sigmoid_like_linear_func.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_pars(k, b, x0, x1, c0, c1):\n",
    "    n0 = k / (k * x0 + b - c0)\n",
    "    n1 = k / (k * x1 + b - c1)\n",
    "    m0 = k / (n0 * np.exp(n0 * x0))\n",
    "    m1 = k / (n1 * np.exp(n1 * x1))\n",
    "    return n0, n1, m0, m1\n",
    "\n",
    "\n",
    "def linear_sigmoid(x,\n",
    "                   k,\n",
    "                   b,\n",
    "                   x0,\n",
    "                   x1,\n",
    "                   c0,\n",
    "                   c1,\n",
    "                   n0=None,\n",
    "                   n1=None,\n",
    "                   m0=None,\n",
    "                   m1=None):\n",
    "    if n0 is None or n1 is None or m0 is None or m1 is None:\n",
    "        n0, n1, m0, m1 = get_pars(k, b, x0, x1, c0, c1)\n",
    "    y = np.zeros_like(x)\n",
    "    y[x < x0] = m0 * np.exp(n0 * x[x < x0]) + c0\n",
    "    y[x > x1] = m1 * np.exp(n1 * x[x > x1]) + c1\n",
    "    y[(x >= x0) & (x <= x1)] = k * x[(x >= x0) & (x <= x1)] + b\n",
    "    return y\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "k = 1.2\n",
    "b = 0\n",
    "x0 = -1.5\n",
    "x1 = 1.5\n",
    "c0 = -2.5\n",
    "c1 = 2.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.linspace(-3, 3, 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = linear_sigmoid(x, k, b, x0, x1, c0, c1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fa0bbd26a60>]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "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": "3.9.16"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}