IA 2022-11-16

# distance_metrics_v3.py
import math

import numpy as np

"""
Python implementation of some distance metrics
Manhattan
Euclidian
Minkowski

These metrics are used by several higher level
algorithms, namely KNN, for Machine Learning
"""

def dManhattan_v0 (pSample1, pSample2):
    bCaution = len(pSample1)==len(pSample2)
    if (bCaution):
        sum = 0
        for idx in range(len(pSample1)):
            e1 = pSample1[idx]
            e2 = pSample2[idx]
            diff = e2-e1
            if(diff<0):
                diff=-1*diff
            # if
            sum+=diff
        # for
        return sum
    # if caution
    return False
# def dManhattan_v0

# using numpy
def dManhattan_np(pS1, pS2):
    pS1 = np.array(pS1)
    pS2 = np.array(pS2)
    diff:np.ndarray = np.abs(pS2-pS1) # array with the differences, component-by-component
    return diff.sum()
# def dManhattan_np

def dEuclidian_v0 (pS1, pS2):
    bCaution = len(pS1)==len(pS2)
    if(bCaution):
        sum = 0
        for address in range(len(pS1)):
            e1 = pS1[address]
            e2 = pS2[address]
            diff = (e2-e1)**2
            sum+=diff
        # for
        return math.sqrt(diff)
    # if
    return False
# def dEuclidian_v0

# using numpy
def dEuclidian_np(pS1, pS2):
    pS1=np.array(pS1)
    pS2=np.array(pS2)
    diff = (pS2-pS1)**2
    theSum = diff.sum()
    return math.sqrt(theSum)
# def dEuclidian_np

def feedback(pS1, pS2, pMetric, pDescription):
    print("sample1: ", pS1)
    print("sample2: ", pS2)
    distance = pMetric(pS1, pS2)
    print(f"distance by {pDescription} = {distance}")
# def feedback

def dMinkowski_np(pS1, pS2, pN=2):
    bCaution = len(pS1)==len(pS2)
    if(bCaution):
        pS1 = np.array(pS1)
        pS2 = np.array(pS2)
        diff = pS2-pS1
        diff = diff**pN
        theSum = diff.sum()
        return theSum**(1/pN)
    # if
    return False
# def dMinkowski_np

s1 = [10, 11, 13, 15, 17]
s2 = [10, 9, 11, 18, 14]
feedback(s1, s1, dManhattan_v0, "dManhattan_v0")
feedback(s1, s2, dManhattan_v0, "dManhattan_v0")

feedback(s1, s1, dManhattan_np, "dManhattan_np")
feedback(s1, s2, dManhattan_np, "dManhattan_np")

feedback(s1, s1, dEuclidian_v0, "dEuclidian_v0")
feedback(s1, s2, dEuclidian_v0, "dEuclidian_v0")

feedback(s1, s1, dEuclidian_np, "dEuclidian_np")
feedback(s1, s2, dEuclidian_np, "dEuclidian_np")

feedback(s1, s1, dMinkowski_np, "dMinkowski_np")
feedback(s1, s2, dMinkowski_np, "dMinkowski_np")