Different ways to calculate the euclidean distance in python

There are already many ways to do the euclidean distance in python, you don’t need to do it actually. But it is a very good exercise for programming as long as you do it by yourself.

summary:

• no numpy
• numpy.dot(vector.T, vector)
• numpy.linalg.norm(vector, order, axis)
• scipy.spatial.distance.euclidean(vector1, vector2)
• matplotlib.pyplot.mlab.dist()
• It is also important to care about the Error Handling, this is useful link to learn deal with errors.
  • python tutorial
  • python build-in exceptions
  • python raise-statement quick reference
  • python exception handling with many examples

I wrote the following script, just try it!

import numpy as np
import sys
import math
import matplotlib.pyplot as plt
from scipy.spatial import distance

vector1 = [3.0, 104.0]
vector2 = [18.0, 90.0]
vectorOther = [1.0, 2.0, 3.0]

v1 = np.array(vector1)
v2 = np.array(vector2)
vOther = np.array(vectorOther)

def diff_Length_Error():
    raise RuntimeWarning("The length of the two vectors are not the same!")

def euclidean0_0 (vector1, vector2):
''' calculate the euclidean distance
    input: numpy.arrays or lists
    return: 1. quard distance, 2. euclidean distance
'''
    quar_distance = 0
    try:
        if(len(vector1) != len(vector2)):
            diff_Length_Error()
        zipVector = zip(vector1, vector2)

        for member in zipVector:
            quar_distance += (member[1] - member[0]) ** 2

        return quar_distance, math.sqrt(quar_distance)

    except Exception, err:
        sys.stderr.write('WARNING: %s\n' % str(err))
        return -1, -1

def euclidean0_1(vector1, vector2):
    '''calculate the euclidean distance, no numpy
    input: numpy.arrays or lists
    return: euclidean distance
    '''
    dist = [(a - b)**2 for a, b in zip(vector1, vector2)]
    dist = math.sqrt(sum(dist))
    return dist

def euclidean2(vector1, vector2):
    '''calculate the euclidean distance, use numpy.dot() function
    input: numpy.arrays or lists
    return: euclidean distance
    '''
    try:
        if type(vector1) == list:
            vector1 = np.array(vector1)
        if type(vector2) == list:
            vector2 = np.array(vector2)
        diff = vector2 - vector1
        squareDistance = np.dot(diff.T, diff)
        return squareDistance, math.sqrt(squareDistance)
    except TypeError as e:
        print "Type error: {}".format(e.message)
        raise
    except ValueError as e:
        print "Value error: {}".format(e.message)
        raise
    except:
        print "Unexpected error:", sys.exc_info()[0]
        raise

def euclidean3(vector1, vector2):
    ''' use numpy.linalg.norm to calculate the euclidean distance. '''
    vector1, vector2 = list_to_npArray(vector1, vector2)
    distance = np.linalg.norm(vector1-vector2, 2, 0) # the third argument "0" means the column, and "1" means the line.
    return distance

def euclidean4(vector1, vector2):
    ''' use scipy to calculate the euclidean distance. '''
    dist = distance.euclidean(vector1, vector2)
    return dist

def euclidean5(vector1, vector2):
    ''' use matplotlib.mlab to calculate the euclidean distance. '''
    vector1, vector2 = list_to_npArray(vector1, vector2)
    dist = plt.mlab.dist(vector1, vector2)
    return dist

def list_to_npArray(vector1, vector2):
    '''convert the list to numpy array'''
    if type(vector1) == list:
        vector1 = np.array(vector1)
    if type(vector2) == list:
        vector2 = np.array(vector2)
    return vector1, vector2