Recurrent neuralnetwork

Constructors

Properties

Accessors

Methods

Constructors

  • Parameters

    • Optionalmethod: "rnn" | "lstm" | "gru"

      Method name

    • Optionalwindow: number

      Window size

    • Optionalunit: number

      Size of recurrent unit

    • Optionalout_size: number

      Output size

    • Optionaloptimizer: string

      Optimizer of the network

    Returns RNN

Properties

_epoch: number
_layers: { type: string }[]
_method: "rnn" | "lstm" | "gru"
_window: number

Accessors

  • get epoch(): number
  • Epoch

    Returns number

  • get method(): "rnn" | "lstm" | "gru"
  • Method

    Returns "rnn" | "lstm" | "gru"

Methods

  • Fit model.

    Parameters

    • train_x: number[][]

      Training data

    • train_y: number[][]

      Target values

    • iteration: number

      Iteration count

    • rate: number

      Learning rate

    • batch: number

      Batch size

    Returns number

    Loss value

  • Returns predicted future values.

    Parameters

    • data: number[][]

      Sample data

    • k: number

      Prediction count

    Returns number[][]

    Predicted values