CircularDescriptiveAnalysis Class 
Namespace: Accord.Statistics.Analysis
[SerializableAttribute] public class CircularDescriptiveAnalysis : IMultivariateAnalysis, IAnalysis, IDescriptiveLearning<CircularDescriptiveAnalysis, double[]>
The CircularDescriptiveAnalysis type exposes the following members.
Name  Description  

CircularDescriptiveAnalysis(Double) 
Constructs the Circular Descriptive Analysis.
 
CircularDescriptiveAnalysis(Double, Double)  Obsolete.
Constructs the Circular Descriptive Analysis.
 
CircularDescriptiveAnalysis(Double, String) 
Constructs the Circular Descriptive Analysis.
 
CircularDescriptiveAnalysis(Double, Double)  Obsolete.
Constructs the Circular Descriptive Analysis.
 
CircularDescriptiveAnalysis(Double, Double, String)  Obsolete.
Constructs the Circular Descriptive Analysis.
 
CircularDescriptiveAnalysis(Double, Double, Boolean)  Obsolete.
Constructs the Circular Descriptive Analysis.
 
CircularDescriptiveAnalysis(Double, Double, String)  Obsolete.
Constructs the Circular Descriptive Analysis.
 
CircularDescriptiveAnalysis(Double, Double, String, Boolean)  Obsolete.
Constructs the Circular Descriptive Analysis.

Name  Description  

Angles  Obsolete.
Gets the source matrix from which the analysis was run.
 
Array  Obsolete.
Gets the source matrix from which the analysis was run.
 
ColumnNames 
Gets the column names from the variables in the data.
 
Concentration 
Gets an array containing the circular concentration for each data column.
 
Confidence 
Gets the 95% confidence intervals for the Means.
 
CosineSum 
Gets an array containing the sum of cosines for each data column.
 
Deviance 
Gets the 95% deviance intervals for the Means.
 
Distinct 
Gets a vector containing the number of distinct elements for each data column.
 
InnerFences 
Gets an array containing the inner fences of each data column.
 
Kurtosis 
Gets an array containing the kurtosis for of each data column.
 
Lazy 
Gets or sets whether the properties of this class should
be computed only when necessary. If set to true, a copy
of the input data will be maintained inside an instance
of this class, using more memory.
 
Lengths 
Gets a vector containing the length of
the circular domain for each data column.
 
Means 
Gets a vector containing the Mean of each data column.
 
Measures 
Gets a collection of DescriptiveMeasures objects that can be bound to a DataGridView.
 
Medians 
Gets a vector containing the Median of each data column.
 
Modes 
Gets a vector containing the Mode of each data column.
 
OuterFences 
Gets an array containing the outer fences of each data column.
 
QuantileMethod 
Gets or sets the method to be used when computing quantiles (median and quartiles).
 
Quartiles 
Gets an array containing the interquartile range of each data column.
 
Ranges 
Gets an array containing the Ranges of each data column.
 
Samples 
Gets the number of samples (or observations) in the data.
 
SineSum 
Gets an array containing the sum of sines for each data column.
 
Skewness 
Gets an array containing the skewness for of each data column.
 
Source  Obsolete.
Gets the source matrix from which the analysis was run.
 
StandardDeviations 
Gets a vector containing the Standard Deviation of each data column.
 
StandardErrors 
Gets a vector containing the Standard Error of the Mean of each data column.
 
Sums 
Gets an array containing the sum of each data column. If
the analysis has been computed in place, this will contain
the sum of the transformed angle values instead.
 
UseStrictRanges 
Gets or sets whether all reported statistics should respect the circular
interval. For example, setting this property to false would allow
the Confidence, Deviance, InnerFences
and OuterFences properties report minimum and maximum values
outside the variable's allowed circular range. Default is true.
 
Variables 
Gets the number of variables (or features) in the data.
 
Variances 
Gets a vector containing the Variance of each data column.

Name  Description  

Compute  Obsolete.
Computes the analysis using given source data and parameters.
 
Equals  Determines whether the specified object is equal to the current object. (Inherited from Object.)  
Finalize  Allows an object to try to free resources and perform other cleanup operations before it is reclaimed by garbage collection. (Inherited from Object.)  
GetConfidenceInterval 
Gets a confidence interval for the Means
within the given confidence level percentage.
 
GetDevianceInterval 
Gets a deviance interval for the Means
within the given confidence level percentage (i.e. uses
the standard deviation rather than the standard error to
compute the range interval for the variable).
 
GetHashCode  Serves as the default hash function. (Inherited from Object.)  
GetType  Gets the Type of the current instance. (Inherited from Object.)  
Learn 
Learns a model that can map the given inputs to the desired outputs.
 
MemberwiseClone  Creates a shallow copy of the current Object. (Inherited from Object.)  
ToString  Returns a string that represents the current object. (Inherited from Object.) 
Name  Description  

HasMethod 
Checks whether an object implements a method with the given name.
(Defined by ExtensionMethods.)  
IsEqual 
Compares two objects for equality, performing an elementwise
comparison if the elements are vectors or matrices.
(Defined by Matrix.)  
To(Type)  Overloaded.
Converts an object into another type, irrespective of whether
the conversion can be done at compile time or not. This can be
used to convert generic types to numeric types during runtime.
(Defined by ExtensionMethods.)  
ToT  Overloaded.
Converts an object into another type, irrespective of whether
the conversion can be done at compile time or not. This can be
used to convert generic types to numeric types during runtime.
(Defined by ExtensionMethods.) 