API / geotoolkit / controls / util / regression / Exponential / Exponential
regression.Exponential.Exponential
generate a exponential regression model
↳
Exponential
Constructors
Methods
Methods
▸ dispose(): void
Dispose.
void
▸ fit(dataX, dataY, weights): void
fit curve
| Name | Type | Description |
|---|---|---|
dataX | number[] | data array of observations x |
dataY | number[] | data array in observations y |
weights | number[] | of data point |
void
▸ getClassName(): string
string
▸ getCoefficients(): number[]
get coefficients
number[]
RegressionBase.getCoefficients
▸ getConfidenceInterval(x): number
get confidence interval for given x value
| Name | Type | Description |
|---|---|---|
x | number | given value to determine the confidence interval |
number
return confidence interval for x
RegressionBase.getConfidenceInterval
▸ getConfidenceProbability(): Probability
get probability for determine confidence interval
probability
RegressionBase.getConfidenceProbability
▸ getData(): DataOut
get all data for training
▸ getDegreeOfFreedom(): number
get degree of freedom
number
return degree of freedom
RegressionBase.getDegreeOfFreedom
▸ getEdge(scale?): number
get x value of edge point where derivative of regression line is 1 or -1 after adjust scale of x and y
| Name | Type | Description |
|---|---|---|
Optional scale | number | scale |
number
▸ getMean(axis): number
get mean value for data set x or y
| Name | Type | Description |
|---|---|---|
axis | "x" | "y" | axis could be 'x' or 'y' |
number
return mean value of data set
▸ getModelType(): RegressionModelType
Returns regression model type
Model type
▸ getOptions(): OptionsOut
get options
options
▸ getPredictionInterval(x): number
get prediction interval for given x value
| Name | Type | Description |
|---|---|---|
x | number | given value to determine the prediction interval |
number
return prediction interval for x
RegressionBase.getPredictionInterval
▸ getPredictionProbability(): Probability
get probability for determine prediction interval
probability
RegressionBase.getPredictionProbability
▸ getProperties(): OptionsOut
get properties
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
▸ getSumSquaredResidual(): number
get sum squared of residual
number
return sum squared of residual
RegressionBase.getSumSquaredResidual
▸ getTotalSumSquared(axis): number
get sum squared for data set x or y
| Name | Type | Description |
|---|---|---|
axis | "x" | "y" | axis could be 'x' or 'y' |
number
return sum squared value of data set
RegressionBase.getTotalSumSquared
▸ hasEventListener(type, callback?): boolean
Check if a list of event listeners for this type contains this listener
| Name | Type | Description |
|---|---|---|
type | string | type of event or property |
Optional callback | Function | to be called, if null, check if any callback is registered |
boolean
RegressionBase.hasEventListener
▸ inversePredict(dataY): number
predict x value with given y value
| Name | Type | Description |
|---|---|---|
dataY | number | single value y |
number
predictedDataX predicted x value
▸ inversePredict(dataY): number[]
predict x values with given y value
| Name | Type | Description |
|---|---|---|
dataY | number[] | data set of Y |
number[]
predictedDataX predicted x values
▸ isCustomizedCoef(): boolean
get customized coefficients flag
boolean
customizedCoef
RegressionBase.isCustomizedCoef
▸ isDisposed(): boolean
Returns whether this object has been disposed
boolean
▸ isSilent(): boolean
Return true if the event dispatcher doesn't notify any events
boolean
▸ notify<E>(type, source, args?): Exponential
Notify listeners
| Name | Type |
|---|---|
E | extends string |
| Name | Type | Description |
|---|---|---|
type | E | event types |
source | RegressionBase | of the event |
Optional args | EventMap[E] | arguments of the event |
this
▸ off<E>(type?, callback?): Exponential
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.
| Name | Type |
|---|---|
E | extends string |
| Name | Type | Description |
|---|---|---|
Optional type | E | type of the event |
Optional callback | (eventType: E, sender: Exponential, args: EventMap[E]) => void | function to be called |
this
▸ on<E>(type, callback): Exponential
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.
| Name | Type |
|---|---|
E | extends string |
| Name | Type | Description |
|---|---|---|
type | E | type of event or property |
callback | (eventType: E, sender: Exponential, args: EventMap[E]) => void | to be called |
this
▸ predict(dataX): number
predict y value with given x value
| Name | Type | Description |
|---|---|---|
dataX | number | single value x |
number
predictedDataY predicted y value
▸ predict(dataX): number[]
predict y values with given x value
| Name | Type | Description |
|---|---|---|
dataX | number[] | data set of X |
number[]
predictedDataY predicted y values
▸ resetModel(): void
reset the model including all intermediate variables and training data
void
▸ setConfidenceProbability(prob): Exponential
set probability for looking up t-table to determine confidence interval
| Name | Type | Description |
|---|---|---|
prob | Probability | probability |
this
RegressionBase.setConfidenceProbability
▸ setOptions(options): Exponential
set options
| Name | Type | Description |
|---|---|---|
options | Options | options |
this
▸ setPredictionProbability(prob): Exponential
set probability for looking up t-table to determine prediction interval
| Name | Type | Description |
|---|---|---|
prob | Probability | probability |
this
RegressionBase.setPredictionProbability
▸ setProperties(properties?): Exponential
Sets properties
| Name | Type | Description |
|---|---|---|
Optional properties | Options | options |
this
▸ setSilent(bool): Exponential
Set silent mode
| Name | Type | Description |
|---|---|---|
bool | boolean | flag to enable silent mode |
this
▸ Static getClassName(): string
string