case class Landmark[D](id: String, point: Point[D], description: Option[String] = None, uncertainty: Option[MultivariateNormalDistribution] = None)(implicit evidence$1: NDSpace[D]) extends Product with Serializable
Linear Supertypes
Ordering
- Alphabetic
- By Inheritance
Inherited
- Landmark
- Serializable
- Product
- Equals
- AnyRef
- Any
- Hide All
- Show All
Visibility
- Public
- Protected
Instance Constructors
- new Landmark(id: String, point: Point[D], description: Option[String] = None, uncertainty: Option[MultivariateNormalDistribution] = None)(implicit arg0: NDSpace[D])
Value Members
- final def !=(arg0: Any): Boolean
- Definition Classes
- AnyRef → Any
- final def ##(): Int
- Definition Classes
- AnyRef → Any
- final def ==(arg0: Any): Boolean
- Definition Classes
- AnyRef → Any
- final def asInstanceOf[T0]: T0
- Definition Classes
- Any
- def clone(): AnyRef
- Attributes
- protected[lang]
- Definition Classes
- AnyRef
- Annotations
- @throws(classOf[java.lang.CloneNotSupportedException]) @native()
- val description: Option[String]
- final def eq(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef
- def finalize(): Unit
- Attributes
- protected[lang]
- Definition Classes
- AnyRef
- Annotations
- @throws(classOf[java.lang.Throwable])
- final def getClass(): Class[_ <: AnyRef]
- Definition Classes
- AnyRef → Any
- Annotations
- @native()
- val id: String
- final def isInstanceOf[T0]: Boolean
- Definition Classes
- Any
- final def ne(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef
- final def notify(): Unit
- Definition Classes
- AnyRef
- Annotations
- @native()
- final def notifyAll(): Unit
- Definition Classes
- AnyRef
- Annotations
- @native()
- val point: Point[D]
- def productElementNames: Iterator[String]
- Definition Classes
- Product
- final def synchronized[T0](arg0: => T0): T0
- Definition Classes
- AnyRef
- def transform(transformation: Transformation[D])(implicit random: Random): Landmark[D]
Transforms a landmark point with the given transformation.
Transforms a landmark point with the given transformation. The method transforms both the point and the uncertainty. The new uncertainty is estimated stochastically and is only an approximation to the real uncertainty (for non-rigid transformations, the uncertainty would not even be gaussian)
- val uncertainty: Option[MultivariateNormalDistribution]
- final def wait(): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws(classOf[java.lang.InterruptedException])
- final def wait(arg0: Long, arg1: Int): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws(classOf[java.lang.InterruptedException])
- final def wait(arg0: Long): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws(classOf[java.lang.InterruptedException]) @native()