grid-search.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "metadata": {}
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "import numpy as np \n",
    "import matplotlib.pyplot as plt\n",
    "import spatial_maps as sm\n",
    "from scipy.integrate import cumtrapz\n",
    "\n",
    "from tqdm import tqdm\n",
    "from models import FFGC, RNNGC\n",
    "from plotting_functions import *\n",
    "from dataset import DatasetMaker\n",
    "\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "import scipy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "metadata": {}
   },
   "outputs": [],
   "source": [
    "plt.style.use(\"project_style.mplstyle\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "metadata": {}
   },
   "outputs": [],
   "source": [
    "## Training parameters\n",
    "train_steps = 10000\n",
    "timesteps = 10\n",
    "bs = 64 # batch size\n",
    "ng = 256 # no. of recurrent units"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "metadata": {}
   },
   "outputs": [],
   "source": [
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "metadata": {}
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.01      , 0.05083333, 0.09166667, 0.1325    , 0.17333333,\n",
       "       0.21416667, 0.255     , 0.29583333, 0.33666667, 0.3775    ,\n",
       "       0.41833333, 0.45916667, 0.5       , 0.54083333, 0.58166667,\n",
       "       0.6225    , 0.66333333, 0.70416667, 0.745     , 0.78583333,\n",
       "       0.82666667, 0.8675    , 0.90833333, 0.94916667, 0.99      ])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ABoQkAAAGhCQAAAaEJAAABoQkAAAGhCQAAAaEJAAABoQkAAAGhCQAAAaEJAAABoQkAAAGhCQAAAaEJAAABoQkAAAGhCQAAAaEJAAABoQkAAAGhCQAAAaEJAAABoQkAAAGhCQAAAaEJAAABoQkAAAGhCQAAAaEJAAABoQkAAAGhCQAAAaEJAAABoQkAAAGhCQAAAaEJAAABoQkAAAGhCQAAAaEJAAABoQkAAAGhCQAAAaEJAAABoQkAAAGhCQAAAaEJAAABoQkAAAGhCQAAAaEJAAABoQkAAAGhCQAAAaEJAAABoQkAAAGhCQAAAaEJAAABoQkAAAGhCQAAAaEJAAABoQkAAAGhCQAAAbthmRVVZWWL1+uUaNGKTo6WpGRkRo6dKgWLlyoffv2BXQHTpw4oWXLlunmm29Wz549FR0drZtuukmPP/64Pvvss4D2AgDAG2FXrti6davmz5+v+vp6t/UVFRWqqKhQUVGR0tPTlZ+fr9DQUL+aFxYW6sknn9SlS5fc1peXl6u8vFyFhYXKzs5WRkaGX30AAPCFW0iWlJQoNTVVly9fliQlJCRo1qxZCg8P1/79+7V9+3ZZlqWCggJJ0tq1a31u/MYbb+jRRx+1lydNmqRp06ZJknbv3q29e/equblZmZmZ6tGjh5555hmfewEA4As7JJ1OpxYsWGAHZEZGhnJzc+VwOOziHTt2KCUlRd9//70KCgqUmpqqO++80+um33zzjX7zm99IkhwOh9atW6clS5a41bz11lv2/qxYsUKzZ8/WsGHDfPoiAQDwhf2e5Pr163X27FlJUlJSkvLy8twCUpLuuusuvf766/ayry+Dvvjii7p48aIk6ZFHHmkTkJL00EMPKS8vT5LU3NyslStX+tQLAABf2SFZVFRkr3z22WeNE+bMmaPhw4dLkvbt26evvvrKq4YtLS168803O9TrySefVO/evSVJW7Zs0ffff+9VLwAA/BEiSbW1tTp8+LAkKSYmRpMnTzZOcDgcmjlzpr28ZcsWrxoeOnRItbW1kqSRI0cqPj7eWBsREWG/T+l0OvXBBx941QsAAH+ESD8Gl2VZkqRx48Z5PGt1woQJ9risrMyrhgcPHrTHEydO9FjvTy8AAPwRIknHjx+3VwwaNMjjJNdnf65zOyKYvQAA8EeIJJ07d85eMXDgQI+T4uLi7HF1dbVXDYPZCwAAf4RJUl1dnb0iMjLS4yTXGte5HRHMXiY1NTX2+6Id9cUXX7gtV1RU+Nz/orOmzbrPP//c79rO6udtT2+20dX7meqv537ebqOzvqcco8HpZ6oP9jHTmf/fngwePFjh4eHt/6NlWVZaWpolyZJkrVu3zvLE6XTa9SEhIR7rXU2fPt2eW1xc7LH+v//9r10/aNAgr3qZZGdn29vkwYMHDx4/78exY8eMeREiSSEhvl/n/MrPUnoSzF4AAPgjRJKioqLsFY2NjR4nOZ1Oe2x8imoQzF4AAPgjTHIProaGBo+TXGt69erlVcNg9jJJT0/X/fff79Wc+vp6ffLJJ7rhhhsUExOjgQMHqkePHledU1FRoZSUFHt569atGjJkiC+7DAQFxyx+SgJ1vA4ePNj4b2GS+xmkVVVVHjdYWVlpj/v16+fVzgSzl0lsbKxiY2O9nteRz3VezZAhQzRixAi/tgEEE8csfko643gNkeS20VOnTnmcdPr0aXvs7UXHg9kLAAB/hEg/3hKr9aSYAwcO2FffMSktLbXHY8eO9arhLbfcYo87cgUdf3oBAOCPEMn9eq01NTU6cOCAcYJlWXr//fft5RkzZnjVMCEhwb6KzuHDh90uLnClhoYG7dq1S5LUrVs3TZ8+3ateAAD4w/48xty5c+2VOTk5xgmbNm1SeXm5JOnWW2/1+vVfh8OhOXPmSJIuX76s3NxcY+3q1at14cIFSdLs2bMVExPjVS8AAPxhh+SCBQvsM3yKi4v1xBNPqLm52a14x44dbvd+9PUej08//bQdeIWFhXrxxRfb1Lz55pvKzs6WJIWGhiorK8unXgAA+CqsdRAeHq7XXntNM2bMUFNTk/Lz81VcXKyUlBRFRUWprKxMxcXF9vuVixYtcrtlVquioiItXLjQXm7v/c3Y2Fi98sormj9/viTp97//vf72t78pOTlZYWFh2rVrlz7++GO7Pjc3VwkJCYH7qgEA6IAw14Vp06bpnXfe0fz581VXV6cTJ060+yxv8eLFKigo8KvxvHnz5HQ69eSTT6qxsVGHDx+272nZKiQkRFlZWVqxYoVfvQAA8EXYlStmz56t8vJyrVmzRu+++65Onjwpp9Opvn37atKkSVq6dKkSExMD0nzJkiVKSkrSmjVrtH37dp05c0ZNTU3q37+/EhMT9dhjj2n06NEB6QUAgLcclqfPe8Bnn3/+uUaOHGkvHzt2jA9m47rGMYufkmAcr75fbRwAgC6OkAQAwKDNe5IInD59+tgfY2ldBq5nHLP4KQnG8cp7kgAAGPByKwAABoQkAAAGhCQAAAaEJAAABoQkAAAGhCQAAAaEJAAABoQkAAAGhCQAAAaEJAAABoQkAAAGhCQAAAaE5FVUVVVp+fLlGjVqlKKjoxUZGamhQ4dq4cKF2rdvX0B7nThxQsuWLdPNN9+snj17Kjo6WjfddJMef/xxffbZZwHtha4rGMdsc3OzwsPD5XA4OvwAOsqyLN1+++1yOBwqKioK6LYPHTqktLQ0DR48WBEREYqJiVFCQoKWL1+ukydPGncI7diyZYt1ww03WJKMj/T0dKu5udnvXuvWrbN69Ohh7BMWFmbl5eUF4KtCVxasY/bo0aNX7dHeA+io5557zj5uNmzYELDtZmZmWiEhIcZjNDIy0lq/fn2bedxPsh0lJSVKTU3V5cuXJUkJCQmaNWuWwsPDtX//fm3fvl2WZamgoECStHbtWp97vfHGG3r00Uft5UmTJmnatGmSpN27d2vv3r1qbm5WZmamevTooWeeecaPrwxdVTCP2cOHD9vj1NRUjR8/3r+dB/7fhg0blJGREfDt5ubmKi8vz16+++67NXHiRDU2Nqq4uFhHjx5VQ0ODFi1apOjoaD3wwAP/mxywmO4iGhoarIEDB9p/XWRkZFgtLS1uNf/+97+tnj172jUlJSU+9Tp37pwVFRVlSbIcDodVWFjYpubNN9+0QkND7WeU5eXlPvVC1xXMY9ayLGvZsmX2dnbv3u3v7gNWS0uLlZubazkcDrdnd4F4JnnkyBH7GWT37t2tbdu2tal54YUX7J4xMTHW+fPn7X8jJK+Qn59vf7OSkpKMdZs2bbLrJk2a5FOvp59+2t7GokWLjHXPP/+8XTd37lyfeqHrCuYxa1mWNWXKFPsPu7q6Op+3A1jWj08WkpOT230JNBAhed9999nbe+6554x1ixcvtutWrFhhryckrzBmzJgO/ZXc0tJiDR8+3K798ssvvepz+fJlq0+fPvb8U6dOGWsbGhqs3r17W5KsiIgI6+LFi171QtcWrGO2dRut73sOHTrUn93Gz1xDQ4OVl5dnv5omyYqOjramTp0asJA8f/68FRYWZkmyoqKirvq7s7Ky0n7GGR8fb6/n7FYXtbW19vstMTExmjx5srHW4XBo5syZ9vKWLVu86nXo0CHV1tZKkkaOHKn4+HhjbUREhP0+pdPp1AcffOBVL3RdwTxmJenLL79UfX29JGnMmDFezwdabd68WZmZmbp48aKkH4+n0tJSJSYmBqzHzp071dzcLElKTExUz549jbVxcXEaPXq0JOn06dM6ePCgJD4C4ubQoUOyLEuSNG7cOIWGhl61fsKECfa4rKzMq16t/wGSNHHiRI/1/vRC1xXMY7a1X6uxY8dKks6cOaPNmzfr1VdfVVFRkcrKytTS0uL1tvHzdOONN+rll19WWVmZRowYEdBtB+L3LGe3ujh+/Lg9HjRokMd612d/rnOvt17ouoJ9HLme2frDDz9o+vTpKikpaVPXv39/5ebm6uGHH/a6B34e+vbtq1WrVmnp0qWKiYnplB6B+PkgJF2cO3fOHg8cONBjfVxcnD2urq6+bnuh6wr2ceQaklc7Vb+yslJpaWkqKSlRUVGRwsL4VQN3ycnJSk5O7tQegfj54OVWF3V1dfY4MjLSY71rjevc660Xuq5gH0euIelwOPTwww9r//79qq+vV319vfbs2eP2GbONGzfqt7/9rdd9gEAIxM8HIeni0qVL9jgiIsJjvWuN69zrrRe6rmAeR5WVlaqpqZEkhYaG6p///Kf+8pe/aPz48YqOjlZ0dLSmTJmizZs36+WXX7bn5efnu703BARLIH4+CEkXISG+fzu8vT5lMHuh6wrmcRQXF6ezZ89qz549KikpUUpKirH2qaeecnspbfXq1b7uJuCzQPx88EaBi6ioKHvc2Njosd7pdNrj8PDw67YXuq5gHkcOh0MDBgzQgAEDOlSfnp6u4uJiSdKHH37oVS8gEALx88EzSReu39CGhgaP9a41vXr1um57oeu6no+j2267zR7X1tban4cDgiUQPx+EpAvXM5uqqqo81ldWVtrjfv36Xbe90HVdz8dR79693ZZbL0IABEsgfj4ISReuH2Q9deqUx/rTp0/b42HDhl23vdB1XYvjqKmpqUMn/Xz33Xduy531WTjAJBA/H4Ski4SEBPvN2gMHDthXMjEpLS21x61XH+moW265xR535Mon/vRC1xXMY3bZsmXq3bu3unfvrpdeeslj/aeffmqP4+PjO3QKPhBIgfg9S0i6cL32ZU1NjQ4cOGCstSxL77//vr08Y8YMr3olJCTYV3c4fPiw24der9TQ0KBdu3ZJkrp166bp06d71QtdVzCP2T59+ujChQuSZJ+QczVvv/22PU5KSvKqFxAI06ZNs/84KykpcTsx50pff/21jhw5IkmKjY21r+NKSF5h7ty59jgnJ8dYt2nTJpWXl0uSbr31Vq+vOehwODRnzhxJ0uXLl5Wbm2usXb16tf3Lafbs2bxsBTfBOmZTU1Pt8d69e+0/3Npz8OBBbdiwwV5OT0/3qhcQCFFRUbrnnnskSefPn1d+fr6xduXKlfYrMfPmzfvfx0f8ug9JF+R0Oq3Bgwfbt2pZtmyZ1dTU5FZz5Q1s33vvPZ96VVdXWzExMfZ2/vSnP7WpeeONN+ybLoeGhlqffvqpT73QdQXzmE1NTbW3ERsba5WVlbWp+fjjj91uA3e1e6UCV8rOzg7o/SSPHTtmdevWzb5x/caNG9vUrFq1yu12Xd988439b4RkO3bu3Gl/U/X/98175plnrJUrV1ozZ850u3u26RfAhg0b3G4eavLXv/7VrW706NHWihUrrKysLPvmtq2Pq90wFD9vwTpmq6urrSFDhtg1ISEh1qxZs6zc3FwrKyvLSkpKcus1efJk7n8Kr3gTkq61rveAvFJubq7bsX3HHXdYWVlZ1ooVK6xRo0a5/dtbb73lNpeQNNi6davVq1evdu+W3fpYvHix1dzc3O78joakZVlWYWGhFR4ebuwTEhJi5eTkdMaXiS4kWMfs119/3eYPuPYec+fOterr6zvry0UX1RkhaVmWlZmZab8q194jPDzcev3119vM44o7BrNnz1Z5ebnWrFmjd999VydPnpTT6VTfvn01adIkLV26NGA3B12yZImSkpK0Zs0abd++XWfOnFFTU5P69++vxMREPfbYY/abyIBJsI7Z/v3766OPPtK2bdu0ceNGlZWVqbq6Wt26dVNcXJzuuOMOzZs376o3gAaCLTc3V/fdd58KCwu1c+dOVVVVybIsxcfHKykpScuWLdOQIUPazHNYlodzxgEA+Jni7FYAAAwISQAADAhJAAAMCEkAAAwISQAADAhJAAAM/g+B+rVYXcSKjAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 480x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get even distribution from distribution function\n",
    "\n",
    "def inverse_transform_sampling(prob_density_func, num_samples):\n",
    "    # Create an array across the interval [0, 1]\n",
    "    x = np.linspace(0.01, 0.99, 10000)\n",
    "\n",
    "    # Calculate the cumulative distribution function (CDF)\n",
    "    cdf = cumtrapz(prob_density_func(x), x)\n",
    "    cdf = np.insert(cdf, 0, 0)\n",
    "\n",
    "    # Use the CDF to get the inverse CDF\n",
    "    inverse_cdf = np.interp(np.linspace(0, cdf[-1], num_samples), cdf, x)\n",
    "\n",
    "    return inverse_cdf\n",
    "\n",
    "# alphas = inverse_transform_sampling(lambda x: 1/(x*(1-x)), 100)\n",
    "# alphas = inverse_transform_sampling(lambda x: (1-x)/x + x/(1-x), 32)\n",
    "alphas = inverse_transform_sampling(lambda x: np.ones(len(x)), 25)\n",
    "plt.hist(alphas, bins = 60)\n",
    "alphas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Alpha: 0.01\n",
      "Sigma: 1.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\kingt\\anaconda3\\envs\\torch2\\lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n",
      "  2%|▏         | 1219/50000 [00:01<01:15, 647.97it/s]\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[78], line 26\u001b[0m\n\u001b[0;32m     24\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m tqdm(\u001b[38;5;28mrange\u001b[39m(train_steps\u001b[38;5;241m-\u001b[39m\u001b[38;5;28mlen\u001b[39m(model\u001b[38;5;241m.\u001b[39mtotal_loss_history))):\n\u001b[0;32m     25\u001b[0m     r \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mrand((bs, \u001b[38;5;241m2\u001b[39m), device \u001b[38;5;241m=\u001b[39m device)\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m4\u001b[39m\u001b[38;5;241m*\u001b[39mnp\u001b[38;5;241m.\u001b[39mpi \u001b[38;5;241m-\u001b[39m \u001b[38;5;241m2\u001b[39m\u001b[38;5;241m*\u001b[39mnp\u001b[38;5;241m.\u001b[39mpi\n\u001b[1;32m---> 26\u001b[0m     loss \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain_step\u001b[49m\u001b[43m(\u001b[49m\u001b[43minputs\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mr\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlabels\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mr\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moptimizer\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43moptimizer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     28\u001b[0m \u001b[38;5;66;03m# Plot loss history\u001b[39;00m\n\u001b[0;32m     29\u001b[0m \u001b[38;5;66;03m# fig,axs = plt.subplots(1,3,figsize=(9,3))\u001b[39;00m\n\u001b[0;32m     30\u001b[0m \u001b[38;5;66;03m# axs[0].semilogy(np.array(model.total_loss_history)-model.loss_minima(), label = 'Total loss', color = 'black')\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m     37\u001b[0m \n\u001b[0;32m     38\u001b[0m \u001b[38;5;66;03m# evaluate on 64x64 grid\u001b[39;00m\n\u001b[0;32m     39\u001b[0m res \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m128\u001b[39m\n",
      "File \u001b[1;32mc:\\Users\\kingt\\Dropbox\\Jobb\\BioAI\\SimGC\\models.py:90\u001b[0m, in \u001b[0;36mFFGC.train_step\u001b[1;34m(self, inputs, labels, optimizer)\u001b[0m\n\u001b[0;32m     87\u001b[0m loss \u001b[38;5;241m=\u001b[39m distance_loss \u001b[38;5;241m+\u001b[39m capacity_loss\n\u001b[0;32m     89\u001b[0m loss\u001b[38;5;241m.\u001b[39mbackward()\n\u001b[1;32m---> 90\u001b[0m \u001b[43moptimizer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstep\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     92\u001b[0m \u001b[38;5;66;03m# log losses \u001b[39;00m\n\u001b[0;32m     93\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdistance_loss_history\u001b[38;5;241m.\u001b[39mappend(distance_loss\u001b[38;5;241m.\u001b[39mitem())\n",
      "File \u001b[1;32mc:\\Users\\kingt\\anaconda3\\envs\\torch2\\lib\\site-packages\\torch\\optim\\optimizer.py:373\u001b[0m, in \u001b[0;36mOptimizer.profile_hook_step.<locals>.wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    368\u001b[0m         \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    369\u001b[0m             \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[0;32m    370\u001b[0m                 \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfunc\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m must return None or a tuple of (new_args, new_kwargs), but got \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mresult\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    371\u001b[0m             )\n\u001b[1;32m--> 373\u001b[0m out \u001b[38;5;241m=\u001b[39m func(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m    374\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_optimizer_step_code()\n\u001b[0;32m    376\u001b[0m \u001b[38;5;66;03m# call optimizer step post hooks\u001b[39;00m\n",
      "File \u001b[1;32mc:\\Users\\kingt\\anaconda3\\envs\\torch2\\lib\\site-packages\\torch\\optim\\optimizer.py:76\u001b[0m, in \u001b[0;36m_use_grad_for_differentiable.<locals>._use_grad\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m     74\u001b[0m     torch\u001b[38;5;241m.\u001b[39mset_grad_enabled(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdefaults[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdifferentiable\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[0;32m     75\u001b[0m     torch\u001b[38;5;241m.\u001b[39m_dynamo\u001b[38;5;241m.\u001b[39mgraph_break()\n\u001b[1;32m---> 76\u001b[0m     ret \u001b[38;5;241m=\u001b[39m func(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m     77\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m     78\u001b[0m     torch\u001b[38;5;241m.\u001b[39m_dynamo\u001b[38;5;241m.\u001b[39mgraph_break()\n",
      "File \u001b[1;32mc:\\Users\\kingt\\anaconda3\\envs\\torch2\\lib\\site-packages\\torch\\optim\\adam.py:163\u001b[0m, in \u001b[0;36mAdam.step\u001b[1;34m(self, closure)\u001b[0m\n\u001b[0;32m    152\u001b[0m     beta1, beta2 \u001b[38;5;241m=\u001b[39m group[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbetas\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[0;32m    154\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_init_group(\n\u001b[0;32m    155\u001b[0m         group,\n\u001b[0;32m    156\u001b[0m         params_with_grad,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    160\u001b[0m         max_exp_avg_sqs,\n\u001b[0;32m    161\u001b[0m         state_steps)\n\u001b[1;32m--> 163\u001b[0m     \u001b[43madam\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    164\u001b[0m \u001b[43m        \u001b[49m\u001b[43mparams_with_grad\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    165\u001b[0m \u001b[43m        \u001b[49m\u001b[43mgrads\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    166\u001b[0m \u001b[43m        \u001b[49m\u001b[43mexp_avgs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    167\u001b[0m \u001b[43m        \u001b[49m\u001b[43mexp_avg_sqs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    168\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmax_exp_avg_sqs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    169\u001b[0m \u001b[43m        \u001b[49m\u001b[43mstate_steps\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    170\u001b[0m \u001b[43m        \u001b[49m\u001b[43mamsgrad\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mamsgrad\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    171\u001b[0m \u001b[43m        \u001b[49m\u001b[43mbeta1\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbeta1\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    172\u001b[0m \u001b[43m        \u001b[49m\u001b[43mbeta2\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbeta2\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    173\u001b[0m \u001b[43m        \u001b[49m\u001b[43mlr\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mlr\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    174\u001b[0m \u001b[43m        \u001b[49m\u001b[43mweight_decay\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mweight_decay\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    175\u001b[0m \u001b[43m        \u001b[49m\u001b[43meps\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43meps\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    176\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmaximize\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mmaximize\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    177\u001b[0m \u001b[43m        \u001b[49m\u001b[43mforeach\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mforeach\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    178\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcapturable\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mcapturable\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    179\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdifferentiable\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mdifferentiable\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    180\u001b[0m \u001b[43m        \u001b[49m\u001b[43mfused\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mfused\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    181\u001b[0m \u001b[43m        \u001b[49m\u001b[43mgrad_scale\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mgetattr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mgrad_scale\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    182\u001b[0m \u001b[43m        \u001b[49m\u001b[43mfound_inf\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mgetattr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mfound_inf\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    183\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    185\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m loss\n",
      "File \u001b[1;32mc:\\Users\\kingt\\anaconda3\\envs\\torch2\\lib\\site-packages\\torch\\optim\\adam.py:311\u001b[0m, in \u001b[0;36madam\u001b[1;34m(params, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, foreach, capturable, differentiable, fused, grad_scale, found_inf, amsgrad, beta1, beta2, lr, weight_decay, eps, maximize)\u001b[0m\n\u001b[0;32m    308\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    309\u001b[0m     func \u001b[38;5;241m=\u001b[39m _single_tensor_adam\n\u001b[1;32m--> 311\u001b[0m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mparams\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    312\u001b[0m \u001b[43m     \u001b[49m\u001b[43mgrads\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    313\u001b[0m \u001b[43m     \u001b[49m\u001b[43mexp_avgs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    314\u001b[0m \u001b[43m     \u001b[49m\u001b[43mexp_avg_sqs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    315\u001b[0m \u001b[43m     \u001b[49m\u001b[43mmax_exp_avg_sqs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    316\u001b[0m \u001b[43m     \u001b[49m\u001b[43mstate_steps\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    317\u001b[0m \u001b[43m     \u001b[49m\u001b[43mamsgrad\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mamsgrad\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    318\u001b[0m \u001b[43m     \u001b[49m\u001b[43mbeta1\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbeta1\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    319\u001b[0m \u001b[43m     \u001b[49m\u001b[43mbeta2\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbeta2\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    320\u001b[0m \u001b[43m     \u001b[49m\u001b[43mlr\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlr\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    321\u001b[0m \u001b[43m     \u001b[49m\u001b[43mweight_decay\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mweight_decay\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    322\u001b[0m \u001b[43m     \u001b[49m\u001b[43meps\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43meps\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    323\u001b[0m \u001b[43m     \u001b[49m\u001b[43mmaximize\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmaximize\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    324\u001b[0m \u001b[43m     \u001b[49m\u001b[43mcapturable\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcapturable\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    325\u001b[0m \u001b[43m     \u001b[49m\u001b[43mdifferentiable\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdifferentiable\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    326\u001b[0m \u001b[43m     \u001b[49m\u001b[43mgrad_scale\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgrad_scale\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    327\u001b[0m \u001b[43m     \u001b[49m\u001b[43mfound_inf\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfound_inf\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mc:\\Users\\kingt\\anaconda3\\envs\\torch2\\lib\\site-packages\\torch\\optim\\adam.py:496\u001b[0m, in \u001b[0;36m_multi_tensor_adam\u001b[1;34m(params, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, grad_scale, found_inf, amsgrad, beta1, beta2, lr, weight_decay, eps, maximize, capturable, differentiable)\u001b[0m\n\u001b[0;32m    493\u001b[0m device_params \u001b[38;5;241m=\u001b[39m [torch\u001b[38;5;241m.\u001b[39mview_as_real(x) \u001b[38;5;28;01mif\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mis_complex(x) \u001b[38;5;28;01melse\u001b[39;00m x \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m device_params]\n\u001b[0;32m    495\u001b[0m \u001b[38;5;66;03m# update steps\u001b[39;00m\n\u001b[1;32m--> 496\u001b[0m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_foreach_add_\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdevice_state_steps\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m    498\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m weight_decay \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m    499\u001b[0m     \u001b[38;5;66;03m# Re-use the intermediate memory (device_grads) already allocated for maximize\u001b[39;00m\n\u001b[0;32m    500\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m maximize:\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# FF Grid search\n",
    "\n",
    "# alphas = inverse_transform_sampling(lambda x: (1-x)/x + x/(1-x), 32)\n",
    "# alphas = inverse_transform_sampling(lambda x: np.ones(len(x)), 11)\n",
    "# sigmas = [0.4, 0.7, 1.0, 1.2, 1.5]\n",
    "alphas = inverse_transform_sampling(lambda x: np.ones(len(x)), 25)\n",
    "sigmas = [1.0, 1.2, 1.5, 1.8, 2.0, 2.5]\n",
    "\n",
    "# alphas = np.linspace(0.85, 0.94, 100)\n",
    "# alphas = 1-np.geomspace(0.001,1,30)\n",
    "grid_scores = np.zeros((len(sigmas), len(alphas), ng))\n",
    "grid_spacings = np.zeros((len(sigmas), len(alphas)))\n",
    "s_count = 0\n",
    "for sigma in sigmas:\n",
    "    a_count = 0\n",
    "    for alpha in alphas:\n",
    "        print(f'Alpha: {alpha}')\n",
    "        print(f'Sigma: {sigma}')\n",
    "        model = FFGC(alpha = alpha, sigma = sigma, norm = \"l1\")\n",
    "        # model = RNNGC(ng = ng, alpha = alpha, norm = \"l2\")\n",
    "        model.to(device)\n",
    "        optimizer = torch.optim.Adam(model.parameters(), lr = 1e-3)\n",
    "        # Train model\n",
    "        for i in tqdm(range(train_steps-len(model.total_loss_history))):\n",
    "            r = torch.rand((bs, 2), device = device)*4*np.pi - 2*np.pi\n",
    "            loss = model.train_step(inputs = r, labels = r, optimizer = optimizer)\n",
    "\n",
    "        # Plot loss history\n",
    "        # fig,axs = plt.subplots(1,3,figsize=(9,3))\n",
    "        # axs[0].semilogy(np.array(model.total_loss_history)-model.loss_minima(), label = 'Total loss', color = 'black')\n",
    "        # axs[1].semilogy(np.array(model.capacity_loss_history)-model.loss_minima(), label = 'Capacity loss', color = 'red')\n",
    "        # axs[2].semilogy(model.similarity_loss_history, label = 'Similarity loss', color = 'blue')\n",
    "        # fig.tight_layout()\n",
    "        # fig.legend()\n",
    "        # plt.savefig(f'./gridsearch/ffgc_loss_alpha_{alpha}.png')\n",
    "        # plt.close()\n",
    "\n",
    "        # evaluate on 64x64 grid\n",
    "        res = 128\n",
    "        x = np.linspace(-1, 1, res)*2*np.pi\n",
    "        y = np.linspace(-1, 1, res)*2*np.pi\n",
    "        xx, yy = np.meshgrid(x,y)\n",
    "        r = np.stack([xx.ravel(), yy.ravel()], axis = -1)\n",
    "        gs = model(torch.tensor(r.astype(\"float32\"),device = device))\n",
    "        gs = gs.detach().cpu().numpy()\n",
    "        gs = gs.T.reshape(-1,res,res)\n",
    "\n",
    "        # plot spatial map\n",
    "        # multiimshow(gs[:25], figsize=(10,10), normalize=False);\n",
    "        # plt.tight_layout()\n",
    "        # plt.savefig(f'./gridsearch/ffgc_spatial_map_alpha_{alpha}.png')\n",
    "        # plt.close()\n",
    "\n",
    "        # Gridscore\n",
    "        grid_score = np.array([sm.gridness(gs[i]) for i in range(len(gs))])\n",
    "        grid_scores[np.isnan(grid_scores)] = 0\n",
    "        grid_scores[s_count, a_count] = grid_score\n",
    "\n",
    "        acorrs = np.array([sm.autocorrelation(g) for g in gs])\n",
    "        acorrs = acorrs[grid_score > 0.5]\n",
    "        gs = gs[grid_score > 0.5]\n",
    "        peaks = [sm.find_peaks(acorr) for acorr in acorrs]\n",
    "        spacings = np.array([sm.spacing_and_orientation(peak, acorrs.shape[-1])[0] for peak in peaks])\n",
    "        # Ignore nans in spacings\n",
    "        spacings = spacings[~np.isnan(spacings)]\n",
    "        grid_spacings[s_count, a_count] = np.mean(spacings)\n",
    "\n",
    "        a_count += 1\n",
    "    s_count += 1\n",
    "\n",
    "# Save grid scores\n",
    "np.save('./gridsearch/ffgc_grid_scores.npy', grid_scores)\n",
    "np.save('./gridsearch/ffgc_grid_spacings.npy', grid_spacings)\n",
    "np.save('./gridsearch/ffgc_grid_search_alphas.npy', alphas)\n",
    "np.save('./gridsearch/ffgc_grid_search_sigmas.npy', sigmas)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "metadata": {}
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Alpha: 0.01\n",
      "Sigma: 1.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10000/10000 [01:51<00:00, 89.41it/s]\n",
      "c:\\Users\\kingt\\anaconda3\\envs\\torch2\\lib\\site-packages\\numpy\\core\\fromnumeric.py:3474: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "c:\\Users\\kingt\\anaconda3\\envs\\torch2\\lib\\site-packages\\numpy\\core\\_methods.py:189: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Alpha: 0.11888888888888889\n",
      "Sigma: 1.0\n"
     ]
    },
    {
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      "Alpha: 0.22777777777777777\n",
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    {
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      "100%|██████████| 10000/10000 [01:48<00:00, 92.43it/s]\n",
      "c:\\Users\\kingt\\anaconda3\\envs\\torch2\\lib\\site-packages\\spatial_maps\\tools.py:41: RuntimeWarning: invalid value encountered in true_divide\n",
      "  arr1 = (arr1 - np.mean(a_)) / (np.std(a_) * len(a_))\n",
      "c:\\Users\\kingt\\anaconda3\\envs\\torch2\\lib\\site-packages\\spatial_maps\\tools.py:42: RuntimeWarning: invalid value encountered in true_divide\n",
      "  arr2 = (arr2 - np.mean(v_)) / np.std(v_)\n",
      "c:\\Users\\kingt\\anaconda3\\envs\\torch2\\lib\\site-packages\\numpy\\core\\fromnumeric.py:86: UserWarning: Warning: converting a masked element to nan.\n",
      "  return ufunc.reduce(obj, axis, dtype, out, **passkwargs)\n"
     ]
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    {
     "name": "stdout",
     "output_type": "stream",
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      "Alpha: 0.99\n",
      "Sigma: 2.5\n"
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     ]
    }
   ],
   "source": [
    "# RNN Grid search\n",
    "\n",
    "# alphas = inverse_transform_sampling(lambda x: (1-x)/x + x/(1-x), 32)\n",
    "# alphas = inverse_transform_sampling(lambda x: np.ones(len(x)), 25)\n",
    "# sigmas = [1.0, 1.2, 1.5, 1.8, 2.0, 2.5]\n",
    "alphas = inverse_transform_sampling(lambda x: np.ones(len(x)), 10)\n",
    "sigmas = [1.0, 1.5, 2.5]\n",
    "\n",
    "dataset = DatasetMaker()\n",
    "\n",
    "grid_scores = np.zeros((len(sigmas), len(alphas), ng))\n",
    "grid_spacings = np.zeros((len(sigmas), len(alphas)))\n",
    "s_count = 0\n",
    "for sigma in sigmas:\n",
    "    a_count = 0\n",
    "    for alpha in alphas:\n",
    "        print(f'Alpha: {alpha}')\n",
    "        print(f'Sigma: {sigma}')\n",
    "        model = RNNGC(ng = ng, alpha = alpha, sigma = sigma)\n",
    "        model.to(device)\n",
    "        optimizer = torch.optim.Adam(model.parameters(), lr = 1e-3)\n",
    "        # Train model\n",
    "        for i in tqdm(range(train_steps-len(model.total_loss_history))):\n",
    "            r, v = dataset.generate_data(bs, timesteps, device)\n",
    "            loss = model.train_step(inputs = (r[:,0], v), labels = r, optimizer = optimizer)\n",
    "\n",
    "        # evaluate on 64x64 grid\n",
    "        r, v = dataset.generate_data(20000, timesteps, device)\n",
    "        g = model((r[:,0], v))\n",
    "\n",
    "        r = r.detach().cpu().numpy()\n",
    "        g = g.detach().cpu().numpy()\n",
    "        gs = scipy.stats.binned_statistic_2d(r[...,0].ravel(), r[...,1].ravel(), g.reshape(-1, g.shape[-1]).T, bins = 64)[0]\n",
    "\n",
    "        # plot spatial map\n",
    "        # multiimshow(gs[:25], figsize=(10,10), normalize=False);\n",
    "        # plt.tight_layout()\n",
    "        # plt.savefig(f'./gridsearch/ffgc_spatial_map_alpha_{alpha}.png')\n",
    "        # plt.close()\n",
    "\n",
    "        # Gridscore\n",
    "        grid_score = np.array([sm.gridness(gs[i]) for i in range(len(gs))])\n",
    "        grid_scores[np.isnan(grid_scores)] = 0\n",
    "        grid_scores[s_count, a_count] = grid_score\n",
    "\n",
    "        acorrs = np.array([sm.autocorrelation(g) for g in gs])\n",
    "        acorrs = acorrs[grid_score > 0.5]\n",
    "        gs = gs[grid_score > 0.5]\n",
    "        peaks = [sm.find_peaks(acorr) for acorr in acorrs]\n",
    "        spacings = np.array([sm.spacing_and_orientation(peak, acorrs.shape[-1])[0] for peak in peaks])\n",
    "        # Ignore nans in spacings\n",
    "        spacings = spacings[~np.isnan(spacings)]\n",
    "        grid_spacings[s_count, a_count] = np.mean(spacings)\n",
    "\n",
    "        a_count += 1\n",
    "    s_count += 1\n",
    "\n",
    "# Save grid scores\n",
    "np.save('./gridsearch/RNN_grid_scores.npy', grid_scores)\n",
    "np.save('./gridsearch/RNN_grid_spacings.npy', grid_spacings)\n",
    "np.save('./gridsearch/RNN_grid_search_alphas.npy', alphas)\n",
    "np.save('./gridsearch/RNN_grid_search_sigmas.npy', sigmas)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Alpha: 0.001\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  6%|▌         | 3/50 [00:00<00:01, 27.47it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 50/50 [00:01<00:00, 30.74it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Alpha: 0.5000000000000029\n"
     ]
    },
    {
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     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Alpha: 0.999\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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    {
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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      "Alpha: 0.5000000000000029\n"
     ]
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 50/50 [00:01<00:00, 31.27it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Alpha: 0.999\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 50/50 [00:01<00:00, 31.97it/s]\n"
     ]
    }
   ],
   "source": [
    "# RNN Gridsearch\n",
    "\n",
    "alphas = inverse_transform_sampling(lambda x: (1-x)/x + x/(1-x), 3)\n",
    "\n",
    "dataset = DatasetMaker()\n",
    "\n",
    "for norm in [\"l1\", \"l2\"]:\n",
    "    grid_scores = []\n",
    "    for alpha in alphas:\n",
    "        print(f'Alpha: {alpha}')\n",
    "        model = RNNGC(ng = ng, alpha = alpha, norm = norm)\n",
    "        model.to(device)\n",
    "        optimizer = torch.optim.Adam(model.parameters(), lr = 1e-3)\n",
    "        # Train model\n",
    "        for i in tqdm(range(train_steps-len(model.total_loss_history))):\n",
    "            r, v = dataset.generate_data(bs, timesteps, device)\n",
    "            loss = model.train_step(inputs = (r[:,0], v), labels = r, optimizer = optimizer)\n",
    "\n",
    "        # evaluate on 64x64 grid\n",
    "        r, v = dataset.generate_data(20000, timesteps, device)\n",
    "        g = model((r[:,0], v))\n",
    "\n",
    "        r = r.detach().cpu().numpy()\n",
    "        g = g.detach().cpu().numpy()\n",
    "        ratemaps = scipy.stats.binned_statistic_2d(r[...,0].ravel(), r[...,1].ravel(), g.reshape(-1, g.shape[-1]).T, bins = 64)[0]\n",
    "\n",
    "        # Gridscore\n",
    "        grid_score = np.array([sm.gridness(ratemaps[i]) for i in range(len(ratemaps))])\n",
    "        grid_scores.append(grid_score)\n",
    "\n",
    "        model.save(path=f\"./saved-models/{model.__class__.__name__}_{alpha}_{norm}.pkl\")\n",
    "\n",
    "    # Save grid scores\n",
    "    grid_scores = np.array(grid_scores)\n",
    "    np.save(f'./gridsearch/rnngc_grid_scores_{norm}.npy', grid_scores)\n",
    "    np.save(f'./gridsearch/rnnc_grid_scores_alphas_{norm}.npy', alphas)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([64.20082941, 70.00407886, 77.02281004, 73.99509092, 62.95197528,\n",
       "               nan,         nan,         nan,         nan,         nan,\n",
       "               nan,         nan,         nan,         nan,         nan,\n",
       "               nan,         nan,         nan,         nan,         nan,\n",
       "               nan,         nan,         nan,         nan,         nan])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.load('./gridsearch/ffgc_grid_spacings.npy')[4]\n",
    "# np.load('./gridsearch/ffgc_grid_search_sigmas.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "metadata": {}
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10,)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid_scores = np.load('./gridsearch/RNN_grid_scores.npy')\n",
    "grid_spacings = np.load('./gridsearch/RNN_grid_spacings.npy')\n",
    "alphas = np.load('./gridsearch/RNN_grid_search_alphas.npy')\n",
    "sigmas = np.load('./gridsearch/RNN_grid_search_sigmas.npy')\n",
    "\n",
    "# NaNs to zeros\n",
    "grid_scores[np.isnan(grid_scores)] = 0\n",
    "grid_scores_binned = []\n",
    "\n",
    "\n",
    "\n",
    "gs_means = np.mean(grid_scores, axis = 2)\n",
    "gs_meadians = np.median(grid_scores, axis = 2)\n",
    "\n",
    "\n",
    "# mode on 30 bins\n",
    "i_plot = 1\n",
    "grid_scores_i = grid_scores[i_plot]\n",
    "gs_modes = np.zeros(len(grid_scores_i))\n",
    "\n",
    "for i in range(len(grid_scores_i)):\n",
    "    grid_scores_binned.append(np.histogram(grid_scores_i[i], bins = 100))\n",
    "    gs_modes[i] = grid_scores_binned[i][1][np.argmax(grid_scores_binned[i][0])]\n",
    "\n",
    "print(gs_modes.shape)\n",
    "\n",
    "plt.figure(figsize=(3,2))\n",
    "plt.plot(alphas, gs_means[i_plot], label = 'Mean')\n",
    "plt.plot(alphas, gs_meadians[i_plot], label = 'Median')\n",
    "plt.plot(alphas, gs_modes, label = 'Mode')\n",
    "plt.xlabel('Alpha')\n",
    "plt.ylabel('Grid score')\n",
    "plt.legend()\n",
    "# plt.savefig('./gridsearch/ffgc_grid_scores.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "metadata": {}
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mode alpha max: 0.5544444444444444\n",
      "Mean alpha max: [0.88111111 0.55444444 0.11888889]\n",
      "Median alpha max: [0.88111111 0.55444444 0.33666667]\n",
      "\n",
      "Mean max: [0.03353553 1.14535281 0.15877672]\n",
      "Median max: [-0.02158751  1.19961696  0.37830559]\n",
      "Mode max: -0.037771625125429864\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[27.304533361273982, 29.407075375713656, 39.80751680477144]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mode_alpha_max = alphas[np.argmax(gs_modes)]\n",
    "mean_alpha_max = alphas[np.argmax(gs_means, axis = 1)]\n",
    "median_alpha_max = alphas[np.argmax(gs_meadians, axis = 1)]\n",
    "print(f'Mode alpha max: {mode_alpha_max}')\n",
    "print(f'Mean alpha max: {mean_alpha_max}')\n",
    "print(f'Median alpha max: {median_alpha_max}')\n",
    "\n",
    "mean_max = np.max(gs_means, axis = 1)\n",
    "median_max = np.max(gs_meadians, axis = 1)\n",
    "mode_max = np.max(gs_modes)\n",
    "\n",
    "print(f'\\nMean max: {mean_max}')\n",
    "print(f'Median max: {median_max}')\n",
    "print(f'Mode max: {mode_max}')\n",
    "\n",
    "gridscales_at_best = []\n",
    "for i in range(len(sigmas)):\n",
    "    # Gridscale at max mean gridscore\n",
    "    g_scale_best = grid_spacings[i][np.argmax(gs_means, axis = 1)[i]]\n",
    "    gridscales_at_best.append(g_scale_best)\n",
    "\n",
    "grid_spacings_naned = grid_spacings.copy()\n",
    "grid_spacings_naned[np.isnan(grid_spacings_naned)] = 0\n",
    "grid_spacings_max = np.max(grid_spacings_naned, axis = 1)\n",
    "gridscales_at_best"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([60.04526362, 75.41317518, 70.60801718, 70.71801175, 77.02281004,\n",
       "       91.54947599])"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_spacings_max"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "metadata": {}
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(-0.1, 3.5, 'Gridscore')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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      "text/plain": [
       "<Figure size 2100x900 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create the data\n",
    "\n",
    "grid_scores = np.load('./gridsearch/RNN_grid_scores.npy')\n",
    "grid_spacings = np.load('./gridsearch/RNN_grid_spacings.npy')\n",
    "alphas = np.load('./gridsearch/RNN_grid_search_alphas.npy')\n",
    "sigmas = np.load('./gridsearch/RNN_grid_search_sigmas.npy')\n",
    "\n",
    "grid_scores_means = np.mean(grid_scores, axis = 2)\n",
    "\n",
    "n_plots = len(sigmas)\n",
    "\n",
    "# x = rs.randn(500)\n",
    "x = grid_scores_means.T.flatten()\n",
    "g = np.tile([r\"$\\sigma = $\" + str(sigmas[i]) + \", scale: \" + str(f\"{gridscales_at_best[i]:.0f}\") for i in range(len(sigmas))], len(alphas))\n",
    "alphas_plot = np.repeat(alphas, len(sigmas))\n",
    "df = pd.DataFrame(dict(x=x, g=g, alpha=alphas_plot, alpha_max = np.repeat(mean_alpha_max, len(alphas))))\n",
    "\n",
    "# Initialize the FacetGrid object\n",
    "g = sns.FacetGrid(df, row=\"g\", hue=\"g\", aspect=7, height=1)\n",
    "\n",
    "# Plot \n",
    "g.map(sns.lineplot, \"alpha\", \"x\", alpha=1, linewidth=1.5, err_kws={'alpha':0})\n",
    "\n",
    "# Create coloured axes\n",
    "g.refline(y=0, linewidth=2, linestyle=\"-\", color=None, clip_on=False)\n",
    "\n",
    "\n",
    "# Define and use a simple function to label the plot in axes coordinates\n",
    "def label(x, color, label):\n",
    "    ax = plt.gca()\n",
    "    # label = r\"\\sigma\" + f\"{label:.2f}\"\n",
    "    max_y = mean_max[np.where(sigmas == float(label[11:14]))[0][0]]\n",
    "    # Plot the labels in \"g\"\n",
    "    ax.text(0, 0.0, label, fontweight=\"bold\", color=color,\n",
    "            ha=\"left\", va=\"center\", transform=ax.transAxes)\n",
    "    # Plot the max mean gridscore\n",
    "    ax.text(0.85, 0.3, f\"Max gridscore: {max_y:.2f}\", fontweight=\"bold\", color=color,\n",
    "            ha=\"left\", va=\"center\", transform=ax.transAxes)\n",
    "\n",
    "g.map(label, \"x\")\n",
    "\n",
    "# Set the subplots to overlap, NB: Adjust if you get white overlaps\n",
    "g.figure.subplots_adjust(hspace=0.1)\n",
    "\n",
    "# Remove axes details that don't play well with overlap\n",
    "g.set_titles(\"\")\n",
    "g.set(yticks=[0], ylabel=\"\", xlabel=r\"$\\alpha$\", xticks=np.linspace(0,1,11))\n",
    "g.despine(bottom=True, left=True)\n",
    "g.tick_params(left=False, bottom=False)\n",
    "ax = plt.gca()\n",
    "# Remove grid tick lines\n",
    "ax.text(-0.1, 3.5, 'Gridscore', va='center', rotation='vertical', fontweight='bold')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "metadata": {}
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(-0.1, 300, 'Grid scale')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2100x900 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create the data\n",
    "\n",
    "grid_scores = np.load('./gridsearch/RNN_grid_scores.npy')\n",
    "grid_spacings = np.load('./gridsearch/RNN_grid_spacings.npy')\n",
    "alphas = np.load('./gridsearch/RNN_grid_search_alphas.npy')\n",
    "sigmas = np.load('./gridsearch/RNN_grid_search_sigmas.npy')\n",
    "\n",
    "grid_scores_means = np.mean(grid_scores, axis = 2)\n",
    "\n",
    "n_plots = len(sigmas)\n",
    "\n",
    "# x = rs.randn(500)\n",
    "x = grid_spacings.T.flatten()\n",
    "g = np.tile([r\"$\\sigma = $\" + str(sigmas[i]) for i in range(len(sigmas))], len(alphas))\n",
    "alphas_plot = np.repeat(alphas, len(sigmas))\n",
    "df = pd.DataFrame(dict(x=x, g=g, alpha=alphas_plot, alpha_max = np.repeat(mean_alpha_max, len(alphas))))\n",
    "\n",
    "# Initialize the FacetGrid object\n",
    "g = sns.FacetGrid(df, row=\"g\", hue=\"g\", aspect=7, height=1)\n",
    "\n",
    "# Plot \n",
    "g.map(sns.lineplot, \"alpha\", \"x\", alpha=1, linewidth=1.5, err_kws={'alpha':0})\n",
    "\n",
    "# Create coloured axes\n",
    "g.refline(y=0, linewidth=2, linestyle=\"-\", color=None, clip_on=False)\n",
    "\n",
    "\n",
    "# Define and use a simple function to label the plot in axes coordinates\n",
    "def label(x, color, label):\n",
    "    ax = plt.gca()\n",
    "    # label = r\"\\sigma\" + f\"{label:.2f}\"\n",
    "    max_y = grid_spacings_max[np.where(sigmas == float(label[11:14]))[0][0]]\n",
    "    # max_y = mean_max[np.where(sigmas == float(label[11:14]))[0][0]]\n",
    "    # print(max_y)\n",
    "    # Plot the labels in \"g\"\n",
    "    ax.text(0, -0.05, label, fontweight=\"bold\", color=color,\n",
    "            ha=\"left\", va=\"center\", transform=ax.transAxes)\n",
    "    # Plot the max mean gridscore\n",
    "    ax.text(0.95, 0.2, f\"Max scale: {max_y:.0f}\", fontweight=\"bold\", color=color, ha=\"left\", va=\"center\", transform=ax.transAxes)\n",
    "\n",
    "g.map(label, \"x\")\n",
    "\n",
    "# Set the subplots to overlap, NB: Adjust if you get white overlaps\n",
    "g.figure.subplots_adjust(hspace=0.1)\n",
    "\n",
    "# Remove axes details that don't play well with overlap\n",
    "g.set_titles(\"\")\n",
    "g.set(yticks=[0], ylabel=\"\", xlabel=r\"$\\alpha$\", xticks=np.linspace(0,1,11))\n",
    "g.despine(bottom=True, left=True)\n",
    "g.tick_params(left=False, bottom=False)\n",
    "ax = plt.gca()\n",
    "# Remove grid tick lines\n",
    "ax.text(-0.1, 300, 'Grid scale', va='center', rotation='vertical', fontweight='bold')"
   ]
  }
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