def test_predicted_svals():
'''
The predicted_svals function shows the predicted singular values.
'''
t1 = MockTensor()
t1.svals = range(5, 0, -1)
t2 = MockTensor()
t2.svals = range(10, 0, -2)
# Weighting one side heavily should make its svals uniquely show up.
weight = 0.999999
b = Blend([t1, t2], weights=[weight, 1 - weight], k_values=1)
# with origin tracking:
svals = b.predicted_svals(num=5, for_each_tensor=5, track_origin=True)
for expected, (actual, src) in zip(t1.svals, svals):
assert_almost_equal(actual / weight, expected)
eq_(src, 0)
# without origin tracking
svals = b.predicted_svals(num=5, for_each_tensor=5)
for expected, actual in zip(t1.svals, svals):
assert_almost_equal(actual / weight, expected)
# Flip it around.
b.weights = [1 - weight, weight]
# Note: this is an easy way to transpose the "matrix"
sval, src = zip(
*b.predicted_svals(num=5, for_each_tensor=5, track_origin=True))
for actual, expected in zip(sval, t2.svals):
assert_almost_equal(actual / weight, expected)
eq_(src, (1, ) * 5)
def test_predicted_svals():
'''
The predicted_svals function shows the predicted singular values.
'''
t1 = MockTensor()
t1.svals = range(5, 0, -1)
t2 = MockTensor()
t2.svals = range(10, 0, -2)
# Weighting one side heavily should make its svals uniquely show up.
weight = 0.999999
b = Blend([t1, t2], weights=[weight, 1-weight], k_values=1)
# with origin tracking:
svals = b.predicted_svals(num=5, for_each_tensor=5, track_origin=True)
for expected, (actual, src) in zip(t1.svals, svals):
assert_almost_equal(actual/weight, expected)
eq_(src, 0)
# without origin tracking
svals = b.predicted_svals(num=5, for_each_tensor=5)
for expected, actual in zip(t1.svals, svals):
assert_almost_equal(actual/weight, expected)
# Flip it around.
b.weights = [1-weight, weight]
# Note: this is an easy way to transpose the "matrix"
sval, src = zip(*b.predicted_svals(num=5, for_each_tensor=5, track_origin=True))
for actual, expected in zip(sval, t2.svals):
assert_almost_equal(actual/weight, expected)
eq_(src, (1,)*5)
