from_machine_learning_book.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyNznliF+kk0lykpI7Xu+MkB",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Sinrez/pyCoursera/blob/master/from_machine_learning_book.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9dR8GhXptnZI",
        "outputId": "1685d897-e6f7-4273-a85a-a653f7e7d881"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Normal Python: 1.409112 sec\n",
            "Naive NumPy: 1.116826 sec\n",
            "Good NumPy: 0.017017 sec\n"
          ]
        }
      ],
      "source": [
        "import timeit\n",
        "normal_py_sec = timeit.timeit('sum(x*x for x in range(1000))',number=10000)\n",
        "naive_np_sec = timeit.timeit('sum(na*na)',setup=\"import numpy as np; na=np.arange(1000)\", number=10000)\n",
        "good_np_sec = timeit.timeit('na.dot(na)',setup=\"import numpy as np; na=np.arange(1000)\", number=10000)\n",
        "print(\"Normal Python: %f sec\" % normal_py_sec)\n",
        "print(\"Naive NumPy: %f sec\" % naive_np_sec)\n",
        "print(\"Good NumPy: %f sec\" % good_np_sec)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import scipy, numpy\n",
        "scipy.version.full_version\n",
        "scipy.dot is numpy.dot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DCKrufZvuVRh",
        "outputId": "2e243db3-de4e-4703-cda4-3cb14c2b8100"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {},
          "execution_count": 1
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from io import StringIO\n",
        "import numpy as np\n",
        "import scipy as sp\n",
        "# data = sp.genfromtxt(\"/content/web_traffic.tsv\", delimiter = \"\\t\")\n",
        "data = np.genfromtxt(\"/content/web_traffic.tsv\", delimiter=\"\\t\")\n",
        "print(data[:10])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Yrg3ff8FumZX",
        "outputId": "44bdae97-9983-45d3-acda-63b29e0ade6e"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[1.000e+00 2.272e+03]\n",
            " [2.000e+00       nan]\n",
            " [3.000e+00 1.386e+03]\n",
            " [4.000e+00 1.365e+03]\n",
            " [5.000e+00 1.488e+03]\n",
            " [6.000e+00 1.337e+03]\n",
            " [7.000e+00 1.883e+03]\n",
            " [8.000e+00 2.283e+03]\n",
            " [9.000e+00 1.335e+03]\n",
            " [1.000e+01 1.025e+03]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = data[:,0]\n",
        "y = data[:,1]\n"
      ],
      "metadata": {
        "id": "75fYk4zPTcsm"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sp.sum(sp.isnan(y))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aGcH4RrtqhDf",
        "outputId": "c69ddea5-97be-48d7-f2d8-b53b3a6e588a"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-20-d6e39c765d76>:1: DeprecationWarning: scipy.isnan is deprecated and will be removed in SciPy 2.0.0, use numpy.isnan instead\n",
            "  sp.sum(sp.isnan(y))\n",
            "<ipython-input-20-d6e39c765d76>:1: DeprecationWarning: scipy.sum is deprecated and will be removed in SciPy 2.0.0, use numpy.sum instead\n",
            "  sp.sum(sp.isnan(y))\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "8"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ">>> x = x[~sp.isnan(y)]\n",
        ">>> y = y[~sp.isnan(y)]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HZulFA7Dq3OI",
        "outputId": "49593cdf-5c21-4de7-8266-75288ff5d182"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-43-63f409da33d3>:1: DeprecationWarning: scipy.isnan is deprecated and will be removed in SciPy 2.0.0, use numpy.isnan instead\n",
            "  x = x[~sp.isnan(y)]\n",
            "<ipython-input-43-63f409da33d3>:2: DeprecationWarning: scipy.isnan is deprecated and will be removed in SciPy 2.0.0, use numpy.isnan instead\n",
            "  y = y[~sp.isnan(y)]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ">>> import matplotlib.pyplot as plt\n",
        ">>> # представляем точки (x,y) кружочками диаметра 10\n",
        ">>> plt.scatter(x, y, s=10)\n",
        ">>> plt.title(\"Web traffic over the last month\")\n",
        ">>> plt.xlabel(\"Time\")\n",
        ">>> plt.ylabel(\"Hits/hour\")\n",
        ">>> plt.xticks([w*7*24 for w in range(10)],\n",
        "                 ['week %i' % w for w in range(10)])\n",
        ">>> plt.autoscale(tight=True)\n",
        ">>> # рисуем полупрозрачную сетку пунктирными линиями\n",
        ">>> plt.grid(True, linestyle='-', color='0.75')\n",
        ">>> plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "4yQwnvJSq-f8",
        "outputId": "b436439f-5c98-4a5c-998b-778eecf998c1"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def error(f, x, y):\n",
        "     return sp.sum((f(x)-y)**2)"
      ],
      "metadata": {
        "id": "8Xg6P-cjx8mA"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fp1, residuals, rank, sv, rcond = sp.polyfit(x, y, 1, full=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_iACa4CNXglx",
        "outputId": "de4523ad-c345-48ff-b00f-8c50be6ecf19"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-23-176abcef6c22>:1: DeprecationWarning: scipy.polyfit is deprecated and will be removed in SciPy 2.0.0, use numpy.polyfit instead\n",
            "  fp1, residuals, rank, sv, rcond = sp.polyfit(x, y, 1, full=True)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Параметры модели: %s\" % fp1)\n",
        "print(residuals)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AevSACKEXvox",
        "outputId": "943bd1eb-60de-4c78-e927-ba7ef92c461d"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Параметры модели: [  2.59619213 989.02487106]\n",
            "[3.17389767e+08]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        " f(x) = 2.59619213 * x + 989.02487106"
      ],
      "metadata": {
        "id": "A_BLjhgnX9vX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        ">>> f1 = sp.poly1d(fp1)\n",
        ">>> print(error(f1, x, y))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DnTiMMkuXzgj",
        "outputId": "5f8e1e70-c17d-49a8-e502-ac2c565aeb7e"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "317389767.33977807\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-16-4c7a863c8cc8>:2: DeprecationWarning: scipy.sum is deprecated and will be removed in SciPy 2.0.0, use numpy.sum instead\n",
            "  return sp.sum((f(x)-y)**2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fx = sp.linspace(0,x[-1], 1000) # сгенерировать значения X для графика \n",
        "plt.plot(fx, f1(fx), linewidth=4)\n",
        "plt.legend([\"d=%i\" % f1.order], loc=\"upper left\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 342
        },
        "id": "895tLg21YFYe",
        "outputId": "ee8cc54e-13a9-4698-cd6e-b604d60b882d"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-40-b9820c9f9c4a>:1: DeprecationWarning: scipy.linspace is deprecated and will be removed in SciPy 2.0.0, use numpy.linspace instead\n",
            "  fx = sp.linspace(0,x[-1], 1000) # сгенерировать значения X для графика\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fba5cbb57c0>"
            ]
          },
          "metadata": {},
          "execution_count": 40
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ">>> f2p = sp.polyfit(x, y, 2)\n",
        ">>> print(f2p)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XfQbu8ipcYlI",
        "outputId": "15af4cd5-eab6-4b80-8a09-c8ecc5826c64"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[ 1.05322215e-02 -5.26545650e+00  1.97476082e+03]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-35-517e130e6db7>:1: DeprecationWarning: scipy.polyfit is deprecated and will be removed in SciPy 2.0.0, use numpy.polyfit instead\n",
            "  f2p = sp.polyfit(x, y, 2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ">>> f2 = sp.poly1d(f2p)\n",
        ">>> print(error(f2, x, y))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KIc6MRsAcgXX",
        "outputId": "5453ffd2-c8d3-491e-a3a8-a7d0728ca3b1"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "179983507.8781792\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-16-4c7a863c8cc8>:2: DeprecationWarning: scipy.sum is deprecated and will be removed in SciPy 2.0.0, use numpy.sum instead\n",
            "  return sp.sum((f(x)-y)**2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fx = sp.linspace(0,x[-1], 1000) # сгенерировать значения X для графика \n",
        "plt.plot(fx, f2(fx), linewidth=4)\n",
        "plt.legend([\"d=%i\" % f1.order], loc=\"upper left\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 343
        },
        "id": "K0T4u_7JclLY",
        "outputId": "296e1c76-2fe5-499e-8f94-fc094b9fc8d6"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-38-14082f52bd61>:1: DeprecationWarning: scipy.linspace is deprecated and will be removed in SciPy 2.0.0, use numpy.linspace instead\n",
            "  fx = sp.linspace(0,x[-1], 1000) # сгенерировать значения X для графика\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fba5cbb5b50>"
            ]
          },
          "metadata": {},
          "execution_count": 38
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from bisect import bisect_left\n",
        "\n",
        "inflection = 3.5*7*24\n",
        "index_xa = bisect_left(x,inflection,0,len(x)) #binary search to locate the index where inflection should be inserted.\n",
        "xa  =  x[:index_xa]\n",
        "ya  =  y[:index_xa]\n",
        "xb  =  x[index_xa:]\n",
        "yb  =  y[index_xa:]"
      ],
      "metadata": {
        "id": "391VAWxTgOwY"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fa = sp.poly1d(sp.polyfit(xa, ya, 1))\n",
        "fb = sp.poly1d(sp.polyfit(xb, yb, 1))\n",
        "fa_error = error(fa, xa, ya)\n",
        "fb_error = error(fb, xb, yb)\n",
        "print(\"Error inflection=%f\" % (fa_error + fb_error))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wFys13OLg8iC",
        "outputId": "d9f89b89-b474-4265-df83-4c1eae133269"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Error inflection=135280910.179210\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-49-6074f9abf4b1>:1: DeprecationWarning: scipy.polyfit is deprecated and will be removed in SciPy 2.0.0, use numpy.polyfit instead\n",
            "  fa = sp.poly1d(sp.polyfit(xa, ya, 1))\n",
            "<ipython-input-49-6074f9abf4b1>:2: DeprecationWarning: scipy.polyfit is deprecated and will be removed in SciPy 2.0.0, use numpy.polyfit instead\n",
            "  fb = sp.poly1d(sp.polyfit(xb, yb, 1))\n",
            "<ipython-input-16-4c7a863c8cc8>:2: DeprecationWarning: scipy.sum is deprecated and will be removed in SciPy 2.0.0, use numpy.sum instead\n",
            "  return sp.sum((f(x)-y)**2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Kjs6qWQ2hAhq"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}