def test_problem_absorb(self):
"""Test problem object with absorption.
"""
X = Variable((4, 2))
B = np.reshape(np.arange(8, dtype=np.float32), (4, 2), order='F')
# Absorbing lin ops.
prox_fns = sum_squares(-2 * X, b=B) + norm1(5 * mul_elemwise(B, X))
prob = Problem(prox_fns)
prob.set_absorb(True)
prob.solve(solver="admm", eps_rel=1e-6, eps_abs=1e-6)
cvx_X = cvx.Variable((4, 2))
cost = cvx.sum_squares(-2 * cvx_X - B) + cvx.norm(
5 * cvx.multiply(B, cvx_X), 1)
cvx_prob = cvx.Problem(cvx.Minimize(cost))
cvx_prob.solve(solver=cvx.SCS)
self.assertItemsAlmostEqual(X.value, cvx_X.value, places=2)
prob.set_absorb(False)
prob.solve(solver="admm", eps_rel=1e-6, eps_abs=1e-6)
self.assertItemsAlmostEqual(X.value, cvx_X.value, places=2)
# Constant offsets.
prox_fns = sum_squares(-2 * X - B) + norm1(5 * mul_elemwise(B, X))
prob = Problem(prox_fns)
prob.solve(solver="admm", eps_rel=1e-6, eps_abs=1e-6)
self.assertItemsAlmostEqual(X.value, cvx_X.value, places=2)
def test_mul_elemwise(self):
"""Test mul_elemwise lin op.
"""
# Forward.
var = Variable((2, 5))
W = np.arange(10)
W = np.reshape(W, (2, 5))
fn = mul_elemwise(W, var)
x = W.copy()
out = np.zeros(x.shape)
fn.forward([x], [out])
self.assertItemsAlmostEqual(out, W * W)
# Adjoint.
x = W.copy()
out = np.zeros(x.shape)
fn.adjoint([x], [out])
self.assertItemsAlmostEqual(out, W * W)
# Diagonal form.
x = Variable(5)
fn = mul_elemwise(np.arange(5) - 3, x)
assert not fn.is_diag(freq=True)
assert fn.is_diag(freq=False)
self.assertItemsAlmostEqual(
fn.get_diag(freq=False)[x],
np.arange(5) - 3)
def test_mul_elemwise(self):
"""Test mul_elemwise lin op.
"""
# Forward.
var = Variable((2, 5))
W = np.arange(10)
W = np.reshape(W, (2, 5))
fn = mul_elemwise(W, var)
x = W.copy()
out = np.zeros(x.shape)
fn.forward([x], [out])
self.assertItemsAlmostEqual(out, W * W)
# Adjoint.
x = W.copy()
out = np.zeros(x.shape)
fn.adjoint([x], [out])
self.assertItemsAlmostEqual(out, W * W)
# Diagonal form.
x = Variable(5)
fn = mul_elemwise(np.arange(5) - 3, x)
assert not fn.is_diag(freq=True)
assert fn.is_diag(freq=False)
self.assertItemsAlmostEqual(fn.get_diag(freq=False)[x],
np.arange(5) - 3)
def test_problem(self):
"""Test problem object.
"""
X = Variable((4, 2))
B = np.reshape(np.arange(8), (4, 2)) * 1.
prox_fns = [norm1(X), sum_squares(X, b=B)]
prob = Problem(prox_fns)
# prob.partition(quad_funcs = [prox_fns[0], prox_fns[1]])
prob.set_automatic_frequency_split(False)
prob.set_absorb(False)
prob.set_implementation(Impl['halide'])
prob.set_solver('admm')
prob.solve()
true_X = norm1(X).prox(2, B.copy())
self.assertItemsAlmostEqual(X.value, true_X, places=2)
prob.solve(solver="pc")
self.assertItemsAlmostEqual(X.value, true_X, places=2)
prob.solve(solver="hqs", eps_rel=1e-6,
rho_0=1.0, rho_scale=np.sqrt(2.0) * 2.0, rho_max=2**16,
max_iters=20, max_inner_iters=500, verbose=False)
self.assertItemsAlmostEqual(X.value, true_X, places=2)
# CG
prob = Problem(prox_fns)
prob.set_lin_solver("cg")
prob.solve(solver="admm")
self.assertItemsAlmostEqual(X.value, true_X, places=2)
prob.solve(solver="hqs", eps_rel=1e-6,
rho_0=1.0, rho_scale=np.sqrt(2.0) * 2.0, rho_max=2**16,
max_iters=20, max_inner_iters=500, verbose=False)
self.assertItemsAlmostEqual(X.value, true_X, places=2)
# Quad funcs.
prob = Problem(prox_fns)
prob.solve(solver="admm")
self.assertItemsAlmostEqual(X.value, true_X, places=2)
# Absorbing lin ops.
prox_fns = [norm1(5 * mul_elemwise(B, X)), sum_squares(-2 * X, b=B)]
prob = Problem(prox_fns)
prob.set_absorb(True)
prob.solve(solver="admm", eps_rel=1e-6, eps_abs=1e-6)
cvx_X = cvx.Variable(4, 2)
cost = cvx.sum_squares(-2 * cvx_X - B) + cvx.norm(5 * cvx.mul_elemwise(B, cvx_X), 1)
cvx_prob = cvx.Problem(cvx.Minimize(cost))
cvx_prob.solve(solver=cvx.SCS)
self.assertItemsAlmostEqual(X.value, cvx_X.value, places=2)
prob.set_absorb(False)
prob.solve(solver="admm", eps_rel=1e-6, eps_abs=1e-6)
self.assertItemsAlmostEqual(X.value, cvx_X.value, places=2)
# Constant offsets.
prox_fns = [norm1(5 * mul_elemwise(B, X)), sum_squares(-2 * X - B)]
prob = Problem(prox_fns)
prob.solve(solver="admm", eps_rel=1e-6, eps_abs=1e-6)
self.assertItemsAlmostEqual(X.value, cvx_X.value, places=2)
def absorb_params(self):
"""Returns an equivalent sum_squares with alpha = 1.0,
gamma = 0, and c = 0.
"""
new_lin_op = mul_elemwise(self.weight, self.lin_op)
new_b = mul_elemwise(self.weight, self.b).value
return sum_squares(new_lin_op, alpha=self.alpha,
beta=self.beta, b=new_b, c=self.c, gamma=self.gamma).absorb_params()
def absorb_params(self):
"""Returns an equivalent sum_squares with alpha = 1.0,
gamma = 0, and c = 0.
"""
new_lin_op = mul_elemwise(self.weight, self.lin_op)
new_b = mul_elemwise(self.weight, self.b).value
return sum_squares(new_lin_op, alpha=self.alpha,
beta=self.beta, b=new_b, c=self.c, gamma=self.gamma).absorb_params()
def test_sum(self):
# Forward.
x = Variable((2, 3))
y = Variable((2, 3))
fn = sum([x, y])
x_val = np.reshape(np.arange(6) * 1.0, x.shape)
y_val = np.reshape(np.arange(6) * 1.0 - 5, x.shape)
out = np.zeros(fn.shape)
fn.forward([x_val, y_val], [out])
self.assertItemsAlmostEqual(out, 2 * np.arange(6) - 5)
# Adjoint.
x_val = np.reshape(np.arange(6) * 1.0, x.shape)
out = [np.zeros(fn.shape), np.zeros(fn.shape)]
fn.adjoint([x_val], out)
for arr in out:
self.assertItemsAlmostEqual(arr, x_val)
# Constant args.
x = Variable((2, 3))
y = Variable((2, 3))
x_val = np.reshape(np.arange(6) * 1.0, x.shape)
y_val = np.reshape(np.arange(6) * 1.0 - 5, x.shape)
fn = sum([x_val, y_val])
self.assertItemsAlmostEqual(fn.value, 2 * np.arange(6) - 5)
# Diagonal form.
x = Variable(5)
term = mul_elemwise(np.arange(5) - 3, x)
fn = sum([term, x])
assert not fn.is_diag(freq=True)
assert fn.is_diag(freq=False)
self.assertItemsAlmostEqual(
fn.get_diag(freq=False)[x],
np.arange(5) - 3 + np.ones(5))
def test_sum(self):
# Forward.
x = Variable((2, 3))
y = Variable((2, 3))
fn = sum([x, y])
x_val = np.reshape(np.arange(6) * 1.0, x.shape)
y_val = np.reshape(np.arange(6) * 1.0 - 5, x.shape)
out = np.zeros(fn.shape)
fn.forward([x_val, y_val], [out])
self.assertItemsAlmostEqual(out, 2 * np.arange(6) - 5)
# Adjoint.
x_val = np.reshape(np.arange(6) * 1.0, x.shape)
out = [np.zeros(fn.shape), np.zeros(fn.shape)]
fn.adjoint([x_val], out)
for arr in out:
self.assertItemsAlmostEqual(arr, x_val)
# Constant args.
x = Variable((2, 3))
y = Variable((2, 3))
x_val = np.reshape(np.arange(6) * 1.0, x.shape)
y_val = np.reshape(np.arange(6) * 1.0 - 5, x.shape)
fn = sum([x_val, y_val])
self.assertItemsAlmostEqual(fn.value, 2 * np.arange(6) - 5)
# Diagonal form.
x = Variable(5)
term = mul_elemwise(np.arange(5) - 3, x)
fn = sum([term, x])
assert not fn.is_diag(freq=True)
assert fn.is_diag(freq=False)
self.assertItemsAlmostEqual(fn.get_diag(freq=False)[x],
np.arange(5) - 3 + np.ones(5))
def test_problem(self):
"""Test problem object.
"""
X = Variable((4, 2))
B = np.reshape(np.arange(8), (4, 2)) * 1.
prox_fns = [norm1(X), sum_squares(X, b=B)]
prob = Problem(prox_fns)
# prob.partition(quad_funcs = [prox_fns[0], prox_fns[1]])
prob.set_automatic_frequency_split(False)
prob.set_absorb(False)
prob.set_implementation(Impl['halide'])
prob.set_solver('admm')
prob.solve()
true_X = norm1(X).prox(2, B.copy())
self.assertItemsAlmostEqual(X.value, true_X, places=2)
prob.solve(solver="pc")
self.assertItemsAlmostEqual(X.value, true_X, places=2)
prob.solve(solver="hqs",
eps_rel=1e-6,
rho_0=1.0,
rho_scale=np.sqrt(2.0) * 2.0,
rho_max=2**16,
max_iters=20,
max_inner_iters=500,
verbose=False)
self.assertItemsAlmostEqual(X.value, true_X, places=2)
# CG
prob = Problem(prox_fns)
prob.set_lin_solver("cg")
prob.solve(solver="admm")
self.assertItemsAlmostEqual(X.value, true_X, places=2)
prob.solve(solver="hqs",
eps_rel=1e-6,
rho_0=1.0,
rho_scale=np.sqrt(2.0) * 2.0,
rho_max=2**16,
max_iters=20,
max_inner_iters=500,
verbose=False)
self.assertItemsAlmostEqual(X.value, true_X, places=2)
# Quad funcs.
prob = Problem(prox_fns)
prob.solve(solver="admm")
self.assertItemsAlmostEqual(X.value, true_X, places=2)
# Absorbing lin ops.
prox_fns = [norm1(5 * mul_elemwise(B, X)), sum_squares(-2 * X, b=B)]
prob = Problem(prox_fns)
prob.set_absorb(True)
prob.solve(solver="admm", eps_rel=1e-6, eps_abs=1e-6)
cvx_X = cvx.Variable(4, 2)
cost = cvx.sum_squares(-2 * cvx_X - B) + cvx.norm(
5 * cvx.mul_elemwise(B, cvx_X), 1)
cvx_prob = cvx.Problem(cvx.Minimize(cost))
cvx_prob.solve(solver=cvx.SCS)
self.assertItemsAlmostEqual(X.value, cvx_X.value, places=2)
prob.set_absorb(False)
prob.solve(solver="admm", eps_rel=1e-6, eps_abs=1e-6)
self.assertItemsAlmostEqual(X.value, cvx_X.value, places=2)
# Constant offsets.
prox_fns = [norm1(5 * mul_elemwise(B, X)), sum_squares(-2 * X - B)]
prob = Problem(prox_fns)
prob.solve(solver="admm", eps_rel=1e-6, eps_abs=1e-6)
self.assertItemsAlmostEqual(X.value, cvx_X.value, places=2)
def test_op_overloading(self):
"""Test operator overloading.
"""
# Multiplying by a scalar.
# Forward.
var = Variable((2, 5))
W = np.arange(10)
W = np.reshape(W, (2, 5))
fn = -2 * mul_elemwise(W, var)
fn = CompGraph(fn)
x = W.copy()
out = np.zeros(x.shape)
fn.forward(x.flatten(), out)
self.assertItemsAlmostEqual(out, -2 * W * W)
# Adjoint.
x = W.copy()
out = np.zeros(x.shape).flatten()
fn.adjoint(x, out)
self.assertItemsAlmostEqual(out, -2 * W * W)
# Forward.
var = Variable((2, 5))
W = np.arange(10)
W = np.reshape(W, (2, 5))
fn = mul_elemwise(W, var) * 0.5
fn = CompGraph(fn)
x = W.copy()
out = np.zeros(x.shape)
fn.forward(x.flatten(), out)
self.assertItemsAlmostEqual(out, W * W / 2.)
# Adjoint.
x = W.copy()
out = np.zeros(x.shape).flatten()
fn.adjoint(x, out)
self.assertItemsAlmostEqual(out, W * W / 2.)
# Dividing by a scalar.
# Forward.
var = Variable((2, 5))
W = np.arange(10)
W = np.reshape(W, (2, 5))
fn = mul_elemwise(W, var) / 2
fn = CompGraph(fn)
x = W.copy()
out = np.zeros(x.shape)
fn.forward(x, out)
self.assertItemsAlmostEqual(out, W * W / 2.)
# Adding lin ops.
# Forward.
x = Variable((2, 5))
W = np.arange(10)
W = np.reshape(W, (2, 5))
fn = mul_elemwise(W, x)
fn = fn + x + x
self.assertEqual(len(fn.input_nodes), 3)
fn = CompGraph(fn)
x = W.copy()
out = np.zeros(fn.shape)
fn.forward(x, out)
self.assertItemsAlmostEqual(out, W * W + 2 * W)
# Adjoint.
x = W.copy()
out = np.zeros(x.shape).flatten()
fn.adjoint(x, out)
self.assertItemsAlmostEqual(out, W * W + 2 * W)
# Adding in a constant.
# CompGraph should ignore the constant.
x = Variable((2, 5))
W = np.arange(10)
W = np.reshape(W, (2, 5))
fn = mul_elemwise(W, x)
fn = fn + x + W
self.assertEqual(len(fn.input_nodes), 3)
fn = CompGraph(fn)
x = W.copy()
out = np.zeros(fn.shape)
fn.forward(x, out)
self.assertItemsAlmostEqual(out, W * W + W)
# Subtracting lin ops.
# Forward.
x = Variable((2, 5))
W = np.arange(10)
W = np.reshape(W, (2, 5))
fn = -mul_elemwise(W, x)
fn = x + x - fn
self.assertEqual(len(fn.input_nodes), 3)
fn = CompGraph(fn)
x = W.copy()
out = np.zeros(fn.shape)
fn.forward(x, out)
self.assertItemsAlmostEqual(out, W * W + 2 * W)
# Adjoint.
x = W.copy()
out = np.zeros(x.shape).flatten()
fn.adjoint(x, out)
self.assertItemsAlmostEqual(out, W * W + 2 * W)
def test_absorb_lin_op(self):
"""Test absorb lin op operator.
"""
# norm1.
tmp = Variable(10)
v = np.arange(10) * 1.0 - 5.0
fn = norm1(mul_elemwise(-v, tmp), alpha=5.)
rho = 2
new_prox = absorb_lin_op(fn)[0]
x = new_prox.prox(rho, v.copy())
self.assertItemsAlmostEqual(x, np.sign(v) * np.maximum(np.abs(v) - 5. *
np.abs(v) / rho, 0))
fn = norm1(mul_elemwise(-v, mul_elemwise(2 * v, tmp)), alpha=5.)
rho = 2
new_prox = absorb_lin_op(fn)[0]
x = new_prox.prox(rho, v.copy())
self.assertItemsAlmostEqual(x, np.sign(v) * np.maximum(np.abs(v) - 5. *
np.abs(v) / rho, 0))
new_prox = absorb_lin_op(new_prox)[0]
x = new_prox.prox(rho, v.copy())
new_v = 2 * v * v
self.assertItemsAlmostEqual(x, np.sign(new_v) *
np.maximum(np.abs(new_v) - 5. * np.abs(new_v) / rho, 0))
# nonneg.
tmp = Variable(10)
v = np.arange(10) * 1.0 - 5.0
fn = nonneg(mul_elemwise(-v, tmp), alpha=5.)
rho = 2
new_prox = absorb_lin_op(fn)[0]
x = new_prox.prox(rho, v.copy())
self.assertItemsAlmostEqual(x, fn.prox(rho, -np.abs(v)))
# sum_squares.
tmp = Variable(10)
v = np.arange(10) * 1.0 - 5.0
alpha = 5.
val = np.arange(10)
fn = sum_squares(mul_elemwise(-v, tmp), alpha=alpha, c=val)
rho = 2
new_prox = absorb_lin_op(fn)[0]
x = new_prox.prox(rho, v.copy())
cvx_x = cvx.Variable(10)
prob = cvx.Problem(cvx.Minimize(cvx.sum_squares(cvx_x - v) * (rho / 2) +
5 * cvx.sum_squares(cvx.mul_elemwise(-v,
cvx_x)) + (val * -v).T * cvx_x
))
prob.solve()
self.assertItemsAlmostEqual(x, cvx_x.value, places=3)
# Test scale.
tmp = Variable(10)
v = np.arange(10) * 1.0 - 5.0
fn = norm1(10 * tmp)
rho = 2
new_prox = absorb_lin_op(fn)[0]
x = new_prox.prox(rho, v.copy())
cvx_x = cvx.Variable(10)
prob = cvx.Problem(cvx.Minimize(cvx.sum_squares(cvx_x - v) + cvx.norm(10 * cvx_x, 1)))
prob.solve()
self.assertItemsAlmostEqual(x, cvx_x.value, places=3)
val = np.arange(10)
fn = norm1(10 * tmp, c=val, b=val, gamma=0.01)
rho = 2
new_prox = absorb_lin_op(fn)[0]
x = new_prox.prox(rho, v.copy())
cvx_x = cvx.Variable(10)
prob = cvx.Problem(cvx.Minimize(cvx.sum_squares(cvx_x - v) +
cvx.norm(10 * cvx_x - val, 1) + 10 * val.T * \
cvx_x + cvx.sum_squares(cvx_x)
))
prob.solve()
self.assertItemsAlmostEqual(x, cvx_x.value, places=2)
# sum_entries
tmp = Variable(10)
v = np.arange(10) * 1.0 - 5.0
fn = sum_entries(sum([10 * tmp, mul_elemwise(v, tmp)]))
funcs = absorb.absorb_all_lin_ops([fn])
c = __builtins__['sum']([func.c for func in funcs])
self.assertItemsAlmostEqual(c, v + 10, places=3)
def test_absorb_lin_op(self):
"""Test absorb lin op operator.
"""
# norm1.
tmp = Variable(10)
v = np.arange(10) * 1.0 - 5.0
fn = norm1(mul_elemwise(-v, tmp), alpha=5.)
rho = 2
new_prox = absorb_lin_op(fn)[0]
x = new_prox.prox(rho, v.copy())
self.assertItemsAlmostEqual(
x,
np.sign(v) * np.maximum(np.abs(v) - 5. * np.abs(v) / rho, 0))
fn = norm1(mul_elemwise(-v, mul_elemwise(2 * v, tmp)), alpha=5.)
rho = 2
new_prox = absorb_lin_op(fn)[0]
x = new_prox.prox(rho, v.copy())
self.assertItemsAlmostEqual(
x,
np.sign(v) * np.maximum(np.abs(v) - 5. * np.abs(v) / rho, 0))
new_prox = absorb_lin_op(new_prox)[0]
x = new_prox.prox(rho, v.copy())
new_v = 2 * v * v
self.assertItemsAlmostEqual(
x,
np.sign(new_v) *
np.maximum(np.abs(new_v) - 5. * np.abs(new_v) / rho, 0))
# nonneg.
tmp = Variable(10)
v = np.arange(10) * 1.0 - 5.0
fn = nonneg(mul_elemwise(-v, tmp), alpha=5.)
rho = 2
new_prox = absorb_lin_op(fn)[0]
x = new_prox.prox(rho, v.copy())
self.assertItemsAlmostEqual(x, fn.prox(rho, -np.abs(v)))
# sum_squares.
tmp = Variable(10)
v = np.arange(10) * 1.0 - 5.0
alpha = 5.
val = np.arange(10)
fn = sum_squares(mul_elemwise(-v, tmp), alpha=alpha, c=val)
rho = 2
new_prox = absorb_lin_op(fn)[0]
x = new_prox.prox(rho, v.copy())
cvx_x = cvx.Variable(10)
prob = cvx.Problem(
cvx.Minimize(
cvx.sum_squares(cvx_x - v) * (rho / 2) +
5 * cvx.sum_squares(cvx.mul_elemwise(-v, cvx_x)) +
(val * -v).T * cvx_x))
prob.solve()
self.assertItemsAlmostEqual(x, cvx_x.value, places=3)
# Test scale.
tmp = Variable(10)
v = np.arange(10) * 1.0 - 5.0
fn = norm1(10 * tmp)
rho = 2
new_prox = absorb_lin_op(fn)[0]
x = new_prox.prox(rho, v.copy())
cvx_x = cvx.Variable(10)
prob = cvx.Problem(
cvx.Minimize(cvx.sum_squares(cvx_x - v) + cvx.norm(10 * cvx_x, 1)))
prob.solve()
self.assertItemsAlmostEqual(x, cvx_x.value, places=3)
val = np.arange(10)
fn = norm1(10 * tmp, c=val, b=val, gamma=0.01)
rho = 2
new_prox = absorb_lin_op(fn)[0]
x = new_prox.prox(rho, v.copy())
cvx_x = cvx.Variable(10)
prob = cvx.Problem(cvx.Minimize(cvx.sum_squares(cvx_x - v) +
cvx.norm(10 * cvx_x - val, 1) + 10 * val.T * \
cvx_x + cvx.sum_squares(cvx_x)
))
prob.solve()
self.assertItemsAlmostEqual(x, cvx_x.value, places=2)
# sum_entries
tmp = Variable(10)
v = np.arange(10) * 1.0 - 5.0
fn = sum_entries(sum([10 * tmp, mul_elemwise(v, tmp)]))
funcs = absorb.absorb_all_lin_ops([fn])
c = __builtins__['sum']([func.c for func in funcs])
self.assertItemsAlmostEqual(c, v + 10, places=3)
def test_op_overloading(self):
"""Test operator overloading.
"""
# Multiplying by a scalar.
# Forward.
var = Variable((2, 5))
W = np.arange(10)
W = np.reshape(W, (2, 5))
fn = -2 * mul_elemwise(W, var)
fn = CompGraph(fn)
x = W.copy()
out = np.zeros(x.shape)
fn.forward(x.flatten(), out)
self.assertItemsAlmostEqual(out, -2 * W * W)
# Adjoint.
x = W.copy()
out = np.zeros(x.shape).flatten()
fn.adjoint(x, out)
self.assertItemsAlmostEqual(out, -2 * W * W)
# Forward.
var = Variable((2, 5))
W = np.arange(10)
W = np.reshape(W, (2, 5))
fn = mul_elemwise(W, var) * 0.5
fn = CompGraph(fn)
x = W.copy()
out = np.zeros(x.shape)
fn.forward(x.flatten(), out)
self.assertItemsAlmostEqual(out, W * W / 2.)
# Adjoint.
x = W.copy()
out = np.zeros(x.shape).flatten()
fn.adjoint(x, out)
self.assertItemsAlmostEqual(out, W * W / 2.)
# Dividing by a scalar.
# Forward.
var = Variable((2, 5))
W = np.arange(10)
W = np.reshape(W, (2, 5))
fn = mul_elemwise(W, var) / 2
fn = CompGraph(fn)
x = W.copy()
out = np.zeros(x.shape)
fn.forward(x, out)
self.assertItemsAlmostEqual(out, W * W / 2.)
# Adding lin ops.
# Forward.
x = Variable((2, 5))
W = np.arange(10)
W = np.reshape(W, (2, 5))
fn = mul_elemwise(W, x)
fn = fn + x + x
self.assertEquals(len(fn.input_nodes), 3)
fn = CompGraph(fn)
x = W.copy()
out = np.zeros(fn.shape)
fn.forward(x, out)
self.assertItemsAlmostEqual(out, W * W + 2 * W)
# Adjoint.
x = W.copy()
out = np.zeros(x.shape).flatten()
fn.adjoint(x, out)
self.assertItemsAlmostEqual(out, W * W + 2 * W)
# Adding in a constant.
# CompGraph should ignore the constant.
x = Variable((2, 5))
W = np.arange(10)
W = np.reshape(W, (2, 5))
fn = mul_elemwise(W, x)
fn = fn + x + W
self.assertEquals(len(fn.input_nodes), 3)
fn = CompGraph(fn)
x = W.copy()
out = np.zeros(fn.shape)
fn.forward(x, out)
self.assertItemsAlmostEqual(out, W * W + W)
# Subtracting lin ops.
# Forward.
x = Variable((2, 5))
W = np.arange(10)
W = np.reshape(W, (2, 5))
fn = -mul_elemwise(W, x)
fn = x + x - fn
self.assertEquals(len(fn.input_nodes), 3)
fn = CompGraph(fn)
x = W.copy()
out = np.zeros(fn.shape)
fn.forward(x, out)
self.assertItemsAlmostEqual(out, W * W + 2 * W)
# Adjoint.
x = W.copy()
out = np.zeros(x.shape).flatten()
fn.adjoint(x, out)
self.assertItemsAlmostEqual(out, W * W + 2 * W)