ai20210620 random graph.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b6436378",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[40.96251803  4.        ]\n",
      " [91.21431419  1.        ]\n",
      " [87.48296541  4.        ]\n",
      " [50.30426012  2.        ]\n",
      " [42.52232012  1.        ]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from numpy import random\n",
    "\n",
    "x = random.uniform(low = 0, high = 100, size=(100))\n",
    "\n",
    "y = random.randint(5, size=(100))\n",
    "\n",
    "z = np.zeros([100, 2])\n",
    "z[:, 0] = x\n",
    "z[:, 1] = y\n",
    "print(z[:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b5e82f1c",
   "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": [
    "from matplotlib import pyplot as plt\n",
    "\n",
    "plt.scatter(x, y, c = 'g')\n",
    "plt.title(\"Random Scatter Graph\")\n",
    "plt.xlabel(\"Float values\")\n",
    "plt.ylabel(\"Integer values\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "69305b2d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[51.91990686  4.        ]\n",
      " [67.73901116  3.        ]\n",
      " [53.97051898  3.        ]\n",
      " [98.49470034  3.        ]\n",
      " [81.1012772   4.        ]]\n"
     ]
    }
   ],
   "source": [
    "a = random.uniform(low = 50, high = 100, size=(100))\n",
    "\n",
    "b = random.randint(3, 5, 100)\n",
    "\n",
    "c = np.zeros([100, 2])\n",
    "c[:, 0] = a\n",
    "c[:, 1] = b\n",
    "print(c[0:5, :])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "60edb9c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "756.5069571464211\n"
     ]
    }
   ],
   "source": [
    "def mse(x, y):\n",
    "    w = (x - y)**2\n",
    "    v = np.sum(w, axis=0)\n",
    "    u = np.sum(v, axis=0)\n",
    "    return u/200\n",
    "\n",
    "print(mse(z, c))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5b44cda0",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}