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# numpy.polynomial.laguerre.lagval2d

All Samples(6)  |  Call(6)  |  Derive(0)  |  Import(0)
Evaluate a 2-D Laguerre series at points (x, y).

This function returns the values:

.. math:: p(x,y) = \sum_{i,j} c_{i,j} * L_i(x) * L_j(y)

The parameters x and y are converted to arrays only if they are
tuples or a lists, otherwise they are treated as a scalars and they
must have the same shape after conversion. In either case, either x
and y or their elements must support multiplication and addition both(more...)


        def lagval2d(x, y, c):
"""
Evaluate a 2-D Laguerre series at points (x, y).

This function returns the values:

.. math:: p(x,y) = \\sum_{i,j} c_{i,j} * L_i(x) * L_j(y)

The parameters x and y are converted to arrays only if they are
tuples or a lists, otherwise they are treated as a scalars and they
must have the same shape after conversion. In either case, either x
and y or their elements must support multiplication and addition both
with themselves and with the elements of c.

If c is a 1-D array a one is implicitly appended to its shape to make
it 2-D. The shape of the result will be c.shape[2:] + x.shape.

Parameters
----------
x, y : array_like, compatible objects
The two dimensional series is evaluated at the points (x, y),
where x and y must have the same shape. If x or y is a list
or tuple, it is first converted to an ndarray, otherwise it is left
unchanged and if it isn't an ndarray it is treated as a scalar.
c : array_like
Array of coefficients ordered so that the coefficient of the term
of multi-degree i,j is contained in c[i,j]. If c has
dimension greater than two the remaining indices enumerate multiple
sets of coefficients.

Returns
-------
values : ndarray, compatible object
The values of the two dimensional polynomial at points formed with
pairs of corresponding values from x and y.

See Also
--------
lagval, laggrid2d, lagval3d, laggrid3d

Notes
-----

.. versionadded::1.7.0

"""
try:
x, y = np.array((x, y), copy=0)
except:
raise ValueError('x, y are incompatible')

c = lagval(x, c)
c = lagval(y, c, tensor=False)
return c


src/n/u/nupic-linux64-HEAD/lib64/python2.6/site-packages/numpy/polynomial/tests/test_laguerre.py   nupic-linux64(Download)
        #test values
tgt = y1*y2
res = lag.lagval2d(x1, x2, self.c2d)
assert_almost_equal(res, tgt)

#test shape
z = np.ones((2,3))
res = lag.lagval2d(z, z, self.c2d)

        c = np.random.random((2, 3))
van = lag.lagvander2d(x1, x2, [1, 2])
tgt = lag.lagval2d(x1, x2, c)
res = np.dot(van, c.flat)
assert_almost_equal(res, tgt)


src/n/u/numpy-1.8.1/numpy/polynomial/tests/test_laguerre.py   numpy(Download)
        #test values
tgt = y1*y2
res = lag.lagval2d(x1, x2, self.c2d)
assert_almost_equal(res, tgt)

#test shape
z = np.ones((2, 3))
res = lag.lagval2d(z, z, self.c2d)

        c = np.random.random((2, 3))
van = lag.lagvander2d(x1, x2, [1, 2])
tgt = lag.lagval2d(x1, x2, c)
res = np.dot(van, c.flat)
assert_almost_equal(res, tgt)