EmpiricalCovariance
Maximum likelihood covariance estimator.
Read more in the User Guide.
Python Reference (opens in a new tab)
Constructors
constructor()
Signature
new EmpiricalCovariance(opts?: object): EmpiricalCovariance;Parameters
| Name | Type | Description |
|---|---|---|
opts? | object | - |
opts.assume_centered? | boolean | If true, data are not centered before computation. Useful when working with data whose mean is almost, but not exactly zero. If false (default), data are centered before computation. Default Value false |
opts.store_precision? | boolean | Specifies if the estimated precision is stored. Default Value true |
Returns
Defined in: generated/covariance/EmpiricalCovariance.ts:23 (opens in a new tab)
Properties
_isDisposed
boolean=false
Defined in: generated/covariance/EmpiricalCovariance.ts:21 (opens in a new tab)
_isInitialized
boolean=false
Defined in: generated/covariance/EmpiricalCovariance.ts:20 (opens in a new tab)
_py
PythonBridge
Defined in: generated/covariance/EmpiricalCovariance.ts:19 (opens in a new tab)
id
string
Defined in: generated/covariance/EmpiricalCovariance.ts:16 (opens in a new tab)
opts
any
Defined in: generated/covariance/EmpiricalCovariance.ts:17 (opens in a new tab)
Accessors
covariance_
Estimated covariance matrix
Signature
covariance_(): Promise<ArrayLike[]>;Returns
Promise<ArrayLike[]>
Defined in: generated/covariance/EmpiricalCovariance.ts:365 (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/covariance/EmpiricalCovariance.ts:446 (opens in a new tab)
location_
Estimated location, i.e. the estimated mean.
Signature
location_(): Promise<ArrayLike>;Returns
Promise<ArrayLike>
Defined in: generated/covariance/EmpiricalCovariance.ts:338 (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/covariance/EmpiricalCovariance.ts:419 (opens in a new tab)
precision_
Estimated pseudo-inverse matrix. (stored only if store_precision is true)
Signature
precision_(): Promise<ArrayLike[]>;Returns
Promise<ArrayLike[]>
Defined in: generated/covariance/EmpiricalCovariance.ts:392 (opens in a new tab)
py
Signature
py(): PythonBridge;Returns
PythonBridge
Defined in: generated/covariance/EmpiricalCovariance.ts:42 (opens in a new tab)
Signature
py(pythonBridge: PythonBridge): void;Parameters
| Name | Type |
|---|---|
pythonBridge | PythonBridge |
Returns
void
Defined in: generated/covariance/EmpiricalCovariance.ts:46 (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/covariance/EmpiricalCovariance.ts:99 (opens in a new tab)
error_norm()
Compute the Mean Squared Error between two covariance estimators.
Signature
error_norm(opts: object): Promise<number>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.comp_cov? | ArrayLike[] | The covariance to compare with. |
opts.norm? | "frobenius" | "spectral" | The type of norm used to compute the error. Available error types: - ‘frobenius’ (default): sqrt(tr(A^t.A)) - ‘spectral’: sqrt(max(eigenvalues(A^t.A)) where A is the error (comp\_cov \- self.covariance\_). Default Value 'frobenius' |
opts.scaling? | boolean | If true (default), the squared error norm is divided by n_features. If false, the squared error norm is not rescaled. Default Value true |
opts.squared? | boolean | Whether to compute the squared error norm or the error norm. If true (default), the squared error norm is returned. If false, the error norm is returned. Default Value true |
Returns
Promise<number>
Defined in: generated/covariance/EmpiricalCovariance.ts:116 (opens in a new tab)
fit()
Fit the maximum likelihood covariance estimator to X.
Signature
fit(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike[] | Training data, where n\_samples is the number of samples and n\_features is the number of features. |
opts.y? | any | Not used, present for API consistency by convention. |
Returns
Promise<any>
Defined in: generated/covariance/EmpiricalCovariance.ts:179 (opens in a new tab)
get_precision()
Getter for the precision matrix.
Signature
get_precision(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.precision_? | ArrayLike[] | The precision matrix associated to the current covariance object. |
Returns
Promise<any>
Defined in: generated/covariance/EmpiricalCovariance.ts:219 (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/covariance/EmpiricalCovariance.ts:55 (opens in a new tab)
mahalanobis()
Compute the squared Mahalanobis distances of given observations.
Signature
mahalanobis(opts: object): Promise<ArrayLike>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike[] | The observations, the Mahalanobis distances of the which we compute. Observations are assumed to be drawn from the same distribution than the data used in fit. |
Returns
Promise<ArrayLike>
Defined in: generated/covariance/EmpiricalCovariance.ts:257 (opens in a new tab)
score()
Compute the log-likelihood of X\_test under the estimated Gaussian model.
The Gaussian model is defined by its mean and covariance matrix which are represented respectively by self.location\_ and self.covariance\_.
Signature
score(opts: object): Promise<number>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X_test? | ArrayLike[] | Test data of which we compute the likelihood, where n\_samples is the number of samples and n\_features is the number of features. X\_test is assumed to be drawn from the same distribution than the data used in fit (including centering). |
opts.y? | any | Not used, present for API consistency by convention. |
Returns
Promise<number>
Defined in: generated/covariance/EmpiricalCovariance.ts:296 (opens in a new tab)