KernelDensity

Kernel Density Estimation.

Constructors

constructor()

Signature

new KernelDensity(opts?: object): KernelDensity;

Parameters

Name	Type	Description
`opts?`	`object`	-
`opts.algorithm?`	`"auto"` \| `"ball_tree"` \| `"kd_tree"`	The tree algorithm to use. `Default Value` `'auto'`
`opts.atol?`	`number`	The desired absolute tolerance of the result. A larger tolerance will generally lead to faster execution. `Default Value` `0`
`opts.bandwidth?`	`number` \| `"scott"` \| `"silverman"`	The bandwidth of the kernel. If bandwidth is a float, it defines the bandwidth of the kernel. If bandwidth is a string, one of the estimation methods is implemented. `Default Value` `1`
`opts.breadth_first?`	`boolean`	If true (default), use a breadth-first approach to the problem. Otherwise use a depth-first approach. `Default Value` `true`
`opts.kernel?`	`"linear"` \| `"cosine"` \| `"exponential"` \| `"gaussian"` \| `"tophat"` \| `"epanechnikov"`	The kernel to use. `Default Value` `'gaussian'`
`opts.leaf_size?`	`number`	Specify the leaf size of the underlying tree. See `BallTree` or `KDTree` for details. `Default Value` `40`
`opts.metric?`	`string`	Metric to use for distance computation. See the documentation of scipy.spatial.distance (opens in a new tab) and the metrics listed in `distance\_metrics` for valid metric values. Not all metrics are valid with all algorithms: refer to the documentation of `BallTree` and `KDTree`. Note that the normalization of the density output is correct only for the Euclidean distance metric. `Default Value` `'euclidean'`
`opts.metric_params?`	`any`	Additional parameters to be passed to the tree for use with the metric. For more information, see the documentation of `BallTree` or `KDTree`.
`opts.rtol?`	`number`	The desired relative tolerance of the result. A larger tolerance will generally lead to faster execution. `Default Value` `0`

Returns

KernelDensity

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

Properties

_isDisposed

boolean = false

Defined in: generated/neighbors/KernelDensity.ts:21 (opens in a new tab)

_isInitialized

boolean = false

Defined in: generated/neighbors/KernelDensity.ts:20 (opens in a new tab)

_py

PythonBridge

Defined in: generated/neighbors/KernelDensity.ts:19 (opens in a new tab)

id

string

Defined in: generated/neighbors/KernelDensity.ts:16 (opens in a new tab)

opts

any

Defined in: generated/neighbors/KernelDensity.ts:17 (opens in a new tab)

Accessors

bandwidth_

Value of the bandwidth, given directly by the bandwidth parameter or estimated using the ‘scott’ or ‘silverman’ method.

Signature

bandwidth_(): Promise<number>;

Returns

Promise<number>

Defined in: generated/neighbors/KernelDensity.ts:408 (opens in a new tab)

feature_names_in_

Names of features seen during fit. Defined only when X has feature names that are all strings.

Signature

feature_names_in_(): Promise<ArrayLike>;

Returns

Promise<ArrayLike>

Defined in: generated/neighbors/KernelDensity.ts:383 (opens in a new tab)

n_features_in_

Number of features seen during fit.

Signature

n_features_in_(): Promise<number>;

Returns

Promise<number>

Defined in: generated/neighbors/KernelDensity.ts:335 (opens in a new tab)

py

Signature

py(): PythonBridge;

Returns

PythonBridge

Defined in: generated/neighbors/KernelDensity.ts:97 (opens in a new tab)

Signature

py(pythonBridge: PythonBridge): void;

Parameters

Name	Type
`pythonBridge`	`PythonBridge`

Returns

void

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

tree_

The tree algorithm for fast generalized N-point problems.

Signature

tree_(): Promise<any>;

Returns

Promise<any>

Defined in: generated/neighbors/KernelDensity.ts:360 (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/KernelDensity.ts:158 (opens in a new tab)

fit()

Fit the Kernel Density model on the data.

Signature

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

Parameters

Name	Type	Description
`opts`	`object`	-
`opts.X?`	`ArrayLike`[]	List of n_features-dimensional data points. Each row corresponds to a single data point.
`opts.sample_weight?`	`ArrayLike`	List of sample weights attached to the data X.
`opts.y?`	`any`	Ignored. This parameter exists only for compatibility with `Pipeline`.

Returns

Promise<any>

Defined in: generated/neighbors/KernelDensity.ts:175 (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/KernelDensity.ts:110 (opens in a new tab)

sample()

Generate random samples from the model.

Currently, this is implemented only for gaussian and tophat kernels.

Signature

sample(opts: object): Promise<ArrayLike[]>;

Parameters

Name	Type	Description
`opts`	`object`	-
`opts.n_samples?`	`number`	Number of samples to generate. `Default Value` `1`
`opts.random_state?`	`number`	Determines random number generation used to generate random samples. Pass an int for reproducible results across multiple function calls. See Glossary.

Returns

Promise<ArrayLike[]>

Defined in: generated/neighbors/KernelDensity.ts:224 (opens in a new tab)

score()

Compute the total log-likelihood under the model.

Signature

score(opts: object): Promise<number>;

Parameters

Name	Type	Description
`opts`	`object`	-
`opts.X?`	`ArrayLike`[]	List of n_features-dimensional data points. Each row corresponds to a single data point.
`opts.y?`	`any`	Ignored. This parameter exists only for compatibility with `Pipeline`.

Returns

Promise<number>

Defined in: generated/neighbors/KernelDensity.ts:264 (opens in a new tab)

score_samples()

Compute the log-likelihood of each sample under the model.

Signature

score_samples(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 (n_features).

Returns

Promise<ArrayLike>

Defined in: generated/neighbors/KernelDensity.ts:302 (opens in a new tab)

KernelCenterer KernelPCA