scipy.stats.boltzmann¶
- scipy.stats.boltzmann = <scipy.stats._discrete_distns.boltzmann_gen object at 0x450c01ac>[source]¶
A Boltzmann (Truncated Discrete Exponential) random variable.
As an instance of the rv_discrete class, boltzmann object inherits from it a collection of generic methods (see below for the full list), and completes them with details specific for this particular distribution.
Notes
The probability mass function for boltzmann is:
boltzmann.pmf(k) = (1-exp(-lambda_)*exp(-lambda_*k)/(1-exp(-lambda_*N))
for k = 0,..., N-1.
boltzmann takes lambda_ and N as shape parameters.
The probability mass function above is defined in the “standardized” form. To shift distribution use the loc parameter. Specifically, boltzmann.pmf(k, lambda_, N, loc) is identically equivalent to boltzmann.pmf(k - loc, lambda_, N).
Examples
>>> from scipy.stats import boltzmann >>> import matplotlib.pyplot as plt >>> fig, ax = plt.subplots(1, 1)
Calculate a few first moments:
>>> lambda_, N = 1.4, 19 >>> mean, var, skew, kurt = boltzmann.stats(lambda_, N, moments='mvsk')
Display the probability mass function (pmf):
>>> x = np.arange(boltzmann.ppf(0.01, lambda_, N), ... boltzmann.ppf(0.99, lambda_, N)) >>> ax.plot(x, boltzmann.pmf(x, lambda_, N), 'bo', ms=8, label='boltzmann pmf') >>> ax.vlines(x, 0, boltzmann.pmf(x, lambda_, N), colors='b', lw=5, alpha=0.5)
Alternatively, the distribution object can be called (as a function) to fix the shape and location. This returns a “frozen” RV object holding the given parameters fixed.
Freeze the distribution and display the frozen pmf:
>>> rv = boltzmann(lambda_, N) >>> ax.vlines(x, 0, rv.pmf(x), colors='k', linestyles='-', lw=1, ... label='frozen pmf') >>> ax.legend(loc='best', frameon=False) >>> plt.show()
Check accuracy of cdf and ppf:
>>> prob = boltzmann.cdf(x, lambda_, N) >>> np.allclose(x, boltzmann.ppf(prob, lambda_, N)) True
Generate random numbers:
>>> r = boltzmann.rvs(lambda_, N, size=1000)
Methods
rvs(lambda_, N, loc=0, size=1, random_state=None) Random variates. pmf(x, lambda_, N, loc=0) Probability mass function. logpmf(x, lambda_, N, loc=0) Log of the probability mass function. cdf(x, lambda_, N, loc=0) Cumulative density function. logcdf(x, lambda_, N, loc=0) Log of the cumulative density function. sf(x, lambda_, N, loc=0) Survival function (1 - cdf — sometimes more accurate). logsf(x, lambda_, N, loc=0) Log of the survival function. ppf(q, lambda_, N, loc=0) Percent point function (inverse of cdf — percentiles). isf(q, lambda_, N, loc=0) Inverse survival function (inverse of sf). stats(lambda_, N, loc=0, moments='mv') Mean(‘m’), variance(‘v’), skew(‘s’), and/or kurtosis(‘k’). entropy(lambda_, N, loc=0) (Differential) entropy of the RV. expect(func, lambda_, N, loc=0, lb=None, ub=None, conditional=False) Expected value of a function (of one argument) with respect to the distribution. median(lambda_, N, loc=0) Median of the distribution. mean(lambda_, N, loc=0) Mean of the distribution. var(lambda_, N, loc=0) Variance of the distribution. std(lambda_, N, loc=0) Standard deviation of the distribution. interval(alpha, lambda_, N, loc=0) Endpoints of the range that contains alpha percent of the distribution