All Known Implementing Classes:

ExponentialRegression, LinearRegression, PolynomialRegression, PowerRegression, RobustPolynomialRegression, TheilSenRegression
```
public interface IsRegression
```
Maps all common methods of a regression.

Author:

Andrea "Stock" Stocchero

Method Summary

All Methods Static Methods Instance Methods Abstract Methods
Modifier and Type	Method	Description
`static <T extends IsRegression> T`	`checkAndGetIfValid(T regression)`	Checks if regression passed as argument is not `null` and if consistent as well. If not, throw a `IllegalArgumentException`, otherwise it returns the regression.
`static void`	`checkIfValid(IsRegression regression)`	Checks if regression passed as argument is not `null` and if consistent as well. If not, throw a `IllegalArgumentException`.
`List<Double>`	`getCoefficients()`	Returns all calculated coefficients as a list.
`RegressionDescriptor`	`getDescriptor()`	Returns the regression descriptor.
`RegressionType`	`getType()`	Returns the regression type.
`boolean`	`isConsistent()`	Returns `true` if the regression is consistent and usable.
`double`	`predict(double x)`	Returns the Y value, calculated by the regression formula at specific X value.
`List<Double>`	`predict(double... xValues)`	Returns a list of Y values, calculated by the regression formula for specific X values.
`double`	`predict(Date x)`	Returns the Y value, calculated by the regression formula at specific X value.
`List<Double>`	`predict(List<Double> xValues)`	Returns a list of Y values, calculated by the regression formula for specific X values.
`double`	`predict(DataPoint dataPoint)`	Returns the Y value, calculated by the regression formula at specific X value.
`double`	`predict(TimeSeriesItem item)`	Returns the Y value, calculated by the regression formula at specific X value.
`List<Double>`	`predictByDataPoints(List<DataPoint> dataPoints)`	Returns a list of Y values, calculated by the regression formula for specific X values.
`List<Double>`	`predictByDataPoints(DataPoint... dataPoints)`	Returns a list of Y values, calculated by the regression formula for specific X values.
`List<Double>`	`predictByDates(Date... xValues)`	Returns a list of Y values, calculated by the regression formula for specific X values.
`List<Double>`	`predictByDates(List<Date> xValues)`	Returns a list of Y values, calculated by the regression formula for specific X values.
`List<Double>`	`predictByTimeSeriesItems(List<TimeSeriesItem> items)`	Returns a list of Y values, calculated by the regression formula for specific X values.
`List<Double>`	`predictByTimeSeriesItems(TimeSeriesItem... items)`	Returns a list of Y values, calculated by the regression formula for specific X values.
`RegressionScore`	`score(List<Double> y)`	Evaluates the regression formula if the model fits enough. X values are creating starting from 0 with increment of 1.
`RegressionScore`	`score(List<Double> y, double starting)`	Evaluates the regression formula if the model fits enough. X values are creating starting from passed argument with increment of 1.
`RegressionScore`	`score(List<Double> y, double starting, double increment)`	Evaluates the regression formula if the model fits enough. X values are creating starting from passed argument with increment of passed argument.
`RegressionScore`	`score(List<Double> x, List<Double> y)`	Evaluates the regression formula if the model fits enough.
`RegressionScore`	`scoreByDataPoints(List<DataPoint> dataPoints)`	Evaluates the regression formula if the model fits enough.
`RegressionScore`	`scoreByDate(List<Date> x, List<Double> y)`	Evaluates the regression formula if the model fits enough.
`RegressionScore`	`scoreByTimeSeriesItems(List<TimeSeriesItem> items)`	Evaluates the regression formula if the model fits enough.
`String`	`toFormula()`	Returns the formula of the regression.
`String`	`toFormula(int precision)`	Returns the formula of the regression, using the requested precision.
`String`	`toLaTeX()`	Returns the formula of the regression.
`String`	`toLaTeX(int precision)`	Returns the formula of the regression, using the requested precision.

- Method Detail
  - checkIfValid
```
static void checkIfValid(IsRegression regression)
```
    Checks if regression passed as argument is not null and if consistent as well.
    If not, throw a IllegalArgumentException.
    
    Parameters:
    
    regression - regression to be checked
  - checkAndGetIfValid
```
static <T extends IsRegression> T checkAndGetIfValid(T regression)
```
    Checks if regression passed as argument is not null and if consistent as well.
    If not, throw a IllegalArgumentException, otherwise it returns the regression.
    
    Type Parameters:
    
    T - type of regression
    
    Parameters:
    
    regression - regression to be checked
    
    Returns:
    
    the same regression passed as argument
  - getType
```
RegressionType getType()
```
    Returns the regression type.
    
    Returns:
    
    the regression type
  - getDescriptor
```
RegressionDescriptor getDescriptor()
```
    Returns the regression descriptor.
    
    Returns:
    
    the regression descriptor
  - getCoefficients
```
List<Double> getCoefficients()
```
    Returns all calculated coefficients as a list.
    
    Returns:
    
    all calculated coefficients as a list
  - isConsistent
```
boolean isConsistent()
```
    Returns true if the regression is consistent and usable.
    
    Returns:
    
    true if the regression is consistent and usable
  - predict
```
double predict(double x)
```
    Returns the Y value, calculated by the regression formula at specific X value.
    
    Parameters:
    
    x - value to use to get the predicted value
    
    Returns:
    
    the Y value, calculated by the regression formula at specific X value
  - predict
```
double predict(Date x)
```
    Returns the Y value, calculated by the regression formula at specific X value.
    
    Parameters:
    
    x - value to use to get the predicted value
    
    Returns:
    
    the Y value, calculated by the regression formula at specific X value
  - predict
```
double predict(DataPoint dataPoint)
```
    Returns the Y value, calculated by the regression formula at specific X value.
    
    Parameters:
    
    dataPoint - value to use to get the predicted value
    
    Returns:
    
    the Y value, calculated by the regression formula at specific X value
  - predict
```
double predict(TimeSeriesItem item)
```
    Returns the Y value, calculated by the regression formula at specific X value.
    
    Parameters:
    
    item - value to use to get the predicted value
    
    Returns:
    
    the Y value, calculated by the regression formula at specific X value
  - predict
```
List<Double> predict(double... xValues)
```
    Returns a list of Y values, calculated by the regression formula for specific X values.
    
    Parameters:
    
    xValues - values to use to get the predicted values
    
    Returns:
    
    a list of Y values, calculated by the regression formula for specific X values
  - predict
```
List<Double> predict(List<Double> xValues)
```
    Returns a list of Y values, calculated by the regression formula for specific X values.
    
    Parameters:
    
    xValues - values to use to get the predicted values
    
    Returns:
    
    a list of Y values, calculated by the regression formula for specific X values
  - predictByDates
```
List<Double> predictByDates(Date... xValues)
```
    Returns a list of Y values, calculated by the regression formula for specific X values.
    
    Parameters:
    
    xValues - values to use to get the predicted values
    
    Returns:
    
    a list of Y values, calculated by the regression formula for specific X values
  - predictByDates
```
List<Double> predictByDates(List<Date> xValues)
```
    Returns a list of Y values, calculated by the regression formula for specific X values.
    
    Parameters:
    
    xValues - values to use to get the predicted values
    
    Returns:
    
    a list of Y values, calculated by the regression formula for specific X values
  - predictByDataPoints
```
List<Double> predictByDataPoints(DataPoint... dataPoints)
```
    Returns a list of Y values, calculated by the regression formula for specific X values.
    
    Parameters:
    
    dataPoints - values to use to get the predicted values
    
    Returns:
    
    a list of Y values, calculated by the regression formula for specific X values
  - predictByDataPoints
```
List<Double> predictByDataPoints(List<DataPoint> dataPoints)
```
    Returns a list of Y values, calculated by the regression formula for specific X values.
    
    Parameters:
    
    dataPoints - values to use to get the predicted values
    
    Returns:
    
    a list of Y values, calculated by the regression formula for specific X values
  - predictByTimeSeriesItems
```
List<Double> predictByTimeSeriesItems(TimeSeriesItem... items)
```
    Returns a list of Y values, calculated by the regression formula for specific X values.
    
    Parameters:
    
    items - values to use to get the predicted values
    
    Returns:
    
    a list of Y values, calculated by the regression formula for specific X values
  - predictByTimeSeriesItems
```
List<Double> predictByTimeSeriesItems(List<TimeSeriesItem> items)
```
    Returns a list of Y values, calculated by the regression formula for specific X values.
    
    Parameters:
    
    items - values to use to get the predicted values
    
    Returns:
    
    a list of Y values, calculated by the regression formula for specific X values
  - score
```
RegressionScore score(List<Double> y)
```
    Evaluates the regression formula if the model fits enough.
    X values are creating starting from 0 with increment of 1.
    
    Parameters:
    
    y - Y values to use for evaluation
    
    Returns:
    
    the score of the regression
  - score
```
RegressionScore score(List<Double> y,
                      double starting)
```
    Evaluates the regression formula if the model fits enough.
    X values are creating starting from passed argument with increment of 1.
    
    Parameters:
    
    y - Y values to use for evaluation
    
    starting - starting value to create X values
    
    Returns:
    
    the score of the regression
  - score
```
RegressionScore score(List<Double> y,
                      double starting,
                      double increment)
```
    Evaluates the regression formula if the model fits enough.
    X values are creating starting from passed argument with increment of passed argument.
    
    Parameters:
    
    y - Y values to use for evaluation
    
    starting - starting value to create X values
    
    increment - increment to apply to the starting value
    
    Returns:
    
    the score of the regression
  - score
```
RegressionScore score(List<Double> x,
                      List<Double> y)
```
    Evaluates the regression formula if the model fits enough.
    
    Parameters:
    
    x - X values to use for evaluation
    
    y - Y values to use for evaluation
    
    Returns:
    
    the score of the regression
  - scoreByDate
```
RegressionScore scoreByDate(List<Date> x,
                            List<Double> y)
```
    Evaluates the regression formula if the model fits enough.
    
    Parameters:
    
    x - X values to use for evaluation
    
    y - Y values to use for evaluation
    
    Returns:
    
    the score of the regression
  - scoreByDataPoints
```
RegressionScore scoreByDataPoints(List<DataPoint> dataPoints)
```
    Evaluates the regression formula if the model fits enough.
    
    Parameters:
    
    dataPoints - X and Y values to use for evaluation
    
    Returns:
    
    the score of the regression
  - scoreByTimeSeriesItems
```
RegressionScore scoreByTimeSeriesItems(List<TimeSeriesItem> items)
```
    Evaluates the regression formula if the model fits enough.
    
    Parameters:
    
    items - X and Y values to use for evaluation
    
    Returns:
    
    the score of the regression
  - toFormula
```
String toFormula()
```
    Returns the formula of the regression.
    
    Returns:
    
    the formula of the regression
  - toFormula
```
String toFormula(int precision)
```
    Returns the formula of the regression, using the requested precision.
    
    Parameters:
    
    precision - precision to apply to the numbers of the formula
    
    Returns:
    
    the formula of the regression
  - toLaTeX
```
String toLaTeX()
```
    Returns the formula of the regression.
    
    Returns:
    
    the formula of the regression
  - toLaTeX
```
String toLaTeX(int precision)
```
    Returns the formula of the regression, using the requested precision.
    
    Parameters:
    
    precision - precision to apply to the numbers of the formula
    
    Returns:
    
    the formula of the regression

Interface IsRegression

Method Summary

Method Detail

checkIfValid

checkAndGetIfValid

getType

getDescriptor

getCoefficients

isConsistent

predict

predict

predict

predict

predict

predict

predictByDates

predictByDates

predictByDataPoints

predictByDataPoints

predictByTimeSeriesItems

predictByTimeSeriesItems

score

score

score

score

scoreByDate

scoreByDataPoints

scoreByTimeSeriesItems

toFormula

toFormula

toLaTeX

toLaTeX