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neural_network/simple_adaline.py

@@ -1,59 +1,69 @@
 # This program implements the AND and OR gates using the Adaline algorithm.
-def weight_change_or(weight: list[float], threshold: float, learning_rate: float) -> list[float]:
- output = weight[0]*0 + weight[1]*0
+def weight_change_or(
+ weight: list[float], threshold: float, learning_rate: float
+) -> list[float]:
+ output = weight[0] * 0 + weight[1] * 0
  if output <= threshold:
- output_left = weight[0]*0 + weight[1]*1
+ output_left = weight[0] * 0 + weight[1] * 1
  if output_left >= threshold:
- output_left_down = weight[0]*1 + weight[1]*0
+ output_left_down = weight[0] * 1 + weight[1] * 0
  if output_left_down >= threshold:
- output_all = weight[0]*1 + weight[1]*1
+ output_all = weight[0] * 1 + weight[1] * 1
  if output_all >= threshold:
  return weight
  else:
- weight[0] = weight[0] + learning_rate*1*1
- weight[1] = weight[1] + learning_rate*1*1
+ weight[0] = weight[0] + learning_rate * 1 * 1
+ weight[1] = weight[1] + learning_rate * 1 * 1
  return weight_change_or(weight, threshold, learning_rate)
  else:
- weight[0] = weight[0] + learning_rate*1*1
- weight[1] = weight[1] + learning_rate*1*0
+ weight[0] = weight[0] + learning_rate * 1 * 1
+ weight[1] = weight[1] + learning_rate * 1 * 0
  return weight_change_or(weight, threshold, learning_rate)
  else:
- weight[0] = weight[0] + learning_rate*1*0
- weight[1] = weight[1] + learning_rate*1*1
+ weight[0] = weight[0] + learning_rate * 1 * 0
+ weight[1] = weight[1] + learning_rate * 1 * 1
  return weight_change_or(weight, threshold, learning_rate)
  else:
  threshold += learning_rate
  return weight_change_or(weight, threshold, learning_rate)
 
 
-def weight_change_and(weight: list[float], threshold: float, learning_rate: float) -> list[float]:
- output = weight[0]*0 + weight[1]*0
+def weight_change_and(
+ weight: list[float], threshold: float, learning_rate: float
+) -> list[float]:
+ output = weight[0] * 0 + weight[1] * 0
  if output <= threshold:
- output_left = weight[0]*0 + weight[1]*1
+ output_left = weight[0] * 0 + weight[1] * 1
  if output_left <= threshold:
- output_left_down = weight[0]*1 + weight[1]*0
+ output_left_down = weight[0] * 1 + weight[1] * 0
  if output_left_down <= threshold:
- output_all = weight[0]*1 + weight[1]*1
+ output_all = weight[0] * 1 + weight[1] * 1
  if output_all >= threshold:
  return weight
  else:
- weight[0] = weight[0] + (learning_rate*1*1)
- weight[1] = weight[1] + (learning_rate*1*1)
+ weight[0] = weight[0] + (learning_rate * 1 * 1)
+ weight[1] = weight[1] + (learning_rate * 1 * 1)
  return weight_change_and(weight, threshold, learning_rate)
  else:
- weight[0] = weight[0] - (learning_rate*1*1)
- weight[1] = weight[1] - (learning_rate*1*0)
+ weight[0] = weight[0] - (learning_rate * 1 * 1)
+ weight[1] = weight[1] - (learning_rate * 1 * 0)
  return weight_change_and(weight, threshold, learning_rate)
  else:
- weight[0] = weight[0] - (learning_rate*1*0)
- weight[1] = weight[1] - (learning_rate*1*1)
+ weight[0] = weight[0] - (learning_rate * 1 * 0)
+ weight[1] = weight[1] - (learning_rate * 1 * 1)
  return weight_change_and(weight, threshold, learning_rate)
  else:
  threshold += learning_rate
  return weight_change_and(weight, threshold, learning_rate)
 
 
-def and_gate(weight: list[float], input_a: int, input_b: int, threshold: float, learning_rate: float) -> int:
+def and_gate(
+ weight: list[float],
+ input_a: int,
+ input_b: int,
+ threshold: float,
+ learning_rate: float,
+) -> int:
  """
  This function implements the AND gate using the Adaline algorithm.
 
@@ -68,14 +78,20 @@ def and_gate(weight: list[float], input_a: int, input_b: int, threshold: float,
  int: The output of the AND gate.
  """
  weight = weight_change_and(weight, threshold, learning_rate)
- output = weight[0]*input_a + weight[1]*input_b
+ output = weight[0] * input_a + weight[1] * input_b
  if output >= threshold:
  return 1
  else:
  return 0
 
 
-def or_gate(weight: list[float], input_a: int, input_b: int, threshold: float, learning_rate: float) -> int:
+def or_gate(
+ weight: list[float],
+ input_a: int,
+ input_b: int,
+ threshold: float,
+ learning_rate: float,
+) -> int:
  """
  This function implements the OR gate using the Adaline algorithm.
 
@@ -90,7 +106,7 @@ def or_gate(weight: list[float], input_a: int, input_b: int, threshold: float, l
  int: The output of the OR gate.
  """
  weight = weight_change_or(weight, threshold, learning_rate)
- output = weight[0]*input_a + weight[1]*input_b
+ output = weight[0] * input_a + weight[1] * input_b
  if output >= threshold:
  return 1
  else:
@@ -104,5 +120,11 @@ def or_gate(weight: list[float], input_a: int, input_b: int, threshold: float, l
 input_a, input_b = input("Input the value of A and B:").split()
 input_a = int(input_a)
 input_b = int(input_b)
-print("\nThe output of OR is:", or_gate(weight, input_a, input_b, threshold, learning_rate))
-print("\nThe output of AND is:", and_gate(weight2, input_a, input_b, threshold, learning_rate))
+print(
+ "\nThe output of OR is:",
+ or_gate(weight, input_a, input_b, threshold, learning_rate),
+)
+print(
+ "\nThe output of AND is:",
+ and_gate(weight2, input_a, input_b, threshold, learning_rate),
+)