def initializeModels():
model = newModel()
generations = Generation(model, mChance)
for i in range(2):
board = [0 for x in range(10)]
board = [list(board) for x in range(20)]
species = Species(model, [board] * 2,
[[1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0]],
mChance)
species.evolve()
species.addName(f'Gen 0, Species {i}')
print(species.name)
playGame(species)
generations.population.append(species)
print(f'For a total of {species.score:.2f}!')
generations.breed()
generations.child.addName('Gen 0, Species Child')
generations.saveGen()
print('Finished initialization!')
return generations
def initializeModels():
model = Sequential()
model.add(LSTM(200,activation='sigmoid',input_shape=(10,20)))
model.add(LSTM(1800,activation='softplus'))
model.add(LSTM(8,activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
generations = [Generation(model,9)]
for i in range(2):
species = Species(model,[[[0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0]],[[0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0]]],[[1,0,0,0,0,0,0,0],[1,0,0,0,0,0,0,0]],5)
species.evolve()
species.addName('Gen 0, Species {}'.format(i))
print(species.name)
playGame(species)
generations[0].population.append(species)
print('For a total of {:.2f}!'.format(species.score))
generations[0].breed()
generations[0].child.addName('Gen 0, Species Child')
generations[0].saveGen()
print('Finished initialization!')
return generations