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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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
-
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
crossValidate(nx: Int = 10, show: Boolean = false): Array[Statistic]
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'.
- nx
the number of crosses and cross-validations (defaults to 10x).
- show
the show flag (show result from each iteration)
- Definition Classes
- GMM → 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
- GMM → Classifier
-
def
eval(xx: MatriD, yy: VectoD): GMM
Evaluate the model's Quality of Fit (QoF) as well as the importance of its parameters (e.g., if 0 is in a parameter's confidence interval, it is a candidate for removal from the model).
Evaluate the model's Quality of Fit (QoF) as well as the importance of its parameters (e.g., if 0 is in a parameter's confidence interval, it is a candidate for removal from the model). Extending traits and classess should implement various diagnostics for the test and full (training + test) datasets.
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def
exp_step(): Unit
Execute the Expectation (E) Step in the EM algoithm.
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final
def
flaw(method: String, message: String): Unit
- Definition Classes
- Error
-
def
hparameter: HyperParameter
Return the model hyper-parameters (if none, return null).
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def
max_step(): Unit
Execute the Maximumization (M) Step in the EM algoithm.
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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
-
def
parameter: VectoD
Return the vector of model parameter/coefficient values.
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def
report: String
Return a basic report on the trained model.
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def
reset(): Unit
Reset ...
Reset ... FIX
- Definition Classes
- GMM → 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
size: Int
Return the size of the feature set.
Return the size of the feature set.
- Definition Classes
- GMM → Classifier
-
def
test(itest: Ints): 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
-
def
train(itest: Ints): 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
-
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