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}
public static MsItem SetMsNonLinearRegr(MsId ms, ulong modelKey, ulong eqKey, MsModel eq)
{ // Get input variable
MsFormulaTransformVariable inp = eq.transform.inputs.its.GetValue(0) as MsFormulaTransformVariable;
// Set operation execution parameters
var setMsOp = new SetMs();
setMsOp.tMs = ms;
setMsOp.tArg = new SetMsArg()
{ // Set data change pattern
pattern = new MsMdPattern()
{
item = new MsItemPattern()
{ // Specify model key
key = modelKey,
problem = new MsProblemPattern()
{
metamodel = new MsMetaModelPattern()
{
calculationChain = ListOperation.Change
}
}
}
}, // Set data that must be changed
meta = new MsMd()
{
item = new MsItem()
{ // Specify model key
k = modelKey,
type = MsItemType.Problem,
problemMd = new MsProblem()
{
metamodel = new MsMetaModel()
{
calculationChain = new MsCalculationChainEntries()
{
its = new MsCalculationChainEntry[]
{
new MsCalculationChainEntry()
{
k = eqKey,
type = MsCalculationChainType.Model,
model = new MsModel()
{ // Set calculation periods
period = new MsModelPeriod()
{
autoPeriod = 0,
identificationStartDate = DateTime.Parse("01.01.1990"),
identificationEndDate = DateTime.Parse("01.01.2006"),
forecastStartDate = DateTime.Parse("02.01.2006"),
forecastEndDate = DateTime.Parse("01.01.2010")
},
transform = new MsFormulaTransform()
{
formulas = new TsFormulas()
{
its = new TsFormula[]
{
new TsFormula()
{
kind = TsFormulaKind.NonLinearRegression,
method = new TsMethod()
{ // Set method calculation parameters
nonLinearRegression = new TsNonLinearRegressionMethod()
{ // Set missing data treatment method
missingData = new StatMissingData(){method = StatMissingDataMethod.LinTrend},
//Set confidence limits relevance
confidenceLevel = 0.90,
// Use derivatives for improving accuracy of calculation
useDerivatives = true,
// Set maximum number of iterations for method calculation
maxIterations = 500,
// Set accuracy of determining normalization parameter
dampingFactorTolerance = 0.6,
// Set variables used in calculation
operands = new MsFormulaTerms()
{
its = new MsFormulaTerm[]
{
new MsFormulaTerm()
{slice = inp.slices.its.GetValue(0) as MsFormulaTransformSlice}
}
}, // Set calculation formula
expression = "@_" + inp.k + ":0[] * a + b",
// Set coefficients
coefficients = new string[] {"a", "b"},
// Set initial approximations of coefficients
initialApproximation = new double[] {0.15, 0.09},
// Set maximum distance between approximations
maxStep = 0.4
}
}
}
}
}
}
}
}
}
}
}
}
}
}, // Set changed data getting pattern
metaGet = new MsMdPattern()
{
scenarios = ListOperation.Get,
period = true,
item = new MsItemPattern()
{
key = modelKey,
problem = new MsProblemPattern()
{
details = true,
scenarios = ListOperation.Get,
useSavedCoefficients = true,
metamodel = new MsMetaModelPattern()
{
calculationChain = ListOperation.Get,
calcChainPattern = new MsCalculationChainPattern()
{
modelPattern = new MsModelPattern()
{
transform = new MsFormulaTransformPattern()
{
kind = true,
formulaCount = true,
formulas = new TsFormulaPattern()
{
method = new TsMethodPattern() { }
},
equationsFormula = new TsFormulaPattern()
{
method = new TsMethodPattern() { }
}
}
}
},
visualControllerPattern = new MsMetaModelVisualControllerPattern()
{
variableRubricatorKey = true,
levelFormat = MsLevelFormat.Short,
useSourceName = false,
freeVariables = true
}
}
}
}
}
}; // Create a proxy object for operation execution
var somClient = new SomPortTypeClient();
// Set operation execution parameters
var result = somClient.SetMs(setMsOp);
return result.meta.item;
}