Carreau/MyFirstNotebook.ipynb

## MyFirstNotebook.ipynb
{
 "metadata": {
  "name": "",
  "signature": "sha256:25538ee28915986cbadd1d9a5680a2421a30db181f794e989349918793754b6d"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Fibonacci Sequence"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Hi, I have issues with my fibonacci implementation, can you help me ? It is much too slow !"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def fib(n):\n",
      "    \"\"\" return the nthnumber of fibonnaci sequence \"\"\"\n",
      "    if n<=2:\n",
      "        return 1\n",
      "    else: \n",
      "        return fib(n-1)+fib(n-2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "[fib(i) for i in range(1,15)]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 2,
       "text": [
        "[1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144, 233, 377]"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "r = []\n",
      "for u in range(33):\n",
      "    s = %timeit -n1 -r1 -q -o fib({u})\n",
      "    r.append(s.best)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "import matplotlib.pyplot as plt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.semilogy(r,'+')\n",
      "plt.ylabel('time (in s)')\n",
      "plt.xlabel('fib(#)')\n",
      "plt.title('seem to be quadratic!')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "<matplotlib.text.Text at 0x107401f90>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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hUJi7atWqXRcuXJhh7HgGAoxFAIC2TDZZmJmZddnZ2TW3t7dbc7ncGmPHMxBg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\n9H/nW9VY2R81tgAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1073eb610>"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}
	{
	"metadata": {
	"name": "",
	"signature": "sha256:25538ee28915986cbadd1d9a5680a2421a30db181f794e989349918793754b6d"
	},
	"nbformat": 3,
	"nbformat_minor": 0,
	"worksheets": [
	{
	"cells": [
	{
	"cell_type": "heading",
	"level": 1,
	"metadata": {},
	"source": [
	"Fibonacci Sequence"
	]
	},
	{
	"cell_type": "markdown",
	"metadata": {},
	"source": [
	"Hi, I have issues with my fibonacci implementation, can you help me ? It is much too slow !"
	]
	},
	{
	"cell_type": "code",
	"collapsed": false,
	"input": [
	"def fib(n):\n",
	" \"\"\" return the nthnumber of fibonnaci sequence \"\"\"\n",
	" if n<=2:\n",
	" return 1\n",
	" else: \n",
	" return fib(n-1)+fib(n-2)"
	],
	"language": "python",
	"metadata": {},
	"outputs": [],
	"prompt_number": 1
	},
	{
	"cell_type": "code",
	"collapsed": false,
	"input": [
	"[fib(i) for i in range(1,15)]"
	],
	"language": "python",
	"metadata": {},
	"outputs": [
	{
	"metadata": {},
	"output_type": "pyout",
	"prompt_number": 2,
	"text": [
	"[1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144, 233, 377]"
	]
	}
	],
	"prompt_number": 2
	},
	{
	"cell_type": "code",
	"collapsed": false,
	"input": [
	"r = []\n",
	"for u in range(33):\n",
	" s = %timeit -n1 -r1 -q -o fib({u})\n",
	" r.append(s.best)"
	],
	"language": "python",
	"metadata": {},
	"outputs": [],
	"prompt_number": 3
	},
	{
	"cell_type": "code",
	"collapsed": false,
	"input": [
	"%matplotlib inline\n",
	"import matplotlib.pyplot as plt"
	],
	"language": "python",
	"metadata": {},
	"outputs": [],
	"prompt_number": 4
	},
	{
	"cell_type": "code",
	"collapsed": false,
	"input": [
	"plt.semilogy(r,'+')\n",
	"plt.ylabel('time (in s)')\n",
	"plt.xlabel('fib(#)')\n",
	"plt.title('seem to be quadratic!')"
	],
	"language": "python",
	"metadata": {},
	"outputs": [
	{
	"metadata": {},
	"output_type": "pyout",
	"prompt_number": 5,
	"text": [
	"<matplotlib.text.Text at 0x107401f90>"
	]
	},
	{
	"metadata": {},
	"output_type": "display_data",
	"png": 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hUJi7atWqXRcuXJhh7HgGAoxFAIC2TDZZmJmZddnZ2TW3t7dbc7ncGmPHMxBg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\n9H/nW9VY2R81tgAAAABJRU5ErkJggg==\n",
	"text": [
	"<matplotlib.figure.Figure at 0x1073eb610>"
	]
	}
	],
	"prompt_number": 5
	},
	{
	"cell_type": "code",
	"collapsed": false,
	"input": [],
	"language": "python",
	"metadata": {},
	"outputs": []
	}
	],
	"metadata": {}
	}
	]
	}