IterativeClustering (FSharp.Stats)

IterativeClustering Module

Namespace: FSharp.Stats.ML.Unsupervised
Assembly: FSharp.Stats.dll

Types

Type	Description
CentroidsFactory<'a>
KClusteringResult<'a>	Result of a kmeans clustering
ToCentroid<'a>

Functions and values

Function or value	Description
`Dispersion dist lCentroids dataset` Full Usage: `Dispersion dist lCentroids dataset` Parameters: dist : `Distance<'a>` - lCentroids : `'a list` - dataset : `seq<'a>` - Returns: `float`	Calculates the average squared distance from the data points to the cluster centroid (also refered to as error) dist : `Distance<'a>` lCentroids : `'a list` dataset : `seq<'a>` Returns: `float` Example
`DispersionOfClusterResult kmeansResult` Full Usage: `DispersionOfClusterResult kmeansResult` Parameters: kmeansResult : `KClusteringResult<'a>` - Returns: `float`	Calculates the average squared distance from the data points to the cluster centroid (also refered to as error) kmeansResult : `KClusteringResult<'a>` Returns: `float` Example
`avgCentroid current sample` Full Usage: `avgCentroid current sample` Parameters: current : `float[]` sample : `float[] array` Returns: `float[]`	current : `float[]` sample : `float[] array` Returns: `float[]`
`compute dist factory aggregator dataset k` Full Usage: `compute dist factory aggregator dataset k` Parameters: dist : `Distance<'a>` factory : `CentroidsFactory<'a>` aggregator : `ToCentroid<'a>` dataset : `'a array` k : `int` Returns: `KClusteringResult<'a>`	dist : `Distance<'a>` factory : `CentroidsFactory<'a>` aggregator : `ToCentroid<'a>` dataset : `'a array` k : `int` Returns: `KClusteringResult<'a>`
`createKClusteringResult centroids classifier closestDistances distanceMetric` Full Usage: `createKClusteringResult centroids classifier closestDistances distanceMetric` Parameters: centroids : `(int * 'a) array` - classifier : `'a -> int * 'a` - closestDistances : `(int * float) array` - distanceMetric : `Distance<'a>` - Returns: `KClusteringResult<'a>`	Creates a k-clustering result centroids : `(int * 'a) array` classifier : `'a -> int * 'a` closestDistances : `(int * float) array` distanceMetric : `Distance<'a>` Returns: `KClusteringResult<'a>` Example
`initCVMAX sampleRows k` Full Usage: `initCVMAX sampleRows k` Parameters: sampleRows : `float[][]` k : `int` Returns: `float[][]`	sampleRows : `float[][]` k : `int` Returns: `float[][]`
`kmeans dist factory dataset k` Full Usage: `kmeans dist factory dataset k` Parameters: dist : `Distance<float[]>` factory : `CentroidsFactory<float[]>` dataset : `float[] array` k : `int` Returns: `KClusteringResult<float[]>`	dist : `Distance<float[]>` factory : `CentroidsFactory<float[]>` dataset : `float[] array` k : `int` Returns: `KClusteringResult<float[]>`
`nearest dist lCentroids datapoint` Full Usage: `nearest dist lCentroids datapoint` Parameters: dist : `Distance<'a>` lCentroids : `'a array` datapoint : `'a` Returns: `int * 'a`	dist : `Distance<'a>` lCentroids : `'a array` datapoint : `'a` Returns: `int * 'a`
`nearestDistance dist lCentroids datapoint` Full Usage: `nearestDistance dist lCentroids datapoint` Parameters: dist : `Distance<'a>` - lCentroids : `'a array` - datapoint : `'a` - Returns: `float`	Calculates the distance from the data point to the centroid dist : `Distance<'a>` lCentroids : `'a array` datapoint : `'a` Returns: `float` Example
`randomCentroids rng sample k` Full Usage: `randomCentroids rng sample k` Parameters: rng : `Random` sample : `'a array` k : `int` Returns: `'a[]`	rng : `Random` sample : `'a array` k : `int` Returns: `'a[]`