Removed old training data.

Untitled.ipynb

@@ -1,137 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "aeccefd6-7729-4830-afd8-e8ed0423d92c",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "from functools import lru_cache, wraps\n",
-    "import numpy as np\n",
-    "import time"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "id": "65e726fb-d3d3-4b47-a96e-9a09a7c774d7",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "CacheInfo(hits=1, misses=1, maxsize=256, currsize=1)"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "def np_cache(*lru_args, array_argument_indexs=(0,), **lru_kwargs):\n",
-    "    \"\"\"\n",
-    "    LRU cache implementation for functions whose parameter at ``array_argument_index`` is a numpy array of dimensions <= 2\n",
-    "\n",
-    "    Example:\n",
-    "    >>> from sem_env.utils.cache import np_cache\n",
-    "    >>> array = np.array([[1, 2, 3], [4, 5, 6]])\n",
-    "    >>> @np_cache(maxsize=256)\n",
-    "    ... def multiply(array, factor):\n",
-    "    ...     return factor * array\n",
-    "    >>> multiply(array, 2)\n",
-    "    >>> multiply(array, 2)\n",
-    "    >>> multiply.cache_info()\n",
-    "    CacheInfo(hits=1, misses=1, maxsize=256, currsize=1)\n",
-    "    \"\"\"\n",
-    "\n",
-    "    def decorator(function):\n",
-    "        @wraps(function)\n",
-    "        def wrapper(*args, **kwargs):\n",
-    "            for array_argument_index in array_argument_indexs:\n",
-    "                np_array = args[array_argument_index]\n",
-    "                if len(np_array.shape) > 2:\n",
-    "                    raise RuntimeError(\n",
-    "                        f\"np_cache is currently only supported for arrays of dim. less than 3 but got shape: {np_array.shape}\"\n",
-    "                    )\n",
-    "                hashable_array = tuple(map(tuple, np_array))\n",
-    "                args = list(args)\n",
-    "                args[array_argument_index] = hashable_array\n",
-    "            return cached_wrapper(*args, **kwargs)\n",
-    "\n",
-    "        @lru_cache(*lru_args, **lru_kwargs)\n",
-    "        def cached_wrapper(*args, **kwargs):\n",
-    "            for array_argument_index in array_argument_indexs:\n",
-    "                hashable_array = args[array_argument_index]\n",
-    "                array = np.array(hashable_array)\n",
-    "                args = list(args)\n",
-    "                args[array_argument_index] = array\n",
-    "            return function(*args, **kwargs)\n",
-    "\n",
-    "        # copy lru_cache attributes over too\n",
-    "        wrapper.cache_info = cached_wrapper.cache_info\n",
-    "        wrapper.cache_clear = cached_wrapper.cache_clear\n",
-    "        return wrapper\n",
-    "\n",
-    "    return decorator\n",
-    "\n",
-    "\n",
-    "@np_cache(maxsize=256, array_argument_indexs=(0, 1))\n",
-    "def multiply(array, array2):\n",
-    "    return array2 * array\n",
-    "\n",
-    "\n",
-    "array = np.array([[1, 2, 3], [4, 5, 6]])\n",
-    "multiply(array, array)\n",
-    "multiply(array, array)\n",
-    "multiply.cache_info()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "2e064b94-aa85-4a5f-a99f-0adeeb897153",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "arg"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "33887602-8325-4bde-8e8a-faf7155ff143",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "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": "3.10.8"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}