def learn(args):
# 1. Obtengo los patrones de entrenamiento
# inputs, outputs = fp.parse_file('terrains/terrain4.txt', -1)
_inputs, _outputs = fp.parse_file('terrains/terrain4.txt', -1)
inputs, outputs, _inputs_test, _outputs_test = randomTerrain(300, _inputs, _outputs)
patterns = len(outputs)
arquitecture = [2, args.hl1, args.hl2, 1]
fun = args.g_fun
ecm = args.ecm
eta = args.eta
alfa = args.alpha
#los valores a,b,k en cero desactiva el eta adaptativo
a = args.a
b = args.b
k = args.k
# 2. Entreno la red
#multilayer_perceptron(arquitecture, input, output, bias, beta, eta, error_cuad, fun, alfa, a, b, k):
errors, epoch, out, weights = bp.train(arquitecture, inputs, outputs, -1, 0.5, eta, ecm, fun, alfa, a, b, k)
fp.plotX1X2Z(inputs, out)
#fp.plotOriginals(inputs,outputs)
# 3. Obtengo los patrones de testeo
inputs, outputs = fp.parse_file('terrains/terrain4-test-1.txt', -1)
# 4. Testeo la red
# out = bp.test(arquitecture, inputs, outputs, -1, 0.5, eta, fun, weights)
out = bp.test(arquitecture, _inputs_test, _outputs_test, -1, 0.5, eta, fun, weights)
# 5. Calculo porcentaje de aciertos y aproximaciones
hit_p, apprx_p = percentage(outputs, out, ecm)
print('Porcentaje de aciertos: ' + "%.2f" % round(hit_p,2) + '% - Porcentaje de aproximaciones: ' + "%.2f" % round(apprx_p,2) + '%')
fp.plotX1X2Z(inputs, out)
#fp.plotOriginals(inputs, outputs)
plt.plot(range(1, epoch), errors)
plt.xlabel('Epoca')
plt.ylabel('Error cuadratico medio')
plt.title('Red neuronal con arquitectura ' + str(arquitecture) + ', cantidad de patrones: ' + str(patterns) + ', funcion de activacion: ' + fun)
plt.show()
return
def get_closest_store(current_location):
store_list = parse_file('com/resources/store-locations.csv')
closest_store = find_closest_store(current_location, store_list)
if closest_store is None:
sys.exit('No stores found, something went wrong')
return closest_store
def main(argv):
input_files = []
help_text = 'main.py -i <input_file>'
try:
opts, args = getopt.getopt(argv, "h:i:", ["ifile="])
except getopt.GetoptError:
print(help_text)
sys.exit()
if len(opts) < 1:
print(help_text)
sys.exit()
for opt, arg in opts:
if opt == "-h":
print(help_text)
sys.exit()
elif opt in ("-i", "--ifile"):
input_files.append(arg)
if len(input_files) > 0:
for file in input_files:
print("Opening file" + file)
parse_file(file)
else:
print(help_text)
sys.exit()
def insert_blocks(self, mineral_deposit, block_model, data_file):
model = self.fetch_block_model(mineral_deposit, block_model)
headers, amount_headers, data_map, weight_column, weight_column_index, grades_data_map = self.get_params_from_model(
model)
data = parse_file(data_file)
data_array = []
grade_units = ['tonn', 'percentage', 'oz/tonn', 'ppm']
self.print_units(grade_units)
my_units = self.select_options_for_units(grade_units, grades_data_map)
for item in data:
document = self.create_block_document(mineral_deposit, block_model,
amount_headers, headers,
grades_data_map, item,
weight_column_index,
grade_units, my_units)
data_array.append(document)
self.db.blocks.insert_many(data_array)
def get_game_file_stats(filename):
game, winner = file_parser.parse_file(filename)
game = _normalize_game(game)
num_turns = len(game)
final_board = game[num_turns -1]
stats_teamA = {}
stats_teamB = {}
stats_teamA['wins'] = 0
stats_teamA['looses'] = 0
stats_teamA['draws'] = 0
stats_teamA['turns_winning'] = 0
stats_teamA['turns_losing'] = 0
stats_teamB['wins'] = 0
stats_teamB['looses'] = 0
stats_teamB['draws'] = 0
stats_teamB['turns_winning'] = 0
stats_teamB['turns_losing'] = 0
if winner == 1:
stats_teamA['wins'] = 1
stats_teamB['looses'] = 1
stats_teamA['turns_winning'] = num_turns
stats_teamB['turns_losing'] = num_turns
elif winner == -1:
stats_teamA['looses'] = 1
stats_teamB['wins'] = 1
stats_teamA['turns_losing'] = num_turns
stats_teamB['turns_winning'] = num_turns
else:
stats_teamA['draws'] = 1
stats_teamB['draws'] = 1
stats_teamA['num_pieces'], stats_teamB['num_pieces'] = _count_pieces(final_board)
stats_teamA['val_pieces'], stats_teamB['val_pieces'] = _count_values(final_board)
stats_teamA['max_death'], stats_teamB['max_death'] = _check_death(6, game)
return (stats_teamA, stats_teamB)
def load_sentries(world, level):
# load all the sentries for a given level from the file
file_name = SENTRY_FILE
print(file_parser.parse_file(SENTRY_FILE))
with open(file_name, 'r') as file:
lines = file.readlines() # read the whole file into a string array
i = 0
while i < len(lines):
while i < len(lines) and lines[
i][:-1] != SENTRY_START: # look for the start of a sentry definition
i += 1
if i < len(lines):
all_sentries = []
name = lines[i + 1][:-1] # strip trailing CRLF
sentry_level = eval(lines[i + 2])
position = eval(lines[i + 3])
display_program = []
if sentry_level == level: # only create the sentries for this game level
s = Sentry(world, position, name)
all_sentries.append(s)
i += 3 # skip on to next sentry
return all_sentries
import file_parser
import search
import GUI
def search_word(word):
return search.do_search(word)
if __name__ == "__main__":
file_parser.parse_file("test-data.txt")
# docsList=[]
# app.run(host="localhost")
GUI.vp_start_gui()
import matplotlib.pyplot as plt
from matplotlib import cm
import numpy as np
from celluloid import Camera
from file_parser import parse_file
if __name__ == '__main__':
N, L, t_max, x_values, y_values = parse_file("../CIMOutput.txt")
camera = Camera(plt.figure())
for i in range(t_max):
show_x = x_values[i]
show_y = y_values[i]
plt.scatter(x=show_x, y=show_y, c="black", marker='.', s=15)
camera.snap()
anim = camera.animate(blit=True)
anim.save('scatter.gif')
header_file_name = header_folder+'/'+class_name + '.h' cpp_file_name = cpp_folder+'/'+class_name + '.cpp' test_file_name = '' if namespace_name != 'GUI': test_file_name = test_folder+'/'+class_name + 'Test.cpp' #define template names header_template = '' cpp_template = '' if is_factory: header_template = 'FactoryHTemplate.txt' cpp_template = 'FactoryCPPTemplate.txt' dictionary['FactoryType'] = factory_type dictionary['FACTORYTYPE'] = factory_type.upper() dictionary['factorytype'] = factory_type.lower() elif is_singleton: header_template = 'SingletonHTemplate.txt' cpp_template = 'SingletonCPPTemplate.txt' elif is_algorithm: header_template = 'AlgorithmHTemplate.txt' cpp_template = 'AlgorithmCPPTemplate.txt' else: header_template = 'ClassHTemplate.txt' cpp_template = 'ClassCPPTemplate.txt' parse_file(template_dir + header_template,root + header_file_name,dictionary) parse_file(template_dir + cpp_template,root + cpp_file_name,dictionary) if len(test_file_name) > 0: parse_file(template_dir + 'ClassTestTemplate.txt',root + test_file_name,dictionary)
