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

matrix.searchsorted(v, side='left', sorter=None) Find indices where elements of v should be inserted in a to maintain order. For full documentation, see numpy.searchsorted See also numpy.searchsorted equivalent function

numpy.random.random(size=None) Return random floats in the half-open interval [0.0, 1.0). Results are from the ?continuous uniform? distribution over the stated interval. To sample multiply the output of random_sample by (b-a) and add a: (b - a) * random_sample() + a Parameters: 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: out : float or

RandomState.zipf(a, size=None) Draw samples from a Zipf distribution. Samples are drawn from a Zipf distribution with specified parameter a > 1. The Zipf distribution (also known as the zeta distribution) is a continuous probability distribution that satisfies Zipf?s law: the frequency of an item is inversely proportional to its rank in a frequency table. Parameters: a : float > 1 Distribution parameter. size : int or tuple of ints, optional Output shape. If the given shape is, e.

ndarray.__ge__ x.__ge__(y) <==> x>=y

Hermite.copy() [source] Return a copy. Returns: new_series : series Copy of self.

numpy.polynomial.chebyshev.chebmul(c1, c2) [source] Multiply one Chebyshev series by another. Returns the product of two Chebyshev series c1 * c2. The arguments are sequences of coefficients, from lowest order ?term? to highest, e.g., [1,2,3] represents the series T_0 + 2*T_1 + 3*T_2. Parameters: c1, c2 : array_like 1-D arrays of Chebyshev series coefficients ordered from low to high. Returns: out : ndarray Of Chebyshev series coefficients representing their product. See also cheba

numpy.polynomial.hermite_e.hermevander(x, deg) [source] Pseudo-Vandermonde matrix of given degree. Returns the pseudo-Vandermonde matrix of degree deg and sample points x. The pseudo-Vandermonde matrix is defined by where 0 <= i <= deg. The leading indices of V index the elements of x and the last index is the degree of the HermiteE polynomial. If c is a 1-D array of coefficients of length n + 1 and V is the array V = hermevander(x, n), then np.dot(V, c) and hermeval(x, c) are the

numpy.fv(rate, nper, pmt, pv, when='end') [source] Compute the future value. Given: a present value, pv an interest rate compounded once per period, of which there are nper total a (fixed) payment, pmt, paid either at the beginning (when = {?begin?, 1}) or the end (when = {?end?, 0}) of each period Return: the value at the end of the nper periods Parameters: rate : scalar or array_like of shape(M, ) Rate of interest as decimal (not per cent) per period nper : scalar or array_like of

numpy.polynomial.chebyshev.chebsub(c1, c2) [source] Subtract one Chebyshev series from another. Returns the difference of two Chebyshev series c1 - c2. The sequences of coefficients are from lowest order term to highest, i.e., [1,2,3] represents the series T_0 + 2*T_1 + 3*T_2. Parameters: c1, c2 : array_like 1-D arrays of Chebyshev series coefficients ordered from low to high. Returns: out : ndarray Of Chebyshev series coefficients representing their difference. See also chebadd, c