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import {LSTMTimeSeries} from 'tensorscript/lib/lstm_time_series.mjs'
public class | source

LSTMTimeSeries

Extends:

lib/deep_learning.js~BaseNeuralNetwork → LSTMTimeSeries

Long Short Term Memory Time Series with Tensorflow

Static Method Summary

Static Public Methods
public static

createDataset(dataset: Array<Array<number>, look_back: Number): [Array<Array<number>>,Array<number>]

Creates dataset data

public static

getTimeseriesDataSet(timeseries: *, look_back: *)

Returns data for predicting values

public static

getTimeseriesShape(x_timeseries: Array<Array<number>): Array<Array<number>>

Reshape input to be [samples, time steps, features]

Constructor Summary

Public Constructor
public

constructor(options: {layers: Array<Object>, compile: Object, fit: Object}, properties: *)

Member Summary

Public Members
public
public
public
public
public
public
public

Method Summary

Public Methods
public
public

generateLayers(x_matrix: Array<Array<number>>, y_matrix: Array<Array<number>>, layers: Array<Object>, x_test: Array<Array<number>>, y_test: Array<Array<number>>)

Adds dense layers to tensorflow classification model

public

async predict()

public

async train()

Static Public Methods

public static createDataset(dataset: Array<Array<number>, look_back: Number): [Array<Array<number>>,Array<number>] source

Creates dataset data

Params:

NameTypeAttributeDescription
dataset Array<Array<number>

array of values

look_back Number

number of values in each feature

Return:

[Array<Array<number>>,Array<number>]

returns x matrix and y matrix for model trainning

Example:

LSTMTimeSeries.createDataset([ [ 1, ], [ 2, ], [ 3, ], [ 4, ], [ 5, ], [ 6, ], [ 7, ], [ 8, ], [ 9, ], [ 10, ], ], 3) // => 
//  [ 
//    [ 
//      [ [ 1 ], [ 2 ], [ 3 ] ],
//      [ [ 2 ], [ 3 ], [ 4 ] ],
//      [ [ 3 ], [ 4 ], [ 5 ] ],
//      [ [ 4 ], [ 5 ], [ 6 ] ],
//      [ [ 5 ], [ 6 ], [ 7 ] ],
//      [ [ 6 ], [ 7 ], [ 8 ] ], 
//   ], //x_matrix
//   [ [ 4 ], [ 5 ], [ 6 ], [ 7 ], [ 8 ], [ 9 ] ] //y_matrix
// ]

public static getTimeseriesDataSet(timeseries: *, look_back: *) source

Returns data for predicting values

Params:

NameTypeAttributeDescription
timeseries *
look_back *

public static getTimeseriesShape(x_timeseries: Array<Array<number>): Array<Array<number>> source

Reshape input to be [samples, time steps, features]

Params:

NameTypeAttributeDescription
x_timeseries Array<Array<number>

dataset array of values

Return:

Array<Array<number>>

returns proper timeseries forecasting shape

Example:

LSTMTimeSeries.getTimeseriesShape([ 
[ [ 1 ], [ 2 ], [ 3 ] ],
[ [ 2 ], [ 3 ], [ 4 ] ],
[ [ 3 ], [ 4 ], [ 5 ] ],
[ [ 4 ], [ 5 ], [ 6 ] ],
[ [ 5 ], [ 6 ], [ 7 ] ],
[ [ 6 ], [ 7 ], [ 8 ] ], 
]) //=> [6, 1, 3,]

Public Constructors

public constructor(options: {layers: Array<Object>, compile: Object, fit: Object}, properties: *) source

Params:

NameTypeAttributeDescription
options {layers: Array<Object>, compile: Object, fit: Object}

neural network configuration and tensorflow model hyperparameters

properties *

extra instance properties

Public Members

public createDataset source

public getTimeseriesDataSet source

public getTimeseriesShape source

public layers source

public model source

public xShape source

public yShape source

Public Methods

public calculate() source

public generateLayers(x_matrix: Array<Array<number>>, y_matrix: Array<Array<number>>, layers: Array<Object>, x_test: Array<Array<number>>, y_test: Array<Array<number>>) source

Adds dense layers to tensorflow classification model

Params:

NameTypeAttributeDescription
x_matrix Array<Array<number>>

independent variables

y_matrix Array<Array<number>>

dependent variables

layers Array<Object>

model dense layer parameters

x_test Array<Array<number>>

validation data independent variables

y_test Array<Array<number>>

validation data dependent variables

public async predict() source

public async train() source