ISampleSet

References
public interface ISampleSet : IDimensional
Base interface (no generics) for n-dimensional sample sets.

Properties

Coordinates

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IEnumerator<PointN> Coordinates { get; }
Enumerate over all sample coordinates.

Count

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int Count { get; }
Gets the number of samples in this set.

Description

string Description { get; }
Gets a description of this object type.
inherited fromIDimensional

Dimensionality

int Dimensionality { get; }
Gets the dimensionality of this object.
inherited fromIDimensional

Samples

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IEnumerable<object> Samples { get; }
Enumerate over all samples.

Indexers

this[]

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object this[int index] { get; }
Gets the item at the given index.
Parameters:
index (int)Index of item to retrieve.
Returns: Item at index.

Methods

ComputeAxisDomain

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bool ComputeAxisDomain(int axis, int[] indices, out double min, out double mean, out double max)
Compute the domain and average of all samples along some axis.
Parameters:
axis (int)Axis to measure.
indices (int[])Indices to measure.
min (out double)Lowest value will be returned here.
mean (out double)Average (mean) value will be returned here.
max (out double)Highest value will be returned here.
Returns: True on success, false on failure. This method only returns false if the set is empty.

ComputeAxisDomain

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bool ComputeAxisDomain(int axis, out double min, out double mean, out double max)
Compute the domain and average of all samples along some axis.
Parameters:
axis (int)Axis to measure.
min (out double)Lowest value will be returned here.
mean (out double)Average (mean) value will be returned here.
max (out double)Highest value will be returned here.
Returns: True on success, false on failure. This method only returns false if the set is empty.

ComputeAxisMean

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double ComputeAxisMean(int axis, int maxSamples, int[] indices)
Compute the exact or estimated mean for this set.
Parameters:
axis (int)Spatial axis.
maxSamples (int)Maximum number of samples to take into account. If this limit is less than the total number of potential samples, the resulting mean will be an estimate.
indices (int[])Set of sample indices to take into account.
Returns: Computed mean along given axis.

ComputeAxisMean

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double ComputeAxisMean(int axis, int maxSamples)
Compute the exact or estimated mean for this set.
Parameters:
axis (int)Spatial axis.
maxSamples (int)Maximum number of samples to take into account. If this limit is less than the total number of potential samples, the resulting mean will be an estimate.
Returns: Computed mean along given axis.

ComputeBoundingBox

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BoxN ComputeBoundingBox()
Compute the bounding box for all items.

ComputeBoundingBox

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BoxN ComputeBoundingBox(IEnumerable<int> indices)
Compute the bounding box for a subset of samples.
Parameters:
indices (IEnumerable<int>)Indices to include.
Returns: Bounding box.

ComputeKdTree

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KdTree ComputeKdTree()
Compute the k-d tree associated with this sample set. Once a k-d tree has been computed once, additional calls to this method will return a cached version.
Returns: k-d tree or null if this sampling set is empty.

ComputeKMeansClusters

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KMeansCluster[] ComputeKMeansClusters(int count, KMeansIteration callback)
Compute a distribution of k-means clusters.
Parameters:
count (int)Number of clusters to compute.
callback (KMeansIteration)Callback method.
Returns: Array of clusters.

ComputeKMeansClusters

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KMeansCluster[] ComputeKMeansClusters(int count)
Compute a distribution of k-means clusters.
Parameters:
count (int)Number of clusters to compute.
Returns: Array of clusters.

ComputeKMeansClusters

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KMeansCluster[] ComputeKMeansClusters(PointN[] initialMeans, KMeansIteration callback)
Compute a distribution of k-means clusters.
Parameters:
initialMeans (PointN[])Number of clusters to compute.
callback (KMeansIteration)Callback method.
Returns: Array of clusters.

ComputeKMeansClusters

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KMeansCluster[] ComputeKMeansClusters(PointN[] initialMeans)
Compute a distribution of k-means clusters.
Parameters:
initialMeans (PointN[])Number of clusters to compute.
Returns: Array of clusters.

ComputeMean

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PointN ComputeMean()
Compute the exact or estimated mean for this set.
Returns: Computed mean.

ComputeMean

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PointN ComputeMean(int maxSamples, int[] indices)
Compute the exact or estimated mean for this set.
Parameters:
maxSamples (int)Maximum number of samples to actually use.
indices (int[])Set of sample indices to take into account.
Returns: Computed mean.

ComputeMean

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PointN ComputeMean(int[] indices)
Compute the exact or estimated mean for this set.
Parameters:
indices (int[])Set of sample indices to take into account.
Returns: Computed mean.

DistributePoints

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PointN[] DistributePoints(int count, bool limitToSet)
Randomly distribute N points within the space. This method performs pretty poorly when 'count' is large.
Parameters:
count (int)Number of points to distribute.
limitToSet (bool)If true, distributed points are constrainted to the existing point set.
Returns: Array of randomly distributed points.

NormaliseProjection

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ISampleSet NormaliseProjection()
Normalise the entire projection to the unit hypercube.

NormaliseProjection

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ISampleSet NormaliseProjection(double min, double max)
Normalise the entire projection to a hypercube in a specific domain.
Parameters:
min (double)Hypercube domain start.
max (double)Hypercube domain end.

NormaliseProjection

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ISampleSet NormaliseProjection(double size)
Normalise the entire projection to a hypercube of a specific size.
Parameters:
size (double)Edge length of hypercube.

Point

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PointN Point(int index)
Gets the point associated with the given index.
Parameters:
index (int)Index.
Returns: Point associated with index.

Points

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PointN[] Points(int[] indices)
Get an array of points.
Parameters:
indices (int[])Point indices to retrieve.
Returns: Array of points.