def crossover(self):
#Introduce crossover
i = 0
while len(self.new_generation) < self.number_of_agents:
new_agent = NeuralNetwork()
#Copy half of all weights from parent i and parent i+1
new_agent.weights_left = np.concatenate((self.list_of_neural_nets[i].weights_left[:int(len(self.list_of_neural_nets[i].weights_left)/2)], \
self.list_of_neural_nets[i+1].weights_left[int(len(self.list_of_neural_nets[i+1].weights_left)/2):]))
new_agent.weights_down = np.concatenate((self.list_of_neural_nets[i].weights_down[:int(len(self.list_of_neural_nets[i].weights_down)/2)], \
self.list_of_neural_nets[i+1].weights_down[int(len(self.list_of_neural_nets[i+1].weights_down)/2):]))
new_agent.weights_right = np.concatenate((self.list_of_neural_nets[i].weights_right[:int(len(self.list_of_neural_nets[i].weights_right)/2)], \
self.list_of_neural_nets[i+1].weights_right[int(len(self.list_of_neural_nets[i+1].weights_right)/2):]))
new_agent.weights_rotate = np.concatenate((self.list_of_neural_nets[i].weights_rotate[:int(len(self.list_of_neural_nets[i].weights_rotate)/2)], \
self.list_of_neural_nets[i+1].weights_rotate[int(len(self.list_of_neural_nets[i+1].weights_rotate)/2):]))
self.new_generation.append(new_agent)
i += 1
print(f"Number of agents for next generation: {len(self.list_of_neural_nets)}")
i = 0
while len(new_generation) < number_of_agents:
new_agent = NeuralNetwork()
#Copy half of all weights from parent i and parent i+1
new_agent.weights_left = np.concatenate((list_of_neural_nets[i].weights_left[:int(len(list_of_neural_nets[i].weights_left)/2)], \
list_of_neural_nets[i+1].weights_left[int(len(list_of_neural_nets[i+1].weights_left)/2):]))
new_agent.weights_down = np.concatenate((list_of_neural_nets[i].weights_down[:int(len(list_of_neural_nets[i].weights_down)/2)], \
list_of_neural_nets[i+1].weights_down[int(len(list_of_neural_nets[i+1].weights_down)/2):]))
new_agent.weights_right = np.concatenate((list_of_neural_nets[i].weights_right[:int(len(list_of_neural_nets[i].weights_right)/2)], \
list_of_neural_nets[i+1].weights_right[int(len(list_of_neural_nets[i+1].weights_right)/2):]))
new_agent.weights_rotate = np.concatenate((list_of_neural_nets[i].weights_rotate[:int(len(list_of_neural_nets[i].weights_rotate)/2)], \
list_of_neural_nets[i+1].weights_rotate[int(len(list_of_neural_nets[i+1].weights_rotate)/2):]))
new_generation.append(new_agent)
i += 1
# for i, agent in enumerate(list_of_neural_nets):
# print(list_of_neural_nets[0].weights_left - list_of_neural_nets[1].weights_left)
# print(list_of_neural_nets[1].weights_left)
# agent[i].weights_left
print(f"Number of agents for next generation: {len(list_of_neural_nets)}")
