The info-output level of detail.</param>
protected PseudoSqrtMatrixDecomposer(InfoOutputDetailLevel infoOutputDetailLevel = InfoOutputDetailLevel.Middle)
{
Annotation = String.Empty;
InfoOutputDetailLevel = infoOutputDetailLevel;
}
/// <summary>Initializes a new instance of the <see cref="QuadraticProgram"/> class.
/// </summary>
/// <param name="infoOutputDetailLevel">The info-output level of detail in its <see cref="Dodoni.BasicComponents.Containers.InfoOutputDetailLevel"/> representation.</param>
protected QuadraticProgram(InfoOutputDetailLevel infoOutputDetailLevel = InfoOutputDetailLevel.Full)
: base(infoOutputDetailLevel)
{
}
/// <summary>Creates a new <see cref="State"/> object.
/// </summary>
/// <param name="rank">The rank of the decomposed correlation matrix, i.e. of matrix B where B * B^t represents the correlation matrix.</param>
/// <param name="iterationsNeeded">The number of iterations needed by the algorithm to reach the desired accuracy.</param>
/// <param name="infoOutputDetailLevel">The info-output level of detail.</param>
/// <param name="details">Additional details in its <see cref="IInfoOutputQueriable"/> representation together with a specific name.</param>
/// <returns>A <see cref="State"/> object that represents the state of a specific calculation.</returns>
public static State Create(int rank, int iterationsNeeded = Int32.MaxValue, InfoOutputDetailLevel infoOutputDetailLevel = InfoOutputDetailLevel.High, params Tuple<string, IInfoOutputQueriable>[] details)
{
return new State(rank, details, iterationsNeeded, infoOutputDetailLevel);
}
/// <summary>Sets the <see cref="IInfoOutputQueriable.InfoOutputDetailLevel"/> property.
/// </summary>
/// <param name="infoOutputDetailLevel">The info-output level of detail.</param>
/// <returns>A value indicating whether the <see cref="IInfoOutputQueriable.InfoOutputDetailLevel"/> has been set to <paramref name="infoOutputDetailLevel"/>.</returns>
bool IInfoOutputQueriable.TrySetInfoOutputDetailLevel(InfoOutputDetailLevel infoOutputDetailLevel)
{
return(infoOutputDetailLevel == InfoOutputDetailLevel.Full);
}
/// <summary>Initializes a new instance of the <see cref="EznMatrixDecomposer" /> class.
/// </summary>
/// <param name="infoOutputDetailLevel">The info-output level of detail.</param>
public EznMatrixDecomposer(InfoOutputDetailLevel infoOutputDetailLevel = InfoOutputDetailLevel.Middle)
: base(infoOutputDetailLevel)
{
MaximalRank = null;
m_Name = new IdentifierString("EZN Decomposer");
}
/// <summary>Initializes a new instance of the <see cref="DensityEstimator" /> class.
/// </summary>
/// <param name="infoOutputDetailLevel">The info output detail level.</param>
/// <param name="annotation">The annotation of the current instance.</param>
protected DensityEstimator(InfoOutputDetailLevel infoOutputDetailLevel = InfoOutputDetailLevel.High, string annotation = "")
{
Annotation = annotation;
InfoOutputDetailLevel = infoOutputDetailLevel;
}
/// <summary>Sets the <see cref="IInfoOutputQueriable.InfoOutputDetailLevel" /> property.
/// </summary>
/// <param name="infoOutputDetailLevel">The info-output level of detail.</param>
protected void SetInfoOutputDetailLevel(InfoOutputDetailLevel infoOutputDetailLevel)
{
InfoOutputDetailLevel = infoOutputDetailLevel;
}
/// <summary>Initializes a new instance of the <see cref="MLEstimator" /> class.
/// </summary>
/// <param name="infoOutputDetailLevel">A value indicating the level of detail.</param>
internal MLEstimator(InfoOutputDetailLevel infoOutputDetailLevel)
{
InfoOutputDetailLevel = infoOutputDetailLevel;
Name = new IdentifierString("ML Estimator");
LongName = new IdentifierString("Log-Normal distribution: ML Estimator");
}
/// <summary>Creates a specific parameter estimating approach with respect to the Log-Normal distribution.
/// </summary>
/// <param name="method">A value indicating the method how to estimate the parameters.</param>
/// <param name="infoOutputDetailLevel">A value indicating the level of detail.</param>
/// <returns>The specified parameter estimating approach with respect to the Normal distribution.</returns>
public static IProbabilityDistributionEstimator <LogNormalDistribution> Create(Method method, InfoOutputDetailLevel infoOutputDetailLevel = InfoOutputDetailLevel.Middle)
{
switch (method)
{
case Method.MaximumLikelihood:
return(new MLEstimator(infoOutputDetailLevel));
case Method.MethodsOfMoments:
return(new MoMEstimator(infoOutputDetailLevel));
default:
throw new NotImplementedException();
}
}
/// <summary>Sets the <see cref="IInfoOutputQueriable.InfoOutputDetailLevel"/> property.
/// </summary>
/// <param name="infoOutputDetailLevel">The info-output level of detail.</param>
/// <returns>A value indicating whether the <see cref="IInfoOutputQueriable.InfoOutputDetailLevel"/> has been set to <paramref name="infoOutputDetailLevel"/>.
/// </returns>
public bool TrySetInfoOutputDetailLevel(InfoOutputDetailLevel infoOutputDetailLevel)
{
return(m_DateScheduleRule.TrySetInfoOutputDetailLevel(infoOutputDetailLevel));
}
/// <summary>Creates a specific parameter estimating approach with respect to the Exponential distribution.
/// </summary>
/// <param name="method">A value indicating the method how to estimate the parameters.</param>
/// <param name="infoOutputDetailLevel">A value indicating the level of detail.</param>
/// <returns>The specified parameter estimating approach with respect to the Exponential distribution.</returns>
public static IProbabilityDistributionEstimator <ExponentialDistribution> Create(Method method = Method.Standard, InfoOutputDetailLevel infoOutputDetailLevel = InfoOutputDetailLevel.Middle)
{
switch (method)
{
case Method.Standard:
return(new StandardEstimator(infoOutputDetailLevel));
default:
throw new NotImplementedException();
}
}
/// <summary>Initializes a new instance of the <see cref="EznMatrixDecomposer" /> class.
/// </summary>
/// <param name="maximalRank">The maximal rank of the resulting matrix.</param>
/// <param name="infoOutputDetailLevel">The info-output level of detail.</param>
public EznMatrixDecomposer(int maximalRank, InfoOutputDetailLevel infoOutputDetailLevel = InfoOutputDetailLevel.Middle)
: base(infoOutputDetailLevel)
{
MaximalRank = maximalRank;
m_Name = new IdentifierString(String.Format("EZN Decomposer; max. rank: {0}", maximalRank));
}
/// <summary>Sets the <see cref="P:Dodoni.BasicComponents.Containers.IInfoOutputQueriable.InfoOutputDetailLevel"/> property.
/// </summary>
/// <param name="infoOutputDetailLevel">The info-output level of detail.</param>
/// <returns>A value indicating whether the <see cref="P:Dodoni.BasicComponents.Containers.IInfoOutputQueriable.InfoOutputDetailLevel"/> has been set to <paramref name="infoOutputDetailLevel"/>.</returns>
public virtual bool TrySetInfoOutputDetailLevel(InfoOutputDetailLevel infoOutputDetailLevel)
{
return(infoOutputDetailLevel == InfoOutputDetailLevel);
}
/// <summary>Sets the <see cref="IInfoOutputQueriable.InfoOutputDetailLevel" /> property.
/// </summary>
/// <param name="infoOutputDetailLevel">The info-output level of detail.</param>
/// <returns>A value indicating whether the <see cref="IInfoOutputQueriable.InfoOutputDetailLevel" /> has been set to <paramref name="infoOutputDetailLevel" />.</returns>
public bool TrySetInfoOutputDetailLevel(InfoOutputDetailLevel infoOutputDetailLevel)
{
return(infoOutputDetailLevel == this.InfoOutputDetailLevel);
}
/// <summary>Initializes a new instance of the <see cref="OneDimOptimizer"/> class.
/// </summary>
/// <param name="infoOutputDetailLevel">The info-output level of detail in its <see cref="Dodoni.BasicComponents.Containers.InfoOutputDetailLevel"/> representation.</param>
protected OneDimOptimizer(InfoOutputDetailLevel infoOutputDetailLevel = InfoOutputDetailLevel.Full)
{
InfoOutputDetailLevel = infoOutputDetailLevel;
}
/// <summary>Initializes a new instance of the <see cref="StandardEstimator" /> class.
/// </summary>
/// <param name="infoOutputDetailLevel">A value indicating the level of detail.</param>
internal StandardEstimator(InfoOutputDetailLevel infoOutputDetailLevel)
{
InfoOutputDetailLevel = infoOutputDetailLevel;
Name = new IdentifierString("Standard Estimator");
LongName = new IdentifierString("Exponential distribution: Standard Estimator");
}
/// <summary>Sets the <see cref="IInfoOutputQueriable.InfoOutputDetailLevel"/> property.
/// </summary>
/// <param name="infoOutputDetailLevel">The info-output level of detail.</param>
/// <returns>A value indicating whether the <see cref="IInfoOutputQueriable.InfoOutputDetailLevel"/> has been set to <paramref name="infoOutputDetailLevel"/>.</returns>
public bool TrySetInfoOutputDetailLevel(InfoOutputDetailLevel infoOutputDetailLevel)
{
return(m_OrdinaryFunctionDescriptor.TrySetInfoOutputDetailLevel(infoOutputDetailLevel));
}
/// <summary>Sets the <see cref="P:Dodoni.BasicComponents.Containers.IInfoOutputQueriable.InfoOutputDetailLevel"/> property.
/// </summary>
/// <param name="infoOutputDetailLevel">The info-output level of detail.</param>
/// <returns>A value indicating whether the <see cref="P:Dodoni.BasicComponents.Containers.IInfoOutputQueriable.InfoOutputDetailLevel"/> has been set to <paramref name="infoOutputDetailLevel"/>.</returns>
public bool TrySetInfoOutputDetailLevel(InfoOutputDetailLevel infoOutputDetailLevel)
{
return(infoOutputDetailLevel == InfoOutputDetailLevel.Full);
}
/// <summary>Initializes a new instance of the <see cref="OrdinaryMultiDimOptimizer"/> class.
/// </summary>
/// <param name="infoOutputDetailLevel">The info-output level of detail in its <see cref="Dodoni.BasicComponents.Containers.InfoOutputDetailLevel"/> representation.</param>
protected OrdinaryMultiDimOptimizer(InfoOutputDetailLevel infoOutputDetailLevel = InfoOutputDetailLevel.Full)
: base(infoOutputDetailLevel)
{
}
