def __init__(self):
self.gene_target = Gene.new_random_gene()
self.active_gene_range = abs(
float(
dgamma.rvs(Virus.ACTIVE_GENE_RANGE_A,
Virus.ACTIVE_GENE_RANGE_LOC,
Virus.ACTIVE_GENE_RANGE_SCALE,
size=1)[0]))
def make_block_flow(self,max_steps):
step = 0
# default parameters from multichain
while step <= max_steps:
time_block = dgamma.rvs(2.0000170661444634, 5.4611854838492295, 0.8244588748930897)
time_block = int(round(time_block)) * 1000
step += time_block
self.block_closed_flow.append(step)
def make_blocks_flow_control(self,max_steps):
step = 0
while step <= max_steps:
if(time_block > 1):
time_block = dgamma.rvs(2.0000170661444634, 5.4611854838492295, 0.8244588748930897)
time_block = int(round(time_block)) * 1000
step += time_block
self.new_blocks_flow_control.append(step)
return
def make_block_miner_flow(self, max_steps):
step = 0
while step <= max_steps:
time_block = dgamma.rvs(2.0000170661444634, 5.4611854838492295,
0.8244588748930897)
time_block = int(round(time_block)) * 1000
step += time_block
self.block_miner_flow.append(step)
return
def make_block_miner_flow(self,max_steps):
step = 0
while step <= max_steps:
if(time_block > 1):
time_block = dgamma.rvs(2.0000170661444634, 5.4611854838492295, 0.8244588748930897)
time_block = int(round(time_block)) * 1000
step += time_block
self.block_miner_flow.append(step)
return
def make_keys(self):
(self.public_key, self.private_key) = rsa.newkeys(KEYSIZE)
def get_private_key(self):
return self.private_key
def get_dict(self):
data = self.__dict__
return data
def make_ingress_transactions(self):
self.transaction = Ingress()
self.transaction.sign_transactions(self)
def set_timestamp(self):
self.timestamp = dt.datetime.now()
def update_tables(self):
pass
def check_bloom_filter(self,bf_public_key):
bf = bloom_filter.BloomFilter(5,0.1)
bf.add(str(bf_public_key.n))
if(bf.check(str(self.public_key))):
print("sou juiz desse cara")
self.trust_table[bf_public_key.n] = 6.0
def __str__(self):
return "Pub:{}\nPriv:{}".format(self.public_key.n, self.private_key)
# Display the probability density function (``pdf``): x = np.linspace(dgamma.ppf(0.01, a), dgamma.ppf(0.99, a), 100) ax.plot(x, dgamma.pdf(x, a), 'r-', lw=5, alpha=0.6, label='dgamma pdf') # Alternatively, the distribution object can be called (as a function) # to fix the shape, location and scale parameters. This returns a "frozen" # RV object holding the given parameters fixed. # Freeze the distribution and display the frozen ``pdf``: rv = dgamma(a) ax.plot(x, rv.pdf(x), 'k-', lw=2, label='frozen pdf') # Check accuracy of ``cdf`` and ``ppf``: vals = dgamma.ppf([0.001, 0.5, 0.999], a) np.allclose([0.001, 0.5, 0.999], dgamma.cdf(vals, a)) # True # Generate random numbers: r = dgamma.rvs(a, size=1000) # And compare the histogram: ax.hist(r, normed=True, histtype='stepfilled', alpha=0.2) ax.legend(loc='best', frameon=False) plt.show()
