Math

Value Members

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

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

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3. final def ##(): Int

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4. final def ==(arg0: AnyRef): Boolean

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

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6. final def asInstanceOf[T0]: T0

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7. def avg(b: Array[Float], off: Int, len: Int): Float

   Calculates the mean of a vector

   Calculates the mean of a vector

   b

   the vector

   off

   the offset into the vector

   len

   the number of samples to take into account

   returns

   the average of the len samples starting at offset off in vector b.

8. def clone(): AnyRef

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9. def correlate(a: Array[Array[Float]], aMean: Double, aStdDev: Double, numFrames: Int, numChannels: Int, b: Array[Array[Float]], bMean: Double, bStdDev: Double, bFrameOff: Int, bChanOff: Int): Float

10. def correlateHalf(numChannels: Int, halfWinSize: Int, a: Array[Array[Float]], frameOff: Int, chanOff: Int): Float

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

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12. def equals(arg0: Any): Boolean

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13. def finalize(): Unit

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

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15. def hashCode(): Int

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16. final def isInstanceOf[T0]: Boolean

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

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18. def normalize(normBuf: Array[Array[Float]], b: Array[Array[Float]], bOff: Int, bLen: Int): Unit

    Normalizes a matrix with respect to a normalization vector.

    Normalizes a matrix with respect to a normalization vector. For each row in the matrix, given the rows minimum and maximum value through the normalization vector, every cell is offset by -minimum and then divided by maximum - minimum.

    normBuf

    provides normalization vector. The outer array must be of size b.length. each element of that outer array holds an array of size 2, specifying minimum and maximum value for the index. The normBuf argument may be null, in which case this method simply returns without any adjustments.

    b

    the matrix which is normalized in-place (the values are scaled and overwritten).

    bOff

    a frame of column offset in the matrix b.

    bLen

    the number of frames or columns to process in the matrix b.

19. final def notify(): Unit

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20. final def notifyAll(): Unit

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21. def stat(mat: Array[Array[Float]], frameOff: Int, frameLen: Int, chanOff: Int, chanLen: Int): (Double, Double)

    Calculates the mean and standard deviation of a given matrix

    Calculates the mean and standard deviation of a given matrix

    mat

    the matrix to analyse

    frameOff

    0 if the whole matrix is to be considered, otherwise column offset

    frameLen

    the number of columns to analyse

    chanOff

    0 if the whole matrix is to be considered, otherwise row offset

    chanLen

    the number of rows to analyse

    returns

    the tuple (mean, stddev)

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

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23. def toString(): String

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24. final def wait(): Unit

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

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

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