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_BlockMatrix():
n = diofant.Symbol('n', integer=True)
A = diofant.MatrixSymbol('A', n, n)
B = diofant.MatrixSymbol('B', n, n)
C = diofant.MatrixSymbol('C', n, n)
D = diofant.MatrixSymbol('D', n, n)
At, Bt, Ct, Dt = map(theano_code, (A, B, C, D))
Block = diofant.BlockMatrix([[A, B], [C, D]])
Blockt = theano_code(Block)
solutions = [
tt.join(0, tt.join(1, At, Bt), tt.join(1, Ct, Dt)),
tt.join(1, tt.join(0, At, Ct), tt.join(0, Bt, Dt))
]
assert any(theq(Blockt, solution) for solution in solutions)
def test_MatrixSlice_2():
n = diofant.Symbol('n', integer=True)
X = diofant.MatrixSymbol('X', n, n)
Y = X[1:2:3, 4:5:6]
Yt = theano_code(Y)
assert tuple(Yt.owner.op.idx_list) == (slice(1, 2, 3), slice(4, 5, 6))
def test_MatrixSlice():
n = diofant.Symbol('n', integer=True)
X = diofant.MatrixSymbol('X', n, n)
Y = X[1:2:3, 4:5:6]
Yt = theano_code(Y)
# assert tuple(Yt.owner.op.idx_list) == (slice(1,2,3), slice(4,5,6))
assert Yt.owner.inputs[0] == theano_code(X)
k = diofant.Symbol('k')
kt = theano_code(k, dtypes={k: 'int32'})
start, stop, step = 4, k, 2
Y = X[start:stop:step]
Yt = theano_code(Y, dtypes={n: 'int32', k: 'int32'})
def test_MatAdd():
X = diofant.MatrixSymbol('X', 4, 4)
Y = diofant.MatrixSymbol('X', 4, 4)
Z = diofant.MatrixSymbol('X', 4, 4)
expr = X + Y + Z
assert isinstance(theano_code(expr).owner.op, tt.Elemwise)
def test_Transpose():
X = diofant.MatrixSymbol('X', 4, 4)
assert isinstance(theano_code(X.T).owner.op, tt.DimShuffle)
def test_MatMul():
X = diofant.MatrixSymbol('X', 4, 4)
Y = diofant.MatrixSymbol('X', 4, 4)
Z = diofant.MatrixSymbol('X', 4, 4)
expr = X * Y * Z
assert isinstance(theano_code(expr).owner.op, tt.Dot)
def test_MatrixSymbol():
X = diofant.MatrixSymbol('X', 4, 5)
Xt = theano_code(X)
assert isinstance(Xt, tt.TensorVariable)
assert Xt.broadcastable == (False, False)