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Change to predict/update terminology.

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I originally used sense/update as my function names. But all the
literature uses update/predict, so I am normalizing on that.

I also wrote a test to ensure the update/predict functions in the
1d kalman filter chapter gives the same output as the full blown
kalman filter. Of course, they do. This is in the 1d_kf_compare
notebook. I will add this as an exercise in the multidimensional KF
chapter.
This commit is contained in:
Roger Labbe 2014-08-29 22:38:59 -07:00
parent a12322e2ef
commit 87d4d973ac
3 changed files with 397 additions and 444 deletions
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"Compare 1d kalman filter with full kalman filter with only 1 observed state variable."
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"from __future__ import division\n",
"import numpy as np\n",
"\n",
"def predict(pos, variance, movement, movement_variance):\n",
" return (pos + movement, variance + movement_variance)\n",
" \n",
"def update(mean, variance, measurement, measurement_variance):\n",
" return multiply(mean, variance, measurement, measurement_variance)\n",
" \n",
"def multiply(mu1, var1, mu2, var2):\n",
" mean = (var1*mu2 + var2*mu1) / (var1+var2)\n",
" variance = 1 / (1/var1 + 1/var2)\n",
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"from numpy.random import randn\n",
"from filterpy.kalman import KalmanFilter\n",
"\n",
"f = KalmanFilter(dim_x=1, dim_z=1, dim_u=1)\n",
"f.P = 500\n",
"f.H = np.array([[1.]])\n",
"f.F = np.array([[1.]])\n",
"f.B = np.array([[1.]])\n",
"f.Q = 3\n",
"f.R = 5\n",
"\n",
"pos = (0., 500.)\n",
"\n",
"for i in range(100):\n",
" z = i + randn()\n",
" pos = update(pos[0], pos[1], z, 5)\n",
" f.update(z)\n",
" \n",
" assert abs(pos[0]- f.x[0,0]) < 1.e-12\n",
" \n",
" pos = predict(pos[0], pos[1], 1., 3.)\n",
" f.predict(u=1)\n",
" print pos[0], f.x[0,0]"
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