def parse(self, entry):
fieldkeys = [
'ENTRY', 'NAME', 'FORMULA', 'EXACT_MASS', 'MOL_WEIGHT', 'SEQUENCE',
'REMARK', 'COMMENT', 'REACTION', 'PATHWAY', 'BRITE', 'ENZYME',
'PATHWAY', 'OTHER DBS', 'LINKDB', 'MODULE', 'STRUCTURE', 'ATOM',
'BOND', 'DBLINKS'
]
pattern = '|'.join(fieldkeys)
data = [x.strip() for x in re.split(pattern, entry)[1:]]
keys = re.findall(pattern, entry)
for key, val in zip(keys, data):
if key == 'ENTRY':
self.id = re.search('C\\d{5}', val).group(0)
elif key == 'NAME':
self.names = [x.strip('; ') for x in val.split('\n')]
elif key == 'FORMULA':
self.formula = val.strip()
elif key == 'COMMENT':
self.comment = val.strip()
elif key == 'REACTION':
self.reactions = re.findall('R\d{5}', val)
elif key == 'MOL_WEIGHT':
self.molweight = helper.f(val.strip())
elif key == 'PATHWAY':
self.pathways = re.findall('map(\d{5})', val)
elif key == 'ATOM':
self.__parse_atomtypes(val.strip())
elif key == 'BRITE':
self.brite = re.findall('(br\d{5}|ko\d{5})', val)
elif key == 'REMARK':
self.remark = re.findall('[DG]\\d{5}', val)
def __parse_equation(self, equation):
tokens = ['-->', '<==>', '<=>']
pattern = '|'.join(tokens)
left, right = [x.strip() for x in re.split(pattern, equation)[0:]]
self.reactants = dict()
self.__reactants = list()
self.__substrates = list()
self.__products = list()
for m in re.finditer(
r'((?:[0-9]*|\([^)]*\)|[n,m]))\s*((?:C|G)[0-9]{5})', left):
self.reactants[m.group(
2)] = -1.0 if m.group(1) is '' else -helper.f(m.group(1))
self.__reactants.append(m.group(2))
self.__substrates.append(m.group(2))
for m in re.finditer(
r'((?:[0-9]*|\([^)]*\)|[n,m]))\s*((?:C|G)[0-9]{5})', right):
stoich = 1.0 if m.group(1) is '' else helper.f(m.group(1))
self.reactants[m.group(2)] = self.reactants[m.group(
2)] + stoich if m.group(2) in self.reactants else stoich
self.__reactants.append(m.group(2))
self.__products.append(m.group(2))
def encrypt(message, key):
simulator.simulate_inputs(message, key)
simulator.simulate_init_binary(helper.getBinary(message),
helper.getBinary(key))
M = helper.applyTable(helper.getBinary(message), 'ip')
L0, R0 = helper.makeHalf(M)
simulator.simulate_init_iptable_effects(M, L0, R0)
K = helper.applyTable(helper.getBinary(key), "pc1")
C0, D0 = helper.makeHalf(K)
simulator.simulate_init_pc1table_effects(K, C0, D0)
Ln = L0
Rn = R0
Cn = C0
Dn = D0
Kn = K
for n in range(16):
simulator.simulate_round_number(n + 1)
Cn = helper.leftShift(Cn, n + 1)
Dn = helper.leftShift(Dn, n + 1)
Kn = helper.applyTable(Cn + Dn, 'pc2')
tempL = Ln[:]
Ln = Rn
ern, xor, sbox, sbox_result = helper.f(Rn, Kn)
simulator.simulate_f_calculations(ern, xor, sbox, sbox_result)
Rn = bin(int(tempL, 2) ^ int(sbox_result, 2))[2:].zfill(32)
simulator.simulate_round_results(n + 1, Cn, Dn, Kn, Ln, Rn)
ip1_effect = helper.applyTable(Rn + Ln, 'ip-1')
final_result = helper.getHexa(ip1_effect)
simulator.simulate_final_result(ip1_effect, final_result)
return final_result
import numpy as np
from helper import f
from helper import f_prime_x0
from helper import f_prime_x1
from helper import plot_rosenbrock
if __name__ == "__main__":
x0 = np.random.uniform(-2, 2)
x1 = np.random.uniform(-2, 2)
x_start = (x0, x1)
y_start = f(x0, x1)
print(f"Global minimum: {(1, 1)}")
print(f"X_start = {x_start}")
print(f"Y_start = {y_start}")
plot_rosenbrock(x_start)
learning_rate = 0.005 # [0.001, 0.00001]
num_iterations = 1000
gradient_steps = []
for it in range(num_iterations):
x0 = x0 - learning_rate * f_prime_x0(x0, x1)
x1 = x1 - learning_rate * f_prime_x1(x0, x1)
y = f(x0, x1)
if it % 100 == 0:
print(f"x0 = {x0} x1 = {x1} y = {y}")
gradient_steps.append((x0, x1))
Created on Tue Feb 12 11:41:30 2019 @author: qinzhen """ import helper as hlp import numpy as np import matplotlib.pyplot as plt #设置随机种子，保证每次结果一致 seed = 42 rnd = np.random.RandomState(seed) #(a)(b)(c) N = 20 d = 2 a, b, c, X, y, s, w = hlp.f(N, d, rnd) hlp.plot_helper(a, b, c, X, y, s, w) #(d) N = 100 a, b, c, X, y, s, w = hlp.f(N, d, rnd) hlp.plot_helper(a, b, c, X, y, s, w) #(e) N = 1000 a, b, c, X, y, s, w = hlp.f(N, d, rnd) hlp.plot_helper(a, b, c, X, y, s, w) #(f) #修改数据维度 N = 1000