ValueError: non-broadcastable output operand with shape (3,1) doesn't match the broadcast shape (3,4)

Change self.synaptic_weights += adjustment to

self.synaptic_weights = self.synaptic_weights + adjustment

---

self.synaptic_weights must have a shape of (3,1) and adjustment must have a shape of (3,4). While the shapes are broadcastable numpy must not like trying to assign the result with shape (3,4) to an array of shape (3,1)

a = np.ones((3,1))
b = np.random.randint(1,10, (3,4))

>>> a
array([[1],
       [1],
       [1]])
>>> b
array([[8, 2, 5, 7],
       [2, 5, 4, 8],
       [7, 7, 6, 6]])

>>> a + b
array([[9, 3, 6, 8],
       [3, 6, 5, 9],
       [8, 8, 7, 7]])

>>> b += a
>>> b
array([[9, 3, 6, 8],
       [3, 6, 5, 9],
       [8, 8, 7, 7]])
>>> a
array([[1],
       [1],
       [1]])

>>> a += b
Traceback (most recent call last):
  File "<pyshell#24>", line 1, in <module>
    a += b
ValueError: non-broadcastable output operand with shape (3,1) doesn't match the broadcast shape (3,4)

The same error occurs when using numpy.add and specifying a as the output array

>>> np.add(a,b, out = a)
Traceback (most recent call last):
  File "<pyshell#31>", line 1, in <module>
    np.add(a,b, out = a)
ValueError: non-broadcastable output operand with shape (3,1) doesn't match the broadcast shape (3,4)
>>> 

A new a needs to be created

>>> a = a + b
>>> a
array([[10,  4,  7,  9],
       [ 4,  7,  6, 10],
       [ 9,  9,  8,  8]])
>>>