KernelDensity
Kernel Density Estimation.
Read more in the User Guide.
Python Reference (opens in a new tab)
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
Defined in: generated/neighbors/KernelDensity.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/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)
get_metadata_routing()
Get metadata routing of this object.
Please check User Guide on how the routing mechanism works.
Signature
get_metadata_routing(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.routing? | any | A MetadataRequest encapsulating routing information. |
Returns
Promise<any>
Defined in: generated/neighbors/KernelDensity.ts:224 (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:261 (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:301 (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:339 (opens in a new tab)
set_fit_request()
Request metadata passed to the fit method.
Note that this method is only relevant if enable\_metadata\_routing=True (see sklearn.set\_config). Please see User Guide on how the routing mechanism works.
The options for each parameter are:
Signature
set_fit_request(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.sample_weight? | string | boolean | Metadata routing for sample\_weight parameter in fit. |
Returns
Promise<any>
Defined in: generated/neighbors/KernelDensity.ts:376 (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:482 (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:457 (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:409 (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:434 (opens in a new tab)