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Won't transform it back from sym to numeric, please see script


By : , Category : matlab

Judging from this page the answer is that you should use the double command: http://www.mathworks.nl/help/symbolic/double.html

If that does not work, perhaps try eval.

ReLated :

You need to track the original T from where the value came, and then define a conversion from double to that T.

For example:

trait ConvertTemp[T] extends (Double ⇒ T)
object ConvertTemp {
    def apply[T](f: Double ⇒ T) = new ConvertTemp[T] { override def apply(v: Double) = f(v) }
    implicit val convertToInt = apply(Math round _)
    implicit val convertToDouble = apply(identity)
}

sealed case class Temperature[T](kelvin: Double)(implicit convert: ConvertTemp[T]) {
  import Temperature._
  override def toString(): String = s"$kelvin Kelvin"

  def asKelvin = convert(kelvin)

  def asFahrenheight = ctof(ktoc(kelvin))

  def asCelsius = ktoc(kelvin)

  def asK = asKelvin

  def asF = asFahrenheight

  def asC = asCelsius
}

object Temperature {
  private val absoluteZeroC = -273.15

  private def ftoc(f: Double) = (f - 32) * 5 / 9

  private def ctok(c: Double) = c - absoluteZeroC

  private[temperature] def ktoc(k: Double) = k + absoluteZeroC

  private[temperature] def ctof(c: Double) = c * 9 / 5 + 32

  private[temperature] def Fahrenheit[T](f: Double)(implicit convert: ConvertTemp[T]) = Temperature(ctok(ftoc(f)))

  private[temperature] def Celsius[T](c: Double)(implicit convert: ConvertTemp[T]) = Temperature(ctok(c))

  implicit def toTempOps(n: Int) = TempOps(n)
}

sealed case class TempOps[N](t: N)(implicit n: Numeric[N]) {
  implicit val Fahrenheit = Temperature.Fahrenheit(n.toDouble(t))
  implicit val Celsius = Temperature.Celsius(n.toDouble(t))
  implicit val Kelvin = Temperature(n.toDouble(t))
}

Direct conversion of a factor to numeric yields the factor levels, and nothing to do with the values themselves. You need to convert to character first:

results[,3] <- as.numeric(as.character(results$Successes))

Equivalently (see ?factor), you can convert the levels to numeric, and index by the (implicit) numeric conversion of the factor.

as.numeric(levels(results$Successes))[results$Successes]

What I found when testing is, that whilte the NDC_dir.x and NDC_dir.y seem to be reasonable, the NDC_dir.w and NDC_dir.z are always almost equal. Therefor when dividing by "w" the z-Value is always about 1. I dont think this is how it should be right? (Same for NDC_pos).

Actually yes, that is how it should be. If your input is formed as {x,y,z,0} then it will be interpreted as a point infinitely far in the direction of {x,y,z}. Therefore the depth component should always be most distant, if visible at all. By the way a point infinitely far away is still translation invariant

But you should know that a point infinitely far away is probably not what you want within NDC.

A vector in 3D space is invariant to translation, it just stays the same vector, because a vector has only a direction, not a position. This is not really possible in the distorted NDC.

If you want to transform a vector, you should better transform two points, and then take the difference in NDC again. But you should know that your result then depends on the position.

You shouldn't. The weather condition is a categorical variable, which random forest handles natively. Leave it as it is and let the algorithm work as it should.

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