public void TestChangeIndex()
{
IndexedDataTable indexedTable = new IndexedDataTable(new string[] { "Year", "Name" });
indexedTable.SetIndex(new object[] { 2000, "Name1" });
indexedTable.SetValues("A", new double[] { 1, 2, 3, 4 }); // vector
indexedTable.Set("B", 1234); // scalar
indexedTable.SetIndex(new object[] { 2001, "Name2" });
indexedTable.SetValues("A", new double[] { 5, 6, 7, 8 }); // vector
indexedTable.Set("B", 5678); // scalar
Assert.IsTrue(
Utilities.CreateTable(new string[] { "Year", "Name", "A", "B" },
new List <object[]> {
new object[] { 2000, "Name1", 1, 1234 },
new object[] { 2000, "Name1", 2, 1234 },
new object[] { 2000, "Name1", 3, 1234 },
new object[] { 2000, "Name1", 4, 1234 },
new object[] { 2001, "Name2", 5, 5678 },
new object[] { 2001, "Name2", 6, 5678 },
new object[] { 2001, "Name2", 7, 5678 },
new object[] { 2001, "Name2", 8, 5678 }
})
.IsSame(indexedTable.ToTable()));
}
/// <summary>Main run method for performing our calculations and storing data.</summary>
public void Run()
{
// If the target table has not been modified during the simulation run, don't do anything.
if (dataStore?.Writer != null && !dataStore.Writer.TablesModified.Contains(TableName))
{
return;
}
if (string.IsNullOrWhiteSpace(TableName))
{
throw new Exception(string.Format("Error in probability model {0}: TableName is null", Name));
}
else if (!dataStore.Reader.TableNames.Contains(TableName))
{
throw new Exception(string.Format("Error in probability model {0}: table '{1}' does not exist in the database.", Name, TableName));
}
DataTable simulationData = dataStore.Reader.GetData(TableName, fieldNames: dataStore.Reader.ColumnNames(TableName));
if (simulationData != null)
{
IndexedDataTable simData = new IndexedDataTable(simulationData, new string[] { FieldToSplitOn });
IndexedDataTable probabilityData = new IndexedDataTable(new string[] { FieldToSplitOn });
foreach (var group in simData.Groups())
{
object keyValue = group.IndexValues[0];
// Add in our key column
probabilityData.SetIndex(new object[] { keyValue });
probabilityData.Set <object>(FieldToSplitOn, keyValue);
// Add in all other numeric columns.
bool haveWrittenProbabilityColumn = false;
foreach (DataColumn column in simulationData.Columns)
{
if (column.DataType == typeof(double))
{
var values = group.Get <double>(column.ColumnName).ToList();
values.Sort();
if (!haveWrittenProbabilityColumn)
{
// Add in the probability column
double[] probabilityValues = MathUtilities.ProbabilityDistribution(values.Count, this.Exceedence);
probabilityData.SetValues("Probability", probabilityValues);
haveWrittenProbabilityColumn = true;
}
probabilityData.SetValues(column.ColumnName, values);
}
}
}
// Write the stats data to the DataStore
DataTable t = probabilityData.ToTable();
t.TableName = this.Name;
dataStore.Writer.WriteTable(t);
}
}
public void TestIterateThroughGroups()
{
IndexedDataTable indexedTable = new IndexedDataTable(new string[] { "Year", "Name" });
indexedTable.SetIndex(new object[] { 2000, "Name1" });
indexedTable.SetValues("A", new double[] { 1, 2, 3, 4 }); // vector
indexedTable.Set("B", 1234); // scalar
indexedTable.SetIndex(new object[] { 2001, "Name2" });
indexedTable.SetValues("A", new double[] { 5, 6, 7, 8 }); // vector
indexedTable.Set("B", 5678); // scalar
int i = 1;
foreach (var group in indexedTable.Groups())
{
var a = group.Get <double>("A");
if (i == 1)
{
Assert.AreEqual(a, new double[] { 1, 2, 3, 4 });
}
else
{
Assert.AreEqual(a, new double[] { 5, 6, 7, 8 });
}
i++;
}
}
public Form1()
{
InitializeComponent();
using (var xmlStream = Assembly.GetExecutingAssembly().GetManifestResourceStream("LiveLinqToDataSet.Northwind.xml"))
{
System.Diagnostics.Debug.Assert(xmlStream != null);
XmlReaderSettings settings = new XmlReaderSettings {
IgnoreWhitespace = true
};
XmlReader reader = XmlReader.Create(xmlStream, settings);
untypedDataSet.ReadXml(reader);
}
using (var xmlStream = Assembly.GetExecutingAssembly().GetManifestResourceStream("LiveLinqToDataSet.Northwind.xml"))
{
System.Diagnostics.Debug.Assert(xmlStream != null);
XmlReaderSettings settings = new XmlReaderSettings {
IgnoreWhitespace = true
};
XmlReader reader = XmlReader.Create(xmlStream, settings);
foreach (DataColumn col in typedDataSet.Tables["Orders"].Columns)
{
col.ColumnMapping = MappingType.Attribute; // to make our XML file more compact
}
typedDataSet.Namespace = ""; // need this because our XML file does not have xmlns defined
typedDataSet.ReadXml(reader);
}
untypedCustomers = untypedDataSet.Tables["Customers"].AsIndexed();
untypedOrders = untypedDataSet.Tables["Orders"].AsIndexed();
customers = typedDataSet.Customers.AsIndexed();
orders = typedDataSet.Orders.AsIndexed();
}
public void TestGetColumn()
{
IndexedDataTable indexedTable = new IndexedDataTable(new string[] { "Year", "Name" });
indexedTable.SetIndex(new object[] { 2000, "Name1" });
indexedTable.SetValues("A", new double[] { 1, 2, 3, 4 }); // vector
indexedTable.Set("B", 1234); // scalar
indexedTable.SetIndex(new object[] { 2001, "Name2" });
indexedTable.SetValues("A", new double[] { 5, 6, 7, 8 }); // vector
indexedTable.Set("B", 5678); // scalar
IndexedDataTable indexedTable2 = new IndexedDataTable(indexedTable.ToTable(), new string[] { "Year" });
indexedTable2.SetIndex(new object[] { 2000 });
var a = indexedTable2.Get <double>("A");
Assert.AreEqual(a, new double[] { 1, 2, 3, 4 });
var names = indexedTable2.Get <string>("Name");
Assert.AreEqual(names, new string[] { "Name1", "Name1", "Name1", "Name1" });
indexedTable2.SetIndex(new object[] { 2001 });
var b = indexedTable2.Get <int>("B");
Assert.AreEqual(b, new int[] { 5678, 5678, 5678, 5678 });
}
/// <summary>Main run method for performing our post simulation calculations</summary>
public void Run()
{
// Note - we seem to be assuming that the predicted data table is called Report.
// If the predicted table has not been modified during the most recent simulations run, don't do anything.
if (dataStore?.Writer != null && !dataStore.Writer.TablesModified.Contains("Report"))
{
return;
}
string sql = "SELECT * FROM [Report]";
DataTable predictedData = dataStore.Reader.GetDataUsingSql(sql);
if (predictedData != null)
{
IndexedDataTable predictedDataIndexed = new IndexedDataTable(predictedData, null);
string outputNames = StringUtilities.Build(Outputs, ",", "\"", "\"");
string inputNames = StringUtilities.Build(Inputs, ",", "\"", "\"");
string anovaVariableValuesFileName = GetTempFileName("anovaVariableValues", ".csv");
// Write variables file
using (var writer = new StreamWriter(anovaVariableValuesFileName))
DataTableUtilities.DataTableToText(predictedDataIndexed.ToTable(), 0, ",", true, writer, excelFriendly: true);
string script = string.Format(
"inputs <- c({0})" + Environment.NewLine +
"inputs <- inputs[inputs != \"\"]" + Environment.NewLine +
"outputs <- c({1})" + Environment.NewLine +
"outputs <- outputs[outputs != \"\"]" + Environment.NewLine +
"factorial_data <- read.csv(\"{2}\")" + Environment.NewLine +
"indices <- data.frame(matrix(ncol = 4, nrow = 0))" + Environment.NewLine +
"colnames(indices) <- c(\"Input\", \"Output\", \"FirstOrder\", \"TotalOrder\")" + Environment.NewLine +
"for (output in outputs){{" + Environment.NewLine +
" data <- factorial_data[, names(factorial_data) %in% inputs | names(factorial_data) == output]" + Environment.NewLine +
" data[, names(data) %in% inputs] <- lapply(data[, names(data) %in% inputs], factor)" + Environment.NewLine +
" output_mean <- mean(data[[output]])" + Environment.NewLine +
" TSS <- sum((data[[output]] - output_mean)^2)" + Environment.NewLine +
" anova_model <- aov(data[[output]] ~ (.)^1000, data = data[, names(data) %in% inputs])" + Environment.NewLine +
" SSi <- summary(anova_model)[[1]][2]" + Environment.NewLine +
" variance_contributions <- SSi / TSS" + Environment.NewLine +
" parameter_names <- trimws(rownames(SSi), which = \"both\")" + Environment.NewLine +
" all_results <- data.frame(parameter_names, variance_contributions, row.names = NULL)" + Environment.NewLine +
" names(all_results) <- list(\"input\", \"% of variance\") " + Environment.NewLine +
" for (input in inputs){{" + Environment.NewLine +
" first <- all_results[all_results$input == input, colnames(all_results) == \"% of variance\"]" + Environment.NewLine +
" total <- sum(all_results[grepl(input, all_results$input), colnames(all_results) == \"% of variance\"])" + Environment.NewLine +
" result <- data.frame(Input=c(input), Output=c(output), FirstOrder=c(first), TotalOrder=c(total))" + Environment.NewLine +
" indices <- rbind(indices, result)" + Environment.NewLine +
" }}" + Environment.NewLine +
"}}" + Environment.NewLine +
"write.table(indices, sep=\",\", row.names=FALSE)" + Environment.NewLine
,
inputNames, outputNames, anovaVariableValuesFileName.Replace("\\", "/"));
DataTable results = RunR(script);
results.TableName = Name + "Statistics";
dataStore.Writer.WriteTable(results);
}
}
protected IncrementalLinearSolver(IncrementalLinearSolver original, Cloner cloner)
: base(original, cloner)
{
problemTypeParam = cloner.Clone(original.problemTypeParam);
qualityUpdateIntervalParam = cloner.Clone(original.qualityUpdateIntervalParam);
if (original.qualityPerClock != null)
{
qualityPerClock = cloner.Clone(original.qualityPerClock);
}
}
public void TestNoIndexSetScalarThenSetVector()
{
IndexedDataTable indexedTable = new IndexedDataTable(null);
indexedTable.Set("A", 1234); // scalar
indexedTable.SetValues("B", new double[] { 1, 2, 3, 4 }); // vector
string expected = ReflectionUtilities.GetResourceAsString("UnitTests.APSIMShared.TestNoIndexSetScalarThenSetVector.Expected.txt");
Assert.AreEqual(Utilities.TableToString(indexedTable.ToTable()), expected);
}
public void TestSetVectorThenSetScalar()
{
IndexedDataTable indexedTable = new IndexedDataTable(new string[] { "Year", "Name" });
indexedTable.SetIndex(new object[] { 2000, "Name1" });
indexedTable.SetValues("A", new double[] { 1, 2, 3, 4 }); // vector
indexedTable.Set("B", 1234); // scalar
string expected = ReflectionUtilities.GetResourceAsString("UnitTests.APSIMShared.IndexedDataTableTests.TestSetVectorThenScalar.Expected.txt");
Assert.AreEqual(Utilities.TableToString(indexedTable.ToTable()), expected);
}
public void TestNoIndexSetScalarThenSetVector()
{
IndexedDataTable indexedTable = new IndexedDataTable(null);
indexedTable.Set("A", 1234); // scalar
indexedTable.SetValues("B", new double[] { 1, 2, 3, 4 }); // vector
Assert.AreEqual(Utilities.TableToString(indexedTable.ToTable()),
" A, B\r\n" +
"1234,1.000\r\n" +
"1234,2.000\r\n" +
"1234,3.000\r\n" +
"1234,4.000\r\n");
}
public void TestSetScalarThenSetVector()
{
IndexedDataTable indexedTable = new IndexedDataTable(new string[] { "Year", "Name" });
indexedTable.SetIndex(new object[] { 2000, "Name1" });
indexedTable.Set("A", 1234); // scalar
indexedTable.SetValues("B", new double[] { 1, 2, 3, 4 }); // vector
Assert.AreEqual(Utilities.TableToString(indexedTable.ToTable()),
"Year, Name, A, B\r\n" +
"2000,Name1,1234,1.000\r\n" +
"2000,Name1,1234,2.000\r\n" +
"2000,Name1,1234,3.000\r\n" +
"2000,Name1,1234,4.000\r\n");
}
/// <summary>
/// The main run method called to fill tables in the specified DataStore.
/// </summary>
/// <param name="dataStore">The DataStore to work with</param>
public void Run(IStorageReader dataStore)
{
dataStore.DeleteDataInTable(this.Name);
DataTable simulationData = dataStore.GetData(TableName, fieldNames: dataStore.GetTableColumns(TableName));
if (simulationData != null)
{
IndexedDataTable simData = new IndexedDataTable(simulationData, new string[] { FieldToSplitOn });
IndexedDataTable probabilityData = new IndexedDataTable(new string[] { FieldToSplitOn });
foreach (var group in simData.Groups())
{
object keyValue = group.IndexValues[0];
// Add in our key column
probabilityData.SetIndex(new object[] { keyValue });
probabilityData.Set <object>(FieldToSplitOn, keyValue);
// Add in all other numeric columns.
bool haveWrittenProbabilityColumn = false;
foreach (DataColumn column in simulationData.Columns)
{
if (column.DataType == typeof(double))
{
var values = group.Get <double>(column.ColumnName).ToList();
values.Sort();
if (!haveWrittenProbabilityColumn)
{
// Add in the probability column
double[] probabilityValues = MathUtilities.ProbabilityDistribution(values.Count, this.Exceedence);
probabilityData.SetValues("Probability", probabilityValues);
haveWrittenProbabilityColumn = true;
}
probabilityData.SetValues(column.ColumnName, values);
}
}
}
// Write the stats data to the DataStore
DataTable t = probabilityData.ToTable();
t.TableName = this.Name;
dataStore.WriteTable(t);
}
}
public void TestNoIndexSetScalarThenSetVector()
{
IndexedDataTable indexedTable = new IndexedDataTable(null);
indexedTable.Set("A", 1234); // scalar
indexedTable.SetValues("B", new double[] { 1, 2, 3, 4 }); // vector
Assert.IsTrue(
Utilities.CreateTable(new string[] { "A", "B" },
new List <object[]> {
new object[] { 1234, 1 },
new object[] { 1234, 2 },
new object[] { 1234, 3 },
new object[] { 1234, 4 }
})
.IsSame(indexedTable.ToTable()));
}
public void TestSetVectorThenSetScalar()
{
IndexedDataTable indexedTable = new IndexedDataTable(new string[] { "Year", "Name" });
indexedTable.SetIndex(new object[] { 2000, "Name1" });
indexedTable.SetValues("A", new double[] { 1, 2, 3, 4 }); // vector
indexedTable.Set("B", 1234); // scalar
Assert.IsTrue(
Utilities.CreateTable(new string[] { "Year", "Name", "A", "B" },
new List <object[]> {
new object[] { 2000, "Name1", 1, 1234 },
new object[] { 2000, "Name1", 2, 1234 },
new object[] { 2000, "Name1", 3, 1234 },
new object[] { 2000, "Name1", 4, 1234 }
})
.IsSame(indexedTable.ToTable()));
}
public void TestIndexedDataTable()
{
var dt = new IndexedDataTable <int>("test", "test description");
var dr = new IndexedDataRow <int>("test row");
dr.Values.Add(Tuple.Create(1, 1.0));
dr.Values.Add(Tuple.Create(2, 2.0));
dr.Values.Add(Tuple.Create(3, 3.0));
dt.Rows.Add(dr);
var ser = new ProtoBufSerializer();
ser.Serialize(dt, tempFile);
var dt2 = (IndexedDataTable <int>)ser.Deserialize(tempFile);
Assert.AreEqual(dt.Rows["test row"].Values[0], dt2.Rows["test row"].Values[0]);
Assert.AreEqual(dt.Rows["test row"].Values[1], dt2.Rows["test row"].Values[1]);
Assert.AreEqual(dt.Rows["test row"].Values[2], dt2.Rows["test row"].Values[2]);
}
private static IndexedDataTable <double> NMSEGraph(double[,] coeff, double[] lambda, double[] trainNMSE, double[] testNMSE)
{
var errorTable = new IndexedDataTable <double>("NMSE", "Path of NMSE values over different lambda values");
var numNonZeroCoeffs = new int[lambda.Length];
errorTable.VisualProperties.YAxisMaximumAuto = false;
errorTable.VisualProperties.YAxisMinimumAuto = false;
errorTable.VisualProperties.XAxisMaximumAuto = false;
errorTable.VisualProperties.XAxisMinimumAuto = false;
for (int i = 0; i < coeff.GetLength(0); i++)
{
for (int j = 0; j < coeff.GetLength(1); j++)
{
if (!coeff[i, j].IsAlmost(0.0))
{
numNonZeroCoeffs[i]++;
}
}
}
errorTable.VisualProperties.YAxisMinimumFixedValue = 0;
errorTable.VisualProperties.YAxisMaximumFixedValue = 1.0;
errorTable.VisualProperties.XAxisLogScale = true;
errorTable.VisualProperties.XAxisTitle = "Lambda";
errorTable.VisualProperties.YAxisTitle = "Normalized mean of squared errors (NMSE)";
errorTable.VisualProperties.SecondYAxisTitle = "Number of variables";
errorTable.Rows.Add(new IndexedDataRow <double>("NMSE (train)", "Path of NMSE values over different lambda values", lambda.Zip(trainNMSE, (l, v) => Tuple.Create(l, v))));
errorTable.Rows.Add(new IndexedDataRow <double>("NMSE (test)", "Path of NMSE values over different lambda values", lambda.Zip(testNMSE, (l, v) => Tuple.Create(l, v))));
errorTable.Rows.Add(new IndexedDataRow <double>("Number of variables", "The number of non-zero coefficients for each step in the path", lambda.Zip(numNonZeroCoeffs, (l, v) => Tuple.Create(l, (double)v))));
if (lambda.Length > 2)
{
errorTable.VisualProperties.XAxisMinimumFixedValue = Math.Pow(10, Math.Floor(Math.Log10(lambda.Last())));
errorTable.VisualProperties.XAxisMaximumFixedValue = Math.Pow(10, Math.Ceiling(Math.Log10(lambda.Skip(1).First())));
}
errorTable.Rows["NMSE (train)"].VisualProperties.ChartType = DataRowVisualProperties.DataRowChartType.Points;
errorTable.Rows["NMSE (test)"].VisualProperties.ChartType = DataRowVisualProperties.DataRowChartType.Points;
errorTable.Rows["Number of variables"].VisualProperties.ChartType = DataRowVisualProperties.DataRowChartType.Points;
errorTable.Rows["Number of variables"].VisualProperties.SecondYAxis = true;
return(errorTable);
}
public void TestChangeIndex()
{
IndexedDataTable indexedTable = new IndexedDataTable(new string[] { "Year", "Name" });
indexedTable.SetIndex(new object[] { 2000, "Name1" });
indexedTable.SetValues("A", new double[] { 1, 2, 3, 4 }); // vector
indexedTable.Set("B", 1234); // scalar
indexedTable.SetIndex(new object[] { 2001, "Name2" });
indexedTable.SetValues("A", new double[] { 5, 6, 7, 8 }); // vector
indexedTable.Set("B", 5678); // scalar
Assert.AreEqual(Utilities.TableToString(indexedTable.ToTable()),
"Year, Name, A, B\r\n" +
"2000,Name1,1.000,1234\r\n" +
"2000,Name1,2.000,1234\r\n" +
"2000,Name1,3.000,1234\r\n" +
"2000,Name1,4.000,1234\r\n" +
"2001,Name2,5.000,5678\r\n" +
"2001,Name2,6.000,5678\r\n" +
"2001,Name2,7.000,5678\r\n" +
"2001,Name2,8.000,5678\r\n");
}
private static IndexedDataTable <double> CoefficientGraph(double[,] coeff, double[] lambda, IEnumerable <string> allowedVars, IDataset ds, bool showOnlyRelevantBasisFuncs = true)
{
var coeffTable = new IndexedDataTable <double>("Coefficients", "The paths of standarized coefficient values over different lambda values");
coeffTable.VisualProperties.YAxisMaximumAuto = false;
coeffTable.VisualProperties.YAxisMinimumAuto = false;
coeffTable.VisualProperties.XAxisMaximumAuto = false;
coeffTable.VisualProperties.XAxisMinimumAuto = false;
coeffTable.VisualProperties.XAxisLogScale = true;
coeffTable.VisualProperties.XAxisTitle = "Lambda";
coeffTable.VisualProperties.YAxisTitle = "Coefficients";
coeffTable.VisualProperties.SecondYAxisTitle = "Number of variables";
var nLambdas = lambda.Length;
var nCoeff = coeff.GetLength(1);
var dataRows = new IndexedDataRow <double> [nCoeff];
var numNonZeroCoeffs = new int[nLambdas];
var doubleVariables = allowedVars.Where(ds.VariableHasType <double>);
var factorVariableNames = allowedVars.Where(ds.VariableHasType <string>);
var factorVariablesAndValues = ds.GetFactorVariableValues(factorVariableNames, Enumerable.Range(0, ds.Rows)); //must consider all factor values (in train and test set)
for (int i = 0; i < coeff.GetLength(0); i++)
{
for (int j = 0; j < coeff.GetLength(1); j++)
{
if (!coeff[i, j].IsAlmost(0.0))
{
numNonZeroCoeffs[i]++;
}
}
}
{
int i = 0;
foreach (var factorVariableAndValues in factorVariablesAndValues)
{
foreach (var factorValue in factorVariableAndValues.Value)
{
double sigma = ds.GetStringValues(factorVariableAndValues.Key)
.Select(s => s == factorValue ? 1.0 : 0.0)
.StandardDeviation(); // calc std dev of binary indicator
var path = Enumerable.Range(0, nLambdas).Select(r => Tuple.Create(lambda[r], coeff[r, i] * sigma)).ToArray();
dataRows[i] = new IndexedDataRow <double>(factorVariableAndValues.Key + "=" + factorValue, factorVariableAndValues.Key + "=" + factorValue, path);
i++;
}
}
foreach (var doubleVariable in doubleVariables)
{
double sigma = ds.GetDoubleValues(doubleVariable).StandardDeviation();
var path = Enumerable.Range(0, nLambdas).Select(r => Tuple.Create(lambda[r], coeff[r, i] * sigma)).ToArray();
dataRows[i] = new IndexedDataRow <double>(doubleVariable, doubleVariable, path);
i++;
}
// add to coeffTable by total weight (larger area under the curve => more important);
foreach (var r in dataRows.OrderByDescending(r => r.Values.Select(t => t.Item2).Sum(x => Math.Abs(x))))
{
coeffTable.Rows.Add(r);
}
}
if (lambda.Length > 2)
{
coeffTable.VisualProperties.XAxisMinimumFixedValue = Math.Pow(10, Math.Floor(Math.Log10(lambda.Last())));
coeffTable.VisualProperties.XAxisMaximumFixedValue = Math.Pow(10, Math.Ceiling(Math.Log10(lambda.Skip(1).First())));
}
coeffTable.Rows.Add(new IndexedDataRow <double>("Number of variables", "The number of non-zero coefficients for each step in the path", lambda.Zip(numNonZeroCoeffs, (l, v) => Tuple.Create(l, (double)v))));
coeffTable.Rows["Number of variables"].VisualProperties.ChartType = DataRowVisualProperties.DataRowChartType.Points;
coeffTable.Rows["Number of variables"].VisualProperties.SecondYAxis = true;
return(coeffTable);
}
/// <summary>Main run method for performing our post simulation calculations</summary>
/// <param name="dataStore">The data store.</param>
public void Run(IDataStore dataStore)
{
DataTable predictedData = dataStore.Reader.GetData("Report", filter: "SimulationName LIKE '" + Name + "%'", orderBy: "SimulationID");
if (predictedData != null)
{
// Determine how many years we have per simulation
DataView view = new DataView(predictedData);
view.RowFilter = "SimulationName='" + Name + "Simulation1'";
Years = DataTableUtilities.GetColumnAsIntegers(view, "Clock.Today.Year");
// Create a table of all predicted values
DataTable predictedValues = new DataTable();
List <string> descriptiveColumnNames = new List <string>();
List <string> variableNames = new List <string>();
foreach (double year in Years)
{
view.RowFilter = "Clock.Today.Year=" + year;
foreach (DataColumn predictedColumn in view.Table.Columns)
{
if (predictedColumn.DataType == typeof(double))
{
double[] valuesForYear = DataTableUtilities.GetColumnAsDoubles(view, predictedColumn.ColumnName);
if (valuesForYear.Distinct().Count() == 1)
{
if (!descriptiveColumnNames.Contains(predictedColumn.ColumnName))
{
descriptiveColumnNames.Add(predictedColumn.ColumnName);
}
}
else
{
DataTableUtilities.AddColumn(predictedValues, predictedColumn.ColumnName + year, valuesForYear);
if (!variableNames.Contains(predictedColumn.ColumnName))
{
variableNames.Add(predictedColumn.ColumnName);
}
}
}
}
}
// Run R
DataTable eeDataRaw;
DataTable statsDataRaw;
RunRPostSimulation(predictedValues, out eeDataRaw, out statsDataRaw);
// Get ee data from R and store in ee table.
// EE data from R looks like:
// "ResidueWt", "FASW", "CN2", "Cona", "variable","path"
// - 22.971008269563,0.00950570342209862,-0.00379987333757356,56.7587080430652,"FallowEvaporation1996",1
// - 25.790599484188, 0.0170777988614538, -0.0265991133629069,58.0240658644712,"FallowEvaporation1996",2
// - 26.113599477728, 0.0113851992409871, 0.0113996200126667,57.9689677010766,"FallowEvaporation1996",3
// - 33.284199334316, 0.0323193916349732, -0.334388853704853,60.5376820772641,"FallowEvaporation1996",4
DataView eeView = new DataView(eeDataRaw);
IndexedDataTable eeTableKey = new IndexedDataTable(new string[] { "Parameter", "Year" });
// Create a path variable.
var pathValues = Enumerable.Range(1, NumPaths).ToArray();
foreach (var parameter in Parameters)
{
foreach (DataColumn column in predictedValues.Columns)
{
eeView.RowFilter = "variable = '" + column.ColumnName + "'";
if (eeView.Count != NumPaths)
{
throw new Exception("Found only " + eeView.Count + " paths for variable " + column.ColumnName + " in ee table");
}
int year = Convert.ToInt32(column.ColumnName.Substring(column.ColumnName.Length - 4));
string variableName = column.ColumnName.Substring(0, column.ColumnName.Length - 4);
eeTableKey.SetIndex(new object[] { parameter.Name, year });
List <double> values = DataTableUtilities.GetColumnAsDoubles(eeView, parameter.Name).ToList();
for (int i = 0; i < values.Count; i++)
{
values[i] = Math.Abs(values[i]);
}
var runningMean = MathUtilities.RunningAverage(values);
eeTableKey.SetValues("Path", pathValues);
eeTableKey.SetValues(variableName + ".MuStar", runningMean);
}
}
DataTable eeTable = eeTableKey.ToTable();
eeTable.TableName = Name + "PathAnalysis";
// Get stats data from R and store in MuStar table.
// Stats data coming back from R looks like:
// "mu", "mustar", "sigma", "param","variable"
// -30.7331368183818, 30.7331368183818, 5.42917964248002,"ResidueWt","FallowEvaporation1996"
// -0.0731299918470997,0.105740687296631,0.450848277601353, "FASW","FallowEvaporation1996"
// -0.83061431285624,0.839772007599748, 1.75541097254145, "CN2","FallowEvaporation1996"
// 62.6942591520838, 62.6942591520838, 5.22778043503867, "Cona","FallowEvaporation1996"
// -17.286285468283, 19.4018404625051, 24.1361388348929,"ResidueWt","FallowRunoff1996"
// 8.09850688306722, 8.09852589447407, 15.1988107373113, "FASW","FallowRunoff1996"
// 18.6196168461051, 18.6196168461051, 15.1496277765849, "CN2","FallowRunoff1996"
// -7.12794888887507, 7.12794888887507, 5.54014788597839, "Cona","FallowRunoff1996"
IndexedDataTable tableKey = new IndexedDataTable(new string[2] {
"Parameter", "Year"
});
foreach (DataRow row in statsDataRaw.Rows)
{
string variable = row["variable"].ToString();
int year = Convert.ToInt32(variable.Substring(variable.Length - 4));
variable = variable.Substring(0, variable.Length - 4);
tableKey.SetIndex(new object[] { row["param"], year });
tableKey.Set(variable + ".Mu", row["mu"]);
tableKey.Set(variable + ".MuStar", row["mustar"]);
tableKey.Set(variable + ".Sigma", row["sigma"]);
// Need to bring in the descriptive values.
view.RowFilter = "Clock.Today.Year=" + year;
foreach (var descriptiveColumnName in descriptiveColumnNames)
{
var values = DataTableUtilities.GetColumnAsStrings(view, descriptiveColumnName);
if (values.Distinct().Count() == 1)
{
tableKey.Set(descriptiveColumnName, view[0][descriptiveColumnName]);
}
}
}
DataTable muStarTable = tableKey.ToTable();
muStarTable.TableName = Name + "Statistics";
dataStore.Writer.WriteTable(eeTable);
dataStore.Writer.WriteTable(muStarTable);
}
}
/// <summary>Main run method for performing our post simulation calculations</summary>
public void Run()
{
// If the predicted table has not been modified, don't do anything.
// This can happen if other simulations were run but the Morris model was not.
if (dataStore?.Writer != null && !dataStore.Writer.TablesModified.Contains(TableName))
{
return;
}
DataTable predictedData = dataStore.Reader.GetData(TableName);
if (predictedData != null)
{
// Determine how many aggregation values we have per simulation
DataView view = new DataView(predictedData);
view.RowFilter = "SimulationName='" + Name + "Simulation1'";
AggregationValues = DataTableUtilities.GetColumnAsStrings(view, AggregationVariableName);
// Create a table of all predicted values
DataTable predictedValues = new DataTable();
List <string> descriptiveColumnNames = new List <string>();
List <string> variableNames = new List <string>();
foreach (string aggregationValue in AggregationValues)
{
string value = aggregationValue;
if (DateTime.TryParse(value, out DateTime date))
{
value = date.ToString("yyyy-MM-dd");
}
view.RowFilter = $"{AggregationVariableName}='{value}'";
foreach (DataColumn predictedColumn in view.Table.Columns)
{
if (predictedColumn.DataType == typeof(double))
{
double[] values = DataTableUtilities.GetColumnAsDoubles(view, predictedColumn.ColumnName);
if (values.Distinct().Count() == 1)
{
if (!descriptiveColumnNames.Contains(predictedColumn.ColumnName))
{
descriptiveColumnNames.Add(predictedColumn.ColumnName);
}
}
else
{
DataTableUtilities.AddColumn(predictedValues, predictedColumn.ColumnName + "_" + value, values);
if (!variableNames.Contains(predictedColumn.ColumnName))
{
variableNames.Add(predictedColumn.ColumnName);
}
}
}
}
}
// Run R
DataTable eeDataRaw;
DataTable statsDataRaw;
RunRPostSimulation(predictedValues, out eeDataRaw, out statsDataRaw);
// Get ee data from R and store in ee table.
// EE data from R looks like:
// "ResidueWt", "FASW", "CN2", "Cona", "variable","path"
// - 22.971008269563,0.00950570342209862,-0.00379987333757356,56.7587080430652,"FallowEvaporation1996",1
// - 25.790599484188, 0.0170777988614538, -0.0265991133629069,58.0240658644712,"FallowEvaporation1996",2
// - 26.113599477728, 0.0113851992409871, 0.0113996200126667,57.9689677010766,"FallowEvaporation1996",3
// - 33.284199334316, 0.0323193916349732, -0.334388853704853,60.5376820772641,"FallowEvaporation1996",4
DataView eeView = new DataView(eeDataRaw);
IndexedDataTable eeTableKey = new IndexedDataTable(new string[] { "Parameter", AggregationVariableName });
// Create a path variable.
var pathValues = Enumerable.Range(1, NumPaths).ToArray();
foreach (var parameter in Parameters)
{
foreach (DataColumn column in predictedValues.Columns)
{
eeView.RowFilter = "variable = '" + column.ColumnName + "'";
if (eeView.Count != NumPaths)
{
throw new Exception("Found only " + eeView.Count + " paths for variable " + column.ColumnName + " in ee table");
}
string aggregationValue = StringUtilities.GetAfter(column.ColumnName, "_");
string variableName = StringUtilities.RemoveAfter(column.ColumnName, '_');
eeTableKey.SetIndex(new object[] { parameter.Name, aggregationValue });
List <double> values = DataTableUtilities.GetColumnAsDoubles(eeView, parameter.Name).ToList();
for (int i = 0; i < values.Count; i++)
{
values[i] = Math.Abs(values[i]);
}
var runningMean = MathUtilities.RunningAverage(values);
eeTableKey.SetValues("Path", pathValues);
eeTableKey.SetValues(variableName + ".MuStar", runningMean);
}
}
DataTable eeTable = eeTableKey.ToTable();
eeTable.TableName = Name + "PathAnalysis";
// Get stats data from R and store in MuStar table.
// Stats data coming back from R looks like:
// "mu", "mustar", "sigma", "param","variable"
// -30.7331368183818, 30.7331368183818, 5.42917964248002,"ResidueWt","FallowEvaporation1996"
// -0.0731299918470997,0.105740687296631,0.450848277601353, "FASW","FallowEvaporation1996"
// -0.83061431285624,0.839772007599748, 1.75541097254145, "CN2","FallowEvaporation1996"
// 62.6942591520838, 62.6942591520838, 5.22778043503867, "Cona","FallowEvaporation1996"
// -17.286285468283, 19.4018404625051, 24.1361388348929,"ResidueWt","FallowRunoff1996"
// 8.09850688306722, 8.09852589447407, 15.1988107373113, "FASW","FallowRunoff1996"
// 18.6196168461051, 18.6196168461051, 15.1496277765849, "CN2","FallowRunoff1996"
// -7.12794888887507, 7.12794888887507, 5.54014788597839, "Cona","FallowRunoff1996"
IndexedDataTable tableKey = new IndexedDataTable(new string[2] {
"Parameter", AggregationVariableName
});
foreach (DataRow row in statsDataRaw.Rows)
{
string variable = row["variable"].ToString();
string aggregationValue = StringUtilities.GetAfter(variable, "_");
variable = StringUtilities.RemoveAfter(variable, '_');
tableKey.SetIndex(new object[] { row["param"], aggregationValue });
tableKey.Set(variable + ".Mu", row["mu"]);
tableKey.Set(variable + ".MuStar", row["mustar"]);
tableKey.Set(variable + ".Sigma", row["sigma"]);
// Need to bring in the descriptive values.
view.RowFilter = $"{AggregationVariableName}='{aggregationValue}'";
foreach (var descriptiveColumnName in descriptiveColumnNames)
{
var values = DataTableUtilities.GetColumnAsStrings(view, descriptiveColumnName);
if (values.Distinct().Count() == 1)
{
tableKey.Set(descriptiveColumnName, view[0][descriptiveColumnName]);
}
}
}
DataTable muStarTable = tableKey.ToTable();
muStarTable.TableName = Name + "Statistics";
dataStore.Writer.WriteTable(eeTable);
dataStore.Writer.WriteTable(muStarTable);
}
}
/// <summary>Main run method for performing our calculations and storing data.</summary>
public void Run()
{
if (dataStore?.Writer != null && !dataStore.Writer.TablesModified.Contains("Report"))
{
return;
}
DataTable predictedData = dataStore.Reader.GetData("Report", filter: "SimulationName LIKE '" + Name + "%'", orderBy: "SimulationID");
if (predictedData != null)
{
IndexedDataTable variableValues = new IndexedDataTable(null);
// Determine how many years we have per simulation
DataView view = new DataView(predictedData);
view.RowFilter = "SimulationName='" + Name + "Simulation1'";
var Years = DataTableUtilities.GetColumnAsIntegers(view, "Clock.Today.Year");
// Create a results table.
IndexedDataTable results;
if (Years.Count() > 1)
{
results = new IndexedDataTable(new string[] { "Year" });
}
else
{
results = new IndexedDataTable(null);
}
// Loop through all years and perform analysis on each.
List <string> errorsFromR = new List <string>();
foreach (double year in Years)
{
view.RowFilter = "Clock.Today.Year=" + year;
foreach (DataColumn predictedColumn in predictedData.Columns)
{
if (predictedColumn.DataType == typeof(double))
{
var values = DataTableUtilities.GetColumnAsDoubles(view, predictedColumn.ColumnName);
if (values.Distinct().Count() > 1)
{
variableValues.SetValues(predictedColumn.ColumnName, values);
}
}
}
string paramNames = StringUtilities.Build(Parameters.Select(p => p.Name), ",", "\"", "\"");
string sobolx1FileName = GetTempFileName("sobolx1", ".csv");
string sobolx2FileName = GetTempFileName("sobolx2", ".csv");
string sobolVariableValuesFileName = GetTempFileName("sobolvariableValues", ".csv");
// Write variables file
using (var writer = new StreamWriter(sobolVariableValuesFileName))
DataTableUtilities.DataTableToText(variableValues.ToTable(), 0, ",", true, writer, excelFriendly: false, decimalFormatString: "F6");
// Write X1
using (var writer = new StreamWriter(sobolx1FileName))
DataTableUtilities.DataTableToText(X1, 0, ",", true, writer, excelFriendly: false, decimalFormatString: "F6");
// Write X2
using (var writer = new StreamWriter(sobolx2FileName))
DataTableUtilities.DataTableToText(X2, 0, ",", true, writer, excelFriendly: false, decimalFormatString: "F6");
string script = string.Format(
$".libPaths(c('{R.PackagesDirectory}', .libPaths()))" + Environment.NewLine +
$"library('boot', lib.loc = '{R.PackagesDirectory}')" + Environment.NewLine +
$"library('sensitivity', lib.loc = '{R.PackagesDirectory}')" + Environment.NewLine +
"params <- c({0})" + Environment.NewLine +
"n <- {1}" + Environment.NewLine +
"nparams <- {2}" + Environment.NewLine +
"X1 <- read.csv(\"{3}\")" + Environment.NewLine +
"X2 <- read.csv(\"{4}\")" + Environment.NewLine +
"sa <- sobolSalt(model = NULL, X1, X2, scheme=\"A\", nboot = 100)" + Environment.NewLine +
"variableValues = read.csv(\"{5}\")" + Environment.NewLine +
"for (columnName in colnames(variableValues))" + Environment.NewLine +
"{{" + Environment.NewLine +
" sa$y <- variableValues[[columnName]]" + Environment.NewLine +
" tell(sa)" + Environment.NewLine +
" sa$S$Parameter <- params" + Environment.NewLine +
" sa$T$Parameter <- params" + Environment.NewLine +
" sa$S$ColumnName <- columnName" + Environment.NewLine +
" sa$T$ColumnName <- columnName" + Environment.NewLine +
" sa$S$Indices <- \"FirstOrder\"" + Environment.NewLine +
" sa$T$Indices <- \"Total\"" + Environment.NewLine +
" if (!exists(\"allData\"))" + Environment.NewLine +
" allData <- rbind(sa$S, sa$T)" + Environment.NewLine +
" else" + Environment.NewLine +
" allData <- rbind(allData, sa$S, sa$T)" + Environment.NewLine +
"}}" + Environment.NewLine +
"write.table(allData, sep=\",\", row.names=FALSE)" + Environment.NewLine
,
paramNames, NumPaths, Parameters.Count,
sobolx1FileName.Replace("\\", "/"),
sobolx1FileName.Replace("\\", "/"),
sobolVariableValuesFileName.Replace("\\", "/"));
DataTable resultsForYear = null;
try
{
resultsForYear = RunR(script);
// Put output from R into results table.
if (Years.Count() > 1)
{
results.SetIndex(new object[] { year.ToString() });
}
foreach (DataColumn col in resultsForYear.Columns)
{
if (col.DataType == typeof(string))
{
results.SetValues(col.ColumnName, DataTableUtilities.GetColumnAsStrings(resultsForYear, col.ColumnName));
}
else
{
results.SetValues(col.ColumnName, DataTableUtilities.GetColumnAsDoubles(resultsForYear, col.ColumnName));
}
}
}
catch (Exception err)
{
string msg = err.Message;
if (Years.Count() > 1)
{
msg = "Year " + year + ": " + msg;
}
errorsFromR.Add(msg);
}
}
var resultsRawTable = results.ToTable();
resultsRawTable.TableName = Name + "Statistics";
dataStore.Writer.WriteTable(resultsRawTable);
if (errorsFromR.Count > 0)
{
string msg = StringUtilities.BuildString(errorsFromR.ToArray(), Environment.NewLine);
throw new Exception(msg);
}
}
}
private void CreateSolutionPath()
{
double[] lambda;
double[] trainNMSE;
double[] testNMSE;
double[,] coeff;
double[] intercept;
RunElasticNetLinearRegression(Problem.ProblemData, Penality, out lambda, out trainNMSE, out testNMSE, out coeff, out intercept);
var coeffTable = new IndexedDataTable <double>("Coefficients", "The paths of standarized coefficient values over different lambda values");
coeffTable.VisualProperties.YAxisMaximumAuto = false;
coeffTable.VisualProperties.YAxisMinimumAuto = false;
coeffTable.VisualProperties.XAxisMaximumAuto = false;
coeffTable.VisualProperties.XAxisMinimumAuto = false;
coeffTable.VisualProperties.XAxisLogScale = true;
coeffTable.VisualProperties.XAxisTitle = "Lambda";
coeffTable.VisualProperties.YAxisTitle = "Coefficients";
coeffTable.VisualProperties.SecondYAxisTitle = "Number of variables";
var nLambdas = lambda.Length;
var nCoeff = coeff.GetLength(1);
var dataRows = new IndexedDataRow <double> [nCoeff];
var allowedVars = Problem.ProblemData.AllowedInputVariables.ToArray();
var numNonZeroCoeffs = new int[nLambdas];
var ds = Problem.ProblemData.Dataset;
var doubleVariables = allowedVars.Where(ds.VariableHasType <double>);
var factorVariableNames = allowedVars.Where(ds.VariableHasType <string>);
var factorVariablesAndValues = ds.GetFactorVariableValues(factorVariableNames, Enumerable.Range(0, ds.Rows)); // must consider all factor values (in train and test set)
{
int i = 0;
foreach (var factorVariableAndValues in factorVariablesAndValues)
{
foreach (var factorValue in factorVariableAndValues.Value)
{
double sigma = ds.GetStringValues(factorVariableAndValues.Key)
.Select(s => s == factorValue ? 1.0 : 0.0)
.StandardDeviation(); // calc std dev of binary indicator
var path = Enumerable.Range(0, nLambdas).Select(r => Tuple.Create(lambda[r], coeff[r, i] * sigma)).ToArray();
dataRows[i] = new IndexedDataRow <double>(factorVariableAndValues.Key + "=" + factorValue, factorVariableAndValues.Key + "=" + factorValue, path);
i++;
}
}
foreach (var doubleVariable in doubleVariables)
{
double sigma = ds.GetDoubleValues(doubleVariable).StandardDeviation();
var path = Enumerable.Range(0, nLambdas).Select(r => Tuple.Create(lambda[r], coeff[r, i] * sigma)).ToArray();
dataRows[i] = new IndexedDataRow <double>(doubleVariable, doubleVariable, path);
i++;
}
// add to coeffTable by total weight (larger area under the curve => more important);
foreach (var r in dataRows.OrderByDescending(r => r.Values.Select(t => t.Item2).Sum(x => Math.Abs(x))))
{
coeffTable.Rows.Add(r);
}
}
for (int i = 0; i < coeff.GetLength(0); i++)
{
for (int j = 0; j < coeff.GetLength(1); j++)
{
if (!coeff[i, j].IsAlmost(0.0))
{
numNonZeroCoeffs[i]++;
}
}
}
if (lambda.Length > 2)
{
coeffTable.VisualProperties.XAxisMinimumFixedValue = Math.Pow(10, Math.Floor(Math.Log10(lambda.Last())));
coeffTable.VisualProperties.XAxisMaximumFixedValue = Math.Pow(10, Math.Ceiling(Math.Log10(lambda.Skip(1).First())));
}
coeffTable.Rows.Add(new IndexedDataRow <double>("Number of variables", "The number of non-zero coefficients for each step in the path", lambda.Zip(numNonZeroCoeffs, (l, v) => Tuple.Create(l, (double)v))));
coeffTable.Rows["Number of variables"].VisualProperties.ChartType = DataRowVisualProperties.DataRowChartType.Points;
coeffTable.Rows["Number of variables"].VisualProperties.SecondYAxis = true;
Results.Add(new Result(coeffTable.Name, coeffTable.Description, coeffTable));
var errorTable = new IndexedDataTable <double>("NMSE", "Path of NMSE values over different lambda values");
errorTable.VisualProperties.YAxisMaximumAuto = false;
errorTable.VisualProperties.YAxisMinimumAuto = false;
errorTable.VisualProperties.XAxisMaximumAuto = false;
errorTable.VisualProperties.XAxisMinimumAuto = false;
errorTable.VisualProperties.YAxisMinimumFixedValue = 0;
errorTable.VisualProperties.YAxisMaximumFixedValue = 1.0;
errorTable.VisualProperties.XAxisLogScale = true;
errorTable.VisualProperties.XAxisTitle = "Lambda";
errorTable.VisualProperties.YAxisTitle = "Normalized mean of squared errors (NMSE)";
errorTable.VisualProperties.SecondYAxisTitle = "Number of variables";
errorTable.Rows.Add(new IndexedDataRow <double>("NMSE (train)", "Path of NMSE values over different lambda values", lambda.Zip(trainNMSE, (l, v) => Tuple.Create(l, v))));
errorTable.Rows.Add(new IndexedDataRow <double>("NMSE (test)", "Path of NMSE values over different lambda values", lambda.Zip(testNMSE, (l, v) => Tuple.Create(l, v))));
errorTable.Rows.Add(new IndexedDataRow <double>("Number of variables", "The number of non-zero coefficients for each step in the path", lambda.Zip(numNonZeroCoeffs, (l, v) => Tuple.Create(l, (double)v))));
if (lambda.Length > 2)
{
errorTable.VisualProperties.XAxisMinimumFixedValue = Math.Pow(10, Math.Floor(Math.Log10(lambda.Last())));
errorTable.VisualProperties.XAxisMaximumFixedValue = Math.Pow(10, Math.Ceiling(Math.Log10(lambda.Skip(1).First())));
}
errorTable.Rows["NMSE (train)"].VisualProperties.ChartType = DataRowVisualProperties.DataRowChartType.Points;
errorTable.Rows["NMSE (test)"].VisualProperties.ChartType = DataRowVisualProperties.DataRowChartType.Points;
errorTable.Rows["Number of variables"].VisualProperties.ChartType = DataRowVisualProperties.DataRowChartType.Points;
errorTable.Rows["Number of variables"].VisualProperties.SecondYAxis = true;
Results.Add(new Result(errorTable.Name, errorTable.Description, errorTable));
}
public Form1()
{
InitializeComponent();
// Filling the data set with data
string dataPath = string.Empty;
foreach (DataColumn col in typedDataSet.Tables["Orders"].Columns)
{
col.ColumnMapping = MappingType.Attribute; // to make our XML file more compact
}
typedDataSet.Namespace = ""; // need this because our XML file does not have xmlns defined
using (var xmlStream = Assembly.GetExecutingAssembly().GetManifestResourceStream("LiveViewsDataSet.Northwind.xml"))
{
System.Diagnostics.Debug.Assert(xmlStream != null);
XmlReaderSettings settings = new XmlReaderSettings {
IgnoreWhitespace = true
};
XmlReader reader = XmlReader.Create(xmlStream, settings);
typedDataSet.ReadXml(reader);
}
// Creating indexes. It is optional, only for performance optimization.
// If you delete this, the functionality will be the same and slowdown will be noticeable only on large data sets.
// If you don't need to create indexes, you can use typedDataSet.Customers.AsLive() below instead of customers.AsLive(), so you don't need the
// IndexedDataTable<> class, can proceed directly to View<NorthwindDataSet.CustomersRow>.
IndexedDataTable <NorthwindDataSet.CustomersRow> customers = typedDataSet.Customers.AsIndexed();
IndexedDataTable <NorthwindDataSet.OrdersRow> orders = typedDataSet.Orders.AsIndexed();
customers.Indexes.Add(c => c.CustomerID, true);
orders.Indexes.Add(o => o.CustomerID);
orders.Indexes.Add(o => o.ShipCity);
//...end of creating indexes
// Make the tables live:
customersView = typedDataSet.Customers.AsLive();
ordersView = typedDataSet.Orders.AsLive().AsUpdatable(); // AsUpdatable() makes it possible for
// the user to change data directly in the grid showing the join view below.
// Order fields can be changed by the user there, Customer fields can't be changed by the user
// Specifying this argument is needed only if you need to change data directly in the view instead
// of changing the base data, and then only for query operators with more than one argument such as Join.
// Creating a live view over the base data
IListSource view =
(from o in ordersView
where o.ShipCity == "London" || o.ShipCity == "Colchester"
select new { OrderID = o.OrderID, CustomerID = o.CustomerID, ShipCity = o.ShipCity }).AsDynamic();
ShowViewInGrid(view, dataGridView1);
// Creating another live view over the base data
customerOrderView =
from c in customersView
join o in ordersView on c.CustomerID equals o.CustomerID
where o.ShipCity == "London" || o.ShipCity == "Colchester"
// Using a user-defined class like CustomerOrder is not mandatory. Anonymous class could be used instead as in the
// query above, but in that case we could not assign it to a variable like customerOrderView, defined outside
// of the scope of this method, because anonymous classes are only available in local scope.
select new CustomerOrder {
CustomerID = c.CustomerID, City = c.City, OrderID = o.OrderID, ShipCity = o.ShipCity
};
ShowViewInGrid(customerOrderView, dataGridView2);
// See the readme.txt file in the project folder for a description of the live view functionality you can try in this sample
}