public void Main()
{
// Set the number of items and features under study.
const int numberOfItems = 12;
int numberOfFeatures = 7;
// Define a partition that must be explained.
// Three parts (clusters) are included,
// containing, respectively, items 0 to 3,
// 5 to 8, and 9 to 11.
var partition = IndexPartition.Create(
new double[numberOfItems]
{
0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2
});
// Create a matrix that will represent
// an artificial data set,
// having 12 items (rows) and 7 features (columns).
// This will store the observations which
// explanation will be based on.
var data = DoubleMatrix.Dense(
numberOfRows: numberOfItems,
numberOfColumns: numberOfFeatures);
// The first 5 features are built to be almost
// surely non informative, since they result
// as samples drawn from a same distribution.
var g = new GaussianDistribution(mu: 0, sigma: .01);
for (int j = 0; j < 5; j++)
{
data[":", j] = g.Sample(sampleSize: numberOfItems);
}
// Features 5 to 6 are instead built to be informative,
// since they are sampled from different distributions
// while filling rows whose indexes are in different parts
// of the partition to be explained.
var partIdentifiers = partition.Identifiers;
double mu = 1.0;
for (int i = 0; i < partIdentifiers.Count; i++)
{
var part = partition[partIdentifiers[i]];
int partSize = part.Count;
g.Mu = mu;
data[part, 5] = g.Sample(sampleSize: partSize);
mu += 2.0;
g.Mu = mu;
data[part, 6] = g.Sample(sampleSize: partSize);
mu += 2.0;
}
Console.WriteLine("The data set:");
Console.WriteLine(data);
// Define how many features must be selected
// for explanation.
int numberOfExplanatoryFeatures = 2;
// Select the best features.
IndexCollection optimalExplanatoryFeatureIndexes =
Clusters.Explain(
data,
partition,
numberOfExplanatoryFeatures);
// Show the results.
Console.WriteLine();
Console.WriteLine(
"The {0} features best explaining the given partition have column indexes:",
numberOfExplanatoryFeatures);
Console.WriteLine(optimalExplanatoryFeatureIndexes);
Console.WriteLine();
Console.WriteLine("The Davies-Bouldin Index for the selected features:");
var dbi = IndexPartition.DaviesBouldinIndex(
data[":", optimalExplanatoryFeatureIndexes],
partition);
Console.WriteLine(dbi);
}
public void ExplainTest()
{
// data is null
{
string parameterName = "data";
ArgumentExceptionAssert.Throw(
() =>
{
Clusters.Explain(
data: null,
partition: IndexPartition.Create(
DoubleMatrix.Dense(10, 1)),
numberOfExplanatoryFeatures: 2);;
},
expectedType: typeof(ArgumentNullException),
expectedPartialMessage:
ArgumentExceptionAssert.NullPartialMessage,
expectedParameterName: parameterName);
}
// data is null
{
string parameterName = "partition";
ArgumentExceptionAssert.Throw(
() =>
{
Clusters.Explain(
data: DoubleMatrix.Dense(10, 5),
partition: null,
numberOfExplanatoryFeatures: 2);;
},
expectedType: typeof(ArgumentNullException),
expectedPartialMessage:
ArgumentExceptionAssert.NullPartialMessage,
expectedParameterName: parameterName);
}
// numberOfExplanatoryFeatures is zero
{
var STR_EXCEPT_PAR_MUST_BE_POSITIVE =
ImplementationServices.GetResourceString(
"STR_EXCEPT_PAR_MUST_BE_POSITIVE");
string parameterName = "numberOfExplanatoryFeatures";
ArgumentExceptionAssert.Throw(
() =>
{
Clusters.Explain(
data: DoubleMatrix.Dense(10, 5),
partition: IndexPartition.Create(
DoubleMatrix.Dense(10, 1)),
numberOfExplanatoryFeatures: 0);
},
expectedType: typeof(ArgumentOutOfRangeException),
expectedPartialMessage:
STR_EXCEPT_PAR_MUST_BE_POSITIVE,
expectedParameterName: parameterName);
}
// numberOfExplanatoryFeatures is negative
{
var STR_EXCEPT_PAR_MUST_BE_POSITIVE =
ImplementationServices.GetResourceString(
"STR_EXCEPT_PAR_MUST_BE_POSITIVE");
string parameterName = "numberOfExplanatoryFeatures";
ArgumentExceptionAssert.Throw(
() =>
{
Clusters.Explain(
data: DoubleMatrix.Dense(10, 5),
partition: IndexPartition.Create(
DoubleMatrix.Dense(10, 1)),
numberOfExplanatoryFeatures: -1);
},
expectedType: typeof(ArgumentOutOfRangeException),
expectedPartialMessage:
STR_EXCEPT_PAR_MUST_BE_POSITIVE,
expectedParameterName: parameterName);
}
// numberOfExplanatoryFeatures is equal to the number of columns in data
{
var STR_EXCEPT_PAR_MUST_BE_LESS_THAN_OTHER_COLUMNS =
string.Format(
ImplementationServices.GetResourceString(
"STR_EXCEPT_PAR_MUST_BE_LESS_THAN_OTHER_COLUMNS"),
"numberOfExplanatoryFeatures",
"data");
string parameterName = "numberOfExplanatoryFeatures";
ArgumentExceptionAssert.Throw(
() =>
{
Clusters.Explain(
data: DoubleMatrix.Dense(10, 5),
partition: IndexPartition.Create(
DoubleMatrix.Dense(10, 1)),
numberOfExplanatoryFeatures: 5);
},
expectedType: typeof(ArgumentException),
expectedPartialMessage:
STR_EXCEPT_PAR_MUST_BE_LESS_THAN_OTHER_COLUMNS,
expectedParameterName: parameterName);
}
// numberOfExplanatoryFeatures is greater than the number of columns in data
{
var STR_EXCEPT_PAR_MUST_BE_LESS_THAN_OTHER_COLUMNS =
string.Format(
ImplementationServices.GetResourceString(
"STR_EXCEPT_PAR_MUST_BE_LESS_THAN_OTHER_COLUMNS"),
"numberOfExplanatoryFeatures",
"data");
string parameterName = "numberOfExplanatoryFeatures";
ArgumentExceptionAssert.Throw(
() =>
{
Clusters.Explain(
data: DoubleMatrix.Dense(10, 5),
partition: IndexPartition.Create(
DoubleMatrix.Dense(10, 1)),
numberOfExplanatoryFeatures: 6);
},
expectedType: typeof(ArgumentException),
expectedPartialMessage:
STR_EXCEPT_PAR_MUST_BE_LESS_THAN_OTHER_COLUMNS,
expectedParameterName: parameterName);
}
// Valid input
{
const int numberOfItems = 12;
var source = DoubleMatrix.Dense(numberOfItems, 1,
new double[numberOfItems]
{
0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2
});
var partition = IndexPartition.Create(source);
var data = DoubleMatrix.Dense(numberOfItems, 7);
// features 0 to 4
var g = new GaussianDistribution(mu: 0, sigma: .1);
for (int j = 0; j < 5; j++)
{
data[":", j] = g.Sample(sampleSize: numberOfItems);
}
var partIdentifiers = partition.Identifiers;
// feature 5 to 6
double mu = 1.0;
for (int i = 0; i < partIdentifiers.Count; i++)
{
var part = partition[partIdentifiers[i]];
int partSize = part.Count;
g.Mu = mu;
data[part, 5] = g.Sample(sampleSize: partSize);
mu += 2.0;
g.Mu = mu;
data[part, 6] = g.Sample(sampleSize: partSize);
mu += 2.0;
}
IndexCollection actualFeatureIndexes =
Clusters.Explain(
data: data,
partition: partition,
numberOfExplanatoryFeatures: 2);
IndexCollectionAssert.AreEqual(
expected: IndexCollection.Range(5, 6),
actual: actualFeatureIndexes);
}
}
