Interim check in.

dog_track_1d is just a file to test code before putting it into the
Kalman filter chapter.

dog_track_1d.py

@@ -0,0 +1,35 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Wed Apr 30 10:35:19 2014
+
+@author: rlabbe
+"""
+import numpy.random as random
+
+class dog_sensor(object):
+    def __init__(self, x0 = 0, motion=1, noise=0.0):
+        self.x      = x0
+        self.motion = motion
+        self.noise  = math.sqrt(noise)
+
+    def sense(self):
+        self.x = self.x + self.motion
+        self.x += random.randn() * self.noise
+        return self.x
+
+
+def measure_dog ():
+    if not hasattr(measure_dog, "x"):
+        measure_dog.x = 0
+        measure_dog.motion = 1
+
+
+if __name__ == '__main__':
+
+    dog = dog_sensor(noise = 1)
+    for i in range(10):
+        print (dog.sense())
+
+
+
+

gaussian.py

@@ -1,6 +1,6 @@
 import numpy as np
 import math
-
+import matplotlib.pyplot as plt
 
 def _to_array(x):
     """ returns any of a scalar, matrix, or array as a 1D numpy array
@@ -63,6 +63,14 @@ def multivariate_gaussian (x, mu, cov):
     fprime = (x - mu)**2
     return frac * np.exp(-0.5*np.dot(fprime, 1./np.diag(cov)))
 
+def norm_plot (mean, var):
+    min_x = mean - var * 1.5
+    max_x = mean + var * 1.5
+
+    xs = np.arange (min_x, max_x, 0.1)
+    ys = [gaussian (x,23,5) for x in xs]
+    plt.plot (xs,ys)
+    plt.show()
 
 if __name__ == '__main__':
     from scipy.stats import norm