From 3125480b947682dfa56761590f607b5a42fd52e8 Mon Sep 17 00:00:00 2001 From: Jakob Robnik <43053552+JakobRobnik@users.noreply.github.com> Date: Fri, 13 Oct 2023 15:07:16 +0200 Subject: [PATCH 1/8] Created using Colaboratory --- notebooks/tutorials/intro_tutorial.ipynb | 74 +++++++++++++++--------- 1 file changed, 46 insertions(+), 28 deletions(-) diff --git a/notebooks/tutorials/intro_tutorial.ipynb b/notebooks/tutorials/intro_tutorial.ipynb index 1af1213..c1a19e9 100644 --- a/notebooks/tutorials/intro_tutorial.ipynb +++ b/notebooks/tutorials/intro_tutorial.ipynb @@ -3,11 +3,11 @@ { "cell_type": "markdown", "metadata": { - "colab_type": "text", - "id": "view-in-github" + "id": "view-in-github", + "colab_type": "text" }, "source": [ - "\"Open" + "\"Open" ] }, { @@ -21,30 +21,49 @@ }, { "cell_type": "markdown", + "source": [ + "\n", + "\n", + "First, let's import the MCHMC code.\n" + ], + "metadata": { + "id": "MbK7Gv7hIc-i" + } + }, + { + "cell_type": "code", + "source": [ + "!git clone https://github.com/JakobRobnik/MicroCanonicalHMC.git" + ], "metadata": { - "id": "rEAjgH6b6MiR" + "id": "zS-IBX_rIY8B" }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", "source": [ "We will be using jax, as it can automatically compute gradients." - ] + ], + "metadata": { + "id": "B42CrO21Ijph" + } }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 5, "metadata": { "id": "Rrzbg6xjz2gm" }, "outputs": [], "source": [ - "import sys \n", - "sys.path.insert(0, '../../')\n", - "\n", "import jax\n", "import jax.numpy as jnp\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", - "from sampling.sampler import Sampler" + "from MicroCanonicalHMC.sampling.sampler import Sampler" ] }, { @@ -100,7 +119,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 13, "metadata": { "id": "n2nW60C-zDIF" }, @@ -123,7 +142,7 @@ " \"\"\"Args: jax random key\n", " Returns: one random sample from the prior\"\"\"\n", "\n", - " return jax.random.normal(key, shape = (self.d, ), dtype = 'float64') * 4" + " return jax.random.normal(key, shape = (self.d, )) * 4" ] }, { @@ -148,7 +167,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 14, "metadata": { "id": "aMvFvS4Lz8Up" }, @@ -173,7 +192,7 @@ " `sampler.frac_tune1 = 0.05`\n", " before the sampling starts.\n", "\n", - " **WARNING: sometimes the default energy error 0.005 (which is used to tune the stepsize) is not optimal. Try decreasing it, say by a factor = 10 - 100 and accordingly increase the number of samples by factor^1/6 to see if the posteriors remain the same. On some other problems 0.005 is to conservative and you can improve the performance by increasing it.**" + " **WARNING: sometimes the default energy error 0.005 (which is used to tune the stepsize) is not optimal, especially if you are not interested in all of the parameters but specifialcally in the hardest-to-sample parameters (as is the case for example in hierarchical Bayesian models). Try decreasing the energy error, say by a factor = 10 - 100 and accordingly increase the number of samples by factor^1/6 to see if the posteriors remain the same. On some other problems 0.005 is to conservative and you can improve the performance by increasing it.**" ] }, { @@ -187,7 +206,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 16, "metadata": { "id": "sucJHLMi0Jfh" }, @@ -207,18 +226,18 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 17, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "yypd11zL5Cof", - "outputId": "d1093e20-4e6c-4ec1-d943-03167976ce01" + "outputId": "7a5cdb58-fdf0-4a35-df49-9a44fbf1bea1" }, "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ "(3, 5000, 2)\n" ] @@ -239,25 +258,25 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 18, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 455 }, "id": "FQWghKb41Yvy", - "outputId": "d8e9316d-5f37-4ad7-d44c-55818d06ae64" + "outputId": "b2035935-6e3b-422e-adb0-806be26b3c3f" }, "outputs": [ { + "output_type": "display_data", "data": { - "image/png": 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", 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4+eef4ebmhpkzZ2LmzJlwc3PDunXrSq138/zzz+OTTz5BcnIyIiMj8f3332PatGmIiIio8nuKiopCZGQkDh8+jLfffhv79u3Dzz//jODg4HI/x1dffYWHH34YH374IYYNG1bq7LZ7cXV1xZYtW9CyZUt89NFHmDFjBurUqYNt27aVuf/KlSvRuHFjTJkyBc899xxef/31cmesaZIsYvZUOURFRWHkyJFYuHAhOnXqhHnz5mHNmjWIi4uDl5fXPR+XkJCAhx9+GAEBAXB3dy/3dUcyMzOh1WqRkZFR4lRFIqp55RmlSZgdVuXnqMrzm7s7d+4gPj4eDRs2hFqtFh1HiJs3b8LT0xOvvPIKFi5cKDoOVUJ5vo8r8vlttiM9//vf/zBmzBiEh4ejRYsWWLhwITQaDZYsWXLPx+j1ejz//POYOXNmmafIERGRdcrLyyu1bfbs2QBQYh4O2TaznNOj0+lw7NgxTJ482bhNoVAgNDQUBw4cuOfj/vOf/8DLywsvvvgi9u3bd9/XyM/PR35+vvHrzMzMqgcnIiIh+vbtiwYNGqB9+/YwGAyIjo7Gpk2b0KVLF051ICOzLD1paWnQ6/Xw9vYusd3b2xtnz54t8zH79+/Hd999h5iYmHK9xqxZszBz5syqRiUiM5SZmYnMI+uRdXwzCtOTS96pUMKxUUe4dhgAlV8rizvllsrWr18//N///R/WrVuHvLw81KtXD2+//TamT59+33ViyLaYZempqKysLIwYMQKLFy+Gp6dnuR4zefJkREZGGr/OzMyEn59fdUUkohpw6dIlfPHFF1iyZEmJU45LMOiRd/4g8s4fhIN3I7h0GACn5t0gKe1rNiyZ1Ntvv423335bdAwyc2ZZejw9PaFUKnH9+vUS269fvw4fH59S+1+8eBEJCQklTpErXqTKzs4OcXFxaNSoUYnHqFQqqFSqakhPRDVNlmVMmzYNH330kfHfvr2HH1w6DIAmsBPwj8sT6LNvIevEZuSc2gXd9Yu4ufl/SN/3PbwGvw8HL84FJLJmZjmR2cHBAcHBwYiOjjZuKz5G27lz51L7N2vWDH/99RdiYmKMtyeffBI9e/ZETEwMR3CIrFh+fj6GDx+ODz74AAaDAX369MG2bdvg++JXcAl6HErnWlBqtMabg1dDePQZh7qvLYVb95FQOrtDn5mKlJXvIu/SMdFvh4iqkVmO9ABAZGQkRo0ahQ4dOiAkJATz5s1DTk4OwsPDAQAjR45E3bp1MWvWLKjVarRq1arE44svMvfv7URkPW7duoVBgwZh7969sLOzw6JFizB69GgAgLTr/qesKx1doe08BC7t+iJ13UfITzyJ1J9mwr3POLi07V0T8WuEma5KQlQupv7+NdvSM3ToUNy4cQPTpk1DSkoKgoKCsG3bNuPk5sTExFJX1CUi2xEfH4++ffvi7NmzcHFxwdq1ayt1arJC7QzvITNxc+sXyIndhVvbvkBhxnXIcl+LnuRsZ1f0472yF4kkMgfF37/F389VZbaLE9Y0Lk5IZD4etLBgYWYq9OumICUlBfXq1cOWLVvQunXrCj3Hv8myjIx93yPjQBSAoksMzJ8/v2LBzYgsyzh//jycnJxQt25d0XGIKuXq1avIyclB48aN7/lLSEU+v812pIeIqCxyYQFurJ8FXUoKWrZsie3bt5vkQ12SJLh1HwGl1hu3ts3Hl19+iY4dO2LkyJEmSF3zJEmCl5cXkpOToVKp4OTkZNEjV2RbZFlGTk4OMjMz4evra7LvXZYeIrIot6K/gS75PGrVqoWNGzeafBTDpW1v6LNuIOP3H/DKK6+gbdu2aNu2rUlfo6ZotVrk5eUhLS0NN27cEB2HqEIkSYKbm1uJi7xWFUsPEVmM7L+ikR2zFYCElStXomHDhtXyOtquz6Gzy21s27YNTz31FI4ePYpatWpVy2tVJ0mS4OvrCy8vLxQUFIiOQ1Qh9vb2Jl9YkqWHiCyC7vol3NqxAEBRKXniiSeq7bUkSYHvv/8ewcHBuHTpEkaOHIkNGzZY7MkTSqWSqxITwUzX6SEi+if9nWzcWP8R5EId1AHB0HZ9ttpf08PDA2vXroVKpcKmTZswa9asan9NIqpeHOkhIrN3a/sCFKanQKn1hme/CZAkRYXPzqqM4OBgLFiwAC+99BKmTp2KRx99tMwFUonIMnCkh4jMWl5CDHLP7gMkBWoPmASlo0uNvv6LL76IESNGQJZljBs3Dnq9vkZfn4hMh6WHiMyWrC/ArZ0LAQAu7fpC5dtYSI45c+bAzc0Nx48fxzfffCMkAxFVHUsPEZmtzKO/oPDWFSg0Wrh1Gy4sh5eXF/773/8CAN577z2kpaUJy0JElcfSQ0RmqTArDRl/rAYA1HrkBSjUzkLzREREoG3btrh9+zYmT54sNAsRVQ5LDxGZpdu7lkLW5cGhTlM4tX5MdBzY2dlhwYKiU+a/++47HD58WHAiIqoolh4iMjt3Ek8i98weABLce70KSTKPH1Vdu3bFyJEjIcsyxo4dy0nNRBbGPH6SEBHdVVDw9+Rl53ZPQOUTKDhRSR9//DFcXV1x9OhRfPfdd6LjEFEFsPQQkVlZsWIFCtISoXB0hVu3EaLjlOLj44OZM2cCAKZNm4a8vDzBiYiovFh6iMhsFBYW4sMPPwQAaB96psbX5CmvsWPHokGDBrh+/ToWL14sOg4RlRNLDxGZjVWrVuHSpUtQaLRwDqq+a2tVlb29PaZMmQKg6HDXnTt3BCciovJg6SEis6DX6/HBBx8AAFxDBkHhoBac6P5GjRoFPz8/XLt2DUuWLBEdh4jKgaWHiMxCVFQUzp8/Dw8PD7i0CxMd54FUKhUmTZoEAJg1axby8/MFJyKiB2HpISLh/jnKExkZCYWDo+BE5TN69GjUqVMHV65cwfLly0XHIaIHYOkhIuHWrl2LM2fOwM3NDePGjRMdp9zUajXeffddAMBHH32EgoICwYmI6H5YeohIKIPBYLyu1VtvvQVXV1fBiSpmzJgx8Pb2xuXLl7FixQrRcYjoPiRZlmXRIcxBZmYmtFotMjIyLO6HLpEl+/nnnzF48GC4urri8uXLcHNzg/+kzaJjPVDC7L/nHc2dOxcTJkxAQEAA4uLiYGdnJzAZkW2pyOc3R3qISBhZlvHRRx8BAN544w24ubmJDVRJERER8PT0xKVLl/Djjz+KjkNE98DSQ0TCHDhwAMeOHYNKpcL48eNFx6k0JycnvPHGGwCAL774QnAaIroXlh4iEmb+/PkAgGHDhsHT01Nwmqp5+eWX4eDggEOHDuHIkSOi4xBRGVh6iEiI5ORk/PTTTwCA119/XXCaqvP29saQIUMAAF9++aXgNERUFpYeIhJi0aJFKCwsRNeuXdGuXTvRcUyiuLytXr0aqampgtMQ0b+x9BBRjdPpdFi0aBEA6xjlKRYSEoKQkBDodDp8++23ouMQ0b+w9BBRjfvpp5+QkpICX19fPPXUU6LjmFTx4opff/01CgsLBachon9i6SGiGlc8gTkiIgL29vaC05jWkCFD4OXlhStXrmD9+vWi4xDRP3AFLSKqUUePHsXBgwdhb2+Pl19+WXScSrvfAor5jXoCqVGYP38+nn766RpMRUT3w5EeIqpRxWc2DRkyBD4+PoLTVA/ndk8ACiX27t2LkydPio5DRHex9BBRjblx4wZWr14NwLomMP+bnYsnNE26AODp60TmhIe3iKjGLFu2DPn5+ejQoQNCQkJEx6lWLu3DkHt2H75d9n/Y5tQLCpWm1D7/vH4XEVU/jvQQUY2QZRlLly4FULR6sSRJghNVL1W9lrBzrwe5IB85Z/eLjkNEYOkhohpy+PBhnDlzBo6Ojhg6dKjoONVOkiQ4twkFAOT8tVNwGiICWHqIqIYsWbIEADB48GC4uroKTlMznFo+CkgK5F89g4KbV0THIbJ5LD1EVO1yc3ONE5hHjx4tOE3NsXN2h2NAMAAg+9SvgtMQEUsPEVW7devWITMzE/7+/njkkUdEx6lRTq3vHuI69Rtkg15wGiLbxtJDRNWueALzCy+8AIXCtn7saAJDoHB0hT77Fu7EHxcdh8im2dZPHyKqcQkJCYiOjgYAjBo1SnCamicp7eHUogcAIPsvHuIiEomlh4iq1fLlywEAjz32GPz9/cWGEaT4LK7c84egz80QnIbIdrH0EFG1MRgMWLZsGQAgPDxcbBiBHLwC4ODdCDAUIuf0HtFxiGwWV2QmomqzZ88eJCQkwNXVFYMGDRIdRyin1qHQXb+I7L9+hWuHJwHc/6KlAFdsJjI1jvQQUbUpXpvn2WefhUZT+jIMtsSpxSOA0g4FqZegu35RdBwim8SRHiIyOf9Jm2HQ5eFK1BoAwC+5TbD9H6MatjiCoXR0hSbwIeTG7Uf2qd/g7t1IdCQim8ORHiKqFnkXDkEuyIddLV841GkqOo5ZcGrZEwCQe3Yf1+whEoClh4iqRfGEXafmj1j9xUXLy7FheyhUTkVr9iSdEh2HyObw8BYRmZw+LxN5dxfic2peegXmB03gtVaSnT00Tbsi++QO5J7eA8cGbUVHIrIpHOkhIpPLjfsDMOhh7xUAe08/0XHMiqZFUQnMPfcH5MICwWmIbAtLDxGZXM7p3QAApxbdxQYxQ2q/VlA6u8NwJ9s4GkZENYOlh4hM6urVq8hPigUAODVn6fk3SaGEplk3AEDOGS5USFSTWHqIyKSioqIAyFDVawE7Vy/RccxScRnMu3AIBl2e4DREtoOlh4hMatWqVQDKnsBMRRx8m8DOzRdyQT7yLhwSHYfIZrD0EJHJnDt3DseOHQMkBTTNHhYdx2xJkmQc7eG1uIhqDksPEZnMDz/8AABQ+7eDUqMVnMa8FZ/FlRd/HPq8TMFpiGwDSw8RmYQsy8bS49SCh7YexMGzPuy9GgIGfdEp/kRU7Vh6iMgkYmJiEBcXB7VaDU3jh0THsQjF8554FhdRzWDpISKTWL16NQCgX79+UKhs+4rq5VU8ryc/8RQKs24KTkNk/Vh6iKjKZFnGTz/9BAAYOnSo4DSWw07rdfdirDLyzh8QHYfI6rH0EFGVxcTE4NKlS3B0dMQTTzwhOo5FcWraFQCQE/e74CRE1o+lh4iqrHiUp2/fvnBychKcxrJo7pae/KRY6HNuC05DZN1YeoioSmRZxpo1awAATz/9tOA0lsdO6w0Hn8aAbEDu+YOi4xBZNZYeIqqSU6dO4fz581CpVAgLCxMdxyIVj/bknuUhLqLqxNJDRFVSfGjr8ccfh4uLi+A0lknTtAsA4E7iSS5USFSNWHqIqEqKSw8PbVWefa06sPcKAGQD8niIi6jasPQQUaWdPn0ap0+fhr29Pfr37y86jkXjWVxE1c+sS8+CBQvg7+8PtVqNTp064fDhw/fc9+eff0aHDh3g5uYGJycnBAUFYcWKFTWYlsj2rF27FgDQu3dvaLW81lZVFM/ruZPwJ/R3sgWnIbJOZlt6oqKiEBkZienTp+P48eNo27Yt+vTpg9TU1DL3d3d3x3vvvYcDBw7g5MmTCA8PR3h4OLZv317DyYlsBw9tmY69Rz3YezYADIXIu3BIdBwiq2S2ped///sfxowZg/DwcLRo0QILFy6ERqPBkiVLyty/R48eGDRoEJo3b45GjRph/PjxaNOmDfbv31/m/vn5+cjMzCxxI6LyO3fuHE6ePAk7Ozs8+eSTouNYBeNZXDzERVQtzLL06HQ6HDt2DKGhocZtCoUCoaGhOHDgwUu1y7KM6OhoxMXFoXv37mXuM2vWLGi1WuPNz8/PZPmJbEHxoa3HHnsM7u7ugtNYh+LSkxd/HIb8XMFpiKyPWZaetLQ06PV6eHt7l9ju7e2NlJSUez4uIyMDzs7OcHBwQFhYGObPn49evXqVue/kyZORkZFhvCUlJZn0PRBZOx7aMj17z/qwc68H6AuRd/HecxiJqHLsRAcwJRcXF8TExCA7OxvR0dGIjIxEQEAAevToUWpflUoFlUpV8yGJrEB8fDyOHz8OhUKBAQMGiI5jNSRJgqZpV2QeiEJu3B+i4xBZHbMsPZ6enlAqlbh+/XqJ7devX4ePj889H6dQKBAYGAgACAoKwpkzZzBr1qwySw8RVd6GDRsAAN27d0ft2rUFp7EumiadkXkgCnnxx5CXlwdHR0fRkYishlke3nJwcEBwcDCio6ON2wwGA6Kjo9G5c+dyP4/BYEB+fn51RCSyaevWrQMADBw4UGwQK+Tg3QhK19qQC/Kxc+dO0XGIrIpZlh4AiIyMxOLFi7F8+XKcOXMGr776KnJychAeHg4AGDlyJCZPnmzcf9asWdi5cycuXbqEM2fOYO7cuVixYgWGDx8u6i0QWaUbN24Yz4pk6TE9SZKgafwQAGD9+vViwxBZGbM8vAUAQ4cOxY0bNzBt2jSkpKQgKCgI27ZtM05uTkxMhELxd2fLycnBa6+9hitXrsDR0RHNmjXD999/j6FDh4p6C0RWadOmTTAYDGjXrh0aNGggOo5V0jTujKxjG7F89VpEew6EpFCWuV/CbF7glagizLb0AMC4ceMwbty4Mu/bvXt3ia8/+OADfPDBBzWQisi28dBW9VP5tYRC7QJDXibyr5yGun5r0ZGIrILZHt4iIvOTnZ2NHTt2AAAGDRokOI31khRKOAaGAAByzz14bTIiKh+WHiIqtx07diA/Px8BAQFo1aqV6DhWrXheT+75g5BlWXAaIuvA0kNE5VZ8aGvQoEGQJElwGuumbtgOkp0K+sxUFKTGi45DZBXMek4PEdU8/0mby9wu6wtxZU1R6fk+xQs/3WM/Mg2FvRrqhu2Qd/4gcs8dgIN3gOhIRBaPIz1EVC53kk7BkJ8DhUYLVZ1mouPYBE3jonXJcs9zXg+RKVS69ISHh+PgwYOmzEJEZizv7gevJrDTPU+hJtNyDOwISAoU3EhAQfq9rztIROVT6dKzfPlydO3aFa1bt8YXX3yB27dvmzIXEZkRWZaRe67olxzHJuVfFZ2qRunoCpVf0YTxPJ7FRVRllS4933//Pbp3747Y2Fi89dZbqFu3LkaMGIG9e/eaMh8RmQFdynnos29CcnCEY4O2ouPYFE0THuIiMpVKl55hw4Zh165dOH/+PCZOnAitVouVK1eiZ8+eaN68OebOnYu0tDRTZiUiQXLP3x3laRgMyc5BcBrbomncCQCQf+UM9DnpYsMQWbgqT2Ru1KgRZs+ejaSkJPz000/o06ePsQjVq1cPzz77bIkLhxKR5ckrLj13P4Cp5ti5esHBuxEAGXkXj4iOQ2TRTHb2lp2dHZ566ils2bIF8fHxGDt2LHQ6HdasWYPevXsjMDAQn332GXJzc031kkRUAwpuJ6MgLRGQFHBs1FF0HJvkGFhUNnMvHBKchMiymfyU9d9++w3vvPMOvv32WwCAo6MjunbtisuXL2PChAlo0aIFTp06ZeqXJaJqknfhMABAXb8VlGpnwWlsU/HqzHfiT8BQkC84DZHlMknpuX79OmbPno3GjRujV69eiIqKQmBgIL744gtcu3YNe/fuRXx8PCIiIpCYmIg33njDFC9LRDUg98LdQ1uBPLQlir1XQyhdakMuzMedy3+KjkNksSq9IrMsy9i2bRsWL16MzZs3o6CgACqVCs899xwiIiLw8MMPl9i/Xr16WLBgAeLi4ri+D5GF0OdlIT8pFgBLj0iSJEHTOARZxzcj7/xBaO5ejJSIKqbSpcff3x9XrlyBLMsIDAzEyy+/jPDwcHh4eDzwcbt27arsyxJRDcq7dBSQDbCv7Q97Nx/RcWyaY+BDyDq+GbkXD8NdNkCSuKA+UUVVuvRcu3YNgwYNQkREBEJDQ8v9uHfeeQcjRoyo7MsSUQ3KO180cZajPOKp67eC5OAIQ046dMnnoarTVHQkIotT6dKTlJQEH5+K/+bXpEkTNGnSpLIvS0Q1RC4sQF78MQDg4RQzICnt4RjQAbln9yH3wiGWHqJKqPT46JQpU7BkyZIH7rds2TKMHj26si9DRILcSfoLsi4PSmd3OPg2Fh2H8PdChcXrJhFRxVS69Cxbtgz79+9/4H6///47li9fXtmXISJBcosPbTUK4fwRM6EO6FB0AdK0RBTcThYdh8jiVPtPMp1OB6WSV2QmsiSyLCPv7kJ4XIXZfCjVzn9fgPTu+klEVH6VntNTHrIs4/jx46hdu3Z1vgwRmVhB6iXos9Ig2at4gVEzo2ncCfmJJ5F74SD8J21+4P4Js8NqIBWRZahQ6Xn00UdLfL1t27ZS24oVFhbi4sWLSElJ4dlaRBam+AKj6obteYFRM+MY2Am3oxcjPykW+rwsKB1dREcishgVKj27d+82/lmSJKSkpCAlJeWe+9vb26Nfv36YM2dOpQMSUc0rPnSi4anqZsfezQf2ng1QkHYZeZeOwrllT9GRiCxGhUpPfHw8gKLDVgEBAXj66afx6aeflrmvg4MDPD09YW9vX/WURFRjCjNvQHf9Ii8wasYcGz9UVHrOH2LpIaqACpWeBg0aGP88ffp0tGvXrsQ2IrJ8xaM8qjrNoNRoBaehsmgCQ5B5IAp58ccg6wsgKfnLJVF5VHoi8/Tp002Zg4jMRO7d0uPYmAsSmisH38ZQOLnBkJOOO0mxcPQPEh2JyCJw8Q0iMsrKysKdxKKreGsacT6PuZIkBTSNikpp8dICRPRg5S49CoUCdnZ2OHfuHABAqVSW+2ZnV61nxhORiezcuRPQF8LOzRd2HvVEx6H7KL4eWu6Fw5BlWXAaIstQ7jZSv359SJJknJjs5+cHSZKqLRgR1byNGzcCABwDQ/jv28yp/dtCsnOAPuM6CtIuw6G2v+hIRGav3KUnISHhvl8TkWXT6/XYtGkTAJ6qbgkU9mqoG7RF3sUjyLtwmKWHqBw4p4eIAACHDh1CWloaJJUTVPVaiI5D5fD3IS7O6yEqj2orPWlpaSgsLKyupyciEzMe2goIhqTkPDxLULyOku7aOehzbgtOQ2T+Kl16jh49iv/85z84ffp0ie3r1q2Dj48PvL294eHhgc8//7zKIYmo+v3yyy8AeGjLkti5eMDBJxCAjLyLR0THITJ7lS498+fPx0cffQRvb2/jtvj4eDz77LNITU2Fj48PcnJyEBkZWeLyFURkfi5evIjTp09DqVRCHRAsOg5VwD/P4iKi+6t06Tl48CDatWsHDw8P47YlS5agoKAAc+bMwdWrV3Ho0CEoFAqO9hCZueJDW926dYNS7Sw4DVWEJrBovZ47CScgF+oEpyEyb5UuPdevX0f9+vVLbNu5cyecnJwwbtw4AEBwcDC6deuGP//8s2opiahaFZeeJ598UnASqih7rwAoXTwhF+TjzmX+rCW6n0qXHr1eX2KicnZ2No4fP46uXbvCwcHBuL1OnTr3vRI7EYmVnp6OvXv3AgD69+8vOA1VlCRJcLw72sNDXET3V+nSU79+fRw7dsz49ebNm1FYWIjQ0NAS+2VmZkKr5UULiczVtm3bUFhYiGbNmiEwMFB0HKqEvy9JwdWZie6n0qWnf//+SExMxFNPPYX58+djwoQJUCgUGDBgQIn9Tpw4wSuxE5kxHtqyfOoGbSDZq6HPvgnd9Yui4xCZrUqXngkTJsDf3x/r16/H+PHjcfXqVbz55pto3LixcZ9Dhw7h6tWr6N69u0nCEpFpFRQUYMuWLQB4aMuSSXYOUDdsB4AXICW6n0qvQObp6YmTJ0/ip59+wo0bNxAcHIxHH320xD4pKSkYP348hg8fXuWgRGR6v//+O9LT0+Hh4YHOnTuLjkNVoAnshLxzB5B34TDcHn5edBwis1SlZVednZ3xwgsv3PP+AQMGlDrcRUTmo3hBwrCwMCiVSsFpqCocAzoAkKC7fhGFmWmwc/UUHYnI7PDaW0Q2SpZlzuexIkonN6jqNgMA5F3kWVxEZanyBXbi4+Oxb98+JCcnIz8/v8x9JEnC1KlTq/pSRGRCcXFxuHDhAhwcHNC7d2/RccgEHANDkH/1DPIuHIZLu76i4xCZnUqXHp1Oh5deegkrV64EgPueJsnSQ2R+ig9t9ezZEy4uLoLTkCk4BnZC+p7lyLv8Jwy6O1A4qEVHIjIrlS4906ZNw/fffw83NzcMHz4cTZo04Q9OIgtSfGiLZ21ZD3sPP9i5+aAwPQV3Ek5A04ST04n+qdKlZ9WqVXBzc+M6PEQWKC0tDX/88QcAlh5rUrQ6cydkHd2A3AuHWHqI/qXSE5lTU1PRrVs3Fh4iC7RlyxYYDAYEBQWVuoYeWbbiS1LkXTwC2aAXnIbIvFS69LDsEFmu4vk8HOWxPup6LSGpnGDIzYAu+ZzoOERmpdKlZ/To0di9ezdu3LhhyjxEVM3y8/Oxfft2ADxV3RpJSjs4BgQD4AVIif6t0qVn4sSJeOKJJ9CzZ0/s2rWLF7kjshC7d+9GdnY2fH190b59e9FxqBpoig9x8ZIURCVUeiJz8dWYL1++jNDQUNjb28PHxwcKRekeJUkSLl7kRfCIzME/z9oq698rWT51QAdAUqAgLRGXLl1CQECA6EhEZqHSpSchIaHE1zqdDomJiVXNQ0TVSJZlzuexAUq1M1R+LZGf+Bc2btyI8ePHi45EZBYq/WuewWCo0I2IxDt58iSSkpLg6OiIxx57THQcqkaawE4A/p60TkS89haRTdmwYQMAoFevXnB0dBSchqqT493Ss3fvXqSnp4sNQ2QmWHqIbEjxb/0DBgwQnISqm30tX9h71EdhYSG2bNkiOg6RWahy6dmxYwcGDRqEunXrQqVS4cUXXzTet337dkRGRuLatWtVfRkiqqIrV67g2LFjkCQJ/fr1Ex2HaoBjYx7iIvqnKpWe8ePH44knnsCGDRuQlZWFgoKCEqeu+/r6Yt68eYiKiqpyUCKqmuIPvs6dO8PLy0twGqoJxfN6tm7dCp1OJzgNkXiVLj3/93//h/nz5yM4OBjHjx9HZmZmqX3atGkDPz8/4ymyRCQOD23ZHoc6TeDj44PMzEzs2bNHdBwi4Spder7++mu4ublh8+bNCAoKuud+bdq0waVLlyr7MkRkApmZmfjtt98AsPTYEklSGJcmKJ7ETmTLKl16Tp06hS5duqB27dr33U+r1eL69euVfRkiMoHt27ejoKAATZo0QdOmTUXHoRpUfKmRX375hSvnk82r0pweSZIeuM+1a9d4aiyRYMW/5XOUx/Y89thj0Gg0SEpKQkxMjOg4REJVuvQ0btwYx48fR0FBwT33ycrKQkxMDFq2bFnZlyGiKiooKMDmzZsB8AKjtsjR0RG9e/cGwENcRJUuPc888wySk5MxadKke+4zefJkZGRk4Nlnn63syxBRFe3fvx/p6enw9PRE586dRcchAYpH+HjqOtm6SpeeN998E61bt8a8efPQuXNnzJ49GwBw8eJFfPbZZ+jevTu++uortGvXDmPGjDFZYCKqmOIPun79+kGpVApOQyKEhYVBoVDgxIkTvEYi2bRKlx5HR0f8+uuvePzxx3Ho0CG89957AIB9+/bh7bffxv79+9GrVy9s3boVDg4OJgtMROUnyzLn8xBq166NLl26AOBoD9m2Kk1krl27NjZv3owTJ05g9uzZePXVV/HKK6/gv//9Lw4ePIjt27c/8Oyu+1mwYAH8/f2hVqvRqVMnHD58+J77Ll68GN26dUOtWrVQq1YthIaG3nd/IlsQGxuL+Ph4qNVq9OrVS3QcEuifZ3ER2So7UzxJ27Zt0bZtW1M8lVFUVBQiIyOxcOFCdOrUCfPmzUOfPn0QFxdX5mqyu3fvxnPPPYcuXbpArVbj448/Ru/evREbG4u6deuaNBuRpSge5QkNDYWTkxMAwH/SZpGRSJABAwbgnXfewe7du5GRkQGtVis6ElGNk+RyLtzwf//3f1V6oZEjR1Zo/06dOqFjx4748ssvAQAGgwF+fn54/fXX7zt5upher0etWrXw5Zdflvna+fn5yM/PN36dmZkJPz8/ZGRkwNXVtUJZicxVSEgIjhw5gm+++cY4t46lx7YkzA4z/rlZs2aIi4vDDz/8wBNMyGpkZmZCq9WW6/O73CM9L7zwQrnW5fk3WZYhSVKFSo9Op8OxY8cwefJk4zaFQoHQ0FAcOHCgXM+Rm5uLgoICuLu7l3n/rFmzMHPmzHJnIrI0V65cwZEjRyBJknFVXrJtAwcOxMcff4z169ez9JBNKnfpmTZtWqnSc/HiRXz//ffQaDTo3bs3/P39AQCXL1/Gjh07kJOTg+HDh6NRo0YVCpWWlga9Xg9vb+8S2729vXH27NlyPce7776LOnXqIDQ0tMz7J0+ejMjISOPXxSM9RNai+NBWly5d4OPjIzgNifLPkb38a0VTA35c9wsOTFgPyc6+xEgQkbUrd+mZMWNGia/Pnz+PkJAQDB8+HPPmzSs1onL79m28+eab2LhxIw4ePGiSsOU1e/ZsrF69Grt374ZarS5zH5VKBZVKVaO5iGrSunXrABT9dk8EAA6+jaF0doc++xbuXI6BY6OOoiMR1ahKn701efJk1KpVC0uXLi3zEFKtWrXw3Xffwc3NrcRhqvLw9PSEUqksdc2u69evP/A31jlz5mD27NnYsWMH2rRpU6HXJbIWt2/fxu7duwEAgwYNEhuGzIYkKeDY+CEAQO75mv1llMgcVLr07N69Gw899NB9Fzuzs7PDQw89hD179lTouR0cHBAcHIzo6GjjNoPBgOjo6PuuKPvJJ5/gv//9L7Zt24YOHTpU6DWJrMmmTZug1+vRunXrCh9eJuumaVz0MzT3/CHIBr3gNEQ1q9KnrOfl5SE5OfmB+6WkpODOnTsVfv7IyEiMGjUKHTp0QEhICObNm4ecnByEh4cDKDobrG7dupg1axYA4OOPP8a0adOwatUq+Pv7IyUlBQDg7OwMZ2fnCr8+kSXjoS26F3X9VpBUTjDkpiP/WpzoOEQ1qtIjPW3atMG+ffvw66+/3nOf6Oho7N27t1KHmYYOHYo5c+Zg2rRpCAoKQkxMDLZt22ac3JyYmFiidH399dfQ6XR4+umn4evra7zNmTOn4m+OyILl5uZi27ZtAHhoi0qTlPbQ3J3Lk3eufGfDElmLcq/T82+//PILBg4cCAcHBwwbNgxDhw5FgwYNABSdvfXjjz9i5cqVKCgowLp168z+6s4VOc+fyJxt2LABAwcORIMGDRAfH1/qrEuu00M5Z/cjbcNs2Ln5QHfrWqWWIyEyF9WyTs+/Pfnkk/jqq68QGRmJZcuWYfny5SXul2UZKpUK8+fPN/vCQ2RN/nloix9mVBbHgGBAaY/C9BScOnUKrVu3Fh2JqEZU6TIUERER6Nu3L7777jvs378f165dAwD4+vqiW7duCA8PN67dQ0TVr7CwECuifgYArE71wXqO6lAZFA6OcGzYDnkXDmPdunUsPWQzqnztrfr163NlYyIzsW/fPhjuZEHh6ApVvRai45AZ0zR+CHkXDmP9+vWYNm2a6DhENaJKV1knIvNSfGjLMTAEkuLey0kQOQZ2AiQFTpw4gYSEBNFxiGoESw+RlZBlGevXrwcAaJrcez0rIgBQarTG0cDi7xsia8fSQ2Qljh8/jqSkJEj2aqgbBImOQxageKHC4hFCImvH0kNkJdauXQsAcGzYHgp7XleOHqx4RHD//v1ITU0VnIao+rH0EFkBWZbx008/AQA0TbsKTkOWwk7rhQ4dOsBgMPAQF9kElh4iK3Dq1CmcP38eKpWKV86mChk8eDAAGEszkTVj6SGyAsUfWH369IFCpRGchixJcen57bffcPPmTcFpiKoXSw+RFSiez/P0008LTkKWpnHjxmjbti30ej1++eUX0XGIqhVLD5GFO3v2LGJjY2Fvb4/+/fuLjkMWiIe4yFaw9BBZuOJRntDQULi5uYkNQxapeIRw586dyMjIEJyGqPqw9BBZuOLfzot/WyeqqObNm6N58+YoKCjAxo0bRcchqjYsPUQW7OLFi4iJiYFSqcSAAQNExyELVjzaw0NcZM1YeogsWPGhrZ49e8LT01NwGrJkxaVn27ZtyMrKEpyGqHqw9BBZMB7aIlNp3bo1AgMDkZ+fjy1btoiOQ1QtWHqILNTly5dx5MgRSJKEQYMGiY5DFk6SJB7iIqvH0kNkoX7++WcAQPfu3eHt7S04DVmD4tKzZcsW5ObmCk5DZHosPUQWioe2yNTat28Pf39/5ObmYuvWraLjEJmcnegARPQ3/0mbH7hPwuwwJCUl4Y8//oAkSXjqqadqIBnZAkmSMHjwYMydOxdr1qxhoSarw5EeIgv0448/AgC6deuGunXrCk5D1uTZZ58FAGzcuBE5OTmC0xCZFksPkQVavXo1AGDo0KGCk5C1CQ4ORkBAAHJzc7lQIVkdlh4iC3Px4kUcPXoUCoWCFxglk5MkyTjaExUVJTgNkWmx9BBZmOIPosceewxeXl6C05A1Ki49W7Zs4bW4yKqw9BBZmOLSw0NbVF1atWqF5s2bQ6fTYcOGDaLjEJkMSw+RBdGlJeLkyZOwt7fngoRUbf55iKt4/hiRNeAp60QWJPfMPgCAXf0gtP/kgOA0ZM2GDh2K6dOnY+fOnbh58yY8PDxERyKqMo70EFkIWZaRc7ao9Giadxechqxd06ZNERQUhMLCQuPq30SWjqWHyEIUpMaj8NYVSHYO0AR2Eh2HbAAPcZG1YekhshA5Z/cCABwDOkCh0ghOQ7ZgyJAhAIDdu3cjJSVFcBqiqmPpIbIAsiwj5wwPbVHNatiwITp16gSDwcArr5NVYOkhsgC65HPQZ1yHZK+GY6MOouOQDeEhLrImLD1EFiDn9G4AgGNgJyjs1WLDkE155plnIEkSfv/9dyQkJIiOQ1QlLD1EZk7WFyLnTNF8HueWPcSGIZtTt25d9OzZEwCwcuVKwWmIqobr9BCZuTsJJ2DIzYBCo4Xav53oOGRl/Cdtvu/9CbPDMHz4cPz2229YsWIFpkyZAkmSaigdkWlxpIfIzGXH7gIAODXvDknJ31Oo5g0ePBhqtRpxcXE4duyY6DhElcafoERmzJCfi7zzBwEATi0fFZyGbFHxSJCiYQhwZi8ejfgP3ENfMd6fMDtMVDSiCuNID5EZy437HXKhDnbu9eDgEyg6Dtkw55ZF83pyzuyFrC8UnIaoclh6iMxYzumiQ1vOrR7lPAoSSt2wPRQaLQy5GchLOCE6DlGlsPQQmanCzDTcufwXAMCpxSOC05CtkxRKON1dGDPn1G+C0xBVDksPkZkqWptHhsqvFey03qLjEBnnleVdOARDfo7gNEQVx9JDZIZkWUZObNFv005351IQiebgEwg793qQC3XIjftddByiCmPpITJDBamXUJCWCCjt4dS0q+g4RAAASZLg3KpotKd4KQUiS8LSQ2SGij9QNIGdoFA7C05D9DenFj0AAPmJf6EwM1VsGKIKYukhMjOyQY/c03sA8NAWmR87rRdUfq0AADmxu8WGIaoglh4iM5N36Sj0ObehcHSFY0B70XGISjEe4vrrV8iyLDgNUfmx9BCZmeyTOwEATq0ehaS0F5yGqDRN04ch2atRePsa9u3bJzoOUbmx9BCZEX32beRdOAwAcG7TW3AaorIpVBpomnUDAHz33XeC0xCVH0sPkRnJjo0GZANUdZrBwbO+6DhE9+TStqiUr1mzBhkZGYLTEJUPSw+RmZBl+e9DWxzlITPnUKcZ7D38kJeXh9WrV4uOQ1QuLD1EZmL//v0ovHUVkr0aTs0eFh2H6L4kSYJzm14AeIiLLIed6ABEtsR/0uZ73pe2+TMAgKZZNyhUmpqKRFRpTi0fRda+/8ORI0dw8uRJtGnTRnQkovviSA+RGTDk5yA3bj8ATmAmy6F0csOTTz4JgKM9ZBlYeojMQM6ZvZAL8mHv4QdV3Wai4xCV20svvQQA+P7775Gfny84DdH9sfQQmYHskzsAAM5tekGSJMFpiMqvd+/eqFevHm7duoX169eLjkN0Xyw9RILpbiRAl3weUCjh1PJR0XGIKkSpVOKFF14AwENcZP5YeogEy/5zO4Cii4sqndzEhiGqhPDwcADAr7/+ivj4eMFpiO6NpYdIIIMuD9l/RQMAnNv2EZyGqHICAgLQq1cvyLKMhQsXio5DdE8sPUQC5ZzeDVmXC7tavlA3bCc6DlGljR07FkDRIa68vDzBaYjKxtJDJIgsy8g6tgkA4NIuDJLEf45kufr164f69evj5s2biIqKEh2HqEz8KUskSP6VWBSkXYZkp4JT61DRcYiqRKlUIiIiAgCwYMECwWmIysbSQyRI1vGi1ZmdWjwCpdpZcBqiqnvppZfg4OCAo0eP4vDhw6LjEJXCy1AQCVCYfQu55/4AALi07yc4DVHl/fvSKvaNu0IXuwuPvjgZnmGRAICE2WEiohGVwpEeIgGyY7YBBj1UdVvAwTtAdBwikyku8Tln9kGfmyE4DVFJLD1ENUzWFyL7z20AAJf2/A2YrIuDbxM4+AQC+gJkn9wpOg5RCWZbehYsWAB/f3+o1Wp06tTpvseHY2NjMXjwYPj7+0OSJMybN6/mghJVUO75g9Bn34LCyQ2apl1ExyEyKUmS4NKuqMxnndgC2aAXnIjob2ZZeqKiohAZGYnp06fj+PHjaNu2Lfr06YPU1NQy98/NzUVAQABmz54NHx+fGk5LVDFZx++ept6mDySlveA0RKanad4dCrUL9JmpyLt0VHQcIiOzLD3/+9//MGbMGISHh6NFixZYuHAhNBoNlixZUub+HTt2xKeffopnn30WKpWqhtMSlZ8uNR75SacASQHnoCdExyGqFgp7FZzb9AIAZB3dKDgN0d/MrvTodDocO3YMoaF/r1uiUCgQGhqKAwcOmOx18vPzkZmZWeJGVN0yD/8MANA07Qo7V0/BaYiqj0v7MEBS4M7lGMTExIiOQwTADEtPWloa9Ho9vL29S2z39vZGSkqKyV5n1qxZ0Gq1xpufn5/JnpuoLFeuXEHOmb0AANeQpwSnIapedlpvaJo9DACYO3eu4DRERcyu9NSUyZMnIyMjw3hLSkoSHYms3Oeff150mrpfK6h8G4uOQ1Ttisv96tWr+TOWzILZlR5PT08olUpcv369xPbr16+bdJKySqWCq6triRtRdcnIyMCiRYsAAK6dBgtOQ1QzVD6BUNVvg8LCQp5VS2bB7EqPg4MDgoODER0dbdxmMBgQHR2Nzp07C0xGVHmLFy9GVlYW7D384BgQLDoOUY3RhgwCAHzzzTdIT08XG4ZsntmVHgCIjIzE4sWLsXz5cpw5cwavvvoqcnJyEB4eDgAYOXIkJk+ebNxfp9MhJqZospxOp8PVq1cRExODCxcuiHoLREY6nc74W65ryCBeTZ1sijqgA1q0aIHs7Gx88803ouOQjTPLn75Dhw7FnDlzMG3aNAQFBSEmJgbbtm0zTm5OTExEcnKycf9r166hXbt2aNeuHZKTkzFnzhy0a9cOL730kqi3QGQUFRWFq1evwsfHB04teoqOQ1SjJEnChAkTABTNa9PpdIITkS2TZFmWRYcwB5mZmdBqtcjIyOD8HjIZWZYRFBSEkydP4qOPPsKijDaiIxHVuLiZoWjYsCGSk5OxbNkyjBo1SnQksiIV+fw2y5EeImuxc+dOnDx5Ek5OToiIiBAdh0gIlUqFN954AwAwZ84c8HdtEoWlh6gazZ49GwDw0ksvoVatWoLTEIkTEREBZ2dnnDp1Cps3bxYdh2wUSw9RNdmzZw927doFe3t7REZGio5DJJSbmxtee+01AMDMmTM52kNCsPQQVZOZM2cCKBrlqV+/vuA0ROJNmDABGo0GR48exZYtW0THIRvE0kNUDf45yvPP5RWIbFnt2rUxbtw4AMCMGTM42kM1jmdv3cWzt8gU/CcVzVVI+WEK8hNPwrldX3j0fk1wKiKxEmaHGf9848YN+Pv7Izc3F5s2bUJYWNh9Hkn0YDx7i0igO4l/IT/xJKCwg/ahZ0THITIrHO0hkVh6iEws/fcfAADObXvDzrW24DRE5odze0gUO9EBiKwJR3mISis+7PtPdq2fAA6txVMvvYU71/pCkiQBycjWcKSHyIQ4ykNUPq4hT0GyV0GXcp6jPVRjWHqITOS3337jKA9ROSk1Wri07wcAmDp1KgwGg+BEZAtYeohMwGAwGC+q6BL0OEd5iMrBNeQpSA4anDhxAqtWrRIdh2wASw+RCaxcuRInTpyA5KCBtutzouMQWQSlRgtt56JR0SlTpiAvL09wIrJ2LD1EVZSXl4f33nsPAKDtPARKjVZwIiLL4RL8JPz8/JCUlITPP/9cdByyciw9RFX0+eefIykpCX5+fnAJ7i86DpFFUdir8NFHHwEAZs2ahRs3bghORNaMpYeoCm7cuGH8gf3RRx9BYa8SnIjI8gwbNgzt27dHZmYm/vOf/4iOQ1aMpYeoCmbOnImsrCy0b98ew4YNEx2HyCIpFArMmTMHALBw4ULExcUJTkTWiosTElVSXFwcFi5cCACYM2cOFAr+DkFUGcWLFzo26oi8i0fQrv8L8HrqfeP9/7x2F1FV8Kc0USVNnDgRer0e/fr1Q8+ePUXHIbJ4tXqMBiQF8s4fxJ3Ek6LjkBVi6SGqhA0bNmDjxo2ws7PDJ598IjoOkVWw9/SDc9ATAIBbO76GrC8QnIisDUsPUQVlZ2fj9ddfB1B04cTmzZsLTkRkPdy6j4BC44aCm0nIPLxOdByyMiw9RBU0Y8YMJCUlwd/fH1OnThUdh8iqKNXOqPXoiwCAjD9Wo+B2suBEZE1Yeogq4M8//8S8efMAAAsWLIBGoxEbiMgKObXoAXWDtpALdbi1cyFkWRYdiawESw9RORkMBkRERECv1+Ppp59G3759RUciskqSJMG992uA0g534o9hzZo1oiORlWDpISqnxYsX4+DBg3BxcTGO9hBR9bB3rwvtQ0MAAG+++SYyMjIEJyJrwNJDVA4pKSmYNGkSAOCDDz5A3bp1BScisn7ah56GXa06SE5ONl7fjqgqWHqIHkCWZYwZMwbp6elo3749XnvtNdGRiGyCZOdQdJgLRXPofvvtN8GJyNJxRWaiB/j222+xadMmODg4YNmyZbCz4z8bopri6B8E56DHkR2zDb0HDkWd0V9CoXYusQ9XbKby4k9von8oXg6/WMHta0he+gYAwKnrcLRu3VpELCKbVqvni7hz+U8U3k7GzZ1fo3b/iaIjkYXi4S2ie5ANeqRtmgu54A5U9VvDpeNA0ZGIbJLCwRGeYW8DkgK5p/cg5/Qe0ZHIQrH0EN1DxsE10F2Lg+SggWfftyBJ/OdCJIqqbjNoOw8FANza8RUKs9IEJyJLxMNbRGXITz6PjN9/AAC4934VdlovAKUPfxFRzdF2GYq8+KPQJZ/Hzc3z4DX0P/xlhCqE3y1E/2LIz0Haxk8Bgx6apg/DqUUP0ZGICICktINn2NuQ7FS4czmG1+aiCmPpIfoHWTYgbdP/UHj7GpQunnDv8xokSRIdi4jusveoh1qPvQQASN+zHHkJMWIDkUVh6SH6h4wDPyLvwiFAaYfag6ZA6egqOhIR/Ytz28fh1CoUkA1I++UTXL58WXQkshAsPUR3bd26FRn7VgIA3Hu9BpVvE8GJiKgsRdfmehUO3o1gyMvE4MGDkZeXJzoWWQCWHiIAFy9exLBhwwDIcA56HC5te4uORET3obBXofag96BwdMWxY8fw2muv8Wrs9EAsPWTzcnJyMGjQIKSnp8OhTlO4P/aK6EhEVA52Wi94PvkOFAoFli1bhoULF4qORGaOpYdsWmFhIZ599ln89ddf8Pb2Ru2BUyDZ2YuORUTl5OgfhNmzZwMA3njjDWzdulVwIjJnLD1ks2RZxssvv4xNmzZBrVbj559/hp2Lh+hYRFRBEyZMwIgRI1BYWIinn34ahw4dEh2JzBRLD9msKVOmYOnSpVAqlfjxxx/RpUsX0ZGIqBIkScJ3332Hxx9/HLm5uejbty/OnDkjOhaZIZYesknz5s0zDol/88036N+/v+BERFQV9vb2WLNmDUJCQnDr1i306dMHV65cER2LzIwkc7o7ACAzMxNarRYZGRlwdeXaLNao+BISOaf3FK24DMCt+0hoOw8RGYuIqihhdpjxz2lpaXj44YcRFxeHli1bYt++fahVq5bAdFTdKvL5zZEesik5p/cgbdNcAIBLcH+4PvSM4EREZEqenp7Yvn076tSpg9jYWISGhiItjRcnpSIsPWQzsk/uQNrGOYBsgFOrR1HrsTG8xASRFWrQoAG2b98OLy8vHD9+HD169EBycrLoWGQGWHrIJsyfPx83t36B4sUHPfq+yaszE1mxVq1aYc+ePcYRn+7duyMxMVF0LBKMP/XJ6n388cd44403AAAuHQbAvfdYFh4iG9CsWTPs27cP/v7+uHDhArp164YLFy6IjkUC8Sc/WS29Xo8JEyZg0qRJAABtl2dR69GXeEiLyIYEBARg3759aNKkCRITE9GtWzccPnxYdCwShKWHrFJGRgb69euHuXOLJi3PmjULbt2Gs/AQ2aB69eph7969aNOmDVJSUtC9e3esXLlSdCwSgKWHrM758+fRqVMnbNu2DY6Ojli9erVxtIeIbJO3tzf27duH/v37Iz8/H8OHD8ekSZOg1+tFR6MaxNJDVmXnzp0ICQlBXFwc6tWrh3379mHo0KGiYxGRGXB1dcX69esxefJkAEXz/QYOHIjMzEzByaimsPSQVdDpdJg8eTL69OmD9PR0dO7cGUeOHEFwcLDoaERkRhQKBT766COsXLkSarUamzZtQrt27fDHH3+IjkY1gCsy38UVmS3XmTNn8Pzzz+PEiRMAgJdeeglffvklVCpVif2KV2QmItvyzxWb/+nIkSN45plncPnyZSgUCrz33nuYOnUq7O3tazghVQVXZCabIMsyFixYgPbt2+PEiRNwd3fH2rVrsXjx4lKFh4jo3zp27Ig///wTI0aMgMFgwH//+1907doV586dEx2NqglHeu7iSI9lOXXqFMaNG4c9e/YAAPr06YMlS5agTp0693wMR3qI6F4+bpeNiIgIpKenw9HREVOmTMGECROgVqtFR6MH4EgPWa309HS8+eabCAoKwp49e+Do6Ij58+dj69at9y08RET3M3ToUPz1118IDQ1FXl4epk6dipYtW2LTpk2io5EJsfSQRdDr9Vi6dCmaNm2Kzz//HHq9HoMHD8aZM2cwbtw4rr9DRFVWr1497NixAz/88APq1KmDS5cuoX///ujXrx/i4uJExyMT4OGtu3h4yzzp9XqsXr0a//nPf4zH2Zs2bYr58+ejV69eJfbl4Ssiqqx/T3bOysrCBx98gM8++wwFBQVQKBR4/vnn8f7776NJkyaCUlJZKvL5zdJzF0uPeSmr7Li7u2PSpEkYP348HBwcSj2GpYeITK3gZhJu71qCvItHABSd8j5s2DC8//77aNq0qeB0BLD0VApLj3j+kzZDn5uB7JM7kHViK/SZqQAAhdoFriGDcHnj/Pv+v2HpIaLq8tPT3pg5c6Zxjo8kSQgLC8Nrr72GPn36QKHgbBFROJGZLIosyzhw4ADSNs3Fla9GIX3PcugzU6FQu8Ct+0jUjfgO2s5DWEaJSJgOHTpg48aNOHLkCPr16wdZlrFp0yb07dsXjRs3xpw5c5CWliY6Jj0ASw8Jc+rUKbz//vto3LgxunTpgpzYXYC+EA4+jeHR903UfW0ZtJ2HQKHSiI5KRATg7/Jz9uxZvPnmm9Bqtbh06RImTpwIX19fhIWFYcWKFby0hZni4a27eHir+un1ehw5cgRbt27F2rVrERsba7xPo9FACugMl/ZhUPlykiARWQaD7g5yzuyBf+rvOH78uHG7SqVC37590b9/fzz++OPw9fUVmNK6cU5PJbD0mJ4sy0hISMDevXuxbds27NixA7du3TLe7+DggCeeeALPPvss+vfvj5b/3S0sKxFRVSTMDsPZs2cRFRWFH374odQp7kFBQXj88cfRq1cvdOrUCU5OToKSWh+Wnkpg6am63Nxc/PXXXzh48CB+//137N+/H8nJySX20Wq16N27N8LCwjBgwAC4ubkZ7+NEZCKyVP885V2WZfz555/4+eefsXXrVhw9erTEvkqlEu3atUPXrl3RtWtXBAcHo2HDhlxvrJJYeiqBpaf8dDodLl26hLi4OJw5cwZ//vknYmJicO7cORgMhhL72tnZITg4GGcU/nAMaA9VnWaQFEpByYmIqse9LmoKAKmpqWjx4ifIu3QU+Umx0GeVnvAsOWjQ7aEOCAoKQuvWrdGkSRM0bdoUXl5eLEMPUJHPb7saylQpCxYswKeffoqUlBS0bdsW8+fPR0hIyD33X7NmDaZOnYqEhAQ0btwYH3/8Mfr27VuDia1Dbm4uUlJScO3aNVy+fBmXL19GYmIiLl++jAsXLiA+Ph56vb7Mx/r4+KB9+/bG32A6duwIjUbDURwislleXl5wbtkTzi17AgAKM1ORf+UM8q+eRv7Vs9ClXYasy8XevXuxd+/eEo/VarVo0qQJGjZsiPr166NBgwZo0KAB/Pz84OvrC09PTyiV/EWyvMx2pCcqKgojR47EwoUL0alTJ8ybNw9r1qxBXFwcvLy8Su3/xx9/oHv37pg1axb69euHVatW4eOPP8bx48fRqlWrB76etY306HQ6ZGdnIycnB9nZ2cjIyChxS09Px82bN423tLQ0XL9+HSkpKeU668DZ2RlNmjRBkyZN0LZtW3x2XAcHrwAonWvVwLsjIrIesr4QBbeu4OMerjhx4gROnz6Nc+fOISEhAQ/6iFYqlfDy8oKPjw9q164NDw8PeHh4wNPTE+7u7tBqtSVuLi4ucHZ2hrOzMzQajVWsL2QVh7c6deqEjh074ssvvwQAGAwG+Pn54fXXX8ekSZNK7T906FDk5OSUuDjcQw89hKCgICxcuPCBr1ddpSc5ORlr166FwWCAwWCAXq8v8efCwkLo9XrjrfjrwsJCFBYWoqCgADqdDgUFBcY/5+fnl7jduXMHeXl5xltubi4KCgqqlFutVsPX17fEbxYNGjRAQEAAmjZtCl9f3xJDrhzJISKqmn8fIrtz5w4uXLiAc+fO4fLly5i+cjcKs25An5GKwqw0GHIzAVTtI1yj0cDR0dF4S0gvgGTnAElpB0lpD0lpD9jZQ1LYQVIoMaRTQ9jb28Pe3h52dnZQKpWws7Mz/vnfN4VCAYVCYfyzr68vnn766Spl/jeLP7yl0+lw7NgxTJ482bhNoVAgNDQUBw4cKPMxBw4cQGRkZIltffr0wfr168vcv7gwFMvIyAAAk6+tEBsbi9dff92kz1khCiXcXF3g4uICV1dXuLq6QqvVwtXVFR4eHnB3d4e7uzs+/DUJSictlBo3KJ3cIDloUChJuATgEgBkATgF4FQaAC7ARURkavXfWnOfe+vBrdvwEltkgx76vEzos2/DkJuOD57wx61bt3Dz5k3cunULt2/fRmZmJjIyMoz/zcnJQU5OjvE5cnNzkZubW+6MS0/vruC7Kqljx47o3bt3lZ7j34o/t8szhmOWpSctLQ16vR7e3t4ltnt7e+Ps2bNlPiYlJaXM/VNSUsrcf9asWZg5c2ap7X5+fpVMbaYMeqSnpyM9PV10EiIiqkYRG0QneLAjR45Aq9VWy3NnZWU98LnNsvTUhMmTJ5cYGTIYDLh16xY8PDxMPlM+MzMTfn5+SEpKsor5Qv9m7e8PsP73yPdn+az9PfL9Wb7qeo+yLCMrKwt16tR54L5mWXqKZ6Nfv369xPbr16/Dx8enzMf4+PhUaH+VSgWVSlVi2z/XjKkOxYeXrJW1vz/A+t8j35/ls/b3yPdn+arjPZZ39Mgsp207ODggODgY0dHRxm0GgwHR0dHo3LlzmY/p3Llzif0BYOfOnffcn4iIiGyLWY70AEBkZCRGjRqFDh06ICQkBPPmzUNOTg7Cw8MBACNHjkTdunUxa9YsAMD48ePxyCOPYO7cuQgLC8Pq1atx9OhRfPPNNyLfBhEREZkJsy09Q4cOxY0bNzBt2jSkpKQgKCgI27ZtM05WTkxMLLG+QJcuXbBq1Sq8//77mDJlCho3boz169eXa42e6qZSqTB9+vRSh9OshbW/P8D63yPfn+Wz9vfI92f5zOE9mu06PURERESmZJZzeoiIiIhMjaWHiIiIbAJLDxEREdkElh4iIiKyCSw9guTn5yMoKAiSJCEmJkZ0HJN58sknUb9+feMFS0eMGIFr166JjmUyCQkJePHFF9GwYUM4OjqiUaNGmD59OnQ6nehoJvPhhx+iS5cu0Gg01b5gZ01ZsGAB/P39oVar0alTJxw+fFh0JJPZu3cv+vfvjzp16kCSpHteb9BSzZo1Cx07doSLiwu8vLwwcOBAxMXFiY5lMl9//TXatGljXLCvc+fO2Lp1q+hY1Wb27NmQJAlvvvmmkNdn6RHknXfeKdeS2ZamZ8+e+PHHHxEXF4e1a9fi4sWLJr+irkhnz56FwWDAokWLEBsbi88++wwLFy7ElClTREczGZ1Oh2eeeQavvvqq6CgmERUVhcjISEyfPh3Hjx9H27Zt0adPH6SmpoqOZhI5OTlo27YtFixYIDpKtdizZw/Gjh2LgwcPYufOnSgoKEDv3r1LXDTTktWrVw+zZ8/GsWPHcPToUTz66KMYMGAAYmNjRUczuSNHjmDRokVo06aNuBAy1bgtW7bIzZo1k2NjY2UA8okTJ0RHqjYbNmyQJUmSdTqd6CjV5pNPPpEbNmwoOobJLV26VNZqtaJjVFlISIg8duxY49d6vV6uU6eOPGvWLIGpqgcAed26daJjVKvU1FQZgLxnzx7RUapNrVq15G+//VZ0DJPKysqSGzduLO/cuVN+5JFH5PHjxwvJwZGeGnb9+nWMGTMGK1asgEajER2nWt26dQsrV65Ely5dYG9vLzpOtcnIyIC7u7voGFQGnU6HY8eOITQ01LhNoVAgNDQUBw4cEJiMKisjIwMArPLfnF6vx+rVq5GTk2N1l1AaO3YswsLCSvxbFIGlpwbJsowXXngBERER6NChg+g41ebdd9+Fk5MTPDw8kJiYiA0bNoiOVG0uXLiA+fPn45VXXhEdhcqQlpYGvV5vXMm9mLe3N1JSUgSlosoyGAx488030bVrV7NYbd9U/vrrLzg7O0OlUiEiIgLr1q1DixYtRMcymdWrV+P48ePGy0aJxNJjApMmTYIkSfe9nT17FvPnz0dWVhYmT54sOnKFlPf9FZs4cSJOnDiBHTt2QKlUYuTIkZDNfOHvir5HALh69Soef/xxPPPMMxgzZoyg5OVTmfdHZG7Gjh2LU6dOYfXq1aKjmFTTpk0RExODQ4cO4dVXX8WoUaNw+vRp0bFMIikpCePHj8fKlSuhVqtFx+FlKEzhxo0buHnz5n33CQgIwJAhQ7Bx40ZIkmTcrtfroVQq8fzzz2P58uXVHbVSyvv+HBwcSm2/cuUK/Pz88Mcff5j1cG1F3+O1a9fQo0cPPPTQQ1i2bFmJ68CZo8r8P1y2bBnefPNNpKenV3O66qPT6aDRaPDTTz9h4MCBxu2jRo1Cenq61Y1CSpKEdevWlXiv1mLcuHHYsGED9u7di4YNG4qOU61CQ0PRqFEjLFq0SHSUKlu/fj0GDRoEpVJp3KbX6yFJEhQKBfLz80vcV93M9oKjlqR27dqoXbv2A/f74osv8MEHHxi/vnbtGvr06YOoqCh06tSpOiNWSXnfX1kMBgOAolP0zVlF3uPVq1fRs2dPBAcHY+nSpWZfeICq/T+0ZA4ODggODkZ0dLSxCBgMBkRHR2PcuHFiw1G5yLKM119/HevWrcPu3butvvAARd+j5v4zs7wee+wx/PXXXyW2hYeHo1mzZnj33XdrtPAALD01qn79+iW+dnZ2BgA0atQI9erVExHJpA4dOoQjR47g4YcfRq1atXDx4kVMnToVjRo1MutRnoq4evUqevTogQYNGmDOnDm4ceOG8T4fHx+ByUwnMTERt27dQmJiIvR6vXEdqcDAQOP3rCWJjIzEqFGj0KFDB4SEhGDevHnIyclBeHi46GgmkZ2djQsXLhi/jo+PR0xMDNzd3Uv9zLFEY8eOxapVq7Bhwwa4uLgY52JptVo4OjoKTld1kydPxhNPPIH69esjKysLq1atwu7du7F9+3bR0UzCxcWl1Pyr4jmfQuZlCTlnjGRZluX4+HirOmX95MmTcs+ePWV3d3dZpVLJ/v7+ckREhHzlyhXR0Uxm6dKlMoAyb9Zi1KhRZb6/Xbt2iY5WafPnz5fr168vOzg4yCEhIfLBgwdFRzKZXbt2lfn/a9SoUaKjmcS9/r0tXbpUdDSTGD16tNygQQPZwcFBrl27tvzYY4/JO3bsEB2rWok8ZZ1zeoiIiMgmmP9kBCIiIiITYOkhIiIim8DSQ0RERDaBpYeIiIhsAksPERER2QSWHiIiIrIJLD1ERERkE1h6iIiIyCaw9BAREZFNYOkhIiIim8DSQ0RW4fnnn4ckSfjggw9K3XfgwAFoNBp4eHjg7NmzAtIRkTngtbeIyCpcvHgRzZs3h7OzM+Lj46HVagEA58+fR5cuXZCTk4Nff/0VXbp0EZyUiEThSA8RWYVGjRrhxRdfxO3bt/HZZ58BAG7cuIEnnngCt2/fxg8//MDCQ2TjWHqIyGpMnToVjo6OmDdvHq5evYr+/fvj4sWL+OqrrzBgwADjfkePHsXIkSMRGBgISZLw/vvvC0xNRDWFpYeIrEadOnUwbtw4ZGRkICgoCIcOHcLUqVPx8ssvl9jv999/x8GDB/Hwww8bD4MRkfXjnB4isirJycmoV68eDAYDXnjhBSxdurTUPgaDAQpF0e98/v7+GD58eJkToInIunCkh4ishizLiIyMhMFgAADY2dmVuV9x4SEi28J/+URkNSZOnIjVq1ejb9++8PX1xbJly3D+/HnRsYjITLD0EJFV+PzzzzF37lyEhIRgzZo1mDRpEgoLCzF16lTR0YjITLD0EJHFW7NmDd566y00atQImzZtgkajwcsvv4y6devixx9/RExMjOiIRGQGWHqIyKLt3bsXI0aMgKenJ7Zt24batWsDANRqNSZPngxZlvHee+8JTklE5oClh4gs1unTpzFgwAAolUps3LgRgYGBJe4fM2YM/Pz8sGXLFuzfv19QSiIyF2Wf2kBEZAFatGiB27dv3/N+BwcHJCYm1mAiIjJnLD1EZHNu3LiBPXv2AAByc3Nx9uxZ/PTTT3BycsITTzwhOB0RVRcuTkhENmf37t3o2bNnqe0NGjRAQkJCzQciohrB0kNEREQ2gROZiYiIyCaw9BAREZFNYOkhIiIim8DSQ0RERDaBpYeIiIhsAksPERER2QSWHiIiIrIJLD1ERERkE1h6iIiIyCaw9BAREZFNYOkhIiIim/D/6JAOt7BHGE4AAAAASUVORK5CYII=\n" }, - "metadata": {}, - "output_type": "display_data" + "metadata": {} } ], "source": [ @@ -277,9 +296,8 @@ ], "metadata": { "colab": { - "authorship_tag": "ABX9TyNK51le231/TYVAdYnFNA86", - "include_colab_link": true, - "provenance": [] + "provenance": [], + "include_colab_link": true }, "kernelspec": { "display_name": "Python 3", @@ -306,4 +324,4 @@ }, "nbformat": 4, "nbformat_minor": 0 -} +} \ No newline at end of file From 3e96e36dcabd55847c6784268b9e90874af2d055 Mon Sep 17 00:00:00 2001 From: Jakob Robnik <43053552+JakobRobnik@users.noreply.github.com> Date: Fri, 13 Oct 2023 15:27:10 +0200 Subject: [PATCH 2/8] Delete notebooks/benchmark_sampling.py --- notebooks/benchmark_sampling.py | 396 -------------------------------- 1 file changed, 396 deletions(-) delete mode 100644 notebooks/benchmark_sampling.py diff --git a/notebooks/benchmark_sampling.py b/notebooks/benchmark_sampling.py deleted file mode 100644 index 480f2f5..0000000 --- a/notebooks/benchmark_sampling.py +++ /dev/null @@ -1,396 +0,0 @@ -import numpy as np -import pandas as pd -import matplotlib.pyplot as plt -import os -import jax -import jax.numpy as jnp - -num_cores = 6 #specific to my PC -os.environ["XLA_FLAGS"] = '--xla_force_host_platform_device_count=' + str(num_cores) - -import sys,os -home = os.getcwd() + '/../' -sys.path.append(home) -from sampling import sampler as mchmc -from sampling import standardKinetic -from benchmarks.benchmarks_mchmc import * -from sampling import grid_search -from HMC import myHMC -from benchmarks import german_credit - - -print(jax.local_device_count()) -### Runs the bencmark problems. """ - -def parallel_run(function, values): - parallel_function= jax.pmap(jax.vmap(function)) - results = jnp.array(parallel_function(values.reshape(num_cores, len(values) // num_cores))) - return results.reshape([len(values), ] + [results.shape[i] for i in range(2, len(results.shape))]) - - -def bounce_frequency(d, alpha, generalized = False): - """For scaning over the L parameter""" - - - length =alpha * jnp.sqrt(d) - sampler = mchmc.Sampler(StandardNormal(d=d), 'LF', generalized) - eps= 7.3 * jnp.sqrt(d / 100.0) - - def f(l): - sampler.set_hyperparameters(l, eps) - return sampler.sample(30000, 10, output= 'ess') - - ess = parallel_run(f, length) - - return jnp.average(ess, 1), jnp.std(ess, 1) - - #eta = (2.93 * np.power(d, -0.78) * np.logspace(-0.8, 0.8, 24))[n] - - #length = 1.5 * np.sqrt(d) - #sampler = standardKinetic.Sampler(Target = IllConditionedGaussian(d= d, condition_number=100), eps= 3) - #sampler = ESH.Sampler(Target= Rosenbrock(d= d), eps= 0.5) - #a= np.sqrt(np.concatenate((np.ones(d//2) * 2.0, np.ones(d//2) * 10.498957879911487))) - #sampler = ESH.Sampler(Target= DiagonalPreconditioned(Rosenbrock(d= d), a), eps= 0.5) - #return [ess, length, sampler.eps, d] - - - -def full_bias(): - length = [2, 5, 30, 90, 1e20] - d= 200 - steps = 1000000 - reduced_steps = mchmc.point_reduction(steps, 100) - bias = np.empty((len(length), len(reduced_steps))) - for n in range(len(length)): - print(n) - sampler = mchmc.Sampler(StandardNormal(d= d), length[n], 1.0, 'LF', False) - bias[n, :] = sampler.sample(steps, output= 'ess') - - np.save('data/full_bias.npy', bias) - - - -def ill_conditioned(): - condition_numbers = jnp.logspace(0, 5, 18) - integrator= 'MN' - generalized = True - name_sampler = integrator + ('_g' if generalized else '') - - targets = [IllConditionedGaussian(d= 100, condition_number= kappa) for kappa in condition_numbers] - num_samples = [2000 * (int)(np.power(kappa, 0.3)) for kappa in condition_numbers] - - - def ESS(alpha, eps, target, num_samples): #sequential mode. Only runs a handful of chains to average ESS over the initial conditions - sampler = mchmc.Sampler(target, alpha* np.sqrt(target.d), eps, integrator, generalized) - return jnp.average(sampler.sample(num_samples, 12, output = 'ess', tune= 'none')) - - def std_ESS(alpha, eps, target, num_samples): #sequential mode. Only runs a handful of chains to average ESS over the initial conditions - sampler = mchmc.Sampler(target, alpha * np.sqrt(target.d), eps, integrator, generalized) - return jnp.std(sampler.sample(num_samples, 12, output= 'ess', tune = 'none')) - - - borders_eps = np.array([[7.0 / np.power(kappa, 0.25) / 1.5, 7.0 / np.power(kappa, 0.25) * 1.5] for kappa in condition_numbers]) - - if integrator == 'MN': - borders_eps *= np.sqrt(10.9) - - borders_alpha = np.array([[0.5 * np.power(kappa, 0.1), 2 * np.power(kappa, 0.1)] for kappa in condition_numbers]) - results = np.array([grid_search.search_wrapper(lambda a, e: ESS(a, e, targets[i], num_samples[i]), borders_alpha[i, 0], borders_alpha[i, 1], borders_eps[i, 0], borders_eps[i, 1], save_name = 'kappa_num'+str(i)) for i in range(len(targets))]) - - ess_errors = np.array([std_ESS(results[i, 1], results[i, 2], targets[i], num_samples[i]) for i in range(len(condition_numbers))]) - - df = pd.DataFrame({'Condition number': condition_numbers, 'ESS': results[:, 0], 'err ESS': ess_errors, 'alpha': results[:, 1], 'eps': results[:, 2]}) - - df.to_csv('submission/MCHMC/ICG/New__' + name_sampler + '.csv', index=False) - print(df) - - - -def ill_conditioned_tuning_free(): - condition_numbers = jnp.logspace(0, 5, 18) - integrator= 'LF' - generalized = True - - targets = [IllConditionedGaussian(d= 100, condition_number= kappa) for kappa in condition_numbers] - num_samples = [2000 * (int)(np.power(kappa, 0.4)) for kappa in condition_numbers] - print([k//40 for k in num_samples]) - - def ESS(target, num_samples): #sequential mode. Only runs a handful of chains to average ESS over the initial conditions - sampler = mchmc.Sampler(target, integrator= integrator, generalized= generalized) - sampler.num = 40 - ess = sampler.sample(num_samples, 12, output= 'ess', tune= 'cheap') - return jnp.average(ess), jnp.std(ess) - - results = np.array([ESS(targets[i], num_samples[i]) for i in range(len(targets))]) - - df = pd.DataFrame({'Condition number': condition_numbers, 'ESS': results[:, 0], 'err ESS': results[:, 1]}) - - df.to_csv('submission/MCHMC/ICG/tuning_free'+'_'+integrator +('_g' if generalized else '')+'.csv', index=False) - print(df) - - - -def dimension_dependence(): - - dimensions = [100, 300, 1000, 3000] - alpha = (1.5 * jnp.logspace(-0.8, 0.8, 12)) - #condition_numbers = np.logspace(0, 5, 18) - dict = {'alpha': alpha} - generalized = False - for d in dimensions: - print(d) - avg, std = bounce_frequency(d, alpha, generalized) - dict.update({'ess (d='+str(d)+')': avg, 'err ess (d='+str(d)+')': std}) - df = pd.DataFrame.from_dict(dict) - df.to_csv('data/dimensions/StandardNormal'+('_g' if generalized else '')+'_eps4.csv', sep='\t', index=False) - - - -def table1(): - """For generating Table 1 in the paper""" - - #version of the sampler - q = 0 #choice of the Hamiltonian (q = 0 or q = 2) - generalized = True #choice of the momentum decoherence mechanism - alpha = -1.0 #bounce frequency (1.0 for generalized, 1.6 for bounces, something very large if no bounces). If -1, alpha is tuned by a grid search. - integrator = 'LF' #integrator (Leapfrog (LF) or Minimum Norm (MN)) - HMC = False - - #name of the version - if alpha > 1e10: - generalized_string= 'no-bounces_' - alpha_string = '' - else: - generalized_string = 'generalized_' if generalized else 'bounces_' - alpha_string = '_tuning-free' if (alpha > 0) else '' - #parallel_string = '_parallel' if parallel else '' - name_sampler = generalized_string + integrator + '_q=' + str(q) + alpha_string #parallel_string - - if HMC: - name_sampler = 'HMC' + '_' + integrator - - print(name_sampler) - - #targets - indexes = [0, 1, 2, 3, 4, 5] - names = ['Ill-Conditioned', 'Bi-Modal', 'Rosenbrock', "Neal's Funnel", 'German Credit', 'Stochastic Volatility'] - targets = [IllConditionedGaussian(100, 100.0), BiModal(), Rosenbrock(), Funnel(), german_credit.Target(), StochasticVolatility()] - - - #dimensions = [100, 300, 1000, 3000, 10000] - #names= [str(d) for d in dimensions] - #targets= [StandardNormal(d) for d in dimensions] - #targets = [IllConditionedGaussian(d, 100.0) for d in dimensions] - #targets= [Rosenbrock(d) for d in dimensions] - - key = jax.random.PRNGKey(0) - - if HMC: - - def ESS(length, eps, target, num_samples): #sequential mode. Only runs a handful of chains to average ESS over the initial conditions - return jnp.average(myHMC.Sampler(Target=target, L = length, eps=eps, integrator= integrator).parallel_sample(10, num_samples, random_key= key, ess=True)) - - eps = np.array([[0.02, 0.6], [0.01, 1.0], [0.01, 1.0], [0.01, 1.0], [0.01, 1.0], [0.002, 0.05]]) - L = np.array([[0.3, 2], [0.5, 3.0], [3, 25.0], [0.1, 5.0], [0.1, 5.0], [0.1, 3]]) - - num_samples= [100000, 300000, 500000, 300000, 300000, 100000] - - - results = np.array([grid_search.search_wrapper(lambda a, e: ESS(a, e, targets[i], num_samples[i]), L[i][0], L[i][1], eps[i][0], eps[i][1]) for i in indexes]) - - df = pd.DataFrame({'Target ': [names[i] for i in indexes], 'ESS': results[:, 0], 'L': results[:, 1], 'eps': results[:, 2]}) - - - elif q == 2: - - def ess_ctv_function(alpha, eps, target, num_steps=300000): - return jnp.average(standardKinetic.Sampler(Target=target, eps=eps).parallel_sample(10, num_steps, alpha * np.sqrt(target.d), key)) - - def tuning_ctv(target, eps_min=0.5, eps_max=5.0, num_steps=300000): - return grid_search.search_wrapper(lambda a, e: ess_ctv_function(a, e, target, num_steps), 0.3, 20, eps_min, eps_max, original_esh) - - borders_ctv = [[0.5, 5.0], [2.0, 9.0], [0.1, 5.0], [0.0001, 0.005], [5000, 10000], [0.004, 0.02]] - num_steps_ctv = [300000, 300000, 3000000, 3000000, 300000] - i = -1 - #6.769621324307172e-05 - #standardKinetic.Sampler(Target=targets[i], eps=10000.0).sample(jnp.zeros(targets[i].d), 300000, 1.5 * jnp.sqrt(targets[i].d), key) - - tuning_ctv(targets[i], borders_ctv[i][0], borders_ctv[i][1]) - #results = np.array([np.array(tuning_ctv(targets[i], borders_ctv[i][0], borders_ctv[i][1], num_steps= num_steps_ctv[i])) for i in range(4)]) - - - else: - - def ESS(alpha, eps, target, num_samples): #sequential mode. Only runs a handful of chains to average ESS over the initial conditions - sampler = mchmc.Sampler(target, alpha * np.sqrt(target.d), eps, integrator, generalized) - return jnp.average(sampler.sample(num_samples, 12, output= 'ess')) - - def ESS_tf(target, num_samples): #tuning-free sequential mode. Only runs a handful of chains to average ESS over the initial conditions - sampler = mchmc.Sampler(target, integrator= integrator, generalized= generalized) - sampler.tune_hyperparameters() - ess= jnp.average(sampler.sample(num_samples, 12, output= 'ess')) - print(ess) - return ess, sampler.L / np.sqrt(target.d), sampler.eps - - #1.0 for Ross, 5.6 for kappa 1, 2.5 for kappa 100 - #borders_eps = 1.0* np.array([[0.5 * np.sqrt(d/100.0), 2 * np.sqrt(d/100.0)] for d in dimensions]) - #num_samples= [100000 for d in dimensions] - borders_eps = np.array([[1.0, 4.0], [0.5, 10.0], [0.1, 1.0], [0.1, 1.5], [0.1, 1.0], [0.1, 1.0]]) - borders_alpha = np.array([[0.3, 3], [0.3, 3], [10, 40], [0.3, 10], [0.3, 3], [0.3, 3]]) - - - num_samples= [30000, 100000, 300000, 100000, 100000, 10000] - - # - # print(ESS(0.7, 0.23, targets[-3], num_samples[-3])) - # print(ESS(0.7, 0.15, targets[-3], num_samples[-3])) - # exit() - - # df = pd.read_csv('submission/Table generalized_LF_q=0.csv') - # df_tf = pd.read_csv('submission/Table generalized_LF_q=0_tuning-free3.csv') - # L = 0.4 * np.array(df_tf['eps'] / df_tf['ESS']) - # eps = np.array(df_tf['eps']) - # for i_target in range(5, 6): - # print(names[i_target] + ': ' +str(ESS(L[i_target] / np.sqrt(targets[i_target].d), eps[i_target], targets[i_target], num_samples[i_target]))) - # - # exit() - #print(ESS(0.19, 0.6, targets[-1], 30000)) - - - if integrator[:2] == 'MN': - print('is minimal norm') - borders_eps *= np.sqrt(10.9) - - if alpha < 0: #do a grid scan over alpha and epsilon - results = np.array([grid_search.search_wrapper(lambda a, e: ESS(a, e, targets[i], num_samples[i]), borders_alpha[i][0], borders_alpha[i][1], borders_eps[i][0], borders_eps[i][1]) for i in indexes]) - print(results) - df = pd.DataFrame({'Target ': [names[i] for i in indexes], 'ESS': results[:, 0], 'alpha': results[:, 1], 'eps': results[:, 2]}) - - - else: - - results = np.array([ESS_tf(targets[i], num_samples[i]) for i in indexes]) - #results = np.array([grid_search.search_wrapper_1d(lambda e: ESS(alpha * sigma[i], e, targets[i], num_samples[i]), borders_eps[i][0], borders_eps[i][1]) for i in range(len(targets))]) - df = pd.DataFrame({'Target ': [names[i] for i in indexes], 'ESS': results[:, 0], 'alpha': results[:, 1], 'eps': results[:, 2]}) - - #df.to_csv('data/dimensions_dependence/Rossenbrockg.csv', index=False) - - df.to_csv('submission/MCHMC/NewTable ' + name_sampler + '.csv', index=False) - print(df) - - - - - - -def stochastic_volatility(): - - target = StochasticVolatility() - - sampler = mchmc.Sampler(target, 1.61 * jnp.sqrt(target.d), 0.63, 'LF', True) - - - X, nburnin = sampler.sample(300000) - - thin = 10 - X= X[nburnin::thin, :] - print('done sampling') - - def posterior_band(R, W): - - percentiles = [0.25, 0.5, 0.75] - band = np.empty((len(percentiles), len(R[0]))) - for i in range(len(R[0])): - perm = np.argsort(R[:, i]) - Ri = R[perm, i] - Wi = W[perm] - - P = np.cumsum(Wi) - P /= P[-1] - - band[:, i] = Ri[[np.argmin(np.abs(P - frac)) for frac in percentiles]] - - return band - - band = posterior_band(np.exp(X[:, :-2])) - np.save('data/stochastic_volatility/MCHMC_posterior_band.npy', band) - - - #np.savez('data/stochastic_volatility/MCHMC_samples.npz', s= X[:, :-2], sigma = X[:, :-2], nu= X[:, :-1], w = W) - - - -def esh_not_converging(): - - target = IllConditionedESH() - bounces= False - L = np.sqrt(np.average(target.variance)) * np.sqrt(target.d) if bounces else np.inf - - sampler = mchmc.Sampler(target, L, 0.5, 'LF', False) - - X, nburnin = sampler.sample(10000, 500) - - np.save('ESH_not_converging/data/ESHexample_'+('MCHMC' if bounces else 'ESH')+'.npy', X[:, [0, 100, 1000, 10000], :]) - - -def energy_time_chains(): - - target = Rosenbrock(d = 100) - #target = StochasticVolatility() - - sampler = mchmc.Sampler(target, np.sqrt(target.d), 10.0, integrator= 'LF') - epsilon = np.logspace(np.log10(0.05), np.log10(0.3), 10) - dE= np.empty((len(epsilon), 4)) - - for i in range(len(epsilon)): - print(epsilon[i]) - sampler.eps = epsilon[i] - X, E, nburnin = sampler.sample(2000, 300, output = 'energy') - E = E[:, np.max(nburnin):] - de1 = np.std(E, axis = 1)**2 / target.d - de2 = np.average(np.square(E[:, 1:] - E[:, :-1]), axis= 0) / target.d - - dE[i, 0] = np.average(de1) - dE[i, 1] = np.std(de1) - dE[i, 2] = np.average(de2) - dE[i, 3] = np.std(de2) - - df = pd.DataFrame(dE, columns = ['avg time', 'std time', 'avg chains', 'std chains']) - df['epsilon'] = epsilon - df.to_csv('energy_fluctuations_time_chains_Rosenbrock.csv') - - - -def plot_energy_time_chains(): - from scipy.stats import linregress - words = ['time', 'chains'] - fmts= ['s:', 'o:'] - colors = ['tab:blue', 'tab:red'] - target_names = ['STN', "Rosenbrock"] - DF= [pd.read_csv('energy_fluctuations_time_chains_'+target_name+'.csv') for target_name in target_names] - - for j in range(len(target_names)): #targets - df = DF[j] - for i in range(2): #time vs chains - word = words[i] - x, y = df['epsilon'], df['avg '+word] - yerr = df['std '+word] - label = target_names[j] + '('+word+')' - plt.errorbar(x, y, yerr = yerr, capsize= 2.0, fmt =fmts[i], label = label, color = colors[j]) - - print(label, linregress(np.log(x), np.log(y))) - - plt.legend() - plt.ylabel('Var[E]/d') - plt.xlabel('stepsize') - plt.yscale('log') - plt.xscale('log') - plt.show() - - -if __name__ == '__main__': - #ill_conditioned() - 0 - #ill_conditioned_tuning_free() - #table1() - From d1c3ef99c654fca8329f1c591fee22d12a8b4846 Mon Sep 17 00:00:00 2001 From: Jakob Robnik <43053552+JakobRobnik@users.noreply.github.com> Date: Fri, 13 Oct 2023 15:29:25 +0200 Subject: [PATCH 3/8] Delete notebooks/mathematica directory --- notebooks/mathematica/ErrorAnalysis.nb | 3969 ----------------- notebooks/mathematica/Fokker-Planck.nb | 1098 ----- .../mathematica/Microcanonical_Nose-Hoover.nb | 438 -- notebooks/mathematica/poisson_brackets.nb | 395 -- .../theoretically_worst_case_convex.nb | 123 - 5 files changed, 6023 deletions(-) delete mode 100644 notebooks/mathematica/ErrorAnalysis.nb delete mode 100644 notebooks/mathematica/Fokker-Planck.nb delete mode 100644 notebooks/mathematica/Microcanonical_Nose-Hoover.nb delete mode 100644 notebooks/mathematica/poisson_brackets.nb delete mode 100644 notebooks/mathematica/theoretically_worst_case_convex.nb diff --git a/notebooks/mathematica/ErrorAnalysis.nb b/notebooks/mathematica/ErrorAnalysis.nb deleted file mode 100644 index a9507b0..0000000 --- a/notebooks/mathematica/ErrorAnalysis.nb +++ /dev/null @@ -1,3969 +0,0 @@ -(* Content-type: application/vnd.wolfram.mathematica *) - 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0.5*jnp.sum(jnp.square(x)) +value_grad = jax.value_and_grad(nlogp) + +class StandardGaussian(): + + def __init__(self, d): + self.d = d + + def grad_nlogp(self, x): + """should return nlogp and gradient of nlogp""" + return value_grad(x) + + def transform(self, x): + return x[:2] + #return x + + def prior_draw(self, key): + """Args: jax random key + Returns: one random sample from the prior""" + + return jax.random.normal(key, shape = (self.d, ), dtype = 'float64') * 4 + +target = StandardGaussian(d = 10) +sampler = Sampler(target, varEwanted = 5e-4) + + +# def test_mclmc(): +# samples1 = sampler.sample(100, 1, random_key=jax.random.PRNGKey(0)) +# samples2 = sampler.sample(100, 1, random_key=jax.random.PRNGKey(0)) +# samples3 = sampler.sample(100, 1, random_key=jax.random.PRNGKey(1)) +# assert jnp.array_equal(samples1,samples2), "sampler should be pure" +# assert not jnp.array_equal(samples1,samples3), "this suggests that seed is not being used" +# # run with multiple chains +# sampler.sample(100, 3) From 2029a0cdf1620ffb8077a08e8808817f92f40c6f Mon Sep 17 00:00:00 2001 From: = Date: Sat, 14 Oct 2023 12:05:26 +0200 Subject: [PATCH 5/8] fix tests --- tests/test_mclmc.py | 16 ++++++++-------- tests/test_momentum_update.py | 13 ++++++------- 2 files changed, 14 insertions(+), 15 deletions(-) diff --git a/tests/test_mclmc.py b/tests/test_mclmc.py index 7b21ce5..e804518 100644 --- a/tests/test_mclmc.py +++ b/tests/test_mclmc.py @@ -35,11 +35,11 @@ def prior_draw(self, key): sampler = Sampler(target, varEwanted = 5e-4) -# def test_mclmc(): -# samples1 = sampler.sample(100, 1, random_key=jax.random.PRNGKey(0)) -# samples2 = sampler.sample(100, 1, random_key=jax.random.PRNGKey(0)) -# samples3 = sampler.sample(100, 1, random_key=jax.random.PRNGKey(1)) -# assert jnp.array_equal(samples1,samples2), "sampler should be pure" -# assert not jnp.array_equal(samples1,samples3), "this suggests that seed is not being used" -# # run with multiple chains -# sampler.sample(100, 3) +def test_mclmc(): + samples1 = sampler.sample(100, 1, random_key=jax.random.PRNGKey(0)) + samples2 = sampler.sample(100, 1, random_key=jax.random.PRNGKey(0)) + samples3 = sampler.sample(100, 1, random_key=jax.random.PRNGKey(1)) + assert jnp.array_equal(samples1,samples2), "sampler should be pure" + assert not jnp.array_equal(samples1,samples3), "this suggests that seed is not being used" + # run with multiple chains + sampler.sample(100, 3) diff --git a/tests/test_momentum_update.py b/tests/test_momentum_update.py index 746bbd5..8827a43 100644 --- a/tests/test_momentum_update.py +++ b/tests/test_momentum_update.py @@ -7,9 +7,9 @@ import jax.numpy as jnp -def update_momentum_unstable(d, eps): +def update_momentum_unstable(d): - def update(u, g): + def update(eps, u, g): g_norm = jnp.linalg.norm(g) e = - g / g_norm delta = eps * g_norm / (d-1) @@ -26,11 +26,10 @@ def test_momentum_update(): u = jax.random.uniform(key=jax.random.PRNGKey(0),shape=(d,)) u = u / jnp.linalg.norm(u) g = jax.random.uniform(key=jax.random.PRNGKey(1),shape=(d,)) - update_stable = update_momentum(d, eps) - update_unstable = update_momentum_unstable(d, eps) - update1 = update_stable(u, g)[0] - update2 = update_unstable(u, g) + update_stable = update_momentum(d, sequential=True) + update_unstable = update_momentum_unstable(d) + update1 = update_stable(eps, u, g)[0] + update2 = update_unstable(eps, u, g) print(update1, update2) assert jnp.allclose(update1,update2) - From 0440290a0579cda27bbf3629549be859ecce3c2a Mon Sep 17 00:00:00 2001 From: = Date: Sat, 14 Oct 2023 19:02:25 +0200 Subject: [PATCH 6/8] target class --- sampling/correlation_length.py | 2 +- sampling/sampler.py | 16 +++++++++++++++- tests/test_mclmc.py | 18 ++++++------------ 3 files changed, 22 insertions(+), 14 deletions(-) diff --git a/sampling/correlation_length.py b/sampling/correlation_length.py index 46aac53..f485576 100644 --- a/sampling/correlation_length.py +++ b/sampling/correlation_length.py @@ -1,7 +1,7 @@ import jax import jax.numpy as jnp import numpy as np -from scipy.fftpack import next_fast_len +from scipy.fftpack import next_fast_len #type: ignore diff --git a/sampling/sampler.py b/sampling/sampler.py index e389d52..b66dc2a 100644 --- a/sampling/sampler.py +++ b/sampling/sampler.py @@ -7,12 +7,26 @@ from . import dynamics from .correlation_length import ess_corr +class Target(): + def __init__(self, d, nlogp): + self.d = d + self.nlogp = nlogp + self.grad_nlogp = jax.value_and_grad(self.nlogp) + + def transform(self, x): + return x + + def prior_draw(self, key): + """Args: jax random key + Returns: one random sample from the prior""" + + raise Exception("Not implemented") class Sampler: """the MCHMC (q = 0 Hamiltonian) sampler""" - def __init__(self, Target, L = None, eps = None, + def __init__(self, Target : Target, L = None, eps = None, integrator = 'MN', varEwanted = 5e-4, diagonal_preconditioning= False, frac_tune1 = 0.1, frac_tune2 = 0.1, frac_tune3 = 0.1, diff --git a/tests/test_mclmc.py b/tests/test_mclmc.py index e804518..28f58da 100644 --- a/tests/test_mclmc.py +++ b/tests/test_mclmc.py @@ -7,31 +7,25 @@ import numpy as np import matplotlib.pyplot as plt -from sampling.sampler import Sampler +from sampling.sampler import Sampler, Target nlogp = lambda x: 0.5*jnp.sum(jnp.square(x)) -value_grad = jax.value_and_grad(nlogp) -class StandardGaussian(): +class StandardGaussian(Target): - def __init__(self, d): - self.d = d - - def grad_nlogp(self, x): - """should return nlogp and gradient of nlogp""" - return value_grad(x) + def __init__(self, d, nlogp): + Target.__init__(self,d,nlogp) def transform(self, x): return x[:2] - #return x - + def prior_draw(self, key): """Args: jax random key Returns: one random sample from the prior""" return jax.random.normal(key, shape = (self.d, ), dtype = 'float64') * 4 -target = StandardGaussian(d = 10) +target = StandardGaussian(d = 10, nlogp=nlogp) sampler = Sampler(target, varEwanted = 5e-4) From 336f29b5e48cc88e7ba177f307f4f05060fffa39 Mon Sep 17 00:00:00 2001 From: = Date: Sat, 14 Oct 2023 19:05:43 +0200 Subject: [PATCH 7/8] simple --- tests/test_mclmc.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/tests/test_mclmc.py b/tests/test_mclmc.py index 28f58da..e8e2e49 100644 --- a/tests/test_mclmc.py +++ b/tests/test_mclmc.py @@ -28,6 +28,7 @@ def prior_draw(self, key): target = StandardGaussian(d = 10, nlogp=nlogp) sampler = Sampler(target, varEwanted = 5e-4) +target_simple = Target(d = 10, nlogp=nlogp) def test_mclmc(): samples1 = sampler.sample(100, 1, random_key=jax.random.PRNGKey(0)) @@ -37,3 +38,5 @@ def test_mclmc(): assert not jnp.array_equal(samples1,samples3), "this suggests that seed is not being used" # run with multiple chains sampler.sample(100, 3) + + Sampler(target).sample(100, x_initial = jax.random.normal(shape=(10,), key=jax.random.PRNGKey(0))) From b06994712b84697eb8db056db16867f57ccfaa4d Mon Sep 17 00:00:00 2001 From: = Date: Sat, 14 Oct 2023 19:06:38 +0200 Subject: [PATCH 8/8] simple --- tests/test_mclmc.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/test_mclmc.py b/tests/test_mclmc.py index e8e2e49..fd75b01 100644 --- a/tests/test_mclmc.py +++ b/tests/test_mclmc.py @@ -39,4 +39,4 @@ def test_mclmc(): # run with multiple chains sampler.sample(100, 3) - Sampler(target).sample(100, x_initial = jax.random.normal(shape=(10,), key=jax.random.PRNGKey(0))) + Sampler(target_simple).sample(100, x_initial = jax.random.normal(shape=(10,), key=jax.random.PRNGKey(0)))