只有整数、切片(`:`)、省略号(`...`)、numpy.newaxis (`None`)和整数或布尔数组才是生成rnn的有效索引

import numpy as np

arr = np.array([1, 2, 3])
try:
    print(arr[3])  # 这将引发IndexError
except IndexError as e:
    print(f"Error: {e}")

arr = np.array([1, 2, 3, 4])
bool_index = np.array([True, False])
try:
    print(arr[bool_index])  # 这将引发IndexError
except IndexError as e:
    print(f"Error: {e}")

arr = np.array([1, 2, 3])
arr_new_dim = arr[np.newaxis, :]  # 正确增加一个维度
print(arr_new_dim.shape)  # 输出 (1, 3)

import numpy as np

# 假设我们有一个简单的序列数据
sequences = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])

# 使用切片获取所有序列的第一个元素
first_elements = sequences[:, 0]
print(first_elements)  # 输出 [1 4 7]

# 使用numpy.newaxis增加时间步维度
sequences_with_time_step = sequences[:, :, np.newaxis]
print(sequences_with_time_step.shape)  # 输出 (3, 3, 1)

# 使用布尔数组索引筛选出大于5的元素
bool_index = sequences > 5
filtered_elements = sequences[bool_index]
print(filtered_elements)  # 输出 [6 7 8 9]