NumPy Won't Append Arrays
<p>I'm currently working on a neural network to play Rock-Paper-Scissors, but I've run into an enormous issue.</p>

<p>I'm having the neural network predict what will happen next based on a history of three moves, where with every move by the human, <strong>a new list is made in an array that contains the two previous moves and the new one</strong>. The neural network then trains and learns off of this. My code for that can be found below.</p>

<pre><code>#add new situation, with what is currently happening to make current prediction with adjusted weights

current_turn = np.array([[input_data[len(input_data) - 1][1], input_data[len(input_data) - 1][2], output_data[len(output_data) - 1][0]]])
np.append(input_data, current_turn, axis = 0)
</code></pre>

<p>I'm using the Python system NumPy, and it is refusing to append these two arrays, such that the neural network isn't learning.</p>

<p><em>Edit</em>: One of the responses recognized that one must reassign the array to this newly appended array. When I tried this later on, as shown below, it once again would not work.</p>

<pre><code>if human_choice == "r":
        output_data = np.append(output_data, ([0]))
elif human_choice == "p":
        output_data = np.append(output_data, ([0.5]))
elif human_choice == "s":
        output_data = np.append(output_data, ([1]))
</code></pre>

<p><strong><em>Is there a better way to join these arrays such that the algorithm can learn?</em></strong></p>

<p><em>Note: The "append" isn't drawing any errors, yet seems to not do its job.</em></p>