如何使用Python将整数序列向量化并编码为二进制矩阵？

本文共计705个文字，预计阅读时间需要3分钟。

将整数序列编码为二进制矩阵，参考以下步骤：

1. 使用`numpy`库，导入`numpy`模块。

2.创建一个整数序列数组`t`，例如：`t=np.array([1, 2, 3, 4, 5, 6, 7, 8, 9])`。

3.创建一个与`t`长度相同且全为零的矩阵`r`，例如：`r=np.zeros((len(t), 1))`。

示例代码如下：

pythonimport numpy as npt=np.array([1, 2, 3, 4, 5, 6, 7, 8, 9])r=np.zeros((len(t), 1))

向量化序列说明_将整数序列编码为二进制矩阵
参考：
​​​ www.pythonheidong.com/blog/article/187614/​​

import numpy as np
t = np.array([1,2,3,4,5,6,7,8,9])
r = np.zeros((len(t), 10))
t
array([1, 2, 3, 4, 5, 6, 7, 8, 9])
r
array([[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]])
for i, s in enumerate(t): r[i,s] = 1.
t
array([1, 2, 3, 4, 5, 6, 7, 8, 9])
r
r
array([[0., 1., 0., 0., 0., 0., 0., 0., 0., 0.],
[0., 0., 1., 0., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 1., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 1., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 1., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0., 1., 0., 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 1., 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 0., 1., 0.],
[0., 0., 0., 0., 0., 0., 0., 0., 0., 1.]])
def vectorize_sequences(sequences, dimension=10000):
# Create an all-zero matrix of shape (len(sequences), dimension)
results = np.zeros((len(sequences), dimension))
for i, sequence in enumerate(sequences):
results[i, sequence] = 1. # set specific indices of results[i] to 1s
return results
x_train = vectorize_sequences(t)
x_train
def vectorize_sequences(sequences, dimension=10000):
# Create an all-zero matrix of shape (len(sequences), dimension)
results = np.zeros((len(sequences), dimension))
for i, sequence in enumerate(sequences):
results[i, sequence] = 1. # set specific indices of results[i] to 1s
return results
x_train = vectorize_sequences(t)
x_train
array([[0., 1., 0., ..., 0., 0., 0.],
[0., 0., 1., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
...,
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.]])
import numpy as np
t = np.array([[1,2,3,4,5,6,7,8,9],[1,2,3,4,5,6,7,8,9]])
r = np.zeros((len(t), 10))
def vectorize_sequences(sequences, dimension=10000):
# Create an all-zero matrix of shape (len(sequences), dimension)
results = np.zeros((len(sequences), dimension))
for i, sequence in enumerate(sequences):
results[i, sequence] = 1. # set specific indices of results[i] to 1s
return results
x_train = vectorize_sequences(t)
x_train
array([[0., 1., 0., ..., 0., 0., 0.],
[0., 0., 1., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
...,
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.]])