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2 changes: 1 addition & 1 deletion adaptive_repo
Submodule adaptive_repo updated 140 files
22 changes: 21 additions & 1 deletion figures.ipynb
Original file line number Diff line number Diff line change
Expand Up @@ -947,9 +947,29 @@
" # Isolines\n",
" vertices, lines = learner._get_iso(level, which=\"line\")\n",
" paths = np.array([[vertices[i], vertices[j]] for i, j in lines]).T\n",
" print(\"{} line segments\".format(len(paths.T)))\n",
" ax.plot(*paths, c=\"k\", lw=0.7)\n",
"\n",
" # Distance between the estimated isoline and the true isoline,\n",
" # y = cbrt(level - x^2), resampling both densely\n",
" from scipy.spatial import cKDTree\n",
"\n",
" xs_true = np.linspace(*bounds[0], 200_001)\n",
" true_pts = np.column_stack([xs_true, np.cbrt(level - xs_true ** 2)])\n",
" seg_pts = []\n",
" for i_, j_ in lines:\n",
" p, q = np.asarray(vertices[i_]), np.asarray(vertices[j_])\n",
" n = max(2, int(np.ceil(np.linalg.norm(q - p) / 1e-3)))\n",
" seg_pts.append(p + np.linspace(0, 1, n)[:, None] * (q - p))\n",
" seg_pts = np.vstack(seg_pts)\n",
" d_est = cKDTree(true_pts).query(seg_pts)[0]\n",
" d_true = cKDTree(seg_pts).query(true_pts)[0]\n",
" mean_dist = (d_est.mean() + d_true.mean()) / 2\n",
" hausdorff = max(d_est.max(), d_true.max())\n",
" print(\n",
" f\"{kind}: {len(lines)} segments, mean distance {mean_dist:.2g}, \"\n",
" f\"Hausdorff distance {hausdorff:.2g}\"\n",
" )\n",
"\n",
" values = np.array(list(learner.data.values()))\n",
" ax.imshow(\n",
" learner.plot(npoints if kind == \"homogeneous\" else None).Image.I.data,\n",
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2 changes: 2 additions & 0 deletions pandoc/revtex.template
Original file line number Diff line number Diff line change
Expand Up @@ -84,7 +84,9 @@
$for(author)$
$if(author.name)$
\author{$author.name$}
$if(author.email)$
\email[Electronic address: ]{$author.email$}
$endif$
$for(author.affiliation)$
\affiliation{$author.affiliation$}
$endfor$
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26 changes: 26 additions & 0 deletions paper.bib
Original file line number Diff line number Diff line change
Expand Up @@ -141,6 +141,19 @@ @article{Bommer2019
journal = {Phys. Rev. Lett.}
}

@article{Chaloner1995,
doi = {10.1214/ss/1177009939},
year = 1995,
month = {aug},
publisher = {Institute of Mathematical Statistics},
volume = {10},
number = {3},
pages = {273--304},
author = {Kathryn Chaloner and Isabella Verdinelli},
title = {Bayesian Experimental Design: A Review},
journal = {Stat. Sci.}
}

@article{Chen2017,
doi = {10.1088/1361-6501/aa7d31},
year = 2017,
Expand Down Expand Up @@ -281,6 +294,19 @@ @article{Nijholt2016
journal = {Phys. Rev. B}
}

@article{Ryan2015,
doi = {10.1111/insr.12107},
year = 2015,
month = {jun},
publisher = {Wiley},
volume = {84},
number = {1},
pages = {128--154},
author = {Elizabeth G. Ryan and Christopher C. Drovandi and James M. McGree and Anthony N. Pettitt},
title = {A Review of Modern Computational Algorithms for Bayesian Optimal Design},
journal = {Int. Stat. Rev.}
}

@article{Visvalingam1990,
doi = {10.1111/j.1467-8659.1990.tb00398.x},
year = 1990,
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