def test_BlockMatrix_Inverse_execution():
k, n = 2, 4
dtype = 'float32'
A = diofant.MatrixSymbol('A', n, k)
B = diofant.MatrixSymbol('B', n, n)
inputs = A, B
output = B.I * A
cutsizes = {
A: [(n // 2, n // 2), (k // 2, k // 2)],
B: [(n // 2, n // 2), (n // 2, n // 2)]
}
cutinputs = [diofant.blockcut(i, *cutsizes[i]) for i in inputs]
cutoutput = output.subs(dict(zip(inputs, cutinputs)))
dtypes = dict(zip(inputs, [dtype] * len(inputs)))
f = theano_function(inputs, [output], dtypes=dtypes, cache={})
fblocked = theano_function(inputs, [diofant.block_collapse(cutoutput)],
dtypes=dtypes,
cache={})
ninputs = [np.random.rand(*x.shape).astype(dtype) for x in inputs]
ninputs = [
np.arange(n * k).reshape(A.shape).astype(dtype),
np.eye(n).astype(dtype)
]
ninputs[1] += np.ones(B.shape) * 1e-5
assert np.allclose(f(*ninputs), fblocked(*ninputs), rtol=1e-5)
def test_theano_function_kwargs():
import numpy as np
f = theano_function([x, y, z], [x + y],
dim=1,
on_unused_input='ignore',
dtypes={
x: 'float64',
y: 'float64',
z: 'float64'
})
assert np.linalg.norm(f([1, 2], [3, 4], [0, 0]) -
np.asarray([4, 6])) < 1e-9
f = theano_function([x, y, z], [x + y],
dtypes={
x: 'float64',
y: 'float64',
z: 'float64'
},
dim=1,
on_unused_input='ignore')
xx = np.arange(3).astype('float64')
yy = 2 * np.arange(3).astype('float64')
zz = 2 * np.arange(3).astype('float64')
assert np.linalg.norm(f(xx, yy, zz) - 3 * np.arange(3)) < 1e-9
def test_theano_function_numpy():
f = theano_function([x, y], [x + y],
dim=1,
dtypes={
x: 'float64',
y: 'float64'
})
assert np.linalg.norm(f([1, 2], [3, 4]) - np.asarray([4, 6])) < 1e-9
f = theano_function([x, y], [x + y],
dtypes={
x: 'float64',
y: 'float64'
},
dim=1)
xx = np.arange(3).astype('float64')
yy = 2 * np.arange(3).astype('float64')
assert np.linalg.norm(f(xx, yy) - 3 * np.arange(3)) < 1e-9
def test_bad_keyword_args_raise_error():
pytest.raises(Exception, lambda: theano_function([x], [x + 1], foobar=3))
def test_theano_function_simple():
f = theano_function([x, y], [x + y])
assert f(2, 3) == 5
