numpy.random.geometric(p, size=None) Draw samples from the geometric distribution. Bernoulli trials are experiments with one of two outcomes: success or failure (an example of such an experiment is flipping a coin). The geometric distribution models the number of trials that must be run in order to achieve success. It is therefore supported on the positive integers, k = 1, 2, .... The probability mass function of the geometric distribution is where p is the probability of success of an i

numpy.random.gamma(shape, scale=1.0, size=None) Draw samples from a Gamma distribution. Samples are drawn from a Gamma distribution with specified parameters, shape (sometimes designated ?k?) and scale (sometimes designated ?theta?), where both parameters are > 0. Parameters: shape : scalar > 0 The shape of the gamma distribution. scale : scalar > 0, optional The scale of the gamma distribution. Default is equal to 1. size : int or tuple of ints, optional Output shape. If the

numpy.random.f(dfnum, dfden, size=None) Draw samples from an F distribution. Samples are drawn from an F distribution with specified parameters, dfnum (degrees of freedom in numerator) and dfden (degrees of freedom in denominator), where both parameters should be greater than zero. The random variate of the F distribution (also known as the Fisher distribution) is a continuous probability distribution that arises in ANOVA tests, and is the ratio of two chi-square variates. Parameters: dfnu

numpy.random.exponential(scale=1.0, size=None) Draw samples from an exponential distribution. Its probability density function is for x > 0 and 0 elsewhere. is the scale parameter, which is the inverse of the rate parameter . The rate parameter is an alternative, widely used parameterization of the exponential distribution [R218]. The exponential distribution is a continuous analogue of the geometric distribution. It describes many common situations, such as the size of raindrops mea

numpy.random.dirichlet(alpha, size=None) Draw samples from the Dirichlet distribution. Draw size samples of dimension k from a Dirichlet distribution. A Dirichlet-distributed random variable can be seen as a multivariate generalization of a Beta distribution. Dirichlet pdf is the conjugate prior of a multinomial in Bayesian inference. Parameters: alpha : array Parameter of the distribution (k dimension for sample of dimension k). size : int or tuple of ints, optional Output shape. If th

numpy.random.choice(a, size=None, replace=True, p=None) Generates a random sample from a given 1-D array New in version 1.7.0. Parameters: a : 1-D array-like or int If an ndarray, a random sample is generated from its elements. If an int, the random sample is generated as if a was np.arange(n) size : int or tuple of ints, optional Output shape. If the given shape is, e.g., (m, n, k), then m * n * k samples are drawn. Default is None, in which case a single value is returned. replac

numpy.random.chisquare(df, size=None) Draw samples from a chi-square distribution. When df independent random variables, each with standard normal distributions (mean 0, variance 1), are squared and summed, the resulting distribution is chi-square (see Notes). This distribution is often used in hypothesis testing. Parameters: df : int Number of degrees of freedom. size : int or tuple of ints, optional Output shape. If the given shape is, e.g., (m, n, k), then m * n * k samples are drawn

numpy.random.bytes(length) Return random bytes. Parameters: length : int Number of random bytes. Returns: out : str String of length length. Examples >>> np.random.bytes(10) ' eh\x85\x022SZ\xbf\xa4' #random

numpy.random.binomial(n, p, size=None) Draw samples from a binomial distribution. Samples are drawn from a binomial distribution with specified parameters, n trials and p probability of success where n an integer >= 0 and p is in the interval [0,1]. (n may be input as a float, but it is truncated to an integer in use) Parameters: n : float (but truncated to an integer) parameter, >= 0. p : float parameter, >= 0 and <=1. size : int or tuple of ints, optional Output shape. I

numpy.random.beta(a, b, size=None) Draw samples from a Beta distribution. The Beta distribution is a special case of the Dirichlet distribution, and is related to the Gamma distribution. It has the probability distribution function where the normalisation, B, is the beta function, It is often seen in Bayesian inference and order statistics. Parameters: a : float Alpha, non-negative. b : float Beta, non-negative. size : int or tuple of ints, optional Output shape. If the given sh