HiddenMarkov

Companion object HiddenMarkov

class HiddenMarkov extends ClassifierInt

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 'x(t)' and an observed symbol/value 'y(t)' at time 't', which may be viewed as a time series.

pi(i) = P(x(t) = i) a(i, j) = P(x(t+1) = j | x(t) = i) b(i, k) = P(y(t) = k | x(t) = i)

model (pi, a, b)

Linear Supertypes

ClassifierInt, Classifier, Model, ConfusionFit, Error, QoF, AnyRef, Any

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HiddenMarkov
ClassifierInt
Classifier
Model
ConfusionFit
Error
QoF
AnyRef
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Instance Constructors

new HiddenMarkov(y: VectoI, m: Int, n: Int, cn_: Strings = null, pi: VectoD = null, a: MatriD = null, b: MatriD = null, hp: HyperParameter = null)
y
the observation vector/observed discrete-valued time series
m
the number of observation symbols/values {0, 1, ... m-1}
n
the number of (hidden) states in the model
cn_
the class names for the states, e.g., ("Hot", "Cold")
pi
the probabilty vector for the initial state
a
the state transition probability matrix (n-by-n)
b
the observation probability matrix (n-by-m)

Value Members

final def !=(arg0: Any): Boolean

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

Definition Classes
AnyRef → Any
final def ==(arg0: Any): Boolean

Definition Classes
AnyRef → Any
def accuracy: Double
Compute the accuracy of the classification, i.e., the fraction of correct classifications.
Compute the accuracy of the classification, i.e., the fraction of correct classifications. Note, the correct classifications 'tp_i' are in the main diagonal of the confusion matrix.

Definition Classes
ConfusionFit
final def asInstanceOf[T0]: T0

Definition Classes
Any
def backwardEval(): MatriD
The beta-pass: a backward pass from time 't = tt-1' to 0 that computes beta 'bet' with scaling.
The beta-pass: a backward pass from time 't = tt-1' to 0 that computes beta 'bet' with scaling. Requires: 'alp' the scaled alpha matrix 'c' the vector of scaling factors
def backwardEval0(): MatriD
The beta-pass: a backward pass from time 't = tt-1' to 0 that computes beta 'bet'.
def calcCorrelation: MatriD
Calculate the correlation matrix for the feature vectors 'fea'.
Calculate the correlation matrix for the feature vectors 'fea'. If the correlations are too high, the independence assumption may be dubious.

Definition Classes
ClassifierInt
def calcCorrelation2(zrg: Range, xrg: Range): MatriD
Calculate the correlation matrix for the feature vectors of Z (Level 3) and those of X (level 2).
Calculate the correlation matrix for the feature vectors of Z (Level 3) and those of X (level 2). If the correlations are too high, the independence assumption may be dubious.
zrg
the range of Z-columns
xrg
the range of X-columns

Definition Classes
ClassifierInt
def classify(z: VectoI): (Int, String, Double)
Given a new discrete data vector 'z', determine which class it belongs to, returning the best class, its name and its relative probability.
Given a new discrete data vector 'z', determine which class it belongs to, returning the best class, its name and its relative probability.
z
the observation vector/time series to classify

Definition Classes
HiddenMarkov → Classifier
def classify(xx: MatriI = x): VectoI
Classify all of the row vectors in matrix 'xx'.
Classify all of the row vectors in matrix 'xx'.
xx
the row vectors to classify (defaults to x)

Definition Classes
ClassifierInt
def classify(z: VectoD): (Int, String, Double)
Given a new continuous data vector 'z', determine which class it fits into, returning the best class, its name and its relative probability.
Given a new continuous data vector 'z', determine which class it fits into, returning the best class, its name and its relative probability. Override in classes that require precise real values for classification.
z
the real vector to classify

Definition Classes
ClassifierInt → Classifier
def clearConfusion(): Unit
Clear the total cummulative confusion matrix.
Clear the total cummulative confusion matrix.

Definition Classes
ConfusionFit
def clone(): AnyRef

Attributes
protected[lang]
Definition Classes
AnyRef
Annotations
@throws( ... ) @native() @HotSpotIntrinsicCandidate()
var cn: Strings

Attributes
protected
Definition Classes
ClassifierInt
def confusion(yp: VectoI, yy: VectoI = y): MatriI
Compare the actual class 'y' vector versus the predicted class 'yp' vector, returning the confusion matrix 'cmat', which for 'k = 2' is
Compare the actual class 'y' vector versus the predicted class 'yp' vector, returning the confusion matrix 'cmat', which for 'k = 2' is
yp 0 1 ---------- y 0 | tn fp | 1 | fn tp | ----------
Note: ScalaTion's confusion matrix is Actual × Predicted, but to swap the position of actual 'y' (rows) with predicted 'yp' (columns) simply use 'cmat.t', the transpose of 'cmat'.
yp
the precicted class values/labels
yy
the actual class values/labels for full (y) or test (y_e) dataset

Definition Classes
ConfusionFit
See also
www.dataschool.io/simple-guide-to-confusion-matrix-terminology
def contrast(yp: VectoI, yy: VectoI = y): Unit
Contract the actual class 'yy' vector versus the predicted class 'yp' vector.
Contract the actual class 'yy' vector versus the predicted class 'yp' vector.
yp
the predicted class values/labels
yy
the actual class values/labels for full (y) or test (y_e) dataset

Definition Classes
ConfusionFit
def crossValidate(nx: Int = 10, show: Boolean = false): Array[Statistic]
Test the accuracy of the classified results by cross-validation, returning the Quality of Fit (QoF) measures such as accuracy.
Test the accuracy of the classified results by cross-validation, returning the Quality of Fit (QoF) measures such as accuracy. This method slices out instances/rows to form the test dataset.
nx
number of crosses and cross-validations (defaults to 10x).
show
the show flag (show result from each iteration)

Definition Classes
ClassifierInt → Classifier
def crossValidateRand(nx: Int = 10, show: Boolean = false): Array[Statistic]
Test the accuracy of the classified results by cross-validation, returning the Quality of Fit (QoF) measures such as accuracy.
Test the accuracy of the classified results by cross-validation, returning the Quality of Fit (QoF) measures such as accuracy. This method randomizes the instances/rows selected for the test dataset.
nx
number of crosses and cross-validations (defaults to 10x).
show
the show flag (show result from each iteration)

Definition Classes
ClassifierInt → Classifier
def diagnose(e: VectoD, yy: VectoD, yp: VectoD, w: VectoD = null, ym: Double = noDouble): Unit
Diagnose the health of the model by computing the Quality of Fit (QoF) measures, from the error/residual vector and the predicted & actual responses.
Diagnose the health of the model by computing the Quality of Fit (QoF) measures, from the error/residual vector and the predicted & actual responses. For some models the instances may be weighted.
e
the m-dimensional error/residual vector (yy - yp)
yy
the actual response vector to use (test/full)
yp
the predicted response vector (test/full)
w
the weights on the instances (defaults to null)
ym
the mean of the actual response vector to use (test/full)

Definition Classes
ConfusionFit → QoF
See also
Regression_WLS
final def eq(arg0: AnyRef): Boolean

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

Definition Classes
AnyRef → Any
def eval(xx: MatriD, yy: VectoD = null): ClassifierInt
Test the quality of the training with a test dataset and return the fraction of correct classifications.
Test the quality of the training with a test dataset and return the fraction of correct classifications.
xx
the integer-valued test vectors stored as rows of a matrix
yy
the classification vector (impl. classes should ignore or default yy to y)

Definition Classes
ClassifierInt → Model
def f1_measure(p: Double, r: Double): Double
Compute the F1-measure, i.e., the harmonic mean of the precision and recall.
Compute the F1-measure, i.e., the harmonic mean of the precision and recall.
p
the precision
r
the recall

Definition Classes
ConfusionFit
def f1v: VectoD
Compute the micro-F1-measure vector, i.e., the harmonic mean of the precision and recall.
Compute the micro-F1-measure vector, i.e., the harmonic mean of the precision and recall.

Definition Classes
ConfusionFit
def f_(z: Double): String
Format a double value.
Format a double value.
z
the double value to format

Definition Classes
QoF
def featureSelection(TOL: Double = 0.01): Unit
Perform feature selection on the classifier.
Perform feature selection on the classifier. Use backward elimination technique, that is, remove the least significant feature, in terms of cross- validation accuracy, in each round.
TOL
tolerance indicating negligible accuracy loss when removing features

Definition Classes
ClassifierInt
def fit: VectoD
Return the Quality of Fit (QoF) measures corresponding to the labels given above in the 'fitLabel' method.
Return the Quality of Fit (QoF) measures corresponding to the labels given above in the 'fitLabel' method.

Definition Classes
ConfusionFit → QoF
def fitLabel: Seq[String]
Return the labels for the Quality of Fit (QoF) measures.
Return the labels for the Quality of Fit (QoF) measures. Override to add additional QoF measures.

Definition Classes
ConfusionFit → QoF
def fitLabel_v: Seq[String]
Return the labels for the Quality of Fit (QoF) measures.
Return the labels for the Quality of Fit (QoF) measures. Override to add additional QoF measures.

Definition Classes
ConfusionFit
def fitMap: Map[String, String]
Build a map of quality of fit measures (use of LinkedHashMap makes it ordered).
Build a map of quality of fit measures (use of LinkedHashMap makes it ordered).

Definition Classes
QoF
def fitMicroMap: Map[String, VectoD]
Return the Quality of Fit (QoF) vector micor-measures, i.e., measures for each class.
Return the Quality of Fit (QoF) vector micor-measures, i.e., measures for each class.

Definition Classes
ConfusionFit
final def flaw(method: String, message: String): Unit

Definition Classes
Error
var fn: Strings

Attributes
protected
Definition Classes
ClassifierInt
def forwardEval(): MatriD
The alpha-pass: a forward pass from time 't = 0' to 'tt-1' that computes alpha 'alp' with scaling.
def forwardEval0(): MatriD
The alpha-pass: a forward pass from time 't = 0' to 'tt-1' that computes alpha 'alp'.
val fset: Array[Boolean]
the set of features to turn on or off.
the set of features to turn on or off. All features are on by default. Used for feature selection.

Attributes
protected
Definition Classes
ClassifierInt
def gamma(): MatriD
The gamma-pass: a forward pass from time 't = 0' to 'tt-2' that computes gamma 'gam'.
The gamma-pass: a forward pass from time 't = 0' to 'tt-2' that computes gamma 'gam'. Given the observation vector 'y', find the most probable sequence of states. Requires: 'alp' the unscaled alpha matrix 'bet' the unscaled beta matrix
def getC: VectoD
Return the vector of scaling factors 'c'.
final def getClass(): Class[_]

Definition Classes
AnyRef → Any
Annotations
@native() @HotSpotIntrinsicCandidate()
def getY: VectoI
Return the response (class label) vector.
Return the response (class label) vector.

Definition Classes
ClassifierInt
def hashCode(): Int

Definition Classes
AnyRef → Any
Annotations
@native() @HotSpotIntrinsicCandidate()
def help: String
Return the help string that describes the Quality of Fit (QoF) measures provided by the ConfusionFit class.
Return the help string that describes the Quality of Fit (QoF) measures provided by the ConfusionFit class. Override to correspond to 'fitLabel'.

Definition Classes
ConfusionFit → QoF
def hparameter: HyperParameter
Return the model hyper-parameters (if none, return null).
Return the model hyper-parameters (if none, return null). Hyper-parameters may be used to regularize parameters or tune the optimizer.

Definition Classes
ClassifierInt → Model
final def isInstanceOf[T0]: Boolean

Definition Classes
Any
def jointProb(x: VectoI): Double
Compute the joint probability P(x, y).
Compute the joint probability P(x, y).
x
the state vector
def kappa: Double
Compute Cohen's 'kappa' coefficient that measures agreement between actual 'y' and predicted 'yp' classifications.
Compute Cohen's 'kappa' coefficient that measures agreement between actual 'y' and predicted 'yp' classifications.

Definition Classes
ConfusionFit
See also
en.wikipedia.org/wiki/Cohen%27s_kappa
def logProbY(scaled: Boolean = false): Double
Compute the -log of the probability of seeing observation vector 'y', given the model 'pi, 'a' and 'b'.
Compute the -log of the probability of seeing observation vector 'y', given the model 'pi, 'a' and 'b'.
scaled
whether the alpha matrix is scaled Requires: 'alp' the unscaled alpha matrix or 'c' the vector of scaling factors
val m: Int
the number of data vectors in training/test-set (# rows)
the number of data vectors in training/test-set (# rows)

Attributes
protected
Definition Classes
ClassifierInt
val md: Double
the training-set size as a Double
the training-set size as a Double

Attributes
protected
Definition Classes
ClassifierInt
val modelConcept: URI
An optional reference to an ontological concept
An optional reference to an ontological concept

Definition Classes
Model
def modelName: String
An optional name for the model (or modeling technique)
An optional name for the model (or modeling technique)

Definition Classes
Model
val n: Int
the number of features/variables (# columns)
the number of features/variables (# columns)

Attributes
protected
Definition Classes
ClassifierInt
val nd: Double
the feature-set size as a Double
the feature-set size as a Double

Attributes
protected
Definition Classes
ClassifierInt
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
AnyRef
Annotations
@native() @HotSpotIntrinsicCandidate()
final def notifyAll(): Unit

Definition Classes
AnyRef
Annotations
@native() @HotSpotIntrinsicCandidate()
def p_r_s(): Unit
Compute the micro-precision, micro-recall and micro-specificity vectors which have elements for each class i in {0, 1, ...
Compute the micro-precision, micro-recall and micro-specificity vectors which have elements for each class i in {0, 1, ... k-1}. -------------------------------------------------------------------------- Precision is the fraction classified as true that are actually true. Recall (sensitivity) is the fraction of the actually true that are classified as true. Specificity is the fraction of the actually false that are classified as false. -------------------------------------------------------------------------- Note, for 'k = 2', ordinary precision 'p', recall 'r' and specificity 's' will correspond to the last elements in the 'pv', 'rv' and 'sv' micro vectors.

Definition Classes
ConfusionFit
def parameter: VectoD
Return the parameter vector 'pi'.
Return the parameter vector 'pi'.

Definition Classes
HiddenMarkov → Model
def parameters: (MatriD, MatriD)
Return the parameter matrices 'a' and 'b'.
def probY(scaled: Boolean = false): Double
Compute the probability of seeing observation vector 'y', given the model 'pi, 'a' and 'b'.
Compute the probability of seeing observation vector 'y', given the model 'pi, 'a' and 'b'.
scaled
whether the alpha matrix is scaled Requires: 'alp' the unscaled alpha matrix or 'c' the vector of scaling factors
def pseudo_rSq: Double
Compute the Efron's pseudo R-squared value.
Compute the Efron's pseudo R-squared value. Override to McFadden's, etc.

Definition Classes
ConfusionFit
def reestimate(): Unit
Re-estimate the probability vector 'pi' and the probability matrices 'a' and 'b'.
Re-estimate the probability vector 'pi' and the probability matrices 'a' and 'b'. Requires: 'gam' the gamma matrix 'gat' the gamma tensor
def report: String
Return a basic report on the trained model.
Return a basic report on the trained model.

Definition Classes
HiddenMarkov → ClassifierInt → Model
def reset(): Unit
Reset global variables.
Reset global variables. So far, not needed.

Definition Classes
HiddenMarkov → Classifier
def setStream(str: Int = 0): Unit
Set the random number 'stream' to 'str'.
Set the random number 'stream' to 'str'. This is useful for testing purposes, since a fixed stream will follow the same sequence each time.
str
the new fixed random number stream

Definition Classes
Classifier
def shiftToZero(): Unit
Shift the 'x' Matrix so that the minimum value for each column equals zero.
Shift the 'x' Matrix so that the minimum value for each column equals zero.

Definition Classes
ClassifierInt
def size: Int
Return the size of the (hidden) state space.
Return the size of the (hidden) state space.

Definition Classes
HiddenMarkov → ClassifierInt → Classifier
val stream: Int
the random number stream {0, 1, ..., 999} to be used
the random number stream {0, 1, ..., 999} to be used

Attributes
protected
Definition Classes
Classifier
def summary(b: VectoD = null, show: Boolean = false): String
Produce a summary report with diagnostics and the overall quality of fit.
Produce a summary report with diagnostics and the overall quality of fit.
b
the parameters of the model
show
flag indicating whether to print the summary

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

Definition Classes
AnyRef
def test(xx: MatriI, yy: VectoI): Double
Test the quality of the training with a test-set and return the fraction of correct classifications.
Test the quality of the training with a test-set and return the fraction of correct classifications.
xx
the integer-valued test vectors stored as rows of a matrix
yy
the test classification vector, where 'yy_i = class' for row 'i' of 'xx'

Definition Classes
ClassifierInt
def test(itest: Ints): Double
Test the quality of the training with a test-set and return the fraction of correct classifications.
Test the quality of the training with a test-set and return the fraction of correct classifications.
itest
indices of the instances considered test data

Definition Classes
ClassifierInt → Classifier
def test(testStart: Int, testEnd: Int): Double
Test the quality of the training with a test dataset and return the fraction of correct classifications.
Test the quality of the training with a test dataset and return the fraction of correct classifications. Can be used when the dataset is randomized so that the testing/training part of a dataset corresponds to simple slices of vectors and matrices.
testStart
the beginning of test region (inclusive).
testEnd
the end of test region (exclusive).

Definition Classes
Classifier
def tn_fp_fn_tp(con: MatriI = cmat): (Double, Double, Double, Double)
Return the confusion matrix for 'k = 2' as a tuple (tn, fp, fn, tp).
Return the confusion matrix for 'k = 2' as a tuple (tn, fp, fn, tp).
con
the confusion matrix (defaults to cmat)

Definition Classes
ConfusionFit
def toString(): String

Definition Classes
AnyRef → Any
def total_cmat(): MatriI
Return a copy of the total cummulative confusion matrix 'tcmat' and clear 'tcmat'.
Return a copy of the total cummulative confusion matrix 'tcmat' and clear 'tcmat'.

Definition Classes
ConfusionFit
def train(itest: Ints): HiddenMarkov
Train the Hidden Markov Model using the observation vector 'y' to determine the model 'pi, 'a' and 'b'.
Train the Hidden Markov Model using the observation vector 'y' to determine the model 'pi, 'a' and 'b'.
itest
the indices of the test data

Definition Classes
HiddenMarkov → Classifier
def train(xx: MatriD = null, yy: VectoD = null): Classifier
Train the classifier by computing the probabilities from a training dataset of data vectors and their classifications.
Train the classifier by computing the probabilities from a training dataset of data vectors and their classifications. Must be implemented in any extending class. Can be used when the whole dataset is used for training.
xx
the data/input matrix (impl. classes should ignore or default xx to x)
yy
the response/classification vector (impl. classes should ignore or default yy to y)

Definition Classes
Classifier → Model
def train(testStart: Int, testEnd: Int): Classifier
Train the classifier by computing the probabilities from a training dataset of data vectors and their classifications.
Train the classifier by computing the probabilities from a training dataset of data vectors and their classifications. Must be implemented in any extending class. Can be used when the dataset is randomized so that the training part of a dataset corresponds to simple slices of vectors and matrices.
testStart
starting index of test region (inclusive) used in cross-validation
testEnd
ending index of test region (exclusive) used in cross-validation

Definition Classes
Classifier
def vc_default: Array[Int]
Return default values for binary input data (value count 'vc' set to 2).
Return default values for binary input data (value count 'vc' set to 2).

Definition Classes
ClassifierInt
def vc_fromData: Array[Int]
Return value counts calculated from the input data.
Return value counts calculated from the input data. May wish to call 'shiftToZero' before calling this method.

Definition Classes
ClassifierInt
def vc_fromData2(rg: Range): Array[Int]
Return value counts calculated from the input data.
Return value counts calculated from the input data. May wish to call 'shiftToZero' before calling this method.
rg
the range of columns to be considered

Definition Classes
ClassifierInt
def viterbiDecode(): MatriD
The gamma-pass: a forward pass from time 't = 0' to 'tt-2' that computes gamma 'gam' and 'gat'.
The gamma-pass: a forward pass from time 't = 0' to 'tt-2' that computes gamma 'gam' and 'gat'. Given the observation vector 'y', find the most probable sequence of states. Note: 'gat' is computed as a side-effect. Requires: 'alp' the scaled alpha matrix 'bet' the scaled beta matrix
final def wait(arg0: Long, arg1: Int): Unit

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

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

Definition Classes
AnyRef
Annotations
@throws( ... )

Deprecated Value Members

def finalize(): Unit

Attributes
protected[lang]
Definition Classes
AnyRef
Annotations
@throws( classOf[java.lang.Throwable] ) @Deprecated
Deprecated

Packages

HiddenMarkov

Companion object HiddenMarkov

class HiddenMarkov extends ClassifierInt

Instance Constructors

Value Members

Deprecated Value Members

Inherited from ClassifierInt

Inherited from Classifier

Inherited from Model

Inherited from ConfusionFit

Inherited from Error

Inherited from QoF

Inherited from AnyRef

Inherited from Any

Ungrouped

Packages

HiddenMarkov 

Companion object HiddenMarkov

class HiddenMarkov extends ClassifierInt

Instance Constructors

Value Members

Deprecated Value Members

Inherited from ClassifierInt

Inherited from Classifier

Inherited from Model

Inherited from ConfusionFit

Inherited from Error

Inherited from QoF

Inherited from AnyRef

Inherited from Any

Ungrouped

HiddenMarkov