diff --git a/learning.ipynb b/learning.ipynb
index 16df6a021..091fce32d 100644
--- a/learning.ipynb
+++ b/learning.ipynb
@@ -12,7 +12,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -113,194 +113,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": null,
    "metadata": {
     "collapsed": true
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[1;32mclass\u001b[0m \u001b[0mDataSet\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m    \u001b[1;34m\"\"\"\n",
-      "    A data set for a machine learning problem. It has the following fields:\n",
-      "\n",
-      "    d.examples   A list of examples. Each one is a list of attribute values.\n",
-      "    d.attrs      A list of integers to index into an example, so example[attr]\n",
-      "                 gives a value. Normally the same as range(len(d.examples[0])).\n",
-      "    d.attr_names Optional list of mnemonic names for corresponding attrs.\n",
-      "    d.target     The attribute that a learning algorithm will try to predict.\n",
-      "                 By default the final attribute.\n",
-      "    d.inputs     The list of attrs without the target.\n",
-      "    d.values     A list of lists: each sublist is the set of possible\n",
-      "                 values for the corresponding attribute. If initially None,\n",
-      "                 it is computed from the known examples by self.set_problem.\n",
-      "                 If not None, an erroneous value raises ValueError.\n",
-      "    d.distance   A function from a pair of examples to a non-negative number.\n",
-      "                 Should be symmetric, etc. Defaults to mean_boolean_error\n",
-      "                 since that can handle any field types.\n",
-      "    d.name       Name of the data set (for output display only).\n",
-      "    d.source     URL or other source where the data came from.\n",
-      "    d.exclude    A list of attribute indexes to exclude from d.inputs. Elements\n",
-      "                 of this list can either be integers (attrs) or attr_names.\n",
-      "\n",
-      "    Normally, you call the constructor and you're done; then you just\n",
-      "    access fields like d.examples and d.target and d.inputs.\n",
-      "    \"\"\"\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m    \u001b[1;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mexamples\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mattrs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mattr_names\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtarget\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m                 \u001b[0mvalues\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdistance\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmean_boolean_error\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m''\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msource\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m''\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mexclude\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m        \u001b[1;34m\"\"\"\n",
-      "        Accepts any of DataSet's fields. Examples can also be a\n",
-      "        string or file from which to parse examples using parse_csv.\n",
-      "        Optional parameter: exclude, as documented in .set_problem().\n",
-      "        >>> DataSet(examples='1, 2, 3')\n",
-      "        <DataSet(): 1 examples, 3 attributes>\n",
-      "        \"\"\"\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m        \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m        \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msource\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msource\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m        \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mvalues\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m        \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdistance\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdistance\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m        \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgot_values_flag\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbool\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m        \u001b[1;31m# initialize .examples from string or list or data directory\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m        \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mexamples\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m            \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexamples\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mparse_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mexamples\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m        \u001b[1;32melif\u001b[0m \u001b[0mexamples\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m            \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexamples\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mparse_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mopen_data\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;34m'.csv'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m        \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m            \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexamples\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mexamples\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m        \u001b[1;31m# attrs are the indices of examples, unless otherwise stated.\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m        \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexamples\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mattrs\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m            \u001b[0mattrs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexamples\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m        \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mattrs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mattrs\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m        \u001b[1;31m# initialize .attr_names from string, list, or by default\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m        \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mattr_names\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m            \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mattr_names\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mattr_names\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m        \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m            \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mattr_names\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mattr_names\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mattrs\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m        \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_problem\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtarget\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0minputs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mexclude\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mexclude\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m    \u001b[1;32mdef\u001b[0m \u001b[0mset_problem\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtarget\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mexclude\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m        \u001b[1;34m\"\"\"\n",
-      "        Set (or change) the target and/or inputs.\n",
-      "        This way, one DataSet can be used multiple ways. inputs, if specified,\n",
-      "        is a list of attributes, or specify exclude as a list of attributes\n",
-      "        to not use in inputs. Attributes can be -n .. n, or an attr_name.\n",
-      "        Also computes the list of possible values, if that wasn't done yet.\n",
-      "        \"\"\"\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m        \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtarget\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mattr_num\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtarget\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m        \u001b[0mexclude\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mattr_num\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mexclude\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m        \u001b[1;32mif\u001b[0m \u001b[0minputs\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m            \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minputs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mremove_all\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtarget\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m        \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m            \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minputs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0ma\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mattrs\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0ma\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtarget\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0ma\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mexclude\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m        \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m            \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate_values\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m        \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcheck_me\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m    \u001b[1;32mdef\u001b[0m \u001b[0mcheck_me\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m        \u001b[1;34m\"\"\"Check that my fields make sense.\"\"\"\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m        \u001b[1;32massert\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mattr_names\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mattrs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m        \u001b[1;32massert\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtarget\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mattrs\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m        \u001b[1;32massert\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtarget\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minputs\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m        \u001b[1;32massert\u001b[0m \u001b[0mset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minputs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0missubset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mattrs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m        \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgot_values_flag\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m            \u001b[1;31m# only check if values are provided while initializing DataSet\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m            \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcheck_example\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexamples\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m    \u001b[1;32mdef\u001b[0m \u001b[0madd_example\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mexample\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m        \u001b[1;34m\"\"\"Add an example to the list of examples, checking it first.\"\"\"\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m        \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcheck_example\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mexample\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m        \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexamples\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mexample\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m    \u001b[1;32mdef\u001b[0m \u001b[0mcheck_example\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mexample\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m        \u001b[1;34m\"\"\"Raise ValueError if example has any invalid values.\"\"\"\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m        \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m            \u001b[1;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mattrs\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m                \u001b[1;32mif\u001b[0m \u001b[0mexample\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m                    \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Bad value {} for attribute {} in {}'\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m                                     \u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mexample\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mattr_names\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mexample\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m    \u001b[1;32mdef\u001b[0m \u001b[0mattr_num\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mattr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m        \u001b[1;34m\"\"\"Returns the number used for attr, which can be a name, or -n .. n-1.\"\"\"\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m        \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mattr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m            \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mattr_names\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mattr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m        \u001b[1;32melif\u001b[0m \u001b[0mattr\u001b[0m \u001b[1;33m<\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m            \u001b[1;32mreturn\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mattrs\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mattr\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m        \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m            \u001b[1;32mreturn\u001b[0m \u001b[0mattr\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m    \u001b[1;32mdef\u001b[0m \u001b[0mupdate_values\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m        \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0munique\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexamples\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m    \u001b[1;32mdef\u001b[0m \u001b[0msanitize\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mexample\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m        \u001b[1;34m\"\"\"Return a copy of example, with non-input attributes replaced by None.\"\"\"\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m        \u001b[1;32mreturn\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mattr_i\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minputs\u001b[0m \u001b[1;32melse\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mattr_i\u001b[0m \u001b[1;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mexample\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m    \u001b[1;32mdef\u001b[0m \u001b[0mclasses_to_numbers\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mclasses\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m        \u001b[1;34m\"\"\"Converts class names to numbers.\"\"\"\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m        \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mclasses\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m            \u001b[1;31m# if classes were not given, extract them from values\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m            \u001b[0mclasses\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msorted\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtarget\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m        \u001b[1;32mfor\u001b[0m \u001b[0mitem\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexamples\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m            \u001b[0mitem\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtarget\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mclasses\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtarget\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m    \u001b[1;32mdef\u001b[0m \u001b[0mremove_examples\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m''\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m        \u001b[1;34m\"\"\"Remove examples that contain given value.\"\"\"\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m        \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexamples\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mx\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexamples\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mvalue\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m        \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate_values\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m    \u001b[1;32mdef\u001b[0m \u001b[0msplit_values_by_classes\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m        \u001b[1;34m\"\"\"Split values into buckets according to their class.\"\"\"\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m        \u001b[0mbuckets\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdefaultdict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;32mlambda\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m        \u001b[0mtarget_names\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtarget\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m        \u001b[1;32mfor\u001b[0m \u001b[0mv\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexamples\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m            \u001b[0mitem\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0ma\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mv\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0ma\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mtarget_names\u001b[0m\u001b[1;33m]\u001b[0m  \u001b[1;31m# remove target from item\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m            \u001b[0mbuckets\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mv\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtarget\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m  \u001b[1;31m# add item to bucket of its class\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m        \u001b[1;32mreturn\u001b[0m \u001b[0mbuckets\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m    \u001b[1;32mdef\u001b[0m \u001b[0mfind_means_and_deviations\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m        \u001b[1;34m\"\"\"\n",
-      "        Finds the means and standard deviations of self.dataset.\n",
-      "        means     : a dictionary for each class/target. Holds a list of the means\n",
-      "                    of the features for the class.\n",
-      "        deviations: a dictionary for each class/target. Holds a list of the sample\n",
-      "                    standard deviations of the features for the class.\n",
-      "        \"\"\"\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m        \u001b[0mtarget_names\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtarget\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m        \u001b[0mfeature_numbers\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minputs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m        \u001b[0mitem_buckets\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msplit_values_by_classes\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m        \u001b[0mmeans\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdefaultdict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;32mlambda\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0mfeature_numbers\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m        \u001b[0mdeviations\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdefaultdict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;32mlambda\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0mfeature_numbers\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m        \u001b[1;32mfor\u001b[0m \u001b[0mt\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mtarget_names\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m            \u001b[1;31m# find all the item feature values for item in class t\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m            \u001b[0mfeatures\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0m_\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfeature_numbers\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m            \u001b[1;32mfor\u001b[0m \u001b[0mitem\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mitem_buckets\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mt\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m                \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfeature_numbers\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m                    \u001b[0mfeatures\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m            \u001b[1;31m# calculate means and deviations fo the class\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m            \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfeature_numbers\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m                \u001b[0mmeans\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mt\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmean\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfeatures\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m                \u001b[0mdeviations\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mt\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mstdev\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfeatures\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m        \u001b[1;32mreturn\u001b[0m \u001b[0mmeans\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdeviations\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m    \u001b[1;32mdef\u001b[0m \u001b[0m__repr__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n",
-      "\u001b[0m        \u001b[1;32mreturn\u001b[0m \u001b[1;34m'<DataSet({}): {:d} examples, {:d} attributes>'\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexamples\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mattrs\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"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "%psource DataSet"
    ]
@@ -363,7 +180,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": null,
    "metadata": {
     "collapsed": true
    },
@@ -382,18 +199,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[5.1, 3.5, 1.4, 0.2, 'setosa']\n",
-      "[0, 1, 2, 3]\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "print(iris.examples[0])\n",
     "print(iris.inputs)"
@@ -417,17 +225,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[0, 2, 3]\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "iris2 = DataSet(name=\"iris\",exclude=[1])\n",
     "print(iris2.inputs)"
@@ -447,17 +247,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[5.1, 3.5, 1.4, 0.2, 'setosa'], [4.9, 3.0, 1.4, 0.2, 'setosa'], [4.7, 3.2, 1.3, 0.2, 'setosa']]\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "print(iris.examples[:3])"
    ]
@@ -472,20 +264,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "attrs: [0, 1, 2, 3, 4]\n",
-      "attrnames (by default same as attrs): [0, 1, 2, 3, 4]\n",
-      "target: 4\n",
-      "inputs: [0, 1, 2, 3]\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "print(\"attrs:\", iris.attrs)\n",
     "print(\"attrnames (by default same as attrs):\", iris.attr_names)\n",
@@ -503,17 +284,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[4.7, 5.5, 5.0, 4.9, 5.1, 4.6, 5.4, 4.4, 4.8, 4.3, 5.8, 7.0, 7.1, 4.5, 5.9, 5.6, 6.9, 6.5, 6.4, 6.6, 6.0, 6.1, 7.6, 7.4, 7.9, 5.7, 5.3, 5.2, 6.3, 6.7, 6.2, 6.8, 7.3, 7.2, 7.7]\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "print(iris.values[0])"
    ]
@@ -528,18 +301,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "name: iris\n",
-      "source: \n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "print(\"name:\", iris.name)\n",
     "print(\"source:\", iris.source)"
@@ -555,17 +319,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['versicolor', 'setosa', 'virginica']\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "print(iris.values[iris.target])"
    ]
@@ -594,18 +350,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Sanitized: [5.1, 3.5, 1.4, 0.2, None]\n",
-      "Original: [5.1, 3.5, 1.4, 0.2, 'setosa']\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "print(\"Sanitized:\",iris.sanitize(iris.examples[0]))\n",
     "print(\"Original:\",iris.examples[0])"
@@ -621,17 +368,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['versicolor', 'setosa']\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "iris2 = DataSet(name=\"iris\")\n",
     "\n",
@@ -649,18 +388,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Class of first example: setosa\n",
-      "Class of first example: 0\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "print(\"Class of first example:\",iris2.examples[0][iris2.target])\n",
     "iris2.classes_to_numbers()\n",
@@ -685,20 +415,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Setosa feature means: [5.006, 3.418, 1.464, 0.244]\n",
-      "Versicolor mean for first feature: 5.936\n",
-      "Setosa feature deviations: [0.3524896872134513, 0.38102439795469095, 0.17351115943644546, 0.10720950308167838]\n",
-      "Virginica deviation for second feature: 0.32249663817263746\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "means, deviations = iris.find_means_and_deviations()\n",
     "\n",
@@ -723,40 +442,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "iris = DataSet(name=\"iris\")\n",
     "\n",
@@ -789,17 +477,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Manhattan Distance between (1,2) and (3,4) is 4\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "def manhattan_distance(X, Y):\n",
     "    return sum([abs(x - y) for x, y in zip(X, Y)])\n",
@@ -821,17 +501,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Euclidean Distance between (1,2) and (3,4) is 2.8284271247461903\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "import math\n",
     "\n",
@@ -855,17 +527,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Hamming Distance between 'abc' and 'abb' is 1\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "def hamming_distance(X, Y):\n",
     "    return sum(x != y for x, y in zip(X, Y))\n",
@@ -887,17 +551,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Mean Boolean Error Distance between (1,2,3) and (1,4,5) is 0.6666666666666666\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "def mean_boolean_error(X, Y):\n",
     "    return mean(int(x != y) for x, y in zip(X, Y))\n",
@@ -919,17 +575,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Mean Error Distance between (1,0,5) and (3,10,5) is 4\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "def mean_error(X, Y):\n",
     "    return mean([abs(x - y) for x, y in zip(X, Y)])\n",
@@ -951,17 +599,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Mean Square Distance between (1,0,5) and (3,10,5) is 34.666666666666664\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "def ms_error(X, Y):\n",
     "    return mean([(x - y)**2 for x, y in zip(X, Y)])\n",
@@ -983,17 +623,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Root of Mean Error Distance between (1,0,5) and (3,10,5) is 5.887840577551898\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "def rms_error(X, Y):\n",
     "    return math.sqrt(ms_error(X, Y))\n",
@@ -1033,135 +665,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": null,
    "metadata": {
     "collapsed": true
    },
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-       "\n",
-       "  </style>\n",
-       "</head>\n",
-       "<body>\n",
-       "<h2></h2>\n",
-       "\n",
-       "<div class=\"highlight\"><pre><span></span><span class=\"k\">def</span> <span class=\"nf\">PluralityLearner</span><span class=\"p\">(</span><span class=\"n\">dataset</span><span class=\"p\">):</span>\n",
-       "<span class=\"w\">    </span><span class=\"sd\">&quot;&quot;&quot;</span>\n",
-       "<span class=\"sd\">    A very dumb algorithm: always pick the result that was most popular</span>\n",
-       "<span class=\"sd\">    in the training data. Makes a baseline for comparison.</span>\n",
-       "<span class=\"sd\">    &quot;&quot;&quot;</span>\n",
-       "    <span class=\"n\">most_popular</span> <span class=\"o\">=</span> <span class=\"n\">mode</span><span class=\"p\">([</span><span class=\"n\">e</span><span class=\"p\">[</span><span class=\"n\">dataset</span><span class=\"o\">.</span><span class=\"n\">target</span><span class=\"p\">]</span> <span class=\"k\">for</span> <span class=\"n\">e</span> <span class=\"ow\">in</span> <span class=\"n\">dataset</span><span class=\"o\">.</span><span class=\"n\">examples</span><span class=\"p\">])</span>\n",
-       "\n",
-       "    <span class=\"k\">def</span> <span class=\"nf\">predict</span><span class=\"p\">(</span><span class=\"n\">example</span><span class=\"p\">):</span>\n",
-       "<span class=\"w\">        </span><span class=\"sd\">&quot;&quot;&quot;Always return same result: the most popular from the training set.&quot;&quot;&quot;</span>\n",
-       "        <span class=\"k\">return</span> <span class=\"n\">most_popular</span>\n",
-       "\n",
-       "    <span class=\"k\">return</span> <span class=\"n\">predict</span>\n",
-       "</pre></div>\n",
-       "</body>\n",
-       "</html>\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "psource(PluralityLearner)"
    ]
@@ -1188,17 +696,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mammal\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "zoo = DataSet(name=\"zoo\")\n",
     "\n",
@@ -1255,132 +755,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": null,
    "metadata": {
     "collapsed": true
    },
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-       "\n",
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-       "</head>\n",
-       "<body>\n",
-       "<h2></h2>\n",
-       "\n",
-       "<div class=\"highlight\"><pre><span></span><span class=\"k\">def</span> <span class=\"nf\">NearestNeighborLearner</span><span class=\"p\">(</span><span class=\"n\">dataset</span><span class=\"p\">,</span> <span class=\"n\">k</span><span class=\"o\">=</span><span class=\"mi\">1</span><span class=\"p\">):</span>\n",
-       "<span class=\"w\">    </span><span class=\"sd\">&quot;&quot;&quot;k-NearestNeighbor: the k nearest neighbors vote.&quot;&quot;&quot;</span>\n",
-       "\n",
-       "    <span class=\"k\">def</span> <span class=\"nf\">predict</span><span class=\"p\">(</span><span class=\"n\">example</span><span class=\"p\">):</span>\n",
-       "<span class=\"w\">        </span><span class=\"sd\">&quot;&quot;&quot;Find the k closest items, and have them vote for the best.&quot;&quot;&quot;</span>\n",
-       "        <span class=\"n\">best</span> <span class=\"o\">=</span> <span class=\"n\">heapq</span><span class=\"o\">.</span><span class=\"n\">nsmallest</span><span class=\"p\">(</span><span class=\"n\">k</span><span class=\"p\">,</span> <span class=\"p\">((</span><span class=\"n\">dataset</span><span class=\"o\">.</span><span class=\"n\">distance</span><span class=\"p\">(</span><span class=\"n\">e</span><span class=\"p\">,</span> <span class=\"n\">example</span><span class=\"p\">),</span> <span class=\"n\">e</span><span class=\"p\">)</span> <span class=\"k\">for</span> <span class=\"n\">e</span> <span class=\"ow\">in</span> <span class=\"n\">dataset</span><span class=\"o\">.</span><span class=\"n\">examples</span><span class=\"p\">))</span>\n",
-       "        <span class=\"k\">return</span> <span class=\"n\">mode</span><span class=\"p\">(</span><span class=\"n\">e</span><span class=\"p\">[</span><span class=\"n\">dataset</span><span class=\"o\">.</span><span class=\"n\">target</span><span class=\"p\">]</span> <span class=\"k\">for</span> <span class=\"p\">(</span><span class=\"n\">d</span><span class=\"p\">,</span> <span class=\"n\">e</span><span class=\"p\">)</span> <span class=\"ow\">in</span> <span class=\"n\">best</span><span class=\"p\">)</span>\n",
-       "\n",
-       "    <span class=\"k\">return</span> <span class=\"n\">predict</span>\n",
-       "</pre></div>\n",
-       "</body>\n",
-       "</html>\n"
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "psource(NearestNeighborLearner)"
    ]
@@ -1407,17 +786,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "setosa\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "iris = DataSet(name=\"iris\")\n",
     "\n",
@@ -1469,38 +840,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/markdown": [
-       "### AIMA3e\n",
-       "__function__ DECISION-TREE-LEARNING(_examples_, _attributes_, _parent\\_examples_) __returns__ a tree  \n",
-       "&emsp;__if__ _examples_ is empty __then return__ PLURALITY\\-VALUE(_parent\\_examples_)  \n",
-       "&emsp;__else if__ all _examples_ have the same classification __then return__ the classification  \n",
-       "&emsp;__else if__ _attributes_ is empty __then return__ PLURALITY\\-VALUE(_examples_)  \n",
-       "&emsp;__else__  \n",
-       "&emsp;&emsp;&emsp;_A_ &larr; argmax<sub>_a_ &isin; _attributes_</sub> IMPORTANCE(_a_, _examples_)  \n",
-       "&emsp;&emsp;&emsp;_tree_ &larr; a new decision tree with root test _A_  \n",
-       "&emsp;&emsp;&emsp;__for each__ value _v<sub>k</sub>_ of _A_ __do__  \n",
-       "&emsp;&emsp;&emsp;&emsp;&emsp;_exs_ &larr; \\{ _e_ : _e_ &isin; _examples_ __and__ _e_._A_ = _v<sub>k</sub>_ \\}  \n",
-       "&emsp;&emsp;&emsp;&emsp;&emsp;_subtree_ &larr; DECISION-TREE-LEARNING(_exs_, _attributes_ &minus; _A_, _examples_)  \n",
-       "&emsp;&emsp;&emsp;&emsp;&emsp;add a branch to _tree_ with label \\(_A_ = _v<sub>k</sub>_\\) and subtree _subtree_  \n",
-       "&emsp;&emsp;&emsp;__return__ _tree_  \n",
-       "\n",
-       "---\n",
-       "__Figure ??__ The decision\\-tree learning algorithm. The function  IMPORTANCE is described in Section __??__. The function PLURALITY\\-VALUE selects the most common output value among a set of examples, breaking ties randomly."
-      ],
-      "text/plain": [
-       "<IPython.core.display.Markdown object>"
-      ]
-     },
-     "execution_count": 51,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "pseudocode(\"Decision Tree Learning\")"
    ]
@@ -1516,156 +858,9 @@
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-       "<h2></h2>\n",
-       "\n",
-       "<div class=\"highlight\"><pre><span></span><span class=\"k\">class</span> <span class=\"nc\">DecisionFork</span><span class=\"p\">:</span>\n",
-       "<span class=\"w\">    </span><span class=\"sd\">&quot;&quot;&quot;</span>\n",
-       "<span class=\"sd\">    A fork of a decision tree holds an attribute to test, and a dict</span>\n",
-       "<span class=\"sd\">    of branches, one for each of the attribute&#39;s values.</span>\n",
-       "<span class=\"sd\">    &quot;&quot;&quot;</span>\n",
-       "\n",
-       "    <span class=\"k\">def</span> <span class=\"fm\">__init__</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"n\">attr</span><span class=\"p\">,</span> <span class=\"n\">attr_name</span><span class=\"o\">=</span><span class=\"kc\">None</span><span class=\"p\">,</span> <span class=\"n\">default_child</span><span class=\"o\">=</span><span class=\"kc\">None</span><span class=\"p\">,</span> <span class=\"n\">branches</span><span class=\"o\">=</span><span class=\"kc\">None</span><span class=\"p\">):</span>\n",
-       "<span class=\"w\">        </span><span class=\"sd\">&quot;&quot;&quot;Initialize by saying what attribute this node tests.&quot;&quot;&quot;</span>\n",
-       "        <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">attr</span> <span class=\"o\">=</span> <span class=\"n\">attr</span>\n",
-       "        <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">attr_name</span> <span class=\"o\">=</span> <span class=\"n\">attr_name</span> <span class=\"ow\">or</span> <span class=\"n\">attr</span>\n",
-       "        <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">default_child</span> <span class=\"o\">=</span> <span class=\"n\">default_child</span>\n",
-       "        <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">branches</span> <span class=\"o\">=</span> <span class=\"n\">branches</span> <span class=\"ow\">or</span> <span class=\"p\">{}</span>\n",
-       "\n",
-       "    <span class=\"k\">def</span> <span class=\"fm\">__call__</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"n\">example</span><span class=\"p\">):</span>\n",
-       "<span class=\"w\">        </span><span class=\"sd\">&quot;&quot;&quot;Given an example, classify it using the attribute and the branches.&quot;&quot;&quot;</span>\n",
-       "        <span class=\"n\">attr_val</span> <span class=\"o\">=</span> <span class=\"n\">example</span><span class=\"p\">[</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">attr</span><span class=\"p\">]</span>\n",
-       "        <span class=\"k\">if</span> <span class=\"n\">attr_val</span> <span class=\"ow\">in</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">branches</span><span class=\"p\">:</span>\n",
-       "            <span class=\"k\">return</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">branches</span><span class=\"p\">[</span><span class=\"n\">attr_val</span><span class=\"p\">](</span><span class=\"n\">example</span><span class=\"p\">)</span>\n",
-       "        <span class=\"k\">else</span><span class=\"p\">:</span>\n",
-       "            <span class=\"c1\"># return default class when attribute is unknown</span>\n",
-       "            <span class=\"k\">return</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">default_child</span><span class=\"p\">(</span><span class=\"n\">example</span><span class=\"p\">)</span>\n",
-       "\n",
-       "    <span class=\"k\">def</span> <span class=\"nf\">add</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"n\">val</span><span class=\"p\">,</span> <span class=\"n\">subtree</span><span class=\"p\">):</span>\n",
-       "<span class=\"w\">        </span><span class=\"sd\">&quot;&quot;&quot;Add a branch. If self.attr = val, go to the given subtree.&quot;&quot;&quot;</span>\n",
-       "        <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">branches</span><span class=\"p\">[</span><span class=\"n\">val</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">subtree</span>\n",
-       "\n",
-       "    <span class=\"k\">def</span> <span class=\"nf\">display</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"n\">indent</span><span class=\"o\">=</span><span class=\"mi\">0</span><span class=\"p\">):</span>\n",
-       "        <span class=\"n\">name</span> <span class=\"o\">=</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">attr_name</span>\n",
-       "        <span class=\"nb\">print</span><span class=\"p\">(</span><span class=\"s1\">&#39;Test&#39;</span><span class=\"p\">,</span> <span class=\"n\">name</span><span class=\"p\">)</span>\n",
-       "        <span class=\"k\">for</span> <span class=\"p\">(</span><span class=\"n\">val</span><span class=\"p\">,</span> <span class=\"n\">subtree</span><span class=\"p\">)</span> <span class=\"ow\">in</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">branches</span><span class=\"o\">.</span><span class=\"n\">items</span><span class=\"p\">():</span>\n",
-       "            <span class=\"nb\">print</span><span class=\"p\">(</span><span class=\"s1\">&#39; &#39;</span> <span class=\"o\">*</span> <span class=\"mi\">4</span> <span class=\"o\">*</span> <span class=\"n\">indent</span><span class=\"p\">,</span> <span class=\"n\">name</span><span class=\"p\">,</span> <span class=\"s1\">&#39;=&#39;</span><span class=\"p\">,</span> <span class=\"n\">val</span><span class=\"p\">,</span> <span class=\"s1\">&#39;==&gt;&#39;</span><span class=\"p\">,</span> <span class=\"n\">end</span><span class=\"o\">=</span><span class=\"s1\">&#39; &#39;</span><span class=\"p\">)</span>\n",
-       "            <span class=\"n\">subtree</span><span class=\"o\">.</span><span class=\"n\">display</span><span class=\"p\">(</span><span class=\"n\">indent</span> <span class=\"o\">+</span> <span class=\"mi\">1</span><span class=\"p\">)</span>\n",
-       "\n",
-       "    <span class=\"k\">def</span> <span class=\"fm\">__repr__</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">):</span>\n",
-       "        <span class=\"k\">return</span> <span class=\"s1\">&#39;DecisionFork(</span><span class=\"si\">{0!r}</span><span class=\"s1\">, </span><span class=\"si\">{1!r}</span><span class=\"s1\">, </span><span class=\"si\">{2!r}</span><span class=\"s1\">)&#39;</span><span class=\"o\">.</span><span class=\"n\">format</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">attr</span><span class=\"p\">,</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">attr_name</span><span class=\"p\">,</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">branches</span><span class=\"p\">)</span>\n",
-       "</pre></div>\n",
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+   "execution_count": null,
+   "metadata": {},
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    "source": [
     "psource(DecisionFork)"
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@@ -1680,137 +875,11 @@
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-       "\n",
-       "  </style>\n",
-       "</head>\n",
-       "<body>\n",
-       "<h2></h2>\n",
-       "\n",
-       "<div class=\"highlight\"><pre><span></span><span class=\"k\">class</span> <span class=\"nc\">DecisionLeaf</span><span class=\"p\">:</span>\n",
-       "<span class=\"w\">    </span><span class=\"sd\">&quot;&quot;&quot;A leaf of a decision tree holds just a result.&quot;&quot;&quot;</span>\n",
-       "\n",
-       "    <span class=\"k\">def</span> <span class=\"fm\">__init__</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"n\">result</span><span class=\"p\">):</span>\n",
-       "        <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">result</span> <span class=\"o\">=</span> <span class=\"n\">result</span>\n",
-       "\n",
-       "    <span class=\"k\">def</span> <span class=\"fm\">__call__</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"n\">example</span><span class=\"p\">):</span>\n",
-       "        <span class=\"k\">return</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">result</span>\n",
-       "\n",
-       "    <span class=\"k\">def</span> <span class=\"nf\">display</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">):</span>\n",
-       "        <span class=\"nb\">print</span><span class=\"p\">(</span><span class=\"s1\">&#39;RESULT =&#39;</span><span class=\"p\">,</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">result</span><span class=\"p\">)</span>\n",
-       "\n",
-       "    <span class=\"k\">def</span> <span class=\"fm\">__repr__</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">):</span>\n",
-       "        <span class=\"k\">return</span> <span class=\"nb\">repr</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">result</span><span class=\"p\">)</span>\n",
-       "</pre></div>\n",
-       "</body>\n",
-       "</html>\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "psource(DecisionLeaf)"
    ]
@@ -1825,178 +894,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": null,
    "metadata": {
     "collapsed": true
    },
-   "outputs": [
-    {
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-       "\n",
-       "  </style>\n",
-       "</head>\n",
-       "<body>\n",
-       "<h2></h2>\n",
-       "\n",
-       "<div class=\"highlight\"><pre><span></span><span class=\"k\">def</span> <span class=\"nf\">DecisionTreeLearner</span><span class=\"p\">(</span><span class=\"n\">dataset</span><span class=\"p\">):</span>\n",
-       "<span class=\"w\">    </span><span class=\"sd\">&quot;&quot;&quot;[Figure 18.5]&quot;&quot;&quot;</span>\n",
-       "\n",
-       "    <span class=\"n\">target</span><span class=\"p\">,</span> <span class=\"n\">values</span> <span class=\"o\">=</span> <span class=\"n\">dataset</span><span class=\"o\">.</span><span class=\"n\">target</span><span class=\"p\">,</span> <span class=\"n\">dataset</span><span class=\"o\">.</span><span class=\"n\">values</span>\n",
-       "\n",
-       "    <span class=\"k\">def</span> <span class=\"nf\">decision_tree_learning</span><span class=\"p\">(</span><span class=\"n\">examples</span><span class=\"p\">,</span> <span class=\"n\">attrs</span><span class=\"p\">,</span> <span class=\"n\">parent_examples</span><span class=\"o\">=</span><span class=\"p\">()):</span>\n",
-       "        <span class=\"k\">if</span> <span class=\"nb\">len</span><span class=\"p\">(</span><span class=\"n\">examples</span><span class=\"p\">)</span> <span class=\"o\">==</span> <span class=\"mi\">0</span><span class=\"p\">:</span>\n",
-       "            <span class=\"k\">return</span> <span class=\"n\">plurality_value</span><span class=\"p\">(</span><span class=\"n\">parent_examples</span><span class=\"p\">)</span>\n",
-       "        <span class=\"k\">if</span> <span class=\"n\">all_same_class</span><span class=\"p\">(</span><span class=\"n\">examples</span><span class=\"p\">):</span>\n",
-       "            <span class=\"k\">return</span> <span class=\"n\">DecisionLeaf</span><span class=\"p\">(</span><span class=\"n\">examples</span><span class=\"p\">[</span><span class=\"mi\">0</span><span class=\"p\">][</span><span class=\"n\">target</span><span class=\"p\">])</span>\n",
-       "        <span class=\"k\">if</span> <span class=\"nb\">len</span><span class=\"p\">(</span><span class=\"n\">attrs</span><span class=\"p\">)</span> <span class=\"o\">==</span> <span class=\"mi\">0</span><span class=\"p\">:</span>\n",
-       "            <span class=\"k\">return</span> <span class=\"n\">plurality_value</span><span class=\"p\">(</span><span class=\"n\">examples</span><span class=\"p\">)</span>\n",
-       "        <span class=\"n\">A</span> <span class=\"o\">=</span> <span class=\"n\">choose_attribute</span><span class=\"p\">(</span><span class=\"n\">attrs</span><span class=\"p\">,</span> <span class=\"n\">examples</span><span class=\"p\">)</span>\n",
-       "        <span class=\"n\">tree</span> <span class=\"o\">=</span> <span class=\"n\">DecisionFork</span><span class=\"p\">(</span><span class=\"n\">A</span><span class=\"p\">,</span> <span class=\"n\">dataset</span><span class=\"o\">.</span><span class=\"n\">attr_names</span><span class=\"p\">[</span><span class=\"n\">A</span><span class=\"p\">],</span> <span class=\"n\">plurality_value</span><span class=\"p\">(</span><span class=\"n\">examples</span><span class=\"p\">))</span>\n",
-       "        <span class=\"k\">for</span> <span class=\"p\">(</span><span class=\"n\">v_k</span><span class=\"p\">,</span> <span class=\"n\">exs</span><span class=\"p\">)</span> <span class=\"ow\">in</span> <span class=\"n\">split_by</span><span class=\"p\">(</span><span class=\"n\">A</span><span class=\"p\">,</span> <span class=\"n\">examples</span><span class=\"p\">):</span>\n",
-       "            <span class=\"n\">subtree</span> <span class=\"o\">=</span> <span class=\"n\">decision_tree_learning</span><span class=\"p\">(</span><span class=\"n\">exs</span><span class=\"p\">,</span> <span class=\"n\">remove_all</span><span class=\"p\">(</span><span class=\"n\">A</span><span class=\"p\">,</span> <span class=\"n\">attrs</span><span class=\"p\">),</span> <span class=\"n\">examples</span><span class=\"p\">)</span>\n",
-       "            <span class=\"n\">tree</span><span class=\"o\">.</span><span class=\"n\">add</span><span class=\"p\">(</span><span class=\"n\">v_k</span><span class=\"p\">,</span> <span class=\"n\">subtree</span><span class=\"p\">)</span>\n",
-       "        <span class=\"k\">return</span> <span class=\"n\">tree</span>\n",
-       "\n",
-       "    <span class=\"k\">def</span> <span class=\"nf\">plurality_value</span><span class=\"p\">(</span><span class=\"n\">examples</span><span class=\"p\">):</span>\n",
-       "<span class=\"w\">        </span><span class=\"sd\">&quot;&quot;&quot;</span>\n",
-       "<span class=\"sd\">        Return the most popular target value for this set of examples.</span>\n",
-       "<span class=\"sd\">        (If target is binary, this is the majority; otherwise plurality).</span>\n",
-       "<span class=\"sd\">        &quot;&quot;&quot;</span>\n",
-       "        <span class=\"n\">popular</span> <span class=\"o\">=</span> <span class=\"n\">argmax_random_tie</span><span class=\"p\">(</span><span class=\"n\">values</span><span class=\"p\">[</span><span class=\"n\">target</span><span class=\"p\">],</span> <span class=\"n\">key</span><span class=\"o\">=</span><span class=\"k\">lambda</span> <span class=\"n\">v</span><span class=\"p\">:</span> <span class=\"n\">count</span><span class=\"p\">(</span><span class=\"n\">target</span><span class=\"p\">,</span> <span class=\"n\">v</span><span class=\"p\">,</span> <span class=\"n\">examples</span><span class=\"p\">))</span>\n",
-       "        <span class=\"k\">return</span> <span class=\"n\">DecisionLeaf</span><span class=\"p\">(</span><span class=\"n\">popular</span><span class=\"p\">)</span>\n",
-       "\n",
-       "    <span class=\"k\">def</span> <span class=\"nf\">count</span><span class=\"p\">(</span><span class=\"n\">attr</span><span class=\"p\">,</span> <span class=\"n\">val</span><span class=\"p\">,</span> <span class=\"n\">examples</span><span class=\"p\">):</span>\n",
-       "<span class=\"w\">        </span><span class=\"sd\">&quot;&quot;&quot;Count the number of examples that have example[attr] = val.&quot;&quot;&quot;</span>\n",
-       "        <span class=\"k\">return</span> <span class=\"nb\">sum</span><span class=\"p\">(</span><span class=\"n\">e</span><span class=\"p\">[</span><span class=\"n\">attr</span><span class=\"p\">]</span> <span class=\"o\">==</span> <span class=\"n\">val</span> <span class=\"k\">for</span> <span class=\"n\">e</span> <span class=\"ow\">in</span> <span class=\"n\">examples</span><span class=\"p\">)</span>\n",
-       "\n",
-       "    <span class=\"k\">def</span> <span class=\"nf\">all_same_class</span><span class=\"p\">(</span><span class=\"n\">examples</span><span class=\"p\">):</span>\n",
-       "<span class=\"w\">        </span><span class=\"sd\">&quot;&quot;&quot;Are all these examples in the same target class?&quot;&quot;&quot;</span>\n",
-       "        <span class=\"n\">class0</span> <span class=\"o\">=</span> <span class=\"n\">examples</span><span class=\"p\">[</span><span class=\"mi\">0</span><span class=\"p\">][</span><span class=\"n\">target</span><span class=\"p\">]</span>\n",
-       "        <span class=\"k\">return</span> <span class=\"nb\">all</span><span class=\"p\">(</span><span class=\"n\">e</span><span class=\"p\">[</span><span class=\"n\">target</span><span class=\"p\">]</span> <span class=\"o\">==</span> <span class=\"n\">class0</span> <span class=\"k\">for</span> <span class=\"n\">e</span> <span class=\"ow\">in</span> <span class=\"n\">examples</span><span class=\"p\">)</span>\n",
-       "\n",
-       "    <span class=\"k\">def</span> <span class=\"nf\">choose_attribute</span><span class=\"p\">(</span><span class=\"n\">attrs</span><span class=\"p\">,</span> <span class=\"n\">examples</span><span class=\"p\">):</span>\n",
-       "<span class=\"w\">        </span><span class=\"sd\">&quot;&quot;&quot;Choose the attribute with the highest information gain.&quot;&quot;&quot;</span>\n",
-       "        <span class=\"k\">return</span> <span class=\"n\">argmax_random_tie</span><span class=\"p\">(</span><span class=\"n\">attrs</span><span class=\"p\">,</span> <span class=\"n\">key</span><span class=\"o\">=</span><span class=\"k\">lambda</span> <span class=\"n\">a</span><span class=\"p\">:</span> <span class=\"n\">information_gain</span><span class=\"p\">(</span><span class=\"n\">a</span><span class=\"p\">,</span> <span class=\"n\">examples</span><span class=\"p\">))</span>\n",
-       "\n",
-       "    <span class=\"k\">def</span> <span class=\"nf\">information_gain</span><span class=\"p\">(</span><span class=\"n\">attr</span><span class=\"p\">,</span> <span class=\"n\">examples</span><span class=\"p\">):</span>\n",
-       "<span class=\"w\">        </span><span class=\"sd\">&quot;&quot;&quot;Return the expected reduction in entropy from splitting by attr.&quot;&quot;&quot;</span>\n",
-       "\n",
-       "        <span class=\"k\">def</span> <span class=\"nf\">I</span><span class=\"p\">(</span><span class=\"n\">examples</span><span class=\"p\">):</span>\n",
-       "            <span class=\"k\">return</span> <span class=\"n\">information_content</span><span class=\"p\">([</span><span class=\"n\">count</span><span class=\"p\">(</span><span class=\"n\">target</span><span class=\"p\">,</span> <span class=\"n\">v</span><span class=\"p\">,</span> <span class=\"n\">examples</span><span class=\"p\">)</span> <span class=\"k\">for</span> <span class=\"n\">v</span> <span class=\"ow\">in</span> <span class=\"n\">values</span><span class=\"p\">[</span><span class=\"n\">target</span><span class=\"p\">]])</span>\n",
-       "\n",
-       "        <span class=\"n\">n</span> <span class=\"o\">=</span> <span class=\"nb\">len</span><span class=\"p\">(</span><span class=\"n\">examples</span><span class=\"p\">)</span>\n",
-       "        <span class=\"n\">remainder</span> <span class=\"o\">=</span> <span class=\"nb\">sum</span><span class=\"p\">((</span><span class=\"nb\">len</span><span class=\"p\">(</span><span class=\"n\">examples_i</span><span class=\"p\">)</span> <span class=\"o\">/</span> <span class=\"n\">n</span><span class=\"p\">)</span> <span class=\"o\">*</span> <span class=\"n\">I</span><span class=\"p\">(</span><span class=\"n\">examples_i</span><span class=\"p\">)</span> <span class=\"k\">for</span> <span class=\"p\">(</span><span class=\"n\">v</span><span class=\"p\">,</span> <span class=\"n\">examples_i</span><span class=\"p\">)</span> <span class=\"ow\">in</span> <span class=\"n\">split_by</span><span class=\"p\">(</span><span class=\"n\">attr</span><span class=\"p\">,</span> <span class=\"n\">examples</span><span class=\"p\">))</span>\n",
-       "        <span class=\"k\">return</span> <span class=\"n\">I</span><span class=\"p\">(</span><span class=\"n\">examples</span><span class=\"p\">)</span> <span class=\"o\">-</span> <span class=\"n\">remainder</span>\n",
-       "\n",
-       "    <span class=\"k\">def</span> <span class=\"nf\">split_by</span><span class=\"p\">(</span><span class=\"n\">attr</span><span class=\"p\">,</span> <span class=\"n\">examples</span><span class=\"p\">):</span>\n",
-       "<span class=\"w\">        </span><span class=\"sd\">&quot;&quot;&quot;Return a list of (val, examples) pairs for each val of attr.&quot;&quot;&quot;</span>\n",
-       "        <span class=\"k\">return</span> <span class=\"p\">[(</span><span class=\"n\">v</span><span class=\"p\">,</span> <span class=\"p\">[</span><span class=\"n\">e</span> <span class=\"k\">for</span> <span class=\"n\">e</span> <span class=\"ow\">in</span> <span class=\"n\">examples</span> <span class=\"k\">if</span> <span class=\"n\">e</span><span class=\"p\">[</span><span class=\"n\">attr</span><span class=\"p\">]</span> <span class=\"o\">==</span> <span class=\"n\">v</span><span class=\"p\">])</span> <span class=\"k\">for</span> <span class=\"n\">v</span> <span class=\"ow\">in</span> <span class=\"n\">values</span><span class=\"p\">[</span><span class=\"n\">attr</span><span class=\"p\">]]</span>\n",
-       "\n",
-       "    <span class=\"k\">return</span> <span class=\"n\">decision_tree_learning</span><span class=\"p\">(</span><span class=\"n\">dataset</span><span class=\"o\">.</span><span class=\"n\">examples</span><span class=\"p\">,</span> <span class=\"n\">dataset</span><span class=\"o\">.</span><span class=\"n\">inputs</span><span class=\"p\">)</span>\n",
-       "</pre></div>\n",
-       "</body>\n",
-       "</html>\n"
-      ],
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-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "psource(DecisionTreeLearner)"
    ]
@@ -2027,17 +929,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "setosa\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "iris = DataSet(name=\"iris\")\n",
     "\n",
@@ -2085,143 +979,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
-   "metadata": {},
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-       "\n",
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-       "</head>\n",
-       "<body>\n",
-       "<h2></h2>\n",
-       "\n",
-       "<div class=\"highlight\"><pre><span></span><span class=\"k\">def</span> <span class=\"nf\">RandomForest</span><span class=\"p\">(</span><span class=\"n\">dataset</span><span class=\"p\">,</span> <span class=\"n\">n</span><span class=\"o\">=</span><span class=\"mi\">5</span><span class=\"p\">):</span>\n",
-       "<span class=\"w\">    </span><span class=\"sd\">&quot;&quot;&quot;An ensemble of Decision Trees trained using bagging and feature bagging.&quot;&quot;&quot;</span>\n",
-       "\n",
-       "    <span class=\"k\">def</span> <span class=\"nf\">data_bagging</span><span class=\"p\">(</span><span class=\"n\">dataset</span><span class=\"p\">,</span> <span class=\"n\">m</span><span class=\"o\">=</span><span class=\"mi\">0</span><span class=\"p\">):</span>\n",
-       "<span class=\"w\">        </span><span class=\"sd\">&quot;&quot;&quot;Sample m examples with replacement&quot;&quot;&quot;</span>\n",
-       "        <span class=\"n\">n</span> <span class=\"o\">=</span> <span class=\"nb\">len</span><span class=\"p\">(</span><span class=\"n\">dataset</span><span class=\"o\">.</span><span class=\"n\">examples</span><span class=\"p\">)</span>\n",
-       "        <span class=\"k\">return</span> <span class=\"n\">weighted_sample_with_replacement</span><span class=\"p\">(</span><span class=\"n\">m</span> <span class=\"ow\">or</span> <span class=\"n\">n</span><span class=\"p\">,</span> <span class=\"n\">dataset</span><span class=\"o\">.</span><span class=\"n\">examples</span><span class=\"p\">,</span> <span class=\"p\">[</span><span class=\"mi\">1</span><span class=\"p\">]</span> <span class=\"o\">*</span> <span class=\"n\">n</span><span class=\"p\">)</span>\n",
-       "\n",
-       "    <span class=\"k\">def</span> <span class=\"nf\">feature_bagging</span><span class=\"p\">(</span><span class=\"n\">dataset</span><span class=\"p\">,</span> <span class=\"n\">p</span><span class=\"o\">=</span><span class=\"mf\">0.7</span><span class=\"p\">):</span>\n",
-       "<span class=\"w\">        </span><span class=\"sd\">&quot;&quot;&quot;Feature bagging with probability p to retain an attribute&quot;&quot;&quot;</span>\n",
-       "        <span class=\"n\">inputs</span> <span class=\"o\">=</span> <span class=\"p\">[</span><span class=\"n\">i</span> <span class=\"k\">for</span> <span class=\"n\">i</span> <span class=\"ow\">in</span> <span class=\"n\">dataset</span><span class=\"o\">.</span><span class=\"n\">inputs</span> <span class=\"k\">if</span> <span class=\"n\">probability</span><span class=\"p\">(</span><span class=\"n\">p</span><span class=\"p\">)]</span>\n",
-       "        <span class=\"k\">return</span> <span class=\"n\">inputs</span> <span class=\"ow\">or</span> <span class=\"n\">dataset</span><span class=\"o\">.</span><span class=\"n\">inputs</span>\n",
-       "\n",
-       "    <span class=\"k\">def</span> <span class=\"nf\">predict</span><span class=\"p\">(</span><span class=\"n\">example</span><span class=\"p\">):</span>\n",
-       "        <span class=\"nb\">print</span><span class=\"p\">([</span><span class=\"n\">predictor</span><span class=\"p\">(</span><span class=\"n\">example</span><span class=\"p\">)</span> <span class=\"k\">for</span> <span class=\"n\">predictor</span> <span class=\"ow\">in</span> <span class=\"n\">predictors</span><span class=\"p\">])</span>\n",
-       "        <span class=\"k\">return</span> <span class=\"n\">mode</span><span class=\"p\">(</span><span class=\"n\">predictor</span><span class=\"p\">(</span><span class=\"n\">example</span><span class=\"p\">)</span> <span class=\"k\">for</span> <span class=\"n\">predictor</span> <span class=\"ow\">in</span> <span class=\"n\">predictors</span><span class=\"p\">)</span>\n",
-       "\n",
-       "    <span class=\"n\">predictors</span> <span class=\"o\">=</span> <span class=\"p\">[</span><span class=\"n\">DecisionTreeLearner</span><span class=\"p\">(</span><span class=\"n\">DataSet</span><span class=\"p\">(</span><span class=\"n\">examples</span><span class=\"o\">=</span><span class=\"n\">data_bagging</span><span class=\"p\">(</span><span class=\"n\">dataset</span><span class=\"p\">),</span> <span class=\"n\">attrs</span><span class=\"o\">=</span><span class=\"n\">dataset</span><span class=\"o\">.</span><span class=\"n\">attrs</span><span class=\"p\">,</span>\n",
-       "                                              <span class=\"n\">attr_names</span><span class=\"o\">=</span><span class=\"n\">dataset</span><span class=\"o\">.</span><span class=\"n\">attr_names</span><span class=\"p\">,</span> <span class=\"n\">target</span><span class=\"o\">=</span><span class=\"n\">dataset</span><span class=\"o\">.</span><span class=\"n\">target</span><span class=\"p\">,</span>\n",
-       "                                              <span class=\"n\">inputs</span><span class=\"o\">=</span><span class=\"n\">feature_bagging</span><span class=\"p\">(</span><span class=\"n\">dataset</span><span class=\"p\">)))</span> <span class=\"k\">for</span> <span class=\"n\">_</span> <span class=\"ow\">in</span> <span class=\"nb\">range</span><span class=\"p\">(</span><span class=\"n\">n</span><span class=\"p\">)]</span>\n",
-       "\n",
-       "    <span class=\"k\">return</span> <span class=\"n\">predict</span>\n",
-       "</pre></div>\n",
-       "</body>\n",
-       "</html>\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "psource(RandomForest)"
    ]
@@ -2241,18 +1001,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['setosa', 'setosa', 'setosa', 'setosa', 'setosa']\n",
-      "setosa\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "iris = DataSet(name=\"iris\")\n",
     "\n",
@@ -2382,18 +1133,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.3333333333333333\n",
-      "0.10588235294117647\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "dataset = iris\n",
     "\n",
@@ -2425,22 +1167,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'argmax' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[1;32mIn[59], line 9\u001b[0m\n\u001b[0;32m      3\u001b[0m         \u001b[39mreturn\u001b[39;00m (target_dist[targetval] \u001b[39m*\u001b[39m\n\u001b[0;32m      4\u001b[0m                 product(attr_dists[targetval, attr][example[attr]]\n\u001b[0;32m      5\u001b[0m                         \u001b[39mfor\u001b[39;00m attr \u001b[39min\u001b[39;00m dataset\u001b[39m.\u001b[39minputs))\n\u001b[0;32m      6\u001b[0m     \u001b[39mreturn\u001b[39;00m argmax(target_vals, key\u001b[39m=\u001b[39mclass_probability)\n\u001b[1;32m----> 9\u001b[0m \u001b[39mprint\u001b[39m(predict([\u001b[39m5\u001b[39;49m, \u001b[39m3\u001b[39;49m, \u001b[39m1\u001b[39;49m, \u001b[39m0.1\u001b[39;49m]))\n",
-      "Cell \u001b[1;32mIn[59], line 6\u001b[0m, in \u001b[0;36mpredict\u001b[1;34m(example)\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mclass_probability\u001b[39m(targetval):\n\u001b[0;32m      3\u001b[0m     \u001b[39mreturn\u001b[39;00m (target_dist[targetval] \u001b[39m*\u001b[39m\n\u001b[0;32m      4\u001b[0m             product(attr_dists[targetval, attr][example[attr]]\n\u001b[0;32m      5\u001b[0m                     \u001b[39mfor\u001b[39;00m attr \u001b[39min\u001b[39;00m dataset\u001b[39m.\u001b[39minputs))\n\u001b[1;32m----> 6\u001b[0m \u001b[39mreturn\u001b[39;00m argmax(target_vals, key\u001b[39m=\u001b[39mclass_probability)\n",
-      "\u001b[1;31mNameError\u001b[0m: name 'argmax' is not defined"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "def predict(example):\n",
     "    def class_probability(targetval):\n",
@@ -2486,16 +1215,7 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[5.006, 3.418, 1.464, 0.244]\n",
-      "[0.5161711470638634, 0.3137983233784114, 0.46991097723995795, 0.19775268000454405]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "means, deviations = dataset.find_means_and_deviations()\n",
     "\n",
@@ -2523,15 +1243,7 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "setosa\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "def predict(example):\n",
     "    def class_probability(targetval):\n",
@@ -2612,23 +1324,7 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Discrete Classifier\n",
-      "setosa\n",
-      "setosa\n",
-      "setosa\n",
-      "\n",
-      "Continuous Classifier\n",
-      "setosa\n",
-      "versicolor\n",
-      "virginica\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "nBD = NaiveBayesLearner(iris, continuous=False)\n",
     "print(\"Discrete Classifier\")\n",
@@ -2704,17 +1400,7 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "First\n",
-      "Second\n",
-      "Third\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(nBS('aab'))        # We can handle strings\n",
     "print(nBS(['b', 'b']))   # And lists!\n",
@@ -2802,15 +1488,7 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "iris = DataSet(name=\"iris\")\n",
     "iris.classes_to_numbers()\n",
@@ -2871,15 +1549,7 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.2404650656510341\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "iris = DataSet(name=\"iris\")\n",
     "iris.classes_to_numbers()\n",
@@ -2969,16 +1639,7 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Error ratio for Discrete: 0.040000000000000036\n",
-      "Error ratio for Continuous: 0.040000000000000036\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "nBD = NaiveBayesLearner(iris, continuous=False)\n",
     "print(\"Error ratio for Discrete:\", err_ratio(nBD, iris))\n",
@@ -3009,18 +1670,7 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Error ratio for k=1: 0.0\n",
-      "Error ratio for k=3: 0.06000000000000005\n",
-      "Error ratio for k=5: 0.1266666666666667\n",
-      "Error ratio for k=7: 0.19999999999999996\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "kNN_1 = NearestNeighborLearner(iris, k=1)\n",
     "kNN_3 = NearestNeighborLearner(iris, k=3)\n",
@@ -3057,15 +1707,7 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Error ratio for Perceptron: 0.31333333333333335\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "iris2 = DataSet(name=\"iris\")\n",
     "iris2.classes_to_numbers()\n",
@@ -3118,130 +1760,7 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<!DOCTYPE html PUBLIC \"-//W3C//DTD HTML 4.01//EN\"\n",
-       "   \"http://www.w3.org/TR/html4/strict.dtd\">\n",
-       "\n",
-       "<html>\n",
-       "<head>\n",
-       "  <title></title>\n",
-       "  <meta http-equiv=\"content-type\" content=\"text/html; charset=None\">\n",
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-       "<h2></h2>\n",
-       "\n",
-       "<div class=\"highlight\"><pre><span></span><span class=\"k\">def</span> <span class=\"nf\">AdaBoost</span><span class=\"p\">(</span><span class=\"n\">L</span><span class=\"p\">,</span> <span class=\"n\">K</span><span class=\"p\">):</span>\n",
-       "    <span class=\"sd\">&quot;&quot;&quot;[Figure 18.34]&quot;&quot;&quot;</span>\n",
-       "    <span class=\"k\">def</span> <span class=\"nf\">train</span><span class=\"p\">(</span><span class=\"n\">dataset</span><span class=\"p\">):</span>\n",
-       "        <span class=\"n\">examples</span><span class=\"p\">,</span> <span class=\"n\">target</span> <span class=\"o\">=</span> <span class=\"n\">dataset</span><span class=\"o\">.</span><span class=\"n\">examples</span><span class=\"p\">,</span> <span class=\"n\">dataset</span><span class=\"o\">.</span><span class=\"n\">target</span>\n",
-       "        <span class=\"n\">N</span> <span class=\"o\">=</span> <span class=\"nb\">len</span><span class=\"p\">(</span><span class=\"n\">examples</span><span class=\"p\">)</span>\n",
-       "        <span class=\"n\">epsilon</span> <span class=\"o\">=</span> <span class=\"mf\">1.</span> <span class=\"o\">/</span> <span class=\"p\">(</span><span class=\"mi\">2</span> <span class=\"o\">*</span> <span class=\"n\">N</span><span class=\"p\">)</span>\n",
-       "        <span class=\"n\">w</span> <span class=\"o\">=</span> <span class=\"p\">[</span><span class=\"mf\">1.</span> <span class=\"o\">/</span> <span class=\"n\">N</span><span class=\"p\">]</span> <span class=\"o\">*</span> <span class=\"n\">N</span>\n",
-       "        <span class=\"n\">h</span><span class=\"p\">,</span> <span class=\"n\">z</span> <span class=\"o\">=</span> <span class=\"p\">[],</span> <span class=\"p\">[]</span>\n",
-       "        <span class=\"k\">for</span> <span class=\"n\">k</span> <span class=\"ow\">in</span> <span class=\"nb\">range</span><span class=\"p\">(</span><span class=\"n\">K</span><span class=\"p\">):</span>\n",
-       "            <span class=\"n\">h_k</span> <span class=\"o\">=</span> <span class=\"n\">L</span><span class=\"p\">(</span><span class=\"n\">dataset</span><span class=\"p\">,</span> <span class=\"n\">w</span><span class=\"p\">)</span>\n",
-       "            <span class=\"n\">h</span><span class=\"o\">.</span><span class=\"n\">append</span><span class=\"p\">(</span><span class=\"n\">h_k</span><span class=\"p\">)</span>\n",
-       "            <span class=\"n\">error</span> <span class=\"o\">=</span> <span class=\"nb\">sum</span><span class=\"p\">(</span><span class=\"n\">weight</span> <span class=\"k\">for</span> <span class=\"n\">example</span><span class=\"p\">,</span> <span class=\"n\">weight</span> <span class=\"ow\">in</span> <span class=\"nb\">zip</span><span class=\"p\">(</span><span class=\"n\">examples</span><span class=\"p\">,</span> <span class=\"n\">w</span><span class=\"p\">)</span>\n",
-       "                        <span class=\"k\">if</span> <span class=\"n\">example</span><span class=\"p\">[</span><span class=\"n\">target</span><span class=\"p\">]</span> <span class=\"o\">!=</span> <span class=\"n\">h_k</span><span class=\"p\">(</span><span class=\"n\">example</span><span class=\"p\">))</span>\n",
-       "            <span class=\"c1\"># Avoid divide-by-0 from either 0% or 100% error rates:</span>\n",
-       "            <span class=\"n\">error</span> <span class=\"o\">=</span> <span class=\"n\">clip</span><span class=\"p\">(</span><span class=\"n\">error</span><span class=\"p\">,</span> <span class=\"n\">epsilon</span><span class=\"p\">,</span> <span class=\"mi\">1</span> <span class=\"o\">-</span> <span class=\"n\">epsilon</span><span class=\"p\">)</span>\n",
-       "            <span class=\"k\">for</span> <span class=\"n\">j</span><span class=\"p\">,</span> <span class=\"n\">example</span> <span class=\"ow\">in</span> <span class=\"nb\">enumerate</span><span class=\"p\">(</span><span class=\"n\">examples</span><span class=\"p\">):</span>\n",
-       "                <span class=\"k\">if</span> <span class=\"n\">example</span><span class=\"p\">[</span><span class=\"n\">target</span><span class=\"p\">]</span> <span class=\"o\">==</span> <span class=\"n\">h_k</span><span class=\"p\">(</span><span class=\"n\">example</span><span class=\"p\">):</span>\n",
-       "                    <span class=\"n\">w</span><span class=\"p\">[</span><span class=\"n\">j</span><span class=\"p\">]</span> <span class=\"o\">*=</span> <span class=\"n\">error</span> <span class=\"o\">/</span> <span class=\"p\">(</span><span class=\"mf\">1.</span> <span class=\"o\">-</span> <span class=\"n\">error</span><span class=\"p\">)</span>\n",
-       "            <span class=\"n\">w</span> <span class=\"o\">=</span> <span class=\"n\">normalize</span><span class=\"p\">(</span><span class=\"n\">w</span><span class=\"p\">)</span>\n",
-       "            <span class=\"n\">z</span><span class=\"o\">.</span><span class=\"n\">append</span><span class=\"p\">(</span><span class=\"n\">math</span><span class=\"o\">.</span><span class=\"n\">log</span><span class=\"p\">((</span><span class=\"mf\">1.</span> <span class=\"o\">-</span> <span class=\"n\">error</span><span class=\"p\">)</span> <span class=\"o\">/</span> <span class=\"n\">error</span><span class=\"p\">))</span>\n",
-       "        <span class=\"k\">return</span> <span class=\"n\">WeightedMajority</span><span class=\"p\">(</span><span class=\"n\">h</span><span class=\"p\">,</span> <span class=\"n\">z</span><span class=\"p\">)</span>\n",
-       "    <span class=\"k\">return</span> <span class=\"n\">train</span>\n",
-       "</pre></div>\n",
-       "</body>\n",
-       "</html>\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "psource(AdaBoost)"
    ]
@@ -3302,18 +1821,7 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "iris2 = DataSet(name=\"iris\")\n",
     "iris2.classes_to_numbers()\n",
@@ -3335,15 +1843,7 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Error ratio for adaboost:  0.046666666666666634\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(\"Error ratio for adaboost: \", err_ratio(adaboost, iris2))"
    ]