MATLAB File Help: prtRvUniform |
prtRvUniform Uniform random variable RV = prtRvUniform creates a prtRvUniform object with empty upperBounds and lowerBounds. The upperBounds and lowerBounds must be set either directly, or by calling the MLE method. upperBounds and lowerBounds specify the range of the uniform variable. RV = prtRvUniform(PROPERTY1, VALUE1,...) creates a prtRvUniform object RV with properties as specified by PROPERTY/VALUE pairs. A prtRvUniform object inherits all properties from the prtRv class. In addition, it has the following properties: upperBounds - 1 x nDims double vector specifying the upper bound of the region with uniform density lowerBounds - 1 x nDims double vector specifying the lower bound of the region with uniform density A prtRvUniform object inherits all methods from the prtRv class. The MLE method can be used to estimate the distribution parameters from data. Example: dataSet = prtDataGenUnimodal; % Load a dataset consisting of % 2 features dataSet = retainFeatures(dataSet,1); % Retain only the first feature RV = prtRvUniform; % Create a prtRvUniform object RV = RV.mle(dataSet); % Compute the bounds % form the data RV.plotPdf % Plot the pdf
Superclasses | prtRv |
Sealed | false |
Construct on load | false |
prtRvUniform | Uniform random variable |
dataSet | The training prtDataSet, only stored if verboseStorage is true. |
dataSetSummary | Structure that summarizes prtDataSet. |
isCrossValidateValid | |
isSupervised | |
isTrained | Indicates if prtAction object has been trained. |
lowerBounds | The upper bounds of the random variable |
name | |
nameAbbreviation | |
plotOptions | |
showProgressBar | |
upperBounds | The lower bounds of the random variable |
userData | User specified data |
verboseStorage | Specifies whether or not to store the training prtDataset. |
area | ||
cdf | Output the cdf of the random variable evaluated at the points specified | |
crossValidate | Cross validate prtAction using prtDataSet and cross validation keys. | |
draw | Draw random samples from the distribution described by the prtRv object | |
get | get the object properties | |
kfolds | Perform K-folds cross-validation of prtAction | |
logPdf | Output the log pdf of the random variable evaluated at the points specified | |
mle | Compute the maximum likelihood estimate | |
optimize | Optimize action parameter by exhaustive function maximization. | |
Output the pdf of the random variable evaluated at the points specified | ||
plotCdf | Plot the cdf of the prtRv | |
plotLogPdf | Plot the pdf of the prtRv | |
plotPdf | Plot the pdf of the prtRv | |
run | Run a prtAction object on a prtDataSet object. | |
set | set the object properties | |
train | Train a prtAction object using training a prtDataSet object. |