nsaunier/traffic-intelligence: python/utils.py comparison

added kernel smoothing

author	Nicolas Saunier <nicolas.saunier@polymtl.ca>
date	Fri, 29 Nov 2013 02:38:41 -0500
parents	f738fa1b69f0
children	9a714f32fc9f

comparison

equal deleted inserted replaced

-:4970fa64f636
+:d40ad901b272
 #########################
 # maths section
 #########################
+# def kernelSmoothing(sampleX, X, Y, weightFunc, halfwidth):
+#     '''Returns a smoothed weighted version of Y at the predefined values of sampleX
+#     Sum_x weight(sample_x,x) * y(x)'''
+#     from numpy import zeros, array
+#     smoothed = zeros(len(sampleX))
+#     for i,x in enumerate(sampleX):
+#         weights = array([weightFunc(x,xx, halfwidth) for xx in X])
+#         if sum(weights)>0:
+#             smoothed[i] = sum(weights*Y)/sum(weights)
+#         else:
+#             smoothed[i] = 0
+#     return smoothed
+def kernelSmoothing(x, X, Y, weightFunc, halfwidth):
+'''Returns the smoothed estimate of (X,Y) at x
+Sum_x weight(sample_x,x) * y(x)'''
+from numpy import zeros, array
+weights = array([weightFunc(x,observedx, halfwidth) for observedx in X])
+if sum(weights)>0:
+return sum(weights*Y)/sum(weights)
+else:
+return 0
+def uniform(center, x, halfwidth):
+if abs(center-x)<halfwidth:
+return 1.
+else:
+return 0.
+def gaussian(center, x, halfwidth):
+from numpy import exp
+return exp(-((center-x)/halfwidth)**2/2)
+def epanechnikov(center, x, halfwidth):
+diff = abs(center-x)
+if diff<halfwidth:
+return 1.-(diff/halfwidth)**2
+else:
+return 0.
 def argMaxDict(d):
 return max(d.iterkeys(), key=(lambda key: d[key]))
 def framesToTime(nFrames, frameRate, initialTime = time()):

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