numpy.random.randn(d0, d1, ..., dn) Return a sample (or samples) from the ?standard normal? distribution. If positive, int_like or int-convertible arguments are provided, randn generates an array of shape (d0, d1, ..., dn), filled with random floats sampled from a univariate ?normal? (Gaussian) distribution of mean 0 and variance 1 (if any of the are floats, they are first converted to integers by truncation). A single float randomly sampled from the distribution is returned if no argument

numpy.random.randint(low, high=None, size=None, dtype='l') Return random integers from low (inclusive) to high (exclusive). Return random integers from the ?discrete uniform? distribution of the specified dtype in the ?half-open? interval [low, high). If high is None (the default), then results are from [0, low). Parameters: low : int Lowest (signed) integer to be drawn from the distribution (unless high=None, in which case this parameter is the highest such integer). high : int, optiona

numpy.random.rand(d0, d1, ..., dn) Random values in a given shape. Create an array of the given shape and populate it with random samples from a uniform distribution over [0, 1). Parameters: d0, d1, ..., dn : int, optional The dimensions of the returned array, should all be positive. If no argument is given a single Python float is returned. Returns: out : ndarray, shape (d0, d1, ..., dn) Random values. See also random Notes This is a convenience function. If you want an interface

numpy.random.power(a, size=None) Draws samples in [0, 1] from a power distribution with positive exponent a - 1. Also known as the power function distribution. Parameters: a : float parameter, > 0 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. Returns: samples : ndarray or scalar The returned samples lie in [0, 1]. Raises: ValueError If a &

numpy.random.poisson(lam=1.0, size=None) Draw samples from a Poisson distribution. The Poisson distribution is the limit of the binomial distribution for large N. Parameters: lam : float or sequence of float Expectation of interval, should be >= 0. A sequence of expectation intervals must be broadcastable over the requested size. 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

numpy.random.permutation(x) Randomly permute a sequence, or return a permuted range. If x is a multi-dimensional array, it is only shuffled along its first index. Parameters: x : int or array_like If x is an integer, randomly permute np.arange(x). If x is an array, make a copy and shuffle the elements randomly. Returns: out : ndarray Permuted sequence or array range. Examples >>> np.random.permutation(10) array([1, 7, 4, 3, 0, 9, 2, 5, 8, 6]) >>> np.random.permuta

numpy.random.pareto(a, size=None) Draw samples from a Pareto II or Lomax distribution with specified shape. The Lomax or Pareto II distribution is a shifted Pareto distribution. The classical Pareto distribution can be obtained from the Lomax distribution by adding 1 and multiplying by the scale parameter m (see Notes). The smallest value of the Lomax distribution is zero while for the classical Pareto distribution it is mu, where the standard Pareto distribution has location mu = 1. Lomax

numpy.random.normal(loc=0.0, scale=1.0, size=None) Draw random samples from a normal (Gaussian) distribution. The probability density function of the normal distribution, first derived by De Moivre and 200 years later by both Gauss and Laplace independently [R250], is often called the bell curve because of its characteristic shape (see the example below). The normal distributions occurs often in nature. For example, it describes the commonly occurring distribution of samples influenced by a

numpy.random.noncentral_f(dfnum, dfden, nonc, size=None) Draw samples from the noncentral 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 > 1. nonc is the non-centrality parameter. Parameters: dfnum : int Parameter, should be > 1. dfden : int Parameter, should be > 1. nonc : float Parameter, should be >= 0. size : int or tuple of

numpy.random.noncentral_chisquare(df, nonc, size=None) Draw samples from a noncentral chi-square distribution. The noncentral distribution is a generalisation of the distribution. Parameters: df : int Degrees of freedom, should be > 0 as of Numpy 1.10, should be > 1 for earlier versions. nonc : float Non-centrality, should be non-negative. 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 No