Class: RegressionBase

• new RegressionBase(options, customFunc)

• new RegressionBase(options?, customFunc?)

Regression base

EventDispatcher.constructor

▸ dispose(): void

Dispose.

void

EventDispatcher.dispose

▸ fit(dataX, dataY, weights): void

fit the curve with given data set the subclass of RegressionBase need to support incremental fitting by default that means the model have to keep necessary intermediate values as properties which can be used to do incremental calculating when add new data point to model

void

▸ getClassName(): string

string

EventDispatcher.getClassName

▸ getCoefficients(): number[]

get coefficients

number[]

▸ getConfidenceInterval(x): number

get confidence interval for given x value

number

return confidence interval for x

▸ getConfidenceProbability(): Probability

get probability for determine confidence interval

Probability

probability

▸ getData(): DataOut

get all data for training

DataOut

▸ getDegreeOfFreedom(): number

get degree of freedom

number

return degree of freedom

▸ getMean(axis): number

get mean value for data set x or y

number

return mean value of data set

▸ getModelType(): string

Returns regression model type

string

Model type

▸ getOptions(): OptionsOut

get options

OptionsOut

options

▸ getPredictionInterval(x): number

get prediction interval for given x value

number

return prediction interval for x

▸ getPredictionProbability(): Probability

get probability for determine prediction interval

Probability

probability

▸ getProperties(): OptionsOut

get properties

OptionsOut

properties

▸ getResiduals(): number[]

get array of residuals

number[]

residuals array of residuals

▸ getRsquared(): number

get R squared value

number

return R squared value

▸ getStatistics(): Statistics

get statistics of regression analysis

Statistics

▸ getSumSquaredResidual(): number

get sum squared of residual

number

return sum squared of residual

▸ getTotalSumSquared(axis): number

get sum squared for data set x or y

number

return sum squared value of data set

▸ hasEventListener(type, callback?): boolean

Check if a list of event listeners for this type contains this listener

boolean

EventDispatcher.hasEventListener

▸ inversePredict(dataY): number

predict x value with given y value

number

predictedDataX predicted x value

▸ inversePredict(dataY): number[]

predict x values with given y value

number[]

predictedDataX predicted x values

▸ isCustomizedCoef(): boolean

get customized coefficients flag

boolean

customizedCoef

▸ isDisposed(): boolean

Returns whether this object has been disposed

boolean

EventDispatcher.isDisposed

▸ isSilent(): boolean

Return true if the event dispatcher doesn't notify any events

boolean

EventDispatcher.isSilent

▸ notify<E>(type, source, args?): RegressionBase

Notify listeners

RegressionBase

this

EventDispatcher.notify

▸ off<E>(type?, callback?): RegressionBase

Detach listener on event. Calling .off() with no arguments removes all attached listeners. Calling .off(type) with no callback removes all attached listeners for specific type.

RegressionBase

this

EventDispatcher.off

▸ on<E>(type, callback): RegressionBase

Attach listener on event that will be called whenever the specified event is delivered to the target

If the callback function is already in the list of event listeners for this target, the function is not added a second time.

If a particular anonymous function is in the list of event listeners registered for a certain target, and then later in the code, an identical anonymous function is given in an "on" call, the second function will also be added to the list of event listeners for that target.

RegressionBase

this

EventDispatcher.on

▸ predict(dataX): number

predict y value with given x value

number

predictedDataY predicted y value

▸ predict(dataX): number[]

predict y values with given x value

number[]

predictedDataY predicted y values

▸ resetModel(): void

reset the model including all intermediate variables and training data

void

▸ setConfidenceProbability(prob): RegressionBase

set probability for looking up t-table to determine confidence interval

RegressionBase

this

▸ setOptions(options): RegressionBase

set options

RegressionBase

this

▸ setPredictionProbability(prob): RegressionBase

set probability for looking up t-table to determine prediction interval

RegressionBase

this

▸ setProperties(properties?): RegressionBase

Sets properties

RegressionBase

this

▸ setSilent(bool): RegressionBase

Set silent mode

RegressionBase

this

EventDispatcher.setSilent

▸ Static getClassName(): string

string

EventDispatcher.getClassName