for _ in range(3):
print(_)
for __ in range(10):
train_env.maxI = 2
train_env.p = [SNPprob[_], 0]
agent = Agent(FLAGS, False, train_env, train_model)
seq1 = agent.env.seq1
seq2 = agent.env.seq2
start1, start2, lcslen = lcs.longestSubstring(seq1, seq2)
if FLAGS.show_align:
dot_plot = 255 * np.ones((len(seq1), len(seq2)))
for i in range(lcslen):
dot_plot[start1 + i, start2 + i] = 0
if FLAGS.print_align:
record = recordalign.record_align()
print("test", _, __)
print("raw seq len", len(seq1), len(seq2))
print("lcs len 1", start1, lcslen, len(seq1) - start1 - lcslen)
print("lcs len 2", start2, lcslen, len(seq2) - start2 - lcslen)
past = time.time()
if (start1 > 0) and (start2 > 0):
agent.set(seq1[start1 - 1::-1] + [0],
seq2[start2 - 1::-1] + [0])
if FLAGS.show_align and FLAGS.print_align:
rT1, rT2, processingtime, j, dot_plot1 = agent.Global(
sess, record)
dot_plot[:start1, :start2] = dot_plot1[::-1, ::-1]
record.reverse(start1 - 1, start2 - 1)
start = time.time()
startdate = time.localtime()
X = 100 # Greedy-X algorithm parameter
agent.set(seq1, seq2)
path = []
score = []
ptime = []
if FLAGS.show_align:
dot_plot = 255 * np.ones((len(seq1), len(seq2)))
for i in range(lcslen):
dot_plot[start1 + i, start2 + i] = 0
if FLAGS.print_align:
record = recordalign.record_path()
print("Ecoli test")
print("raw seq len", len(seq1), len(seq2))
past = time.time()
SeedNum = REMiner2.REMiner2(1, seq1, seq2)
uX1, uX2, uY1, uY2 = REMiner2.GetSEED(SeedNum, True)
uX1, uX2, uY1, uY2 = function.sortalign(uX1, uX2, uY1, uY2)
print("Pre-processing stage is completed : " + str(np.size(uX1)) +
" seeds are found")
print("Time spent : " + str(time.time() - past))
#순서를 다시 잡도록 노력하자, RC 부분이랑 나눠서 순서 정하기