def evaluation_function(individual):
# start_time = time.time()
weights = individual[0:p.NEEDED_WEIGHTS]
biases = individual[p.NEEDED_WEIGHTS:(p.NEEDED_WEIGHTS + p.NEEDED_BIASES)]
splitted_weights = new_ann.convert_weights_to_arrays(p.LAYER_SIZES, weights)
splitted_biases = new_ann.convert_biases_to_arrays(p.LAYER_SIZES, biases)
# ann = new_ann.neuralnetwork(splitted_weights, splitted_biases, LAYER_SIZES, activation=[nnet.sigmoid, nnet.sigmoid, nnet.sigmoid, nnet.sigmoid])
ann = new_ann.neuralnetwork(splitted_weights, splitted_biases, p.LAYER_SIZES,
activation=[new_ann.relu, new_ann.relu, new_ann.relu, new_ann.relu])
reg_ai_player = Player_fast(None, random_start=p.RANDOM_START)
ann_player = ANN_Player(None, ann)
game = GomokuGame.playToTheEnd(reg_ai_player, ann_player)
fit = len(game.moves)
if game.winner == ann_player.color:
fit += 10000.0
elif game.winner == game.DRAW:
fit += 5000.0
# print("Moves:", len(game.moves))
return len(game.moves),
#
# ann = LeNetConvPoolLayer(rng, input1d, filter_shape=(2, 3, 5, 5), image_shape=(4,3,20,20), poolsize=(2,2))
#
# print(ann.output)
import numpy as np
from ann import new_ann
layer_sizes = [5, 10, 1]
print("Weights needed", new_ann.get_weights_needed(layer_sizes))
print("Biases needed", new_ann.get_biases_needed(layer_sizes))
test_weights = [0.1, 0.3, 0.4, 0.1, 0.3, 0.1, 0.3, 0.4, 0.1, 0.3, 0.1, 0.3, 0.4, 0.1, 0.3, 0.1, 0.3, 0.4, 0.1, 0.3,
0.1, 0.3, 0.4, 0.1, 0.3, 0.1, 0.3, 0.4, 0.1, 0.3, 0.1, 0.3, 0.4, 0.1, 0.3, 0.1, 0.3, 0.4, 0.1, 0.3,
0.1, 0.3, 0.4, 0.1, 0.3, 0.1, 0.3, 0.4, 0.1, 0.3, 0.1, 0.3, 0.4, 0.1, 0.3, 0.1, 0.3, 0.4, 0.1, 0.3]
test_biases = [0.1, 0.3, 0.4, 0.1, 0.2, 0.1, 0.3, 0.4, 0.1, 0.2, 0.1]
actual_weights = [np.array([[0.1, 0.3, 0.4, 0.1, 0.3, 0.1, 0.3, 0.4, 0.1, 0.3], [0.1, 0.3, 0.4, 0.1, 0.3, 0.1, 0.3, 0.4, 0.1, 0.3],
[0.1, 0.3, 0.4, 0.1, 0.3, 0.1, 0.3, 0.4, 0.1, 0.3], [0.1, 0.3, 0.4, 0.1, 0.3, 0.1, 0.3, 0.4, 0.1, 0.3],
[0.1, 0.3, 0.4, 0.1, 0.3, 0.1, 0.3, 0.4, 0.1, 0.3]]),
np.array([[0.1], [0.3], [0.4], [0.1], [0.3], [0.1], [0.3], [0.4], [0.1], [0.3]])] # in --> [out]
actual_biases = [np.array([0.1, 0.3, 0.4, 0.1, 0.2, 0.1, 0.3, 0.4, 0.1, 0.2]), np.array([0.1])] # out
splitted_weights = new_ann.convert_weights_to_arrays(layer_sizes, test_weights)
splitted_biases = new_ann.convert_biases_to_arrays(layer_sizes, test_biases)
ann = new_ann.neuralnetwork(splitted_weights, splitted_biases, layer_sizes=layer_sizes)
input_arr = np.array([1, 0, 0, 0, 1])
print(ann.get_ann_prediction(input_arr))