def run(rule_number):
automaton = automatatron.Engine(rule_number)
def stream_handler(row, _):
print automatatron.default_string_formatter(row)
time.sleep(0.05)
automaton.run(handler=stream_handler, width=101)
def create_swarm_input(rule_number):
this_dir = os.path.dirname(os.path.realpath(__file__))
data_dir = os.path.join(this_dir, DATA_DIR)
swarm_input_file = os.path.join(data_dir, SWARM_INPUT_FILENAME % rule_number)
swarm_desc_file = os.path.join(data_dir, SWARM_DESC_FILENAME)
with open(swarm_desc_file, "r") as swarm_desc_tmpl:
swarm_desc = swarm_desc_tmpl.read()
incl_fields = []
midpoint = BITS / 2
for i in xrange(BITS):
incl_fields.append(dict(
fieldName="bit_%i" % i,
fieldType="string"
))
swarm_desc = swarm_desc.replace("<INCLUDED_FIELDS>", str(incl_fields))
swarm_desc = swarm_desc.replace("<PREDICTED_FIELD>", "bit_%i" % midpoint)
swarm_desc = swarm_desc.replace("<RULE_NUMBER>", str(rule_number))
swarm_desc = swarm_desc.replace("<SOURCE_FILE>", swarm_input_file)
swarm_desc_out = os.path.join(data_dir, "swarm_description_%s.py" % rule_number)
print "Creating swarm description at %s..." % swarm_desc_out
with open(swarm_desc_out, "w") as swarm_desc_out:
swarm_desc_out.write(swarm_desc)
print "Creating swarm input file at %s..." % swarm_input_file
with open(swarm_input_file, "w") as input_file:
writer = csv.writer(input_file)
names = []
types = []
flags = []
for i in xrange(BITS):
names.append("bit_%i" % i)
types.append("string")
flags.append("")
writer.writerow(names)
writer.writerow(types)
writer.writerow(flags)
automaton = automatatron.Engine(int(rule_number))
def stream_handler(row, _):
writer.writerow(row)
automaton.run(iterations=BITS)
automaton.run(handler=stream_handler, width=BITS, iterations=3000)
def test_rule30_prediction_is_perfect_after_600_iterations(self):
"""
Generates Rule 30 elementary cellular automaton and passes it through NuPIC.
Asserts that predictions are perfect after X rows of data.
"""
iterations = 600
model = ModelFactory.create(rule_30_model_params.MODEL_PARAMS)
model.enableInference({"predictedField": PREDICTED_FIELD})
prediction_history = deque(maxlen=500)
counter = [0]
last_prediction = [None]
def stream_handler(row, _):
counter[0] += 1
input_row = {}
for index, field in enumerate(row):
input_row["bit_%i" % index] = str(field)
prediction = last_prediction[0]
predicted_index = int(PREDICTED_FIELD.split("_").pop())
value = str(row[predicted_index])
correct = (value == prediction)
count = counter[0]
if correct:
prediction_history.append(1.0)
else:
prediction_history.append(0.0)
correctness = reduce(lambda x, y: x + y,
prediction_history) / len(prediction_history)
if count == iterations:
unittest.TestCase.assertEqual(
self, 1.0, correctness,
"Predictions should be 100 percent correct after reaching %i "
"iterations." % iterations)
result = model.run(input_row)
prediction = result.inferences["multiStepBestPredictions"][1]
last_prediction[0] = prediction
automaton = automatatron.Engine(RULE_NUMBER)
automaton.run(handler=stream_handler, width=21, iterations=iterations)
def run_io_through_nupic(model, rule_number):
prediction_history = deque(maxlen=500)
counter = [0]
last_prediction = [None]
def stream_handler(row, _):
counter[0] += 1
input_row = {}
for index, field in enumerate(row):
input_row["bit_%i" % index] = str(field)
# Show this input row compared with the last prediction
print_current_row_with_last_prediction(
row, last_prediction[0], int(PREDICTED_FIELD.split("_").pop()),
prediction_history)
result = model.run(input_row)
prediction = result.inferences["multiStepBestPredictions"][1]
last_prediction[0] = prediction
automaton = automatatron.Engine(rule_number)
automaton.run(iterations=21)
automaton.run(handler=stream_handler, width=21)
incl_fields = []
midpoint = BITS / 2
for i in xrange(BITS):
incl_fields.append(dict(fieldName="bit_%i" % i, fieldType="string"))
swarm_desc = swarm_desc.replace("<INCLUDED_FIELDS>", str(incl_fields))
swarm_desc = swarm_desc.replace("<PREDICTED_FIELD>", "bit_%i" % midpoint)
with open("swarm_description.py", "w") as swarm_desc_out:
swarm_desc_out.write(swarm_desc)
with open("swarm_input.csv", "w") as input_file:
writer = csv.writer(input_file)
names = []
types = []
flags = []
for i in xrange(BITS):
names.append("bit_%i" % i)
types.append("string")
flags.append("")
writer.writerow(names)
writer.writerow(types)
writer.writerow(flags)
automaton = automatatron.Engine(30)
def stream_handler(row, _):
writer.writerow(row)
automaton.run(iterations=BITS)
automaton.run(handler=stream_handler, width=BITS, iterations=3000)
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
# THE SOFTWARE.
import automatatron
import time
print "EXAMPLE 1:"
print "Print the first 10 rows of all possible automaton:"
for rule in range(1, 257):
automaton = automatatron.Engine(rule)
automaton.run(iterations=10)
print automaton
print "EXAMPLE 2:"
print "Print the first 50 rows of Rule 30"
automaton = automatatron.Engine(30)
automaton.run(iterations=50)
print automaton
print "EXAMPLE 3:"
print "Run the next 10 iterations, and pass results into specified handler."
def row_handler(row, _):
print row