def test_output_is_zero_when_both_inputs_are_zero(self):
genome = decode('CryogenicStorage/xor_genome.json')
brain = NeuralNetwork.expressed_from(genome)
brain.stimulate([("S1", 0),
("S2", 0)])
brain.electrify()
activation = brain.outputs()["O1"].activation()
self.assertLess(activation, 0.001)
print "0, 0: %.8f" % activation
def test_output_is_one_when_s2_is_one(self):
genome = decode('CryogenicStorage/xor_genome.json')
brain = NeuralNetwork.expressed_from(genome)
brain.stimulate([("S1", 1),
("S2", 0)])
brain.electrify()
activation = brain.outputs()["O1"].activation()
self.assertGreater(activation, 0.001)
print "1, 0: %.8f" % activation
generation = 1
last_improvement = 0
archive = "Experiments/Archive/xor/%s" % datetime.datetime.now().strftime(
"%Y%m%d %H:%M:%S")
os.makedirs(archive)
archive_population(population, generation)
timeline_path = "%s/timeline.txt" % archive
timeline = open(timeline_path, "w")
while best_fitness < target_fitness:
for genome in population.organisms:
genome["champ"] = None
brain = NeuralNetwork.expressed_from(genome)
inputs = [[("S1", 0), ("S2", 0)], [("S1", 0), ("S2", 1)],
[("S1", 1), ("S2", 0)], [("S1", 1), ("S2", 1)]]
desired_outputs = [0, 1, 1, 0]
actual_outputs = []
try:
for sensory_input in inputs:
brain.stimulate(sensory_input)
brain.electrify()
actual_outputs.append(brain.outputs()["O1"].activation())
error = actual_outputs[0] + (1 - actual_outputs[1]) + (
1 - actual_outputs[2]) + actual_outputs[3]
genome["fitness"] = math.pow(4 - error, 2)
best_fitness = 0
generation = 1
last_improvement = 0
archive = "Experiments/Archive/xor/%s" % datetime.datetime.now().strftime("%Y%m%d %H:%M:%S")
os.makedirs(archive)
archive_population(population, generation)
timeline_path = "%s/timeline.txt" % archive
timeline = open(timeline_path, "w")
while best_fitness < target_fitness:
for genome in population.organisms:
genome["champ"] = None
brain = NeuralNetwork.expressed_from(genome)
inputs = [[("S1", 0), ("S2", 0)], [("S1", 0), ("S2", 1)], [("S1", 1), ("S2", 0)], [("S1", 1), ("S2", 1)]]
desired_outputs = [0, 1, 1, 0]
actual_outputs = []
try:
for sensory_input in inputs:
brain.stimulate(sensory_input)
brain.electrify()
actual_outputs.append(brain.outputs()["O1"].activation())
error = actual_outputs[0] + (1 - actual_outputs[1]) + (1 - actual_outputs[2]) + actual_outputs[3]
genome["fitness"] = math.pow(4 - error, 2)
except UnstableNetworkError:
def test_extract_genome(self):
expected_genome = self.read_genome('Experiments/CryogenicStorage/xor_genome.json')
brain = NeuralNetwork.expressed_from(expected_genome)
genome = brain.decode_genome()
self.maxDiff = None
self.assertEqual(genome, expected_genome)
tomorrow = ftse100[min(index + 1, len(ftse100) - 1)]
if tomorrow > today:
if money > 0:
shares = money / ftse100[index]
money = 0
else:
if shares > 0:
money = shares * ftse100[index]
shares = 0
print "Buy and hold: %s" % ((1000 / ftse100[0]) * ftse100[len(ftse100) - 1])
print "Best possible result: %s" % money if money > 0 else shares * ftse100[len(ftse100) - 1]
brain = NeuralNetwork.expressed_from(fund_manager)
money = 1000
shares = 0
print "date,ftse100,prediction,balance,cash,shares"
for index, quote in enumerate(ftse100):
brain.stimulate([("S1", ftse100_t1[index]),
("S2", ftse100_t2[index]),
("S3", ftse100_t3[index]),
("S4", volume_t1[index])
])
brain.electrify()
prediction = brain.outputs()["O1"].activation()
