HiddenMarkov

The HiddenMarkov classes provides Hidden Markov Models (HMM). An HMM model consists of a probability vector 'pi' and probability matrices 'a' and 'b'. The discrete-time system is characterized by a hidden 'state(t)' and an 'observed(t)' symbol at time 't'.

pi(j) = P(state(t) = j) a(i, j) = P(state(t+1) = j|state(t) = i) b(i, k) = P(observed(t) = k|state(t) = i)

See also: http://www.cs.sjsu.edu/faculty/stamp/RUA/HMM.pdf

Linear Supertypes

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Instance Constructors

new HiddenMarkov(ob: VectorI, m: Int, n: Int)

ob
the observation vector
m
the number of observation symbols
n
the number of states in the model

Value Members

final def !=(arg0: AnyRef): Boolean

Definition Classes
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final def !=(arg0: Any): Boolean

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final def ##(): Int

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final def ==(arg0: AnyRef): Boolean

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final def ==(arg0: Any): Boolean

Definition Classes
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def alp_pass(): Unit

The alpha-pass: a forward pass from time t = 0 to tt-1 that computes alpha 'alp'.
final def asInstanceOf[T0]: T0

Definition Classes
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def bet_pass(): Unit

The beta-pass: a backward pass from time t = tt-1 to 0 that computes beta 'bet'.
def clone(): AnyRef

Attributes
protected[java.lang]
Definition Classes
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Annotations
@throws( ... )
final def eq(arg0: AnyRef): Boolean

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def equals(arg0: Any): Boolean

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def finalize(): Unit

Attributes
protected[java.lang]
Definition Classes
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Annotations
@throws( classOf[java.lang.Throwable] )
def gam_pass(): Unit

The gamma-pass: a forward pass from time t = 0 to tt-2 that computes gamma 'gam'.
final def getClass(): Class[_]

Definition Classes
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def hashCode(): Int

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final def isInstanceOf[T0]: Boolean

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def logProb(): Double

Compute the log of the probability of the observation vector 'ob' given the model 'pi, 'a' and 'b'.
final def ne(arg0: AnyRef): Boolean

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final def notify(): Unit

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final def notifyAll(): Unit

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def reestimate(): Unit

Re-estimate the probability vector 'pi' and the probability matrices 'a' and 'b'.
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
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def toString(): String

Definition Classes
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def train(): (VectorD, MatrixD, MatrixD)

Train the Hidden Markov Model using the observation vector 'ob' to determine the model 'pi, 'a' and 'b'.
final def wait(): Unit

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

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

Definition Classes
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@throws( ... )

class HiddenMarkov extends AnyRef

Instance Constructors

new HiddenMarkov(ob: VectorI, m: Int, n: Int)

Value Members

final def !=(arg0: AnyRef): Boolean

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: AnyRef): Boolean

final def ==(arg0: Any): Boolean

def alp_pass(): Unit

final def asInstanceOf[T0]: T0

def bet_pass(): Unit

def clone(): AnyRef

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def finalize(): Unit

def gam_pass(): Unit

final def getClass(): Class[_]

def hashCode(): Int

final def isInstanceOf[T0]: Boolean

def logProb(): Double

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

def reestimate(): Unit

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

def toString(): String

def train(): (VectorD, MatrixD, MatrixD)

final def wait(): Unit

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

final def wait(arg0: Long): Unit

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Ungrouped