def LF_model(zs):
def u(z):
output = campaspe_toy.run(*z, nrow=10)
return numpy.interp(numpy.linspace(0, 1000, 100),
numpy.linspace(0, 1000, 10), output)
return numpy.array(parmap(u, zs.T)).T
def HF_model(zs):
def u(z):
return campaspe_toy.run(*z, nrow=100)
return numpy.array(parmap(u, zs.T)).T
# Defining the random input distributions:
dists = [cp.Uniform(0, 0.5) for i in range(3)]
dists.append(cp.Uniform(-200.0, 50.0))
dist = cp.J(*dists)
num_tests = 100
order = 3
## Polynomial chaos expansion
## using Pseudo-spectral method and Gaussian Quadrature
P, norms = cp.orth_ttr(order - 2, dist, retall=True)
nodes, weights = cp.generate_quadrature(order + 1, dist, rule="G", sparse=False)
# solves = [u(s) for s in nodes.T]
solves = parmap(u, nodes.T) # [u(s) for s in nodes.T]
U_hat = cp.fit_quadrature(P, nodes, weights, solves, norms=norms)
test_inputs = dist.sample(num_tests)
test_outputs = numpy.array([u(s) for s in test_inputs.T])
surrogate_test_outputs = numpy.array([U_hat(*s) for s in test_inputs.T])
print "mean l2 error", numpy.mean(numpy.linalg.norm(test_outputs - surrogate_test_outputs, axis=0))
print "mean l2 norm", numpy.mean(numpy.linalg.norm(test_outputs, axis=0))
# scatter for all QOI
num_qoi = test_outputs.shape[1]
for qoi_i in range(num_qoi):
plt.subplot(numpy.ceil(num_qoi / 3.0), 3, qoi_i + 1)
plt.scatter(test_outputs[:, qoi_i], surrogate_test_outputs[:, qoi_i])
plt.plot([test_outputs[0, qoi_i], test_outputs[-1, qoi_i]], [test_outputs[0, qoi_i], test_outputs[-1, qoi_i]], "--")
dists = [cp.Uniform(0, 0.5) for i in range(3)]
dists.append(cp.Uniform(-200., 50.))
dist = cp.J(*dists)
num_tests = 100
order = 3
## Polynomial chaos expansion
## using Pseudo-spectral method and Gaussian Quadrature
P, norms = cp.orth_ttr(order - 2, dist, retall=True)
nodes, weights = cp.generate_quadrature(order + 1,
dist,
rule="G",
sparse=False)
# solves = [u(s) for s in nodes.T]
solves = parmap(u, nodes.T) #[u(s) for s in nodes.T]
U_hat = cp.fit_quadrature(P, nodes, weights, solves, norms=norms)
test_inputs = dist.sample(num_tests)
test_outputs = numpy.array([u(s) for s in test_inputs.T])
surrogate_test_outputs = numpy.array([U_hat(*s) for s in test_inputs.T])
print "mean l2 error", numpy.mean(
numpy.linalg.norm(test_outputs - surrogate_test_outputs, axis=0))
print "mean l2 norm", numpy.mean(numpy.linalg.norm(test_outputs, axis=0))
# scatter for all QOI
num_qoi = test_outputs.shape[1]
for qoi_i in range(num_qoi):
plt.subplot(numpy.ceil(num_qoi / 3.), 3, qoi_i + 1)
plt.scatter(test_outputs[:, qoi_i], surrogate_test_outputs[:, qoi_i])
def LF_model(zs):
def u(z):
output = campaspe_toy.run(*z, nrow=10)
return numpy.interp(numpy.linspace(0,1000,100), numpy.linspace(0,1000,10),output)
return numpy.array(parmap(u, zs.T)).T
def HF_model(zs):
def u(z):
return campaspe_toy.run(*z, nrow=100)
return numpy.array(parmap(u, zs.T)).T
