RandomState.multinomial(n, pvals, size=None) Draw samples from a multinomial distribution. The multinomial distribution is a multivariate generalisation of the binomial distribution. Take an experiment with one of p possible outcomes. An example of such an experiment is throwing a dice, where the outcome can be 1 through 6. Each sample drawn from the distribution represents n such experiments. Its values, X_i = [X_0, X_1, ..., X_p], represent the number of times the outcome was i. Parameter

dtype.descr Array-interface compliant full description of the data-type. The format is that required by the ?descr? key in the __array_interface__ attribute.

numpy.ma.outerproduct(a, b) [source] Compute the outer product of two vectors. Given two vectors, a = [a0, a1, ..., aM] and b = [b0, b1, ..., bN], the outer product [R51] is: [[a0*b0 a0*b1 ... a0*bN ] [a1*b0 . [ ... . [aM*b0 aM*bN ]] Parameters: a : (M,) array_like First input vector. Input is flattened if not already 1-dimensional. b : (N,) array_like Second input vector. Input is flattened if not already 1-dimensional. out : (M, N) ndarray, optional A loc

numpy.ma.dump(a, F) [source] Pickle a masked array to a file. This is a wrapper around cPickle.dump. Parameters: a : MaskedArray The array to be pickled. F : str or file-like object The file to pickle a to. If a string, the full path to the file.

classmethod Laguerre.basis(deg, domain=None, window=None) [source] Series basis polynomial of degree deg. Returns the series representing the basis polynomial of degree deg. New in version 1.7.0. Parameters: deg : int Degree of the basis polynomial for the series. Must be >= 0. domain : {None, array_like}, optional If given, the array must be of the form [beg, end], where beg and end are the endpoints of the domain. If None is given then the class domain is used. The default is Non

record.view() Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also The

matrix.getI() [source] Returns the (multiplicative) inverse of invertible self. Parameters: None Returns: ret : matrix object If self is non-singular, ret is such that ret * self == self * ret == np.matrix(np.eye(self[0,:].size) all return True. Raises: numpy.linalg.LinAlgError: Singular matrix If self is singular. See also linalg.inv Examples >>> m = np.matrix('[1, 2; 3, 4]'); m matrix([[1, 2], [3, 4]]) >>> m.getI() matrix([[-2. , 1. ], [ 1.5,

record.choose() Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also The

MaskedArray.__imod__ x.__imod__(y) <==> x%=y

ndarray.conjugate() Return the complex conjugate, element-wise. Refer to numpy.conjugate for full documentation. See also numpy.conjugate equivalent function