getEntropy

Compute the entropy of a distribution.

Create adjacency matrices of vocalisation graphs from
dataframes containing sequences of speech and silence intervals,
transforming these matrices into Markov diagrams, and generating
datasets for classification of these diagrams by 'flattening' them
and adding global properties (functionals) etc. Vocalisation
diagrams date back to early work in psychiatry (Jaffe and Feldstein,
1970) and social psychology (Dabbs and Ruback, 1987) but have only
recently been employed as a data representation method for machine
learning tasks including meeting segmentation (Luz, 2012)
<doi:10.1145/2328967.2328970> and classification (Luz,
2013) <doi:10.1145/2522848.2533788>.

Saturnino Luz

vocaldia

Create and Manipulate Vocalisation Diagrams

getEntropy function

<dl><dt>distribution</dt>
<dd>a probability distribution.</dd></dl>

Arguments

getEntropy: safely return the Shannon entropy of a distribution. — getEntropy

<dl>

<dt>distribution</dt>
<dd>a probability distribution.</dd>

</dl>

getEntropy: getEntropy: safely return the Shannon entropy of a distribution.

Description

Usage

Value

Arguments

Details