axis = np.random.randint(0, 2)什么意思是

这是一个使用 NumPy 库的 Python 代码行，它生成一个随机整数，范围在 0 到 2 之间（但不包括 2）。

具体地说，`np.random.randint()` 是 NumPy 库中的一个随机数生成函数，它可以生成给定范围内的整数。在这里，我们将范围设为 0 到 2（不包括 2），因此可能生成的整数只有 0 和 1。

然后，将生成的整数赋值给变量 `axis`，以供后续使用。根据上下文，`axis` 可能是一个用于指定数组维度的参数，表示我们要在哪个维度上进行某种操作。

相关问题

逐行解释np.random.seed(0) X = np.random.randint(0, 10, size=(6, 2)) y = np.array([0, 0, 0, 1, 1, 1]) data = pd.DataFrame(np.concatenate([X, y.reshape(-1, 1)], axis=1), columns=["x1", "x2", "y"]) print(data)

当我们编写机器学习算法时，我们通常需要使用随机数生成器。但是，由于机器学习算法的随机性，不同的随机数生成器可能会导致不同的结果。为了确保我们的实验结果可重复，我们可以使用伪随机数生成器，并通过设置种子(seed)来指定随机数生成器的起始状态。每次运行程序时，生成的随机数序列将是相同的。

下面是对这段代码的逐行解释：

- `np.random.seed(0)`：设置NumPy随机数生成器的种子为0。
- `X = np.random.randint(0, 10, size=(6, 2))`：生成一个6行2列的NumPy数组X，其中元素是0到9之间的随机整数。
- `y = np.array([0, 0, 0, 1, 1, 1])`：生成一个包含6个元素的NumPy数组y，其中前三个元素为0，后三个元素为1。
- `data = pd.DataFrame(np.concatenate([X, y.reshape(-1, 1)], axis=1), columns=["x1", "x2", "y"])`：将X和y按列方向进行拼接，生成一个包含三列的NumPy数组。然后，将该数组转换为一个Pandas DataFrame对象，其中列名分别为"x1"、"x2"和"y"。
- `print(data)`：输出DataFrame对象data的内容。

请在不影响结果的条件下改变代码的样子：import numpy as np import matplotlib.pyplot as plt x1len = 21 x2len = 18 LEN = x1len + x2len POPULATION_SIZE = 100 GENERATIONS = 251 CROSSOVER_RATE = 0.7 MUTATION_RATE = 0.3 pop = np.random.randint(0,2,size=(POPULATION_SIZE,LEN)) def BinToX(pop): x1 = pop[:,0:x1len] x2 = pop[:,x1len:] x1 = x1.dot(2**np.arange(x1len)[::-1]) x2 = x2.dot(2**np.arange(x2len)[::-1]) x1 = -2.9 + x1*(12 + 2.9)/(np.power(2,x1len)-1) x2 = 4.2 + x2*(5.7 - 4.2)/(np.power(2,x2len)-1) return x1,x2 def func(pop): x1,x2 = BinToX(pop) return 21.5 + x1*np.sin(4*np.pi*x1) + x2*np.sin(20*np.pi*x2) def fn(pop): return func(pop); def selection(pop, fitness): idx = np.random.choice(np.arange(pop.shape[0]), size=POPULATION_SIZE, replace=True, p=fitness/fitness.sum()) return pop[idx] def crossover(IdxP1,pop): if np.random.rand() < CROSSOVER_RATE: C = np.zeros((1,LEN)) IdxP2 = np.random.randint(0, POPULATION_SIZE) pt = np.random.randint(0, LEN) C[0,:pt] = pop[IdxP1,:pt] C[0,pt:] = pop[IdxP2, pt:] np.append(pop, C, axis=0) return def mutation(idx,pop): if np.random.rand() < MUTATION_RATE: mut_index = np.random.randint(0, LEN) pop[idx,mut_index] = 1- pop[idx,mut_index] return best_chrom = np.zeros(LEN) best_score = 0 fig = plt.figure() for generation in range(GENERATIONS): fitness = fn(pop) pop = selection(pop, fitness) if generation%50 == 0: ax = fig.add_subplot(2,3,generation//50 +1, projection='3d', title = "generation："+str(generation)+" best="+str(np.max(fitness))) x1,x2 = BinToX(pop) z = func(pop) ax.scatter(x1,x2,z) for idx in range(POPULATION_SIZE): crossover(idx,pop) mutation(idx,pop) idx = np.argmax(fitness) if best_score < fitness[idx]: best_score = fitness[idx] best_chrom = pop[idx, :] plt.show() print('最优解:', best_chrom, '| best score: %.2f' % best_score)

import numpy as np
import matplotlib.pyplot as plt

x1len = 21
x2len = 18
LEN = x1len + x2len
POPULATION_SIZE = 100
GENERATIONS = 251
CROSSOVER_RATE = 0.7
MUTATION_RATE = 0.3

pop = np.random.randint(0,2,size=(POPULATION_SIZE,LEN))

def BinToX(pop):
x1 = pop[:,0:x1len]
x2 = pop[:,x1len:]
x1 = -2.9 + x1*(12 + 2.9)/(np.power(2,x1len)-1)
x2 = 4.2 + x2*(5.7 - 4.2)/(np.power(2,x2len)-1)
return x1,x2

def func(x1,x2):
return 21.5 + x1*np.sin(4*np.pi*x1) + x2*np.sin(20*np.pi*x2)

def fn(pop):
x1,x2 = BinToX(pop)
return func(x1,x2);

def selection(pop, fitness):
idx = np.random.choice(np.arange(pop.shape[0]),size=POPULATION_SIZE, replace=True, p=fitness/fitness.sum())
return pop[idx]

def crossover(IdxP1,pop):
if np.random.rand() < CROSSOVER_RATE:
C = np.zeros((1,LEN))
IdxP2 = np.random.randint(0, POPULATION_SIZE)
pt = np.random.randint(0, LEN)
C[0,:pt] = pop[IdxP1,:pt]
C[0,pt:] = pop[IdxP2, pt:]
np.append(pop, C, axis=0)
return

def mutation(idx,pop):
if np.random.rand() < MUTATION_RATE:
mut_index = np.random.randint(0, LEN)
pop[idx,mut_index] = 1- pop[idx,mut_index]
return

best_chrom = np.zeros(LEN)
best_score = 0
fig = plt.figure()

for generation in range(GENERATIONS):
fitness = fn(pop)
pop = selection(pop, fitness)

if generation%50 == 0:
ax = fig.add_subplot(2,3,generation//50 +1, projection='3d',title = "generation："+str(generation)+" best="+str(np.max(fitness)))
x1,x2 = BinToX(pop)
z = func(x1,x2)
ax.scatter(x1,x2,z)

for idx in range(POPULATION_SIZE):
crossover(idx,pop)
mutation(idx,pop)

idx = np.argmax(fitness)
if best_score < fitness[idx]:
best_score = fitness[idx]
best_chrom = pop[idx, :]

plt.show()
print('最优解:', best_chrom, '| best score: %.2f' % best_score)