prtRvMultinomial Multinomial random variable
RV = prtRvMultinomial creates a prtRvMultinomial object
with an unknown number of categories with unspecified probabilities
These properties can be set manually or by using the MLE method.
prtRvMultinomial operates on count matrices. Therfore, the DRAW()
method outputs a matrix that is N x nCategories and has a single 1
in each row. To draw integer categories you can use the
DRAWINTEGER() method. Similarly, the MLE function takes a count
matrix as an input. Type help prtRvMultinomial.mle for more
information.
RV = prtRvMultinomial(PROPERTY1, VALUE1,...) creates a
prtRvMultinomial object RV with properties as specified by
PROPERTY/VALUE pairs.
A prtRvMultinomial object inherits all properties from the
prtRv class. In addition, it has the following properties:
nCategories - number of integers modeled by the RV
probabilities - A 1 x nCategories vector of doubles less than 1
that sum to 1, representing the probability of
each of the integers
A prtRvMultinomial object inherits all methods from the prtRv
class. The MLE method can be used to set the parameters from data.
In addition, it has the the following methods:
x = R.drawIntegers(N) - Draws N integers with the corresponding
probabilities
Example:
data = rand(100,5); % Uniformly random data
X = bsxfun(@eq,data,max(data,[],2)); % Generate data that has a
% single 1 in each row
RV = prtRvMultinomial; % Generate a prtRvMultinomial
RV = mle(RV,X); % Estimate the parameters
RV.plotPdf() % Plot the pdf (pmf)
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cdf |
Output the cdf of the random variable evaluated at the points specified |
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crossValidate |
Cross validate prtAction using prtDataSet and cross validation keys. |
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draw |
Draw random samples from the distribution described by the prtRv object |
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drawIntegers |
DRAW Draw random integer samples from the distribution described by the prtRv object |
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get |
get the object properties |
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initializeMixtureMembership |
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kfolds |
Perform K-folds cross-validation of prtAction |
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logPdf |
Output the log pdf of the random variable evaluated at the points specified |
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mle |
RV = RV.mle(X) computes the maximum likelihood estimate based |
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optimize |
Optimize action parameter by exhaustive function maximization. |
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pdf |
Output the pdf of the random variable evaluated at the points specified |
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plotCdf |
Not implemented for this prtRv |
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plotLogPdf |
Plot the pdf of the prtRv |
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plotPdf |
Plot the pdf of the RV |
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run |
Run a prtAction object on a prtDataSet object. |
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set |
set the object properties |
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train |
Train a prtAction object using training a prtDataSet object. |