Making AND, OR, NAND logic with Python's Stochastic Gradient Descent, function problems

This problem is about making a AND, OR, NAND logic gate on Python with the Stochastic Gradient Descent algorithm and concept of Perceptron. So, the thing is how to make a valid code with the custom function called SGD. For the whole code,

import numpy as np

class perceptron: 
  def __init__(self, w):
    self.w = w
  
  def output(self, x):
    y_tmp = np.dot(self.w, np.append([1], x))
    return 1.0 * (y_tmp > 0) #output = [1, w1, w2], stored as gate.w

# Your function goes here

x_list = [[0,0], [0,1],[1,0],[1,1]]
t_and = [-1, -1, -1, 1]
t_or = [-1,1,1,1]
t_nand = [1,1,1,-1]

w_init = [0,0,0] #initialize the concept
and_gate = perceptron(w_init)
SGD(and_gate, x_list, t_and)

or_gate = perceptron(w_init)
SGD(or_gate, x_list, t_or)

nand_gate = perceptron(w_init)
SGD(nand_gate, x_list,t_nand)

print('=== AND gate ===')
for x in x_list:
  print(x, '=>' ,and_gate.output(x))

print('=== OR gate ===')
for x in x_list:
  print(x, '=>' ,or_gate.output(x))

print('=== NAND gate ===')
for x in x_list:
  print(x, '=>' ,nand_gate.output(x))

What I have tried is that since the algorithm needs some epoch to evaluate their limitaitons, and learning rate derived from this mathematical logic, I made this code like this on where we should put our function, then ran it. Stochastic Gradient Descent mathematical logic

def SGD(gate, x_list, t):
    eta = 0.001
    for epoch in range(100):
      for i,x  in enumerate(x_list):
        gate.w[1:] += eta * (t[i]-gate.output(x)) * t[i]
        gate.w[0] += eta * (t[i]-gate.output(x))

It made some progress, but it doesn't fit to the concept where the and, or, nand gate operates, like

=== AND gate ===
[0, 0] => 0.0
[0, 1] => 1.0
[1, 0] => 1.0
[1, 1] => 1.0
=== OR gate ===
[0, 0] => 0.0
[0, 1] => 1.0
[1, 0] => 1.0
[1, 1] => 1.0
=== NAND gate ===
[0, 0] => 0.0
[0, 1] => 1.0
[1, 0] => 1.0
[1, 1] => 1.0

According to the logical gate, should I make the if statement to fit all the needs of the logical gate of or and nand? Feel free to ask, or advise me. I wish you have a good time today.

Try this.

import numpy as np

class Perceptron:
    def __init__(self, w):
        self.w = w
        
    def output(self, x):
        y_tmp = np.dot(self.w, np.append([1], x))
        return 1.0 * (y_tmp > 0)

def SGD(gate, x_list, t, learning_rate=0.01, epochs=100):
    for epoch in range(epochs):
        for i in range(len(x_list)):
            x = np.append([1], x_list[i])
            y = np.dot(gate.w, x)
            if y * t[i] <= 0:
                gate.w += learning_rate * t[i] * x

x_list = [[0, 0], [1, 0], [0, 1], [1, 1]]
t_and = [-1, -1, -1, 1]
t_or = [-1, 1, 1, 1]
t_nand = [1, 1, 1, -1]

w_init = np.array([0.0, 0.0, 0.0])
and_gate = Perceptron(w_init)
SGD(and_gate, x_list, t_and)

or_gate = Perceptron(w_init.copy())
SGD(or_gate, x_list, t_or)

nand_gate = Perceptron(w_init.copy())
SGD(nand_gate, x_list, t_nand)

print('=== AND gate ===')
for x in x_list:
    print(x, '->', and_gate.output(x))
    
print('=== OR gate ===')
for x in x_list:
    print(x, '->', or_gate.output(x))
    
print('=== NAND gate ===')
for x in x_list:
    print(x, '->', nand_gate.output(x))

Hope this helps.