gov.sandia.cognition.learning.function.scalar

## Class LinearFunction

• ### Field Summary

Fields
Modifier and Type Field and Description
`static double` `DEFAULT_OFFSET`
The default offset is 0.0.
`static double` `DEFAULT_SLOPE`
The default slope is 1.0.
`protected double` `offset`
The offset (b).
`protected double` `slope`
The slope (m).
• ### Constructor Summary

Constructors
Constructor and Description
`LinearFunction()`
Creates a new `LinearFunction` with a slope of 1 and offset of 0.
```LinearFunction(double slope, double offset)```
Creates a new `LinearFunction` with the given slope and offset.
`LinearFunction(LinearFunction other)`
Creates a copy of a given `LinearFunction`.
• ### Method Summary

All Methods
Modifier and Type Method and Description
`LinearFunction` `clone()`
This makes public the clone method on the `Object` class and removes the exception that it throws.
`double` `differentiate(double input)`
Differentiates the output of the function about the given input
`double` `evaluate(double input)`
Produces a double output for the given double input
`double` `getOffset()`
Gets the offset of the function, which is the b term in: f(x) = m*x + b.
`double` `getSlope()`
Gets the slope of the function, which is the m term in: f(x) = m*x + b.
`void` `setOffset(double offset)`
Sets the offset of the function, which is the b term in: f(x) = m*x + b.
`void` `setSlope(double slope)`
Sets the slope of the function, which is the m term in: f(x) = m*x + b.
• ### Methods inherited from class java.lang.Object

`equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait`
• ### Methods inherited from interface gov.sandia.cognition.math.DifferentiableUnivariateScalarFunction

`differentiate`
• ### Methods inherited from interface gov.sandia.cognition.math.UnivariateScalarFunction

`evaluate, evaluateAsDouble`
• ### Field Detail

• #### DEFAULT_SLOPE

`public static final double DEFAULT_SLOPE`
The default slope is 1.0.
Constant Field Values
• #### DEFAULT_OFFSET

`public static final double DEFAULT_OFFSET`
The default offset is 0.0.
Constant Field Values
• #### slope

`protected double slope`
The slope (m).
• #### offset

`protected double offset`
The offset (b).
• ### Constructor Detail

• #### LinearFunction

`public LinearFunction()`
Creates a new `LinearFunction` with a slope of 1 and offset of 0. This makes f(x) = x.
• #### LinearFunction

```public LinearFunction(double slope,
double offset)```
Creates a new `LinearFunction` with the given slope and offset.
Parameters:
`slope` - The slope.
`offset` - The offset.
• #### LinearFunction

`public LinearFunction(LinearFunction other)`
Creates a copy of a given `LinearFunction`.
Parameters:
`other` - The LinearFunction to copy.
• ### Method Detail

• #### clone

`public LinearFunction clone()`
Description copied from class: `AbstractCloneableSerializable`
This makes public the clone method on the `Object` class and removes the exception that it throws. Its default behavior is to automatically create a clone of the exact type of object that the clone is called on and to copy all primitives but to keep all references, which means it is a shallow copy. Extensions of this class may want to override this method (but call `super.clone()` to implement a "smart copy". That is, to target the most common use case for creating a copy of the object. Because of the default behavior being a shallow copy, extending classes only need to handle fields that need to have a deeper copy (or those that need to be reset). Some of the methods in `ObjectUtil` may be helpful in implementing a custom clone method. Note: The contract of this method is that you must use `super.clone()` as the basis for your implementation.
Specified by:
`clone` in interface `CloneableSerializable`
Overrides:
`clone` in class `AbstractCloneableSerializable`
Returns:
A clone of this object.
• #### evaluate

`public double evaluate(double input)`
Description copied from interface: `UnivariateScalarFunction`
Produces a double output for the given double input
Parameters:
`input` - Input to the Evaluator
Returns:
output at the given input
• #### differentiate

`public double differentiate(double input)`
Description copied from interface: `DifferentiableUnivariateScalarFunction`
Differentiates the output of the function about the given input
Parameters:
`input` - Input about which to compute the derivative of the function output
Returns:
Derivative of the output with respect to the input
• #### getSlope

`public double getSlope()`
Gets the slope of the function, which is the m term in: f(x) = m*x + b.
Returns:
The slope.
• #### setSlope

`public void setSlope(double slope)`
Sets the slope of the function, which is the m term in: f(x) = m*x + b.
Parameters:
`slope` - The slope.
• #### getOffset

`public double getOffset()`
Gets the offset of the function, which is the b term in: f(x) = m*x + b.
Returns:
The offset.
• #### setOffset

`public void setOffset(double offset)`
Sets the offset of the function, which is the b term in: f(x) = m*x + b.
Parameters:
`offset` - The offset.