breeze

signal

package signal

This package provides digital signal processing functions.

Linear Supertypes
AnyRef, Any
Ordering
  1. Alphabetic
  2. By inheritance
Inherited
  1. signal
  2. AnyRef
  3. Any
  1. Hide All
  2. Show all
Learn more about member selection
Visibility
  1. Public
  2. All

Type Members

  1. case class Automatic() extends Opt with Product with Serializable

    Generic option for automatic.

  2. case class None() extends Opt with Product with Serializable

    Generic option for none.

  3. abstract class Opt extends AnyRef

    Base class for all options

  4. abstract class OptKernelType extends Opt

  5. abstract class OptMethod extends Opt

    Option values: how to deal with convolution and filter padding.

  6. abstract class OptOverhang extends Opt

    Option values: how to deal with convolution overhangs.

  7. abstract class OptPadding extends Opt

    Option values: how to deal with convolution and filter padding.

  8. abstract class OptWindowFunction extends Opt

    Option values: window function for filter design.

Value Members

  1. object Automatic extends Serializable

  2. object None extends Serializable

  3. object OptKernelType

  4. object OptMethod

  5. object OptOverhang

  6. object OptPadding

  7. object OptWindowFunction

  8. def convolve[Input, Output](data: Input, kernel: Input, overhang: OptOverhang = OptOverhang.None(), padding: OptPadding = OptPadding.Value(0d), method: OptMethod = Automatic())(implicit canConvolve: CanConvolve[Input, Output]): Output

    Convolves DenseVectors.

    Convolves DenseVectors. Implementation is via the implicit trait CanConvolve[ InputType, OutputType ], which is found in breeze.signal.support.CanConvolve.scala.

    data

    DenseVector or DenseMatrix to be convolved

    kernel

    DenseVector or DenseMatrix kernel

    canConvolve

    implicit delegate which is used for implementation. End-users should not use this argument.

  9. def correlate[Input, Output](data: Input, kernel: Input, overhang: OptOverhang = OptOverhang.None(), padding: OptPadding = OptPadding.Value(0d), method: OptMethod = Automatic())(implicit canConvolve: CanConvolve[Input, Output]): Output

    Correlates DenseVectors.

    Correlates DenseVectors. Implementation is via the implicit trait CanConvolve[ InputType, OutputType ], which is found in breeze.signal.support.CanConvolve.scala. See breeze.signal.convolve for options and other information.

  10. package filter

  11. object fourierTransform extends UFunc

    Returns the fast fourier transform of a DenseVector or DenseMatrix.

  12. def haarTransform[Input, Output](v: Input)(implicit canHaarTransform: CanHaarTransform[Input, Output]): Output

    Return the padded fast haar transformation of a DenseVector or DenseMatrix.

    Return the padded fast haar transformation of a DenseVector or DenseMatrix. Note that the output will always be padded to a power of 2. A matrix will cause a 2D fht. The 2D haar transformation is defined for squared power of 2 matrices. A new matrix will thus be created and the old matrix will be placed in the upper-left part of the new matrix. Avoid calling this method with a matrix that has few cols / many rows or many cols / few rows (e.g. 1000000 x 3) as this will cause a very high memory consumption.

    v

    DenseVector or DenseMatrix to be transformed.

    canHaarTransform

    implicit delegate which is used for implementation. End-users should not use this argument.

    returns

    DenseVector or DenseMatrix

    See also

    https://en.wikipedia.org/wiki/Haar_wavelet

  13. object inverseFourierTransform extends UFunc

    Returns the inverse fast fourier transform of a DenseVector or DenseMatrix.

  14. def inverseHaarTransform[Input, Output](v: Input)(implicit canInverseHaarTransform: CanInverseHaarTransform[Input, Output]): Output

    Returns the inverse fast haar transform for a DenseVector or DenseMatrix.

  15. package support

Deprecated Value Members

  1. val fft: fourierTransform.type

    Annotations
    @deprecated
    Deprecated

    (Since version v.0.6) use fourierTransform

  2. val ifft: inverseFourierTransform.type

    Annotations
    @deprecated
    Deprecated

    (Since version v.0.6) use inverseFourierTransform

Inherited from AnyRef

Inherited from Any

Ungrouped