Class: BernoulliRBM
Bernoulli Restricted Boltzmann Machine (RBM).
A Restricted Boltzmann Machine with binary visible units and binary hidden units. Parameters are estimated using Stochastic Maximum Likelihood (SML), also known as Persistent Contrastive Divergence (PCD) [2].
The time complexity of this implementation is O(d \*\* 2) assuming d ~ n_features ~ n_components.
Read more in the User Guide.
Constructors
new BernoulliRBM()
new BernoulliRBM(
opts?):BernoulliRBM
Parameters
| Parameter | Type | Description |
|---|---|---|
opts? | object | - |
opts.batch_size? | number | Number of examples per minibatch. |
opts.learning_rate? | number | The learning rate for weight updates. It is highly recommended to tune this hyper-parameter. Reasonable values are in the 10**[0., -3.] range. |
opts.n_components? | number | Number of binary hidden units. |
opts.n_iter? | number | Number of iterations/sweeps over the training dataset to perform during training. |
opts.random_state? | number | Determines random number generation for: |
opts.verbose? | number | The verbosity level. The default, zero, means silent mode. Range of values is [0, inf]. |
Returns BernoulliRBM
Defined in generated/neural_network/BernoulliRBM.ts:27
Properties
| Property | Type | Default value | Defined in |
|---|---|---|---|
_isDisposed | boolean | false | generated/neural_network/BernoulliRBM.ts:25 |
_isInitialized | boolean | false | generated/neural_network/BernoulliRBM.ts:24 |
_py | PythonBridge | undefined | generated/neural_network/BernoulliRBM.ts:23 |
id | string | undefined | generated/neural_network/BernoulliRBM.ts:20 |
opts | any | undefined | generated/neural_network/BernoulliRBM.ts:21 |
Accessors
components_
Get Signature
get components_():
Promise<ArrayLike[]>
Weight matrix, where n_features is the number of visible units and n_components is the number of hidden units.
Returns Promise<ArrayLike[]>
Defined in generated/neural_network/BernoulliRBM.ts:511
feature_names_in_
Get Signature
get feature_names_in_():
Promise<ArrayLike>
Names of features seen during fit. Defined only when X has feature names that are all strings.
Returns Promise<ArrayLike>
Defined in generated/neural_network/BernoulliRBM.ts:586
h_samples_
Get Signature
get h_samples_():
Promise<ArrayLike[]>
Hidden Activation sampled from the model distribution, where batch_size is the number of examples per minibatch and n_components is the number of hidden units.
Returns Promise<ArrayLike[]>
Defined in generated/neural_network/BernoulliRBM.ts:536
intercept_hidden_
Get Signature
get intercept_hidden_():
Promise<ArrayLike>
Biases of the hidden units.
Returns Promise<ArrayLike>
Defined in generated/neural_network/BernoulliRBM.ts:461
intercept_visible_
Get Signature
get intercept_visible_():
Promise<ArrayLike>
Biases of the visible units.
Returns Promise<ArrayLike>
Defined in generated/neural_network/BernoulliRBM.ts:486
n_features_in_
Get Signature
get n_features_in_():
Promise<number>
Number of features seen during fit.
Returns Promise<number>
Defined in generated/neural_network/BernoulliRBM.ts:561
py
Get Signature
get py():
PythonBridge
Returns PythonBridge
Set Signature
set py(
pythonBridge):void
Parameters
| Parameter | Type |
|---|---|
pythonBridge | PythonBridge |
Returns void
Defined in generated/neural_network/BernoulliRBM.ts:72
Methods
dispose()
dispose():
Promise<void>
Disposes of the underlying Python resources.
Once dispose() is called, the instance is no longer usable.
Returns Promise<void>
Defined in generated/neural_network/BernoulliRBM.ts:124
fit()
fit(
opts):Promise<any>
Fit the model to the data X.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike | Training data. |
opts.y? | ArrayLike | Target values (undefined for unsupervised transformations). |
Returns Promise<any>
Defined in generated/neural_network/BernoulliRBM.ts:141
fit_transform()
fit_transform(
opts):Promise<any[]>
Fit to data, then transform it.
Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.fit_params? | any | Additional fit parameters. |
opts.X? | ArrayLike[] | Input samples. |
opts.y? | ArrayLike | Target values (undefined for unsupervised transformations). |
Returns Promise<any[]>
Defined in generated/neural_network/BernoulliRBM.ts:180
get_feature_names_out()
get_feature_names_out(
opts):Promise<any>
Get output feature names for transformation.
The feature names out will prefixed by the lowercased class name. For example, if the transformer outputs 3 features, then the feature names out are: \["class_name0", "class_name1", "class_name2"\].
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.input_features? | any | Only used to validate feature names with the names seen in fit. |
Returns Promise<any>
Defined in generated/neural_network/BernoulliRBM.ts:224
get_metadata_routing()
get_metadata_routing(
opts):Promise<any>
Get metadata routing of this object.
Please check User Guide on how the routing mechanism works.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.routing? | any | A MetadataRequest encapsulating routing information. |
Returns Promise<any>
Defined in generated/neural_network/BernoulliRBM.ts:260
gibbs()
gibbs(
opts):Promise<ArrayLike[]>
Perform one Gibbs sampling step.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.v? | ArrayLike[] | Values of the visible layer to start from. |
Returns Promise<ArrayLike[]>
Defined in generated/neural_network/BernoulliRBM.ts:294
init()
init(
py):Promise<void>
Initializes the underlying Python resources.
This instance is not usable until the Promise returned by init() resolves.
Parameters
| Parameter | Type |
|---|---|
py | PythonBridge |
Returns Promise<void>
Defined in generated/neural_network/BernoulliRBM.ts:85
partial_fit()
partial_fit(
opts):Promise<any>
Fit the model to the partial segment of the data X.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike[] | Training data. |
opts.y? | ArrayLike | Target values (undefined for unsupervised transformations). |
Returns Promise<any>
Defined in generated/neural_network/BernoulliRBM.ts:326
score_samples()
score_samples(
opts):Promise<ArrayLike>
Compute the pseudo-likelihood of X.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike | Values of the visible layer. Must be all-boolean (not checked). |
Returns Promise<ArrayLike>
Defined in generated/neural_network/BernoulliRBM.ts:363
set_output()
set_output(
opts):Promise<any>
Set output container.
See Introducing the set_output API for an example on how to use the API.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.transform? | "default" | "pandas" | "polars" | Configure output of transform and fit_transform. |
Returns Promise<any>
Defined in generated/neural_network/BernoulliRBM.ts:397
transform()
transform(
opts):Promise<ArrayLike[]>
Compute the hidden layer activation probabilities, P(h=1|v=X).
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike | The data to be transformed. |
Returns Promise<ArrayLike[]>
Defined in generated/neural_network/BernoulliRBM.ts:429