Removed use of --pylab inline, as I mistakenly thought that was the best
way to get matplot lib to be inline. Also, imported print_function from future to try to be 3.X compliant.
This commit is contained in:
Roger Labbe
parent
3b9bb1fd44
commit
ef22d7e62a
@ -1,7 +1,7 @@
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{
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"metadata": {
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"signature": "sha256:7c1d5ebfcacf654b0862f976d6da64ba34c3743b23833736a6b3e7efb53ba119"
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"nbformat": 3,
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"nbformat_minor": 0,
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@ -48,24 +48,24 @@
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],
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"language": "python",
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"metadata": {},
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"outputs": [],
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"prompt_number": ""
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"outputs": []
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"%matplotlib inline\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"\n",
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"xs = arange(0,10,0.1)\n",
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"xs = np.arange(0,10,0.1)\n",
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"ys = [gaussian (x, 5, 3) for x in xs]\n",
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"plot (xs, ys)\n",
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"show()"
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"plt.plot (xs, ys)\n",
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"plt.show()"
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],
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"language": "python",
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"metadata": {},
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"outputs": [],
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"prompt_number": ""
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"outputs": []
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},
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{
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"cell_type": "markdown",
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@ -103,7 +103,7 @@
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"xs = arange(16,30,0.1)\n",
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"xs = np.arange(16,30,0.1)\n",
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"ys = [gaussian (x,23,1) for x in xs]\n",
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"plt.plot (xs,ys, 'r')\n",
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"plt.axvline(23); plt.axvline(24) \n",
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@ -111,8 +111,7 @@
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],
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"language": "python",
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"metadata": {},
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"outputs": [],
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"prompt_number": ""
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"outputs": []
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},
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{
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"cell_type": "markdown",
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@ -140,8 +139,7 @@
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],
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"language": "python",
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"metadata": {},
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"outputs": [],
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"prompt_number": ""
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"outputs": []
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},
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{
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"cell_type": "markdown",
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@ -177,18 +175,17 @@
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" return math.exp (-0.5 * (x-mu)**2 / sigma) / math.sqrt(2.*math.pi*sigma)\n",
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"\n",
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"def plt_g (mu,variance):\n",
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" xs = arange(0,10,0.15)\n",
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" xs = np.arange(0,10,0.15)\n",
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" ys = [gaussian (x, mu,variance) for x in xs]\n",
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" plot (xs, ys)\n",
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" ylim((0,1))\n",
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" show()\n",
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" plt.plot (xs, ys)\n",
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" plt.ylim((0,1))\n",
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" plt.show()\n",
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"\n",
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"interact (plt_g, mu=(0,10), variance=(0.2,4.5))"
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],
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"language": "python",
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"metadata": {},
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"outputs": [],
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"prompt_number": ""
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"outputs": []
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},
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{
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"cell_type": "markdown",
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@ -219,8 +216,7 @@
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],
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"language": "python",
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"metadata": {},
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"outputs": [],
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"prompt_number": ""
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"outputs": []
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}
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],
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"metadata": {}
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@ -1,7 +1,7 @@
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{
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"metadata": {
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"name": "",
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"signature": "sha256:62f7253d6ad9a6031738dd1335cf418d484588da5b86c0e2b98b648ae1f20f38"
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"signature": "sha256:6ea7b0e562518e41af4e09c3d07bfdc49bd67a20f9d28c17a5b5a053444de18c"
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"nbformat": 3,
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"nbformat_minor": 0,
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@ -50,8 +50,7 @@
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],
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"language": "python",
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"metadata": {},
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"outputs": [],
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"prompt_number": ""
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"outputs": []
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},
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{
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"cell_type": "markdown",
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@ -65,13 +64,13 @@
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"from __future__ import print_function\n",
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"for i in range(20):\n",
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" print (\"%.4f\" % random.randn()),"
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" print(\"%.4f\" % random.randn(),end='\\t')"
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],
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"language": "python",
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"metadata": {},
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"outputs": [],
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"prompt_number": ""
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"outputs": []
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},
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{
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"cell_type": "markdown",
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@ -87,20 +86,20 @@
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"collapsed": false,
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"input": [
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"%matplotlib inline\n",
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"import matplotlib.pyplot as plt\n",
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"\n",
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"dog = DogSensor (noise=0.0)\n",
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"xs = []\n",
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"for i in range(10):\n",
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" x = dog.sense()\n",
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" xs.append(x)\n",
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" print(\"%.4f\" % x),\n",
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"plot(xs)\n",
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"show()"
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" print(\"%.4f\" % x, end=' '),\n",
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"plt.plot(xs)\n",
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"plt.show()"
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],
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"language": "python",
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"metadata": {},
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"outputs": [],
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"prompt_number": ""
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"outputs": []
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},
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{
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"cell_type": "markdown",
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@ -122,21 +121,20 @@
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" for i in range(100):\n",
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" x = dog.sense()\n",
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" xs.append(x)\n",
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" p1, = plot(xs, c='b')\n",
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" p2, = plot([0,99],[1,100], 'r--')\n",
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" xlabel('time')\n",
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" ylabel('pos')\n",
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" ylim([0,100])\n",
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" title('noise = ' + str(noise_scale))\n",
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" legend([p1, p2], ['sensor', 'actual'], loc=2)\n",
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" show()\n",
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" p1, = plt.plot(xs, c='b')\n",
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" p2, = plt.plot([0,99],[1,100], 'r--')\n",
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" plt.xlabel('time')\n",
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" plt.ylabel('pos')\n",
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" plt.ylim([0,100])\n",
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" plt.title('noise = ' + str(noise_scale))\n",
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" plt.legend([p1, p2], ['sensor', 'actual'], loc=2)\n",
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" plt.show()\n",
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" \n",
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"test_sensor(4.0)"
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],
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"language": "python",
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"metadata": {},
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"outputs": [],
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"prompt_number": ""
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"outputs": []
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},
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{
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"cell_type": "markdown",
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],
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"language": "python",
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"metadata": {},
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"outputs": [],
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"prompt_number": ""
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"outputs": []
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},
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{
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"cell_type": "code",
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@ -170,8 +167,7 @@
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],
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"language": "python",
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"metadata": {},
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"outputs": [],
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"prompt_number": ""
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"outputs": []
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},
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{
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"cell_type": "markdown",
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@ -198,8 +194,7 @@
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],
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"language": "python",
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"metadata": {},
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"outputs": [],
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"prompt_number": ""
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"for i in xs:\n",
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" ys.append(dog.sense())\n",
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" \n",
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"plot(xs,ys)\n",
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"show()"
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"plt.plot(xs,ys)\n",
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"plt.show()"
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],
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"language": "python",
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"metadata": {},
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"outputs": [],
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"prompt_number": ""
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"outputs": []
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},
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{
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"cell_type": "markdown",
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@ -277,12 +271,13 @@
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"collapsed": false,
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"input": [
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"import gaussian\n",
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"import numpy as np\n",
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"\n",
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"def multiply(mu1, sig1, mu2, sig2):\n",
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" m = (sig1*mu2 + sig2*mu1) / (sig1+sig2)\n",
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" s = 1. / (1./sig1 + 1./ sig2)\n",
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" return (m,s)\n",
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"\n",
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"\n",
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"xs = np.arange(16, 30, 0.1)\n",
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"\n",
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"\n",
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"m, s = multiply(m1,s1,m1,s1)\n",
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"\n",
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"ys = [gaussian.gaussian(x,m1,s1) for x in xs]\n",
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"p1, =plot (xs,ys)\n",
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"p1, = plt.plot (xs,ys)\n",
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"\n",
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"ys = [gaussian.gaussian(x,m,s) for x in xs]\n",
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"p2, = plot (xs,ys)\n",
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"p2, = plt.plot (xs,ys)\n",
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"\n",
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"legend([p1,p2],['original', 'multiply'])\n",
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"show()"
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"plt.legend([p1,p2],['original', 'multiply'])\n",
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"plt.show()"
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],
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"language": "python",
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"metadata": {},
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"outputs": [],
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"prompt_number": ""
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"input": [
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"xs = np.arange(16, 30, 0.1)\n",
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"\n",
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"\n",
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"m1,s1 = 23, 5\n",
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"m2,s2 = 25, 5\n",
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"m, s = multiply(m1,s1,m2,s2)\n",
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"\n",
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"ys = [gaussian.gaussian(x,m1,s1) for x in xs]\n",
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"p1, = plot (xs,ys)\n",
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"p1, = plt.plot (xs,ys)\n",
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"\n",
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"ys = [gaussian.gaussian(x,m2,s2) for x in xs]\n",
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"p2, = plot (xs,ys)\n",
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"p2, = plt.plot (xs,ys)\n",
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"\n",
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"ys = [gaussian.gaussian(x,m,s) for x in xs]\n",
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"p3, = plot(xs,ys)\n",
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"legend([p1,p2,p3],['measure 1', 'measure 2', 'multiply'])\n",
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"show()"
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"p3, = plt.plot(xs,ys)\n",
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"plt.legend([p1,p2,p3],['measure 1', 'measure 2', 'multiply'])\n",
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"plt.show()"
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],
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"language": "python",
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"metadata": {},
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"outputs": [],
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"prompt_number": ""
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"outputs": []
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},
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{
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"cell_type": "markdown",
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@ -372,8 +364,7 @@
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],
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"language": "python",
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"metadata": {},
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"outputs": [],
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"prompt_number": ""
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"outputs": []
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},
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{
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"cell_type": "markdown",
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],
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"language": "python",
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"metadata": {},
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"outputs": [],
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"prompt_number": ""
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"pos,s = 2, 5\n",
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"for i in range(20):\n",
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" pos,s = sense(pos, s, dog.sense(), 5)\n",
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" print 'time:', i, 'position = ', \"%.3f\" % pos, 'variance = ', \"%.3f\" % s\n",
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"\n"
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" print('time:', i, '\\tposition =', \"%.3f\" % pos, '\\tvariance =', \"%.3f\" % s)"
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],
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"language": "python",
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"metadata": {},
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"outputs": [],
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"prompt_number": ""
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"outputs": []
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},
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{
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"cell_type": "markdown",
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],
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"language": "python",
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"metadata": {},
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"outputs": [],
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"prompt_number": ""
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"\n",
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"for i in range(10):\n",
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" pos = update(pos[0], pos[1], movement, movement_error)\n",
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" print 'UPDATE:', \"%.4f\" %pos[0], \", %.4f\" %pos[1]\n",
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" print('UPDATE: %.4f,\\t%.4f' % (pos[0], pos[1]))\n",
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" \n",
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" Z = dog.sense()\n",
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" zs.append(Z)\n",
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" pos = sense(pos[0], pos[1], Z, sensor_error)\n",
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" ps.append(pos[0])\n",
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" \n",
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" print 'SENSE:', \"%.4f\" %pos[0], \", %.4f\" %pos[1]\n",
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" print\n",
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" print('SENSE: %.4f,\\t%.4f' % (pos[0], pos[1]))\n",
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" print()\n",
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" \n",
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"p1, = plot(zs,c='r', linestyle='dashed')\n",
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"p2, = plot(ps, c='b')\n",
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"legend([p1,p2], ['measurement', 'filter'], 2)\n",
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"show()"
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"p1, = plt.plot(zs,c='r', linestyle='dashed')\n",
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"p2, = plt.plot(ps, c='b')\n",
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"plt.legend([p1,p2], ['measurement', 'filter'], 2)\n",
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"plt.show()"
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],
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"language": "python",
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"metadata": {},
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"outputs": [],
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"prompt_number": ""
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"outputs": []
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},
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{
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"cell_type": "markdown",
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" ps.append(pos[0])\n",
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"\n",
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"\n",
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"p1, = plot(zs,c='r', linestyle='dashed')\n",
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"p2, = plot(ps, c='b')\n",
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"legend([p1,p2], ['measurement', 'filter'], 2)\n",
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"show()"
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"p1, = plt.plot(zs,c='r', linestyle='dashed')\n",
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"p2, = plt.plot(ps, c='b')\n",
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"plt.legend([p1,p2], ['measurement', 'filter'], 2)\n",
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"plt.show()"
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],
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"language": "python",
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"metadata": {},
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"outputs": [],
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"prompt_number": ""
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"outputs": []
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},
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{
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"cell_type": "markdown",
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" ps.append(pos[0])\n",
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"\n",
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"\n",
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"p1, = plot(zs,c='r', linestyle='dashed')\n",
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"p2, = plot(ps, c='b')\n",
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"legend([p1,p2], ['measurement', 'filter'], 2)\n",
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"show()"
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"p1, = plt.plot(zs,c='r', linestyle='dashed')\n",
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"p2, = plt.plot(ps, c='b')\n",
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"plt.legend([p1,p2], ['measurement', 'filter'], 2)\n",
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"plt.show()"
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],
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"language": "python",
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"metadata": {},
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"outputs": [],
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"prompt_number": ""
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"outputs": []
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},
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{
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"cell_type": "markdown",
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" ps.append(pos[0])\n",
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"\n",
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"\n",
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"p1, = plot(zs,c='r', linestyle='dashed')\n",
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"p2, = plot(ps, c='b')\n",
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"legend([p1,p2], ['measurement', 'filter'], 2)\n",
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"show()"
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"p1, = plt.plot(zs,c='r', linestyle='dashed')\n",
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"p2, = plt.plot(ps, c='b')\n",
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"plt.legend([p1,p2], ['measurement', 'filter'], 2)\n",
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"plt.show()"
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],
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"language": "python",
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"metadata": {},
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"outputs": [],
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"prompt_number": ""
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"\n",
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" pos = update (pos[0], pos[1], movement, movement_error)\n",
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"\n",
|
||||
"p1, = plot(zs,c='r', linestyle='dashed')\n",
|
||||
"p2, = plot(ps, c='b')\n",
|
||||
"legend([p1,p2], ['measurement', 'filter'], 2)\n",
|
||||
"show()"
|
||||
"p1, = plt.plot(zs,c='r', linestyle='dashed')\n",
|
||||
"p2, = plt.plot(ps, c='b')\n",
|
||||
"plt.legend([p1,p2], ['measurement', 'filter'], 2)\n",
|
||||
"plt.show()"
|
||||
],
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"prompt_number": ""
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
@ -801,15 +783,14 @@
|
||||
"\n",
|
||||
" pos = update(pos[0], pos[1], movement, movement_error)\n",
|
||||
"\n",
|
||||
"p1, = plot(zs,c='r', linestyle='dashed')\n",
|
||||
"p2, = plot(ps, c='b')\n",
|
||||
"legend([p1,p2], ['measurement', 'filter'], 2)\n",
|
||||
"show()"
|
||||
"p1, = plt.plot(zs,c='r', linestyle='dashed')\n",
|
||||
"p2, = plt.plot(ps, c='b')\n",
|
||||
"plt.legend([p1,p2], ['measurement', 'filter'], 2)\n",
|
||||
"plt.show()"
|
||||
],
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"prompt_number": ""
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
@ -829,18 +810,14 @@
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"collapsed": false,
|
||||
"input": [
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"author notes:\n",
|
||||
" clean up the code - same stuff duplicated over and over - write a 'clean implemntation' at the end.\n",
|
||||
" \n",
|
||||
" "
|
||||
],
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"prompt_number": ""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
@ -864,10 +841,10 @@
|
||||
" ps.append(pos[0])\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"p1, = plot(zs,c='r', linestyle='dashed')\n",
|
||||
"p2, = plot(ps, c='b')\n",
|
||||
"legend([p1,p2], ['measurement', 'filter'], 2)\n",
|
||||
"show()"
|
||||
"p1, = plt.plot(zs,c='r', linestyle='dashed')\n",
|
||||
"p2, = plt.plot(ps, c='b')\n",
|
||||
"plt.legend([p1,p2], ['measurement', 'filter'], 2)\n",
|
||||
"plt.show()"
|
||||
],
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
@ -886,8 +863,7 @@
|
||||
],
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"prompt_number": ""
|
||||
"outputs": []
|
||||
}
|
||||
],
|
||||
"metadata": {}
|
||||
|
||||
@ -1,7 +1,7 @@
|
||||
{
|
||||
"metadata": {
|
||||
"name": "",
|
||||
"signature": "sha256:43e6b1c0a6a87cad97442c35e4be82ce79c07ba2a6b41e95e576e4371959d448"
|
||||
"signature": "sha256:3b71546948dd21caccb388a6147d443538b451fdb64f56494861aa34958fb04a"
|
||||
},
|
||||
"nbformat": 3,
|
||||
"nbformat_minor": 0,
|
||||
@ -85,8 +85,7 @@
|
||||
],
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"prompt_number": ""
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
@ -107,8 +106,7 @@
|
||||
],
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"prompt_number": ""
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
@ -125,8 +123,7 @@
|
||||
],
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"prompt_number": ""
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
@ -143,8 +140,7 @@
|
||||
],
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"prompt_number": ""
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
@ -157,12 +153,11 @@
|
||||
"cell_type": "code",
|
||||
"collapsed": false,
|
||||
"input": [
|
||||
"print multivariate_gaussian(x,mu,cov)"
|
||||
"print(multivariate_gaussian(x,mu,cov))"
|
||||
],
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"prompt_number": ""
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
@ -175,13 +170,14 @@
|
||||
"cell_type": "code",
|
||||
"collapsed": false,
|
||||
"input": [
|
||||
"from __future__ import print_function\n",
|
||||
"\n",
|
||||
"x = np.array([2,7])\n",
|
||||
"print multivariate_gaussian(x,mu,cov)"
|
||||
"print(multivariate_gaussian(x,mu,cov))"
|
||||
],
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"prompt_number": ""
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
@ -195,12 +191,16 @@
|
||||
"collapsed": false,
|
||||
"input": [
|
||||
"%matplotlib inline\n",
|
||||
"\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"import matplotlib.pylab as pylab\n",
|
||||
"from mpl_toolkits.mplot3d import Axes3D\n",
|
||||
"import numpy as np\n",
|
||||
"\n",
|
||||
"pylab.rcParams['figure.figsize'] = 12,6\n",
|
||||
"\n",
|
||||
"from mpl_toolkits.mplot3d import Axes3D\n",
|
||||
"\n",
|
||||
"xs, ys = arange(-8, 13, .75), arange(-8, 20, .75)\n",
|
||||
"xv, yv = meshgrid (xs, ys)\n",
|
||||
"xs, ys = np.arange(-8, 13, .75), np.arange(-8, 20, .75)\n",
|
||||
"xv, yv = np.meshgrid (xs, ys)\n",
|
||||
"\n",
|
||||
"zs = np.array([multivariate_gaussian(np.array([x,y]),mu,cov) \n",
|
||||
" for x,y in zip(np.ravel(xv), np.ravel(yv))])\n",
|
||||
@ -208,13 +208,12 @@
|
||||
"\n",
|
||||
"ax = plt.figure().add_subplot(111, projection='3d')\n",
|
||||
"ax.plot_wireframe(xv, yv, zv)\n",
|
||||
"show()\n",
|
||||
"plt.show()\n",
|
||||
"pylab.rcParams['figure.figsize'] = 6,4"
|
||||
],
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"prompt_number": ""
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
@ -232,28 +231,28 @@
|
||||
"import gaussian as g\n",
|
||||
"pylab.rcParams['figure.figsize'] = 12,4\n",
|
||||
"\n",
|
||||
"cov = array([[2,0],[0,2]])\n",
|
||||
"cov = np.array([[2,0],[0,2]])\n",
|
||||
"e = g.sigma_ellipse (cov, 2, 7)\n",
|
||||
"subplot(131)\n",
|
||||
"plt.subplot(131)\n",
|
||||
"g.plot_sigma_ellipse(e, '|2 0|\\n|0 2|')\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"cov = array([[2,0],[0,9]])\n",
|
||||
"cov = np.array([[2,0],[0,9]])\n",
|
||||
"e = g.sigma_ellipse (cov, 2, 7)\n",
|
||||
"subplot(132)\n",
|
||||
"plt.subplot(132)\n",
|
||||
"g.plot_sigma_ellipse(e, '|2 0|\\n|0 9|')\n",
|
||||
"\n",
|
||||
"subplot(133)\n",
|
||||
"cov = array([[2,1.2],[1.2,3]])\n",
|
||||
"plt.subplot(133)\n",
|
||||
"cov = np.array([[2,1.2],[1.2,3]])\n",
|
||||
"e = g.sigma_ellipse (cov, 2, 7)\n",
|
||||
"g.plot_sigma_ellipse(e,'|2 1.2|\\n|1.2 2|')\n",
|
||||
"show()\n",
|
||||
"plt.show()\n",
|
||||
"\n",
|
||||
"pylab.rcParams['figure.figsize'] = 6,4"
|
||||
],
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"prompt_number": ""
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
@ -299,12 +298,14 @@
|
||||
"cell_type": "code",
|
||||
"collapsed": false,
|
||||
"input": [
|
||||
"scatter ([1,2,3],[1,2,3]);xlim([0,4]);ylim([0,4]);show()"
|
||||
"plt.scatter ([1,2,3],[1,2,3])\n",
|
||||
"plt.xlim([0,4])\n",
|
||||
"plt.ylim([0,4])\n",
|
||||
"plt.show()"
|
||||
],
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"prompt_number": ""
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
@ -317,15 +318,15 @@
|
||||
"cell_type": "code",
|
||||
"collapsed": false,
|
||||
"input": [
|
||||
"scatter ([1,2,3],[1,2,3]);xlim([0,5]);ylim([0,5])\n",
|
||||
"plot([0,5],[0,5],'r')\n",
|
||||
"scatter ([4], [4], c='g', marker='s',s=200)\n",
|
||||
"show()"
|
||||
"plt.scatter ([1,2,3],[1,2,3])\n",
|
||||
"plt.xlim([0,5]); plt.ylim([0,5])\n",
|
||||
"plt.plot([0,5],[0,5],'r')\n",
|
||||
"plt.scatter ([4], [4], c='g', marker='s',s=200)\n",
|
||||
"plt.show()"
|
||||
],
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"prompt_number": ""
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
@ -338,19 +339,18 @@
|
||||
"cell_type": "code",
|
||||
"collapsed": false,
|
||||
"input": [
|
||||
"cov = array([[0.003,0], [0,12]])\n",
|
||||
"sigma = sigma=[0.5,1.,1.5,2]\n",
|
||||
"cov = np.array([[0.003,0], [0,12]])\n",
|
||||
"sigma=[0.5,1.,1.5,2]\n",
|
||||
"e1 = g.sigma_ellipses(cov, x=1, y=1, sigma=sigma)\n",
|
||||
"e2 = g.sigma_ellipses(cov, x=2, y=2, sigma=sigma)\n",
|
||||
"e3 = g.sigma_ellipses(cov, x=3, y=3, sigma=sigma)\n",
|
||||
"g.plot_sigma_ellipses([e1, e2, e3], axis_equal=True,x_lim=[0,4],y_lim=[0,15])\n",
|
||||
"plt.ylim([0,11])\n",
|
||||
"show()"
|
||||
"plt.show()"
|
||||
],
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"prompt_number": ""
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
@ -369,7 +369,7 @@
|
||||
"input": [
|
||||
"from matplotlib.patches import Ellipse\n",
|
||||
"\n",
|
||||
"cov = array([[1,1],[1,1.1]])\n",
|
||||
"cov = np.array([[1,1],[1,1.1]])\n",
|
||||
"ev = g.sigma_ellipses(cov, x=2, y=2, sigma=sigma)\n",
|
||||
"\n",
|
||||
"isct = Ellipse(xy=(2,2), width=.2, height=1.2, edgecolor='r', fc='None', lw=4)\n",
|
||||
@ -380,8 +380,7 @@
|
||||
],
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"prompt_number": ""
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
@ -413,22 +412,20 @@
|
||||
],
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"prompt_number": ""
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"collapsed": false,
|
||||
"input": [
|
||||
"cov = array([[7,4],[4,7.]])\n",
|
||||
"mu = array([0,0])\n",
|
||||
"x = array([0,0])\n",
|
||||
"print multivariate_gaussian(x,mu,cov)"
|
||||
"cov = np.array([[7,4],[4,7.]])\n",
|
||||
"mu = np.array([0,0])\n",
|
||||
"x = np.array([0,0])\n",
|
||||
"print(multivariate_gaussian(x,mu,cov))"
|
||||
],
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"prompt_number": ""
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
@ -443,8 +440,7 @@
|
||||
],
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"prompt_number": ""
|
||||
"outputs": []
|
||||
}
|
||||
],
|
||||
"metadata": {}
|
||||
|
||||
@ -1,7 +1,7 @@
|
||||
{
|
||||
"metadata": {
|
||||
"name": "",
|
||||
"signature": "sha256:d0bfbbe322bb5a6c6b2c484f5f8da49814c5c8d8ec1120813d02a9511ddf94a1"
|
||||
"signature": "sha256:35f19121bea1b617984e84b0fa7dca20a612d6302af68a65f9d7d24120786c2d"
|
||||
},
|
||||
"nbformat": 3,
|
||||
"nbformat_minor": 0,
|
||||
@ -38,12 +38,13 @@
|
||||
"cell_type": "code",
|
||||
"collapsed": false,
|
||||
"input": [
|
||||
"pos = array([.1, .1, .1, .1, .1, .1, .1, .1, .1, .1])"
|
||||
"import numpy as np\n",
|
||||
"\n",
|
||||
"pos = np.array([.1, .1, .1, .1, .1, .1, .1, .1, .1, .1])"
|
||||
],
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"prompt_number": ""
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
@ -56,12 +57,11 @@
|
||||
"cell_type": "code",
|
||||
"collapsed": false,
|
||||
"input": [
|
||||
"hallway = array([1, 1, 0, 0, 0, 0, 0, 0, 1, 0])"
|
||||
"hallway = np.array([1, 1, 0, 0, 0, 0, 0, 0, 1, 0])"
|
||||
],
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"prompt_number": ""
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
@ -75,8 +75,9 @@
|
||||
"collapsed": false,
|
||||
"input": [
|
||||
"%matplotlib inline\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"\n",
|
||||
"pos = array([0.333, 0.333, 0., 0., 0., 0., 0., 0., 0.333, 0.])\n",
|
||||
"pos = np.array([0.333, 0.333, 0., 0., 0., 0., 0., 0., 0.333, 0.])\n",
|
||||
"\n",
|
||||
"def bar_plot(pos):\n",
|
||||
" ax = plt.figure().gca()\n",
|
||||
@ -89,8 +90,7 @@
|
||||
],
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"prompt_number": ""
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
@ -110,8 +110,7 @@
|
||||
],
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"prompt_number": ""
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
@ -132,13 +131,12 @@
|
||||
"cell_type": "code",
|
||||
"collapsed": false,
|
||||
"input": [
|
||||
"pos = array([0,1,0,0,0,0,0,0,0,0])\n",
|
||||
"pos = np.array([0,1,0,0,0,0,0,0,0,0])\n",
|
||||
"print pos"
|
||||
],
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"prompt_number": ""
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
@ -168,7 +166,7 @@
|
||||
"collapsed": false,
|
||||
"input": [
|
||||
"def measure (pos, measure, p_hit, p_miss):\n",
|
||||
" q = array(pos, dtype=float)\n",
|
||||
" q = np.array(pos, dtype=float)\n",
|
||||
" for i in range(len(hallway)):\n",
|
||||
" if hallway[i] == measure:\n",
|
||||
" q[i] = pos[i] * p_hit\n",
|
||||
@ -176,7 +174,7 @@
|
||||
" q[i] = pos[i] * p_miss\n",
|
||||
" return q\n",
|
||||
"\n",
|
||||
"pos = array([0.2]*10)\n",
|
||||
"pos = np.array([0.2]*10)\n",
|
||||
"reading = 1 # 1 is 'door'\n",
|
||||
"pos = measure (pos, 1, .6, .2)\n",
|
||||
"\n",
|
||||
@ -185,8 +183,7 @@
|
||||
],
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"prompt_number": ""
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
@ -205,7 +202,7 @@
|
||||
" p[i] = p[i] / s\n",
|
||||
" \n",
|
||||
"def sense (pos, measure, p_hit, p_miss):\n",
|
||||
" q = array(pos, dtype=float)\n",
|
||||
" q = np.array(pos, dtype=float)\n",
|
||||
" for i in range(len(hallway)):\n",
|
||||
" if hallway[i] == measure:\n",
|
||||
" q[i] = pos[i] * p_hit\n",
|
||||
@ -215,7 +212,7 @@
|
||||
" return q\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"pos = array([0.2]*10)\n",
|
||||
"pos = np.array([0.2]*10)\n",
|
||||
"reading = 1 # 1 is 'door'\n",
|
||||
"pos = sense (pos, 1, .6, .2)\n",
|
||||
"\n",
|
||||
@ -225,8 +222,7 @@
|
||||
],
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"prompt_number": ""
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
@ -260,20 +256,19 @@
|
||||
" is to the left\n",
|
||||
" \"\"\"\n",
|
||||
" n = len(pos)\n",
|
||||
" result = array(pos, dtype=float)\n",
|
||||
" result = np.array(pos, dtype=float)\n",
|
||||
" for i in range(n):\n",
|
||||
" result[i] = pos[(i-move) % n]\n",
|
||||
" return result\n",
|
||||
" \n",
|
||||
"pos = array([.4, .1, .2, .3])\n",
|
||||
"pos = np.array([.4, .1, .2, .3])\n",
|
||||
"print 'pos before update =', pos\n",
|
||||
"pos = perfect_update(pos, 1)\n",
|
||||
"print 'pos after update =', pos\n"
|
||||
],
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"prompt_number": ""
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
@ -296,7 +291,7 @@
|
||||
"input": [
|
||||
"def update (pos, move, p_correct, p_under, p_over):\n",
|
||||
" n = len(pos)\n",
|
||||
" result = array(pos, dtype=float)\n",
|
||||
" result = np.array(pos, dtype=float)\n",
|
||||
" for i in range(n):\n",
|
||||
" result[i] = \\\n",
|
||||
" pos[(i-move) % n] * p_correct + \\\n",
|
||||
@ -304,14 +299,13 @@
|
||||
" pos[(i-move+1) % n] * p_under \n",
|
||||
" return result\n",
|
||||
"\n",
|
||||
"p = array([0,0,0,1,0,0,0,0])\n",
|
||||
"p = np.array([0,0,0,1,0,0,0,0])\n",
|
||||
"res = update (p, 2, .8, .1, .1)\n",
|
||||
"print res"
|
||||
],
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"prompt_number": ""
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
@ -324,14 +318,13 @@
|
||||
"cell_type": "code",
|
||||
"collapsed": false,
|
||||
"input": [
|
||||
"p = array([0, 0, .4, .6, 0, 0, 0, 0])\n",
|
||||
"p = np.array([0, 0, .4, .6, 0, 0, 0, 0])\n",
|
||||
"res = update (p, 2, .8, .1, .1)\n",
|
||||
"print res"
|
||||
],
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"prompt_number": ""
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
@ -350,8 +343,7 @@
|
||||
],
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"prompt_number": ""
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
@ -372,8 +364,7 @@
|
||||
],
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"prompt_number": ""
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
@ -394,8 +385,7 @@
|
||||
],
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"prompt_number": ""
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
@ -416,19 +406,16 @@
|
||||
"cell_type": "code",
|
||||
"collapsed": false,
|
||||
"input": [
|
||||
"p = array([.1]*10)\n",
|
||||
"p = sense (p, 1, .6, .2)\n",
|
||||
"print p\n",
|
||||
"p = update (p, 1, .8, .1, .1)\n",
|
||||
"print p\n",
|
||||
"bar_plot(p)\n",
|
||||
"\n",
|
||||
" "
|
||||
"p = np.array([.1]*10)\n",
|
||||
"p = sense(p, 1, .6, .2)\n",
|
||||
"print(p)\n",
|
||||
"p = update(p, 1, .8, .1, .1)\n",
|
||||
"print(p)\n",
|
||||
"bar_plot(p)"
|
||||
],
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"prompt_number": ""
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
@ -441,14 +428,13 @@
|
||||
"cell_type": "code",
|
||||
"collapsed": false,
|
||||
"input": [
|
||||
"p = sense (p, 1, .6, .2)\n",
|
||||
"print p\n",
|
||||
"p = sense(p, 1, .6, .2)\n",
|
||||
"print(p)\n",
|
||||
"bar_plot(p)"
|
||||
],
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"prompt_number": ""
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
@ -461,14 +447,13 @@
|
||||
"cell_type": "code",
|
||||
"collapsed": false,
|
||||
"input": [
|
||||
"p = update (p, 1, .8, .1, .1)\n",
|
||||
"p = sense (p, 0, .6, .2)\n",
|
||||
"bar_plot (p)"
|
||||
"p = update(p, 1, .8, .1, .1)\n",
|
||||
"p = sense(p, 0, .6, .2)\n",
|
||||
"bar_plot(p)"
|
||||
],
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"prompt_number": ""
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
@ -481,14 +466,13 @@
|
||||
"cell_type": "code",
|
||||
"collapsed": false,
|
||||
"input": [
|
||||
"p = update (p, 1, .8, .1, .1)\n",
|
||||
"p = sense (p, 0, .6, .2)\n",
|
||||
"bar_plot (p)"
|
||||
"p = update(p, 1, .8, .1, .1)\n",
|
||||
"p = sense(p, 0, .6, .2)\n",
|
||||
"bar_plot(p)"
|
||||
],
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"prompt_number": ""
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
@ -513,19 +497,18 @@
|
||||
"collapsed": false,
|
||||
"input": [
|
||||
"hallway = [1,0,1,0,0,1,0,1,0,0]\n",
|
||||
"pos = array([.1]*10)\n",
|
||||
"pos = np.array([.1]*10)\n",
|
||||
"measurements = [1,0,1,0,0]\n",
|
||||
"\n",
|
||||
"for m in measurements:\n",
|
||||
" pos = sense (pos, m, .6, .2)\n",
|
||||
" pos = update (pos, 1, .8, .1, .1)\n",
|
||||
" pos = sense(pos, m, .6, .2)\n",
|
||||
" pos = update(pos, 1, .8, .1, .1)\n",
|
||||
"bar_plot(pos)\n",
|
||||
"print pos"
|
||||
],
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"prompt_number": ""
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
@ -538,14 +521,13 @@
|
||||
"cell_type": "code",
|
||||
"collapsed": false,
|
||||
"input": [
|
||||
"pos = sense (pos, m, .6, .2)\n",
|
||||
"pos = update (pos, 1, .8, .1, .1)\n",
|
||||
"pos = sense(pos, m, .6, .2)\n",
|
||||
"pos = update(pos, 1, .8, .1, .1)\n",
|
||||
"bar_plot(pos)"
|
||||
],
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"prompt_number": ""
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
@ -561,14 +543,13 @@
|
||||
"measurements = [0,1,0,1,0,0]\n",
|
||||
"\n",
|
||||
"for m in measurements:\n",
|
||||
" pos = sense (pos, m, .6, .2)\n",
|
||||
" pos = update (pos, 1, .8, .1, .1)\n",
|
||||
" pos = sense(pos, m, .6, .2)\n",
|
||||
" pos = update(pos, 1, .8, .1, .1)\n",
|
||||
"bar_plot(pos)"
|
||||
],
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"prompt_number": ""
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
@ -606,11 +587,6 @@
|
||||
"**If you can understand this chapter you will be able to understand and implement Kalman filters** I cannot stress this enough. If anything is murky, go back and reread this chapter and play with the code. the rest of this book will build on the algorithms that we use here. If you don't intuitively understand why this histogram filter works, and can at least work through the math, you will have little success with the rest of the material. However, if you grasp the fundamental insight - multiplying probabilities when we measure, and shifting probabilities when we update leads to a converging solution - then you understand everything important you need to grasp the Kalman filter. "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"collapsed": false,
|
||||
@ -624,8 +600,7 @@
|
||||
],
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"prompt_number": ""
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
|
||||
Loading…
Reference in New Issue
Block a user