polyval(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 Horner's method.
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 returned array is described above.
Evaluate a polynomial at points x.
>>> 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.])See :
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