gistfile1.txt

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from PIL import Image\n",
    "import os\n",
    "from tqdm import tqdm\n",
    "import seaborn as sns\n",
    "sns.set_palette('hls', 10)\n",
    "import sys\n",
    "import matplotlib.pyplot as plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.set_printoptions(linewidth=np.inf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "node_i = 12\n",
    "weight = np.random.randint(1,10,(node_i,node_i))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def X_maker(i):\n",
    "    binary = [-1,1]\n",
    "    x = []\n",
    "    randomi = np.random.randint(0,2, size=i)\n",
    "\n",
    "    for i in range(len(randomi)):\n",
    "        x.append(binary[randomi[i]])\n",
    "    x = np.array([x]) \n",
    "#     xt = x.transpose()\n",
    "#     X = np.dot(xt,x)\n",
    "    \n",
    "    return x\n",
    "\n",
    "# s = X_maker(node_i)\n",
    "# print(s)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def W_maker(x):\n",
    "\n",
    "    xt = x.transpose()\n",
    "    W = np.dot(xt,x)    \n",
    "    I = np.identity(node_i)\n",
    "    W = W - I\n",
    "    \n",
    "    return W \n",
    "\n",
    "\n",
    "# test2 = W_maker(s)\n",
    "# print(test2)\n",
    "# # #     I = np.identity(node_i)\n",
    "# # #     weight = weight-I\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def sigmo(a):\n",
    "    y = 1/(1 + np.exp(-a))\n",
    "    return y\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [],
   "source": [
    "d1 = 0\n",
    "d2 = 0\n",
    "d = []\n",
    "f = []\n",
    "fl = 10\n",
    "\n",
    "for l in range(fl):\n",
    "    for i in range(node_i):\n",
    "        w = np.zeros((node_i,node_i))\n",
    "        x = X_maker(node_i)\n",
    "\n",
    "        for i in range(node_i): \n",
    "            w += W_maker(x)\n",
    "        a = w*x\n",
    "        for i in range(node_i):\n",
    "            for j in range(node_i):\n",
    "                d1 += a[i,0]\n",
    "                d2 += a[0,j]\n",
    "        \n",
    "        d.append(np.sign(d1+d2))\n",
    "    f.append(l)\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "d = np.array(d)\n",
    "l = np.array(l)\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(d)\n",
    "plt.show()\n",
    "#     d.shape()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}