From eadc6e52420126e2b66d0db5ec4c54de8622fb17 Mon Sep 17 00:00:00 2001
From: Bas Nijholt <basnijholt@gmail.com>
Date: Mon, 23 Sep 2019 14:31:37 +0200
Subject: [PATCH] separate plotting and calculation
---
figures.ipynb | 74 +++++++++++++++++++++++++++++----------------------
1 file changed, 42 insertions(+), 32 deletions(-)
diff --git a/figures.ipynb b/figures.ipynb
index c497a6d..d954248 100644
--- a/figures.ipynb
+++ b/figures.ipynb
@@ -598,6 +598,7 @@
"metadata": {},
"outputs": [],
"source": [
+ "import collections\n",
"from adaptive import Learner1D, LearnerND\n",
"from scipy import interpolate\n",
"\n",
@@ -612,7 +613,7 @@
" xs_rand = np.random.uniform(*learner.bounds, size=100_000)\n",
"\n",
" xs = np.linspace(*learner.bounds, N)\n",
- " ys = f(xs)\n",
+ " ys = learner.function(xs)\n",
" err_lin = err_1D(xs, ys, xs_rand, learner.function)\n",
"\n",
" xs, ys = zip(*learner.data.items())\n",
@@ -646,12 +647,12 @@
"\n",
" try:\n",
" # Vectorized\n",
- " zs_hom = f(xys_hom.T)\n",
- " zs_rand = f(xys_rand)\n",
+ " zs_hom = learner.function(xys_hom.T)\n",
+ " zs_rand = learner.function(xys_rand)\n",
" except:\n",
" # Non-vectorized\n",
- " zs_hom = np.array([f(xy) for xy in xys_hom])\n",
- " zs_rand = np.array([f(xy) for xy in xys_rand.T])\n",
+ " zs_hom = np.array([learner.function(xy) for xy in xys_hom])\n",
+ " zs_rand = np.array([learner.function(xy) for xy in xys_rand.T])\n",
"\n",
" ip = interpolate.LinearNDInterpolator(xys_hom, zs_hom)\n",
" zs = ip(xys_rand.T)\n",
@@ -664,40 +665,49 @@
" return err_lin, err_learner\n",
"\n",
"\n",
- "N_max = 10000\n",
+ "N_max = 100\n",
"Ns = np.geomspace(4, N_max, 20).astype(int)\n",
"\n",
- "fig, axs = plt.subplots(2, 1, figsize=(fig_width, 1.6 * fig_height))\n",
- "\n",
"loss_1D = adaptive.learner.learner1D.curvature_loss_function()\n",
"loss_2D = adaptive.learner.learnerND.curvature_loss_function()\n",
"\n",
- "for ax, funcs, loss, Learner, get_err in zip(\n",
- " axs,\n",
- " [funcs_1D, funcs_2D],\n",
- " [loss_1D, loss_2D],\n",
- " [Learner1D, LearnerND],\n",
- " [get_err_1D, get_err_2D],\n",
+ "err = collections.defaultdict(dict)\n",
+ "for i, (funcs, loss, Learner, get_err) in enumerate(\n",
+ " zip(\n",
+ " [funcs_1D, funcs_2D],\n",
+ " [loss_1D, loss_2D],\n",
+ " [Learner1D, LearnerND],\n",
+ " [get_err_1D, get_err_2D],\n",
+ " )\n",
"):\n",
- " ax.set_xlabel(\"$N$\")\n",
- " ax.set_ylabel(r\"$\\text{Err}_{1}(\\tilde{f})$\")\n",
- "\n",
- " for i, d in enumerate(funcs):\n",
- " f = d[\"function\"]\n",
- " bounds = d[\"bounds\"]\n",
- " learner = Learner(f, bounds, loss)\n",
+ " for d in funcs:\n",
+ " learner = Learner(d[\"function\"], d[\"bounds\"], loss)\n",
" adaptive.runner.simple(learner, goal=lambda l: l.npoints >= N_max)\n",
" errs = [get_err(learner, N) for N in Ns]\n",
" err_hom, err_adaptive = zip(*errs)\n",
- " color = f\"C{i}\"\n",
- " label = \"abc\"[i]\n",
- " ax.loglog(Ns, err_hom, ls=\"--\", c=color)\n",
- " ax.loglog(\n",
- " Ns, err_adaptive, label=f\"$\\mathrm{{({label})}}$ {d['title']}\", c=color\n",
- " )\n",
+ " err[i][d[\"title\"]] = (err_hom, err_adaptive)\n",
"\n",
" d[\"err_hom\"] = err_hom\n",
- " d[\"err_adaptive\"] = err_adaptive\n",
+ " d[\"err_adaptive\"] = err_adaptive"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "fig, axs = plt.subplots(2, 1, figsize=(fig_width, 1.6 * fig_height))\n",
+ "\n",
+ "for i, ax in enumerate(axs):\n",
+ " ax.set_xlabel(\"$N$\")\n",
+ " ax.set_ylabel(r\"$\\text{Err}_{1}(\\tilde{f})$\")\n",
+ "\n",
+ " for j, (title, (err_hom, err_adaptive)) in enumerate(err[i].items()):\n",
+ " color = f\"C{j}\"\n",
+ " label = \"abc\"[j]\n",
+ " ax.loglog(Ns, err_hom, ls=\"--\", c=color)\n",
+ " ax.loglog(Ns, err_adaptive, label=f\"$\\mathrm{{({label})}}$ {title}\", c=color)\n",
"\n",
"plt.legend()\n",
"plt.savefig(\"figures/line_loss_error.pdf\", bbox_inches=\"tight\", transparent=True)\n",
@@ -711,11 +721,11 @@
"outputs": [],
"source": [
"# Error reduction\n",
- "for d in funcs_1D:\n",
- " print(d[\"err_hom\"][-1] / d[\"err_adaptive\"][-1])\n",
+ "for title, (err_hom, err_adaptive) in err[0].items():\n",
+ " print(title, err_hom[-1] / err_adaptive[-1])\n",
"\n",
- "for d in funcs_2D:\n",
- " print(d[\"err_hom\"][-1] / d[\"err_adaptive\"][-1])"
+ "for title, (err_hom, err_adaptive) in err[1].items():\n",
+ " print(title, err_hom[-1] / err_adaptive[-1])"
]
},
{
--
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