deploy: ae40885

.buildinfo

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+# Sphinx build info version 1
+# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
+config: b97544b7428bf48020e81a5f5a9211ac
+tags: 645f666f9bcd5a90fca523b33c5a78b7

.doctrees/nbsphinx/examples/custom_noise.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "c70df536-cea0-4cdf-bd6a-601824a52768",
+   "metadata": {},
+   "source": [
+    "# Custom Noise CBX\n",
+    "\n",
+    "This notebook showcases, how to use a custom noise function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "33394a7d-caa6-4f01-aa88-4dd69d5c400d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from cbx.dynamics import CBO\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ef05a88a-d000-4ea8-af21-714df66858fa",
+   "metadata": {},
+   "source": [
+    "## Define the custom noise function\n",
+    "\n",
+    "In this case we select that the noise should be zero"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "319270d9-01d4-49c0-8ac5-ab94bd6c327b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def custom_noise(dyn):\n",
+    "    return np.zeros(dyn.drift.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "08dac699-4772-4409-8e25-2a398216a2f4",
+   "metadata": {},
+   "source": [
+    "## Define a loss function and test the method"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "aae8cac4-7a04-41a2-b9ee-a0c8c26e27da",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def f(x):\n",
+    "    return np.linalg.norm(x, axis=-1)\n",
+    "\n",
+    "x0 = np.random.normal(0,1,(4,7,26))\n",
+    "dyn_cn = CBO(f, x=x0, noise=custom_noise, max_it=10, verbosity = 0)\n",
+    "x_cn = dyn_cn.optimize()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "762bcbf2-6861-4c21-8475-3e6b45e35fdb",
+   "metadata": {},
+   "source": [
+    "Using this noise is equivalent to specifying ``sigma=0`` with standard CBO. So let's test, if this is the case:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f1f11124-6a10-4e54-abef-4168cbfd6dbc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dyn = CBO(f, x=x0, sigma=0, max_it=10, verbosity = 0)\n",
+    "x = dyn.optimize()\n",
+    "print('L-infinty Error between the custom noise solution and standard CBO with sigma=0: ' + str(np.abs(x-x_cn).max()))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

.doctrees/nbsphinx/examples/low_level.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "ce7c4dc3-e701-4113-b4f9-ad961e204e20",
+   "metadata": {},
+   "source": [
+    "# Visualiszing CBX dynamics"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1ce71f14-a97a-475a-b027-324bc5864f09",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2\n",
+    "import numpy as np\n",
+    "import cbx as cbx\n",
+    "from cbx.dynamics import CBO, CBS\n",
+    "from cbx.objectives import Rastrigin\n",
+    "from cbx.utils.objective_handling import cbx_objective_fh\n",
+    "from cbx.scheduler import effective_sample_size, scheduler\n",
+    "from cbx.plotting import PlotDynamic, PlotDynamicHistory"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dc027d54-35fc-44c8-bbc1-f6f8655a3863",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.random.seed(420)\n",
+    "conf = {'alpha': 40.0,\n",
+    "        'dt': 0.1,\n",
+    "        'sigma': 1.,#8,#5.1,#8.0,\n",
+    "        'lamda': 1.0,\n",
+    "        'batch_args':{\n",
+    "        'batch_size':200,\n",
+    "        'batch_partial': False},\n",
+    "        'd': 2,\n",
+    "        'max_it': 500,\n",
+    "        'N': 50,\n",
+    "        'M': 3,\n",
+    "        'track_args': {'names':\n",
+    "                       ['update_norm', \n",
+    "                        'energy','x', \n",
+    "                        'consensus', \n",
+    "                        'drift']}\n",
+    "       }"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "abd823be-8498-42c1-aad4-2b7fda0f5adf",
+   "metadata": {},
+   "source": [
+    "## Using the plotting functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b33670f7-14a3-4176-b014-1545b249dbf8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "from IPython import display\n",
+    "\n",
+    "fig, ax = plt.subplots(1,2, figsize=(15,5))\n",
+    "\n",
+    "f = Rastrigin()\n",
+    "x = cbx.utils.init_particles(shape=(conf['M'], conf['N'], conf['d']), x_min=-3., x_max = -1.) # Define the initial positions of the particles\n",
+    "\n",
+    "\n",
+    "dyn = CBO(f, x=x, noise='isotropic', f_dim='3D', **conf) # Define the CBO algorithm\n",
+    "sched = effective_sample_size(maximum=1e12)\n",
+    "plotter = PlotDynamic(dyn, ax=ax[0],\n",
+    "                       objective_args={'x_min':-3, 'x_max':3},\n",
+    "                       plot_consensus=True,\n",
+    "                       plot_drift=True)\n",
+    "plotter.init_plot()\n",
+    "while not dyn.terminate():\n",
+    "    dyn.step()\n",
+    "    sched.update(dyn)\n",
+    "    # update energy plot\n",
+    "    ax[1].clear()\n",
+    "    ax[1].plot([e[0] for e in dyn.history['energy'][-30:]])\n",
+    "    ax[1].set_title('Energy of the last 30 iterations') \n",
+    "    ax[0].set_title('Iteration: ' + str(dyn.it)) \n",
+    "    plotter.update(wait=0.2)\n",
+    "    display.display(fig)\n",
+    "    display.clear_output(wait=True)\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "68fc7a07-cb08-4177-9328-1354ecf0150d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# import matplotlib.pyplot as plt\n",
+    "# plt.close('all')\n",
+    "# plotter = PlotDynamicHistory(\n",
+    "#             dyn, dims=[0,1], \n",
+    "#             objective_args={'x_min':-3, 'x_max':3, 'cmap':'viridis',\n",
+    "#                             'num_pts':300},\n",
+    "#             particle_args = {'s':50, 'c':'xkcd:sky', 'marker':'o'},\n",
+    "#             drift_args = {'color':'pink', 'width':0.003},\n",
+    "#             plot_consensus=True,\n",
+    "#             plot_drift=True)\n",
+    "# plotter.run_plots(wait=0.5, freq=1,)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}