5. step3.py

import codecademylib3_seaborn
import matplotlib.pyplot as plt
import numpy as np
from sklearn import datasets
from copy import deepcopy

iris = datasets.load_iris()

samples = iris.data
samples = iris.data

x = samples[:,0]
y = samples[:,1]

sepal_length_width = np.array(list(zip(x, y)))

# Step 1: Place K random centroids

k = 3

centroids_x = np.random.uniform(min(x), max(x), size=k)
centroids_y = np.random.uniform(min(y), max(y), size=k)

centroids = np.array(list(zip(centroids_x, centroids_y)))

# Step 2: Assign samples to nearest centroid

def distance(a, b):
  one = (a[0] - b[0]) **2
  two = (a[1] - b[1]) **2
  distance = (one+two) ** 0.5
  return distance

# Cluster labels for each point (either 0, 1, or 2)
labels = np.zeros(len(samples))

# Distances to each centroid
distances = np.zeros(k)

for i in range(len(samples)):
  distances[0] = distance(sepal_length_width[i], centroids[0])
  distances[1] = distance(sepal_length_width[i], centroids[1])
  distances[2] = distance(sepal_length_width[i], centroids[2])
  cluster = np.argmin(distances)
  labels[i] = cluster

# Step 3: Update centroids

centroids_old = deepcopy(centroids)

for i in range(k):
  points = [sepal_length_width[j] for j in range(len(sepal_length_width)) if labels[j] == i]
  centroids[i] = np.mean(points, axis=0)
  
print(centroids_old)
print("- - - - - - - - - - - - - -")
print(centroids)