update

mageed · mageed · commit eab09bccd5e6 · 2016-01-17T23:51:43.000-05:00
diff --git a/python_tutorial.ipynb b/python_tutorial.ipynb
@@ -496,6 +496,106 @@
     "greet(\"Nora\")"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'and': 4, 'learning.': 1, '(the': 1, 'family': 1, 'be': 1, 'other.': 1, 'experience,': 1, 'unknown.Artificial': 1, 'number': 1, 'numeric': 1, 'connections': 1, 'as': 1, 'brain)': 1, 'are': 4, 'learning': 1, 'in': 1, 'based': 1, 'tuned': 1, 'nets': 1, 'networks': 3, '(ANNs)': 1, 'functions': 1, 'depend': 1, 'capable': 1, 'nervous': 1, 'exchange': 1, 'generally': 2, 'approximate': 1, 'artificial': 1, 'machine': 1, 'to': 2, 'systems': 2, 'which': 1, 'between': 1, 'adaptive': 1, '\"neurons\"': 1, 'inputs': 2, 'used': 1, 'that': 2, 'models': 1, 'each': 1, 'animals,': 1, 'particular': 1, 'The': 1, 'estimate': 1, 'by': 1, 'a': 2, 'on': 2, 'central': 1, 'cognitive': 1, 'neural': 4, 'of': 5, 'inspired': 1, 'presented': 1, 'messages': 1, 'science,': 1, 'interconnected': 1, 'large': 1, 'weights': 1, 'can': 2, 'have': 1, 'In': 1, 'biological': 1, 'the': 1, 'or': 1, 'making': 1}\n"
+     ]
+    }
+   ],
+   "source": [
+    "# A function can \"return\" an object.\n",
+    "# We provide an example here\n",
+    "\n",
+    "# text below is from https://en.wikipedia.org/wiki/Artificial_neural_network\n",
+    "sentences=[\"In machine learning and cognitive science, artificial neural networks (ANNs)\\\n",
+    "         are a family of models inspired by biological neural networks (the central nervous systems of animals, \\\n",
+    "          in particular the brain) and are used to estimate or approximate functions that can depend on a large\\\n",
+    "          number of inputs and are generally unknown.\"\n",
+    "           \"Artificial neural networks are generally presented as systems of interconnected \\\"neurons\\\" which \\\n",
+    "           exchange messages between each other. The connections have numeric weights that can be tuned based \\\n",
+    "           on experience, making neural nets adaptive to inputs and capable of learning.\"]\n",
+    "def get_dict(sentences):\n",
+    "    \"\"\"\n",
+    "    arguments:\n",
+    "    input: @sentences: a list of sentences\n",
+    "    returns: a dictionary of the words in the sentences.\n",
+    "             dict key is a word and value is word frequency\n",
+    "    \"\"\"\n",
+    "    word_freq={}\n",
+    "    for sent in sentences:\n",
+    "        words=sent.split()\n",
+    "        for w in words:\n",
+    "            if w in word_freq:\n",
+    "                word_freq[w]+=1\n",
+    "            else:\n",
+    "                word_freq[w]=1\n",
+    "    return word_freq\n",
+    "        \n",
+    "    \n",
+    "my_word_freq_dict=get_dict(sentences)\n",
+    "print my_word_freq_dict"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "and 4\n",
+      "are 4\n",
+      "networks 3\n",
+      "neural 4\n",
+      "of 5\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Here's the same function as above, but using python's \"defaultdict\"\n",
+    "from collections import defaultdict\n",
+    "sentences=[\"In machine learning and cognitive science, artificial neural networks (ANNs)\\\n",
+    "         are a family of models inspired by biological neural networks (the central nervous systems of animals, \\\n",
+    "          in particular the brain) and are used to estimate or approximate functions that can depend on a large\\\n",
+    "          number of inputs and are generally unknown.\"\n",
+    "           \"Artificial neural networks are generally presented as systems of interconnected \\\"neurons\\\" which \\\n",
+    "           exchange messages between each other. The connections have numeric weights that can be tuned based \\\n",
+    "           on experience, making neural nets adaptive to inputs and capable of learning.\"]\n",
+    "\n",
+    "def get_dict(sentences):\n",
+    "    \"\"\"\n",
+    "    arguments:\n",
+    "    input: @sentences: a list of sentences\n",
+    "    returns: a dictionary of the words in the sentences.\n",
+    "             dict key is a word and value is word frequency\n",
+    "    \"\"\"\n",
+    "    word_freq=defaultdict(int)\n",
+    "    for sent in sentences:\n",
+    "        words=sent.split()\n",
+    "        for w in words:\n",
+    "            word_freq[w]+=1\n",
+    "    return word_freq\n",
+    "    \n",
+    "my_word_freq_dict=get_dict(sentences)\n",
+    "# Let's print only keys with values > 2 this time\n",
+    "for k in my_word_freq_dict:\n",
+    "    if my_word_freq_dict[k] > 2:\n",
+    "        print k, my_word_freq_dict[k]"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
