def change_N(Max, k, end, n_loop):
data = [[], [], []]
for N in range(100, end, 100):
mode2 = 0
mode3 = 0
for loop in range(n_loop):
s = np.random.rand(N, 2) * Max
e = np.random.rand(N, 2) * Max
start_time = time.time()
improvedMunkres.improved_munkres(s, e, Max, N, k)
print("%d---%s seconds ---" % (N, time.time() - start_time))
mode2 += time.time() - start_time
start_time = time.time()
ver2.improved_munkres(s, e, Max, N, k)
print("%d---%s seconds ---" % (N, time.time() - start_time))
mode3 += time.time() - start_time
data[0].append(N)
data[1].append(mode2 / n_loop)
data[2].append(mode3 / n_loop)
dataframe = pd.DataFrame(data)
dataframe.to_csv("data.csv")
def change_k(Max, k_end, loop, N, color):
i_time = [0] * (k_end - 1)
d_time = [0] * (k_end - 1)
for i in range(1, k_end):
i_sum = 0
d_sum = 0
for j in range(loop):
s = np.random.rand(N, 2) * Max
e = np.random.rand(N, 2) * Max
start_time = time.time()
improvedMunkres.improved_munkres(s, e, Max, N, i)
print("%d%%---%s seconds ---" %
((i + 1) * (j + 1) /
((k_end - 3) * loop) * 100, time.time() - start_time))
i_sum += time.time() - start_time
'''
start_time = time.time()
improvedMunkres.original_munkres(s, e, Max, N, i)
print("%d%%---%s seconds ---" % ((i-1)/(k_end-1)*100 + j/loop*10, time.time() - start_time))
d_sum += time.time() - start_time
'''
i_time[i - 1] = i_sum / loop
# d_time[i-1] = d_sum/loop
plt.title('Check time by changing K')
a = np.array(i_time)
plt.plot(list(range(1, k_end)),
a[0:k_end],
colors[color],
label='improved ' + str(N))
# plt.plot(list(range(1, k_end)), d_time, 'r.-', label='default')
plt.xlabel('k')
plt.ylabel('time(s)')
plt.legend()
plt.show()
print(np.array(i_time))
return i_time
def change_N(Max, k, end, n_loop):
i_time = [0] * (end - k)
d_time = [0] * (end - k)
g_time = [0] * (end - k)
for N in range(k, end):
for loop in range(n_loop):
s = np.random.rand(N, 2) * Max
e = np.random.rand(N, 2) * Max
start_time = time.time()
improvedMunkres.improved_munkres(s, e, Max, N, k)
print("---%s seconds ---" % (time.time() - start_time))
i_time[N - k] += time.time() - start_time
start_time = time.time()
ver2.improved_munkres(s, e, Max, N, k)
print("---%s seconds ---" % (time.time() - start_time))
g_time[N - k] += time.time() - start_time
start_time = time.time()
ver2.original_munkres(s, e, Max, N, k)
print("---%s seconds ---" % (time.time() - start_time))
d_time[N - k] += time.time() - start_time
list(range(k, end))
i_time[N - k] /= n_loop
g_time[N - k] /= n_loop
d_time[N - k] /= n_loop
plt.title('Execution time difference')
plt.plot(list(range(k, end)), d_time, 'kv-', label='mode 1')
plt.plot(list(range(k, end)), i_time, 'r.-', label='mode 2')
plt.plot(list(range(k, end)), g_time, 'b.-', label='mode 3')
plt.xlabel('number of robots')
plt.ylabel('time(s)')
plt.legend()
plt.show()
def change_N_2(Max, k, end, n_loop):
i_time = [0] * (end - k)
d_time = [0] * (end - k)
g_time = [0] * (end - k)
for NNN in range(k, end):
N = NNN * 50
for loop in range(n_loop):
s = np.random.rand(N, 2) * Max
e = np.random.rand(N, 2) * Max
start_time = time.time()
improvedMunkres.improved_munkres(s, e, Max, N, k)
print("---%s seconds ---" % (time.time() - start_time))
i_time[NNN] += time.time() - start_time
start_time = time.time()
ver2.improved_munkres(s, e, Max, N, k)
print("---%s seconds ---" % (time.time() - start_time))
g_time[NNN] += time.time() - start_time
# start_time = time.time()
# ver2.original_munkres(s, e, Max, N, k)
# print("---%s seconds ---" % (time.time() - start_time))
# d_time[N-k] += time.time() - start_time
list(range(k, end))
i_time[NNN] /= n_loop
g_time[NNN] /= n_loop
# d_time[N-k] /= n_loop
dataframe = pd.DataFrame(i_time)
dataframe.to_csv("i_time.csv")
dataframe = pd.DataFrame(g_time)
dataframe.to_csv("g_time.csv")
for i in range(division, d_limit):
buf1.append(0)
buf2.append(0)
for l in range(loop):
s = np.random.rand(N, 2) * Max
e = np.random.rand(N, 2) * Max
s1 = time.time()
standard = improvedMunkres.original_munkres(s, e, Max, N, 1)
print("%d #%dth standard: %f seconds" % (N, l, time.time() - s1))
for k in range(division, d_limit):
s1 = time.time()
r1 = improvedMunkres.improved_munkres(s, e, Max, N, k)
temp = (r1 - standard) / standard * 100
buf1[k - division] += temp
s1 = time.time()
r1 = ver2.improved_munkres(s, e, Max, N, k)
temp = (r1 - standard) / standard * 100
buf2[k - division] += temp
#print("----------- %f seconds s:%f i:%f %f" % (time.time() - s1, standard, r1, temp))
err1 = []
err2 = []
for i in range(len(range(division, d_limit))):
err1.append(100 - buf1[i] / loop)
err2.append(100 - buf2[i] / loop)
import numpy as np
import matplotlib.pylab as plt
time_diff = 0
Max = 1
N = 100
for loop in range(100):
s = np.random.rand(N, 2) * Max
e = np.random.rand(N, 2) * Max
# div 3
st1 = time.time()
improvedMunkres.improved_munkres(s, e, Max, N, 3)
et1 = time.time() - st1
st2 = time.time()
improvedMunkres.original_munkres(s, e, Max, N, 3)
et2 = time.time() - st2
print("%f %f" % (et1, et2))
'''
print("trial : %d" % loop)
print(et1/et2*100)
time_diff += et1/et2*100
'''
'''
print("Final::", end=' ')
print(time_diff/100)