Perceptron classifier function

perceptron.py

import numpy as np

def perceptron(data, label, iteration, y):
	'''
	The perceptron classifier

	Arguments
	data: This is the training data with a certain shape (x, y), meaning there are x samples and each sample has y features.
	label: This is the training data's label with shape (x, 1). The 1 corresponds to the correct classification of the data.
	iteration: This is the number of times the function will iterate.
	
	Returns
	w: This is the separator with the shape (1, y).
	'''
  
	w = np.zeros((1,y))
	for i in range(iteration):
		row = data[i]   # getting the row from the data
		row_tr = np.transpose(row) # transposing the row for using the dot product
		if (np.sign(np.dot(w, row_tr)) != label[i]): # check if the sign is negative, not if update the separator
			w = w + np.dot(row, label[i])
	return w