## Author: Júlio Hoffimann Mendes
import numpy as np
import pylab as pl
from pyhum.decomposition import KernelPCA
# make sure results are reproducible
np.random.seed(2014)
# load ensemble from disk (nfeatures x nsamples)
X = np.loadtxt("ensemble.csv", delimiter=",", skiprows=1, usecols=xrange(100))
fig = pl.figure()
for d in xrange(1, 5):
kpca = KernelPCA(degree=d)
ncomps = kpca.train(X, ncomps=50)
img = kpca.predict(np.ones(ncomps))
denoised = kpca.denoise(img)
pl.subplot(2, 4, d)
pl.title("d = %i" % d)
pl.imshow(img.reshape(250, 250), cmap="PuBu")
pl.axis("off")
pl.subplot(2, 4, d + 4)
pl.imshow(denoised.reshape(250, 250), cmap="PuBu")
pl.axis("off")
fig.tight_layout(h_pad=0.5, w_pad=0.5)
bbox_props = dict(boxstyle="rarrow,pad=0.3", fc="white", ec="b", lw=2)
# only 44550 of 60048 cells are active
mask = np.loadtxt("null.inc", dtype=bool, skiprows=2)
X = X[mask, :]
pool.wait()
# evaluate forward operator on prior ensemble and save results
if pool.is_master():
D = np.array(pool.map(G, [m for m in X.T])).T
np.savetxt("Dprior.dat", D)
pool.proceed()
# ensemble in feature space (ncomps << nfeatures)
kpca = KernelPCA()
kpca.train(X, ncomps=ncomps)
CSI = kpca.featurize(X)
mprior = Nonparametric(CSI.T)
# KDE-based proposal
def kde_proposal(CSI):
return mprior.sample(n_samples=nsamples)
from utils import alltimes, history
timesteps = [1812, 2421, 3029] # chosen timesteps for Bayesian inference
# history-based uncertainty mitigation
