PEP-8 fixes

02_Discrete_Bayes.ipynb

@@ -484,7 +484,7 @@
        "WLWwiMcZ3gAAAABJRU5ErkJggg==\n"
       ],
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fa850d3eac8>"
+       "<matplotlib.figure.Figure at 0x6a80b38>"
       ]
      },
      "metadata": {},
@@ -764,7 +764,7 @@
        "FtXJCJBlAAAAAElFTkSuQmCC\n"
       ],
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fa850a44ef0>"
+       "<matplotlib.figure.Figure at 0x6ac1278>"
       ]
      },
      "metadata": {},
@@ -779,7 +779,7 @@
     "        else:\n",
     "            pos_belief[i] *= p_miss\n",
     "\n",
-    "pos_belief = np.array([0.2]*10)\n",
+    "pos_belief = np.array([0.2] * 10)\n",
     "reading = 1 # 1 is 'door'\n",
     "update(pos_belief, 1, .6, .2)\n",
     "\n",
@@ -959,7 +959,7 @@
        "FtPch8/CAAAAAElFTkSuQmCC\n"
       ],
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fa85097d908>"
+       "<matplotlib.figure.Figure at 0x749b208>"
       ]
      },
      "metadata": {},
@@ -976,7 +976,7 @@
     "            \n",
     "    pos_belief /= sum(pos_belief)\n",
     "\n",
-    "pos_belief = np.array([0.2]*10)\n",
+    "pos_belief = np.array([0.2] * 10)\n",
     "reading = 1 # 1 is 'door'\n",
     "update(pos_belief, 1, .6, .2)\n",
     "\n",
@@ -1201,7 +1201,7 @@
        "ACQirgEAIBFxDQAAiYhrAABIRFwDAEAi4hoAABL5fwpmJuNqHGHfAAAAAElFTkSuQmCC\n"
       ],
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fa85096e4e0>"
+       "<matplotlib.figure.Figure at 0x6be1ef0>"
       ]
      },
      "metadata": {},
@@ -1405,7 +1405,7 @@
        "DQAAiYhrAABIRFwDAEAi4hoAABIR1wAAkIi4BgCARP4/nnsht+19KnYAAAAASUVORK5CYII=\n"
       ],
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fa850704ac8>"
+       "<matplotlib.figure.Figure at 0x6c43e48>"
       ]
      },
      "metadata": {},
@@ -1589,7 +1589,7 @@
        "iGsAAEhEXAMAQCLiGgAAEhHXAACQiLgGAIBExDUAACTy/wCvjTrhgem4kgAAAABJRU5ErkJggg==\n"
       ],
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fa86c351978>"
+       "<matplotlib.figure.Figure at 0x6bcb940>"
       ]
      },
      "metadata": {},
@@ -1757,7 +1757,7 @@
        "iGsAAEhEXAMAQCLiGgAAEhHXAACQiLgGAIBExDUAACTy/wCvjTrhgem4kgAAAABJRU5ErkJggg==\n"
       ],
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fa850659320>"
+       "<matplotlib.figure.Figure at 0x6c51ef0>"
       ]
      },
      "metadata": {},
@@ -1936,7 +1936,7 @@
        "/wDmJxpYHvwEtQAAAABJRU5ErkJggg==\n"
       ],
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fa85073e6a0>"
+       "<matplotlib.figure.Figure at 0x6aac7f0>"
       ]
      },
      "metadata": {},
@@ -1945,7 +1945,7 @@
    ],
    "source": [
     "pos_belief = np.array([1.0, 0, 0, 0, 0, 0, 0, 0, 0, 0])\n",
-    "for i in range (500):\n",
+    "for i in range(500):\n",
     "    predict(pos_belief, 1, .8, .1, .1)\n",
     "print(pos_belief)\n",
     "bp.bar_plot(pos_belief)"
@@ -2003,7 +2003,7 @@
     }
    ],
    "source": [
-    "pos_belief = np.array([.1]*10)\n",
+    "pos_belief = np.array([.1] * 10)\n",
     "update(pos_belief, 1, .6, .2)\n",
     "print(pos_belief)"
    ]
@@ -2170,7 +2170,7 @@
        "QmCC\n"
       ],
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fa8506f84e0>"
+       "<matplotlib.figure.Figure at 0x6aacd68>"
       ]
      },
      "metadata": {},
@@ -2354,7 +2354,7 @@
        "YII=\n"
       ],
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fa8509989e8>"
+       "<matplotlib.figure.Figure at 0x6b2cc88>"
       ]
      },
      "metadata": {},
@@ -2531,7 +2531,7 @@
        "kMj/Azv6KDQ33AZlAAAAAElFTkSuQmCC\n"
       ],
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fa8508ebc50>"
+       "<matplotlib.figure.Figure at 0x75c8eb8>"
       ]
      },
      "metadata": {},
@@ -2709,7 +2709,7 @@
        "SOT/ATXCA84OAAAAA0lEQVQfKKaMWAKYAAAAAElFTkSuQmCC\n"
       ],
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fa85062e5f8>"
+       "<matplotlib.figure.Figure at 0x74d9208>"
       ]
      },
      "metadata": {},
@@ -2916,7 +2916,7 @@
        "rkJggg==\n"
       ],
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fa8508067b8>"
+       "<matplotlib.figure.Figure at 0x6c9cc88>"
       ]
      },
      "metadata": {},
@@ -2925,7 +2925,7 @@
    ],
    "source": [
     "hallway = [1, 0, 1, 0, 0, 1, 0, 1, 0, 0]\n",
-    "pos_belief = np.array([.1]*10)\n",
+    "pos_belief = np.array([.1] * 10)\n",
     "measurements = [1, 0, 1, 0, 0]\n",
     "\n",
     "for m in measurements:\n",
@@ -3097,7 +3097,7 @@
        "gETENQAAJPL/AKVaFt8zsqsBAAAAAElFTkSuQmCC\n"
       ],
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fa85076d7f0>"
+       "<matplotlib.figure.Figure at 0x6b34c18>"
       ]
      },
      "metadata": {},
@@ -3584,7 +3584,7 @@
        "RArh4pqIiIiISCFcXBMRERERKYSLayIiIiIihfx/3pDuDjpTJXEAAAAASUVORK5CYII=\n"
       ],
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fa850873da0>"
+       "<matplotlib.figure.Figure at 0x6aec048>"
       ]
      },
      "metadata": {},
@@ -3594,7 +3594,7 @@
    "source": [
     "measurements = [0, 1, 0, 1, 0, 0]\n",
     "\n",
-    "for i,m in enumerate(measurements):\n",
+    "for i, m in enumerate(measurements):\n",
     "    update(pos_belief, m, .6, .2)\n",
     "    predict(pos_belief, 1, .8, .1, .1)\n",
     "    plt.subplot(3, 2, i+1)\n",
@@ -3788,7 +3788,7 @@
        "rkJggg==\n"
       ],
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fa8505c6d30>"
+       "<matplotlib.figure.Figure at 0x6b9f898>"
       ]
      },
      "metadata": {},