Training_Machine_Learning_Algorithms_for_Classification.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "ename": "URLError",
     "evalue": "<urlopen error [Errno 11001] getaddrinfo failed>",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mgaierror\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32mC:\\Users\\bEgbedokun\\AppData\\Local\\Continuum\\Anaconda3\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36mdo_open\u001b[1;34m(self, http_class, req, **http_conn_args)\u001b[0m\n\u001b[0;32m   1317\u001b[0m                 h.request(req.get_method(), req.selector, req.data, headers,\n\u001b[1;32m-> 1318\u001b[1;33m                           encode_chunked=req.has_header('Transfer-encoding'))\n\u001b[0m\u001b[0;32m   1319\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mOSError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;31m# timeout error\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\bEgbedokun\\AppData\\Local\\Continuum\\Anaconda3\\lib\\http\\client.py\u001b[0m in \u001b[0;36mrequest\u001b[1;34m(self, method, url, body, headers, encode_chunked)\u001b[0m\n\u001b[0;32m   1238\u001b[0m         \u001b[1;34m\"\"\"Send a complete request to the server.\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1239\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_send_request\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbody\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mencode_chunked\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1240\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\bEgbedokun\\AppData\\Local\\Continuum\\Anaconda3\\lib\\http\\client.py\u001b[0m in \u001b[0;36m_send_request\u001b[1;34m(self, method, url, body, headers, encode_chunked)\u001b[0m\n\u001b[0;32m   1284\u001b[0m             \u001b[0mbody\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_encode\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbody\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'body'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1285\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mendheaders\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbody\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mencode_chunked\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mencode_chunked\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1286\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\bEgbedokun\\AppData\\Local\\Continuum\\Anaconda3\\lib\\http\\client.py\u001b[0m in \u001b[0;36mendheaders\u001b[1;34m(self, message_body, encode_chunked)\u001b[0m\n\u001b[0;32m   1233\u001b[0m             \u001b[1;32mraise\u001b[0m \u001b[0mCannotSendHeader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1234\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_send_output\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmessage_body\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mencode_chunked\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mencode_chunked\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1235\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\bEgbedokun\\AppData\\Local\\Continuum\\Anaconda3\\lib\\http\\client.py\u001b[0m in \u001b[0;36m_send_output\u001b[1;34m(self, message_body, encode_chunked)\u001b[0m\n\u001b[0;32m   1025\u001b[0m         \u001b[1;32mdel\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_buffer\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1026\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1027\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\bEgbedokun\\AppData\\Local\\Continuum\\Anaconda3\\lib\\http\\client.py\u001b[0m in \u001b[0;36msend\u001b[1;34m(self, data)\u001b[0m\n\u001b[0;32m    963\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mauto_open\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 964\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconnect\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    965\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\bEgbedokun\\AppData\\Local\\Continuum\\Anaconda3\\lib\\http\\client.py\u001b[0m in \u001b[0;36mconnect\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1391\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1392\u001b[1;33m             \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconnect\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1393\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\bEgbedokun\\AppData\\Local\\Continuum\\Anaconda3\\lib\\http\\client.py\u001b[0m in \u001b[0;36mconnect\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    935\u001b[0m         self.sock = self._create_connection(\n\u001b[1;32m--> 936\u001b[1;33m             (self.host,self.port), self.timeout, self.source_address)\n\u001b[0m\u001b[0;32m    937\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msock\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msetsockopt\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msocket\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mIPPROTO_TCP\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msocket\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTCP_NODELAY\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\bEgbedokun\\AppData\\Local\\Continuum\\Anaconda3\\lib\\socket.py\u001b[0m in \u001b[0;36mcreate_connection\u001b[1;34m(address, timeout, source_address)\u001b[0m\n\u001b[0;32m    703\u001b[0m     \u001b[0merr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 704\u001b[1;33m     \u001b[1;32mfor\u001b[0m \u001b[0mres\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mgetaddrinfo\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhost\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mport\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mSOCK_STREAM\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    705\u001b[0m         \u001b[0maf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msocktype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mproto\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcanonname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msa\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mres\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\bEgbedokun\\AppData\\Local\\Continuum\\Anaconda3\\lib\\socket.py\u001b[0m in \u001b[0;36mgetaddrinfo\u001b[1;34m(host, port, family, type, proto, flags)\u001b[0m\n\u001b[0;32m    742\u001b[0m     \u001b[0maddrlist\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 743\u001b[1;33m     \u001b[1;32mfor\u001b[0m \u001b[0mres\u001b[0m \u001b[1;32min\u001b[0m \u001b[0m_socket\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetaddrinfo\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhost\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mport\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfamily\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mproto\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mflags\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    744\u001b[0m         \u001b[0maf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msocktype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mproto\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcanonname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msa\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mres\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mgaierror\u001b[0m: [Errno 11001] getaddrinfo failed",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mURLError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-21-b50bdfae30ad>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m df = pd.read_csv('https://archive.ics.uci.edu/ml/'\n\u001b[1;32m----> 4\u001b[1;33m         'machine-learning-databases/iris/iris.data', header=None)\n\u001b[0m\u001b[0;32m      5\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtail\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\bEgbedokun\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36mparser_f\u001b[1;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, escapechar, comment, encoding, dialect, tupleize_cols, error_bad_lines, warn_bad_lines, skipfooter, skip_footer, doublequote, delim_whitespace, as_recarray, compact_ints, use_unsigned, low_memory, buffer_lines, memory_map, float_precision)\u001b[0m\n\u001b[0;32m    653\u001b[0m                     skip_blank_lines=skip_blank_lines)\n\u001b[0;32m    654\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 655\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    656\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    657\u001b[0m     \u001b[0mparser_f\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\bEgbedokun\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m_read\u001b[1;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[0;32m    390\u001b[0m     \u001b[0mcompression\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_infer_compression\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcompression\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    391\u001b[0m     filepath_or_buffer, _, compression = get_filepath_or_buffer(\n\u001b[1;32m--> 392\u001b[1;33m         filepath_or_buffer, encoding, compression)\n\u001b[0m\u001b[0;32m    393\u001b[0m     \u001b[0mkwds\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'compression'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcompression\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    394\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\bEgbedokun\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\pandas\\io\\common.py\u001b[0m in \u001b[0;36mget_filepath_or_buffer\u001b[1;34m(filepath_or_buffer, encoding, compression)\u001b[0m\n\u001b[0;32m    184\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0m_is_url\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    185\u001b[0m         \u001b[0murl\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 186\u001b[1;33m         \u001b[0mreq\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_urlopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0murl\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    187\u001b[0m         \u001b[0mcontent_encoding\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mreq\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mheaders\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Content-Encoding'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    188\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mcontent_encoding\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'gzip'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\bEgbedokun\\AppData\\Local\\Continuum\\Anaconda3\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36murlopen\u001b[1;34m(url, data, timeout, cafile, capath, cadefault, context)\u001b[0m\n\u001b[0;32m    221\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    222\u001b[0m         \u001b[0mopener\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_opener\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 223\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mopener\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    224\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    225\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0minstall_opener\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mopener\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\bEgbedokun\\AppData\\Local\\Continuum\\Anaconda3\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36mopen\u001b[1;34m(self, fullurl, data, timeout)\u001b[0m\n\u001b[0;32m    524\u001b[0m             \u001b[0mreq\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmeth\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mreq\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    525\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 526\u001b[1;33m         \u001b[0mresponse\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_open\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mreq\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    527\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    528\u001b[0m         \u001b[1;31m# post-process response\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\bEgbedokun\\AppData\\Local\\Continuum\\Anaconda3\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36m_open\u001b[1;34m(self, req, data)\u001b[0m\n\u001b[0;32m    542\u001b[0m         \u001b[0mprotocol\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mreq\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtype\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    543\u001b[0m         result = self._call_chain(self.handle_open, protocol, protocol +\n\u001b[1;32m--> 544\u001b[1;33m                                   '_open', req)\n\u001b[0m\u001b[0;32m    545\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    546\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\bEgbedokun\\AppData\\Local\\Continuum\\Anaconda3\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36m_call_chain\u001b[1;34m(self, chain, kind, meth_name, *args)\u001b[0m\n\u001b[0;32m    502\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mhandler\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mhandlers\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    503\u001b[0m             \u001b[0mfunc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhandler\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmeth_name\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 504\u001b[1;33m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    505\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mresult\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    506\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\bEgbedokun\\AppData\\Local\\Continuum\\Anaconda3\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36mhttps_open\u001b[1;34m(self, req)\u001b[0m\n\u001b[0;32m   1359\u001b[0m         \u001b[1;32mdef\u001b[0m \u001b[0mhttps_open\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreq\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1360\u001b[0m             return self.do_open(http.client.HTTPSConnection, req,\n\u001b[1;32m-> 1361\u001b[1;33m                 context=self._context, check_hostname=self._check_hostname)\n\u001b[0m\u001b[0;32m   1362\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1363\u001b[0m         \u001b[0mhttps_request\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mAbstractHTTPHandler\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdo_request_\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\bEgbedokun\\AppData\\Local\\Continuum\\Anaconda3\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36mdo_open\u001b[1;34m(self, http_class, req, **http_conn_args)\u001b[0m\n\u001b[0;32m   1318\u001b[0m                           encode_chunked=req.has_header('Transfer-encoding'))\n\u001b[0;32m   1319\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mOSError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;31m# timeout error\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1320\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0mURLError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0merr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1321\u001b[0m             \u001b[0mr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mh\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetresponse\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1322\u001b[0m         \u001b[1;32mexcept\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mURLError\u001b[0m: <urlopen error [Errno 11001] getaddrinfo failed>"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv('https://archive.ics.uci.edu/ml/'\n",
    "        'machine-learning-databases/iris/iris.data', header=None)\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Alternative link"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       0    1    2    3               4\n",
       "145  6.7  3.0  5.2  2.3  Iris-virginica\n",
       "146  6.3  2.5  5.0  1.9  Iris-virginica\n",
       "147  6.5  3.0  5.2  2.0  Iris-virginica\n",
       "148  6.2  3.4  5.4  2.3  Iris-virginica\n",
       "149  5.9  3.0  5.1  1.8  Iris-virginica"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_alt = pd.read_csv('https://raw.githubusercontent.com/\\\n",
    "rasbt/python-machine-learning-book/master/code/datasets/iris/iris.data', header=None)\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Implementing a perceptron learning algorithm in Python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "\n",
    "class Perceptron(object):\n",
    "    \"\"\"Perceptron classifier.\n",
    "\n",
    "    Parameters\n",
    "    ------------\n",
    "    eta : float\n",
    "        Learning rate (between 0.0 and 1.0)\n",
    "    n_iter : int\n",
    "        Passes over the training dataset.\n",
    "\n",
    "    Attributes\n",
    "    -----------\n",
    "    w_ : 1d-array\n",
    "        Weights after fitting.\n",
    "    errors_ : list\n",
    "        Number of misclassifications (updates) in each epoch.\n",
    "\n",
    "    \"\"\"\n",
    "    def __init__(self, eta=0.01, n_iter=10):\n",
    "        self.eta = eta\n",
    "        self.n_iter = n_iter\n",
    "\n",
    "    def fit(self, X, y):\n",
    "        \"\"\"Fit training data.\n",
    "\n",
    "        Parameters\n",
    "        ----------\n",
    "        X : {array-like}, shape = [n_samples, n_features]\n",
    "            Training vectors, where n_samples is the number of samples and\n",
    "            n_features is the number of features.\n",
    "        y : array-like, shape = [n_samples]\n",
    "            Target values.\n",
    "\n",
    "        Returns\n",
    "        -------\n",
    "        self : object\n",
    "\n",
    "        \"\"\"\n",
    "        self.w_ = np.zeros(1 + X.shape[1])\n",
    "        self.errors_ = []\n",
    "\n",
    "        for _ in range(self.n_iter):\n",
    "            errors = 0\n",
    "            for xi, target in zip(X, y):\n",
    "                update = self.eta * (target - self.predict(xi))\n",
    "                self.w_[1:] += update * xi\n",
    "                self.w_[0] += update\n",
    "                errors += int(update != 0.0)\n",
    "            self.errors_.append(errors)\n",
    "        return self\n",
    "\n",
    "    def net_input(self, X):\n",
    "        \"\"\"Calculate net input\"\"\"\n",
    "        return np.dot(X, self.w_[1:]) + self.w_[0]\n",
    "\n",
    "    def predict(self, X):\n",
    "        \"\"\"Return class label after unit step\"\"\"\n",
    "        return np.where(self.net_input(X) >= 0.0, 1, -1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Additional Note (1)\n",
    "\n",
    "Please note that the learning rate η (eta) only has an effect on the classification outcome  if the weights are initialized to non-zero values. If all the weights are initialized to 0, the learning rate parameter eta affects only the scale of the weight vector but not its direction. To have the learning rate influence the classification outcome, the weights need to be initialized to non-zero values. The respective lines in the code that need to be changed to accomplish that are highlighted on below:\n",
    "\n",
    "```python\n",
    "   def __init__(self, eta=0.01, n_iter=50, random_seed=1): # add random_seed=1\n",
    "        ...\n",
    "        self.random_seed = random_seed # add this line\n",
    "\n",
    "    def fit(self, X, y):\n",
    "        ...\n",
    "        # self.w_ = np.zeros(1 + X.shape[1]) ## remove this line\n",
    "        rgen = np.random.RandomState(self.random_seed) # add this line\n",
    "        self.w_ = rgen.normal(loc=0.0, scale=0.01, size=1 + X.shape[1]) # add this line\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Additional Note (2)\n",
    "\n",
    "I received a note by a reader who asked about the net input function:\n",
    "\n",
    ">On page 27, you describe the code.\n",
    "\n",
    "> the net_input method simply calculates the vector product wTx\n",
    "However, there is more than a simple vector product in the code:\n",
    "\n",
    "> def net_input(self, X):  \n",
    "    \"\"\"Calculate net input\"\"\"  \n",
    "    return np.dot(X, self.w_[1:]) + self.w_[0]  \n",
    "   \n",
    "> In addition to the dot product, there is an addition. The text does not mention anything about what is this + self.w_[0]\n",
    "> Can you (or anyone) explain why that's there?\n",
    "\n",
    "-------\n",
    "\n",
    "Sorry that I went over that so briefly. The `self.w_[0]` is basically the \"threshold\" or so-called \"bias unit.\" I simply included the bias unit in the weight vector, which makes the math part easier, but on the other hand, it may make the code more confusing as you mentioned.\n",
    "\n",
    "\n",
    "\n",
    "Let's say we have a 3x2 dimensional dataset `X` (3 training samples with 2 features). Also, let's just assume we have a weight `2` for feature 1 and a weight `3` for feature 2, and we set the bias unit to `4`. \n",
    "\n",
    "```\n",
    "import numpy as np\n",
    ">>> bias = 4.\n",
    ">>> X = np.array([[2., 3.], \n",
    "...              [4., 5.], \n",
    "...              [6., 7.]])\n",
    ">>> w = np.array([bias, 2., 3.])\n",
    "```\n",
    "\n",
    "In order to match the mathematical notation, we would have to add a vector of 1s to compute the dot-product:\n",
    "\n",
    "```\n",
    ">>> ones = np.ones((X.shape[0], 1))\n",
    ">>> X_with1 = np.hstack((ones, X))\n",
    ">>> X_with1\n",
    ">>> np.dot(X_with1, w)\n",
    "array([ 17.,  27.,  37.])\n",
    "```\n",
    "\n",
    "However, I thought that adding a vector of 1s to the training array each time we want to make a prediction would be fairly inefficient. So, instead, we can just \"add\" the bias unit (`w[0]`) to the do product (it's equivalent, since `1.0 * w_0 = w_0`:\n",
    "\n",
    "```\n",
    ">>> np.dot(X, w[1:]) + w[0] \n",
    "array([ 17.,  27.,  37.])\n",
    "```\n",
    "\n",
    "Maybe it is helpful to walk through the matrix-vector multiplication by hand. E.g.,\n",
    "\n",
    "```\n",
    "| 1  2  3 |   | 4 |   | 1*4 + 2*2 + 3*3 |   | 17 |\n",
    "| 1  4  5 | x | 2 | = | 1*4 + 4*2 + 5*3 | = | 27 |\n",
    "| 1  6  7 |   | 3 |   | 1*4 + 6*2 + 7*3 |   | 37 |\n",
    "```\n",
    "\n",
    "which is the same as\n",
    "\n",
    "```\n",
    "| 2  3 |                  | 2*2 + 3*3 |          | 13 + bias |   | 17 |\n",
    "| 4  5 | x | 2 | + bias = | 4*2 + 5*3 | + bias = | 23 + bias | = | 27 |\n",
    "| 6  7 |   | 3 |          | 6*2 + 7*3 |          | 33 + bias |   | 37 |\n",
    "\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Additional Note (3)\n",
    "\n",
    "For simplicity at this point, we don't talk about shuffling at this point; I wanted to introduce concepts incrementally so that it's not too overwhelming all at once. Since a reader asked me about this, I wanted to add a note about shuffling, which you may want to use if you are using a Perceptron in practice. I borrowed the code from the `AdalineSGD` section below to modify the Perceptron algorithm accordingly (new lines are marked by trailing \"`# new`\" inline comment):\n",
    "\n",
    "```python\n",
    "class Perceptron(object):\n",
    "    \"\"\"Perceptron classifier.\n",
    "\n",
    "    Parameters\n",
    "    ------------\n",
    "    eta : float\n",
    "        Learning rate (between 0.0 and 1.0)\n",
    "    n_iter : int\n",
    "        Passes over the training dataset.\n",
    "    shuffle : bool (default: True)\n",
    "        Shuffles training data every epoch if True to prevent cycles.\n",
    "    random_state : int (default: None)\n",
    "        Set random state for shuffling and initializing the weights.\n",
    "\n",
    "    Attributes\n",
    "    -----------\n",
    "    w_ : 1d-array\n",
    "        Weights after fitting.\n",
    "    errors_ : list\n",
    "        Number of misclassifications in every epoch.\n",
    "\n",
    "    \"\"\"\n",
    "    def __init__(self, eta=0.01, n_iter=10,\n",
    "                 shuffle=True, random_state=None):  # new\n",
    "        self.eta = eta\n",
    "        self.n_iter = n_iter\n",
    "        self.shuffle = shuffle  # new\n",
    "        if random_state:  # new\n",
    "            np.random.seed(random_state)  # new\n",
    "\n",
    "    def fit(self, X, y):\n",
    "        \"\"\"Fit training data.\n",
    "\n",
    "        Parameters\n",
    "        ----------\n",
    "        X : {array-like}, shape = [n_samples, n_features]\n",
    "            Training vectors, where n_samples is the number of samples and\n",
    "            n_features is the number of features.\n",
    "        y : array-like, shape = [n_samples]\n",
    "            Target values.\n",
    "\n",
    "        Returns\n",
    "        -------\n",
    "        self : object\n",
    "\n",
    "        \"\"\"\n",
    "        self.w_ = np.zeros(1 + X.shape[1])\n",
    "        self.errors_ = []\n",
    "\n",
    "        for _ in range(self.n_iter):\n",
    "            if self.shuffle:  # new\n",
    "                X, y = self._shuffle(X, y)  # new\n",
    "            errors = 0\n",
    "            for xi, target in zip(X, y):\n",
    "                update = self.eta * (target - self.predict(xi))\n",
    "                self.w_[1:] += update * xi\n",
    "                self.w_[0] += update\n",
    "                errors += int(update != 0.0)\n",
    "            self.errors_.append(errors)\n",
    "        return self\n",
    "\n",
    "    def _shuffle(self, X, y):  # new\n",
    "        \"\"\"Shuffle training data\"\"\"  # new\n",
    "        r = np.random.permutation(len(y))  # new\n",
    "        return X[r], y[r]  # new\n",
    "\n",
    "    def net_input(self, X):\n",
    "        \"\"\"Calculate net input\"\"\"\n",
    "        return np.dot(X, self.w_[1:]) + self.w_[0]\n",
    "\n",
    "    def predict(self, X):\n",
    "        \"\"\"Return class label after unit step\"\"\"\n",
    "        return np.where(self.net_input(X) >= 0.0, 1, -1)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training a perceptron model on Iris dataset  \n",
    "** courtesy rasbt. **"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting the Iris dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0xd05af55ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# select setosa and versicolor\n",
    "y = df.iloc[0:100, 4].values\n",
    "y = np.where(y == 'Iris-setosa', -1, 1)\n",
    "\n",
    "# extract sepal length and petal length\n",
    "X = df.iloc[0:100, [0, 2]].values\n",
    "\n",
    "# plot data\n",
    "plt.scatter(X[:50, 0], X[:50, 1],\n",
    "            color='red', marker='o', label='setosa')\n",
    "plt.scatter(X[50:100, 0], X[50:100, 1],\n",
    "            color='blue', marker='x', label='versicolor')\n",
    "\n",
    "plt.xlabel('sepal length [cm]')\n",
    "plt.ylabel('petal length [cm]')\n",
    "plt.legend(loc='upper left')\n",
    "\n",
    "plt.tight_layout()\n",
    "#plt.savefig('./images/02_06.png', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training the perceptron model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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rZslmlmRm16JefNJIpaUk87Oh/fikcCevf7Ix6jgiDVqYAvXvwJXAF8Am4Ipg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\nOSqgSQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd05940ea90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ppn = Perceptron(eta=0.1, n_iter=10)\n",
    "\n",
    "ppn.fit(X, y)\n",
    "\n",
    "plt.plot(range(1, len(ppn.errors_) + 1), ppn.errors_, marker='o')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Number of updates(misclassifications)')\n",
    "\n",
    "plt.tight_layout()\n",
    "# plt.savefig('./perceptron_1.png', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A function for plotting decision regions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from matplotlib.colors import ListedColormap\n",
    "\n",
    "\n",
    "def plot_decision_regions(X, y, classifier, resolution=0.02):\n",
    "\n",
    "    # setup marker generator and color map\n",
    "    markers = ('s', 'x', 'o', '^', 'v')\n",
    "    colors = ('red', 'blue', 'lightgreen', 'gray', 'cyan')\n",
    "    cmap = ListedColormap(colors[:len(np.unique(y))])\n",
    "\n",
    "    # plot the decision surface\n",
    "    x1_min, x1_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n",
    "    x2_min, x2_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n",
    "    xx1, xx2 = np.meshgrid(np.arange(x1_min, x1_max, resolution),\n",
    "                           np.arange(x2_min, x2_max, resolution))\n",
    "    Z = classifier.predict(np.array([xx1.ravel(), xx2.ravel()]).T)\n",
    "    Z = Z.reshape(xx1.shape)\n",
    "    plt.contourf(xx1, xx2, Z, alpha=0.4, cmap=cmap)\n",
    "    plt.xlim(xx1.min(), xx1.max())\n",
    "    plt.ylim(xx2.min(), xx2.max())\n",
    "\n",
    "    # plot class samples\n",
    "    for idx, cl in enumerate(np.unique(y)):\n",
    "        plt.scatter(x=X[y == cl, 0], y=X[y == cl, 1],\n",
    "                    alpha=0.8, c=cmap(idx),\n",
    "                    edgecolor='black',\n",
    "                    marker=markers[idx], \n",
    "                    label=cl)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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eoNi6KXtj3UFEskgBqgGU23206bXXOKpI99mb4fckuqBKdeE1AYcX2Z8/hPwA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\nSQFKREQy6f8DXA2U+mknCL4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd05af55400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_decision_regions(X, y, classifier=ppn)\n",
    "plt.xlabel('sepal length [cm]')\n",
    "plt.ylabel('petal length [cm]')\n",
    "plt.legend(loc='upper left')\n",
    "\n",
    "plt.tight_layout()\n",
    "# plt.savefig('./perceptron_2.png', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Additional Note (3)\n",
    "\n",
    "The `plt.scatter` function in the `plot_decision_regions` plot may raise errors if you have matplotlib <= 1.5.0 installed if you use this function to plot more than 4 classes as a reader pointed out: \"[...] if there are four items to be displayed as the RGBA tuple is mis-interpreted as a list of colours\".\n",
    "\n",
    "```python\n",
    "plt.scatter(x=X[y == cl, 0], y=X[y == cl, 1],\n",
    "            alpha=0.8, c=cmap(idx),\n",
    "            marker=markers[idx], label=cl)\n",
    "```\n",
    "\n",
    "\n",
    "To address this problem in older matplotlib versions, you can replace `c=cmap(idx)` by `c=colors[idx]`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Adaptive linear neuron and the convergence of learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Implementing adaptive linear neuron in Python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class AdalineGD(object):\n",
    "    \"\"\"ADAptive LInear NEuron classifier.\n",
    "\n",
    "    Parameters\n",
    "    ------------\n",
    "    eta : float\n",
    "        Learning rate (between 0.0 and 1.0)\n",
    "    n_iter : int\n",
    "        Passes over the training dataset.\n",
    "\n",
    "    Attributes\n",
    "    -----------\n",
    "    w_ : 1d-array\n",
    "        Weights after fitting.\n",
    "    cost_ : list\n",
    "        Sum-of-squares cost function value in each epoch.\n",
    "\n",
    "    \"\"\"\n",
    "    def __init__(self, eta=0.01, n_iter=50):\n",
    "        self.eta = eta\n",
    "        self.n_iter = n_iter\n",
    "\n",
    "    def fit(self, X, y):\n",
    "        \"\"\" Fit training data.\n",
    "\n",
    "        Parameters\n",
    "        ----------\n",
    "        X : {array-like}, shape = [n_samples, n_features]\n",
    "            Training vectors, where n_samples is the number of samples and\n",
    "            n_features is the number of features.\n",
    "        y : array-like, shape = [n_samples]\n",
    "            Target values.\n",
    "\n",
    "        Returns\n",
    "        -------\n",
    "        self : object\n",
    "\n",
    "        \"\"\"\n",
    "        self.w_ = np.zeros(1 + X.shape[1])\n",
    "        self.cost_ = []\n",
    "\n",
    "        for i in range(self.n_iter):\n",
    "            net_input = self.net_input(X)\n",
    "            # Please note that the \"activation\" method has no effect\n",
    "            # in the code since it is simply an identity function. We\n",
    "            # could write `output = self.net_input(X)` directly instead.\n",
    "            # The purpose of the activation is more conceptual, i.e.,  \n",
    "            # in the case of logistic regression, we could change it to\n",
    "            # a sigmoid function to implement a logistic regression classifier.\n",
    "            output = self.activation(X)\n",
    "            errors = (y - output)\n",
    "            self.w_[1:] += self.eta * X.T.dot(errors)\n",
    "            self.w_[0] += self.eta * errors.sum()\n",
    "            cost = (errors**2).sum() / 2.0\n",
    "            self.cost_.append(cost)\n",
    "        return self\n",
    "\n",
    "    def net_input(self, X):\n",
    "        \"\"\"Calculate net input\"\"\"\n",
    "        return np.dot(X, self.w_[1:]) + self.w_[0]\n",
    "\n",
    "    def activation(self, X):\n",
    "        \"\"\"Compute linear activation\"\"\"\n",
    "        return self.net_input(X)\n",
    "\n",
    "    def predict(self, X):\n",
    "        \"\"\"Return class label after unit step\"\"\"\n",
    "        return np.where(self.activation(X) >= 0.0, 1, -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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eZ6RXAZU27U87hU/7t6PXqPnc8P4cPurbhrNqV/I6LGOKpNA9OKpqa8OYoPPT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\nmZlZ7rjBMTMzs9z5DxY5E0cgemaIAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd059666cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(8, 4))\n",
    "\n",
    "ada1 = AdalineGD(n_iter=10, eta=0.01).fit(X, y)\n",
    "ax[0].plot(range(1, len(ada1.cost_) + 1), np.log10(ada1.cost_), marker='o')\n",
    "ax[0].set_xlabel('Epochs')\n",
    "ax[0].set_ylabel('log(Sum-squared-error)')\n",
    "ax[0].set_title('Adaline - Learning rate 0.01')\n",
    "\n",
    "ada2 = AdalineGD(n_iter=10, eta=0.0001).fit(X, y)\n",
    "ax[1].plot(range(1, len(ada2.cost_) + 1), ada2.cost_, marker='o')\n",
    "ax[1].set_xlabel('Epochs')\n",
    "ax[1].set_ylabel('Sum-squared-error')\n",
    "ax[1].set_title('Adaline - Learning rate 0.0001')\n",
    "\n",
    "plt.tight_layout()\n",
    "# plt.savefig('./adaline_1.png', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# standardize features\n",
    "X_std = np.copy(X)\n",
    "X_std[:, 0] = (X[:, 0] - X[:, 0].mean()) / X[:, 0].std()\n",
    "X_std[:, 1] = (X[:, 1] - X[:, 1].mean()) / X[:, 1].std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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uWNH8RtA+jTzgXmxmNnClXIN6ff1CPsjrzuWEUw3Tp8Oz9yQkHYDFS7KkM3Zs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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd059743400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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354KhhaSnqedbJJFUoERa4O4sK61ixsJNzHqvjMp9dfTunsXnxpRwzdj+jOjb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\nkaSkAiUiIklJBUpERJKSCpSIiCSl/w/TBUOFsbwxgAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd05ad25550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ada = AdalineGD(n_iter=15, eta=0.01)\n",
    "ada.fit(X_std, y)\n",
    "\n",
    "plot_decision_regions(X_std, y, classifier=ada)\n",
    "plt.title('Adaline - Gradient Descent')\n",
    "plt.xlabel('sepal length [standardized]')\n",
    "plt.ylabel('petal length [standardized]')\n",
    "plt.legend(loc='upper left')\n",
    "plt.tight_layout()\n",
    "# plt.savefig('./adaline_2.png', dpi=300)\n",
    "plt.show()\n",
    "\n",
    "plt.plot(range(1, len(ada.cost_) + 1), ada.cost_, marker='o')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Sum-squared-error')\n",
    "\n",
    "plt.tight_layout()\n",
    "# plt.savefig('./adaline_3.png', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Large scale machine learning and stochastic gradient descent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from numpy.random import seed\n",
    "\n",
    "class AdalineSGD(object):\n",
    "    \"\"\"ADAptive LInear NEuron classifier.\n",
    "\n",
    "    Parameters\n",
    "    ------------\n",
    "    eta : float\n",
    "        Learning rate (between 0.0 and 1.0)\n",
    "    n_iter : int\n",
    "        Passes over the training dataset.\n",
    "\n",
    "    Attributes\n",
    "    -----------\n",
    "    w_ : 1d-array\n",
    "        Weights after fitting.\n",
    "    cost_ : list\n",
    "        Sum-of-squares cost function value averaged over all\n",
    "        training samples in each epoch.\n",
    "    shuffle : bool (default: True)\n",
    "        Shuffles training data every epoch if True to prevent cycles.\n",
    "    random_state : int (default: None)\n",
    "        Set random state for shuffling and initializing the weights.\n",
    "        \n",
    "    \"\"\"\n",
    "    def __init__(self, eta=0.01, n_iter=10, shuffle=True, random_state=None):\n",
    "        self.eta = eta\n",
    "        self.n_iter = n_iter\n",
    "        self.w_initialized = False\n",
    "        self.shuffle = shuffle\n",
    "        if random_state:\n",
    "            seed(random_state)\n",
    "        \n",
    "    def fit(self, X, y):\n",
    "        \"\"\" Fit training data.\n",
    "\n",
    "        Parameters\n",
    "        ----------\n",
    "        X : {array-like}, shape = [n_samples, n_features]\n",
    "            Training vectors, where n_samples is the number of samples and\n",
    "            n_features is the number of features.\n",
    "        y : array-like, shape = [n_samples]\n",
    "            Target values.\n",
    "\n",
    "        Returns\n",
    "        -------\n",
    "        self : object\n",
    "\n",
    "        \"\"\"\n",
    "        self._initialize_weights(X.shape[1])\n",
    "        self.cost_ = []\n",
    "        for i in range(self.n_iter):\n",
    "            if self.shuffle:\n",
    "                X, y = self._shuffle(X, y)\n",
    "            cost = []\n",
    "            for xi, target in zip(X, y):\n",
    "                cost.append(self._update_weights(xi, target))\n",
    "            avg_cost = sum(cost) / len(y)\n",
    "            self.cost_.append(avg_cost)\n",
    "        return self\n",
    "\n",
    "    def partial_fit(self, X, y):\n",
    "        \"\"\"Fit training data without reinitializing the weights\"\"\"\n",
    "        if not self.w_initialized:\n",
    "            self._initialize_weights(X.shape[1])\n",
    "        if y.ravel().shape[0] > 1:\n",
    "            for xi, target in zip(X, y):\n",
    "                self._update_weights(xi, target)\n",
    "        else:\n",
    "            self._update_weights(X, y)\n",
    "        return self\n",
    "\n",
    "    def _shuffle(self, X, y):\n",
    "        \"\"\"Shuffle training data\"\"\"\n",
    "        r = np.random.permutation(len(y))\n",
    "        return X[r], y[r]\n",
    "    \n",
    "    def _initialize_weights(self, m):\n",
    "        \"\"\"Initialize weights to zeros\"\"\"\n",
    "        self.w_ = np.zeros(1 + m)\n",
    "        self.w_initialized = True\n",
    "        \n",
    "    def _update_weights(self, xi, target):\n",
    "        \"\"\"Apply Adaline learning rule to update the weights\"\"\"\n",
    "        output = self.net_input(xi)\n",
    "        error = (target - output)\n",
    "        self.w_[1:] += self.eta * xi.dot(error)\n",
    "        self.w_[0] += self.eta * error\n",
    "        cost = 0.5 * error**2\n",
    "        return cost\n",
    "    \n",
    "    def net_input(self, X):\n",
    "        \"\"\"Calculate net input\"\"\"\n",
    "        return np.dot(X, self.w_[1:]) + self.w_[0]\n",
    "\n",
    "    def activation(self, X):\n",
    "        \"\"\"Compute linear activation\"\"\"\n",
    "        return self.net_input(X)\n",
    "\n",
    "    def predict(self, X):\n",
    "        \"\"\"Return class label after unit step\"\"\"\n",
    "        return np.where(self.activation(X) >= 0.0, 1, -1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calling our predefined class (AdalineSGD) function and plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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bNrwM9dEmmt44+gzwaOORC/Yb/TDnfvvZjL25s4FVU6MW1D9Keq7BcpHG6Vun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\n/wEoJdX6YSg9EgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd059436208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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QThKpBlSpcEANjV0tbfze//8Iuw+8vgPFtPHV/HLeuUWoyszKTSn04rMR5qia\nKvb0EE4AzTsPcMuiF3lu/S5PQW9mQ8LTbVi/TB1fQ3MPY/qNrqxg/s9XcvujL9NQl+Wik47h4pOm\n8j+mjPUXgM1sQBxQ1i83nH88N963jJa2jkNtXc+gfm92HYue28RPlq3njsde5vZHX6axLstFJ03h\n4pOncMIxDiszK5yfQVm/PfBUc5+9+LbubWXRcxv5yTMb+PXKbXQGNNZlufjkXFgdP9lhZXakcieJ\nPA6o4tq6t5WHnt3IwmV5YVWf5V0nTeGibmFVSPiZWXlzQOVxQJWOLXtaeei5jSx8ZgNPrMqF1XH1\nWS4+eSo1VRXctnhFj7cPHVJmI4cDKo8DqjRt3nOARc9u5CfLNvDEqu309kdx2vgafjnvncNbnJml\nxt3MreQdPbaaD5w5i3uvPpMnPtP7d6iad7bw6PLN7NrfNozVmVmxuReflYSjx1YzrZcu7AAf+tYS\nAN44uZbTj53I6cdOYO6xEzh20hh3tjAboRxQVjJ668J+0x+fyIyJY2has4Ola3bwH8+s557fvAJA\nXe0oTps5gbmzJnD6sRN587RxjK7M9PYWZlZGHFBWMg43EzDAWW+oA6CzM/jd5j00rd7Bk2t20LRm\nBz97fhOQmz7k5GlHcfqsCcw9diKnzRzPpNrRgHsImpUbd5KwEWHz7gM8+coOmlbnAuu59bto68j9\n2W6sy1JXO4qn1u481AbuIWhWLIV2kvAVlI0IR4+r5oI3T+GCN08B4EBbB8+s20XTmu0sXb2DR5Zv\nfl0vwZa2Dv7+wWcZW13J7KPHMm1CDZkKP88yKxW+grIjQsO8n/Q89XKe0ZUVNNbXMvvoWt5w9Kuv\nx07KMqrSHV7NhoqvoMzy9DbI7ZSjqvna+07lpU17WbF5Ly9t3svSNTtY8Nv1h/aprBDHThrD7KPH\n5oJrci3H1ed+akblOmT4+ZbZ0HNA2RGhtx6Cn77ghKTb+sTX7L+vtZ2VW/axYsueQ+H1u017ePiF\nTXQkszVKMH1CDbWjKnlp817ak/bmnS3ceN8zAA4ps0HwLT47YgzFVU5rewdrtu3Pu+Law0PPbjwU\nTvkyFeKMxolMHz+GaRNqmDa+hmkTapg+oYZjxlVTmSn8tqGv0Gwk8VBHeRxQlqbDPd+aM2M8zTtb\n2LKn9TXtmQpxzLjcl5OnT6jpFmBjmHJUNdVVr94+7G2KE4eUlSM/gzIbJr0935o2voYHrj0byPUq\nXL+zhebobOT7AAALFklEQVSdLTTvyL2u25FbfmLVdjY83UL3i7D6saOZNr6G5Rt309LW+ZptLW0d\nfH7hC5z9hjrG1VQO6svJvjqzUuWAMhuk3p5v3XD+8YfWq6syNNbX0lhf2+M52jo62bjrwKEAW7ej\nhead+2ne2fK6cOqyeU8rb/ncfwK5Hojjaqo4qqaKcdWVjKupYlx1FeNqKpO2qte0javO7fvfK7by\n2Z88z4HkPXLPz5YBQ/P8zOFng+FbfGZDIM1fxGff/EiPV2gTxlTx1+e9kd0H2tnd0sbuA23samlj\nd0s7uw+0JW3t7GppO9Sxo1CjKis4Z3Yd2dGVZEdXUju6kuyoSrKjM3ltmaQt2Z68VldVICn1W5Np\n/jd3sKarJJ5BSboA+CqQAb4RETd3265k+0XAfuCDEfGkpBnAvwCTgQDujIivJsfcBHwE2JKc5jMR\nsfBwdTigrJwN9hd9RLD/YEcSWvnh1cYn/+23vR534pRx7DvYzr7Wdva2th+6yupLpkKMGZVhX2v7\n625bdtV+6ZypVFdlGF1VwejKDNVVFVRXZnJtlRVUVyVt3dZHV+aOWfzCJv7h359/TU1DFX7lHKxp\nn3+ozl30gJKUAX4HnAesA5YAV0TE83n7XAR8nFxAvQ34akS8TdIUYEoSVmOBpcBlEfF8ElB7I+KL\nhdbigLJyl9Yvnd6uznqag6u9o5P9bR3sa+0KrY5D4dVT27d/tbrX9508bjQH2jo50NZBa3thwVeI\nCsGUo2qoyoiqTAWVmQpGZURlpuLVtorca1XSVpm3XJWp4N+WrGVva/vrzn1UTRV/e8HxVFVUkKkQ\nlRlRWVGRvIpMct7cq8hU5N7r0H4V4pEXN/GFh5ZzIO8zV1dV8L8veTOXnjqVComMRMUARzRJM1yH\n8tylEFBnAjdFxPnJ+o0AEfFPeft8HXgsIu5J1pcDfxARG7qd60HgaxHxsAPKbOik+Qut0PCLCFrb\nO2lt6+RAe8eh0DrQ1vGaEMutd3CgvZO/e+DZXt/3T0+bTntnJ20dnbR1BG0dnbR3BAc7OmnPa2vr\n6KS9M2hr76St89X9egqnYqhQ7mq0QrmfTIVQ0paRkESmgrxl0byzpcfbuVUZMWfG+FywVlRQUfFq\nqFZW6DXrGeVC9dC+yfr3n3ilx/82A5lQtBR68U0D1uatryN3ldTXPtOAQwElaRZwKvBE3n4fl3Ql\n0AR8KiJ2dH9zSVcDVwPMnDlzoJ/BbETrawT5wSik8wiApOQWXoajqCro3PMfe7nX8Lv1vacMqu7e\ngvWYo6p58Nqzae8M2pNwa+8I2js7k9dce0dn0NYZdOS3J9v++ge931K94fzj6ewMOiLoDPKWg87O\nXFtHZ7IeQUcnSXtuvwh4Zfv+Hs/d1hFUVlTQEUFLWwftnblztnfV+Zr1eN16R2e85v9jvvW9zOE2\nFEq6F5+kWuDHwPURsTtpvgP4R3LPpv4RuBX4i+7HRsSdwJ2Qu4IaloLNytBlp05LpQNAKYTfUJ57\n3gUnMHlc9aDOfevPftdrsF77jjcM6twAv1m1vdfz33P1GYM6d2/BPXV8zaDOezhpBlQzMCNvfXrS\nVtA+kqrIhdP3IuK+rh0iYlPXsqS7gP8Y2rLNbKiUY/iVa7Cmff60a+9JmgG1BJgtqYFc6FwOvK/b\nPguAj0m6l9ztv10RsSHp3fdN4IWI+FL+AZKm5D2jejfQ+81oMxux0gq/NM+dZvilff60a+9J2t3M\nLwK+Qq6b+d0R8TlJ1wBExPwkiL4GXECum/mHIqJJ0tuBXwDLgK7uLp+JiIWS/hWYQ+4W32rgo907\nVXTnThJmZqWj6L34SokDysysdBQaUJ6FzczMSpIDyszMSpIDyszMSpIDyszMSpIDyszMStIR0YtP\n0hZgTbHr6EUdsLXYRQxQudZernVD+dbuuodfKdd+bETU97XTERFQpUxSUyHdLUtRudZernVD+dbu\nuodfOdfexbf4zMysJDmgzMysJDmgiu/OYhcwCOVae7nWDeVbu+sefuVcO+BnUGZmVqJ8BWVmZiXJ\nAWVmZiXJAVUkkmZIelTS85Kek/SJYtfUH5Iykp6SVFYTRkoaL+lHkl6U9IKkM4tdUyEkfTL5c/Ks\npHskDW5q1xRJulvSZknP5rVNlPSwpJeS1wnFrLEnvdR9S/Jn5RlJ90saX8wae9JT3XnbPiUpJNUV\no7bBckAVTzvwqYg4ETgDuFbSiUWuqT8+AbxQ7CIG4KvAQxFxAnAKZfAZJE0DrgPmRsSbyc2vdnlx\nqzqsb5Ob4y3fPGBxRMwGFifrpebbvL7uh4E3R8TJwO+AG4e7qAJ8m9fXjaQZwB8Brwx3QUPFAVUk\nEbEhIp5MlveQ+0WZ3tSUQ0jSdOBi4BvFrqU/JB0FnENutmYi4mBE7CxuVQWrBGokVQJjgPVFrqdX\nEfFfwPZuzZcC30mWvwNcNqxFFaCnuiPiZxHRnqz+Gpg+7IX1oZf/3gBfBv6W3OSuZckBVQIkzQJO\nBZ4obiUF+wq5P/idfe1YYhqALcC3ktuT35CULXZRfYmIZuCL5P4lvAHYFRE/K25V/TY5b+brjcDk\nYhYzQH8B/LTYRRRC0qVAc0T8tti1DIYDqsgk1QI/Bq6PiN3Frqcvkt4FbI6IpcWuZQAqgdOAOyLi\nVGAfpXmr6TWS5zWXkgvYqUBW0v8sblUDF7nvtpTVv+ol/T/kbst/r9i19EXSGOAzwN8Xu5bBckAV\nkaQqcuH0vYi4r9j1FOhs4BJJq4F7gXdK+m5xSyrYOmBdRHRdqf6IXGCVuj8EVkXElohoA+4Dzipy\nTf21SdIUgOR1c5HrKZikDwLvAt4f5fHF0ePI/WPmt8nf0+nAk5KOKWpVA+CAKhJJIvcs5IWI+FKx\n6ylURNwYEdMjYha5B/WPRERZ/Gs+IjYCayUdnzSdCzxfxJIK9QpwhqQxyZ+bcymDzh3dLACuSpav\nAh4sYi0Fk3QBudvZl0TE/mLXU4iIWBYRR0fErOTv6TrgtOTPf1lxQBXP2cAHyF2BPJ38XFTsoo4A\nHwe+J+kZYA7w+SLX06fkiu9HwJPAMnJ/b0t2GBtJ9wCPA8dLWifpw8DNwHmSXiJ3RXhzMWvsSS91\nfw0YCzyc/B2dX9Qie9BL3SOChzoyM7OS5CsoMzMrSQ4oMzMrSQ4oMzMrSQ4oMzMrSQ4oMzMrSQ4o\ns2EiqSPvKwVPSxqyUSwkzeppNGuzclZZ7ALMjiAtETGn2EWYlQtfQZkVmaTVkr4gaZmk30h6Q9I+\nS9IjyVxEiyXNTNonJ3MT/Tb56Rr2KCPprmTeqJ9Jqkn2vy6Zd+wZSfcW6WOa9ZsDymz41HS7xffn\nedt2RcRJ5EYu+ErS9n+A7yRzEX0PuC1pvw34eUScQm4sweeS9tnA7RHxJmAn8KdJ+zzg1OQ816T1\n4cyGmkeSMBsmkvZGRG0P7auBd0bEymQA4Y0RMUnSVmBKRLQl7Rsiok7SFmB6RLTmnWMW8HAyISCS\nPg1URcRnJT0E7AUeAB6IiL0pf1SzIeErKLPSEL0s90dr3nIHrz5jvhi4ndzV1pJk0kOzkueAMisN\nf573+niy/Ctendr9/cAvkuXFwF8BSMokMwX3SFIFMCMiHgU+DRwFvO4qzqwU+V9SZsOnRtLTeesP\nRURXV/MJyQjrrcAVSdvHyc3+ewO5mYA/lLR/ArgzGbW6g1xYbaBnGeC7SYgJuK2Mprm3I5yfQZkV\nWfIMam5EbC12LWalxLf4zMysJPkKyszMSpKvoMzMrCQ5oMzMrCQ5oMzMrCQ5oMzMrCQ5oMzMrCT9\nX2j+xOGpg/mTAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd053a19f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ada = AdalineSGD(n_iter=15, eta=0.01, random_state=1)\n",
    "ada.fit(X_std, y)\n",
    "\n",
    "plot_decision_regions(X_std, y, classifier=ada)\n",
    "plt.title('Adaline - Stochastic Gradient Descent')\n",
    "plt.xlabel('sepal length [standardized]')\n",
    "plt.ylabel('petal length [standardized]')\n",
    "plt.legend(loc='upper left')\n",
    "\n",
    "plt.tight_layout()\n",
    "#plt.savefig('./adaline_4.png', dpi=300)\n",
    "plt.show()\n",
    "\n",
    "plt.plot(range(1, len(ada.cost_) + 1), ada.cost_, marker='o')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Average Cost')\n",
    "\n",
    "plt.tight_layout()\n",
    "# plt.savefig('./adaline_5.png', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logistic regression classifier using gradient descent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class LogisticRegressionGD(object):\n",
    "    \"\"\"Logistic regression classifier via gradient descent.\n",
    "\n",
    "    Parameters\n",
    "    ------------\n",
    "    eta : float\n",
    "        Learning rate (between 0.0 and 1.0)\n",
    "    n_iter : int\n",
    "        Passes over the training dataset.\n",
    "\n",
    "    Attributes\n",
    "    -----------\n",
    "    w_ : 1d-array\n",
    "        Weights after fitting.\n",
    "    errors_ : list\n",
    "        Number of misclassifications in every epoch.\n",
    "\n",
    "    \"\"\"\n",
    "    def __init__(self, eta=0.01, n_iter=50):\n",
    "        self.eta = eta\n",
    "        self.n_iter = n_iter\n",
    "\n",
    "    def fit(self, X, y):\n",
    "        \"\"\" Fit training data.\n",
    "\n",
    "        Parameters\n",
    "        ----------\n",
    "        X : {array-like}, shape = [n_samples, n_features]\n",
    "            Training vectors, where n_samples is the number of samples and\n",
    "            n_features is the number of features.\n",
    "        y : array-like, shape = [n_samples]\n",
    "            Target values.\n",
    "\n",
    "        Returns\n",
    "        -------\n",
    "        self : object\n",
    "\n",
    "        \"\"\"\n",
    "        self.w_ = np.zeros(1 + X.shape[1])\n",
    "        self.cost_ = []\n",
    "        \n",
    "        for i in range(self.n_iter):\n",
    "            net_input = self.net_input(X)\n",
    "            output = self.activation(X)\n",
    "            errors = (y - output)\n",
    "            self.w_[1:] += self.eta * X.T.dot(errors)\n",
    "            self.w_[0] += self.eta * errors.sum()\n",
    "            \n",
    "            # note that we compute the logistic `cost` now\n",
    "            # instead of the sum of squared errors cost\n",
    "            cost = -y.dot(np.log(output)) - ((1 - y).dot(np.log(1 - output)))\n",
    "            self.cost_.append(cost)\n",
    "        return self\n",
    "\n",
    "    def net_input(self, X):\n",
    "        \"\"\"Calculate net input\"\"\"\n",
    "        return np.dot(X, self.w_[1:]) + self.w_[0]\n",
    "\n",
    "    def predict(self, X):\n",
    "        \"\"\"Return class label after unit step\"\"\"\n",
    "        # We use the more common convention for logistic\n",
    "        # regression returning class labels 0 and 1\n",
    "        # instead of -1 and 1. Also, the threshold then\n",
    "        # changes from 0.0 to 0.5 \n",
    "        return np.where(self.activation(X) >= 0.5, 1, 0)\n",
    "    \n",
    "    # The Content of `activation` changed \n",
    "    # from linear (Adaline) to sigmoid.\n",
    "    # Note that this method is now returning the\n",
    "    # probability of the positive class\n",
    "    # also \"predict_proba\" in scikit-learn\n",
    "    def activation(self, X):\n",
    "        \"\"\" Compute sigmoid activation.\"\"\"\n",
    "        z = self.net_input(X)\n",
    "        sigmoid = 1.0 / (1.0 + np.exp(-z))\n",
    "        return sigmoid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "\n",
    "iris = load_iris()\n",
    "X, y = iris.data[:100, [0, 2]], iris.target[:100]\n",
    "\n",
    "X_std = np.copy(X)\n",
    "X_std[:, 0] = (X[:, 0] - X[:, 0].mean()) / X[:, 0].std()\n",
    "X_std[:, 1] = (X[:, 1] - X[:, 1].mean()) / X[:, 1].std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
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XAGVmv0rx/hfd/aTsmiMieSgFqVJwgqEZnNrawlVj6dhLV5ObbQbNzXm3buipdAX1FuCf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\nSQFKREQKSQFKREQK6f8DFR6nYvKf+xwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd05ae7e470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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HZCru8x8vekPZcl+4+CT+/M/aKpb70p2Vw/uDb51esdx1D26oWO5/nfdnFcv9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\nLCmgREQklhRQIiISSwooERGJpf8P0TDhn+wfB6wAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd05aeb74a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lr = LogisticRegressionGD(n_iter=25, eta=0.15)\n",
    "lr.fit(X_std, y)\n",
    "\n",
    "plot_decision_regions(X_std, y, classifier=lr)\n",
    "plt.title('Logistic Regression - Gradient Descent')\n",
    "plt.xlabel('sepal length [standardized]')\n",
    "plt.ylabel('sepal width [standardized]')\n",
    "plt.legend(loc='upper left')\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.show()\n",
    "\n",
    "plt.plot(range(1, len(lr.cost_) + 1), lr.cost_, marker='o')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Logistic Cost')\n",
    "\n",
    "plt.tight_layout()\n",
    "# plt.savefig('./adaline_3.png', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}