def standardised_euclidean_distance(data,data_instance1, data_instance2):
"""Returns the euclidean distance of two clusters in the n-dimensional space, which is defined by the number of their attributes,
weighted by the inverse of the corresponding attribute's variance.
"""
if len(data_instance1.feature_vector) != len(data_instance2.feature_vector):
print("The two points must be defined in the same dimensional space.")
return
column_list = pick_up_all_data_columns(data)
std_list = []
for i,column in enumerate(column_list):
if i != 0: #because the first column is always the data labels
std_list.append(standard_deviation(column))
sum_of_squares = 0.0
for i in range(0, len(data_instance1.feature_vector)):
sum_of_squares += 1/pow(std_list[i],2) * pow(data_instance1.feature_vector[i] - data_instance2.feature_vector[i], 2) #the weight here is the variance not the std
return sqrt(sum_of_squares)
def standardisation(data_list):
m = mean(data_list)
sd = standard_deviation(data_list)
return [((row-m)/sd) for row in data_list]