Removed extraneous matrix print out

I don't know why, but the *_ notation ended up printing
the last matrix to the output cell, so I went back to _, _, _, _

I think I was just experiementing with that cell as the *_ notation
is not compatibile with Python 2.7

10-Unscented-Kalman-Filter.ipynb

@@ -239,11 +239,14 @@
        "                },\n",
        "                displayAlign: 'center', // Change this to 'center' to center equations.\n",
        "                \"HTML-CSS\": {\n",
-       "                    scale:95,\n",
+       "                    scale: 95,\n",
        "                        availableFonts: [],\n",
        "                        preferredFont:null,\n",
        "                        webFont: \"TeX\",\n",
        "                    styles: {'.MathJax_Display': {\"margin\": 4}}\n",
+       "                },\n",
+       "                 \"fast-preview\": {\n",
+       "                    \"disabled\": false\n",
        "                }\n",
        "        });\n",
        "</script>\n"
@@ -287,9 +290,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAukAAAEyCAYAAABH1U56AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAukAAAEyCAYAAABH1U56AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1bdc8237978>"
+       "<matplotlib.figure.Figure at 0x260f7929a58>"
       ]
      },
      "metadata": {},
@@ -348,7 +351,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1bdc821ae48>"
+       "<matplotlib.figure.Figure at 0x260f790af60>"
       ]
      },
      "metadata": {},
@@ -393,14 +396,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Difference in mean x=0.026, y=43.017\n"
+      "Difference in mean x=0.076, y=42.452\n"
      ]
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1bdc9889b38>"
+       "<matplotlib.figure.Figure at 0x260f8f603c8>"
       ]
      },
      "metadata": {},
@@ -462,7 +465,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1bdc988d198>"
+       "<matplotlib.figure.Figure at 0x260f68b2550>"
       ]
      },
      "metadata": {},
@@ -512,7 +515,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1bdcae68ba8>"
+       "<matplotlib.figure.Figure at 0x260f956df98>"
       ]
      },
      "metadata": {},
@@ -560,7 +563,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1bdcadd1c50>"
+       "<matplotlib.figure.Figure at 0x260f94d48d0>"
       ]
      },
      "metadata": {},
@@ -615,7 +618,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1bdcaed7a20>"
+       "<matplotlib.figure.Figure at 0x260f9647630>"
       ]
      },
      "metadata": {},
@@ -805,7 +808,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1bdc71a8ac8>"
+       "<matplotlib.figure.Figure at 0x260f68b21d0>"
       ]
      },
      "metadata": {},
@@ -920,11 +923,20 @@
     "scrolled": false
    },
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[ 1.0  0.0  0.0  0.0  0.0  0.0]\n",
+      " [ 0.0  0.0  1.0  0.0  0.0  0.0]\n",
+      " [ 0.0  0.0  0.0  0.0  1.0  0.0]]\n"
+     ]
+    },
     {
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1bdcb071438>"
+       "<matplotlib.figure.Figure at 0x260f9759128>"
       ]
      },
      "metadata": {},
@@ -955,7 +967,7 @@
     "\n",
     "zs = [np.array([i + randn()*std_x, \n",
     "                i + randn()*std_y]) for i in range(100)]               \n",
-    "xs, *_ = kf.batch_filter(zs)\n",
+    "xs, _, _, _ = kf.batch_filter(zs)\n",
     "plt.plot(xs[:, 0], xs[:, 2]);"
    ]
   },
@@ -1037,7 +1049,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvAAAAEaCAYAAAB3kWMDAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1bdcaf999b0>"
+       "<matplotlib.figure.Figure at 0x260f95adf98>"
       ]
      },
      "metadata": {},
@@ -1101,7 +1113,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1bdcb03a470>"
+       "<matplotlib.figure.Figure at 0x260f9707fd0>"
       ]
      },
      "metadata": {},
@@ -1278,7 +1290,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1bdcb0d76a0>"
+       "<matplotlib.figure.Figure at 0x260f97c4f60>"
       ]
      },
      "metadata": {},
@@ -1288,7 +1300,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1bdcb056208>"
+       "<matplotlib.figure.Figure at 0x260f97637b8>"
       ]
      },
      "metadata": {},
@@ -1298,7 +1310,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1bdcb136e48>"
+       "<matplotlib.figure.Figure at 0x260f9849b70>"
       ]
      },
      "metadata": {},
@@ -1383,7 +1395,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1bdcb0b5390>"
+       "<matplotlib.figure.Figure at 0x260f8f74fd0>"
       ]
      },
      "metadata": {},
@@ -1485,7 +1497,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1bdc96259e8>"
+       "<matplotlib.figure.Figure at 0x260f97d5a58>"
       ]
      },
      "metadata": {},
@@ -1550,7 +1562,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1bdc8f95400>"
+       "<matplotlib.figure.Figure at 0x260f97d56a0>"
       ]
      },
      "metadata": {},
@@ -1632,7 +1644,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1bdc8f95e48>"
+       "<matplotlib.figure.Figure at 0x260f8dec358>"
       ]
      },
      "metadata": {},
@@ -1708,7 +1720,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1bdcb0c50f0>"
+       "<matplotlib.figure.Figure at 0x260f8d2fbe0>"
       ]
      },
      "metadata": {},
@@ -1823,7 +1835,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1bdcae6c320>"
+       "<matplotlib.figure.Figure at 0x260f8d2f0f0>"
       ]
      },
      "metadata": {},
@@ -1894,7 +1906,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1bdc9870da0>"
+       "<matplotlib.figure.Figure at 0x260f95375f8>"
       ]
      },
      "metadata": {},
@@ -1939,7 +1951,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1bdcae476d8>"
+       "<matplotlib.figure.Figure at 0x260f8e1c320>"
       ]
      },
      "metadata": {},
@@ -1991,7 +2003,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1bdcc19eef0>"
+       "<matplotlib.figure.Figure at 0x260fb8973c8>"
       ]
      },
      "metadata": {},
@@ -2031,7 +2043,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1bdcc18a208>"
+       "<matplotlib.figure.Figure at 0x260fb884518>"
       ]
      },
      "metadata": {},
@@ -2217,6 +2229,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "So we can implement the sigma points with this code:\n",
+    "\n",
     "```python\n",
     "sigmas = np.zeros((2*n+1, n))\n",
     "U = scipy.linalg.cholesky((n+lambda_)*P) # sqrt\n",
@@ -2433,7 +2447,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1bdcae47e80>"
+       "<matplotlib.figure.Figure at 0x260f9527588>"
       ]
      },
      "metadata": {},
@@ -2443,7 +2457,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1bdcae22ef0>"
+       "<matplotlib.figure.Figure at 0x260fb843048>"
       ]
      },
      "metadata": {},
@@ -2453,7 +2467,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1bdc962b358>"
+       "<matplotlib.figure.Figure at 0x260f9604780>"
       ]
      },
      "metadata": {},
@@ -2532,7 +2546,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1bdcc1aa518>"
+       "<matplotlib.figure.Figure at 0x260fb86df28>"
       ]
      },
      "metadata": {},
@@ -2542,7 +2556,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1bdc989b898>"
+       "<matplotlib.figure.Figure at 0x260f97c46a0>"
       ]
      },
      "metadata": {},
@@ -2552,7 +2566,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1bdcb037cc0>"
+       "<matplotlib.figure.Figure at 0x260f983e400>"
       ]
      },
      "metadata": {},
@@ -2739,7 +2753,7 @@
      "data": {
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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1bdc96b5940>"
+       "<matplotlib.figure.Figure at 0x260f8d2d438>"
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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1bdc96ce860>"
+       "<matplotlib.figure.Figure at 0x260f8d89860>"
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      },
      "metadata": {},
@@ -3262,7 +3276,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1bdcc204048>"
+       "<matplotlib.figure.Figure at 0x260fb9140b8>"
       ]
      },
      "metadata": {},
@@ -3302,7 +3316,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1bdcc351cf8>"
+       "<matplotlib.figure.Figure at 0x260fba4fd68>"
       ]
      },
      "metadata": {},