Python Numpy.random.seed()作用

今日は別のコードでnumpyが使われています.random.seed()は、ネットで調べてみると、使用後は次回に同じ乱数を生成できると言っていますが、これはいったい何の役に立つのでしょうか.私の理解は2つのプログラムの対比で、すべてランダムに始まって、この時seedを設定して、2つのプログラムの初期条件を同じにして、更に対比に有利で、以下のコード:

  import numpy as np
import matplotlib.pyplot as plt

arms = 10
#   seed
seed = np.random.randint(0, 100)

# max = 2.24
variance = 1
mean = np.zeros(10)
Q_table = np.zeros(10)
Q_steps = np.zeros(10)

total_steps = 20000
avg_reward = 0.0
step = 0

def walk_mean(mean):
    for i in xrange(len(mean)):
        if np.random.random() > 0.5:
            mean[i] += 1
        else:
            mean[i] -= 1
    return mean

def greedy_update(avg_reward, step, Q_table, mean):
    mean = walk_mean(mean)
    choice = np.argmax(Q_table)
    step += 1
    reward = np.random.normal(mean[choice], variance)
    avg_reward += (reward-avg_reward)/step
    Q_steps[choice] += 1
    Q_table[choice] += (reward-Q_table[choice])/Q_steps[choice]
    return avg_reward, step, Q_table, mean

def fix_greedy_update(avg_reward, step, Q_table, mean):
    mean = walk_mean(mean)
    choice = np.argmax(Q_table)
    step += 1
    reward = np.random.normal(mean[choice], variance)
    avg_reward += (reward-avg_reward)/step
    Q_steps[choice] += 1
    Q_table[choice] += (reward-Q_table[choice])/10
    return avg_reward, step, Q_table, mean

def e_greedy_update(avg_reward, step, Q_table, p, mean):
    flag = np.random.random()
    if flag <= p:
        mean = walk_mean(mean)
        choice = np.random.randint(0, 10)
        step += 1
        reward = np.random.normal(mean[choice], variance)
        avg_reward += (reward-avg_reward)/step
        Q_steps[choice] += 1
        Q_table[choice] += (reward-Q_table[choice])/Q_steps[choice]
    else:
        avg_reward, step, Q_table, mean = greedy_update(avg_reward, step, Q_table, mean)
    return avg_reward, step, Q_table, mean

def fix_e_greedy_update(avg_reward, step, Q_table, p, mean):
    flag = np.random.random()
    if flag <= p:
        mean = walk_mean(mean)
        choice = np.random.randint(0, 10)
        step += 1
        reward = np.random.normal(mean[choice], variance)
        avg_reward += (reward-avg_reward)/step
        Q_table[choice] += (reward-Q_table[choice])/10
    else:
        avg_reward, step, Q_table, mean = fix_greedy_update(avg_reward, step, Q_table, mean)
    return avg_reward, step, Q_table, mean

# e-greedy with e=0.1
np.random.seed(seed)
r = []
for i in xrange(total_steps):
    avg_reward, step, Q_table, mean = e_greedy_update(avg_reward, step, Q_table, 0.1, mean)
    r.append(avg_reward)
print Q_table
print Q_steps
one, = plt.plot(r, label='e_greedy_0.1')

# fix-e-greedy with e=0.1
Q_table = np.zeros(10)
Q_steps = np.zeros(10)
mean = np.zeros(10)
avg_reward = 0.0
step = 0
np.random.seed(seed)
r = []
for i in xrange(total_steps):
    avg_reward, step, Q_table, mean = fix_e_greedy_update(avg_reward, step, Q_table, 0.1, mean)
    r.append(avg_reward)
print Q_table
print Q_steps
two, = plt.plot(r, label='fix_e_greedy_0.1')


plt.legend(handles=[one, two])

plt.show()

npは、#e-greedy with e=0.1と#fix-e-greedy with e=0.1の下でそれぞれ呼び出されていることがわかる.random.seed(seed)は、flag=npという2つの比較プログラムの初期条件を同じにする.random.random().