void Start()
{
speed = GaussianDistribution.Generate(meanSpeed, 0);
}
public void AtTests()
{
// Verified with WolframAlpha
// (e.g. http://www.wolframalpha.com/input/?i=PDF%5BNormalDistribution%5B0%2C1%5D%2C+0.5%5D )
Assert.AreEqual(0.352065, GaussianDistribution.At(0.5), ERROR_TOLERANCE);
}
public void CumulativeToTests()
{
// Verified with WolframAlpha
// (e.g. http://www.wolframalpha.com/input/?i=CDF%5BNormalDistribution%5B0%2C1%5D%2C+0.5%5D )
Assert.AreEqual(0.691462, GaussianDistribution.CumulativeTo(0.5), ERROR_TOLERANCE);
}
/// <summary>
/// Given a difficulty, return the chance of success for it.
/// </summary>
/// <param name="difficulty"></param>
/// <returns>Higher means easier</returns>
public static double ChanceOfSuccess(double difficulty)
{
return(GaussianDistribution.CumulativeTo(difficulty, World.Mean, World.StandardDeviation));
}
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);
}
}
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);
}
