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numpy.polynomial.polynomial.polyval(x, c, tensor=True)
[source] -
Evaluate a polynomial at points x.
If
c
is of lengthn + 1
, this function returns the valueThe 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, eitherx
or its elements must support multiplication and addition both with themselves and with the elements ofc
.If
c
is a 1-D array, thenp(x)
will have the same shape asx
. Ifc
is multidimensional, then the shape of the result depends on the value oftensor
. Iftensor
is true the shape will be c.shape[1:] + x.shape. Iftensor
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.
Parameters: x : array_like, compatible object
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 ofc
.c : array_like
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 ofc
.tensor : boolean, optional
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 inc
is evaluated for every element ofx
. If False,x
is broadcast over the columns ofc
for the evaluation. This keyword is useful whenc
is multidimensional. The default value is True.New in version 1.7.0.
Returns: values : ndarray, compatible object
The shape of the returned array is described above.
See also
Notes
The evaluation uses Horner?s method.
Examples
>>> from numpy.polynomial.polynomial import polyval >>> polyval(1, [1,2,3]) 6.0 >>> a = np.arange(4).reshape(2,2) >>> a array([[0, 1], [2, 3]]) >>> polyval(a, [1,2,3]) array([[ 1., 6.], [ 17., 34.]]) >>> coef = np.arange(4).reshape(2,2) # multidimensional coefficients >>> coef array([[0, 1], [2, 3]]) >>> polyval([1,2], coef, tensor=True) array([[ 2., 4.], [ 4., 7.]]) >>> polyval([1,2], coef, tensor=False) array([ 2., 7.])
numpy.polynomial.polynomial.polyval()
2017-01-10 18:17:49
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