neighbors

package
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 Go to latest Published: Dec 19, 2023 License: MIT Imports: 10 Imported by: 0

Documentation

Overview

Package neighbors implements the k-nearest neighbors algorithm. it contains NearestCentroid, KNeighborsClassifier and KNeighborsRegressor

Index

Examples

Constants

This section is empty.

Variables

This section is empty.

Functions

func EuclideanDistance 

func EuclideanDistance(a, b mat.Vector) float64

EuclideanDistance is a Distancer

Example 
a, b := mat.NewVecDense(4, []float64{0, 0, 0, 0}), mat.NewVecDense(4, []float64{1, 1, 0, 1})
fmt.Printf("%.8f\n", EuclideanDistance(a, b))

Output:

1.73205081

func MinkowskiDistanceP 

func MinkowskiDistanceP(a, b mat.Vector, p float64) float64

MinkowskiDistanceP ...

func NewKNeighborsRegressor 

func NewKNeighborsRegressor(K int, Weights interface{}) base.Predicter

NewKNeighborsRegressor returns an initialized *KNeighborsRegressor Weights may be "uniform", "distance" or func(dist []float64) []float64

Types

type Distance 

type Distance func(a, b mat.Vector) float64

Distance has Distance(Vector,Vector)float64

func MinkowskiDistance 

func MinkowskiDistance(p float64) Distance

MinkowskiDistance ...

Example 
a, b := mat.NewVecDense(4, []float64{0, 0, 0, 0}), mat.NewVecDense(4, []float64{1, 1, 0, 1})
fmt.Printf("%.8f\n", MinkowskiDistance(1)(a, b))
fmt.Printf("%.8f\n", MinkowskiDistance(2)(a, b))
fmt.Printf("%.8f\n", MinkowskiDistance(math.Inf(1))(a, b))

Output:

3.00000000
1.73205081
1.00000000

type InnerNode 

type InnerNode struct {
	Node
	// contains filtered or unexported fields
}

InnerNode ...

func (*InnerNode) IsLeaf 

func (*InnerNode) IsLeaf() bool

IsLeaf ...

type KDTree 

type KDTree struct {
	Data        *mat.Dense
	LeafSize    int
	Maxes, Mins []float64
	Tree        Node
}

KDTree for quick nearest-neighbor lookup This class provides an index into a set of k-dimensional points which can be used to rapidly look up the nearest neighbors of any point. Parameters Data : (N,K) mat.Dense The data points to be indexed. This array is not copied, and 

so modifying this data will result in bogus results.

Leafsize : int, optional The number of points at which the algorithm switches over to 

brute-force.  Has to be positive.

Notes The algorithm used is described in Maneewongvatana and Mount 1999. The general idea is that the kd-tree is a binary tree, each of whose nodes represents an axis-aligned hyperrectangle. Each node specifies an axis and splits the set of points based on whether their coordinate along that axis is greater than or less than a particular value. During construction, the axis and splitting point are chosen by the "sliding midpoint" rule, which ensures that the cells do not all become long and thin. The tree can be queried for the r closest neighbors of any given point (optionally returning only those within some maximum distance of the point). It can also be queried, with a substantial gain in efficiency, for the r approximate closest neighbors. For large dimensions (20 is already large) do not expect this to run significantly faster than brute force. High-dimensional nearest-neighbor queries are a substantial open problem in computer science. The tree also supports all-neighbors queries, both with arrays of points and with other kd-trees. These do use a reasonably efficient algorithm, but the kd-tree is not necessarily the best data structure for this sort of calculation.

Example 
// v https://github.com/scipy/scipy/blob/v1.1.0/scipy/spatial/kdtree.py
X := mat.NewDense(30, 2, nil)
for i := 0; i < 5; i++ {
	for j := 0; j < 6; j++ {
		X.Set(i*6+j, 0, float64(i))
		X.Set(i*6+j, 1, float64(j+2))
	}
}
//fmt.Println(mat.Formatted(X))
tree := NewKDTree(X, 1)
pts := mat.NewDense(2, 2, []float64{0, 0, 2.1, 2.9})
distances, indices := tree.Query(pts, 1, 1e-15, 2, math.Inf(1))
fmt.Printf("%.6f\n", mat.Formatted(distances.T()))
fmt.Println(mat.Formatted(indices.T()))

Output:

[2.000000  0.141421]
[ 0  13]

func NewKDTree 

func NewKDTree(data mat.Matrix, LeafSize int) *KDTree

NewKDTree ...

func (*KDTree) Query 

func (tr *KDTree) Query(X mat.Matrix, k int, eps, p, distanceUpperBound float64) (dd, ii *mat.Dense)

Query ... Query the kd-tree for nearest neighbors Parameters ---------- x : array_like, last dimension self.m 

An array of points to query.

k : int, optional 

The number of nearest neighbors to return.

eps : nonnegative float, optional 

Return approximate nearest neighbors; the kth returned value
is guaranteed to be no further than (1+eps) times the
distance to the real kth nearest neighbor.

p : float, 1<=p<=infinity, optional 

Which Minkowski p-norm to use.
1 is the sum-of-absolute-values "Manhattan" distance
2 is the usual Euclidean distance
infinity is the maximum-coordinate-difference distance

distance_upper_bound : nonnegative float, optional 

Return only neighbors within this distance. This is used to prune
tree searches, so if you are doing a series of nearest-neighbor
queries, it may help to supply the distance to the nearest neighbor
of the most recent point.

Returns ------- d : float or array of floats 

The distances to the nearest neighbors.
If x has shape tuple+(self.m,), then d has shape tuple if
k is one, or tuple+(k,) if k is larger than one. Missing
neighbors (e.g. when k > n or distance_upper_bound is
given) are indicated with infinite distances.  If k is None,
then d is an object array of shape tuple, containing lists
of distances. In either case the hits are sorted by distance
(nearest first).

i : integer or array of integers 

The locations of the neighbors in self.data. i is the same
shape as d.

type KNeighborsClassifier 

type KNeighborsClassifier struct {
	base.Predicter
	NearestNeighbors
	K        int
	Weight   string
	Scale    bool
	Distance Distance
	// Runtime members
	Xscaled, Y *mat.Dense
	Classes    [][]float64
	// contains filtered or unexported fields
}

KNeighborsClassifier is a Regression based on k-nearest neighbors. The target is predicted by local interpolation of the targets associated of the nearest neighbors in the training set.

Example 
X := mat.NewDense(4, 1, []float64{0, 1, 2, 3})
Y := mat.NewDense(4, 1, []float64{0, 0, 1, 1})
neigh := NewKNeighborsClassifier(3, "uniform")
neigh.Fit(X, Y)
Xtest := mat.NewDense(1, 1, []float64{1.1})
Ypred := mat.NewDense(1, 1, nil)
neigh.Predict(Xtest, Ypred)
fmt.Println(mat.Formatted(Ypred))
Xtest.Set(0, 0, 0.9)

Yprob := mat.NewDense(1, 2, nil)
neigh.PredictProba(Xtest, Yprob)
fmt.Printf("%.8f\n", mat.Formatted(Yprob))

Output:

[0]
[0.66666667  0.33333333]

func NewKNeighborsClassifier 

func NewKNeighborsClassifier(K int, Weights string) *KNeighborsClassifier

NewKNeighborsClassifier returns an initialized *KNeighborsClassifier

func (*KNeighborsClassifier) Fit 

func (m *KNeighborsClassifier) Fit(Xmatrix, Ymatrix mat.Matrix) base.Fiter

Fit ...

func (*KNeighborsClassifier) GetNOutputs 

func (m *KNeighborsClassifier) GetNOutputs() int

GetNOutputs returns output columns number for Y to pass to predict

func (*KNeighborsClassifier) Predict 

func (m *KNeighborsClassifier) Predict(X mat.Matrix, Ymutable mat.Mutable) *mat.Dense

Predict for KNeighborsClassifier

func (*KNeighborsClassifier) PredictProba 

func (m *KNeighborsClassifier) PredictProba(X, Y *mat.Dense) *KNeighborsClassifier

PredictProba for KNeighborsClassifier

func (*KNeighborsClassifier) Score 

func (m *KNeighborsClassifier) Score(X, Y mat.Matrix) float64

Score for KNeighborsClassifier

type KNeighborsRegressor 

type KNeighborsRegressor struct {
	NearestNeighbors
	K int
	// Weights may be "uniform", "distance" or func(dstWeights, srcDists []float64)
	Weights  interface{}
	Scale    bool
	Distance Distance
	// Runtime members
	Xscaled, Y *mat.Dense
}

KNeighborsRegressor is a Regression based on k-nearest neighbors. The target is predicted by local interpolation of the targets associated of the nearest neighbors in the training set.

Example

ExampleKNeighborsRegressor from http://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsRegressor.html#sklearn.neighbors.KNeighborsRegressor 

X := mat.NewDense(4, 1, []float64{0, 1, 2, 3})
Y := mat.NewDense(4, 1, []float64{0, 0, 1, 1})
neigh := NewKNeighborsRegressor(2, "distance")
neigh.Fit(X, Y)
Xtest := mat.NewDense(1, 1, []float64{1.5})
Ypred := mat.NewDense(1, 1, nil)
neigh.Predict(Xtest, Ypred)
fmt.Println(mat.Formatted(Ypred))

Output:

[0.5]

func (*KNeighborsRegressor) Fit 

func (m *KNeighborsRegressor) Fit(Xmatrix, Ymatrix mat.Matrix) base.Fiter

Fit ...

func (*KNeighborsRegressor) GetNOutputs 

func (m *KNeighborsRegressor) GetNOutputs() int

GetNOutputs return Y width

func (*KNeighborsRegressor) IsClassifier 

func (*KNeighborsRegressor) IsClassifier() bool

IsClassifier returns false for KNeighborsRegressor

func (*KNeighborsRegressor) Predict 

func (m *KNeighborsRegressor) Predict(X mat.Matrix, Ymutable mat.Mutable) *mat.Dense

Predict ...

func (*KNeighborsRegressor) PredicterClone 

func (m *KNeighborsRegressor) PredicterClone() base.Predicter

PredicterClone return a (possibly unfitted) copy of predicter

func (*KNeighborsRegressor) Score 

func (m *KNeighborsRegressor) Score(X, Y mat.Matrix) float64

Score for KNeighborsRegressor

type LeafNode 

type LeafNode struct {
	Node
	// contains filtered or unexported fields
}

LeafNode ...

func (*LeafNode) IsLeaf 

func (*LeafNode) IsLeaf() bool

IsLeaf ...

type NearestCentroid 

type NearestCentroid struct {
	base.Predicter
	Metric          string
	ShrinkThreshold float64
	// runtime filled members
	NearestNeighbors
	Classes    [][]float64
	ClassCount [][]int
	Centroids  *mat.Dense
}

NearestCentroid is a Regression based on k-nearest neighbors. The target is predicted by local interpolation of the targets associated of the nearest neighbors in the training set.

Example 
// Example adapted from from http://scikit-learn.org/stable/modules/generated/sklearn.neighbors.NearestCentroid.html#sklearn.neighbors.NearestCentroid
X := mat.NewDense(6, 2, []float64{-1, -1, -2, -1, -3, -2, 1, 1, 2, 1, 3, 2})
Y := mat.NewDense(6, 1, []float64{1, 1, 1, 2, 2, 2})
clf := NewNearestCentroid("euclidean", 0.)
clf.Fit(X, Y)
Xtest := mat.NewDense(1, 2, []float64{-0.8, -1})

Ypred := mat.NewDense(1, 1, nil)
clf.Predict(Xtest, Ypred)
fmt.Println(mat.Formatted(Ypred))

Output:

[1]

func NewNearestCentroid 

func NewNearestCentroid(metric string, shrinkThreshold float64) *NearestCentroid

NewNearestCentroid ... if Metric is "manhattan", centroids are computed using median else mean

func (*NearestCentroid) Fit 

func (m *NearestCentroid) Fit(Xmatrix, Ymatrix mat.Matrix) base.Fiter

Fit ...

func (*NearestCentroid) Predict 

func (m *NearestCentroid) Predict(X mat.Matrix, Ymutable mat.Mutable) *mat.Dense

Predict for NearestCentroid

func (*NearestCentroid) PredictProba 

func (m *NearestCentroid) PredictProba(X mat.Matrix, Y *mat.Dense) *NearestCentroid

PredictProba for NearestCentroid

func (*NearestCentroid) Score 

func (m *NearestCentroid) Score(X, Y mat.Matrix) float64

Score for NearestCentroid

type NearestNeighbors 

type NearestNeighbors struct {
	Algorithm string
	Metric    string
	P         float64
	NJobs     int
	LeafSize  int
	// Runtime filled members
	Distance func(a, b mat.Vector) float64
	X, Y     *mat.Dense
	Tree     *KDTree
}

NearestNeighbors is the unsupervised alog implementing search of k nearest neighbors Algorithm is one of 'auto', 'ball_tree', 'kd_tree', 'brute' defaults to "auto"

Metric = 'cityblock', 'cosine', 'euclidean', 'l1', 'l2', 'manhattan' defaults to euclidean (= minkowski with P=2) P is power for 'minkowski' NJobs: number of concurrent jobs. NJobs<0 means runtime.NumCPU() default to -1

Example 
// test from http://scikit-learn.org/stable/modules/neighbors.html
X := mat.NewDense(6, 2, []float64{-1, -1, -2, -1, -3, -2, 1, 1, 2, 1, 3, 2})
nbrs := NewNearestNeighbors()
nbrs.Fit(X, mat.Matrix(nil))
distances, indices := nbrs.KNeighbors(X, 2)
fmt.Printf("indices:\n%v\n", mat.Formatted(indices))
fmt.Printf("distances:\n%v\n", mat.Formatted(distances))

Output:

	indices:
⎡0  1⎤
⎢1  0⎥
⎢2  1⎥
⎢3  4⎥
⎢4  3⎥
⎣5  4⎦
distances:
⎡                 0                   1⎤
⎢                 0                   1⎥
⎢                 0  1.4142135623730951⎥
⎢                 0                   1⎥
⎢                 0                   1⎥
⎣                 0  1.4142135623730951⎦

func NewNearestNeighbors 

func NewNearestNeighbors() *NearestNeighbors

NewNearestNeighbors returns an *NearestNeighbors

func (*NearestNeighbors) Fit 

func (m *NearestNeighbors) Fit(X, Y mat.Matrix)

Fit for NearestNeighbors. Y is unused

func (*NearestNeighbors) KNeighbors 

func (m *NearestNeighbors) KNeighbors(X mat.Matrix, NNeighbors int) (distances, indices *mat.Dense)

KNeighbors returns distances and indices of first NNeighbors

func (*NearestNeighbors) KNeighborsGraph 

func (m *NearestNeighbors) KNeighborsGraph(X *mat.Dense, NNeighbors int, mode string, includeSelf bool) (graph *mat.Dense)

KNeighborsGraph Computes the (weighted) graph of k-Neighbors for points in X mode : {‘connectivity’, ‘distance’}, optional 

Type of returned matrix: ‘connectivity’ will return the connectivity matrix with ones and zeros, in ‘distance’ the edges are Euclidean distance between points.

Returns: A : shape = [n_samples, n_samples_fit] 

n_samples_fit is the number of samples in the fitted data A[i, j] is assigned the weight of edge that connects i to j.
Example 
X := mat.NewDense(3, 1, []float64{0, 3, 1})
neigh := NewNearestNeighbors()
neigh.Fit(X, mat.Matrix(nil))
A := neigh.KNeighborsGraph(X, 2, "connectivity", true)
fmt.Println(mat.Formatted(A))

Output:

⎡1  0  1⎤
⎢0  1  1⎥
⎣1  0  1⎦

func (*NearestNeighbors) RadiusNeighbors 

func (m *NearestNeighbors) RadiusNeighbors(X *mat.Dense, radius float64) (distances [][]float64, indices [][]int)

RadiusNeighbors Finds the neighbors within a given radius of a point or points. Return the indices and distances of each point from the dataset lying in a ball with size “radius“ around the points of the query array. Points lying on the boundary are included in the results. The result points are *not* necessarily sorted by distance to their query point. Parameters ---------- X : array-like, (n_samples, n_features), optional 

The query point or points.
If not provided, neighbors of each indexed point are returned.
In this case, the query point is not considered its own neighbor.

radius : float 

Limiting distance of neighbors to return.
(default is the value passed to the constructor).
Example 
// adapted example from http://scikit-learn.org/stable/modules/generated/sklearn.neighbors.RadiusNeighborsRegressor.html#sklearn.neighbors.RadiusNeighborsRegressor
samples := mat.NewDense(3, 3, []float64{0, 0, 0, 0, .5, 0, 1, 1, .5})
neigh := NewNearestNeighbors()
neigh.Fit(samples, mat.Matrix(nil))
distances, indices := neigh.RadiusNeighbors(mat.NewDense(1, 3, []float64{1, 1, 1}), 1.6)
fmt.Println(distances[0])
fmt.Println(indices[0])
// unlike the python example, distances are sorted by increasing distances

Output:

[0.5 1.5]
[2 1]

type Node 

type Node interface {
	IsLeaf() bool
}

Node ...

type Rectangle 

type Rectangle struct{ Maxes, Mins []float64 }

Rectangle Hyperrectangle class. Represents a Cartesian product of intervals.

func NewRectangle 

func NewRectangle(Maxes, Mins []float64) *Rectangle

NewRectangle ...

func (*Rectangle) MaxDistancePoint 

func (r *Rectangle) MaxDistancePoint(x []float64, p float64) float64

MaxDistancePoint return the maximum distance between input and points in the hyperrectangle. Parameters x : array_like Input array. p : float, optional Input.

func (*Rectangle) MaxDistanceRectangle 

func (r *Rectangle) MaxDistanceRectangle(other *Rectangle, p float64) float64

MaxDistanceRectangle compute the maximum distance between points in the two hyperrectangles. Parameters other : hyperrectangle Input. p : float, optional Input.

func (*Rectangle) MinDistancePoint 

func (r *Rectangle) MinDistancePoint(x []float64, p float64) float64

MinDistancePoint Return the minimum distance between input and points in the hyperrectangle. Parameters x : array_like Input. p : float, optional Input.

func (*Rectangle) MinDistanceRectangle 

func (r *Rectangle) MinDistanceRectangle(other *Rectangle, p float64) float64

MinDistanceRectangle compute the minimum distance between points in the two hyperrectangles. Parameters other : hyperrectangle Input. p : float Input.

func (*Rectangle) Split 

func (r *Rectangle) Split(d int, split float64) (less, greater *Rectangle)

Split Produce two hyperrectangles by splitting. In general, if you need to compute maximum and minimum distances to the children, it can be done more efficiently by updating the maximum and minimum distances to the parent. Parameters ---------- d : int 

Axis to split hyperrectangle along.

split : float 

Position along axis `d` to split at.

func (*Rectangle) String 

func (r *Rectangle) String() string

String ...

func (*Rectangle) Volume 

func (r *Rectangle) Volume() float64

Volume ...

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