{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Function for getting scores: Sigmoid fucntion</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "75.50813375962909\n"
     ]
    }
   ],
   "source": [
    "\n",
    "mean_stocks = 5\n",
    "value_stock = 4\n",
    "\n",
    "score= (200/(1+math.e**( 0.5*(mean_stocks-value_stock))))\n",
    "\n",
    "print(score)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1e6dab8358774ba1b4a796b47bbf6360",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=1, description='w', max=10), FloatSlider(value=1.0, description='amp', m…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import ipywidgets as widgets\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "# set up plot\n",
    "fig, ax = plt.subplots(figsize=(6, 4))\n",
    "ax.set_ylim([-4, 4])\n",
    "ax.grid(True)\n",
    " \n",
    "# generate x values\n",
    "x = np.linspace(0, 2 * np.pi, 100)\n",
    " \n",
    " \n",
    "def my_sine(x, w, amp, phi):\n",
    "    \"\"\"\n",
    "    Return a sine for x with angular frequeny w and amplitude amp.\n",
    "    \"\"\"\n",
    "    return amp*np.sin(w * (x-phi))\n",
    " \n",
    " \n",
    "@widgets.interact(w=(0, 10, 1), amp=(0, 4, .1), phi=(0, 2*np.pi+0.01, 0.01))\n",
    "def update(w = 1.0, amp=1, phi=0):\n",
    "    \"\"\"Remove old lines from plot and plot new one\"\"\"\n",
    "    [l.remove() for l in ax.lines]\n",
    "    ax.plot(x, my_sine(x, w, amp, phi), color='C0')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "language_info": {
   "name": "python"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}