scipy.stats.randint¶
- scipy.stats.randint = <scipy.stats.distributions.randint_gen object at 0x4ddffd0>[source]¶
A uniform discrete random variable.
Discrete random variables are defined from a standard form and may require some shape parameters to complete its specification. Any optional keyword parameters can be passed to the methods of the RV object as given below:
Parameters : x : array_like
quantiles
q : array_like
lower or upper tail probability
low, high : array_like
shape parameters
loc : array_like, optional
location parameter (default=0)
size : int or tuple of ints, optional
shape of random variates (default computed from input arguments )
moments : str, optional
composed of letters [‘mvsk’] specifying which moments to compute where ‘m’ = mean, ‘v’ = variance, ‘s’ = (Fisher’s) skew and ‘k’ = (Fisher’s) kurtosis. (default=’mv’)
Alternatively, the object may be called (as a function) to fix the shape and
location parameters returning a “frozen” discrete RV object:
rv = randint(low, high, loc=0)
- Frozen RV object with the same methods but holding the given shape and location fixed.
Notes
The probability mass function for randint is:
randint.pmf(k) = 1./(high - low)
for k = low, ..., high - 1.
randint takes low and high as shape parameters.
Note the difference to the numpy random_integers which returns integers on a closed interval [low, high].
Examples
>>> from scipy.stats import randint >>> [ low, high ] = [<Replace with reasonable values>] >>> rv = randint(low, high)
Display frozen pmf
>>> x = np.arange(0, np.minimum(rv.dist.b, 3)) >>> h = plt.vlines(x, 0, rv.pmf(x), lw=2)
Here, rv.dist.b is the right endpoint of the support of rv.dist.
Check accuracy of cdf and ppf
>>> prb = randint.cdf(x, low, high) >>> h = plt.semilogy(np.abs(x - randint.ppf(prb, low, high)) + 1e-20)
Random number generation
>>> R = randint.rvs(low, high, size=100)
Methods
rvs(low, high, loc=0, size=1) Random variates. pmf(x, low, high, loc=0) Probability mass function. logpmf(x, low, high, loc=0) Log of the probability mass function. cdf(x, low, high, loc=0) Cumulative density function. logcdf(x, low, high, loc=0) Log of the cumulative density function. sf(x, low, high, loc=0) Survival function (1-cdf — sometimes more accurate). logsf(x, low, high, loc=0) Log of the survival function. ppf(q, low, high, loc=0) Percent point function (inverse of cdf — percentiles). isf(q, low, high, loc=0) Inverse survival function (inverse of sf). stats(low, high, loc=0, moments=’mv’) Mean(‘m’), variance(‘v’), skew(‘s’), and/or kurtosis(‘k’). entropy(low, high, loc=0) (Differential) entropy of the RV. expect(func, low, high, loc=0, lb=None, ub=None, conditional=False) Expected value of a function (of one argument) with respect to the distribution. median(low, high, loc=0) Median of the distribution. mean(low, high, loc=0) Mean of the distribution. var(low, high, loc=0) Variance of the distribution. std(low, high, loc=0) Standard deviation of the distribution. interval(alpha, low, high, loc=0) Endpoints of the range that contains alpha percent of the distribution