Class/Object

axle.data

Iris

Related Docs: object Iris | package data

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case class Iris(sepalLength: UnittedQuantity[Distance, Double], sepalWidth: UnittedQuantity[Distance, Double], petalLength: UnittedQuantity[Distance, Double], petalWidth: UnittedQuantity[Distance, Double], species: String) extends Product with Serializable

Description: http://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.names

Data url: http://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data

Columns:

  1. sepal length in cm 2. sepal width in cm 3. petal length in cm 4. petal width in cm 5. class: Iris Setosa, Iris Versicolour, or Iris Virginica

Examples:

5.0,3.3,1.4,0.2,Iris-setosa 7.0,3.2,4.7,1.4,Iris-versicolor

For examples of analyzing this dataset with R, see http://horicky.blogspot.com/2012/04/machine-learning-in-r-clustering.html

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Instance Constructors

  1. new Iris(sepalLength: UnittedQuantity[Distance, Double], sepalWidth: UnittedQuantity[Distance, Double], petalLength: UnittedQuantity[Distance, Double], petalWidth: UnittedQuantity[Distance, Double], species: String)

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Value Members

  1. final def !=(arg0: Any): Boolean

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  2. final def ##(): Int

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  3. final def ==(arg0: Any): Boolean

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  4. final def asInstanceOf[T0]: T0

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  5. def clone(): AnyRef

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  6. final def eq(arg0: AnyRef): Boolean

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  7. def finalize(): Unit

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  8. final def getClass(): Class[_]

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  9. final def isInstanceOf[T0]: Boolean

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  10. final def ne(arg0: AnyRef): Boolean

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  11. final def notify(): Unit

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  12. final def notifyAll(): Unit

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  13. val petalLength: UnittedQuantity[Distance, Double]

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  14. val petalWidth: UnittedQuantity[Distance, Double]

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  15. val sepalLength: UnittedQuantity[Distance, Double]

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  16. val sepalWidth: UnittedQuantity[Distance, Double]

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  17. val species: String

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  18. final def synchronized[T0](arg0: ⇒ T0): T0

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  19. final def wait(): Unit

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  20. final def wait(arg0: Long, arg1: Int): Unit

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  21. final def wait(arg0: Long): Unit

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