![]() Thus, the following array specifies realisations for 3 random variables: Multi-dimensional array input is supported based on the convention that *leading dimensions index random variables, with the trailing dimension indexing random variable realisations*. > drv.entropy(, estimator='PERKS', Alphabet_X = np.arange(100)) pseudo-counts of 1/L for each of L possible outcomes) and based on an alphabet specifying L=100 possible outcomes, we may use: Using maximum a posteriori estimation combined with the Perks prior (i.e. For example, we might seek to estimate the entropy in bits for the sequence of realisations ````. In combination with any estimator other than maximum likelihood, it may be useful to specify alphabets containing unobserved outcomes. It's alternatively possible to specify missing data using NumPy masked arrays: > drv.entropy_conditional(,, fill_value=None) Thus, the following method calls yield equivalent results (note use of alternative placeholder value ``None``): Any missing data for random variable ``X`` results in the corresponding realisations for random variable ``Y`` being ignored, and vice versa. In measures expressible in terms of joint entropy (such as conditional entropy, mutual information etc.), equally many realisations of respective random variables are required (with realisations coupled using a common index). Elements equal to the placeholder value are subsequently ignored: ![]() It's possible to specify missing data using placeholder values (the default placeholder value is ``-1``). For example, the analogous method call for computing the entropy of the preceding random variable realisations (with estimated equi-probable outcomes) is: Those methods with the suffix ``_pmf`` operate on arrays specifying probability mass assignments. Let's compute entropy, again based on maximum likelihood estimation, but this time using list input and quantifying entropy in nats: NumPy arrays are created automatically for any input which isn't of the required type, by passing the input to np.array(). Let's compute the entropy for an array containing discrete random variable realisations, based on maximum likelihood estimation and quantifying entropy in bits: The respective methods implemented in ``discrete_random_variable`` accept NumPy arrays as input. Import the module ``discrete_random_variable``, as well as NumPy:įrom pyitlib import discrete_random_variable as drv The codebase for pyitlib is available at. Pyitlib is listed on the Python Package Index at and may be installed using ``pip`` as follows: Missing data are supported, either using placeholder values or NumPy masked arrays. The following estimators are available for each of the measures: * Normalised mutual information (7 variants) * Symmetrised Kullback-Leibler divergence Pyitlib implements the following 19 measures on discrete random variables: Pyitlib is an MIT-licensed library of information-theoretic methods for data analysis and machine learning, implemented in Python and NumPy.ĪPI documentation is available online at.
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