# Open results files
# angluin_result_file = open("angluin.results", "w")
# margulis_result_file = open("margulis.results", "w")
# random3_result_file = open("random3.results", "w")
# random5_result_file = open("random5.results", "w")
# Create results dictionaries for the yaml write
# angluin_results = { ANGLUIN: {} }
# margulis_results = { MARGULIS: {} }
# random3_results = { RANDOM_3: {} }
# random5_results = { RANDOM_5: {} }
print "Starting main program ... \n\n"
# Run only if there is any algorithm configured to run
if check_configured_run(methods.ANGLUIN, methods.MARGULIS, methods.AJTAI, \
methods.RANDOM_3, methods.RANDOM_5):
# Load configuration parameters
config_file = open("config.yaml", "r")
config_vars = yaml.safe_load(config_file)
config_file.close()
# Clean existing .results files
if config_vars['params']['clear_results_files'] == True:
cleanup(".results")
for v in VALUES:
config_vars['params']['n'] = v # update the new n value
# Prepare config file to write the new yaml dictionary
def generate_expanders():
# Read n from configuration file
config_file = open("config.yaml", "r")
config_vars = yaml.safe_load(config_file)
config_file.close()
# Clean existing .out files
if config_vars['params']['cleanup'] == True:
helpers.cleanup(".out")
n = config_vars['params']['n']
EPSILON = config_vars['params']['epsilon']
# Generate Z(n)
#Z_n = list(xrange(n))
size = n * n
# Generate matrices A and B only if the algorithms that require them are
# configured to run
if helpers.check_configured_run(methods.ANGLUIN, methods.MARGULIS):
print "Generating matrices A and B with n = " + str(n) + " ... "
# Generate the elements of A and B using indices from the cross product
indices_of_pairs = numpy.arange(size) # Generate array of indices of the cross product
A_indices = numpy.random.permutation(indices_of_pairs).reshape((n, n)) # Randomize in matrix positions
B_indices = numpy.random.permutation(indices_of_pairs).reshape((n, n)) # Randomize in matrix positions
if config_vars['params']['output_indices_matrices'] == True:
helpers.write_indices_matrices(A_indices, B_indices)
if config_vars['params']['output_initializer_matrices'] == True:
returned_matrices = helpers.generate_pair_matrices(A_indices, B_indices, n)
A = returned_matrices[0]
B = returned_matrices[1]
helpers.write_pair_matrices(A, B)
print "Generated matrices A and B."
# EXPLICIT ALGORITHMS
if config_vars['algorithms'][methods.ANGLUIN] == True:
print ''
algorithms.EXPLICIT_METHOD(method_name=methods.ANGLUIN,
size=size,
A_indices=A_indices,
n=n,
EPSILON=EPSILON)
if config_vars['algorithms'][methods.MARGULIS] == True:
print ''
algorithms.EXPLICIT_METHOD(method_name=methods.MARGULIS,
size=size,
A_indices=A_indices,
n=n,
EPSILON=EPSILON)
if config_vars['algorithms'][methods.AJTAI] == True:
print ''
c = config_vars['params']['c']
s = c * numpy.log(n)
algorithms.EXPLICIT_METHOD(method_name=methods.AJTAI,
size=2 * size,
EPSILON=EPSILON,
s=s)
# RANDOM ALGORITHMS
if config_vars['algorithms'][methods.RANDOM_3] == True:
print ''
samples = config_vars['params']['random_graphs_samples']
algorithms.RANDOM_METHOD(methods.RANDOM_3, 2 * size, EPSILON, samples)
if config_vars['algorithms'][methods.RANDOM_5] == True:
print ''
samples = config_vars['params']['random_graphs_samples']
algorithms.RANDOM_METHOD(methods.RANDOM_5, 2 * size, EPSILON, samples)
