public void DiscoverTest()
{
// data is null
{
string parameterName = "data";
ArgumentExceptionAssert.Throw(
() =>
{
Clusters.Discover(
data: null,
maximumNumberOfParts: 2);
},
expectedType: typeof(ArgumentNullException),
expectedPartialMessage:
ArgumentExceptionAssert.NullPartialMessage,
expectedParameterName: parameterName);
}
// maximumNumberOfParts is one
{
var STR_EXCEPT_PAR_MUST_BE_GREATER_THAN_VALUE =
string.Format(
ImplementationServices.GetResourceString(
"STR_EXCEPT_PAR_MUST_BE_GREATER_THAN_VALUE"),
"1");
string parameterName = "maximumNumberOfParts";
ArgumentExceptionAssert.Throw(
() =>
{
Clusters.Discover(
data: DoubleMatrix.Dense(10, 5),
maximumNumberOfParts: 1);
},
expectedType: typeof(ArgumentOutOfRangeException),
expectedPartialMessage:
STR_EXCEPT_PAR_MUST_BE_GREATER_THAN_VALUE,
expectedParameterName: parameterName);
}
// maximumNumberOfParts is zero
{
var STR_EXCEPT_PAR_MUST_BE_GREATER_THAN_VALUE =
string.Format(
ImplementationServices.GetResourceString(
"STR_EXCEPT_PAR_MUST_BE_GREATER_THAN_VALUE"),
"1");
string parameterName = "maximumNumberOfParts";
ArgumentExceptionAssert.Throw(
() =>
{
Clusters.Discover(
data: DoubleMatrix.Dense(10, 5),
maximumNumberOfParts: 0);
},
expectedType: typeof(ArgumentOutOfRangeException),
expectedPartialMessage:
STR_EXCEPT_PAR_MUST_BE_GREATER_THAN_VALUE,
expectedParameterName: parameterName);
}
// maximumNumberOfParts is negative
{
var STR_EXCEPT_PAR_MUST_BE_GREATER_THAN_VALUE =
string.Format(
ImplementationServices.GetResourceString(
"STR_EXCEPT_PAR_MUST_BE_GREATER_THAN_VALUE"),
"1");
string parameterName = "maximumNumberOfParts";
ArgumentExceptionAssert.Throw(
() =>
{
Clusters.Discover(
data: DoubleMatrix.Dense(10, 5),
maximumNumberOfParts: -1);
},
expectedType: typeof(ArgumentOutOfRangeException),
expectedPartialMessage:
STR_EXCEPT_PAR_MUST_BE_GREATER_THAN_VALUE,
expectedParameterName: parameterName);
}
// maximumNumberOfParts is equal to the number of rows in data
{
var STR_EXCEPT_PAR_MUST_BE_LESS_THAN_OTHER_ROWS =
string.Format(
ImplementationServices.GetResourceString(
"STR_EXCEPT_PAR_MUST_BE_LESS_THAN_OTHER_ROWS"),
"maximumNumberOfParts",
"data");
string parameterName = "maximumNumberOfParts";
ArgumentExceptionAssert.Throw(
() =>
{
Clusters.Discover(
data: DoubleMatrix.Dense(10, 5),
maximumNumberOfParts: 10);
},
expectedType: typeof(ArgumentException),
expectedPartialMessage:
STR_EXCEPT_PAR_MUST_BE_LESS_THAN_OTHER_ROWS,
expectedParameterName: parameterName);
}
// maximumNumberOfParts is greater than the number of rows in data
{
var STR_EXCEPT_PAR_MUST_BE_LESS_THAN_OTHER_ROWS =
string.Format(
ImplementationServices.GetResourceString(
"STR_EXCEPT_PAR_MUST_BE_LESS_THAN_OTHER_ROWS"),
"maximumNumberOfParts",
"data");
string parameterName = "maximumNumberOfParts";
ArgumentExceptionAssert.Throw(
() =>
{
Clusters.Discover(
data: DoubleMatrix.Dense(10, 5),
maximumNumberOfParts: 11);
},
expectedType: typeof(ArgumentException),
expectedPartialMessage:
STR_EXCEPT_PAR_MUST_BE_LESS_THAN_OTHER_ROWS,
expectedParameterName: parameterName);
}
// Valid input
{
const int numberOfItems = 12;
const int numberOfFeatures = 7;
var source = DoubleMatrix.Dense(numberOfItems, 1,
new double[numberOfItems]
{
0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2
});
var expectedPartition = IndexPartition.Create(source);
var data = DoubleMatrix.Dense(numberOfItems, numberOfFeatures);
double mu = 1.0;
var partIdentifiers = expectedPartition.Identifiers;
for (int i = 0; i < partIdentifiers.Count; i++)
{
var part = expectedPartition[partIdentifiers[i]];
int partSize = part.Count;
for (int j = 0; j < partSize; j++)
{
data[part[j], ":"] += mu;
}
mu += 5.0;
}
var actualPartition = Clusters.Discover(
data: data,
maximumNumberOfParts: 3);
IndexPartitionAssert.HaveEqualIdentifiers(
expected: expectedPartition,
actual: actualPartition);
IndexPartitionAssert.HaveEqualParts(
expected: expectedPartition,
actual: actualPartition);
}
}
public void Main()
{
// Set the number of items and features under study.
const int numberOfItems = 12;
int numberOfFeatures = 7;
// Create a matrix that will represent
// an artificial data set,
// having 12 items (rows) and 7 features (columns).
// This will store the observations which
// partition discovery will be based on.
var data = DoubleMatrix.Dense(
numberOfRows: numberOfItems,
numberOfColumns: numberOfFeatures);
// Fill the data rows by sampling from a different
// distribution while, respectively, drawing observations
// for items 0 to 3, 4 to 7, and 8 to 11: these will be the
// three different parts expected to be included in the
// optimal partition.
double mu = 1.0;
var g = new GaussianDistribution(mu: mu, sigma: .01);
IndexCollection range = IndexCollection.Range(0, 3);
for (int j = 0; j < numberOfFeatures; j++)
{
data[range, j] = g.Sample(sampleSize: range.Count);
}
mu += 5.0;
g.Mu = mu;
range = IndexCollection.Range(4, 7);
for (int j = 0; j < numberOfFeatures; j++)
{
data[range, j] = g.Sample(sampleSize: range.Count);
}
mu += 5.0;
g.Mu = mu;
range = IndexCollection.Range(8, 11);
for (int j = 0; j < numberOfFeatures; j++)
{
data[range, j] = g.Sample(sampleSize: range.Count);
}
Console.WriteLine("The data set:");
Console.WriteLine(data);
// Define the maximum number of parts allowed in the
// partition to be discovered.
int maximumNumberOfParts = 3;
// Select the best partition.
IndexPartition <double> optimalPartition =
Clusters.Discover(
data,
maximumNumberOfParts);
// Show the results.
Console.WriteLine();
Console.WriteLine(
"The optimal partition:");
Console.WriteLine(optimalPartition);
Console.WriteLine();
Console.WriteLine("The Davies-Bouldin Index for the optimal partition:");
var dbi = IndexPartition.DaviesBouldinIndex(
data,
optimalPartition);
Console.WriteLine(dbi);
}