iter.ipynb

LagranPolinom.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "def L(arg, x, y):\n",
    "    result = 0\n",
    "    for i in range(len(x)):\n",
    "        basis = 1\n",
    "        for j in range(len(x)):\n",
    "            if i == j: continue\n",
    "            basis *= (arg - x[j]) / (x[i] - x[j])\n",
    "        result += y[i]*basis\n",
    "    return result\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "xr = np.arange(-1, 1, 0.0001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fun(x):\n",
    "    return 1/(25*x**2 + 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "xrav = np.arange(-1, 1.1, 0.2)\n",
    "yrav = [fun(_) for _ in xrav]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1.00000000e+00, -8.00000000e-01, -6.00000000e-01, -4.00000000e-01,\n",
       "       -2.00000000e-01, -2.22044605e-16,  2.00000000e-01,  4.00000000e-01,\n",
       "        6.00000000e-01,  8.00000000e-01,  1.00000000e+00])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xrav"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "xcheb = [np.cos(np.pi*(2*k)/20) for k in range(1, 11)]\n",
    "ycheb = [fun(_) for _ in xcheb]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.9510565162951535,\n",
       " 0.8090169943749475,\n",
       " 0.5877852522924731,\n",
       " 0.30901699437494745,\n",
       " 0,\n",
       " -0.30901699437494734,\n",
       " -0.587785252292473,\n",
       " -0.8090169943749473,\n",
       " -0.9510565162951535,\n",
       " -1.0]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xcheb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.04235006897143931,\n",
       " 0.05759468769926728,\n",
       " 0.10376363605465243,\n",
       " 0.29522146532942556,\n",
       " 1.0,\n",
       " 0.2952214653294257,\n",
       " 0.10376363605465247,\n",
       " 0.05759468769926729,\n",
       " 0.04235006897143931,\n",
       " 0.038461538461538464]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ycheb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "y1 = []\n",
    "y2 = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "y1= [L(_, xrav, yrav) for _ in xr]\n",
    "y2 = [L(_, xcheb, ycheb) for _ in xr]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "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": [
    "plt.plot(xr, y1)\n",
    "plt.plot(xr, y2)\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}