from perceptronClass import Perceptron
from readData import ReadData
from drawPoints import DrawPoints
p = Perceptron()
inputs = [-1, 0.5]
print(p.guess(inputs))
readData = ReadData()
readData.read()
drawPoints = DrawPoints()
drawPoints.draw()
for x, y, z in zip(readData.x, readData.y, readData.z):
inputs = [x, y]
p.train(inputs, z)
from points import *
from readData import ReadData
import csv
from kmeans import *
# read the data first
r = ReadData('exercise-1.csv')
data = r.read()
# print (data)
# make points array
points = []
for arr in data:
p = Point(arr[0], arr[1])
points.append(p)
# generate random centroids
k = Kmeans(2, points)
k.gen_random_centroids()
while not k.converge():
k.group_points()
k.reassign()
for c in k.centroids:
x = []
y = []
for p in k.data:
if p.get_centroid().get_x() == c.get_x() and p.get_centroid().get_y(
) == c.get_y():