src.geometry package

Subpackages

• src.geometry.density package
  • Submodules
  • src.geometry.density.density module
  • Module contents
• src.geometry.embedding package
  • Submodules
  • src.geometry.embedding.laplacian_embedding module
    • laplacian_embedding()
  • src.geometry.embedding.ltsa module
  • Module contents
    • laplacian_embedding()
• src.geometry.matrix package
  • Submodules
  • src.geometry.matrix.adjacency module
    • AdjacencyMatrix
    • AdjacencyMatrixMixin
      • AdjacencyMatrixMixin.create()
      • AdjacencyMatrixMixin.fixed_dtype
    • CsrAdjacencyMatrix
    • DenseAdjacencyMatrix
  • src.geometry.matrix.affinity module
    • AffinityMatrix
      • AffinityMatrix.laplacian()
    • AffinityMatrixMixin
      • AffinityMatrixMixin.create()
    • CsrAffinityMatrix
    • DenseAffinityMatrix
  • src.geometry.matrix.distance module
    • CsrDistanceMatrix
    • DenseDistanceMatrix
    • DistanceMatrix
      • DistanceMatrix.adjacency()
      • DistanceMatrix.affinity()
      • DistanceMatrix.threshold()
      • DistanceMatrix.threshold_distance_iter()
    • DistanceMatrixMixin
      • DistanceMatrixMixin.create()
    • gaussian()
  • src.geometry.matrix.laplacian module
    • CsrLaplacianMatrix
    • DenseLaplacianMatrix
    • LaplacianMatrix
    • LaplacianMatrixMixin
      • LaplacianMatrixMixin.create()
    • eps_adjustment()
  • Module contents
    • AdjacencyMatrix
    • AffinityMatrix
      • AffinityMatrix.laplacian()
    • DistanceMatrix
      • DistanceMatrix.adjacency()
      • DistanceMatrix.affinity()
      • DistanceMatrix.threshold()
      • DistanceMatrix.threshold_distance_iter()
    • LaplacianMatrix

Submodules

src.geometry.normalize module

src.geometry.normalize.normalize(arr, axis=1, degree_exp=1.0, sym_norm=False, in_place=False)

Parameters:

• arr (BaseArray[T])

• axis (int | None)

• degree_exp (float)

• sym_norm (bool)

• in_place (bool)

Return type:

BaseArray[T]

src.geometry.points module

class src.geometry.points.Coordinates(*args, **metadata)

Bases: PointsMixin, DenseArray

Parameters:

• args (Any)

• metadata (Metadata)

property d: int

class src.geometry.points.Data(*args, **metadata)

Bases: Points

Parameters:

• args (Any)

• metadata (Metadata)

property D: int

class src.geometry.points.Embedding(*args, **metadata)

Bases: Points

Parameters:

• args (Any)

• metadata (Metadata)

property p: int

class src.geometry.points.Points(*args, **metadata)

Bases: PointsMixin, DenseArray

Parameters:

• args (Any)

• metadata (Metadata)

pairwise_distance(y_pts=None, dist_type='euclidean')

Compute the pairwise distance matrix between points.

If y_pts is provided, computes distances between self and y_pts. Otherwise, computes distances among points in self.

Parameters:

• y_pts (Optional[Points]) – Another Points object to compute distances to. If None, distances are computed within self.

• dist_type (DistanceType) – The type of distance metric to use (“euclidean”, “cityblock”, “sqeuclidian”). Name of metric is stored in metadata. (default: “euclidian”)

Returns:

A DistanceMatrix containing pairwise distances. The matrix name is constructed from the metadata of the points involved.

Return type:

DistanceMatrix

class src.geometry.points.PointsMixin(*args, **metadata)

Bases: ObjectMixin, ABC

Mixin class for point cloud objects.

Adds fixed dimensionality and dtype constraints, metadata handling, and distance computation functionality to point cloud classes.

Parameters:

• args (Any)

• metadata (Metadata)

fixed_dtype

alias of float64

fixed_ndim: int = 2

metadata: Metadata

property nfeats: int

Number of features (dimensions) per point.

Returns:

Number of features per point.

Return type:

int

property npts: int

Number of points in the point set.

Returns:

Number of points.

Return type:

int

Module contents

class src.geometry.Coordinates(*args, **metadata)

Bases: PointsMixin, DenseArray

Parameters:

• args (Any)

• metadata (Any)

property d: int

class src.geometry.Data(*args, **metadata)

Bases: Points

Parameters:

• args (Any)

• metadata (Any)

property D: int

class src.geometry.Embedding(*args, **metadata)

Bases: Points

Parameters:

• args (Any)

• metadata (Any)

property p: int

class src.geometry.Points(*args, **metadata)

Bases: PointsMixin, DenseArray

Parameters:

• args (Any)

• metadata (Any)

pairwise_distance(y_pts=None, dist_type='euclidean')

Compute the pairwise distance matrix between points.

If y_pts is provided, computes distances between self and y_pts. Otherwise, computes distances among points in self.

Parameters:

• y_pts (Optional[Points]) – Another Points object to compute distances to. If None, distances are computed within self.

• dist_type (DistanceType) – The type of distance metric to use (“euclidean”, “cityblock”, “sqeuclidian”). Name of metric is stored in metadata. (default: “euclidian”)

Returns:

A DistanceMatrix containing pairwise distances. The matrix name is constructed from the metadata of the points involved.

Return type:

DistanceMatrix

src.geometry.normalize(arr, axis=1, degree_exp=1.0, sym_norm=False, in_place=False)

Parameters:

• arr (BaseArray[T])

• axis (int | None)

• degree_exp (float)

• sym_norm (bool)

• in_place (bool)

Return type:

BaseArray[T]