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.
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();
}
}
