Seq (FSharp.Stats)

KNN.Seq Module

Namespace: FSharp.Stats.ML.Unsupervised
Assembly: FSharp.Stats.dll
Parent Module: KNN

Functions and values

Function or value Description

Function or value	Description
`Seq.predict distance labeledPoints k x` Full Usage: `Seq.predict distance labeledPoints k x` Parameters: distance : `Distance<'a>` - the distance function, e.g. `euclidean` labeledPoints : `('a * 'l) seq` - the array of classified (or labeled) points [in the format (point, label)], used for the classification k : `int` - The _positive_ number of nearest neighbors from x to look for. x : `'a` - The point to classify Returns: `'l option` The most common label from the k nearest neighbors for x. Modifiers: inline Type parameters: 'a, 'l	The [k-nearest neighbors algorithm](https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm) to classify a new data point into the target class, depending on the features of its neighboring data points. distance : `Distance<'a>` the distance function, e.g. `euclidean` labeledPoints : `('a * 'l) seq` the array of classified (or labeled) points [in the format (point, label)], used for the classification k : `int` The _positive_ number of nearest neighbors from x to look for. x : `'a` The point to classify Returns: `'l option` The most common label from the k nearest neighbors for x. Example let points = seq { [ 2.0; 4.0 ]; [ 1.0; 3.0 ]; [ 2.0; 4.0 ]; [ 3.0; 2.0 ]; [ 2.0; 1.0 ]; [ 5.0; 6.0 ]; [ 4.0; 5.0 ]; [ 4.0; 6.0 ]; [ 6.0; 6.0 ]; [ 5.0; 4.0 ] } let labels = seq { "red"; "red"; "red"; "red"; "red"; "blue"; "blue"; "blue"; "blue"; "blue" } let labeledPoints = Seq.zip points labels let prediction = FSharp.Stats.ML.Unsupervised.KNN.Seq.KNN.Seq.predict FSharp.Stats.DistanceMetrics.euclidean labeledPoints 3 let color = prediction [3.0; 3.0] // should be: Some "red" let color = prediction [6.0; 6.0] // should be: Some "blue" val points: float list seq Multiple items val seq: sequence: 'T seq -> 'T seq -------------------- type 'T seq = System.Collections.Generic.IEnumerable<'T> val labels: string seq val labeledPoints: (float list * string) seq module Seq from Microsoft.FSharp.Collections val zip: source1: 'T1 seq -> source2: 'T2 seq -> ('T1 * 'T2) seq val prediction: (float list -> obj) namespace Microsoft.FSharp val color: obj


                
                  
                    
                      Seq.predict distance labeledPoints k x

Full Usage: Seq.predict distance labeledPoints k x

Parameters:

distance : Distance<'a> - the distance function, e.g. `euclidean`

labeledPoints : ('a * 'l) seq - the array of classified (or labeled) points [in the format (point, label)], used for the classification

k : int - The _positive_ number of nearest neighbors from x to look for.

x : 'a - The point to classify

Returns: 'l option The most common label from the k nearest neighbors for x.
Modifiers: inline
Type parameters: 'a, 'l

The [k-nearest neighbors algorithm](https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm) to classify a new data point into the target class, depending on the features of its neighboring data points.

distance : Distance<'a>: the distance function, e.g. `euclidean`
labeledPoints : ('a * 'l) seq: the array of classified (or labeled) points [in the format (point, label)], used for the classification
k : int: The _positive_ number of nearest neighbors from x to look for.
x : 'a: The point to classify

Returns: 'l option: The most common label from the k nearest neighbors for x.

Example

 let points = seq { [ 2.0; 4.0 ]; [ 1.0; 3.0 ]; [ 2.0; 4.0 ]; [ 3.0; 2.0 ]; [ 2.0; 1.0 ]; [ 5.0; 6.0 ]; [ 4.0; 5.0 ]; [ 4.0; 6.0 ]; [ 6.0; 6.0 ]; [ 5.0; 4.0 ] }
 let labels = seq { "red"; "red"; "red"; "red"; "red"; "blue"; "blue"; "blue"; "blue"; "blue" }
 let labeledPoints = Seq.zip points labels
 let prediction = FSharp.Stats.ML.Unsupervised.KNN.Seq.KNN.Seq.predict FSharp.Stats.DistanceMetrics.euclidean labeledPoints 3

 let color = prediction [3.0; 3.0] // should be: Some "red"
 let color = prediction [6.0; 6.0] // should be: Some "blue"

val points: float list seq

Multiple items
val seq: sequence: 'T seq -> 'T seq

--------------------
type 'T seq = System.Collections.Generic.IEnumerable<'T>

val labels: string seq

val labeledPoints: (float list * string) seq

module Seq from Microsoft.FSharp.Collections

val zip: source1: 'T1 seq -> source2: 'T2 seq -> ('T1 * 'T2) seq

val prediction: (float list -> obj)

namespace Microsoft.FSharp

val color: obj