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helperFunctions.py
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helperFunctions.py
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# -*- coding: utf-8 -*-
"""
Created on Mon Jan 11 22:24:38 2021
@author: Roneet
"""
import pandas as pd
import numpy as np
import os
from sklearn.metrics.pairwise import euclidean_distances
def read_csv_file(csv_file):
data_frame= None
if os.path.exists(csv_file):
data_frame = pd.read_csv(csv_file)
return data_frame
def standardize(array):
array = np.array(array)
standard_deviation = np.std(array)
mean = np.mean(array)
standardized_array = []
for element in array:
standardized_element = (element - mean)/standard_deviation
standardized_array.append(standardized_element)
return standardized_array
def cluster_assignment(matrix, centers):
min_distance_index = np.argmin(euclidean_distances(matrix, centers), axis = 1)
return min_distance_index
def calculate_starting_centroids(K, matrix):
centers = []
for array in np.array_split(matrix, K):
center = array.mean(0)
center = center.tolist()
# print('center',center)
centers.append(center)
return centers
def write_csv_file(csv_filename, data_frame):
data_frame.to_csv(csv_filename)
print(csv_filename + " created.")