Operators

Value Members

final def !=(arg0: Any): Boolean

Definition Classes
AnyRef → Any
final def ##(): Int

Definition Classes
AnyRef → Any
def +(other: String): String

Implicit information
This member is added by an implicit conversion from Operators to any2stringadd[Operators] performed by method any2stringadd in scala.Predef.
Definition Classes
any2stringadd
def ->[B](y: B): (Operators, B)

Implicit information
This member is added by an implicit conversion from Operators to ArrowAssoc[Operators] performed by method ArrowAssoc in scala.Predef.
Definition Classes
ArrowAssoc
Annotations
@inline()
final def ==(arg0: Any): Boolean

Definition Classes
AnyRef → Any
final def asInstanceOf[T0]: T0

Definition Classes
Any
def clone(): AnyRef

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( ... )
def dwt(t: Array[Double], filter: String): Unit

Discrete wavelet transform.
Discrete wavelet transform.
t
the time series array. The size should be a power of 2. For time series of size no power of 2, 0 padding can be applied.
filter
wavelet filter.
def ensuring(cond: (Operators) ⇒ Boolean, msg: ⇒ Any): Operators

Implicit information
This member is added by an implicit conversion from Operators to Ensuring[Operators] performed by method Ensuring in scala.Predef.
Definition Classes
Ensuring
def ensuring(cond: (Operators) ⇒ Boolean): Operators

Implicit information
This member is added by an implicit conversion from Operators to Ensuring[Operators] performed by method Ensuring in scala.Predef.
Definition Classes
Ensuring
def ensuring(cond: Boolean, msg: ⇒ Any): Operators

Implicit information
This member is added by an implicit conversion from Operators to Ensuring[Operators] performed by method Ensuring in scala.Predef.
Definition Classes
Ensuring
def ensuring(cond: Boolean): Operators

Implicit information
This member is added by an implicit conversion from Operators to Ensuring[Operators] performed by method Ensuring in scala.Predef.
Definition Classes
Ensuring
final def eq(arg0: AnyRef): Boolean

Definition Classes
AnyRef
def equals(arg0: Any): Boolean

Definition Classes
AnyRef → Any
def finalize(): Unit

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( classOf[java.lang.Throwable] )
def formatted(fmtstr: String): String

Implicit information
This member is added by an implicit conversion from Operators to StringFormat[Operators] performed by method StringFormat in scala.Predef.
Definition Classes
StringFormat
Annotations
@inline()
final def getClass(): Class[_]

Definition Classes
AnyRef → Any
def hashCode(): Int

Definition Classes
AnyRef → Any
def idwt(wt: Array[Double], filter: String): Unit

Inverse discrete wavelet transform.
Inverse discrete wavelet transform.
wt
the wavelet coefficients. The size should be a power of 2. For time series of size no power of 2, 0 padding can be applied.
filter
wavelet filter.
final def isInstanceOf[T0]: Boolean

Definition Classes
Any
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

Definition Classes
AnyRef
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
def toString(): String

Definition Classes
AnyRef → Any
final def wait(): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
final def wait(arg0: Long, arg1: Int): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
final def wait(arg0: Long): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
def wavelet(filter: String): Wavelet

Returns the wavelet filter.
Returns the wavelet filter. The filter name is derived from one of four classes of wavelet transform filters: Daubechies, Least Asymetric, Best Localized and Coiflet. The prefixes for filters of these classes are d, la, bl and c, respectively. Following the prefix, the filter name consists of an integer indicating length. Supported lengths are as follows:
Daubechies 2,4,6,8,10,12,14,16,18,20.
Least Asymetric 8,10,12,14,16,18,20.
Best Localized 14,18,20.
Coiflet 6,12,18,24,30.
Additionally "haar" is supported for Haar wavelet. Although Haar wavelet is a special case of the Daubechies wavelet transform filter of length 2, the implementation of "haar" is different from "d2".
Besides, "d4", the simplest and most localized wavelet, uses a different centering method from other Daubechies wavelet.
filter
filter name
def wsdenoise(t: Array[Double], filter: String, soft: Boolean = false): Unit

The wavelet shrinkage is a signal denoising technique based on the idea of thresholding the wavelet coefficients.
The wavelet shrinkage is a signal denoising technique based on the idea of thresholding the wavelet coefficients. Wavelet coefficients having small absolute value are considered to encode mostly noise and very fine details of the signal. In contrast, the important information is encoded by the coefficients having large absolute value. Removing the small absolute value coefficients and then reconstructing the signal should produce signal with lesser amount of noise. The wavelet shrinkage approach can be summarized as follows:
- Apply the wavelet transform to the signal.
- Estimate a threshold value.
- The so-called hard thresholding method zeros the coefficients that are smaller than the threshold and leaves the other ones unchanged. In contrast, the soft thresholding scales the remaining coefficients in order to form a continuous distribution of the coefficients centered on zero.
- Reconstruct the signal (apply the inverse wavelet transform).
The biggest challenge in the wavelet shrinkage approach is finding an appropriate threshold value. In this method, we use the universal threshold T = σ sqrt(2*log(N)), where N is the length of time series and σ is the estimate of standard deviation of the noise by the so-called scaled median absolute deviation (MAD) computed from the high-pass wavelet coefficients of the first level of the transform.
t
the time series array. The size should be a power of 2. For time series of size no power of 2, 0 padding can be applied.
filter
the wavelet filter to transform the time series.
soft
true if apply soft thresholding.
def →[B](y: B): (Operators, B)

Implicit information
This member is added by an implicit conversion from Operators to ArrowAssoc[Operators] performed by method ArrowAssoc in scala.Predef.
Definition Classes
ArrowAssoc

Inherited from AnyRef

Inherited from Any

Related Doc: package wavelet

trait Operators extends AnyRef

Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

def +(other: String): String

def ->[B](y: B): (Operators, B)

final def ==(arg0: Any): Boolean

final def asInstanceOf[T0]: T0

def clone(): AnyRef

def dwt(t: Array[Double], filter: String): Unit

def ensuring(cond: (Operators) ⇒ Boolean, msg: ⇒ Any): Operators

def ensuring(cond: (Operators) ⇒ Boolean): Operators

def ensuring(cond: Boolean, msg: ⇒ Any): Operators

def ensuring(cond: Boolean): Operators

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def finalize(): Unit

def formatted(fmtstr: String): String

final def getClass(): Class[_]

def hashCode(): Int

def idwt(wt: Array[Double], filter: String): Unit

final def isInstanceOf[T0]: Boolean

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

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

def toString(): String

final def wait(): Unit

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

final def wait(arg0: Long): Unit

def wavelet(filter: String): Wavelet

def wsdenoise(t: Array[Double], filter: String, soft: Boolean = false): Unit

def →[B](y: B): (Operators, B)

Inherited from AnyRef

Inherited from Any

Inherited by implicit conversion any2stringadd from Operators to any2stringadd[Operators]

Inherited by implicit conversion StringFormat from Operators to StringFormat[Operators]

Inherited by implicit conversion Ensuring from Operators to Ensuring[Operators]

Inherited by implicit conversion ArrowAssoc from Operators to ArrowAssoc[Operators]

Ungrouped