def compute_d1(self):
# To stay consistent with numpy, we must upgrade 1D arrays to 2D
ar = row(self.a.r) if len(self.a.r.shape)<2 else self.a.r.reshape((-1, self.a.r.shape[-1]))
br = col(self.b.r) if len(self.b.r.shape)<2 else self.b.r.reshape((self.b.r.shape[0], -1))
if ar.ndim <= 2:
return sp.kron(sp.eye(ar.shape[0], ar.shape[0]),br.T)
else:
raise NotImplementedError
def test_maximum(self):
from blender_utils import row, col
from ch import maximum
# Make sure that when we compare the max of two *identical* numbers,
# we get the right derivatives wrt both
the_max = maximum(ch.Ch(1), ch.Ch(1))
self.assertTrue(the_max.r.ravel()[0] == 1.)
self.assertTrue(the_max.dr_wrt(the_max.a)[0, 0] == 1.)
self.assertTrue(the_max.dr_wrt(the_max.b)[0, 0] == 1.)
# Now test given that all numbers are different, by allocating from
# a pool of randomly permuted numbers.
# We test combinations of scalars and 2d arrays.
rnd = np.asarray(np.random.permutation(np.arange(20)), np.float64)
c1 = ch.Ch(rnd[:6].reshape((2, 3)))
c2 = ch.Ch(rnd[6:12].reshape((2, 3)))
s1 = ch.Ch(rnd[12])
s2 = ch.Ch(rnd[13])
eps = .1
for first in [c1, s1]:
for second in [c2, s2]:
the_max = maximum(first, second)
for which_to_change in [first, second]:
max_r0 = the_max.r.copy()
max_r_diff = np.max(
np.abs(max_r0 - np.maximum(first.r, second.r)))
self.assertTrue(max_r_diff == 0)
max_dr = the_max.dr_wrt(which_to_change).copy()
which_to_change.x = which_to_change.x + eps
max_r1 = the_max.r.copy()
emp_diff = (the_max.r - max_r0).ravel()
pred_diff = max_dr.dot(col(
eps * np.ones(max_dr.shape[1]))).ravel()
#print 'comparing the following numbers/vectors:'
#print first.r
#print second.r
#print 'empirical vs predicted difference:'
#print emp_diff
#print pred_diff
#print '-----'
max_dr_diff = np.max(np.abs(emp_diff - pred_diff))
#print 'max dr diff: %.2e' % (max_dr_diff,)
self.assertTrue(max_dr_diff < 1e-14)
def test_transpose(self):
from blender_utils import row, col
from copy import deepcopy
for which in ('C', 'F'): # test in fortran and contiguous mode
a = ch.Ch(
np.require(np.zeros(8).reshape((4, 2)), requirements=which))
b = a.T
b1 = b.r.copy()
#dr = b.dr_wrt(a).copy()
dr = deepcopy(b.dr_wrt(a))
diff = np.arange(a.size).reshape(a.shape)
a.x = np.require(a.r + diff, requirements=which)
b2 = b.r.copy()
diff_pred = dr.dot(col(diff)).ravel()
diff_emp = (b2 - b1).ravel()
np.testing.assert_array_equal(diff_pred, diff_emp)
def set_and_get_r(self, x_in):
self.x = Ch(x_in)
return col(self.r)
