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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

Test:

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

Test:

Public Members

public createDataset source

Test:

public getTimeseriesDataSet source

Test:

public getTimeseriesShape source

Test:

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

Test:

public async train() source

Test: