class GMM extends Classifier
The GMM
class is used for univariate Gaussian Mixture Models. Given a
sample, thought to be generated according to 'k' Normal distributions, estimate
the values for the 'mu' and 'sig2' parameters for the Normal distributions.
Given a new value, determine which class (0, ..., k-1) it is most likely to
have come from.
FIX: need a class for multivariate Gaussian Mixture Models.
FIX: need to adapt for clustering.
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Instance Constructors
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new
GMM(x: VectoD, k: Int = 3)
- x
the data vector
- k
the number of components in the mixture
Value Members
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final
def
!=(arg0: Any): Boolean
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final
def
##(): Int
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final
def
==(arg0: Any): Boolean
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def
actualVpredicted(y: VectoI, yp: VectoI): Map[String, Double]
Compare the actual class 'y' vector versus the predicted class 'yp' vector, returning tp, tn, fn, fp.
Compare the actual class 'y' vector versus the predicted class 'yp' vector, returning tp, tn, fn, fp.
- y
the actual class labels
- yp
the precicted class labels
- Definition Classes
- Classifier
- See also
www.dataschool.io/simple-guide-to-confusion-matrix-terminology
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final
def
asInstanceOf[T0]: T0
- Definition Classes
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def
classify(z: VectoI): (Int, String, Double)
Classify the first point in vector 'z'.
Classify the first point in vector 'z'.
- z
the vector to be classified.
- Definition Classes
- GMM → Classifier
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def
classify(z: VectoD): (Int, String, Double)
Classify the first point in vector 'z'.
Classify the first point in vector 'z'.
- z
the vector to be classified.
- Definition Classes
- GMM → Classifier
-
def
clone(): AnyRef
- Attributes
- protected[java.lang]
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def
crossValidate(nx: Int = 10, show: Boolean = false): Double
Test the accuracy of the classified results by cross-validation, returning the accuracy.
Test the accuracy of the classified results by cross-validation, returning the accuracy. The "test data" starts at 'testStart' and ends at 'testEnd', the rest of the data is "training data'. FIX - should return a StatVector
- nx
the number of crosses and cross-validations (defaults to 10x).
- show
the show flag (show result from each iteration)
- Definition Classes
- Classifier
-
def
crossValidateRand(nx: Int = 10, show: Boolean = false): Double
Test the accuracy of the classified results by cross-validation, returning the accuracy.
Test the accuracy of the classified results by cross-validation, returning the accuracy. This version of cross-validation relies on "subtracting" frequencies from the previously stored global data to achieve efficiency. FIX - are the comments correct? FIX - should return a StatVector
- nx
number of crosses and cross-validations (defaults to 10x).
- show
the show flag (show result from each iteration)
- Definition Classes
- Classifier
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final
def
eq(arg0: AnyRef): Boolean
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def
equals(arg0: Any): Boolean
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def
exp_step(): Unit
Execute the Expectation (E) Step in the EM algoithm.
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def
finalize(): Unit
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final
def
getClass(): Class[_]
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def
hashCode(): Int
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final
def
isInstanceOf[T0]: Boolean
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def
max_step(): Unit
Execute the Maximumization (M) Step in the EM algoithm.
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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
reset(): Unit
Reset ...
Reset ... FIX
- Definition Classes
- GMM → Classifier
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def
size: Int
Return the size of the feature set.
Return the size of the feature set.
- Definition Classes
- GMM → Classifier
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final
def
synchronized[T0](arg0: ⇒ T0): T0
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def
test(itest: IndexedSeq[Int]): Double
Test ...
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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
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def
toString(): String
- Definition Classes
- AnyRef → Any
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def
train(itest: IndexedSeq[Int]): GMM
Train the model to determine values for the parameter vectors 'mu' and 'sig2'.
Train the model to determine values for the parameter vectors 'mu' and 'sig2'.
- itest
the indices of test data
- Definition Classes
- GMM → Classifier
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def
train(): 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.
- Definition Classes
- Classifier
-
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
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final
def
wait(): Unit
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final
def
wait(arg0: Long, arg1: Int): Unit
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final
def
wait(arg0: Long): Unit
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