def runModel(cashDistribution, leverageDistribution):
# Set scale for distributions:
cashScale = setCashScale(cashDistribution)
leverageScale = setLeverageScale(leverageDistribution)
# Generate cash vector
cash_vector = generateCashVector(cashDistribution) * cashScale
# print(cash_vector)
cash_vector[cash_vector <= 0] = 1 * 10 ** -10
# print(cash_vector)
# cash_vector[cash_vector > 5000] = 6500
cash_to_connectivity = lambda x: safe_ln(x)
connectivity_vector = cash_to_connectivity(cash_vector)
# Make the adjacency matrix
mat = make_connections(connectivity_vector)
mat = binarize_probabilities(mat)
# Distribute liabilities
leverage_ratios = generateLeverageRatios(leverageDistribution) * leverageScale
leverage_ratios[leverage_ratios < 5] = 5
# print(leverage_ratios)
liabilities = np.multiply(cash_vector, leverage_ratios)
mat = distribute_liabilities(mat, liabilities)
for i, cash in enumerate(cash_vector):
mat[i, i] = cash
defaults_to_freq = {}
for z in tqdm(range(steps)):
if network == 'TestNetwork':
model = TestNetwork(size, mat)
model.reset_net()
step_result = model.step()
defaults = step_result['cascade_defaults'] + step_result['ratio_defaults']
elif network == 'DeterministicRatioNetwork':
model = DeterministicRatioNetwork(100, mat)
model.reset_net()
ratios, defaults = model.step()
if defaults in defaults_to_freq:
defaults_to_freq[defaults] += 1
else:
defaults_to_freq[defaults] = 1
""" the below code saves the results and distribution configuration
in a .json file with the date and time added to
the name so as to prevent overwriting."""
with open(network + 'result_' + cashString + leverageString + str(time.strftime("%d_%m_%y_%H%M%S")) + '.json', 'w') as fp:
json.dump(defaults_to_freq, fp)
def runModel(cashDistribution, leverageDistribution):
cash_vector = generateCashVector(cashDistribution)
cash_vector[cash_vector <= 0] = 1 * 10**-10
# cash_vector[cash_vector > 5000] = 6500
cash_to_connectivity = lambda x: safe_ln(x)
connectivity_vector = cash_to_connectivity(cash_vector)
# Make the adjacency matrix
mat = make_connections(connectivity_vector)
mat = binarize_probabilities(mat)
# Distribute liabilities
leverage_ratios = generateLeverageRatios(leverageDistribution)
leverage_ratios[leverage_ratios < 5] = 5
liabilities = np.multiply(cash_vector, leverage_ratios)
mat = distribute_liabilities(mat, liabilities)
for i, cash in enumerate(cash_vector):
mat[i, i] = cash
defaults = np.zeros((steps, 2))
for z in tqdm(range(steps)):
model = TestNetwork(100, mat)
model.reset_net()
results = model.step()
defaults[z, 0] = results['ratio_defaults']
defaults[z, 1] = results['cascade_defaults']
""" the below code saves the results in a .csv file with the date and time added to
the name so as to prevent overwriting. The format parameter can be used
to change the variable type that is stored in the .csv file, with the result being
smaller file sizes when more data efficient variable types are utilized."""
# use fmt='%1c' to save data as characters
# use fmt='%1d' to save data as signed decimal integers
# use fmt='%1e' to save data in scientific notation
# use fmt='%1f' to save data as decimal floating points
# use fmt='%1g' to save data as the most efficient of e or f
# use fmt='%1o' to save data as signed octals
# use fmt='%1s' to save data as string characters
# use fmt='%1u' to save data as unsigned decimal integers
# use fmt='%1x' to save data as unsigned hexadecimal integer
np.savetxt('defaults_' + str(time.strftime("%d_%m_%y_%H%M%S")) + '.csv',
defaults,
delimiter=',',
fmt='%1f')
""" Cursory analysis reveals that with the standard settings and no fmt parameter used
cash_vector = np.random.normal(10000, 10000, 100)
cash_vector[cash_vector <= 0] = 1 * 10**-10
# cash_vector[cash_vector > 5000] = 6500
cash_to_connectivity = lambda x: np.log(x).astype(int)
connectivity_vector = cash_to_connectivity(cash_vector)
# Make the adjacency matrix
mat = make_connections(connectivity_vector)
mat = binarize_probabilities(mat, cash_vector)
# Distribute liabilities
leverage_ratios = np.random.normal(10, 2, 100)
leverage_ratios[leverage_ratios < 5] = 5
liabilities = np.multiply(cash_vector, leverage_ratios)
mat = distribute_liabilities(mat, liabilities)
for i, cash in enumerate(cash_vector):
mat[i, i] = cash
defaults_to_freq = {}
for z in tqdm(range(100000)):
model = TestNetwork(100, mat)
model.reset_net()
step_result = model.step()
defaults = step_result['cascade_defaults'] + step_result[
'ratio_defaults']
# print(defaults)
if defaults in defaults_to_freq:
defaults_to_freq[defaults] += 1