Laguerre.convert(domain=None, kind=None, window=None) [source] Convert series to a different kind and/or domain and/or window. Parameters: domain : array_like, optional The domain of the converted series. If the value is None, the default domain of kind is used. kind : class, optional The polynomial series type class to which the current instance should be converted. If kind is None, then the class of the current instance is used. window : array_like, optional The window of the conver

numpy.linalg.solve(a, b) [source] Solve a linear matrix equation, or system of linear scalar equations. Computes the ?exact? solution, x, of the well-determined, i.e., full rank, linear matrix equation ax = b. Parameters: a : (..., M, M) array_like Coefficient matrix. b : {(..., M,), (..., M, K)}, array_like Ordinate or ?dependent variable? values. Returns: x : {(..., M,), (..., M, K)} ndarray Solution to the system a x = b. Returned shape is identical to b. Raises: LinAlgError

class numpy.ndarray [source] An array object represents a multidimensional, homogeneous array of fixed-size items. An associated data-type object describes the format of each element in the array (its byte-order, how many bytes it occupies in memory, whether it is an integer, a floating point number, or something else, etc.) Arrays should be constructed using array, zeros or empty (refer to the See Also section below). The parameters given here refer to a low-level method (ndarray(...)) for

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.

Laguerre.mapparms() [source] Return the mapping parameters. The returned values define a linear map off + scl*x that is applied to the input arguments before the series is evaluated. The map depends on the domain and window; if the current domain is equal to the window the resulting map is the identity. If the coefficients of the series instance are to be used by themselves outside this class, then the linear function must be substituted for the x in the standard representation of the base

numpy.polynomial.laguerre.lagcompanion(c) [source] Return the companion matrix of c. The usual companion matrix of the Laguerre polynomials is already symmetric when c is a basis Laguerre polynomial, so no scaling is applied. Parameters: c : array_like 1-D array of Laguerre series coefficients ordered from low to high degree. Returns: mat : ndarray Companion matrix of dimensions (deg, deg). Notes

matrix.argmin(axis=None, out=None) [source] Indexes of the minimum values along an axis. Return the indexes of the first occurrences of the minimum values along the specified axis. If axis is None, the index is for the flattened matrix. Parameters: See `numpy.argmin` for complete descriptions. See also numpy.argmin Notes This is the same as ndarray.argmin, but returns a matrix object where ndarray.argmin would return an ndarray. Examples >>> x = -np.matrix(np.arange(12).reshape((

numpy.polynomial.laguerre.lagx = array([ 1, -1])

numpy.less(x1, x2[, out]) = Return the truth value of (x1 < x2) element-wise. Parameters: x1, x2 : array_like Input arrays. If x1.shape != x2.shape, they must be broadcastable to a common shape (which may be the shape of one or the other). Returns: out : bool or ndarray of bool Array of bools, or a single bool if x1 and x2 are scalars. See also greater, less_equal, greater_equal, equal, not_equal Examples >>> np.less([1, 2], [2, 2]) array([ True, False], dtype=bool)

numpy.matlib.repmat(a, m, n) [source] Repeat a 0-D to 2-D array or matrix MxN times. Parameters: a : array_like The array or matrix to be repeated. m, n : int The number of times a is repeated along the first and second axes. Returns: out : ndarray The result of repeating a. Examples >>> import numpy.matlib >>> a0 = np.array(1) >>> np.matlib.repmat(a0, 2, 3) array([[1, 1, 1], [1, 1, 1]]) >>> a1 = np.arange(4) >>> np.matlib.repma