static TestableMultipleCorrespondence00()
{
// Create a data stream.
string[] data = new string[6] {
"COLOR,NUMBER",
"Red,Negative",
"Green,Zero",
"Red,Negative",
"Blue,Negative",
"Blue,Positive"
};
MemoryStream stream = new();
StreamWriter writer = new(stream);
for (int i = 0; i < data.Length; i++)
{
writer.WriteLine(data[i].ToCharArray());
writer.Flush();
}
stream.Position = 0;
// Encode the categorical data set
StreamReader streamReader = new(stream);
char columnDelimiter = ',';
IndexCollection extractedColumns = IndexCollection.Range(0, 1);
bool firstLineContainsColumnHeaders = true;
dataSet = CategoricalDataSet.Encode(
streamReader,
columnDelimiter,
extractedColumns,
firstLineContainsColumnHeaders);
}
public void Main()
{
// Create a data stream.
string[] data = new string[6] {
"COLOR,NUMBER",
"Red,Negative",
"Green,Zero",
"Red,Negative",
"Black,Negative",
"Black,Positive"
};
MemoryStream stream = new();
StreamWriter writer = new(stream);
for (int i = 0; i < data.Length; i++)
{
writer.WriteLine(data[i].ToCharArray());
writer.Flush();
}
stream.Position = 0;
// Encode the categorical data set.
StreamReader streamReader = new(stream);
char columnDelimiter = ',';
IndexCollection extractedColumns = IndexCollection.Range(0, 1);
bool firstLineContainsColumnHeaders = true;
CategoricalDataSet dataset = CategoricalDataSet.Encode(
streamReader,
columnDelimiter,
extractedColumns,
firstLineContainsColumnHeaders);
// Decode and show the data set.
Console.WriteLine("Decoded data set:");
Console.WriteLine();
var decodedDataSet = dataset.Decode();
int numberOfInstances = dataset.Data.NumberOfRows;
int numberOfVariables = dataset.Data.NumberOfColumns;
foreach (var variable in dataset.Variables)
{
Console.Write(variable.Name + ",");
}
Console.WriteLine();
for (int i = 0; i < numberOfInstances; i++)
{
for (int j = 0; j < numberOfVariables; j++)
{
Console.Write(decodedDataSet[i][j] + ",");
}
Console.WriteLine();
}
}
public void Main()
{
// Create a data stream.
string[] data = new string[7] {
"COLOR,NUMBER",
"Red,Negative",
"Green,Zero",
"White,Positive",
"Red,Negative",
"Blue,Negative",
"Blue,Positive"
};
MemoryStream stream = new();
StreamWriter writer = new(stream);
for (int i = 0; i < data.Length; i++)
{
writer.WriteLine(data[i].ToCharArray());
writer.Flush();
}
stream.Position = 0;
// Encode the categorical data set.
StreamReader streamReader = new(stream);
char columnDelimiter = ',';
IndexCollection extractedColumns = IndexCollection.Range(0, 1);
bool firstLineContainsColumnHeaders = true;
var dataSet = CategoricalDataSet.Encode(
streamReader,
columnDelimiter,
extractedColumns,
firstLineContainsColumnHeaders);
// Assign the categories of variable NUMBER
// to the rows of the table.
int rowVariableIndex = 1;
// Assign the categories of variable COLOR
// to the columns of the table.
int columnVariableIndex = 0;
// Get the NUMBER-by-COLOR table.
var table = dataSet.GetContingencyTable(
rowVariableIndex,
columnVariableIndex);
// Show the table.
Console.WriteLine("Contingency table:");
Console.WriteLine(table);
}
public void Main()
{
// Create a data stream.
const int numberOfInstances = 27;
string[] data = new string[numberOfInstances + 1] {
"NUMERICAL,TARGET",
"0,A",
"0,A",
"0,A",
"1,B",
"1,B",
"1,B",
"1,B",
"2,B",
"2,B",
"3,C",
"3,C",
"3,C",
"4,B",
"4,B",
"4,B",
"4,C",
"5,A",
"5,A",
"6,A",
"7,C",
"7,C",
"7,C",
"8,C",
"8,C",
"9,C",
"9,C",
"9,C"
};
MemoryStream stream = new();
StreamWriter writer = new(stream);
for (int i = 0; i < data.Length; i++)
{
writer.WriteLine(data[i].ToCharArray());
writer.Flush();
}
stream.Position = 0;
// Identify the special categorizer for variable NUMERICAL.
StreamReader streamReader = new(stream);
char columnDelimiter = ',';
IndexCollection numericalColumns = IndexCollection.Range(0, 0);
bool firstLineContainsColumnHeaders = true;
int targetColumn = 1;
IFormatProvider provider = CultureInfo.InvariantCulture;
var specialCategorizers = CategoricalDataSet.CategorizeByEntropyMinimization(
streamReader,
columnDelimiter,
numericalColumns,
firstLineContainsColumnHeaders,
targetColumn,
provider);
// Encode the categorical data set using the special categorizer.
stream.Position = 0;
IndexCollection extractedColumns = IndexCollection.Range(0, 1);
CategoricalDataSet dataset = CategoricalDataSet.Encode(
streamReader,
columnDelimiter,
extractedColumns,
firstLineContainsColumnHeaders,
specialCategorizers,
provider);
// Decode and show the data set.
Console.WriteLine("Decoded data set:");
Console.WriteLine();
var decodedDataSet = dataset.Decode();
int numberOfVariables = dataset.Data.NumberOfColumns;
foreach (var variable in dataset.Variables)
{
Console.Write(variable.Name + ",");
}
Console.WriteLine();
for (int i = 0; i < numberOfInstances; i++)
{
for (int j = 0; j < numberOfVariables; j++)
{
Console.Write(decodedDataSet[i][j] + ",");
}
Console.WriteLine();
}
}
public void FromEncodedDataTest()
{
// variables is null
{
List <CategoricalVariable> variables = null;
DoubleMatrix data = DoubleMatrix.Identity(3);
ArgumentExceptionAssert.Throw(
() =>
{
var actual = CategoricalDataSet.FromEncodedData(
variables: variables,
data: data);
},
expectedType: typeof(ArgumentNullException),
expectedPartialMessage: ArgumentExceptionAssert.NullPartialMessage,
expectedParameterName: "variables");
}
// data is null
{
List <CategoricalVariable> variables =
new();
DoubleMatrix data = null;
ArgumentExceptionAssert.Throw(
() =>
{
var actual = CategoricalDataSet.FromEncodedData(
variables: variables,
data: data);
},
expectedType: typeof(ArgumentNullException),
expectedPartialMessage: ArgumentExceptionAssert.NullPartialMessage,
expectedParameterName: "data");
}
// variables count unequal to data number of columns
{
List <CategoricalVariable> variables =
new()
{
new CategoricalVariable("var0"),
new CategoricalVariable("var1")
};
DoubleMatrix data = DoubleMatrix.Dense(6, 3);
ArgumentExceptionAssert.Throw(
() =>
{
var actual = CategoricalDataSet.FromEncodedData(
variables: variables,
data: data);
},
expectedType: typeof(ArgumentOutOfRangeException),
expectedPartialMessage: String.Format(
ImplementationServices.GetResourceString(
"STR_EXCEPT_CAT_COLUMNS_NOT_EQUAL_TO_VARIABLES_COUNT"),
"variables"),
expectedParameterName: "data");
}
// category not included in variable
{
List <CategoricalVariable> variables =
new()
{
new CategoricalVariable("var0"),
new CategoricalVariable("var1")
};
variables[0].Add(0.0);
variables[1].Add(1.0);
DoubleMatrix data = DoubleMatrix.Dense(1, 2);
ArgumentExceptionAssert.Throw(
() =>
{
var actual = CategoricalDataSet.FromEncodedData(
variables: variables,
data: data);
},
expectedType: typeof(ArgumentException),
expectedPartialMessage:
ImplementationServices.GetResourceString(
"STR_EXCEPT_CAT_MATRIX_ENTRY_NOT_IN_VARIABLE_LIST"),
expectedParameterName: "data");
}
// Valid input
{
// Create a data stream
string[] data = new string[6] {
"COLOR,NUMBER",
"Red, -2.2",
"Green, 0.0",
"Red, -3.3",
"Black,-1.1",
"Black, 4.4"
};
MemoryStream stream = new();
StreamWriter writer = new(stream);
for (int i = 0; i < data.Length; i++)
{
writer.WriteLine(data[i].ToCharArray());
writer.Flush();
}
stream.Position = 0;
// Define a special categorizer for variable NUMBER
string numberCategorizer(string token, IFormatProvider provider)
{
double datum = Convert.ToDouble(token, provider);
if (datum == 0)
{
return("Zero");
}
else if (datum < 0)
{
return("Negative");
}
else
{
return("Positive");
}
}
// Attach the special categorizer to variable NUMBER
int numberColumnIndex = 1;
var specialCategorizers = new Dictionary <int, Categorizer>
{
{ numberColumnIndex, numberCategorizer }
};
// Encode the categorical data set
StreamReader streamReader = new(stream);
char columnDelimiter = ',';
IndexCollection extractedColumns = IndexCollection.Range(0, 1);
bool firstLineContainsColumnHeaders = true;
CategoricalDataSet actual = CategoricalDataSet.Encode(
streamReader,
columnDelimiter,
extractedColumns,
firstLineContainsColumnHeaders,
specialCategorizers,
CultureInfo.InvariantCulture);
CategoricalVariable color = new("COLOR")
{
{ 0, "Red" },