fixed spelling of directory

experiments/gauss.py

@@ -0,0 +1,78 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Tue Apr 22 11:38:49 2014
+
+@author: rlabbe
+"""
+from __future__ import division, print_function
+import math
+import matplotlib.pyplot as plt
+import numpy.random as random
+
+
+class gaussian(object):
+    def __init__(self, mean, variance):
+        try:
+            self.mean     = float(mean)
+            self.variance = float(variance)
+
+        except:
+            self.mean     = mean
+            self.variance = variance
+
+    def __add__ (a, b):
+       return gaussian (a.mean + b.mean, a.variance + b.variance)
+
+    def __mul__ (a, b):
+        m = (a.variance*b.mean + b.variance*a.mean) / (a.variance + b.variance)
+        v = 1. / (1./a.variance + 1./b.variance)
+        return gaussian (m, v)
+
+    def __call__(self, x):
+        """ Impl
+        """
+        return math.exp (-0.5 * (x-self.mean)**2 / self.variance) / \
+               math.sqrt(2.*math.pi*self.variance)
+
+
+    def __str__(self):
+        return "(%f, %f)" %(self.mean, self.sigma)
+
+    def stddev(self):
+        return math.sqrt (self.variance)
+
+    def as_tuple(self):
+        return (self.mean, self.variance)
+
+    def __tuple__(self):
+        return (self.mean, self.variance)
+
+    def __getitem__ (self,index):
+        """ maybe silly, allows you to access obect as a tuple:
+        a = gaussian(3,4)
+        print (tuple(a))
+        """
+        if index == 0:   return self.mean
+        elif index == 1: return self.variance
+        else:            raise StopIteration
+
+class KF1D(object):
+    def __init__ (self, pos, sigma):
+        self.estimate = gaussian(pos,sigma)
+
+    def update(self, Z,var):
+        self.estimate = self.estimate * gaussian (Z,var)
+
+    def predict(self, U, var):
+        self.estimate = self.estimate + gaussian (U,var)
+
+
+
+
+def mul2 (a, b):
+    m = (a['variance']*b['mean'] + b['variance']*a['mean']) / (a['variance'] + b['variance'])
+    v = 1. / (1./a['variance'] + 1./b['variance'])
+    return gaussian (m, v)
+
+
+#varying_error_kf( noise_factor=100)