change exemplar finding

clustering/affinity_propagation.ipynb

@@ -42,8 +42,6 @@
     "x = np.random.normal(0, 1, size)\n",
     "x = np.append(x, np.random.normal(5, 1, size), axis=0)\n",
     "\n",
-    "# x = StandardScaler().fit_transform(x)\n",
-    "\n",
     "c = ['r' for _ in range(n)] + ['b' for _ in range(n)]\n",
     "plt.scatter(x[:, 0], x[:, 1], c=c)\n",
     "plt.show()"
@@ -125,6 +123,16 @@
     "        R = R * lmda + (1 - lmda) * new_val"
    ]
   },
+  {
+   "cell_type": "code",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "A, R, S = create_matrices()\n",
+    "\n",
+    "%timeit update_r(slow=False)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -192,9 +200,8 @@
     "def plot_iteration(A, R):\n",
     "    fig = plt.figure(figsize=(12, 6))\n",
     "    sol = A + R\n",
-    "    labels = []\n",
+    "    \n",
-    "    for i in range(x.shape[0]):\n",
+    "    labels = np.argmax(sol, axis=1)\n",
-    "        labels.append(np.argmax(sol[i]))\n",
     "\n",
     "    exemplars = np.unique(labels)\n",
     "    colors = dict(zip(exemplars, cycle('bgrcmyk')))\n",
@@ -220,6 +227,14 @@
     "    \n"
    ]
   },
+  {
+   "cell_type": "code",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.median(S)"
+   ]
+  },
   {
    "cell_type": "code",
    "metadata": {
@@ -230,6 +245,7 @@
     "%%time\n",
     "A, R, S = create_matrices()\n",
     "preference = np.median(S)\n",
+    "preference =  -1000\n",
     "\n",
     "np.fill_diagonal(S, preference)\n",
     "damping = 0.5\n",