def calc_error_for_nearest_neighbors_2d(accurate_values, current_values, settings_obj):
error = []
#---turn the lists into dictionaries
mydict_of_acc = dictionarize(accurate_values)
mydict_of_cur = dictionarize(current_values);
for key in mydict_of_cur.keys(): #go through each key and cal error
if key in mydict_of_acc.keys():
#make sure ref1 is the small list
if len(mydict_of_acc[key]) > len(mydict_of_cur[key]):
ref1 = mydict_of_acc
ref2 = mydict_of_cur
else:
ref2 = mydict_of_acc
ref1 = mydict_of_cur
#get the list of indecies of nearest neighbors
indecies = nearest_neighbors_2d(ref2[key][:], ref1[key][:])
#calculate error
for i in range(len(indecies)):
error.append(tuple(numpy.subtract(ref2[key][i], ref1[key][indecies[i]])))
#error.append(find_dis(ref2[key][i], ref1[key][indecies[i]]))
if (len(error) == 0 and not(settings_obj.benchmark_name == "sift")):
print "assert(len(error)>0) unless benchmark =sift"
exit(0)
"""
if (settings.benchmark_name == "sift"):
if len(error) == 0: #the reason for it to be None is b/c in sift there is a
#possibility that the octaves (with sift points) don't overlap
error = float("inf")
"""
return error
def calc_error_for_nearest_neighbors(x_temp,y_temp):
assert(len(x_temp) >0)
assert(len(y_temp) >0)
if (len(x_temp) > len(y_temp)):
x_temp_temp = x_temp
x_temp = y_temp
y_temp = x_temp_temp
x = x_temp
y = y_temp
error = []
nearst_neighbors = nearest_neighbors_sorted(x,y)
print "here is x"
print x
print "here is y"
print y
for i in range(min(len(x), len(y))):
error.append(abs(x[i] - y[nearst_neighbors[i]]))
return error
