def mp():
transiant_num = 10
thr_user_number = 5
#get transiant_num and thr_user_number from input
## set time and data using system time
Day = datetime.date.today().strftime("%A")
Time = time.strftime("%H:%M")
current_time = Time
# start a while loop to calculate and save the results in 'ICNoutput' folder till time 24:00-transiant_num
current_time_in_min = sum(
int(x) * 60**i
for i, x in enumerate(reversed(current_time.split(':'))))
##$print current_time_in_min
#while (sum(int(x) * 60 ** i for i,x in enumerate(reversed(current_time.split(':'))))<(1440-transiant_num)):
##$print ' '
##$print '**********Group Mobility Prediction is runing *****************'
##$print 'transiant time is : ', transiant_num
##$print 'thresh old for user number is :', thr_user_number
##$print 'Current date is: ', Day , ' & Currer time is : ', Time
#print 'Currer day is : ', +Day ' & Current time is: ', +Time
##$print '****************************************************************'
##$print "------The Calculation of the Group Mobility Prediction based on the Trajectory Trace Files------"
#print 'Current time :' +Time
#print 'Current date :' +Day
if Day == 'Saturday':
current_day = 'sat'
elif Day == 'Sunday':
current_day = 'sun'
elif Day == 'Monday':
current_day = 'mon'
elif Day == 'Tuesday':
current_day = 'tue'
elif Day == 'Wednesday':
current_day = 'wed'
elif Day == 'Thursday':
current_day = 'thu'
elif Day == 'Friday':
current_day = 'fri'
#shutil.rmtree('/home/ubuntu/mobaas/Groupeoutput')
if not os.path.exists('/home/ubuntu/mobaas/Groupeoutput'):
#os.makedirs('/home/ubuntu/mobaas/Groupeoutput')
print "Create the folder Groupeoutput"
return 0
folder = '/home/ubuntu/mobaas/Groupeoutput'
for the_file in os.listdir(folder):
file_path = os.path.join(folder, the_file)
if os.path.isfile(file_path):
if "threshold_cell_users.txt" not in file_path:
os.unlink(file_path)
#elif os.path.isdir(file_path): shutil.rmtree(file_path)
all_users_start_move_cell_prob = [
] # a matrix to save all_users_start_move_cell_prob
f8 = open(
'/home/ubuntu/mobaas/Groupeoutput/all_users_start_move_cell_prob.txt',
'a')
for file in dirs:
user_id = int(file)
#print '...................'
##$print 'User ID :' , user_id
##$print '...................'
#transiant_num =20 # after how many minutes!
path = '/home/ubuntu/mobaas/test_data/Trace_Files/' '%d%s' % (
user_id, '/Step_1')
number_of_files_all_day = len([
item for item in os.listdir(path)
if os.path.isfile(os.path.join(path, item))
]) #total number of files
#print number_of_files_all_day
total_input_file = number_of_files_all_day / 7 #total input trace file for each day
#print total_input_file
file_num = int(
total_input_file * 0.8
) # 80% number of the input traced files for each week day for estimation
#print file_num
total_file_num = 1 * file_num # number of total input traced files
array_of_traces = [
] # an empty array to input traced files as matrix for all dayes
#print " "
#print " "
#print "------The Calculation of the Probabilities based on the Trajectory Trace Files -------"
## bades on the date read the trace files for specifie day
##Saturdays
if current_day == 'sat':
sat_array_of_traces = [
0
] * file_num # an empty array to input traced files as matrix for all Saturday
for i in range(0, file_num):
sat_trace_file = numpy.loadtxt(
'%s/Sat_%d.dat' %
(path, i)) #read the traced files(Saturday)
size = len(sat_trace_file) # size of trace files step
sat_array_of_traces[
i] = sat_trace_file # input each traces fiel in the array
#array_of_traces.extend(sat_array_of_traces)
array_of_traces = sat_array_of_traces
#print 'Current day is :' , current_day
##Sundays
elif current_day == 'sun':
sun_array_of_traces = [
0
] * file_num # an empty array to input traced files as matrix for all Sundays
for i in range(0, file_num):
sun_trace_file = numpy.loadtxt(
'%s/Sun_%d.dat' %
(path, i)) #read the traced files(Sundays)
size = len(sun_trace_file) # size of trace files step
sun_array_of_traces[
i] = sun_trace_file # input each traces fiel in the array
#array_of_traces.extend(sat_array_of_traces)
array_of_traces = sun_array_of_traces
#print 'Current day =' , current_day
##Mondays
elif current_day == 'mon':
mon_array_of_traces = [
0
] * file_num # an empty array to input traced files as matrix for all Monday
for i in range(0, file_num):
mon_trace_file = numpy.loadtxt(
'%s/Mon_%d.dat' %
(path, i)) #read the traced files(Monday)
size = len(mon_trace_file) # size of trace files step
mon_array_of_traces[
i] = mon_trace_file # input each traces fiel in the array
#array_of_traces.extend(sat_array_of_traces)
array_of_traces = mon_array_of_traces
#print 'Current day =' , current_day
##Tusedays
elif current_day == 'tue':
tue_array_of_traces = [
0
] * file_num # an empty array to input traced files as matrix for all Tuseday
for i in range(0, file_num):
tue_trace_file = numpy.loadtxt(
'%s/Tue_%d.dat' %
(path, i)) #read the traced files(Tuseday)
size = len(tue_trace_file) # size of trace files step
tue_array_of_traces[
i] = tue_trace_file # input each traces fiel in the array
#array_of_traces.extend(sat_array_of_traces)
array_of_traces = tue_array_of_traces
#print 'Current day =', current_day
##Wednesdays
elif current_day == 'wed':
wed_array_of_traces = [
0
] * file_num # an empty array to input traced files as matrix for all Wednesday
for i in range(0, file_num):
wed_trace_file = numpy.loadtxt(
'%s/Wed_%d.dat' %
(path, i)) #read the traced files(Wednesday)
size = len(wed_trace_file) # size of trace files step
wed_array_of_traces[
i] = wed_trace_file # input each traces fiel in the array
#array_of_traces.extend(sat_array_of_traces)
array_of_traces = wed_array_of_traces
#print 'Current day =' , current_day
##Thursdays
elif current_day == 'thu':
thu_array_of_traces = [
0
] * file_num # an empty array to input traced files as matrix for all Thursday
for i in range(0, file_num):
thu_trace_file = numpy.loadtxt(
'%s/Thu_%d.dat' %
(path, i)) #read the traced files(Thursday)
size = len(thu_trace_file) # size of trace files step
thu_array_of_traces[
i] = thu_trace_file # input each traces fiel in the array
#array_of_traces.extend(sat_array_of_traces)
array_of_traces = thu_array_of_traces
#print 'Current day =' , current_day
##Fraidays
elif current_day == 'fri':
fri_array_of_traces = [
0
] * file_num # an empty array to input traced files as for all Friday
for i in range(0, file_num):
fri_trace_file = numpy.loadtxt(
'%s/Fri_%d.dat' %
(path, i)) #read the traced files(Friday)
size = len(fri_trace_file) # size of trace files step
fri_array_of_traces[
i] = fri_trace_file # input each traces fiel in the array
#array_of_traces.extend(sat_array_of_traces)
array_of_traces = fri_array_of_traces
#print 'Current day =' , current_day
#*********************************************************************************************************************
##count number of visited cell
arry_of_temp_cell = [
0
] * total_file_num #an array to put the cell ID in each step
arry_of_number_cell = [
0
] * size #an array to count the number of visited cell ID
arry_of_number_cell_copy = [
0
] * size #an array to count the number of visited cell ID
for j in range(0, size):
for i in range(0, total_file_num):
arry_of_temp_cell[i] = array_of_traces[i][j][1]
arry_of_number_cell[j] = [[x, arry_of_temp_cell.count(x)]
for x in set(arry_of_temp_cell)]
arry_of_temp_cell[i] = array_of_traces[i][j][1]
arry_of_number_cell_copy[j] = [[x,
arry_of_temp_cell.count(x)]
for x in set(arry_of_temp_cell)]
##count percentage of visited cell
arry_of_percentage_cell = arry_of_number_cell_copy #an array to calculate the percentage for each visited cell
for j in range(0, size):
sum_cell = 0.
for n in range(0, len(arry_of_percentage_cell[j])):
sum_cell += arry_of_percentage_cell[j][n][
1] #calculate all visited cell
for n in range(0, len(arry_of_percentage_cell[j])):
arry_of_percentage_cell[j][n][1] = arry_of_percentage_cell[j][
n][1] / sum_cell #calculate the percentage for each visited cell
##make an array to save time,percentage and cell ID
arry_of_time_percentage_cell = arry_of_percentage_cell #an array to save [time_cellID, percentage]
for j in range(0, ):
for n in range(0, len(arry_of_percentage_cell[j])):
arry_of_time_percentage_cell[j][n][0] = [
'%d%s%d' % (array_of_traces[0][j][0], '-',
arry_of_percentage_cell[j][n][0])
]
#*********************************************************************************************************************
#open file to save the state file (state file is calculated from this file that has non zero transient probability)
f1 = open('/home/ubuntu/mobaas/Groupeoutput/statefile.txt', 'w')
f1 = open('/home/ubuntu/mobaas/Groupeoutput/statefile.txt', 'a')
##calculate the percentage of going from once cell to other cell in each step
#matrix_of_number_next_cell_percentage=[0]*t
for j in range(0, size - 2):
for n in range(
0, len(arry_of_number_cell[j])
): #an array which has the number of visited cell in each step
#print (n)
arry_of_temp_next_cell = []
arry_of_number_next_cell = []
arry_of_number_next_cell_copy = []
temp = arry_of_number_cell[j][n][
0] # temp get each of the visited cell
for i in range(0, total_file_num):
if array_of_traces[i][j][1] == temp:
#an array to save visited cell in the next step
arry_of_temp_next_cell.append(array_of_traces[i][j +
1][1])
#an array which has the number of visited cell in the next step
arry_of_number_next_cell = [[
x, arry_of_temp_next_cell.count(x)
] for x in set(arry_of_temp_next_cell)]
#make a copy
arry_of_number_next_cell_copy = [[
x, arry_of_temp_next_cell.count(x)
] for x in set(arry_of_temp_next_cell)]
arry_of_number_next_cell_percentage = arry_of_number_next_cell_copy
#calculate the percentage of going to the next cell
sum_next_cell = 0.
for n in range(
0, len(arry_of_number_next_cell_percentage)):
sum_next_cell += arry_of_number_next_cell_percentage[
n][1]
#print(sum_next_cell)
for n in range(
0, len(arry_of_number_next_cell_percentage)):
arry_of_number_next_cell_percentage[n][
1] = arry_of_number_next_cell_percentage[n][
1] / sum_next_cell
#print(sum_next_cell)
#save the the transient probability in state file
for m in range(0, len(arry_of_number_next_cell_percentage)):
f1.write('%s%d' % ('c', array_of_traces[0][j][0]))
f1.write('%s%d ' % ('_', temp))
f1.write('%s%d' % ('c', array_of_traces[0][j + 1][0]))
f1.write('%s%d %.3f \n' %
('_', arry_of_number_next_cell_percentage[m][0],
arry_of_number_next_cell_percentage[m][1]))
f1.close()
#*********************************************************************************************************************
##calculate the transient probability
state_mat = pykov.readmat(
'/home/ubuntu/mobaas/Groupeoutput/statefile.txt')
#print len(state_mat)
#print state_mat.keys()
## change hh:mm to time step (e.g, 06:21-->381 )
current_time_setp = sum(
int(x) * 60**i
for i, x in enumerate(reversed(current_time.split(':'))))
#current_time_setp = current_time_setp - transiant_num # set the start time "transiant_num" ago to from current time to start M.C
current_time_setp = current_time_setp # set the start time "transiant_num" ago to from current time to start M.C
#print current_time_setp
#print len(state_mat)
#Clear the cellprobabilityfile.txt for new calculation
f4 = open('/home/ubuntu/mobaas/Groupeoutput/cellprobabilityfile.txt',
'w')
f4.close()
#check the "current_time_setp - transiant_num" in state_mat to find the valid cell on that time
for i in range(0, len(state_mat)):
if int(state_mat.keys()[i][0]
[1:state_mat.keys()[i][0].index('_')]) == current_time_setp:
if int(state_mat.keys()[i][0]
[state_mat.keys()[i][0].index('_') + 1:]) != 0:
##$print '**************************************************************************************'
#print state_mat.keys()[i][0]
current_time_cell = state_mat.keys()[i][0]
#verey important point here is that now "current_cell" is not the cell at current time,
#here it shows the cell that user was ther at "current_time_setp -transiant_num "
current_cell = int(state_mat.keys()[i][0]
[state_mat.keys()[i][0].index('_') +
1:])
#print current_time_cell
#print current_cell
## make time step and cell as 'c381_5384 '
initial_state = pykov.Vector([(current_time_cell, 1)])
#print initial_state
next_cell_probability = state_mat.pow(
initial_state, transiant_num)
f3 = open('/home/ubuntu/mobaas/Groupeoutput/state_mat.txt',
'w')
for i in range(0, len(state_mat.values())):
f3.write('%s %f \n' %
(state_mat.keys()[i], state_mat.values()[i]))
f3.close()
#the result file
x = []
cell_name = []
y = []
#return next_cell_probability
## print the caclulation of probability in the screen and save it in the "cellprobabilityfile.txt" file, this file only show the probability of moving fron start cell to current cell
##$print "Starting time for estimation = "'{:02d}:{:02d}'.format(*divmod(current_time_setp, 60)), " , User is in cell = ", (current_cell)
##$print "......................................................................................"
#f4 = open('/home/ubuntu/mypath/groupprediction/cellprobabilityfile.txt', 'w')
f4 = open(
'/home/ubuntu/mobaas/Groupeoutput/cellprobabilityfile.txt',
'a')
for i in range(0, len(next_cell_probability.values())):
x.append(i + 1)
cell_name.append(next_cell_probability.keys()[i])
y.append(next_cell_probability.values()[i])
# f4.write('%s %f \n' % (next_cell_probability.keys()[i],next_cell_probability.values()[i]))
temp_next_cell = next_cell_probability.keys()[i]
#f4.write('%s %s %s %f \n' % (current_cell, '{:02d}:{:02d}'.format(*divmod(int(temp_next_cell[1:temp_next_cell.index('_')]), 60)), temp_next_cell[temp_next_cell.index('_') + 1:], next_cell_probability.values()[i]) )
#f4.write('%s %s %f \n' % (current_cell, temp_next_cell[temp_next_cell.index('_') + 1:], next_cell_probability.values()[i]) )
#f4.write('\n------------------')
if temp_next_cell[temp_next_cell.index('_') +
1:] != '0':
f4.write(
'%s %s %f \n' %
(current_cell,
temp_next_cell[temp_next_cell.index('_') +
1:],
next_cell_probability.values()[i]))
##$print 'Next Time = ' '{:02d}:{:02d}'.format(*divmod(int(temp_next_cell[1:temp_next_cell.index('_')]), 60)),' , Current Cell = ', temp_next_cell[temp_next_cell.index('_') + 1:],' , The Probability = ', next_cell_probability.values()[i]
##$elif temp_next_cell[temp_next_cell.index('_') + 1:] == '0':
##$print 'Next Time = ' '{:02d}:{:02d}'.format(*divmod(int(temp_next_cell[1:temp_next_cell.index('_')]), 60)),' , Current Cell = ', temp_next_cell[temp_next_cell.index('_') + 1:],'(Not trace data)',' , The Probability = ', next_cell_probability.values()[i]
#f4.write('--------\n')
f4.close()
# load cellprobabilityfile in a matrix
prob_mat = numpy.loadtxt(
'/home/ubuntu/mobaas/Groupeoutput/cellprobabilityfile.txt')
#print 'original prob_mat : ',prob_mat
#print'...................'
number_of_element_prob_mat = numpy.count_nonzero(prob_mat)
#print 'number of element in original prob_mat', number_of_element_prob_mat
#print'...................'
#find unique row in prob_mat
def unique_rows(a):
a = numpy.ascontiguousarray(a)
unique_a = numpy.unique(a.view([('', a.dtype)] * a.shape[1]))
return unique_a.view(a.dtype).reshape(
(unique_a.shape[0], a.shape[1]))
if number_of_element_prob_mat > 3:
prob_mat = unique_rows(prob_mat)
#print 'unic prob_mat',prob_mat
#print'...................'
number_of_element_prob_mat = numpy.count_nonzero(prob_mat)
#print 'number of element in unic prob_mat', number_of_element_prob_mat
# check if prob_mat dose not have only one raw(it has three elements), inorder to find unic raw in it
if number_of_element_prob_mat == 3:
#print 'number of raw in prob_mat', raw_of_prob_mat
new_prob_mat_1 = numpy.append(prob_mat, 0)
new_prob_mat_2 = numpy.append(new_prob_mat_1, 0)
#print 'now prob_mat', new_prob_mat_2
prob_mat_extend = new_prob_mat_2
#new = numpy.append(prob_mat, 0)
#new1 = numpy.append(new, 0)
#raw_of_punique_rows_prob_mat = len(unique_rows_prob_mat)
#print 'number of raw in raw_of_punique', raw_of_punique_rows_prob_mat
#prob_mat_extend= np.c_[ unique_rows_prob_mat, 0, 0 ]
#unique_rows_prob_mat.extend([0, 0])
#C =[0]*2
#prob_mat_extend= np.c_[ unique_rows_prob_mat, np.zeros(1), np.zeros(1) ]
#print prob_mat_extend
#print len(prob_mat_extend)
size_of_prob_mat_extend = 1
#print size_of_prob_mat_extend
elif number_of_element_prob_mat != 3:
#print 'number of raw in prob_mat', raw_of_prob_mat
unique_rows_prob_mat = unique_rows(prob_mat)
raw_of_punique_rows_prob_mat = len(unique_rows_prob_mat)
#print 'number of raw in raw_of_punique', raw_of_punique_rows_prob_mat
prob_mat_extend = np.c_[unique_rows_prob_mat,
np.zeros(raw_of_punique_rows_prob_mat),
np.zeros(raw_of_punique_rows_prob_mat)]
#print prob_mat_extend
size_of_prob_mat_extend = len(prob_mat_extend)
#print size_of_prob_mat_extend
# calculate the probability of starting from one specific cell
count_start_cell_numb = Counter(unique_rows_prob_mat[:, 0])
#print 'count_start_cell_numb', count_start_cell_numb
uniq_start_cell_numb = 1. * len(unique_rows_prob_mat[:, 0])
#print 'uniq_start_cell_numb', uniq_start_cell_numb
count_start_cell_perc = numpy.zeros(shape=(len(count_start_cell_numb),
2))
for i in range(0, len(count_start_cell_numb)):
count_start_cell_perc[i, 0] = count_start_cell_numb.keys()[i]
count_start_cell_perc[
i,
1] = count_start_cell_numb.values()[i] / uniq_start_cell_numb
#print count_start_cell_perc[i,0]
#print count_start_cell_perc[i,1]
#print count_start_cell_perc
for i in range(0, len(count_start_cell_perc)):
temp_cell = count_start_cell_perc[i, 0]
temp_prob = count_start_cell_perc[
i, 1] #probability that user was in the start cell
#for j in range(0,len(prob_mat_extend[:,0])):
if size_of_prob_mat_extend == 1:
#print 'in size 1 :', size_of_prob_mat_extend
#print 'prob_mat_extend =', prob_mat_extend
#print 'prob_mat_extend[0]=', prob_mat_extend[2]
#for j in range(0,size_of_prob_mat_extend):
if prob_mat_extend[0] == temp_cell:
prob_mat_extend[
3] = temp_prob #probability that user was in the start cell
prob_mat_extend[4] = prob_mat_extend[3] * prob_mat_extend[
2] #probability that user was in the start cell and move to the current cell
else:
#print 'in size more than 1:', size_of_prob_mat_extend
for j in range(0, size_of_prob_mat_extend):
if prob_mat_extend[j, 0] == temp_cell:
prob_mat_extend[
j,
3] = temp_prob #probability that user was in the start cell
prob_mat_extend[
j, 4] = prob_mat_extend[j, 3] * prob_mat_extend[
j,
2] #probability that user was in the start cell and move to the current cell
#find unique row in prob_mat_extend
def unique_rows(a):
a = numpy.ascontiguousarray(a)
unique_a = numpy.unique(a.view([('', a.dtype)] * a.shape[1]))
return unique_a.view(a.dtype).reshape(
(unique_a.shape[0], a.shape[1]))
if size_of_prob_mat_extend != 1:
unique_rows_prob_mat_extend = unique_rows(prob_mat_extend)
#useID_add_prob_mat_extend = prob_mat_extend
useID_add_prob_mat_extend = unique_rows_prob_mat_extend
useID_add_prob_mat_extend = np.insert(useID_add_prob_mat_extend,
0,
values=user_id,
axis=1)
##numpy.savetxt('/home/ubuntu/mobaas/Groupeoutput/users_startcells_movecell_probability_%d.txt' % (user_id), useID_add_prob_mat_extend ,fmt='%.4f')
numpy.savetxt(f8, useID_add_prob_mat_extend, fmt='%.4f')
if size_of_prob_mat_extend == 1:
unique_rows_prob_mat_extend = prob_mat_extend
useID_add_prob_mat_extend = unique_rows_prob_mat_extend
useID_add_prob_mat_extend = np.insert(useID_add_prob_mat_extend, 0,
user_id)
#useID_add_prob_mat_extend = useID_add_prob_mat_extend.T
##numpy.savetxt('/home/ubuntu/mobaas/Groupeoutput/users_startcells_movecell_probability_%d.txt' % (user_id), useID_add_prob_mat_extend[None] ,fmt='%.4f')
numpy.savetxt(f8, useID_add_prob_mat_extend[None], fmt='%.4f')
#np.savetxt(f,a)
f8.close()
## in this matrix: the rows are : userID-start cell-visited cell-pobability to move to visited cell(P1)-pobalility that user was in start cell(P2)-P1*P2
#numpy.savetxt('/home/ubuntu/mypath/groupprediction/Groupeoutput/users_startcells_movecell_probability_%d.txt' % (user_id), useID_add_prob_mat_extend ,fmt='%.4f')
#^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
#^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
all_users_start_move_cell_prob = numpy.loadtxt(
'/home/ubuntu/mobaas/Groupeoutput/all_users_start_move_cell_prob.txt')
#all_users_start_move_cell_prob is sorted based on row 2 (move cell) to find all users that move to 'move cell'
sorted_all_users_start_move_cell_prob = all_users_start_move_cell_prob[
all_users_start_move_cell_prob[:, 2].argsort()]
numpy.savetxt(
'/home/ubuntu/mobaas/Groupeoutput/sorted_all_users_start_move_cell_prob.txt',
sorted_all_users_start_move_cell_prob,
fmt='%.4f')
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# count how many number of users(with unic ID) are moved to 'move cell'
count_user_in_move_cell = Counter(sorted_all_users_start_move_cell_prob[:,
2])
for i in range(0, len(count_user_in_move_cell)):
temp_mat = sorted_all_users_start_move_cell_prob[numpy.where(
sorted_all_users_start_move_cell_prob[:, 2] ==
count_user_in_move_cell.keys()[i])]
#print 'temp_mat',temp_mat[0,2]
unic_user = Counter(temp_mat[:, 0])
unic_user_number = len(unic_user)
#print 'unic_user', unic_user_number
count_user_in_move_cell[temp_mat[0, 2]] = unic_user_number
#print 'count_user_in_move_cell.values()[i]', count_user_in_move_cell.values()[i]
#print 'count_user_in_move_cell second', count_user_in_move_cell
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# make another matrix to save the probabilites of users(for all users P1*P2)
count_user_in_move_cell_extend = numpy.zeros(
shape=(len(count_user_in_move_cell), 4))
for i in range(0, len(count_user_in_move_cell)):
count_user_in_move_cell_extend[i,
0] = count_user_in_move_cell.keys()[i]
count_user_in_move_cell_extend[i,
1] = count_user_in_move_cell.values()[i]
#print 'number is user is : ' count_user_in_move_cell.values()[i]
# sort the 'count_user_in_move_cell_extend' based on number of users
sorted_count_user_in_move_cell_extend = count_user_in_move_cell_extend[
count_user_in_move_cell_extend[:, 1].argsort()]
#print 'sorted_count_user_in_move_cell_extend :' , sorted_count_user_in_move_cell_extend
Temp_time = '{:02d}:{:02d}'.format(
*divmod(int(temp_next_cell[1:temp_next_cell.index('_')]), 60))
##$print ' '
##$print '******** Notification******'
##$print 'It is estimated at time (',Temp_time,') number of users in following cell will be more than defined threshold!'
numpy.savetxt(
'/home/ubuntu/mobaas/Groupeoutput/sorted_count_user_in_move_cell_extend.txt',
sorted_count_user_in_move_cell_extend,
fmt='%.4f')
f9 = open('/home/ubuntu/mobaas/Groupeoutput/threshold_cell_users.txt', 'w')
#f9 = open('/home/ubuntu/mobaas/Groupeoutput/threshold_cell_users.txt', 'a')
for i in range(0, len(sorted_count_user_in_move_cell_extend)):
if sorted_count_user_in_move_cell_extend[i, 1] > thr_user_number:
##$print 'In cell (', sorted_count_user_in_move_cell_extend[i,0], ') --> ', sorted_count_user_in_move_cell_extend[i,1] , 'users'
f9.write('%s %s %s \n' %
(Temp_time, sorted_count_user_in_move_cell_extend[i, 0],
sorted_count_user_in_move_cell_extend[i, 1]))
f9.close()
#####################
# list for time, cell, number of users
threshold_cell_users = [
line.strip() for line in open(
'/home/ubuntu/mobaas/Groupeoutput/threshold_cell_users.txt')
]
print threshold_cell_users
####################
for i in range(0, len(sorted_count_user_in_move_cell_extend)):
temp_cell = sorted_count_user_in_move_cell_extend[i, 0]
#print temp_cell
temp_prob_1 = 1.
temp_prob_2 = 1.
n = 0.
for j in range(0, len(sorted_all_users_start_move_cell_prob)):
if temp_cell == sorted_all_users_start_move_cell_prob[j, 2]:
n = n + 1
temp_prob_1 = temp_prob_1 * sorted_all_users_start_move_cell_prob[
j, 5] + 0.0000001
#if temp_cell != sorted_all_users_start_move_cell_prob[j,2]:
# n=n+1
# temp_prob_2=temp_prob_2*(1-sorted_all_users_start_move_cell_prob[j,5])
#print n
#print 'temp_prob_1 =' , temp_prob_1
#print 'temp_prob_2 =' , temp_prob_2
sorted_count_user_in_move_cell_extend[i, 2] = (temp_prob_1 *
temp_prob_2) * 100
#print '(temp_prob_1*temp_prob_2)*100 = x=', (temp_prob_1*temp_prob_2)*100
#sorted_count_user_in_move_cell_extend[i,3]=math.log10(temp_prob_1*temp_prob_2)
sorted_count_user_in_move_cell_extend[i, 3] = log10(temp_prob_1 *
temp_prob_2)
numpy.savetxt(
'/home/ubuntu/mobaas/Groupeoutput/sorted_count_user_in_move_cell_extend.txt',
sorted_count_user_in_move_cell_extend,
fmt='%.4f')
'''
def mp(user_id,current_time,current_day,transiant_num):
# Dict to store all the individual results, to be returned at the end of the code
result = {}
## print current input time and dat
print '********************************************************************************'
print 'Current time is: ', current_time, ' & Currer day is : ' , current_day
print '********************************************************************************'
## set time and data using system time
Day = current_day
if Day=='Saturday':
current_day = 'sat'
elif Day=='Sunday':
current_day = 'sun'
elif Day=='Monday':
current_day = 'mon'
elif Day=='Tuesday':
current_day = 'tue'
elif Day=='Wednesday':
current_day = 'wed'
elif Day=='Thursday':
current_day = 'thu'
elif Day=='Friday':
current_day = 'fri'
path_user = '******'
dirs = os.listdir( path_user)
# remove the 'ICNoutput' and create a new one to save results
shutil.rmtree('./mobaas/ICNoutput')
if not os.path.exists('./mobaas/ICNoutput'):
os.makedirs('./mobaas/ICNoutput')
# start a while loop to calculate and save the results in 'ICNoutput' folder till time 24:00-transiant_num
current_time_in_min = sum(int(x) * 60 ** i for i,x in enumerate(reversed(current_time.split(':'))))
#while (sum(int(x) * 60 ** i for i,x in enumerate(reversed(current_time.split(':'))))<(1440-transiant_num)):
#stop prediction at 18:40, for the purpose of demo to limit the period of prediction
while (sum(int(x) * 60 ** i for i,x in enumerate(reversed(current_time.split(':'))))<(1130-transiant_num)):
#change current time to min, add ransiant_num to it, change to xx:yy format, put in Temp_time
current_time_in_min = sum(int(x) * 60 ** i for i,x in enumerate(reversed(current_time.split(':'))))
next_time_in_min = current_time_in_min + transiant_num
Temp_time = '{:02d}:{:02d}'.format(*divmod(next_time_in_min, 60))
#open and save the result in an new file
f4 = open('./mobaas/ICNoutput/cellprobability_file_ICN_%s.txt' % Temp_time, 'w')
f4 = open('./mobaas/ICNoutput/cellprobability_file_ICN_%s.txt' % Temp_time, 'a')
f4.write('%s %s \n' % (Day, current_time))
f4.write('%s \n' % ('---------------------------------------------------'))
f4.write('%s \n' % ('user--current cell--next time--next cell--probability'))
f4.write('%s \n' % ('---------------------------------------------------'))
for i in range(0, len(dirs)):
user_id=int(dirs[i])
##print 'Current user : '******'./mobaas/test_data/Trace_Files/''%d%s' % (user_id,'/Step_1')
number_of_files_all_day = len([item for item in os.listdir(path) if os.path.isfile(os.path.join(path, item))]) #total number of files
#print number_of_files_all_day
total_input_file = number_of_files_all_day/7 #total input trace file for each day
#print total_input_file
file_num = int(total_input_file*0.7 ) # 70% number of the input traced files for each week day for estimation
#print file_num
total_file_num=1*file_num # number of total input traced files
array_of_traces=[] # an empty array to input traced files as matrix for all dayes
## bades on the date read the trace files for specifie day
##Saturdays
if current_day == 'sat':
sat_array_of_traces = [0]*file_num # an empty array to input traced files as matrix for all Saturday
for i in range(0, file_num):
sat_trace_file=numpy.loadtxt('%s/Sat_%d.dat' % (path, i)) #read the traced files(Saturday)
size=len(sat_trace_file) # size of trace files step
sat_array_of_traces[i] = sat_trace_file # input each traces fiel in the array
#array_of_traces.extend(sat_array_of_traces)
array_of_traces = sat_array_of_traces
#print 'Current day is :' , current_day
##Sundays
elif current_day == 'sun':
sun_array_of_traces = [0]*file_num # an empty array to input traced files as matrix for all Sundays
for i in range(0, file_num):
sun_trace_file=numpy.loadtxt('%s/Sun_%d.dat' % (path, i)) #read the traced files(Sundays)
size=len(sun_trace_file) # size of trace files step
sun_array_of_traces[i] = sun_trace_file # input each traces fiel in the array
#array_of_traces.extend(sat_array_of_traces)
array_of_traces = sun_array_of_traces
#print 'Current day =' , current_day
##Mondays
elif current_day == 'mon':
mon_array_of_traces = [0]*file_num # an empty array to input traced files as matrix for all Monday
for i in range(0, file_num):
mon_trace_file=numpy.loadtxt('%s/Mon_%d.dat' % (path, i)) #read the traced files(Monday)
size=len(mon_trace_file) # size of trace files step
mon_array_of_traces[i] = mon_trace_file # input each traces fiel in the array
#array_of_traces.extend(sat_array_of_traces)
array_of_traces = mon_array_of_traces
#print 'Current day =' , current_day
##Tusedays
elif current_day == 'tue':
tue_array_of_traces = [0]*file_num # an empty array to input traced files as matrix for all Tuseday
for i in range(0, file_num):
tue_trace_file=numpy.loadtxt('%s/Tue_%d.dat' % (path, i)) #read the traced files(Tuseday)
size=len(tue_trace_file) # size of trace files step
tue_array_of_traces[i] = tue_trace_file # input each traces fiel in the array
#array_of_traces.extend(sat_array_of_traces)
array_of_traces = tue_array_of_traces
#print 'Current day =', current_day
##Wednesdays
elif current_day == 'wed':
wed_array_of_traces = [0]*file_num # an empty array to input traced files as matrix for all Wednesday
for i in range(0, file_num):
wed_trace_file=numpy.loadtxt('%s/Wed_%d.dat' % (path, i)) #read the traced files(Wednesday)
size=len(wed_trace_file) # size of trace files step
wed_array_of_traces[i] =wed_trace_file # input each traces fiel in the array
#array_of_traces.extend(sat_array_of_traces)
array_of_traces = wed_array_of_traces
#print 'Current day =' , current_day
##Thursdays
elif current_day == 'thu':
thu_array_of_traces = [0]*file_num # an empty array to input traced files as matrix for all Thursday
for i in range(0, file_num):
thu_trace_file=numpy.loadtxt('%s/Thu_%d.dat' % (path, i)) #read the traced files(Thursday)
size=len(thu_trace_file) # size of trace files step
thu_array_of_traces[i] =thu_trace_file # input each traces fiel in the array
#array_of_traces.extend(sat_array_of_traces)
array_of_traces = thu_array_of_traces
#print 'Current day =' , current_day
##Fraidays
elif current_day == 'fri':
fri_array_of_traces = [0]*file_num # an empty array to input traced files as for all Friday
for i in range(0, file_num):
fri_trace_file=numpy.loadtxt('%s/Fri_%d.dat' % (path, i)) #read the traced files(Friday)
size=len(fri_trace_file) # size of trace files step
fri_array_of_traces[i] = fri_trace_file # input each traces fiel in the array
#array_of_traces.extend(sat_array_of_traces)
array_of_traces = fri_array_of_traces
#print 'Current day =' , current_day
#*********************************************************************************************************************
##count number of visited cell
arry_of_temp_cell=[0]*total_file_num #an array to put the cell ID in each step
arry_of_number_cell=[0]*size #an array to count the number of visited cell ID
arry_of_number_cell_copy=[0]*size #an array to count the number of visited cell ID
for j in range(0, size):
for i in range(0, total_file_num):
arry_of_temp_cell[i]=array_of_traces[i][j][1]
arry_of_number_cell[j]=[[x,arry_of_temp_cell.count(x)] for x in set(arry_of_temp_cell)]
arry_of_temp_cell[i]=array_of_traces[i][j][1]
arry_of_number_cell_copy[j]=[[x,arry_of_temp_cell.count(x)] for x in set(arry_of_temp_cell)]
##count percentage of visited cell
arry_of_percentage_cell=arry_of_number_cell_copy #an array to calculate the percentage for each visited cell
for j in range(0, size):
sum_cell=0.
for n in range(0,len(arry_of_percentage_cell[j])):
sum_cell += arry_of_percentage_cell[j][n][1] #calculate all visited cell
for n in range(0,len(arry_of_percentage_cell[j])):
arry_of_percentage_cell[j][n][1]=arry_of_percentage_cell[j][n][1]/sum_cell #calculate the percentage for each visited cell
##make an array to save time,percentage and cell ID
arry_of_time_percentage_cell=arry_of_percentage_cell #an array to save [time_cellID, percentage]
for j in range(0, ):
for n in range(0,len(arry_of_percentage_cell[j])):
arry_of_time_percentage_cell[j][n][0]=['%d%s%d' %(array_of_traces[0][j][0],'-',arry_of_percentage_cell[j][n][0])]
#*********************************************************************************************************************
#open file to save the state file (state file is calculated from this file that has non zero transient probability)
f1 = open('./mobaas/ICNoutput/statefile_ICN.txt', 'w')
f1 = open('./mobaas/ICNoutput/statefile_ICN.txt', 'a')
##calculate the percentage of going from once cell to other cell in each step
#matrix_of_number_next_cell_percentage=[0]*t
for j in range(0,size-2):
for n in range(0,len(arry_of_number_cell[j])): #an array which has the number of visited cell in each step
#print (n)
arry_of_temp_next_cell=[]
arry_of_number_next_cell=[]
arry_of_number_next_cell_copy=[]
temp=arry_of_number_cell[j][n][0] # temp get each of the visited cell
for i in range(0, total_file_num):
if array_of_traces[i][j][1]==temp:
#an array to save visited cell in the next step
arry_of_temp_next_cell.append(array_of_traces[i][j+1][1])
#an array which has the number of visited cell in the next step
arry_of_number_next_cell=[[x,arry_of_temp_next_cell.count(x)] for x in set(arry_of_temp_next_cell)]
#make a copy
arry_of_number_next_cell_copy=[[x,arry_of_temp_next_cell.count(x)] for x in set(arry_of_temp_next_cell)]
arry_of_number_next_cell_percentage=arry_of_number_next_cell_copy
#calculate the percentage of going to the next cell
sum_next_cell=0.
for n in range(0,len(arry_of_number_next_cell_percentage)):
sum_next_cell += arry_of_number_next_cell_percentage[n][1]
#print(sum_next_cell)
for n in range(0,len(arry_of_number_next_cell_percentage)):
arry_of_number_next_cell_percentage[n][1]=arry_of_number_next_cell_percentage[n][1]/sum_next_cell
#print(sum_next_cell)
#save the the transient probability in state file
for m in range(0,len(arry_of_number_next_cell_percentage)):
f1.write('%s%d' % ('c', array_of_traces[0][j][0]))
f1.write('%s%d ' % ('_',temp))
f1.write('%s%d' % ('c', array_of_traces[0][j+1][0]))
f1.write('%s%d %.3f \n' % ('_',arry_of_number_next_cell_percentage[m][0],arry_of_number_next_cell_percentage[m][1]))
f1.close()
#********************************************************************************************************************
## change hh:mm to time step (e.g, 06:21-->381 )
current_time_setp = sum(int(x) * 60 ** i for i,x in enumerate(reversed(current_time.split(':'))))
##calculate the transient probability
state_mat = pykov.readmat('./mobaas/ICNoutput/statefile_ICN.txt')
## save list of cell in current time
current_cell_list = []
for i in range(0,len(state_mat)):
temp_time = int(state_mat.keys()[i][0][1:state_mat.keys()[i][0].index('_')])
if temp_time == current_time_setp:
#print '-------------'
temp_cell=int(state_mat.keys()[i][0][state_mat.keys()[i][0].index('_')+1:])
if temp_cell !=0:
current_cell_list.append(temp_cell)
## consider only one possibility form all currrent cell
## chech if 'current_cell_list' is not empty!
if current_cell_list:
current_cell = current_cell_list[0]
#print 'current cell : ', current_cell
## make time step and cell as 'c381_5384 '
current_time_cell = ('%s%d%s%d' % ('c', current_time_setp ,'_',current_cell ))
#print current_time_cell
#initial_state = pykov.Vector(c614_39184 = 1)
initial_state = pykov.Vector([(current_time_cell, 1)])
next_cell_probability = state_mat.pow(initial_state, transiant_num)
result[(user_id, current_cell, Temp_time)] = []
for prob_key in next_cell_probability.keys():
next_cell = prob_key[prob_key.index('_') + 1:]
if next_cell != '0':
result[(user_id, current_cell, Temp_time)] += [(next_cell, "%.4f" % next_cell_probability[prob_key])]
f3 = open('./mobaas/ICNoutput/state_mat_ICN.txt', 'w')
for i in range(0,len(state_mat.values())):
f3.write('%s %f \n' % (state_mat.keys()[i], state_mat.values()[i]))
f3.close()
#the result file
x=[]
cell_name=[]
y=[]
for i in range(0,len(next_cell_probability.values())):
x.append(i+1)
cell_name.append(next_cell_probability.keys()[i])
y.append(next_cell_probability.values()[i])
temp_next_cell = next_cell_probability.keys()[i]
if temp_next_cell[temp_next_cell.index('_') + 1:] != '0':
f4.write('%d ' % (user_id))
f4.write('%d ' % (current_cell))
f4.write('%s %s %f \n' % ('{:02d}:{:02d}'.format(*divmod(int(temp_next_cell[1:temp_next_cell.index('_')]), 60)), temp_next_cell[temp_next_cell.index('_') + 1:], next_cell_probability.values()[i]) )
f4.write('%s \n' % (' '))
f4.close()
# put the (current_time)=(current_time+transiant_num)
current_time=Temp_time
###print current_time
print "result:"
pprint.pprint(result)
return result
def mp():
transiant_num=30
thr_user_number=5
#get transiant_num and thr_user_number from input
## set time and data using system time
Day = datetime.date.today().strftime("%A")
Time = time.strftime("%H:%M")
current_time = Time
# start a while loop to calculate and save the results in 'ICNoutput' folder till time 24:00-transiant_num
current_time_in_min = sum(int(x) * 60 ** i for i,x in enumerate(reversed(current_time.split(':'))))
##$print current_time_in_min
while (sum(int(x) * 60 ** i for i,x in enumerate(reversed(current_time.split(':'))))<(1440-transiant_num)):
##$print ' '
##$print '**********Group Mobility Prediction is runing *****************'
##$print 'transiant time is : ', transiant_num
##$print 'thresh old for user number is :', thr_user_number
##$print 'Current date is: ', Day , ' & Currer time is : ', Time
#print 'Currer day is : ', +Day ' & Current time is: ', +Time
##$print '****************************************************************'
##$print "------The Calculation of the Group Mobility Prediction based on the Trajectory Trace Files------"
#print 'Current time :' +Time
#print 'Current date :' +Day
if Day=='Saturday':
current_day = 'sat'
elif Day=='Sunday':
current_day = 'sun'
elif Day=='Monday':
current_day = 'mon'
elif Day=='Tuesday':
current_day = 'tue'
elif Day=='Wednesday':
current_day = 'wed'
elif Day=='Thursday':
current_day = 'thu'
elif Day=='Friday':
current_day = 'fri'
shutil.rmtree('./mobaas/Groupeoutput')
if not os.path.exists('./mobaas/Groupeoutput'):
os.makedirs('./mobaas/Groupeoutput')
all_users_start_move_cell_prob=[] # a matrix to save all_users_start_move_cell_prob
f8=open('./mobaas/Groupeoutput/all_users_start_move_cell_prob.txt','a')
for file in dirs:
user_id = int(file)
#print '...................'
##$print 'User ID :' , user_id
##$print '...................'
#transiant_num =20 # after how many minutes!
path = './mobaas/test_data/Trace_Files/''%d%s' % (user_id,'/Step_1')
number_of_files_all_day = len([item for item in os.listdir(path) if os.path.isfile(os.path.join(path, item))]) #total number of files
#print number_of_files_all_day
total_input_file = number_of_files_all_day/7 #total input trace file for each day
#print total_input_file
file_num = int(total_input_file*0.8 ) # 80% number of the input traced files for each week day for estimation
#print file_num
total_file_num=1*file_num # number of total input traced files
array_of_traces=[] # an empty array to input traced files as matrix for all dayes
#print " "
#print " "
#print "------The Calculation of the Probabilities based on the Trajectory Trace Files -------"
## bades on the date read the trace files for specifie day
##Saturdays
if current_day == 'sat':
sat_array_of_traces = [0]*file_num # an empty array to input traced files as matrix for all Saturday
for i in range(0, file_num):
sat_trace_file=numpy.loadtxt('%s/Sat_%d.dat' % (path, i)) #read the traced files(Saturday)
size=len(sat_trace_file) # size of trace files step
sat_array_of_traces[i] = sat_trace_file # input each traces fiel in the array
#array_of_traces.extend(sat_array_of_traces)
array_of_traces = sat_array_of_traces
#print 'Current day is :' , current_day
##Sundays
elif current_day == 'sun':
sun_array_of_traces = [0]*file_num # an empty array to input traced files as matrix for all Sundays
for i in range(0, file_num):
sun_trace_file=numpy.loadtxt('%s/Sun_%d.dat' % (path, i)) #read the traced files(Sundays)
size=len(sun_trace_file) # size of trace files step
sun_array_of_traces[i] = sun_trace_file # input each traces fiel in the array
#array_of_traces.extend(sat_array_of_traces)
array_of_traces = sun_array_of_traces
#print 'Current day =' , current_day
##Mondays
elif current_day == 'mon':
mon_array_of_traces = [0]*file_num # an empty array to input traced files as matrix for all Monday
for i in range(0, file_num):
mon_trace_file=numpy.loadtxt('%s/Mon_%d.dat' % (path, i)) #read the traced files(Monday)
size=len(mon_trace_file) # size of trace files step
mon_array_of_traces[i] = mon_trace_file # input each traces fiel in the array
#array_of_traces.extend(sat_array_of_traces)
array_of_traces = mon_array_of_traces
#print 'Current day =' , current_day
##Tusedays
elif current_day == 'tue':
tue_array_of_traces = [0]*file_num # an empty array to input traced files as matrix for all Tuseday
for i in range(0, file_num):
tue_trace_file=numpy.loadtxt('%s/Tue_%d.dat' % (path, i)) #read the traced files(Tuseday)
size=len(tue_trace_file) # size of trace files step
tue_array_of_traces[i] = tue_trace_file # input each traces fiel in the array
#array_of_traces.extend(sat_array_of_traces)
array_of_traces = tue_array_of_traces
#print 'Current day =', current_day
##Wednesdays
elif current_day == 'wed':
wed_array_of_traces = [0]*file_num # an empty array to input traced files as matrix for all Wednesday
for i in range(0, file_num):
wed_trace_file=numpy.loadtxt('%s/Wed_%d.dat' % (path, i)) #read the traced files(Wednesday)
size=len(wed_trace_file) # size of trace files step
wed_array_of_traces[i] =wed_trace_file # input each traces fiel in the array
#array_of_traces.extend(sat_array_of_traces)
array_of_traces = wed_array_of_traces
#print 'Current day =' , current_day
##Thursdays
elif current_day == 'thu':
thu_array_of_traces = [0]*file_num # an empty array to input traced files as matrix for all Thursday
for i in range(0, file_num):
thu_trace_file=numpy.loadtxt('%s/Thu_%d.dat' % (path, i)) #read the traced files(Thursday)
size=len(thu_trace_file) # size of trace files step
thu_array_of_traces[i] =thu_trace_file # input each traces fiel in the array
#array_of_traces.extend(sat_array_of_traces)
array_of_traces = thu_array_of_traces
#print 'Current day =' , current_day
##Fraidays
elif current_day == 'fri':
fri_array_of_traces = [0]*file_num # an empty array to input traced files as for all Friday
for i in range(0, file_num):
fri_trace_file=numpy.loadtxt('%s/Fri_%d.dat' % (path, i)) #read the traced files(Friday)
size=len(fri_trace_file) # size of trace files step
fri_array_of_traces[i] = fri_trace_file # input each traces fiel in the array
#array_of_traces.extend(sat_array_of_traces)
array_of_traces = fri_array_of_traces
#print 'Current day =' , current_day
#*********************************************************************************************************************
##count number of visited cell
arry_of_temp_cell=[0]*total_file_num #an array to put the cell ID in each step
arry_of_number_cell=[0]*size #an array to count the number of visited cell ID
arry_of_number_cell_copy=[0]*size #an array to count the number of visited cell ID
for j in range(0, size):
for i in range(0, total_file_num):
arry_of_temp_cell[i]=array_of_traces[i][j][1]
arry_of_number_cell[j]=[[x,arry_of_temp_cell.count(x)] for x in set(arry_of_temp_cell)]
arry_of_temp_cell[i]=array_of_traces[i][j][1]
arry_of_number_cell_copy[j]=[[x,arry_of_temp_cell.count(x)] for x in set(arry_of_temp_cell)]
##count percentage of visited cell
arry_of_percentage_cell=arry_of_number_cell_copy #an array to calculate the percentage for each visited cell
for j in range(0, size):
sum_cell=0.
for n in range(0,len(arry_of_percentage_cell[j])):
sum_cell += arry_of_percentage_cell[j][n][1] #calculate all visited cell
for n in range(0,len(arry_of_percentage_cell[j])):
arry_of_percentage_cell[j][n][1]=arry_of_percentage_cell[j][n][1]/sum_cell #calculate the percentage for each visited cell
##make an array to save time,percentage and cell ID
arry_of_time_percentage_cell=arry_of_percentage_cell #an array to save [time_cellID, percentage]
for j in range(0, ):
for n in range(0,len(arry_of_percentage_cell[j])):
arry_of_time_percentage_cell[j][n][0]=['%d%s%d' %(array_of_traces[0][j][0],'-',arry_of_percentage_cell[j][n][0])]
#*********************************************************************************************************************
#open file to save the state file (state file is calculated from this file that has non zero transient probability)
f1 = open('./mobaas/Groupeoutput/statefile.txt', 'w')
f1 = open('./mobaas/Groupeoutput/statefile.txt', 'a')
##calculate the percentage of going from once cell to other cell in each step
#matrix_of_number_next_cell_percentage=[0]*t
for j in range(0,size-2):
for n in range(0,len(arry_of_number_cell[j])): #an array which has the number of visited cell in each step
#print (n)
arry_of_temp_next_cell=[]
arry_of_number_next_cell=[]
arry_of_number_next_cell_copy=[]
temp=arry_of_number_cell[j][n][0] # temp get each of the visited cell
for i in range(0, total_file_num):
if array_of_traces[i][j][1]==temp:
#an array to save visited cell in the next step
arry_of_temp_next_cell.append(array_of_traces[i][j+1][1])
#an array which has the number of visited cell in the next step
arry_of_number_next_cell=[[x,arry_of_temp_next_cell.count(x)] for x in set(arry_of_temp_next_cell)]
#make a copy
arry_of_number_next_cell_copy=[[x,arry_of_temp_next_cell.count(x)] for x in set(arry_of_temp_next_cell)]
arry_of_number_next_cell_percentage=arry_of_number_next_cell_copy
#calculate the percentage of going to the next cell
sum_next_cell=0.
for n in range(0,len(arry_of_number_next_cell_percentage)):
sum_next_cell += arry_of_number_next_cell_percentage[n][1]
#print(sum_next_cell)
for n in range(0,len(arry_of_number_next_cell_percentage)):
arry_of_number_next_cell_percentage[n][1]=arry_of_number_next_cell_percentage[n][1]/sum_next_cell
#print(sum_next_cell)
#save the the transient probability in state file
for m in range(0,len(arry_of_number_next_cell_percentage)):
f1.write('%s%d' % ('c', array_of_traces[0][j][0]))
f1.write('%s%d ' % ('_',temp))
f1.write('%s%d' % ('c', array_of_traces[0][j+1][0]))
f1.write('%s%d %.3f \n' % ('_',arry_of_number_next_cell_percentage[m][0],arry_of_number_next_cell_percentage[m][1]))
f1.close()
#*********************************************************************************************************************
##calculate the transient probability
state_mat = pykov.readmat('./mobaas/Groupeoutput/statefile.txt')
#print len(state_mat)
#print state_mat.keys()
## change hh:mm to time step (e.g, 06:21-->381 )
current_time_setp = sum(int(x) * 60 ** i for i,x in enumerate(reversed(current_time.split(':'))))
#current_time_setp = current_time_setp - transiant_num # set the start time "transiant_num" ago to from current time to start M.C
current_time_setp = current_time_setp # set the start time "transiant_num" ago to from current time to start M.C
#print current_time_setp
#print len(state_mat)
#Clear the cellprobabilityfile.txt for new calculation
f4 = open('./mobaas/Groupeoutput/cellprobabilityfile.txt', 'w')
f4.close()
#check the "current_time_setp - transiant_num" in state_mat to find the valid cell on that time
for i in range(0,len(state_mat)):
if int(state_mat.keys()[i][0][1:state_mat.keys()[i][0].index('_')]) == current_time_setp:
if int(state_mat.keys()[i][0][state_mat.keys()[i][0].index('_') + 1:])!=0:
##$print '**************************************************************************************'
#print state_mat.keys()[i][0]
current_time_cell = state_mat.keys()[i][0]
#verey important point here is that now "current_cell" is not the cell at current time,
#here it shows the cell that user was ther at "current_time_setp -transiant_num "
current_cell =int(state_mat.keys()[i][0][state_mat.keys()[i][0].index('_') + 1:])
#print current_time_cell
#print current_cell
## make time step and cell as 'c381_5384 '
initial_state = pykov.Vector([(current_time_cell, 1)])
#print initial_state
next_cell_probability = state_mat.pow(initial_state, transiant_num)
f3 = open('./mobaas/Groupeoutput/state_mat.txt', 'w')
for i in range(0,len(state_mat.values())):
f3.write('%s %f \n' % (state_mat.keys()[i], state_mat.values()[i]))
f3.close()
#the result file
x=[]
cell_name=[]
y=[]
#return next_cell_probability
## print the caclulation of probability in the screen and save it in the "cellprobabilityfile.txt" file, this file only show the probability of moving fron start cell to current cell
##$print "Starting time for estimation = "'{:02d}:{:02d}'.format(*divmod(current_time_setp, 60)), " , User is in cell = ", (current_cell)
##$print "......................................................................................"
#f4 = open('./mypath/groupprediction/cellprobabilityfile.txt', 'w')
f4 = open('./mobaas/Groupeoutput/cellprobabilityfile.txt', 'a')
for i in range(0,len(next_cell_probability.values())):
x.append(i+1)
cell_name.append(next_cell_probability.keys()[i])
y.append(next_cell_probability.values()[i])
# f4.write('%s %f \n' % (next_cell_probability.keys()[i],next_cell_probability.values()[i]))
temp_next_cell = next_cell_probability.keys()[i]
#f4.write('%s %s %s %f \n' % (current_cell, '{:02d}:{:02d}'.format(*divmod(int(temp_next_cell[1:temp_next_cell.index('_')]), 60)), temp_next_cell[temp_next_cell.index('_') + 1:], next_cell_probability.values()[i]) )
#f4.write('%s %s %f \n' % (current_cell, temp_next_cell[temp_next_cell.index('_') + 1:], next_cell_probability.values()[i]) )
#f4.write('\n------------------')
if temp_next_cell[temp_next_cell.index('_') + 1:] != '0':
f4.write('%s %s %f \n' % (current_cell, temp_next_cell[temp_next_cell.index('_') + 1:], next_cell_probability.values()[i]) )
##$print 'Next Time = ' '{:02d}:{:02d}'.format(*divmod(int(temp_next_cell[1:temp_next_cell.index('_')]), 60)),' , Current Cell = ', temp_next_cell[temp_next_cell.index('_') + 1:],' , The Probability = ', next_cell_probability.values()[i]
##$elif temp_next_cell[temp_next_cell.index('_') + 1:] == '0':
##$print 'Next Time = ' '{:02d}:{:02d}'.format(*divmod(int(temp_next_cell[1:temp_next_cell.index('_')]), 60)),' , Current Cell = ', temp_next_cell[temp_next_cell.index('_') + 1:],'(Not trace data)',' , The Probability = ', next_cell_probability.values()[i]
#f4.write('--------\n')
f4.close()
# load cellprobabilityfile in a matrix
prob_mat = numpy.loadtxt('./mobaas/Groupeoutput/cellprobabilityfile.txt')
#print 'original prob_mat : ',prob_mat
#print'...................'
number_of_element_prob_mat = numpy.count_nonzero(prob_mat)
#print 'number of element in original prob_mat', number_of_element_prob_mat
#print'...................'
#find unique row in prob_mat
def unique_rows(a):
a = numpy.ascontiguousarray(a)
unique_a = numpy.unique(a.view([('', a.dtype)]*a.shape[1]))
return unique_a.view(a.dtype).reshape((unique_a.shape[0], a.shape[1]))
if number_of_element_prob_mat > 3:
prob_mat = unique_rows(prob_mat)
#print 'unic prob_mat',prob_mat
#print'...................'
number_of_element_prob_mat = numpy.count_nonzero(prob_mat)
#print 'number of element in unic prob_mat', number_of_element_prob_mat
# check if prob_mat dose not have only one raw(it has three elements), inorder to find unic raw in it
if number_of_element_prob_mat == 3:
#print 'number of raw in prob_mat', raw_of_prob_mat
new_prob_mat_1 = numpy.append(prob_mat, 0)
new_prob_mat_2 = numpy.append(new_prob_mat_1, 0)
#print 'now prob_mat', new_prob_mat_2
prob_mat_extend = new_prob_mat_2
#new = numpy.append(prob_mat, 0)
#new1 = numpy.append(new, 0)
#raw_of_punique_rows_prob_mat = len(unique_rows_prob_mat)
#print 'number of raw in raw_of_punique', raw_of_punique_rows_prob_mat
#prob_mat_extend= np.c_[ unique_rows_prob_mat, 0, 0 ]
#unique_rows_prob_mat.extend([0, 0])
#C =[0]*2
#prob_mat_extend= np.c_[ unique_rows_prob_mat, np.zeros(1), np.zeros(1) ]
#print prob_mat_extend
#print len(prob_mat_extend)
size_of_prob_mat_extend=1
#print size_of_prob_mat_extend
elif number_of_element_prob_mat !=3:
#print 'number of raw in prob_mat', raw_of_prob_mat
unique_rows_prob_mat = unique_rows(prob_mat)
raw_of_punique_rows_prob_mat = len(unique_rows_prob_mat)
#print 'number of raw in raw_of_punique', raw_of_punique_rows_prob_mat
prob_mat_extend= np.c_[ unique_rows_prob_mat, np.zeros(raw_of_punique_rows_prob_mat), np.zeros(raw_of_punique_rows_prob_mat) ]
#print prob_mat_extend
size_of_prob_mat_extend=len(prob_mat_extend)
#print size_of_prob_mat_extend
# calculate the probability of starting from one specific cell
count_start_cell_numb= Counter(unique_rows_prob_mat[:,0])
#print 'count_start_cell_numb', count_start_cell_numb
uniq_start_cell_numb= 1. * len(unique_rows_prob_mat[:,0])
#print 'uniq_start_cell_numb', uniq_start_cell_numb
count_start_cell_perc = numpy.zeros(shape=(len(count_start_cell_numb),2))
for i in range(0,len(count_start_cell_numb)):
count_start_cell_perc[i,0]= count_start_cell_numb.keys()[i]
count_start_cell_perc[i,1]= count_start_cell_numb.values()[i]/uniq_start_cell_numb
#print count_start_cell_perc[i,0]
#print count_start_cell_perc[i,1]
#print count_start_cell_perc
for i in range(0,len(count_start_cell_perc)):
temp_cell = count_start_cell_perc[i,0]
temp_prob = count_start_cell_perc[i,1] #probability that user was in the start cell
#for j in range(0,len(prob_mat_extend[:,0])):
if size_of_prob_mat_extend==1:
#print 'in size 1 :', size_of_prob_mat_extend
#print 'prob_mat_extend =', prob_mat_extend
#print 'prob_mat_extend[0]=', prob_mat_extend[2]
#for j in range(0,size_of_prob_mat_extend):
if prob_mat_extend[0]== temp_cell:
prob_mat_extend[3]=temp_prob #probability that user was in the start cell
prob_mat_extend[4]=prob_mat_extend[3]* prob_mat_extend[2] #probability that user was in the start cell and move to the current cell
else:
#print 'in size more than 1:', size_of_prob_mat_extend
for j in range(0,size_of_prob_mat_extend):
if prob_mat_extend[j,0]== temp_cell:
prob_mat_extend[j,3]=temp_prob #probability that user was in the start cell
prob_mat_extend[j,4]=prob_mat_extend[j,3]* prob_mat_extend[j,2] #probability that user was in the start cell and move to the current cell
#find unique row in prob_mat_extend
def unique_rows(a):
a = numpy.ascontiguousarray(a)
unique_a = numpy.unique(a.view([('', a.dtype)]*a.shape[1]))
return unique_a.view(a.dtype).reshape((unique_a.shape[0], a.shape[1]))
if size_of_prob_mat_extend!=1:
unique_rows_prob_mat_extend = unique_rows(prob_mat_extend)
#useID_add_prob_mat_extend = prob_mat_extend
useID_add_prob_mat_extend = unique_rows_prob_mat_extend
useID_add_prob_mat_extend = np.insert(useID_add_prob_mat_extend, 0, values= user_id, axis=1)
##numpy.savetxt('./mobaas/Groupeoutput/users_startcells_movecell_probability_%d.txt' % (user_id), useID_add_prob_mat_extend ,fmt='%.4f')
numpy.savetxt(f8, useID_add_prob_mat_extend,fmt='%.4f')
if size_of_prob_mat_extend==1:
unique_rows_prob_mat_extend = prob_mat_extend
useID_add_prob_mat_extend = unique_rows_prob_mat_extend
useID_add_prob_mat_extend = np.insert(useID_add_prob_mat_extend, 0, user_id)
#useID_add_prob_mat_extend = useID_add_prob_mat_extend.T
##numpy.savetxt('./mobaas/Groupeoutput/users_startcells_movecell_probability_%d.txt' % (user_id), useID_add_prob_mat_extend[None] ,fmt='%.4f')
numpy.savetxt(f8, useID_add_prob_mat_extend[None] ,fmt='%.4f')
#np.savetxt(f,a)
f8.close()
## in this matrix: the rows are : userID-start cell-visited cell-pobability to move to visited cell(P1)-pobalility that user was in start cell(P2)-P1*P2
#numpy.savetxt('./mypath/groupprediction/Groupeoutput/users_startcells_movecell_probability_%d.txt' % (user_id), useID_add_prob_mat_extend ,fmt='%.4f')
#^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
#^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
all_users_start_move_cell_prob = numpy.loadtxt('./mobaas/Groupeoutput/all_users_start_move_cell_prob.txt')
#all_users_start_move_cell_prob is sorted based on row 2 (move cell) to find all users that move to 'move cell'
sorted_all_users_start_move_cell_prob = all_users_start_move_cell_prob[all_users_start_move_cell_prob[:,2].argsort()]
numpy.savetxt('./mobaas/Groupeoutput/sorted_all_users_start_move_cell_prob.txt', sorted_all_users_start_move_cell_prob ,fmt='%.4f')
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# count how many number of users(with unic ID) are moved to 'move cell'
count_user_in_move_cell= Counter(sorted_all_users_start_move_cell_prob[:,2])
for i in range(0,len(count_user_in_move_cell)):
temp_mat=sorted_all_users_start_move_cell_prob[numpy.where(sorted_all_users_start_move_cell_prob[:,2] == count_user_in_move_cell.keys()[i])]
#print 'temp_mat',temp_mat[0,2]
unic_user=Counter(temp_mat[:,0])
unic_user_number=len(unic_user)
#print 'unic_user', unic_user_number
count_user_in_move_cell[temp_mat[0,2]]= unic_user_number
#print 'count_user_in_move_cell.values()[i]', count_user_in_move_cell.values()[i]
#print 'count_user_in_move_cell second', count_user_in_move_cell
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# make another matrix to save the probabilites of users(for all users P1*P2)
count_user_in_move_cell_extend = numpy.zeros(shape=(len(count_user_in_move_cell),4))
for i in range(0,len(count_user_in_move_cell)):
count_user_in_move_cell_extend[i,0]= count_user_in_move_cell.keys()[i]
count_user_in_move_cell_extend[i,1]= count_user_in_move_cell.values()[i]
#print 'number is user is : ' count_user_in_move_cell.values()[i]
# sort the 'count_user_in_move_cell_extend' based on number of users
sorted_count_user_in_move_cell_extend =count_user_in_move_cell_extend[count_user_in_move_cell_extend[:,1].argsort()]
#print 'sorted_count_user_in_move_cell_extend :' , sorted_count_user_in_move_cell_extend
Temp_time='{:02d}:{:02d}'.format(*divmod(int(temp_next_cell[1:temp_next_cell.index('_')]), 60))
##$print ' '
##$print '******** Notification******'
##$print 'It is estimated at time (',Temp_time,') number of users in following cell will be more than defined threshold!'
numpy.savetxt('./mobaas/Groupeoutput/sorted_count_user_in_move_cell_extend.txt', sorted_count_user_in_move_cell_extend ,fmt='%.4f')
f9 = open('./mobaas/Groupeoutput/threshold_cell_users.txt', 'w')
f9 = open('./mobaas/Groupeoutput/threshold_cell_users.txt', 'a')
for i in range(0,len(sorted_count_user_in_move_cell_extend)):
if sorted_count_user_in_move_cell_extend[i,1] > thr_user_number:
##$print 'In cell (', sorted_count_user_in_move_cell_extend[i,0], ') --> ', sorted_count_user_in_move_cell_extend[i,1] , 'users'
f9.write('%s %s %s \n' % (Temp_time, sorted_count_user_in_move_cell_extend[i,0], sorted_count_user_in_move_cell_extend[i,1]))
f9.close()
#####################
# list for time, cell, number of users
threshold_cell_users = [line.strip() for line in open('./mobaas/Groupeoutput/threshold_cell_users.txt')]
print threshold_cell_users
####################
for i in range(0,len(sorted_count_user_in_move_cell_extend)):
temp_cell=sorted_count_user_in_move_cell_extend[i,0]
#print temp_cell
temp_prob_1=1.
temp_prob_2=1.
n=0.
for j in range(0,len(sorted_all_users_start_move_cell_prob)):
if temp_cell == sorted_all_users_start_move_cell_prob[j,2]:
n=n+1
temp_prob_1=temp_prob_1*sorted_all_users_start_move_cell_prob[j,5]+0.0000001
#if temp_cell != sorted_all_users_start_move_cell_prob[j,2]:
# n=n+1
# temp_prob_2=temp_prob_2*(1-sorted_all_users_start_move_cell_prob[j,5])
#print n
#print 'temp_prob_1 =' , temp_prob_1
#print 'temp_prob_2 =' , temp_prob_2
sorted_count_user_in_move_cell_extend[i,2]=(temp_prob_1*temp_prob_2)*100
#print '(temp_prob_1*temp_prob_2)*100 = x=', (temp_prob_1*temp_prob_2)*100
#sorted_count_user_in_move_cell_extend[i,3]=math.log10(temp_prob_1*temp_prob_2)
sorted_count_user_in_move_cell_extend[i,3]=log10(temp_prob_1*temp_prob_2)
numpy.savetxt('./mobaas/Groupeoutput/sorted_count_user_in_move_cell_extend.txt', sorted_count_user_in_move_cell_extend ,fmt='%.4f')
'''
def mp(user_id, current_time, current_day, transiant_num):
# Dict to store all the individual results, to be returned at the end of the code
result = {}
## print current input time and dat
print '********************************************************************************'
print 'Current time is: ', current_time, ' & Currer day is : ', current_day
print '********************************************************************************'
## set time and data using system time
Day = current_day
if Day == 'Saturday':
current_day = 'sat'
elif Day == 'Sunday':
current_day = 'sun'
elif Day == 'Monday':
current_day = 'mon'
elif Day == 'Tuesday':
current_day = 'tue'
elif Day == 'Wednesday':
current_day = 'wed'
elif Day == 'Thursday':
current_day = 'thu'
elif Day == 'Friday':
current_day = 'fri'
path_user = '******'
dirs = os.listdir(path_user)
# remove the 'ICNoutput' and create a new one to save results
shutil.rmtree('./mobaas/ICNoutput')
if not os.path.exists('./mobaas/ICNoutput'):
os.makedirs('./mobaas/ICNoutput')
# start a while loop to calculate and save the results in 'ICNoutput' folder till time 24:00-transiant_num
current_time_in_min = sum(
int(x) * 60**i
for i, x in enumerate(reversed(current_time.split(':'))))
#while (sum(int(x) * 60 ** i for i,x in enumerate(reversed(current_time.split(':'))))<(1440-transiant_num)):
#stop prediction at 18:40, for the purpose of demo to limit the period of prediction
while (sum(
int(x) * 60**i
for i, x in enumerate(reversed(current_time.split(':')))) <
(1130 - transiant_num)):
#change current time to min, add ransiant_num to it, change to xx:yy format, put in Temp_time
current_time_in_min = sum(
int(x) * 60**i
for i, x in enumerate(reversed(current_time.split(':'))))
next_time_in_min = current_time_in_min + transiant_num
Temp_time = '{:02d}:{:02d}'.format(*divmod(next_time_in_min, 60))
#open and save the result in an new file
f4 = open(
'./mobaas/ICNoutput/cellprobability_file_ICN_%s.txt' % Temp_time,
'w')
f4 = open(
'./mobaas/ICNoutput/cellprobability_file_ICN_%s.txt' % Temp_time,
'a')
f4.write('%s %s \n' % (Day, current_time))
f4.write('%s \n' %
('---------------------------------------------------'))
f4.write('%s \n' %
('user--current cell--next time--next cell--probability'))
f4.write('%s \n' %
('---------------------------------------------------'))
for i in range(0, len(dirs)):
user_id = int(dirs[i])
##print 'Current user : '******'./mobaas/test_data/Trace_Files/' '%d%s' % (user_id,
'/Step_1')
number_of_files_all_day = len([
item for item in os.listdir(path)
if os.path.isfile(os.path.join(path, item))
]) #total number of files
#print number_of_files_all_day
total_input_file = number_of_files_all_day / 7 #total input trace file for each day
#print total_input_file
file_num = int(
total_input_file * 0.7
) # 70% number of the input traced files for each week day for estimation
#print file_num
total_file_num = 1 * file_num # number of total input traced files
array_of_traces = [
] # an empty array to input traced files as matrix for all dayes
## bades on the date read the trace files for specifie day
##Saturdays
if current_day == 'sat':
sat_array_of_traces = [
0
] * file_num # an empty array to input traced files as matrix for all Saturday
for i in range(0, file_num):
sat_trace_file = numpy.loadtxt(
'%s/Sat_%d.dat' %
(path, i)) #read the traced files(Saturday)
size = len(sat_trace_file) # size of trace files step
sat_array_of_traces[
i] = sat_trace_file # input each traces fiel in the array
#array_of_traces.extend(sat_array_of_traces)
array_of_traces = sat_array_of_traces
#print 'Current day is :' , current_day
##Sundays
elif current_day == 'sun':
sun_array_of_traces = [
0
] * file_num # an empty array to input traced files as matrix for all Sundays
for i in range(0, file_num):
sun_trace_file = numpy.loadtxt(
'%s/Sun_%d.dat' %
(path, i)) #read the traced files(Sundays)
size = len(sun_trace_file) # size of trace files step
sun_array_of_traces[
i] = sun_trace_file # input each traces fiel in the array
#array_of_traces.extend(sat_array_of_traces)
array_of_traces = sun_array_of_traces
#print 'Current day =' , current_day
##Mondays
elif current_day == 'mon':
mon_array_of_traces = [
0
] * file_num # an empty array to input traced files as matrix for all Monday
for i in range(0, file_num):
mon_trace_file = numpy.loadtxt(
'%s/Mon_%d.dat' %
(path, i)) #read the traced files(Monday)
size = len(mon_trace_file) # size of trace files step
mon_array_of_traces[
i] = mon_trace_file # input each traces fiel in the array
#array_of_traces.extend(sat_array_of_traces)
array_of_traces = mon_array_of_traces
#print 'Current day =' , current_day
##Tusedays
elif current_day == 'tue':
tue_array_of_traces = [
0
] * file_num # an empty array to input traced files as matrix for all Tuseday
for i in range(0, file_num):
tue_trace_file = numpy.loadtxt(
'%s/Tue_%d.dat' %
(path, i)) #read the traced files(Tuseday)
size = len(tue_trace_file) # size of trace files step
tue_array_of_traces[
i] = tue_trace_file # input each traces fiel in the array
#array_of_traces.extend(sat_array_of_traces)
array_of_traces = tue_array_of_traces
#print 'Current day =', current_day
##Wednesdays
elif current_day == 'wed':
wed_array_of_traces = [
0
] * file_num # an empty array to input traced files as matrix for all Wednesday
for i in range(0, file_num):
wed_trace_file = numpy.loadtxt(
'%s/Wed_%d.dat' %
(path, i)) #read the traced files(Wednesday)
size = len(wed_trace_file) # size of trace files step
wed_array_of_traces[
i] = wed_trace_file # input each traces fiel in the array
#array_of_traces.extend(sat_array_of_traces)
array_of_traces = wed_array_of_traces
#print 'Current day =' , current_day
##Thursdays
elif current_day == 'thu':
thu_array_of_traces = [
0
] * file_num # an empty array to input traced files as matrix for all Thursday
for i in range(0, file_num):
thu_trace_file = numpy.loadtxt(
'%s/Thu_%d.dat' %
(path, i)) #read the traced files(Thursday)
size = len(thu_trace_file) # size of trace files step
thu_array_of_traces[
i] = thu_trace_file # input each traces fiel in the array
#array_of_traces.extend(sat_array_of_traces)
array_of_traces = thu_array_of_traces
#print 'Current day =' , current_day
##Fraidays
elif current_day == 'fri':
fri_array_of_traces = [
0
] * file_num # an empty array to input traced files as for all Friday
for i in range(0, file_num):
fri_trace_file = numpy.loadtxt(
'%s/Fri_%d.dat' %
(path, i)) #read the traced files(Friday)
size = len(fri_trace_file) # size of trace files step
fri_array_of_traces[
i] = fri_trace_file # input each traces fiel in the array
#array_of_traces.extend(sat_array_of_traces)
array_of_traces = fri_array_of_traces
#print 'Current day =' , current_day
#*********************************************************************************************************************
##count number of visited cell
arry_of_temp_cell = [
0
] * total_file_num #an array to put the cell ID in each step
arry_of_number_cell = [
0
] * size #an array to count the number of visited cell ID
arry_of_number_cell_copy = [
0
] * size #an array to count the number of visited cell ID
for j in range(0, size):
for i in range(0, total_file_num):
arry_of_temp_cell[i] = array_of_traces[i][j][1]
arry_of_number_cell[j] = [[x,
arry_of_temp_cell.count(x)]
for x in set(arry_of_temp_cell)]
arry_of_temp_cell[i] = array_of_traces[i][j][1]
arry_of_number_cell_copy[j] = [[
x, arry_of_temp_cell.count(x)
] for x in set(arry_of_temp_cell)]
##count percentage of visited cell
arry_of_percentage_cell = arry_of_number_cell_copy #an array to calculate the percentage for each visited cell
for j in range(0, size):
sum_cell = 0.
for n in range(0, len(arry_of_percentage_cell[j])):
sum_cell += arry_of_percentage_cell[j][n][
1] #calculate all visited cell
for n in range(0, len(arry_of_percentage_cell[j])):
arry_of_percentage_cell[j][n][
1] = arry_of_percentage_cell[j][n][
1] / sum_cell #calculate the percentage for each visited cell
##make an array to save time,percentage and cell ID
arry_of_time_percentage_cell = arry_of_percentage_cell #an array to save [time_cellID, percentage]
for j in range(0, ):
for n in range(0, len(arry_of_percentage_cell[j])):
arry_of_time_percentage_cell[j][n][0] = [
'%d%s%d' % (array_of_traces[0][j][0], '-',
arry_of_percentage_cell[j][n][0])
]
#*********************************************************************************************************************
#open file to save the state file (state file is calculated from this file that has non zero transient probability)
f1 = open('./mobaas/ICNoutput/statefile_ICN.txt', 'w')
f1 = open('./mobaas/ICNoutput/statefile_ICN.txt', 'a')
##calculate the percentage of going from once cell to other cell in each step
#matrix_of_number_next_cell_percentage=[0]*t
for j in range(0, size - 2):
for n in range(
0, len(arry_of_number_cell[j])
): #an array which has the number of visited cell in each step
#print (n)
arry_of_temp_next_cell = []
arry_of_number_next_cell = []
arry_of_number_next_cell_copy = []
temp = arry_of_number_cell[j][n][
0] # temp get each of the visited cell
for i in range(0, total_file_num):
if array_of_traces[i][j][1] == temp:
#an array to save visited cell in the next step
arry_of_temp_next_cell.append(
array_of_traces[i][j + 1][1])
#an array which has the number of visited cell in the next step
arry_of_number_next_cell = [[
x, arry_of_temp_next_cell.count(x)
] for x in set(arry_of_temp_next_cell)]
#make a copy
arry_of_number_next_cell_copy = [[
x, arry_of_temp_next_cell.count(x)
] for x in set(arry_of_temp_next_cell)]
arry_of_number_next_cell_percentage = arry_of_number_next_cell_copy
#calculate the percentage of going to the next cell
sum_next_cell = 0.
for n in range(
0,
len(arry_of_number_next_cell_percentage)):
sum_next_cell += arry_of_number_next_cell_percentage[
n][1]
#print(sum_next_cell)
for n in range(
0,
len(arry_of_number_next_cell_percentage)):
arry_of_number_next_cell_percentage[n][
1] = arry_of_number_next_cell_percentage[
n][1] / sum_next_cell
#print(sum_next_cell)
#save the the transient probability in state file
for m in range(0,
len(arry_of_number_next_cell_percentage)):
f1.write('%s%d' % ('c', array_of_traces[0][j][0]))
f1.write('%s%d ' % ('_', temp))
f1.write('%s%d' % ('c', array_of_traces[0][j + 1][0]))
f1.write(
'%s%d %.3f \n' %
('_', arry_of_number_next_cell_percentage[m][0],
arry_of_number_next_cell_percentage[m][1]))
f1.close()
#********************************************************************************************************************
## change hh:mm to time step (e.g, 06:21-->381 )
current_time_setp = sum(
int(x) * 60**i
for i, x in enumerate(reversed(current_time.split(':'))))
##calculate the transient probability
state_mat = pykov.readmat('./mobaas/ICNoutput/statefile_ICN.txt')
## save list of cell in current time
current_cell_list = []
for i in range(0, len(state_mat)):
temp_time = int(state_mat.keys()[i][0]
[1:state_mat.keys()[i][0].index('_')])
if temp_time == current_time_setp:
#print '-------------'
temp_cell = int(state_mat.keys()[i][0]
[state_mat.keys()[i][0].index('_') + 1:])
if temp_cell != 0:
current_cell_list.append(temp_cell)
## consider only one possibility form all currrent cell
## chech if 'current_cell_list' is not empty!
if current_cell_list:
current_cell = current_cell_list[0]
#print 'current cell : ', current_cell
## make time step and cell as 'c381_5384 '
current_time_cell = (
'%s%d%s%d' % ('c', current_time_setp, '_', current_cell))
#print current_time_cell
#initial_state = pykov.Vector(c614_39184 = 1)
initial_state = pykov.Vector([(current_time_cell, 1)])
next_cell_probability = state_mat.pow(initial_state,
transiant_num)
result[(user_id, current_cell, Temp_time)] = []
for prob_key in next_cell_probability.keys():
next_cell = prob_key[prob_key.index('_') + 1:]
if next_cell != '0':
result[(user_id, current_cell, Temp_time)] += [
(next_cell,
"%.4f" % next_cell_probability[prob_key])
]
f3 = open('./mobaas/ICNoutput/state_mat_ICN.txt', 'w')
for i in range(0, len(state_mat.values())):
f3.write('%s %f \n' %
(state_mat.keys()[i], state_mat.values()[i]))
f3.close()
#the result file
x = []
cell_name = []
y = []
for i in range(0, len(next_cell_probability.values())):
x.append(i + 1)
cell_name.append(next_cell_probability.keys()[i])
y.append(next_cell_probability.values()[i])
temp_next_cell = next_cell_probability.keys()[i]
if temp_next_cell[temp_next_cell.index('_') + 1:] != '0':
f4.write('%d ' % (user_id))
f4.write('%d ' % (current_cell))
f4.write(
'%s %s %f \n' %
('{:02d}:{:02d}'.format(*divmod(
int(temp_next_cell[1:temp_next_cell.index('_')]
), 60)),
temp_next_cell[temp_next_cell.index('_') + 1:],
next_cell_probability.values()[i]))
f4.write('%s \n' % (' '))
f4.close()
# put the (current_time)=(current_time+transiant_num)
current_time = Temp_time
###print current_time
print "result:"
pprint.pprint(result)
return result
def main():
global log
log = logging.getLogger()
loglevel = 'DEBUG'
coloredlogs.install(fmt='%(asctime)s %(levelname)s:: %(message)s',
datefmt='%H:%M:%S', level=loglevel, logger=log)
log.debug("Let's start!")
for _, dirs, _ in os.walk(path):
for directory in sorted(dirs):
if re.match('user[\d]+', directory):
try:
print(" ")
log.info("Processing: %s", directory)
log.info("Reading Data Frame wireless...")
df_w = pd.read_csv(
path + os.path.join(directory, "df_wireless_HDBSCAN.csv"), index_col=0)
log.info("Reading Data Frame cellular...")
df_c = pd.read_csv(
path + os.path.join(directory, "df_cell_HDBSCAN.csv"), index_col=0)
kkw = set([k for k, _ in df_w.groupby(['day', 'hour'])])
log.debug("wireless key set len: %d", len(kkw))
kkc = set([k for k, _ in df_c.groupby(['day', 'hour'])])
log.debug("cell key set len: %d", len(kkc))
kk = kkc & kkw
log.debug("common key set len: %d", len(kk))
mcv_w_list = list()
mcv_c_list = list()
for k in sorted(kk):
_w = Counter(df_w.groupby(['day', 'hour']).get_group(k)[
'HDBSCAN'].values)
mcv_w_list.append(_w.most_common(1)[0][0])
_c = Counter(df_c.groupby(['day', 'hour']).get_group(k)[
'HDBSCAN'].values)
mcv_c_list.append(_c.most_common(1)[0][0])
mapping_w = {i: c for c, i in enumerate(
sorted(list(set(mcv_w_list))))}
log.debug(mapping_w)
mapping_c = {i: c for c, i in enumerate(
sorted(list(set(mcv_c_list))))}
log.debug(mapping_c)
hours = list()
for c, k in enumerate(sorted(kk)):
hours.append(k[2])
n = len(set(mcv_w_list))
mat_w = np.zeros([n, n])
c1 = 0
c2 = 0
for d, h in zip(list(zip(mcv_w_list[:-1], mcv_w_list[1:])), list(zip(hours[:-1], (hours[1:])))):
c1 += 1
if (h[1] == (h[0] + 1)) or ((h[1] == 0) and (h[0] == 23)):
c2 += 1
mat_w[mapping_w[d[0]]][mapping_w[d[1]]] += 1
markov_w = mat_w / mat_w.sum(axis=1)[:, np.newaxis]
markov_w = np.nan_to_num(markov_w)
markov_w = np.around(markov_w, decimals=2)
log.warning("Inserted %d/%d", c2, c1)
n = len(set(mcv_c_list))
mat_c = np.zeros([n, n])
c1 = 0
c2 = 0
for d, h in zip(list(zip(mcv_c_list[:-1], mcv_c_list[1:])), list(zip(hours[:-1], (hours[1:])))):
c1 += 1
if (h[1] == (h[0] + 1)) or ((h[1] == 0) and (h[0] == 23)):
c2 += 1
mat_c[mapping_c[d[0]]][mapping_c[d[1]]] += 1
markov_c = mat_c / mat_c.sum(axis=1)[:, np.newaxis]
markov_c = np.nan_to_num(markov_c)
markov_c = np.around(markov_c, decimals=2)
log.warning("Inserted %d/%d", c2, c1)
plt.imshow(markov_w, cmap='Blues', interpolation='nearest')
plt.xlabel('Meaningful locations')
plt.ylabel('Meaningful locations')
plt.savefig(path + os.path.join(directory,
"_markov_wireless.png"), dpi=300)
plt.imshow(markov_c, cmap='Blues', interpolation='nearest')
plt.xlabel('Meaningful locations')
plt.ylabel('Meaningful locations')
plt.savefig(path + os.path.join(directory,
"_markov_cell.png"), dpi=300)
with open(path + os.path.join(directory, "mat_w.markov"), 'w') as f_w:
for i in range(mat_w.shape[0]):
for j in range(mat_w.shape[1]):
f_w.writelines("%d %d %d\n" %
(i, j, mat_w[i, j]))
with open(path + os.path.join(directory, "mat_c.markov"), 'w') as f_w:
for i in range(mat_c.shape[0]):
for j in range(mat_c.shape[1]):
f_w.writelines("%d %d %d\n" %
(i, j, mat_c[i, j]))
P_c = pykov.readmat(
path + os.path.join(directory, "mat_c.markov"))
P_w = pykov.readmat(
path + os.path.join(directory, "mat_c.markov"))
try:
P_c.stochastic()
P_w.stochastic()
except pykov.PykovError:
log.critical("Cannot get a right stochastic matrix")
except Exception:
log.exception("Skipping %s", directory)
log.debug("Finished!")
import pykov
import pymongo
from pymongo import MongoClient
client = MongoClient()
database = client.recommender
collection = database.data_lagu
collection2 = database.history_lagu
P = pykov.readmat('matriks.csv')
#print(T)
#print(P)
i = 0
for data in collection.find():
print(data["index"],data["track name"], ' - ', data["artist name"])
response = input("Lagu yang akan diplay: ")
next = collection2.find_one({"index":int(response)})
if next == None:
a = 0
else:
a = next['played']
json = {
'index': int(response),
'played': a + 1
}
collection2.update({'index':int(response)}, json, upsert=True)
next = collection.find_one({"index":int(response)})
print(next['track name'], ' - ', next['artist name'])
i = input("1. Cari lagu, 2. Putar lagu rekomendasi berikutnya, 3. Top 10 list lagu\n")
while i == '1' or i =='2' or i == '3':
if i == '1':
for data in collection.find():
