Updated for Python 3.6

Author: rlabbe

02-Discrete-Bayes.ipynb

@@ -20,7 +20,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from __future__ import division, print_function\n",
     "%matplotlib inline"
    ]
   },
@@ -36,7 +35,7 @@
        "        <style>\n",
        "        .output_wrapper, .output {\n",
        "            height:auto !important;\n",
-       "            max-height:100000px; \n",
+       "            max-height:100000px;\n",
        "        }\n",
        "        .output_scroll {\n",
        "            box-shadow:none !important;\n",
@@ -582,7 +581,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "6a0f5d2b16e54121bd8a3b11957d0966",
+       "model_id": "69e1cf784fd743ddaeae41d5fa161216",
        "version_major": 2,
        "version_minor": 0
       },
@@ -758,7 +757,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "5e1bbeaf87af4835869cd7a6676fa209",
+       "model_id": "b7f90f29b9394f82b4c64982c8eab901",
        "version_major": 2,
        "version_minor": 0
       },
@@ -783,7 +782,7 @@
     "# make interactive plot\n",
     "def show_prior(step):\n",
     "    book_plots.bar_plot(predict_beliefs[step-1])\n",
-    "    plt.title('Step {}'.format(step))\n",
+    "    plt.title(f'Step {step}')\n",
     "\n",
     "interact(show_prior, step=IntSlider(value=1, max=len(predict_beliefs)));"
    ]
@@ -1247,7 +1246,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "9f7338d5d95a4133ae3825b18020b9cd",
+       "model_id": "5e52dc4ac04b4244ad4357a4fd837902",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1300,7 +1299,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ffb4353460494a15ab0c0e9299dde120",
+       "model_id": "562f60dcb79f4651953d34fd40c1cab7",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1382,7 +1381,7 @@
     "        posterior = update(likelihood, prior)\n",
     "        prior = predict(posterior, 1, kernel)\n",
     "        plt.subplot(5, 2, i+1)\n",
-    "        book_plots.bar_plot(posterior, ylim=(0, .4), title='step {}'.format(i+1))\n",
+    "        book_plots.bar_plot(posterior, ylim=(0, .4), title=f'step {i+1}')\n",
     "    plt.tight_layout()"
    ]
   },
@@ -1515,13 +1514,9 @@
     "        index = np.argmax(posterior)\n",
     "\n",
     "        if do_print:\n",
-    "            print('''time {}: pos {}, sensed {}, '''\n",
-    "                  '''at position {}'''.format(\n",
-    "                    i, robot.pos, m, track[robot.pos]))\n",
-    "\n",
-    "            print('''        estimated position is {}'''\n",
-    "                  ''' with confidence {:.4f}%:'''.format(\n",
-    "                  index, posterior[index]*100))            \n",
+    "            print(f'time {i}: pos {robot.pos}, sensed {m}, at position {track[robot.pos]}')\n",
+    "            conf = posterior[index] * 100\n",
+    "            print(f'        estimated position is {index} with confidence {conf:.4f}%:')            \n",
     "\n",
     "    book_plots.bar_plot(posterior)\n",
     "    if do_print:\n",
@@ -1661,7 +1656,7 @@
     "        train_filter(148+i, kernel=[.1, .8, .1], \n",
     "                     sensor_accuracy=.8,\n",
     "                     move_distance=4, do_print=False)\n",
-    "        plt.title ('iteration {}'.format(148+i))"
+    "        plt.title (f'iteration {148 + i}')"
    ]
   },
   {
@@ -1777,7 +1772,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.6"
+   "version": "3.7.4"
   },
   "widgets": {
    "application/vnd.jupyter.widget-state+json": {