moved chapter 14 due to reorg

14_Smoothing.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:4d079ba596720ac4462749b002a8e25677c1666a2db55f444e3770f2e640e242"
+  "signature": "sha256:5a4ad28382e572d04a1a7439f49a860005d391b9ebc799a3c5099f681dea1ac9"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -32,7 +32,7 @@
       "from __future__ import division, print_function\n",
       "import matplotlib.pyplot as plt\n",
       "import sys\n",
-      "sys.path.insert(0, '../code') # allow us to format the book\n",
+      "sys.path.insert(0, './code') # allow us to format the book\n",
       "import book_format\n",
       "book_format.load_style()"
      ],
@@ -261,7 +261,7 @@
        "output_type": "pyout",
        "prompt_number": 1,
        "text": [
-        "<IPython.core.display.HTML at 0x7ff9bcc59128>"
+        "<IPython.core.display.HTML at 0x7f58495a0400>"
        ]
       }
      ],
@@ -300,7 +300,7 @@
        "output_type": "display_data",
        "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7ff9bcc592b0>"
+        "<matplotlib.figure.Figure at 0x7f5849591ba8>"
        ]
       }
      ],
@@ -341,7 +341,7 @@
        "output_type": "display_data",
        "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7ff9a04a4e48>"
+        "<matplotlib.figure.Figure at 0x7f5849591c50>"
        ]
       }
      ],
@@ -377,7 +377,7 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7ff9a0437e80>"
+        "<matplotlib.figure.Figure at 0x7f582c67eb38>"
        ]
       }
      ],
@@ -527,13 +527,13 @@
       "    fk.F = np.array([[1., 1.],\n",
       "                     [0., 1.]])    # state transition matrix\n",
       "\n",
-      "    fk.H = [[1., 0.]]              # Measurement function\n",
+      "    fk.H = np.array([[1., 0.]])    # Measurement function\n",
       "    fk.P = 10.                     # covariance matrix\n",
       "    fk.R = noise                   # state uncertainty\n",
       "    fk.Q = 0.001                   # process uncertainty\n",
       "\n",
       "    # create noisy data\n",
-      "    zs = [t + random.randn()*noise for t in range (40)]\n",
+      "    zs = np.asarray([t + random.randn()*noise for t in range (40)])\n",
       "\n",
       "    # filter data with Kalman filter, than run smoother on it\n",
       "    mu, cov, _, _ = fk.batch_filter(zs)\n",
@@ -558,7 +558,7 @@
        "output_type": "display_data",
        "png": "iVBORw0KGgoAAAANSUhEUgAAAyUAAAGNCAYAAAAPRN41AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7ff9a03ad358>"
+        "<matplotlib.figure.Figure at 0x7f582c5f7b38>"
        ]
       }
      ],
@@ -587,7 +587,7 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7ff9a03d6550>"
+        "<matplotlib.figure.Figure at 0x7f582c641240>"
        ]
       }
      ],