def __init__(self, identical_testcases, width, height):
self.identical_testcases = identical_testcases
self.width = width
self.height = height
self.cell_count = height * height
self.testcase1 = rand_test_case(width, height)
self.testcase2 = None
if identical_testcases:
self.testcase2 = self.testcase1
else:
self.testcase2 = rand_test_case(width, height)
self.cost_table = init_cost_table(height)
self.similarity = float("inf")
def __init__(self, identical_testcases, width, height):
self.identical_testcases = identical_testcases
self.width = width
self.height = height
self.cell_count = height * height
self.testcase1 = rand_test_case(width, height)
self.testcase2 = None
if identical_testcases:
self.testcase2 = self.testcase1
else:
self.testcase2 = rand_test_case(width, height)
self.cost_table = init_cost_table(height)
self.similarity = float("inf")
def dtw_between_two_testcases(width, height, iteration):
ts_sum = 0
tp_sum = 0
for i in range(iteration):
a = rand_test_case(width, height)
b = rand_test_case(width, height)
cs, ts = find_similarity_in_sequential(a,b,True)
cp, tp =find_similarity_in_parallel(a,b,2,True)
ts_sum += ts
tp_sum += tp
ts_avg = ts_sum / iteration
tp_avg = tp_sum / iteration
print
print 'sequential: {0:.4f}'.format(ts_avg)
print 'parallel: {0:.4f}'.format(tp_avg)
def dtw_between_two_testcases(width, height, iteration):
ts_sum = 0
tp_sum = 0
for i in range(iteration):
a = rand_test_case(width, height)
b = rand_test_case(width, height)
cs, ts = find_similarity_in_sequential(a, b, True)
cp, tp = find_similarity_in_parallel(a, b, 2, True)
ts_sum += ts
tp_sum += tp
ts_avg = ts_sum / iteration
tp_avg = tp_sum / iteration
print
print 'sequential: {0:.4f}'.format(ts_avg)
print 'parallel: {0:.4f}'.format(tp_avg)
def dtw_among_n_testcases(w, h, n, iteration, log):
global width
global height
global workload
global captured
global predefined
width = w
height = h
workload = n
ts = 0
tp = 0
for i in range(iteration):
captured = rand_test_case(width, height)
predefined = [rand_test_case(width, height) for i in range(workload)]
if log:
print '----------------------'
start_t = time.time()
if log:
print 'running sequential DTWs...'
for i in range(workload):
find_similarity(i)
run_t = time.time() - start_t
ts += run_t
if log:
print 'run in sequential for {0:.4f} seconds'.format(run_t)
start_t = time.time()
if log:
print 'running parallel DTWs...'
result = multiprocessing.Pool(processes=4).map(find_similarity,
range(workload))
run_t = time.time() - start_t
tp += run_t
if log:
print 'run in parallel for {0:.4f} seconds'.format(run_t)
print '----------------------'
print
#print 'sequential:{0:.4f}'.format(ts/iteration)
#print 'parallel:{0:.4f}'.format(tp/iteration)
print "{0},{1:.4f},{2:.4f}".format(n, ts / iteration, tp / iteration)
def dtw_among_n_testcases(w, h, n, iteration, log):
global width
global height
global workload
global captured
global predefined
width = w
height = h
workload = n
ts = 0
tp = 0
for i in range(iteration):
captured = rand_test_case(width, height)
predefined = [rand_test_case(width, height) for i in range(workload)]
if log:
print '----------------------'
start_t = time.time()
if log:
print 'running sequential DTWs...'
for i in range(workload):
find_similarity(i)
run_t = time.time() - start_t
ts += run_t
if log:
print 'run in sequential for {0:.4f} seconds'.format(run_t)
start_t = time.time()
if log:
print 'running parallel DTWs...'
result = multiprocessing.Pool(processes=4).map(find_similarity, range(workload))
run_t = time.time() - start_t
tp += run_t
if log:
print 'run in parallel for {0:.4f} seconds'.format(run_t)
print '----------------------'
print
#print 'sequential:{0:.4f}'.format(ts/iteration)
#print 'parallel:{0:.4f}'.format(tp/iteration)
print "{0},{1:.4f},{2:.4f}".format(n,ts/iteration,tp/iteration)
