KDTree

KDTree for fast generalized N-point problems

Constructors

constructor()

Signature

new KDTree(opts?: object): KDTree;

Parameters

Name	Type	Description
`opts?`	`object`	-
`opts.X?`	`ArrayLike`[]	n_samples is the number of points in the data set, and n_features is the dimension of the parameter space. Note: if X is a C-contiguous array of doubles then data will not be copied. Otherwise, an internal copy will be made.
`opts.leaf_size?`	`any`	Number of points at which to switch to brute-force. Changing leaf_size will not affect the results of a query, but can significantly impact the speed of a query and the memory required to store the constructed tree. The amount of memory needed to store the tree scales as approximately n_samples / leaf_size. For a specified `leaf\_size`, a leaf node is guaranteed to satisfy `leaf\_size <= n\_points <= 2 \* leaf\_size`, except in the case that `n\_samples < leaf\_size`. `Default Value` `40`
`opts.metric?`	`string`	Metric to use for distance computation. Default is “minkowski”, which results in the standard Euclidean distance when p = 2. A list of valid metrics for KDTree is given by `KDTree.valid\_metrics`. See the documentation of scipy.spatial.distance (opens in a new tab) and the metrics listed in `distance\_metrics` for more information on any distance metric. `Default Value` `'minkowski'`

Returns

KDTree

Defined in: generated/neighbors/KDTree.ts:23 (opens in a new tab)

Methods

dispose()

Disposes of the underlying Python resources.

Once dispose() is called, the instance is no longer usable.

Signature

dispose(): Promise<void>;

Returns

Promise<void>

Defined in: generated/neighbors/KDTree.ts:101 (opens in a new tab)

get_arrays()

Get data and node arrays.

Signature

get_arrays(opts: object): Promise<any>;

Parameters

Name	Type
`opts`	`object`

Returns

Promise<any>

Defined in: generated/neighbors/KDTree.ts:118 (opens in a new tab)

get_n_calls()

Get number of calls.

Signature

get_n_calls(opts: object): Promise<any>;

Parameters

Name	Type
`opts`	`object`

Returns

Promise<any>

Defined in: generated/neighbors/KDTree.ts:144 (opens in a new tab)

get_tree_stats()

Get tree status.

Signature

get_tree_stats(opts: object): Promise<any>;

Parameters

Name	Type
`opts`	`object`

Returns

Promise<any>

Defined in: generated/neighbors/KDTree.ts:170 (opens in a new tab)

init()

Initializes the underlying Python resources.

This instance is not usable until the Promise returned by init() resolves.

Signature

init(py: PythonBridge): Promise<void>;

Parameters

Name	Type
`py`	`PythonBridge`

Returns

Promise<void>

Defined in: generated/neighbors/KDTree.ts:60 (opens in a new tab)

kernel_density()

Compute the kernel density estimate at points X with the given kernel, using the distance metric specified at tree creation.

Signature

kernel_density(opts: object): Promise<any>;

Parameters

Name	Type	Description
`opts`	`object`	-
`opts.X?`	`ArrayLike`[]	An array of points to query. Last dimension should match dimension of training data.
`opts.atol?`	`number`	Specify the desired absolute tolerance of the result. If the true result is `K\_true`, then the returned result `K\_ret` satisfies `abs(K\_true \- K\_ret) < atol + rtol \* K\_ret` The default is zero (i.e. machine precision). `Default Value` `0`
`opts.breadth_first?`	`boolean`	If `true`, use a breadth-first search. If `false` (default) use a depth-first search. Breadth-first is generally faster for compact kernels and/or high tolerances. `Default Value` `false`
`opts.h?`	`number`	the bandwidth of the kernel
`opts.kernel?`	`string`	specify the kernel to use. Options are - ‘gaussian’ - ‘tophat’ - ‘epanechnikov’ - ‘exponential’ - ‘linear’ - ‘cosine’ Default is kernel = ‘gaussian’ `Default Value` `'gaussian'`
`opts.return_log?`	`boolean`	Return the logarithm of the result. This can be more accurate than returning the result itself for narrow kernels. `Default Value` `false`
`opts.rtol?`	`number`	Specify the desired relative tolerance of the result. If the true result is `K\_true`, then the returned result `K\_ret` satisfies `abs(K\_true \- K\_ret) < atol + rtol \* K\_ret` The default is `1e-8` (i.e. machine precision). `Default Value` `1e-8`

Returns

Promise<any>

Defined in: generated/neighbors/KDTree.ts:196 (opens in a new tab)

query()

query the tree for the k nearest neighbors

Signature

query(opts: object): Promise<any>;

Parameters

Name	Type	Description
`opts`	`object`	-
`opts.X?`	`ArrayLike`[]	An array of points to query
`opts.breadth_first?`	`boolean`	if `true`, then query the nodes in a breadth-first manner. Otherwise, query the nodes in a depth-first manner. `Default Value` `false`
`opts.dualtree?`	`boolean`	if `true`, use the dual tree formalism for the query: a tree is built for the query points, and the pair of trees is used to efficiently search this space. This can lead to better performance as the number of points grows large. `Default Value` `false`
`opts.k?`	`number`	The number of nearest neighbors to return `Default Value` `1`
`opts.return_distance?`	`boolean`	if `true`, return a tuple (d, i) of distances and indices if `false`, return array i `Default Value` `true`
`opts.sort_results?`	`boolean`	if `true`, then distances and indices of each point are sorted on return, so that the first column contains the closest points. Otherwise, neighbors are returned in an arbitrary order. `Default Value` `true`

Returns

Promise<any>

Defined in: generated/neighbors/KDTree.ts:275 (opens in a new tab)

query_radius()

query the tree for neighbors within a radius r

Signature

query_radius(opts: object): Promise<any>;

Parameters

Name	Type	Description
`opts`	`object`	-
`opts.X?`	`ArrayLike`[]	An array of points to query
`opts.count_only?`	`boolean`	if `true`, return only the count of points within distance r if `false`, return the indices of all points within distance r If return_distance==`true`, setting count_only=`true` will result in an error. `Default Value` `false`
`opts.r?`	`any`	r can be a single value, or an array of values of shape x.shape[:-1] if different radii are desired for each point.
`opts.return_distance?`	`boolean`	if `true`, return distances to neighbors of each point if `false`, return only neighbors Note that unlike the query() method, setting return_distance=`true` here adds to the computation time. Not all distances need to be calculated explicitly for return_distance=`false`. Results are not sorted by default: see `sort\_results` keyword. `Default Value` `false`
`opts.sort_results?`	`boolean`	if `true`, the distances and indices will be sorted before being returned. If `false`, the results will not be sorted. If return_distance == `false`, setting sort_results = `true` will result in an error. `Default Value` `false`

Returns

Promise<any>

Defined in: generated/neighbors/KDTree.ts:349 (opens in a new tab)

reset_n_calls()

Reset number of calls to 0.

Signature

reset_n_calls(opts: object): Promise<any>;

Parameters

Name	Type
`opts`	`object`

Returns

Promise<any>

Defined in: generated/neighbors/KDTree.ts:414 (opens in a new tab)

two_point_correlation()

Compute the two-point correlation function

Signature

two_point_correlation(opts: object): Promise<ArrayLike>;

Parameters

Name	Type	Description
`opts`	`object`	-
`opts.X?`	`ArrayLike`[]	An array of points to query. Last dimension should match dimension of training data.
`opts.dualtree?`	`boolean`	If `true`, use a dualtree algorithm. Otherwise, use a single-tree algorithm. Dual tree algorithms can have better scaling for large N. `Default Value` `false`
`opts.r?`	`ArrayLike`	A one-dimensional array of distances