y = np.arange([5*x[i] + random.randint(1, 20) for i in range(len(x))])

这行代码存在一个错误。np.arange()函数的参数应该是一个数字，而不是一个列表。请修改代码如下：

y = np.array([5*x[i] + random.randint(1, 20) for i in range(len(x))])

这样就可以生成一个包含随机整数的 NumPy 数组 y，其中每个元素都是 5x[i]+随机整数 的结果。

相关问题

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)

import numpy as np from mpl_toolkits.mplot3d import Axes3D import matplotlib.pyplot as plt np.random.seed(42) # 设置随机种子，保证每次运行结果相同 x = np.random.randn(100, 3) y = x.dot(np.array([4, 5, 6])) + np.random.randn(100) * 0.1 def loss_function(w, x, y): return 0.5 * np.mean((np.dot(x, w) - y) ** 2) def gradient_function(w, x, y): return np.dot(x.T, np.dot(x, w) - y) / len(y) def SGD(x, y, w_init, alpha, max_iter): w = w_init for i in range(max_iter): rand_idx = np.random.randint(len(y)) x_i = x[rand_idx, :].reshape(1, -1) y_i = y[rand_idx] grad_i = gradient_function(w, x_i, y_i) w = w - alpha * grad_i return w fig = plt.figure() ax = Axes3D(fig) W0 = np.arange(0, 10, 0.1) W1 = np.arange(0, 10, 0.1) W0, W1 = np.meshgrid(W0, W1) W2 = np.array([SGD(x, y, np.array([w0, w1, 0]), 0.01, 1000)[2] for w0, w1 in zip(np.ravel(W0), np.ravel(W1))]) W2 = W2.reshape(W0.shape) ax.plot_surface(W0, W1, W2, cmap='coolwarm') ax.set_xlabel('w0') ax.set_ylabel('w1') ax.set_zlabel('loss') plt.show() 代码11行为何报错

在代码的第11行，报错是因为 x 中的索引应该是整数类型，而不是浮点数类型。在这行代码中，索引 `x[rand_idx, :]` 访问的是 x 中的第 rand_idx 行。但是由于 np.random.randint() 返回的是整数类型，因此索引应该使用整数值而不是浮点数值。

为了修复这个错误，可以将第11行代码修改为以下形式：

x_i = x[int(rand_idx), :].reshape(1, -1)

这样就会将 rand_idx 转换为整数类型，然后使用整数索引来访问 x 中的行。