从头开始的双向关联存储器 (BAM) 实现

先决条件： ANN |双向联想记忆 (BAM) 学习算法
为了实现 BAM 模型，这里有一些基本的考虑和方法——

1. 考虑 M 的值，因为 BAM 将由 M 对模式构成。这里 M 的值为 4。
2. A 组：输入模式
3. B组：对应的目标模式
4. 分配输入和输出层的神经元。这里，输入层的神经元为 6，输出层的神经元为 3
5. 使用 BAM 算法计算权重矩阵
6. 测试 BAM 模型的输入模式，该模式将返回相应的目标模式作为输出。对于每个目标模式，BAM 模型将返回相应的输入模式。

BAM 的Python实现：

Python3

# Import Python Libraries
import numpy as np
 
# Take two sets of patterns:
# Set A: Input Pattern
x1 = np.array([1, 1, 1, 1, 1, 1]).reshape(6, 1)
x2 = np.array([-1, -1, -1, -1, -1, -1]).reshape(6, 1)
x3 = np.array([1, 1, -1, -1, 1, 1]).reshape(6, 1)
x4 = np.array([-1, -1, 1, 1, -1, -1]).reshape(6, 1)
 
# Set B: Target Pattern
y1 = np.array([1, 1, 1]).reshape(3, 1)
y2 = np.array([-1, -1, -1]).reshape(3, 1)
y3 = np.array([1, -1, 1]).reshape(3, 1)
y4 = np.array([-1, 1, -1]).reshape(3, 1)
 
'''
print("Set A: Input Pattern, Set B: Target Pattern")
print("\nThe input for pattern 1 is")
print(x1)
print("\nThe target for pattern 1 is")
print(y1)
print("\nThe input for pattern 2 is")
print(x2)
print("\nThe target for pattern 2 is")
print(y2)
print("\nThe input for pattern 3 is")
print(x3)
print("\nThe target for pattern 3 is")
print(y3)
print("\nThe input for pattern 4 is")
print(x4)
print("\nThe target for pattern 4 is")
print(y4)
 
print("\n------------------------------")
'''
# Calculate weight Matrix: W
inputSet = np.concatenate((x1, x2, x3, x4), axis = 1)
targetSet = np.concatenate((y1.T, y2.T, y3.T, y4.T), axis = 0)
print("\nWeight matrix:")
weight = np.dot(inputSet, targetSet)
print(weight)
 
print("\n------------------------------")
 
# Testing Phase
# Test for Input Patterns: Set A
print("\nTesting for input patterns: Set A")
def testInputs(x, weight):
  # Multiply the input pattern with the weight matrix
  # (weight.T X x)
  y = np.dot(weight.T, x)
  y[y < 0] = -1
  y[y >= 0] = 1
  return np.array(y)
 
print("\nOutput of input pattern 1")
print(testInputs(x1, weight))
print("\nOutput of input pattern 2")
print(testInputs(x2, weight))
print("\nOutput of input pattern 3")
print(testInputs(x3, weight))
print("\nOutput of input pattern 4")
print(testInputs(x4, weight))
 
# Test for Target Patterns: Set B
print("\nTesting for target patterns: Set B")
def testTargets(y, weight):
  # Multiply the target pattern with the weight matrix
  # (weight X y)
  x = np.dot(weight, y)
  x[x <= 0] = -1
  x[x > 0] = 1
  return np.array(x)
 
print("\nOutput of target pattern 1")
print(testTargets(y1, weight))
print("\nOutput of target pattern 2")
print(testTargets(y2, weight))
print("\nOutput of target pattern 3")
print(testTargets(y3, weight))
print("\nOutput of target pattern 4")
print(testTargets(y4, weight))

输出：

Weight matrix:
[[4 0 4]
 [4 0 4]
 [0 4 0]
 [0 4 0]
 [4 0 4]
 [4 0 4]]

------------------------------

Testing for input patterns: Set A

Output of input pattern 1
[[1]
 [1]
 [1]]

Output of input pattern 2
[[-1]
 [-1]
 [-1]]

Output of input pattern 3
[[ 1]
 [-1]
 [ 1]]

Output of input pattern 4
[[-1]
 [ 1]
 [-1]]

Testing for target patterns: Set B

Output of target pattern 1
[[1]
 [1]
 [1]
 [1]
 [1]
 [1]]

Output of target pattern 2
[[-1]
 [-1]
 [-1]
 [-1]
 [-1]
 [-1]]

Output of target pattern 3
[[ 1]
 [ 1]
 [-1]
 [-1]
 [ 1]
 [ 1]]

Output of target pattern 4
[[-1]
 [-1]
 [ 1]
 [ 1]
 [-1]
 [-1]]

在这里，对于每个输入模式，BAM 模型给出了正确的目标模式，对于目标模式，模型也给出了相应的输入模式。
因此，这表明 BAM 模型得到了正确实现。