Tutorial2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "# For large library, functions are collected in different submodules.\n",
    "# For this import you need to import the submodule.\n",
    "import matplotlib.pyplot as plt\n",
    "import sklearn.linear_model as skl\n",
    "#add this command to snable matplotlib plot in notebook\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-5.         -4.65517241 -4.31034483 -3.96551724 -3.62068966 -3.27586207\n",
      " -2.93103448 -2.5862069  -2.24137931 -1.89655172 -1.55172414 -1.20689655\n",
      " -0.86206897 -0.51724138 -0.17241379  0.17241379  0.51724138  0.86206897\n",
      "  1.20689655  1.55172414  1.89655172  2.24137931  2.5862069   2.93103448\n",
      "  3.27586207  3.62068966  3.96551724  4.31034483  4.65517241  5.        ]\n",
      "[-85.         -64.55471729 -47.19279183 -32.66821108 -20.73496248\n",
      " -11.1470335   -3.65841158   1.97691582   6.00496125   8.67173726\n",
      "  10.22325639  10.90553118  10.96457419  10.64639797  10.19701505\n",
      "   9.86243798   9.88867932  10.52175161  12.00766739  14.59243921\n",
      "  18.52207963  24.04260117  31.4000164   40.84033786  52.60957809\n",
      "  66.95374964  84.11886506 104.3509369  127.89597769 155.        ]\n"
     ]
    }
   ],
   "source": [
    "x = np.linspace(-5,5,30)\n",
    "y = x**3 + x**2 -x +10\n",
    "print(x)\n",
    "print(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## About matplotlib\n",
    "Remembering the names of all the components in Matplotlib will help you create/revise the figures for your needs easily.\n",
    "![image.png](resources/matplotlib.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "plt.scatter? give you its documentation. Also try shift+tab."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'y one')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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.scatter(x, y)\n",
    "plt.plot(x, y)\n",
    "plt.xlabel(\"x label\")\n",
    "plt.ylabel(\"y label\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Scikit-learn expect data to be formatted as a 2D array with shape (n_samples, n_features), so we need to expand the dimensions of our x/y."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(30,)\n",
      "(30,)\n"
     ]
    }
   ],
   "source": [
    "print(x.shape)\n",
    "print(y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.expand_dims(x, axis=1)\n",
    "y = np.expand_dims(y, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(30, 1)\n",
      "(30, 1)\n"
     ]
    }
   ],
   "source": [
    "print(x.shape)\n",
    "print(y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-5.        ],\n",
       "       [-4.65517241],\n",
       "       [-4.31034483],\n",
       "       [-3.96551724],\n",
       "       [-3.62068966],\n",
       "       [-3.27586207],\n",
       "       [-2.93103448],\n",
       "       [-2.5862069 ],\n",
       "       [-2.24137931],\n",
       "       [-1.89655172],\n",
       "       [-1.55172414],\n",
       "       [-1.20689655],\n",
       "       [-0.86206897],\n",
       "       [-0.51724138],\n",
       "       [-0.17241379],\n",
       "       [ 0.17241379],\n",
       "       [ 0.51724138],\n",
       "       [ 0.86206897],\n",
       "       [ 1.20689655],\n",
       "       [ 1.55172414],\n",
       "       [ 1.89655172],\n",
       "       [ 2.24137931],\n",
       "       [ 2.5862069 ],\n",
       "       [ 2.93103448],\n",
       "       [ 3.27586207],\n",
       "       [ 3.62068966],\n",
       "       [ 3.96551724],\n",
       "       [ 4.31034483],\n",
       "       [ 4.65517241],\n",
       "       [ 5.        ]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this demo we can use several linear models on simulated data.\n",
    "See Sklearn documentation for details."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-65.58250304]\n",
      " [-65.58250304]\n",
      " [-48.1385734 ]\n",
      " [-33.53198846]\n",
      " [-21.51673569]\n",
      " [-11.84680252]\n",
      " [ -4.27617642]\n",
      " [  1.44115517]\n",
      " [  5.55120478]\n",
      " [  8.29998497]\n",
      " [  9.93350828]\n",
      " [ 10.69778725]\n",
      " [ 10.83883445]\n",
      " [ 10.6026624 ]\n",
      " [ 10.23528367]\n",
      " [  9.98271078]\n",
      " [ 10.09095631]\n",
      " [ 10.80603277]\n",
      " [ 12.37395274]\n",
      " [ 15.04072874]\n",
      " [ 19.05237334]\n",
      " [ 24.65489907]\n",
      " [ 32.09431848]\n",
      " [ 41.61664412]\n",
      " [ 53.46788853]\n",
      " [ 67.89406426]\n",
      " [ 85.14118387]\n",
      " [105.45525988]\n",
      " [129.08230486]\n",
      " [129.08230486]]\n"
     ]
    }
   ],
   "source": [
    "# Basic procedure of using a classifier\n",
    "# Fit: training on the data (features + labels).\n",
    "# Predict: predicting on the features\n",
    "# model = skl.LinearRegression()\n",
    "import sklearn.neighbors\n",
    "model = sklearn.neighbors.KNeighborsRegressor(n_neighbors=3)\n",
    "model.fit(x, y)\n",
    "predict_y = model.predict(x)\n",
    "print(predict_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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.scatter(x,y)\n",
    "plt.plot(x,predict_y) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}