fixed description of IMM transition matrix #271

14-Adaptive-Filtering.ipynb

@@ -194,7 +194,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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 truncated
+      "image/png": "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 truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -297,7 +297,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -341,7 +341,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -382,7 +382,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "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 truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -420,7 +420,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -483,7 +483,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -550,7 +550,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 2 Axes>"
       ]
@@ -647,7 +647,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -695,7 +695,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -776,7 +776,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 2 Axes>"
       ]
@@ -871,7 +871,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 2 Axes>"
       ]
@@ -898,7 +898,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 2 Axes>"
       ]
@@ -925,7 +925,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 2 Axes>"
       ]
@@ -935,7 +935,7 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 2 Axes>"
       ]
@@ -965,7 +965,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 2 Axes>"
       ]
@@ -975,7 +975,7 @@
     },
     {
      "data": {
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truncated
       "text/plain": [
        "<Figure size 900x400 with 2 Axes>"
       ]
@@ -985,7 +985,7 @@
     },
     {
      "data": {
-      "image/png": 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truncated
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truncated
       "text/plain": [
        "<Figure size 900x400 with 2 Axes>"
       ]
@@ -995,7 +995,7 @@
     },
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 2 Axes>"
       ]
@@ -1005,7 +1005,7 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 2 Axes>"
       ]
@@ -1076,7 +1076,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 4 Axes>"
       ]
@@ -1202,7 +1202,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 2 Axes>"
       ]
@@ -1233,7 +1233,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 2 Axes>"
       ]
@@ -1286,7 +1286,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 2 Axes>"
       ]
@@ -1336,7 +1336,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 2 Axes>"
       ]
@@ -1412,7 +1412,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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 truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -1549,7 +1549,7 @@
     "\n",
     "$$\\mathbf M = \\begin{bmatrix}.97 & .03\\\\.05 & .95\\end{bmatrix}$$\n",
     "\n",
-    "In other words $\\mathbf M[i, j]$ is the probability of mode being $i$ given that the last mode was $j$. In this example the probability of the mode currently being straight $(i=0)$ given that the last mode was a turn $(j=1)$ is $\\mathbf M[1,\\ 0] = 0.05$. In Python we'd write:"
+    "In other words $\\mathbf M[i, j]$ is the probability of mode being $j$ given that the last mode was $i$. In this example the probability of the mode currently being straight $(j=0)$ given that the last mode was a turn $(i=1)$ is $\\mathbf M[1,\\ 0] = 0.05$. In Python we'd write:"
    ]
   },
   {
@@ -1558,20 +1558,19 @@
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "array([[0.97, 0.03],\n",
-       "       [0.05, 0.95]])"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[0.97 0.03]\n",
+      " [0.05 0.95]]\n",
+      "From turn to straight probablility is 0.05 percent\n"
+     ]
     }
    ],
    "source": [
     "M = np.array([[.97, .03], [.05, .95]])\n",
-    "M"
+    "print(M)\n",
+    "print('From turn to straight probablility is', M[1, 0], 'percent')"
    ]
   },
   {
@@ -1788,7 +1787,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 2 Axes>"
       ]
@@ -1878,7 +1877,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -1930,7 +1929,7 @@
       "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
       "\u001b[1;32m<ipython-input-35-e96cb02ea05b>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      5\u001b[0m                   [0.03, 0.97]])\n\u001b[0;32m      6\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 7\u001b[1;33m \u001b[0mimm\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mIMMEstimator\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mca\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcv\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;36m0.5\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0.5\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrans\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[1;32mC:\\Users\\rlabbe\\Dropbox\\filterpy\\filterpy\\kalman\\IMM.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, filters, mu, M)\u001b[0m\n\u001b[0;32m    141\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mx_shape\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    142\u001b[0m                 raise ValueError(\n\u001b[1;32m--> 143\u001b[1;33m                     'All filters must have the same state dimension')\n\u001b[0m\u001b[0;32m    144\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    145\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mzeros\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilters\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mC:\\pcloud\\filterpy\\filterpy\\kalman\\IMM.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, filters, mu, M)\u001b[0m\n\u001b[0;32m    142\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mx_shape\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    143\u001b[0m                 raise ValueError(\n\u001b[1;32m--> 144\u001b[1;33m                     'All filters must have the same state dimension')\n\u001b[0m\u001b[0;32m    145\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    146\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mzeros\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilters\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
       "\u001b[1;31mValueError\u001b[0m: All filters must have the same state dimension"
      ]
     }