all tests fixed and little refactored

Author: amogorkon

Showcase.ipynb

@@ -56,37 +56,57 @@
     "bar.plot()\n",
     "foo.plot()\n",
     "baz = foo + bar\n",
-    "baz.plot()"
+    "baz.plot()\n",
+    "\n",
+    "numbers(11)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD8CAYAAACMwORRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nb9SakRC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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f7c9cf24c18>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "\n",
+    "from fuzzy.classes import Domain, Set\n",
+    "from fuzzy.functions import alpha, triangular, gauss\n",
+    "from fuzzy.hedges import plus, minus, very\n",
+    "\n",
+    "numbers = Domain(\"numbers\", low=0, high=20, res=0.1)\n",
+    "numbers.close_to_10 = Set(numbers, gauss(10, 10))\n",
+    "\n",
+    "numbers.close_to_10.plot()"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "## Rules\n",
-    "\n",
     "Rules aka *Fuzzy Inference* is done when domains and sets are defined to combine sets of different domains. For this to work, first we need to make sure we only get a single membership value if we query a domain with multiple sets. "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'fromiter' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-4-d8c7a3e2d7ad>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 19\u001b[0;31m \u001b[0mfromiter\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m: name 'fromiter' is not defined"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from numpy import outer\n",
     "\n",