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.
Python Reference (opens in a new tab)
Constructors
constructor()
Signature
new BernoulliRBM(opts?: object): BernoulliRBM;Parameters
| Name | Type | Description |
|---|---|---|
opts? | object | - |
opts.batch_size? | number | Number of examples per minibatch. Default Value 10 |
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. Default Value 0.1 |
opts.n_components? | number | Number of binary hidden units. Default Value 256 |
opts.n_iter? | number | Number of iterations/sweeps over the training dataset to perform during training. Default Value 10 |
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]. Default Value 0 |
Returns
Defined in: generated/neural_network/BernoulliRBM.ts:27 (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/neural_network/BernoulliRBM.ts:131 (opens in a new tab)
fit()
Fit the model to the data X.
Signature
fit(opts: object): Promise<any>;Parameters
| Name | 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:148 (opens in a new tab)
fit_transform()
Fit to data, then transform it.
Fits transformer to X and y with optional parameters fit\_params and returns a transformed version of X.
Signature
fit_transform(opts: object): Promise<any[]>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike[] | Input samples. |
opts.fit_params? | any | Additional fit parameters. |
opts.y? | ArrayLike | Target values (undefined for unsupervised transformations). |
Returns
Promise<any[]>
Defined in: generated/neural_network/BernoulliRBM.ts:190 (opens in a new tab)
get_feature_names_out()
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: \["class\_name0", "class\_name1", "class\_name2"\].
Signature
get_feature_names_out(opts: object): Promise<any>;Parameters
| Name | 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:239 (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/neural_network/BernoulliRBM.ts:277 (opens in a new tab)
gibbs()
Perform one Gibbs sampling step.
Signature
gibbs(opts: object): Promise<ArrayLike[]>;Parameters
| Name | 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:312 (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/neural_network/BernoulliRBM.ts:85 (opens in a new tab)
partial_fit()
Fit the model to the partial segment of the data X.
Signature
partial_fit(opts: object): Promise<any>;Parameters
| Name | 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:345 (opens in a new tab)
score_samples()
Compute the pseudo-likelihood of X.
Signature
score_samples(opts: object): Promise<ArrayLike>;Parameters
| Name | 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:385 (opens in a new tab)
set_output()
Set output container.
See Introducing the set_output API for an example on how to use the API.
Signature
set_output(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.transform? | "default" | "pandas" | Configure output of transform and fit\_transform. |
Returns
Promise<any>
Defined in: generated/neural_network/BernoulliRBM.ts:420 (opens in a new tab)
transform()
Compute the hidden layer activation probabilities, P(h=1|v=X).
Signature
transform(opts: object): Promise<ArrayLike[]>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike | The data to be transformed. |
Returns
Promise<ArrayLike[]>
Defined in: generated/neural_network/BernoulliRBM.ts:453 (opens in a new tab)
Properties
_isDisposed
boolean=false
Defined in: generated/neural_network/BernoulliRBM.ts:25 (opens in a new tab)
_isInitialized
boolean=false
Defined in: generated/neural_network/BernoulliRBM.ts:24 (opens in a new tab)
_py
PythonBridge
Defined in: generated/neural_network/BernoulliRBM.ts:23 (opens in a new tab)
id
string
Defined in: generated/neural_network/BernoulliRBM.ts:20 (opens in a new tab)
opts
any
Defined in: generated/neural_network/BernoulliRBM.ts:21 (opens in a new tab)
Accessors
components_
Weight matrix, where n\_features is the number of visible units and n\_components is the number of hidden units.
Signature
components_(): Promise<ArrayLike[]>;Returns
Promise<ArrayLike[]>
Defined in: generated/neural_network/BernoulliRBM.ts:536 (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/neural_network/BernoulliRBM.ts:611 (opens in a new tab)
h_samples_
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.
Signature
h_samples_(): Promise<ArrayLike[]>;Returns
Promise<ArrayLike[]>
Defined in: generated/neural_network/BernoulliRBM.ts:561 (opens in a new tab)
intercept_hidden_
Biases of the hidden units.
Signature
intercept_hidden_(): Promise<ArrayLike>;Returns
Promise<ArrayLike>
Defined in: generated/neural_network/BernoulliRBM.ts:486 (opens in a new tab)
intercept_visible_
Biases of the visible units.
Signature
intercept_visible_(): Promise<ArrayLike>;Returns
Promise<ArrayLike>
Defined in: generated/neural_network/BernoulliRBM.ts:511 (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/neural_network/BernoulliRBM.ts:586 (opens in a new tab)
py
Signature
py(): PythonBridge;Returns
PythonBridge
Defined in: generated/neural_network/BernoulliRBM.ts:72 (opens in a new tab)
Signature
py(pythonBridge: PythonBridge): void;Parameters
| Name | Type |
|---|---|
pythonBridge | PythonBridge |
Returns
void
Defined in: generated/neural_network/BernoulliRBM.ts:76 (opens in a new tab)