Python Perceptron

import random

# space after comments
# comment in the same line? double space  # comment
# CONSTANT = 1
# variable_name = function_name: Function and variable names should be lowercase, 
# with words separated by underscores as necessary to improve readability
# ClassName
# method_name.py
# 'if var == True:' >>> more pythonic is simply:
# 'if var:' or 'if not var:' for False condition

# comment me
class Train:    
    def __init__(self):
        self.train = [[10,5,3,False],[3,2,16,True],[4,15,11,False]]
        # Initialize training set
        self.fired = False  # activate when over thresh (threshold: don't be lazy :-) )
        self.threshold = 1.0 
        self.x1 = 1  # weights
        self.x2 = -1 
        self.x3 = 0.5
        self.learningRate = 0.1  # not used
        # true if fired state == answers in training set
        self.correct = [False,False,False] 
        self.desire = ''  # input value

    # comment me    
    def train_it(self):
        ''' 
        You can comment
        like this
        too
        ''' 
        # while one or more of the answers are wrong
        # while self.correct[1] == False or self.correct[0] == False or self.correct[2] == False:
        # use parenthesis to divide the logic, is easier to read and to understand
        while (not self.correct[1]) or (not self.correct[0]) or (not self.correct[2]):
            for i in range(3):
                # Perceptron formula
                # fire perceptron
                # You don't need the else in this case. However, leave it if it's more clear to you
                self.fired = True
                if (self.train[i][0]*self.x1+self.train[i][1]*self.x2+self.train[i][2]*self.x3 < self.threshold):
                # under threshold, dont fire
                    self.fired = False  

                if self.fired == self.train[i][3]:
                    # if fired state is correct, log it
                    self.correct[i] = True
                else:
                    # my subsitution for the learning rate
                    self.x1 += random.randrange(-1,2)
                    self.x2 += random.randrange(-1,2)
                    self.x3 += random.randrange(-1,2)

            # prints weights
            # using fstrings
            # prints should be in another method, learn about callback functions
            print(f"W1: {self.x1}")
            print(f"W2: {self.x2}")
            print(f"W3: {self.x3}")

        self.test()

    # what does this method do?             
    def test(self):
        # random point to test perceptron!
        self.test_lst = [3,20,8]
        # reset some values - useless comment!?
        self.fired = False
        self.correct = [False,False,False]

        # fire if over threshold
        self.fired = True
        if self.test_lst[0]*self.x1+self.test_lst[1]*self.x2 < self.threshold:
            self.fired = False

        print(f"Test Result: {self.fired}")
        self.desire = input("Was this your desired resu

import random

# Comment me
class Train:    
    def __init__(self):
        self.train = [[10, 5, 3, False], 
                      [3, 2, 16, True], 
                      [4, 15, 11, False]]
        # Initialize training set
        self.fired = False
        self.threshold = 1.0 
        self.weight = 1
        self.x2 = -1 
        self.x3 = 0.5
        self.correct = [False, False, False] 
        self.input_val = ''

    # Comment me    
    def train_it(self):
        # while one or more of the answers are wrong
        while not all(self.correct):
            # Perceptron formula
            for i in range(3):
                self.fired = True  # fire perceptron

                # under threshold, dont fire
                if ((self.train[i][0] * self.weight)
                   + (self.train[i][1] * self.x2)
                   + (self.train[i][2] * self.x3) < self.threshold):
                    self.fired = False  

                # if fired state is correct, log it
                if self.fired == self.train[i][3]:
                    self.correct[i] = True
                else:
                    # my subsitution for the learning rate
                    self.weight += random.randrange(-1,2)
                    self.x2 += random.randrange(-1,2)
                    self.x3 += random.randrange(-1,2)

            print(f"W1: {self.weight}")
            print(f"W2: {self.x2}")
            print(f"W3: {self.x3}")

        self.test()

    # Comment me            
    def test(self):
        self.test_lst = [3, 20, 8]  # random point to test perceptron!
        self.fired = False
        self.correct = [False, False, False]

        self.fired = True
        # under threshold, dont fire
        if ((self.test_lst[0]*self.weight)
           + (self.test_lst[1]*self.x2) < self.threshold):
            self.fired = False

        print(f"Test Result: {self.fired}")

        input_str = "Was this your desired result? "
        input_str += "'N' for NO. Anything else for YES: "
        self.input_val = input(input_str)

        # print the test result and ask the user if the perceptron was right. 
        # this would be useful in filtering spam
        if self.input_val.lower() == 'n':
            print("Okay! Training...")  # if it was not right, continue training
            self.train_it()


# main function called when module executed directly
# ex. python3 this_module.py
def main(): 
        ptron = Train()
        ptron.train_it()
        ptron.test()


if __name__ == '__main__':
    main()