numpy.polynomial.polynomial.polyval(x, c, tensor=True)
Evaluate a polynomial at points x.
c is of length
n + 1, this function returns the value
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 is a 1-D array, then
p(x) will have the same shape as
c is multidimensional, then the shape of the result depends on the value of
tensor is true the shape will be c.shape[1:] + x.shape. If
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.
>>> 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.])
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