calculator.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.5707963267948966"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = 0\n",
    "b = 1\n",
    "c = 1\n",
    "math.atan2(b,a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.0"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c*math.sqrt((a)**2+(b)**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-24. ],\n",
       "       [  0.8],\n",
       "       [  0. ]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "A = np.array([[12,10,8],[-2/5,0,0],[0,15/4,0]])\n",
    "b = np.array([[-2],[0],[0]])\n",
    "\n",
    "T = np.array([[1,1,-1],[0,-1,1],[-1,0,1]])\n",
    "A1 = np.dot(np.dot(T,A), np.linalg.inv(T))\n",
    "np.dot(A,b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-280. ],\n",
       "       [   9.6],\n",
       "       [   3. ]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.dot(A, np.dot(A,b))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  -2. ,  -20. , -240. ],\n",
       "       [   0. ,    0.8,    8. ],\n",
       "       [   0. ,    0. ,    3. ]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Wc = np.array([[-2,-24,-280],[0,4/5,48/5],[0,0,3]])\n",
    "Wc1 = np.array([[1,-2,4],[0,1,-2],[0,0,1]])\n",
    "\n",
    "np.dot(Wc,Wc1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ -0.5       , -12.5       ,  -6.66666667],\n",
       "       [  0.        ,   1.25      ,  -3.33333333],\n",
       "       [  0.        ,   0.        ,   0.33333333]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.linalg.inv(np.dot(Wc,Wc1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1., 30.,  4.]])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "L1 = np.array([[-2,4,12]])\n",
    "np.dot(L1, np.linalg.inv(np.dot(Wc,Wc1)))"
   ]
  }
 ],
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   "display_name": "base",
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   "name": "python3"
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  "language_info": {
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