entropy#
- Mixture.entropy(*, method=None)[source]#
- Differential entropy - In terms of probability density function \(f(x)\) and support \(\chi\), the differential entropy (or simply “entropy”) of a continuous random variable \(X\) is: \[h(X) = - \int_{\chi} f(x) \log f(x) dx\]- Parameters:
- method{None, ‘formula’, ‘logexp’, ‘quadrature’}
- The strategy used to evaluate the entropy. By default ( - None), the infrastructure chooses between the following options, listed in order of precedence.- 'formula': use a formula for the entropy itself
- 'logexp': evaluate the log-entropy and exponentiate
- 'quadrature': use numerical integration
 - Not all method options are available for all distributions. If the selected method is not available, a - NotImplementedErrorwill be raised.
 
- Returns:
- outarray
- The entropy of the random variable. 
 
 - See also - Notes - This function calculates the entropy using the natural logarithm; i.e. the logarithm with base \(e\). Consequently, the value is expressed in (dimensionless) “units” of nats. To convert the entropy to different units (i.e. corresponding with a different base), divide the result by the natural logarithm of the desired base. - References [1]- Differential entropy, Wikipedia, https://en.wikipedia.org/wiki/Differential_entropy - Examples - Instantiate a distribution with the desired parameters: - >>> from scipy import stats >>> X = stats.Uniform(a=-1., b=1.) - Evaluate the entropy: - >>> X.entropy() 0.6931471805599454