hermeval(x, c, tensor=True)
If c
is of length :None:None:`n + 1`
, this function returns the value:
The parameter x
is converted to an array only if it is a tuple or a list, otherwise it is treated as a scalar. In either case, either x
or its elements must support multiplication and addition both with themselves and with the elements of c
.
If c
is a 1-D array, then :None:None:`p(x)`
will have the same shape as x
. If c
is multidimensional, then the shape of the result depends on the value of :None:None:`tensor`
. If :None:None:`tensor`
is true the shape will be c.shape[1:] + x.shape. If :None:None:`tensor`
is false the shape will be c.shape[1:]. Note that scalars have shape (,).
Trailing zeros in the coefficients will be used in the evaluation, so they should be avoided if efficiency is a concern.
The evaluation uses Clenshaw recursion, aka synthetic division.
If x
is a list or tuple, it is converted to an ndarray, otherwise it is left unchanged and treated as a scalar. In either case, x
or its elements must support addition and multiplication with with themselves and with the elements of c
.
Array of coefficients ordered so that the coefficients for terms of degree n are contained in c[n]. If c
is multidimensional the remaining indices enumerate multiple polynomials. In the two dimensional case the coefficients may be thought of as stored in the columns of c
.
If True, the shape of the coefficient array is extended with ones on the right, one for each dimension of x
. Scalars have dimension 0 for this action. The result is that every column of coefficients in c
is evaluated for every element of x
. If False, x
is broadcast over the columns of c
for the evaluation. This keyword is useful when c
is multidimensional. The default value is True.
The shape of the return value is described above.
Evaluate an HermiteE series at points x.
>>> from numpy.polynomial.hermite_e import hermeval
... coef = [1,2,3]
... hermeval(1, coef) 3.0
>>> hermeval([[1,2],[3,4]], coef) array([[ 3., 14.], [31., 54.]])See :
The following pages refer to to this document either explicitly or contain code examples using this.
numpy.polynomial.hermite_e.hermeval2d
numpy.polynomial.hermite_e.hermegrid2d
numpy.polynomial.hermite_e.hermefit
numpy.polynomial.hermite_e.hermegrid3d
numpy.polynomial.hermite_e.hermeval3d
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