def vectorized_MC_integration(tot_tests, a_nu, batch_size = 100000, rhs = 1., variability = 1./6., mean_field = 5., regular_saves = True, dump_fname = None): # This can be adapted to make it better for later tests. At the moment, it is implemented fast and dirty for our particular setup
final_avg = 0
# Since lists, nd_arrays are not hashable, we need to create a key (unique) for each nu...
hashable_nu = '{}'.format(a_nu)
## This definitely needs to be done better!
if dump_fname is None:
dump_fname = 'MCEstimations_PWLD_6dim_small_var.npy'
if os.path.isfile(dump_fname) is False:
dict_results = {hashable_nu: {}} # or load if the file already exists
batches = range(batch_size, tot_tests + 1, batch_size)
else:
dict_results = np.load(dump_fname).item()
if hashable_nu not in dict_results:
dict_results[hashable_nu] = {}
nb_tests = 0
batches = range(batch_size, tot_tests + 1, batch_size)
else:
computed_tests = sorted(dict_results[hashable_nu].keys())
if tot_tests >= computed_tests[-1]:
batches = range(computed_tests[-1]+batch_size, tot_tests+1, batch_size)
nb_tests = computed_tests[-1]
final_avg = dict_results[hashable_nu][computed_tests[-1]]*computed_tests[-1]
else:
nb_tests = computed_tests[[computed_tests[i]-tot_tests >= 0 for i in xrange(0,len(computed_tests))].index(True)-1]
batches = range(nb_tests+batch_size, tot_tests+1, batch_size)
final_avg = dict_results[hashable_nu][nb_tests]*nb_tests
for a_batch in batches:
print '\tCurrent number of test samples: {}'.format(a_batch)
if a_batch in dict_results[hashable_nu]:
# Don't redo the calculation, and load the one already done:
nb_tests = a_batch
final_avg = dict_results[hashable_nu][nb_tests]*nb_tests
else:
nb_tests = a_batch
zs = np.cos(np.random.uniform(0,np.pi,[batch_size, 6]))
ys = ctaa(zs, rhs, variability, mean_field)
cheb_vals = vmvc(zs,a_nu)
final_avg = final_avg + sum(ys*cheb_vals)
dict_results[hashable_nu][nb_tests] = final_avg/nb_tests
np.save(dump_fname, dict_results)
# one last batch of tests to make sure we have the required number
remaining_tests = tot_tests - nb_tests
if remaining_tests > 0:
nb_tests = nb_tests+remaining_tests
zs = np.cos(np.random.uniform(0,np.pi,[remaining_tests, 6]))
ys = ctaa(zs, rhs, variability, mean_field)
cheb_vals = vmvc(zs,a_nu)
final_avg = final_avg + sum(ys*cheb_vals)
dict_results[hashable_nu][nb_tests] = final_avg/nb_tests
np.save(dump_fname, dict_results)
return final_avg/nb_tests
def coefs_QMCSobol_wrapper(nb_samples, a_nu): # note that a lot of calculations will be repeated by doing this. We need to be smarter!
int_val = 0
seed = 0
dim = 6
for one_sample in xrange(0,nb_samples):
[sample, new_seed] = i4_sobol(6, seed)
int_val = int_val + ctaa(sample, 1.0,1.0/6,5.)*mvc(sample,a_nu)
seed = new_seed
return int_val/nb_samples
def faster_QMC_computations(nb_samples, nus): # note that a lot of calculations will be repeated by doing this. We need to be smarter!
# first find the highest degree considered in the list of nu's
# then go through the samples, one at a time, and everytime we see a new value, we add to the dictionary together with the T_j(value)
max_degree = np.max(nus)
cheb_evals = {}
weights_eval = {}
int_val = 0 # as for integral value
seed = 0 # required for the sobol index
dim = 6 # that hurts!
for one_sample in xrange(0,nb_samples):
[sample, new_seed] = i4_sobol(6, seed)
sample = sample*2 -1 # To set the anchor at (-1,-1,-1 ...) instead of the usual (0,0,...) for QMC methods
not_computed = [a_param for a_param in one_sample if a_param not in cheb_evals] # contains the values that we have not precomputed before
for to_compute in not_computed:
# add these guys to the dictionary.
to_add_to_dict = [1]
for one_deg in xrange(1,max_degree+1):
to_add_to_dict.append(np.polynomial.chebyshev.chebval(to_compute, np.hstack( (np.zeros(one_deg),1) )))
cheb_evals[to_compute] = to_add_to_dict
weights_eval[to_compute] = np.polynomial.chebyshev.chebweight(to_compute)
int_val = int_val + ctaa(sample-1, 1.0,1.0/6,5.)*mvc(sample,a_nu)
seed = new_seed
return int_val/nb_samples
def plot_PWLD(infile, outfile, L_to_plot, v_to_plot, k_to_plot, which_to_plot = None):
### Load
print("Loading {0} ...".format(infile))
results = sorted(shelve.open(infile).values(), key=lambda r: r.L)
### Plot
first_result = results[-1] # At that moment, first_result is a multi-level result
d = first_result.cspde_result[0].d # number of parameters
spde_model = first_result.spde_model
epsilon = first_result.epsilon
d_plot = len(k_to_plot) # k_to_plot corresponds to the number of pointwise convergences that we want to analyze
results_plot = [r for r in results if r.wr_model.weights[0] in v_to_plot and r.L in L_to_plot]
# Note: The number of grid points in the original grid can be recovered from spde_model.mesh_size - Obviously, every time we add a level, we multiply (every component) by two
print len(results_plot)
fig = plt.figure()
# plt.suptitle("{0}: s={1}, epsilon={2}, {3} weights".format("Diffusion Model", s, epsilon, wr_model.weights.name))
outer_grid = gridspec.GridSpec(int(np.ceil(d_plot/2.)), 2)
l = []
# First refine the mesh to a (much) finer level
# spde_model.refine_mesh(ratio=2**(np.max(L_to_plot)+2)) # Refine to 2 levels larger than the largest level we consider.
# This is not required since we have the exact formula for this case
for (index, k) in enumerate(k_to_plot):
print("Plotting k={0} ...".format(k+1))
# Reconstruct
step = 0.02 # Change back to 0.02
T = np.arange(-1+step, 1, step)
Z = np.array([np.hstack((np.zeros(k), t, np.zeros(d-k-1))) for t in T])
# Compute the solutions on that new mesh
# Compute the TRUE solution
# Y = [spde_model.samples(Z)] # This acts as the "True" solution
rhs = spde_model.f.c
variability = spde_model.var
abar = spde_model.abar
# print Z[0]
# Y = [ctaa([np.array([z])], rhs, variability, abar ) for z in Z]
# Y = [cta(z, rhs, variability, abar ) for z in Z]
# Y = ctaa([Z], rhs, variability, abar )
Y = [np.array([float(ctaa([np.array(z)], rhs, variability, abar )) for z in Z])]
# Y = cta(Z, rhs, variability, abar )
# Y = [ctaa(np.array(z), rhs, variability, abar ) for z in Z]
# print Y
for result in results_plot:
# print result
# Evaluation of the estimation / approximation in the ML case:
Y.append(result.wr_model.estimate_ML_samples(result.cspde_result, Z)) # Check that we actually have the level we are testing.
# Evaluation in the SL case:
# F_recon = result.wr_model.operator.create(np.array(result.cspde_result.J_s)[x != 0], Z, normalization=np.sqrt(result.cspde_result.m))
# Y.append(F_recon.apply(x[x != 0]))
# x = result.cspde_result.result.x
# Plot
if which_to_plot is None:
inner_grid = gridspec.GridSpecFromSubplotSpec(2, 1, subplot_spec=outer_grid[index])
ax = plt.Subplot(fig, inner_grid[0])
ax.ticklabel_format(axis='y', style='sci', scilimits=(-2,2))
for y in Y:
l.append(ax.plot(T, y)[0])
fig.add_subplot(ax)
plt.title("$y_{0}$".format(k+1))
ax = plt.Subplot(fig, inner_grid[1])
ax.ticklabel_format(axis='y', style='sci', scilimits=(-2,2))
for y in Y:
ax.plot(T, Y[0]-y)
fig.add_subplot(ax)
else:
inner_grid = gridspec.GridSpecFromSubplotSpec(1, 1, subplot_spec=outer_grid[index])
ax = plt.Subplot(fig, inner_grid[0])
ax.ticklabel_format(axis='y', style='sci', scilimits=(-2,2))
if which_to_plot[index] == 'function':
for y in Y:
l.append(ax.plot(T, y)[0])
fig.add_subplot(ax)
# plt.title("$z_{0}$".format(k+1))
else:
for y in Y:
ax.plot(T, Y[0]-y)
fig.add_subplot(ax)
# plt.title("$z_{0}$ difference".format(k+1))
fig.legend(l, ["Truth"] + ["L={0}".format(int(round(result.L))) for result in results_plot],
loc='lower center', bbox_to_anchor=(0.5, -0.05), bbox_transform=plt.gcf().transFigure,
ncol=len(results_plot)+1, fancybox=True, shadow=True)
all_axes = fig.get_axes()
#show only the outside spines
for ax in all_axes:
if ax.is_last_row():
plt.setp(ax.get_xticklabels(), visible=True)
else:
plt.setp(ax.get_xticklabels(), visible=False)
if which_to_plot is None:
plt.gcf().set_size_inches(17, int(4 * np.ceil(d_plot/2)))
else:
plt.gcf().set_size_inches(17, int(8 * np.ceil(d_plot/2)))
# plt.tight_layout()
plt.savefig(outfile, bbox_inches="tight")
def integrand(x0,x1,x2,x3,x4,x5,nu0,nu1,nu2,nu3,nu4,nu5, with_weights = True, rhs = 1., variability = 1.0/6., abar = 5.):
# print ctaa([np.array([x0,x1,x2,x3,x4,x5])], rhs, variability, abar )
return ctaa([np.array([x0,x1,x2,x3,x4,x5])], rhs, variability, abar )*mvc([x0,x1,x2,x3,x4,x5],np.array([nu0,nu1,nu2,nu3,nu4,nu5]), with_weights)
def coefs_mpmath_wrapper(a_nu): # seems like there is no way to use mpmath for more than 3 dimensions :(
mp.dps = 10 # 10 digits of accuracy should be more than enough.
return quad(lambda x0,x1,x2,x3,x4,x5: ctaa([np.array([x0,x1,x2,x3,x4,x5])], 1.0, 1.0/6.0, 5.0 )*mvc([x0,x1,x2,x3,x4,x5],a_nu),[-1,1],[-1,1],[-1,1],[-1,1],[-1,1],[-1,1], method='tanh-sinh') # tanh-sinh tends to be more accurate when dealing with singularities
def coefs_nquad_wrapper(a_nu):
return spi.nquad(lambda x0,x1,x2,x3,x4,x5: ctaa([np.array([x0,x1,x2,x3,x4,x5])], 1.0, 1.0/6.0, 5.0 )*mvc([x0,x1,x2,x3,x4,x5],a_nu),[[-1,1]]*6)
