{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a873fcbb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.insert(0,'..')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "56023c88",
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "\n",
    "import numpy as np\n",
    "import torch\n",
    "from torch import nn\n",
    "from sklearn.gaussian_process import GaussianProcessRegressor\n",
    "from sklearn.gaussian_process.kernels import RBF, DotProduct, WhiteKernel\n",
    "from priors.utils import get_batch_to_dataloader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "036c690b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_gp():\n",
    "    gp = GaussianProcessRegressor(\n",
    "            kernel=RBF(length_scale=.6, length_scale_bounds='fixed'),\n",
    "            random_state=0, optimizer=None)\n",
    "    return gp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "ff8a3cd1",
   "metadata": {},
   "outputs": [],
   "source": [
    "seq_len = 4\n",
    "num_features = 10\n",
    "x = torch.rand(seq_len, num_features)\n",
    "gpr = get_gp()\n",
    "y = gpr.sample_y(x, random_state=random.randint(0, 2 ** 32)).squeeze()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "46fe34a9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.29995838] [0.90399136]\n",
      "[-0.1039504] [0.98874968]\n",
      "[-0.03414801] [0.99876344]\n",
      "[-0.01104748] [0.99986603]\n",
      "[-0.00356252] [0.9999855]\n",
      "[-0.00114827] [0.99999843]\n",
      "[-0.00037014] [0.99999983]\n",
      "[-0.00011934] [0.99999998]\n",
      "[-3.8486538e-05] [1.]\n",
      "[-1.24147253e-05] [1.]\n",
      "[-4.00568455e-06] [1.]\n",
      "[-1.2927993e-06] [1.]\n",
      "[-4.17353027e-07] [1.]\n",
      "[-1.34771328e-07] [1.]\n",
      "[-4.35327732e-08] [1.]\n",
      "[-1.40657691e-08] [1.]\n",
      "[-4.54613576e-09] [1.]\n",
      "[-1.46979425e-09] [1.]\n",
      "[-4.75345491e-10] [1.]\n"
     ]
    }
   ],
   "source": [
    "for num_copies in range(1,20):\n",
    "    gp = get_gp()\n",
    "    x_copied = x.tile((1,num_copies))\n",
    "    gp.fit(x_copied[:-1],y[:-1])\n",
    "    m,s = gp.predict(x_copied[-1].reshape(1,-1), return_std=True)\n",
    "    print(m,s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "87752b3d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.        , 0.1047567 , 0.17720387, 0.33463634],\n",
       "       [0.1047567 , 1.        , 0.14686013, 0.04858264],\n",
       "       [0.17720387, 0.14686013, 1.        , 0.32035965],\n",
       "       [0.33463634, 0.04858264, 0.32035965, 1.        ]])"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k = RBF(length_scale=.6, length_scale_bounds='fixed')\n",
    "k(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "6a409ae5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.00000000e+00, 2.41799081e-19, 5.26006251e-15, 9.26592960e-10],\n",
       "       [2.41799081e-19, 1.00000000e+00, 1.48311381e-16, 1.10443925e-25],\n",
       "       [5.26006251e-15, 1.48311381e-16, 1.00000000e+00, 4.04686299e-10],\n",
       "       [9.26592960e-10, 1.10443925e-25, 4.04686299e-10, 1.00000000e+00]])"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k = RBF(length_scale=.6, length_scale_bounds='fixed')\n",
    "k(x_copied)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "24141432",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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