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generateRegPool.py
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generateRegPool.py
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# coding: utf-8
# In[8]:
#utility functions
import random, os, subprocess
from shutil import copyfile, rmtree
def write_to_file(file_name,trace):
with open(file_name,'w') as f:
f.write(trace)
def append_to_file(file_name,trace):
with open(file_name,'a') as f:
f.write(trace)
#parameters are file names
def execute_clingo(trace, automata):
script = "clingo " + trace + " " + automata
proc = subprocess.Popen(script,stdout=subprocess.PIPE,shell=True)
return proc
def execute_ILASP(file_name):
script = "ILASP --2i " + file_name
process = subprocess.Popen(script,stdout=subprocess.PIPE,shell=True)
return process
def getILASPSpace(state_num):
extra = ''
for i in range(state_num):
extra += "#constant(st,state{}).\n".format(i)
copyfile('useIlasp/ilaspMode.lp', 'useIlasp/modetmp.lp')
append_to_file('useIlasp/modetmp.lp',extra)
script = "ILASP -s useIlasp/modetmp.lp"
process = subprocess.Popen(script,stdout=subprocess.PIPE,shell=True)
res = []
a = process.stdout.readline()
while(a):
res.append(a)
a = process.stdout.readline()
return res
def getILASP_REGSpace(state_num,reg_limit):
extra = ''
for i in range(state_num):
extra += "#constant(st,state{}).\n".format(i)
for i in range(reg_limit):
extra += "#constant(reg_value,{}).\n".format(i)
tmp_file = 'useIlasp/modeRegTmp.lp'
copyfile('useIlasp/ilaspRegMode.lp', tmp_file)
append_to_file(tmp_file,extra)
script = "ILASP -s {}".format(tmp_file)
process = subprocess.Popen(script,stdout=subprocess.PIPE,shell=True)
res = []
a = process.stdout.readline()
while(a):
res.append(a)
a = process.stdout.readline()
return res
def clearLogDir():
dir_name = 'useIlasp/log'
if(os.path.isdir(dir_name)):
rmtree(dir_name)
os.makedirs(dir_name)
#get the path for that automata
def getLogPath(file_name,auto_id):
log = 'useIlasp/log'
dirname = 'auto'+str(auto_id)
autodir = os.path.join(log,dirname)
if(not os.path.isdir(autodir)):
os.makedirs(autodir)
path = os.path.join(autodir,file_name)
return path
def log(file_name,content,auto_id):
path = getLogPath(file_name, auto_id)
if(os.path.isfile(path)):
append_to_file(path,content)
else:
write_to_file(path,content)
def string_to_trace(string):
res = ''
for i in range(len(string)):
res += 'input({},{}).'.format(i,string[i])
res += 'trace_length({}).\n'.format(len(string))
return res
def get_random_inputs(limit):
sections = random.randint(0,10)
if(sections<2):
ran_int = random.randint(0,3)
elif(sections<5):
ran_int = getNormalRandomWithLimit(3,10,100)
else:
ran_int = random.randint(0,limit)
ran_string = str(bin(ran_int))
ran_string = ran_string[2:]
res = '0'*getNormalRandomWithLimit(1,2,5)+ran_string
return res
# In[9]:
#mutual exclusive condition needed for the deltas\n",
# return a list of tuple , (from state, number of MX)\n",
# e.g. [(0,2)] means state0 need MX for two state\n",
def mutual_required(deltas):
from_states = []
for d in deltas:
from_states.append(d[0])
result = []
for f in from_states:
count = from_states.count(f)
if (count>1) :
result.append((f,count))
return list(set(result))
#only works for integers, take input limit, number of conditions needed
# return list of conditions, guaranteed to be mutually exclusive
# return [[l,u]...]
def generate_conditions(lower_bound,upper_bound,num):
# if(upper_bound - lower_bound +1 < num):
# print "not so possible to generate so many conditions"
# return []
# results = []
# if(upper_bound-lower_bound +1 == num):
# for i in range(lower_bound,upper_bound+1):
# results.append([i,i])
# return results
# #list of ranges available : [[l,u],[l,u],[l,u]]
# ranges = [[lower_bound,upper_bound]]
# while(len(results)<num):
# #choose a range from list of continuous ranges
# chose_range = ranges[random.randint(0,len(ranges)-1)]
# low = chose_range[0]
# upp = chose_range[1]
# l = random.randint(low,upp)
# u = random.randint(l,upp)
# results.append([l,u])
# #update ranges
# left_upper = l-1
# right_lower = u+1
# ranges.remove([low,upp])
# if(left_upper >= low):
# ranges.append([low,left_upper])
# if(right_lower <= upp):
# ranges.append([right_lower,upp])
# #reset if requirement can't be met
# possible_ranges = 0
# for r in ranges:
# possible_ranges += r[1]-r[0]
# if(possible_ranges+len(results) < num):
# results = []
# ranges = [[lower_bound,upper_bound]]
if(num==2):
return [[0,0],[1,1]]
elif(num==1):
one_prob = 4
zero_prob = 8
ran = random.randint(0,10)
if(ran < one_prob):
return [[1,1]]
elif(ran < zero_prob):
return [[0,0]]
else:
return [[0,1]]
else:
print 'shoudnt happen here, generate condition'
return []
def fillUpConditionsForDelta(deltas,automata):
#generate conditions along with deltas
#[(stateNumber,mxNumber)]
mx_needed = mutual_required(deltas)
#index corresponding to deltas
conditions = [[]]*len(deltas)
#index corresponding to states, elements is condition numbers
error_conditions = [[]]*len(automata.states)
#fill up conditions and error_conditions
for mx in mx_needed:
#generate that many conditions, can add a random extra number
# but then need to pick mx[1] out of them, TODO later
mx_conditions = generate_conditions(automata.in_low,automata.in_upp,mx[1])
for d in range(len(deltas)):
#if this delta need MX
from_st = deltas[d][0]
if (from_st == mx[0]) :
if(len(mx_conditions) == 0):
print "something wrong in calculating MX?"
conditions[d] = mx_conditions[0]
#Delta number is corresponding to condition number
# error condition records each state go to error state if
# none of it's condition are met
error_conditions[from_st] = error_conditions[from_st]+[d]
del mx_conditions[0]
#fill up non-mx required conditions
#TODO add more complex conditions
for c in range(len(conditions)):
if (conditions[c]==[]):
conditions[c] = generate_conditions(automata.in_low,automata.in_upp,1)[0]
from_st = deltas[c][0]
error_conditions[from_st] = [c]
return conditions,error_conditions
def draw_graph(a,graph,file_name):
# pass
g = nx.nx_pydot.to_pydot(graph)
for i in range(len(a.deltas)):
edge = g.get_edges()[i]
delta = (int(edge.obj_dict['points'][0]),int(edge.obj_dict['points'][1]))
con = a.deltas.index(delta)
edge.obj_dict['attributes']['label'] = str(a.conditions[con])
g.write_png(file_name)
# In[10]:
import random
# import pkg_resources
# pkg_resources.require("networkx==2.0")
import networkx as nx
import pydot
#ASSUMPTIONS: only one input, represented by number
#input should include an trace_length
cond_param = 'C'
#need to pass time parameter around,
#so that that condition is only true at that time
time_param = 'T'
def gen_states(output, states):
for s in states:
output += "state(" + str(s) + ")."
output += "\n\n"
return output
def gen_condition_tostr(output,conditions):
for c in range(len(conditions)):
output += "condition(" + cond_param+ "," +str(c)+"):- "
output += cond_param+" >= "+ str(conditions[c][0]) +','
output += cond_param+" <= " + str(conditions[c][1]) + ", "
output += "input(_,"+cond_param+"). \n"
output += '\n'
return output
def deltaToString(fr,to,cond):
res = ''
res += "delta("+ fr+ ","
res += cond_param+ ","+ to+ ","+cond+ "):-"
res += 'input('+'_' + "," +cond_param+')' +'.\n'
return res
def gen_deltas_tostr(output,deltas,states):
for d in range(len(deltas)):
from_st = deltas[d][0]
to_st = deltas[d][1]
mx_conditions = []
# output += "delta("+ states[from_st]+ "," + time_param + ","
# output += cond_param+ ","+ states[to_st]+ ","+str(d)+ "):-"
# output += 'input('+time_param + "," +cond_param+')' +'.\n'
output += deltaToString(states[from_st],states[to_st],str(d))
output += '\n'
return output
def gen_state_trans(output, low, init_state):
output += "st(" + str(low) + "," + init_state+ ").\n"
output += "st(T+1,TO):- st(T,FROM),state(FROM),state(TO),delta(FROM,C,TO,ID),condition(C,ID),input(T,C).\n\n"
return output
def getNormalRandomWithLimit(e,std,limit):
ran = int(random.normalvariate(e,std))
if(limit <=0 ):
return ran
res = max(min(ran,limit),0)
return res
def setStartAndEndStateForGraphGen(length):
l = length
if(l<=1):
print "we only interested in graph with more than 1 state"
return
din=[1]*l
dout=[1]*l
#start state must have:
#at least one out,
#at most l in
din[0] = getNormalRandomWithLimit(1,0.5,l)
dout[0] = random.randint(1,2)
#end state must have:
#at least one in
#at most 2 out
din[l-1] = max(1,getNormalRandomWithLimit(1,0.3,l))
dout[l-1] = random.randint(0,2)
if(l>2):
for i in range(l-2):
dout[i] = (random.randint(1,2))
return din,dout
def gen_graph(l):
din,dout = setStartAndEndStateForGraphGen(l)
while(sum(dout)-sum(din) < 0 ):
din,dout = setStartAndEndStateForGraphGen(l)
diff = sum(dout)-sum(din)
while(diff>0):
i = random.randint(0,l-1)
if(din[i] < l):
diff -= 1
din[i] = din[i]+1
D=nx.directed_configuration_model(din,dout)
D=nx.DiGraph(D)
return D
def gen_constraints(output,final_state):
output += "accept :- st(T," + final_state + ")," + "trace_length(T).\n"
output += ":- not accept. \n"
return output
# all states can goto error state if none of it's conditions are met
def gen_error_state_conditions(output, error_conditions,states,deltas):
output += "state(error).\n"
start_id = len(deltas)
for s in range(len(states)):
cond_id = str(start_id +s)
output += "condition("+ cond_param+ "," +cond_id+"):- "
for c in range(len(error_conditions[s])):
condition_number = error_conditions[s][c]
output += "not condition("+ cond_param +','+str(condition_number) +"),"
output += "input(_" + ","+ cond_param +").\n"
output += deltaToString(states[s],'error',cond_id)
output += '\n'
return output
def gen_input_complete():
res = '1{input(T,0);input(T,1)}1:- time(T).\n'
res += 'time(T-1):- trace_length(T).\n'
res += 'time(T-1):- time(T), T>=1.\n'
return res
def find_corresponding_conds(path,deltas,conditions):
res = []
#p is a path e.g.[0,1,3,2,4]
#loop through all element except last one
for i in range(len(path)-1):
con_index = deltas.index((path[i],path[i+1]))
cond = conditions[con_index]
res.append(cond)
return res
def find_conds_for_err(path,conditions,err_cons,in_low,in_upp):
neg_cons_indexs = err_cons[path[-2]]
neg_conditions = []
#get all posible out going edges from that state
for ind in neg_cons_indexs:
neg_conditions.append(conditions[ind])
#then subtract
cond_to_error = range(in_low,in_upp+1)
for rng in neg_conditions:
for i in range(rng[0],rng[1]+1):
cond_to_error.remove(i)
return cond_to_error
def paths_to_conditions(auto,paths):
results = []
for p in range(len(paths)):
conditions = find_corresponding_conds(paths[p],auto.deltas,auto.conditions)
results.append(conditions)
return results
def invalid_path_to_conditions(auto,paths):
results = []
for p in paths:
#if it goes to error
if(p[-1]==auto.state_num):
conditions = find_corresponding_conds(p[:-1],auto.deltas,auto.conditions)
to_err = find_conds_for_err(p,auto.conditions,auto.error_cons,auto.in_low,auto.in_upp)
conditions.append(to_err)
#if it stops at a internal state
else:
conditions = find_corresponding_conds(p,auto.deltas,auto.conditions)
results.append(conditions)
return results
def getMissing_ErrorInputs_index(max_length, m_perc, m_prob, e_perc, e_prob):
clingo_trace = ''
missing_happen = random.randint(0,100) < m_prob*100
error_happen = random.randint(0,100) < e_prob*100
missing_inputs = []
error_inputs = []
if(missing_happen):
clingo_trace += '%% trace below has missing inputs \n'
m_num = int(max_length*m_perc)
missing_inputs = random.sample(range(max_length),m_num)
if(error_happen):
clingo_trace += '%% trace below has error!! inputs \n'
e_num = int(max_length*e_perc)
error_inputs = random.sample(range(max_length), e_num)
return clingo_trace, missing_inputs, error_inputs
def possibleConditions_to_traces(con_traces, auto):
result = []
missing_num = auto.missing_num
miss_prob = auto.missing_prob
error_num = auto.error_num
error_prob = auto.error_prob
#cons: [[0,0],[1,1],[0,1]]
for cons in con_traces:
if ([] in cons):
print 'this trace is impossible to complete'
return []
tmp_res = getMissing_ErrorInputs_index(len(cons), missing_num, miss_prob, error_num, error_prob)
clingo_trace, missing_index, error_index = tmp_res
for c in range(len(cons)):
rng = cons[c]
value = rng[0]
if(len(rng)>1):
value = random.randint(rng[0],rng[1])
if(c in error_index):
value = 1-value
if(not c in missing_index):
clingo_trace += 'input({0},{1}).'.format(c,value)
clingo_trace += 'trace_length({0}).'.format(len(cons))
result.append(clingo_trace)
return result
# from [[[0,1],[1,1]],[trace],[trace]]
# to %input(), input(),...
# randomly select one of the conditions
def conditions_to_traces(path_cond,auto):
con_traces = path_cond
res = possibleConditions_to_traces(con_traces,auto)
return res
def invalid_conditions_to_traces(inv_p_c,auto):
con_traces = inv_p_c
con_possible_traces = []
for cons in con_traces:
if (not [] in cons):
con_possible_traces.append(cons)
res = possibleConditions_to_traces(con_possible_traces,auto)
return res
def graph_with_error_state(auto,graph):
newG = graph.copy()
err = len(auto.states)
newG.add_node(err)
for s in range(err):
newG.add_edge(s,err)
return newG
#ALGORITHM FOR GEN_ALL_PATH:
#first find all simple path,
#then for each circle, add path from start to circle
#and add path to tail
def getAllPossibleCycleStart(graph, fr, to, cycle):
result = []
for st in cycle:
if(nx.has_path(graph,fr,st) and nx.has_path(graph,st,to)):
result.append(st)
return result
def shift(key, array):
return array[key:]+array[:key]
def gen_all_paths(graph,fr,to,more=False):
all_paths = []
final_state = to
simple = nx.all_simple_paths(graph,fr,final_state)
circles = nx.simple_cycles(graph)
for p in simple:
all_paths.append(p)
for cyc in circles:
head = []
tail = []
all_start = [cyc[0]]
if(more):
all_start = getAllPossibleCycleStart(graph,fr,to,cyc)
for start_point in all_start:
c = shift(cyc.index(start_point), cyc)
head = nx.shortest_path(graph,fr,start_point)
tail = nx.shortest_path(graph,start_point,final_state)
if(start_point==fr):
p = c+c+tail
elif(start_point==(final_state)):
p = head + c[1:]+c+[start_point]
p_ext = head+c[1:]+[final_state]
all_paths.append(p_ext)
else:
p = head+c[1:]+tail
all_paths.append(p)
return all_paths
# In[11]:
import os,re
def check_trace_valid(trace, automata_file):
#write trace to file
tmp_file = './trace_tmp.lp'
write_to_file(tmp_file, trace)
#execute script, call clingo
res_clingo = execute_clingo(tmp_file, automata_file)
output = ''
a = res_clingo.stdout.readline()
#check result
result = False
while(a) :
output += a
a = res_clingo.stdout.readline()
if ("UNSATISFIABLE" in a):
result = False
elif("SATISFIABLE" in a ):
result = True
os.remove(tmp_file)
# print output
return result
def checkEdgeValid(automata,edge):
g = automata.graph
fin_state = len(automata.states)-1
fr = edge[0]
to = edge[1]
if((fr == 0 or nx.has_path(g,0,fr)) and( to == fin_state or nx.has_path(g,to,fin_state))):
return True
return False
def getValidEdges(automata):
res = []
for e in automata.deltas:
if(checkEdgeValid(automata,e)):
res.append(e)
return res
def getRawDelta(raw):
res = ''
for r in raw:
if(not 'delta' in r):
break
res += r + '\n'
return res
def getEdgesFromLearning(raw, learn_id):
edges = []
conditions = []
rawOutput = ''
finishedDeltas = False
for d in raw:
if(not 'delta' in d):
finishedDeltas = True
rawOutput+=d
if(not finishedDeltas):
m = re.search(r"delta\(state(\d+),V0,state(\d+),(\d)", d)
groups = map(int,m.groups())
edges.append((groups[0],groups[1]))
conditions.append(groups[2])
log('rawLearningResult',rawOutput,learn_id)
return edges,conditions
def getTimeFromLearning(time_str):
start = time_str.index(':')
end = time_str.index('s')
time = time_str[start+2:end]
return float(time)
def conditions_to_determininistic(path_cond):
rep = [0,1]
modified = []
remain = path_cond[:]
while( len(remain) != 0):
e = remain[0]
if(rep in e):
remain.remove(e)
index = e.index(rep)
replace_to_zero = e[0:index]+[[0,0]]+e[index+1:]
replace_to_one = e[0:index]+[[1,1]]+e[index+1:]
modified.append(replace_to_zero)
modified.append(replace_to_one)
remain.append(replace_to_zero)
remain.append(replace_to_zero)
else:
remain.remove(e)
modified.append(e)
return modified
def gen_bare_minimum_example(auto):
example_str = '#pos(p{0},{{st({1},state{2}),st({3},state{4})}},{{}},{{input({5},{6}).trace_length({7}).}}).'
final_state = len(auto.states)-1
l = len(auto.deltas)
exps = []
for i in range(l):
d = auto.deltas[i]
if(not checkEdgeValid(auto,d)):
continue
con = auto.conditions[i]
input_time = 0
if(d[0]!=0):
input_time = len(nx.shortest_path(auto.graph,0,d[0]))-1
trace_length = input_time+1
if(d[1]!=final_state):
trace_length = input_time + len(nx.shortest_path(auto.graph,d[1],final_state))
e = example_str.format(i,input_time,d[0],input_time+1,d[1],input_time,con[0],trace_length)
if(con == [0,1]):
extra_e = example_str.format('_extra_'+str(i),input_time,d[0],input_time+1,d[1],input_time,con[1],trace_length)
exps.append(extra_e)
exps.append(e)
return '\n'.join(exps)+'\n'
def getConditionCode(cond):
if(cond == [0,0]):
return 0
elif(cond ==[1,1]):
return 1
elif(cond ==[0,1]):
return 2
def StateInfoForClingo(auto):
res = ''
for i in range(auto.state_num):
res += 'state(state{0}).\n'.format(i)
res += ':- not st(T,state{}), trace_length(T).\n'.format(auto.state_num-1)
return res
def getGeneratedAutomataFile(learn_id):
parent = getLogPath('',learn_id)
path = os.path.join(parent,'automata')
if(not os.path.isdir(path)):
os.makedirs(path)
automataFile = os.path.join(path,'generatedAutomata.lp')
return automataFile
def testAutomataAreSame(learned_auto_asp_file,learn_id,test_range,test_time):
log_str = ''
generatedAutomata = getGeneratedAutomataFile(learn_id)
for i in range(test_time):
ran_inputs = get_random_inputs(test_range)
ran_trace = string_to_trace(ran_inputs)
r_g = check_trace_valid(ran_trace,generatedAutomata)
r_l = check_trace_valid(ran_trace,learned_auto_asp_file)
if(r_g!=r_l):
log_str = 'not match,autoID: {}\n trace:{}\n, generated result : {}, leanred result : {}'
log_str = log_str.format(learn_id,ran_trace,r_g, r_l)
return False,log_str
return True,log_str
def checkLearningIsRight_ByTest(auto,learningRes,learn_id):
a = learningRes.stdout.readline()
output = []
while(a):
if ("UNSATISFIABLE" in a):
print 'learning is unsatisiable!, valid edges:', len(getValidEdges(auto))
print 'id:', learn_id
return False,0
output.append(a)
a = learningRes.stdout.readline()
#get learned delta(in asp)
deltas = getRawDelta(output)
time = getTimeFromLearning(output[-1])
learned_auto_asp_file = getLogPath('learnedAutomata.lp',learn_id)
copyfile('useIlasp/ilaspTemplate.lp',learned_auto_asp_file)
append_to_file(learned_auto_asp_file, deltas)
stateInfo = StateInfoForClingo(auto)
append_to_file(learned_auto_asp_file, stateInfo)
result,err_str = testAutomataAreSame(learned_auto_asp_file,learn_id,1000,100)
log('error messages', err_str, learn_id)
learnedEdges,learnedConditions = getEdgesFromLearning(output,learn_id)
return result, time
def checkLearningIsRight_byDeltaConditions(auto,learningRes,learn_id):
a = learningRes.stdout.readline()
output = []
result = True
while(a):
if ("UNSATISFIABLE" in a):
print 'learning is unsatisiable!, valid edges:', len(getValidEdges(auto))
print 'id:', learn_id
result = False
output.append(a)
a = learningRes.stdout.readline()
learnedEdges,learnedConditions = getEdgesFromLearning(output,learn_id)
time = getTimeFromLearning(output[-1])
log_str = ''
error_str = ''
l = len(learnedEdges)
#check each learned edge/delta is actually in automata
#also check if the condition are right
for i in range(l):
ld = learnedEdges[i]
if(not ld in auto.deltas):
error_str += "learned delta {} is not in deltas \n".format(ld)
result = False
else:
indx = auto.deltas.index(ld)
cond = auto.conditions[indx]
learned_cond = int(learnedConditions[i])
if(getConditionCode(cond) != learned_cond):
error_str+= "condition wrong, condition index: {} \n".format(i)
result = False
log_str += "learned delta: {} with condition: {} \n".format(ld, learned_cond)
#check correct number of edges is learned
correct_l = len(getValidEdges(auto))
if(correct_l != l):
error_str += "actual valid edge number: {} learned: {}\n".format(correct_l,l)
log('checking delta',log_str+error_str+ 'learning time: {}'.format(time),learn_id)
return result, time
def stateInfoForIlaspLearning(state_number):
res = '\n'
for i in range(state_number):
res += "state(state{}).\n".format(i)
res += "accept :- st(T,state{}),trace_length(T).\n".format(state_number-1)
return res
def changeLengthOfDelta(mode,num):
modified= []
for m in mode[:-1]:
if(len(m) < 10):
continue
reSearchGroup= re.search(r"delta\(state\d+,V0,state\d+,(\d)", m)
cond_num = reSearchGroup.groups()[0]
if(cond_num=='2'):
modified.append(str(num)+m[1:])
else:
modified.append(m)
return ''.join(modified)
def completeIlaspEncoding(ilasp_file,examples,state_num):
ilasp_template = 'useIlasp/ilaspTemplate.lp'
mode = getILASPSpace(state_num)
mode_str = changeLengthOfDelta(mode,2)
copyfile(ilasp_template,ilasp_file)
append_to_file(ilasp_file,examples)
append_to_file(ilasp_file,mode_str)
stateInfo = stateInfoForIlaspLearning(state_num)
append_to_file(ilasp_file,stateInfo)
append_to_file(ilasp_file,'#max_penalty({}).\n'.format(2*4*state_num+2))
def inputs_to_example_template(head_str, trace,final_state,count=0,error=False):
res = ''
for t in range(len(trace)):
#there might be some comment for the trace, for debugging only
tmp = trace[t].split('\n')
if(len(tmp)>1):
res += '\n'.join(tmp[:-1]) + '\n'
#real format
head = head_str.format(t+count)
trace_length_s = re.search(r"trace_length\((\d+)\)", tmp[-1])
trace_length = int(trace_length_s.groups()[0])
if(not error):
st_info = 'state'+str(final_state)
else:
st_info = '_'
trace_end_info = '{{st({},{})}},{{}},{{'.format(trace_length,st_info)
res += head + trace_end_info + tmp[-1] + '}).\n'
return res
#don't need map for now, probably never tho
def inputs_to_ilasp_examples(final_state,clingo_traces,invalid_traces,invalid_int_traces):
exp = ''
exp += inputs_to_example_template("#pos(p{0},", clingo_traces,final_state)
exp += inputs_to_example_template("#neg(n{0},", invalid_traces,final_state,error=True)
exp += inputs_to_example_template("#neg(n{0},", invalid_int_traces, final_state,count=len(invalid_traces))
return exp
# In[12]:
def allStateValid(auto,graph):
final_state = auto.state_num -1
for s in range(auto.state_num):
check_from = (s==0) or nx.has_path(graph,0,s)
check_to = (s==final_state) or nx.has_path(graph,s,final_state)
if(not (check_from and check_to)):
return False
return True
def checkMutualPossible(deltas):
for (s,m) in mutual_required(deltas):
if(m > 2):
return False
return True
def getValidGraph(auto):
graph = gen_graph(auto.state_num)
deltas = list(graph.edges())
valid_graph = checkMutualPossible(deltas)
count = 1
while( not (valid_graph and allStateValid(auto,graph))):
graph = gen_graph(auto.state_num)
count +=1
deltas = list(graph.edges())
valid_graph = checkMutualPossible(deltas)
return graph
class Automata:
#assume first is the initial state and
#the last one is the accepting state
states = []
#lower and upper bound of the input
in_low = 0
in_upp = 1
minStates = 2
missing_num=0
missing_prob=0
error_num = 0
error_prob=0
def __init__(self, state_num, in_low, in_upp):
self.state_num = state_num
self.states = []
self.in_low = in_low
self.in_upp = in_upp
def setMissingInput(self,num,prob):
self.missing_num=num
self.missing_prob = prob
def setErrorInput(self,num,prob):
self.error_num = num
self.error_prob = prob
def gen_valid_paths(self):
return gen_all_paths(self.graph,0,len(self.states)-1,True)
def gen_invalid_paths(self):
modified_g = self.graph.copy()
err_graph = graph_with_error_state(self,modified_g)
error_paths = gen_all_paths(err_graph,0,len(self.states),True)
internal_paths = []
#traces that stops at internal state
for i in range(0,self.state_num-1):
if(nx.has_path(self.graph,0,i)):
inv_ps = gen_all_paths(self.graph,0,i,True)
internal_paths += inv_ps
return error_paths, internal_paths
def generate_automata(self):
output = ""
num_states = max(self.state_num,self.minStates)
for i in range(num_states):
self.states.append("state"+str(i))
#limiting inputs
output += "in_limit(" + str(self.in_upp) + "). \n"
output = gen_states(output, self.states)
graph = getValidGraph(self)
deltas = list(graph.edges())
# print "generated graph, {} times".format(count)
#condition: [[l,u],...] index is corresponding to delta index
#error_conditions: [[con,con],..],index is state number,
#con is the conditions that should not be satisfied
conditions,error_conditions = fillUpConditionsForDelta(deltas,self)
#output
output = gen_condition_tostr(output,conditions)
output = gen_deltas_tostr(output,deltas,self.states)
output = gen_state_trans(output,self.in_low, self.states[0])
output = gen_constraints(output,self.states[-1])
output += gen_input_complete()
output += "#show st/2.\n"
self.output = output
self.deltas = deltas
self.graph = graph
self.conditions = conditions
self.error_cons = error_conditions
def summarize(self):
res = ''
res += "states:" + str(self.states) + '\n'
res += "deltas:\n" + '\n'.join(str(x) for x in self.deltas) + '\n'
res += "conditions:\n"
for i in range(len(self.deltas)):
res+= str(self.deltas[i]) + ": " + str(self.conditions[i])+" \n"
res += "error_conditions:\n" + '\n'.join(str(x) for x in self.error_cons)
return res
# In[13]:
def logAutomataEncoding(auto, clingo_traces, inv_traces,learn_id):
#setup directory
automataFile = getGeneratedAutomataFile(learn_id)
write_to_file(automataFile,auto.output)
parent = getLogPath('',learn_id)
path = os.path.join(parent,'automata')
results = True
for t in range(len(clingo_traces)):
tFile = 'valid_t'+str(t)+'.lp'
tPath = os.path.join(path,tFile)
write_to_file(tPath,clingo_traces[t])
r = check_trace_valid(clingo_traces[t],automataFile)
if( not r):
results = False
print "somethingwrong with this trace!:",t
for inv in range(len(inv_traces)):
iFile = 'invalid_t'+str(inv)+'.lp'
iPath = os.path.join(path,iFile)
write_to_file(iPath,inv_traces[inv])
r = check_trace_valid(inv_traces[inv],automataFile)
if(r):
results = False