Python Function.grad Beispiele

Beispiel #1

def test_return_grad2(m=3):
	A = np.random.randn(m, m)
	A += A.T
	
	B = np.random.randn(m, m)
	B += B.T

	def func(x, return_grad = False):
		fx = [0.5*x.dot(A.dot(x)), 0.5*x.dot(B.dot(x))]

		if return_grad:
			grad = [A.dot(x), B.dot(x)]
			return fx, grad
		else:
			return fx
	
	dom = BoxDomain(-2*np.ones(m), 2*np.ones(m))

	fun = Function(func, dom, return_grad = True)	


	X = fun.domain.sample(4)
	fX, grads = fun(X, return_grad = True)
	assert fX.shape == (len(X), 2)
	assert grads.shape == (len(X), 2, m)
	
	for x, grad in zip(X, grads):
		assert np.all(np.isclose(grad, fun.grad(x)))

Beispiel #2

def test_finite_diff():
	from psdr.demos.golinski import golinski_volume, build_golinski_design_domain
	
	dom = build_golinski_design_domain()
	fun = Function(golinski_volume, dom, fd_grad = True)

	x = fun.domain.sample()
	print(x)
	print(fun(x))
	print(fun.grad(x))	
	err = check_derivative(x, fun.eval, fun.grad)
	print(err)
	assert err < 1e-5

	# Check asking for multiple gradients
	X = fun.domain.sample(5)
	grads = fun.grad(X)
	for i, x in enumerate(X):
		assert np.all(np.isclose(grads[i], fun.grad(x)))

Beispiel #3

def test_return_grad(m=3):
	A = np.random.randn(m, m)
	A += A.T

	def func(x, return_grad = False):
		fx = 0.5*x.dot(A.dot(x))

		if return_grad:
			grad = A.dot(x)
			return fx, grad
		else:
			return fx
	
	dom = BoxDomain(-2*np.ones(m), 2*np.ones(m))
	x = dom.sample(1)

	fun = Function(func, dom, return_grad = True)	
	# Check the derivative
	x_norm = dom.normalize(x)
	err = check_derivative(x_norm, fun.eval, fun.grad)
	assert err < 1e-5

	# Check wrapping
	fx, grad = func(x, return_grad = True)
	assert np.isclose(fx, fun(x_norm))
	# multiply the grad by two to correct the change of coordinates
	assert np.all(np.isclose(2*grad, fun.grad(x_norm)))

	# Check multiple outputs
	X = dom.sample(10)
	fX, grads = fun(X, return_grad = True)
	for i, x in enumerate(X):
		assert np.isclose(fun(x), fX[i])
		assert np.all(np.isclose(fun.grad(x), grads[i]))

	# Check vectorized functions
	def func2(X, return_grad = False):
		X = np.atleast_2d(X)
		fX = np.vstack([0.5*x.dot(A.dot(x)) for x in X])
		if return_grad:
			grad = X.dot(A)
			return fX, grad
		else:
			return fX

	fun2 = Function(func2, dom, vectorized = True, return_grad = True)

	x = fun2.domain.sample()
	X = fun2.domain.sample(5)
	assert np.isclose(fun2(x), fun(x)) 
	assert np.all(np.isclose(fun2(X), fun(X)))
	assert np.all(np.isclose(fun2.grad(X), fun.grad(X)))

	# Check the __call__ interface
	fX, grad = fun2(X, return_grad = True)
	print(fX.shape, fun(X).shape)
	assert fX.shape == fun(X).shape
	assert np.all(np.isclose(fX, fun(X)))
	print(grad.shape, fun.grad(X).shape)
	assert grad.shape == fun.grad(X).shape
	assert np.all(np.isclose(grad, fun.grad(X)))
	
	fx, grad = fun2(x, return_grad = True)
	print(fx.shape, fun(x).shape)
	assert fx.shape == fun(x).shape
	assert np.all(np.isclose(fx, fun(x)))
	print(grad.shape, fun.grad(x).shape)
	assert grad.shape == fun.grad(x).shape
	assert np.all(np.isclose(grad, fun.grad(x)))
	
	fX, grad = fun(X, return_grad = True)
	print(fX.shape, fun(X).shape)
	assert fX.shape == fun(X).shape
	assert np.all(np.isclose(fX, fun(X)))
	print(grad.shape, fun.grad(X).shape)
	assert grad.shape == fun.grad(X).shape
	assert np.all(np.isclose(grad, fun.grad(X)))
	
	fx, grad = fun(x, return_grad = True)
	print(fx.shape, fun(x).shape)
	assert fx.shape == fun(x).shape
	assert np.all(np.isclose(fx, fun(x)))
	print(grad.shape, fun.grad(x).shape)
	assert grad.shape == fun.grad(x).shape
	assert np.all(np.isclose(grad, fun.grad(x)))