Update simple_adaline.py

neural_network/simple_adaline.py

@@ -1,69 +1,86 @@
 # 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]:
+ """
+ This function updates the weights for the OR gate using the Adaline algorithm.
+
+ Args:
+ weight (list[float]): The weights for the Adaline algorithm.
+ threshold (float): The threshold value for the Adaline algorithm.
+ learning_rate (float): The learning rate for the Adaline algorithm.
+
+ Returns:
+ list[float]: The updated weights for the OR gate.
+ >>> weight_change_or([1.2, 0.6], 1, 0.5)
+ [1.2, 1.1]
+ """
+ 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]:
+ """
+ This function updates the weights for the AND gate using the Adaline algorithm.
+
+ Args:
+ weight (list[float]): The weights for the Adaline algorithm.
+ threshold (float): The threshold value for the Adaline algorithm.
+ learning_rate (float): The learning rate for the Adaline algorithm.
+
+ Returns:
+ list[float]: The updated weights for the AND gate.
+ >>> weight_change_and([1.2, 0.6], 1, 0.5)
+ [0.7, 0.1]
+ """
+ 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.
 
@@ -76,22 +93,24 @@ def and_gate(
 
  Returns:
  int: The output of the AND gate.
+ >>> and_gate([1.2, 0.6], 0, 0, 1, 0.5)
+ 0
+ >>> and_gate([1.2, 0.6], 0, 1, 1, 0.5)
+ 0
+ >>> and_gate([1.2, 0.6], 1, 0, 1, 0.5)
+ 0
+ >>> and_gate([1.2, 0.6], 1, 1, 1, 0.5)
+ 1
  """
  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.
 
@@ -104,27 +123,28 @@ def or_gate(
 
  Returns:
  int: The output of the OR gate.
+ >>> or_gate([1.2, 0.6], 0, 0, 1, 0.5)
+ 0
+ >>> or_gate([1.2, 0.6], 0, 1, 1, 0.5)
+ 1
+ >>> or_gate([1.2, 0.6], 1, 0, 1, 0.5)
+ 1
+ >>> or_gate([1.2, 0.6], 1, 1, 1, 0.5)
+ 1
  """
  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:
  return 0
 
-
 weight = [1.2, 0.6]
 weight2 = [1.2, 0.6]
 threshold = 1
 learning_rate = 0.5
 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))