使用网格搜索在 Python 中创建维度为 n * 3 的矩阵
Use grid search to create a matrix with dimension n * 3 in Python
我想要一个像下面这样的矩阵,3 列,n 行。每行总和为 1。
[[0, 0, 1], [0, 0.1, 0.9], [0.1, 0.1, 0.8], [0.1, 0.2, 0.7] ...]
是否有用于执行此操作的库?
您可以使用 itertools.combinations_with_replacement
从 0.0 到 1.0 之间的 11 个插槽中选择 2 个分区:
from itertools import combinations_with_replacement
[[n / 10 for n in (a, b - a, 10 - b)] for a, b in combinations_with_replacement(range(11), 2)]
这个returns:
[[0.0, 0.0, 1.0],
[0.0, 0.1, 0.9],
[0.0, 0.2, 0.8],
[0.0, 0.3, 0.7],
[0.0, 0.4, 0.6],
[0.0, 0.5, 0.5],
[0.0, 0.6, 0.4],
[0.0, 0.7, 0.3],
[0.0, 0.8, 0.2],
[0.0, 0.9, 0.1],
[0.0, 1.0, 0.0],
[0.1, 0.0, 0.9],
[0.1, 0.1, 0.8],
[0.1, 0.2, 0.7],
[0.1, 0.3, 0.6],
[0.1, 0.4, 0.5],
[0.1, 0.5, 0.4],
[0.1, 0.6, 0.3],
[0.1, 0.7, 0.2],
[0.1, 0.8, 0.1],
[0.1, 0.9, 0.0],
[0.2, 0.0, 0.8],
[0.2, 0.1, 0.7],
[0.2, 0.2, 0.6],
[0.2, 0.3, 0.5],
[0.2, 0.4, 0.4],
[0.2, 0.5, 0.3],
[0.2, 0.6, 0.2],
[0.2, 0.7, 0.1],
[0.2, 0.8, 0.0],
[0.3, 0.0, 0.7],
[0.3, 0.1, 0.6],
[0.3, 0.2, 0.5],
[0.3, 0.3, 0.4],
[0.3, 0.4, 0.3],
[0.3, 0.5, 0.2],
[0.3, 0.6, 0.1],
[0.3, 0.7, 0.0],
[0.4, 0.0, 0.6],
[0.4, 0.1, 0.5],
[0.4, 0.2, 0.4],
[0.4, 0.3, 0.3],
[0.4, 0.4, 0.2],
[0.4, 0.5, 0.1],
[0.4, 0.6, 0.0],
[0.5, 0.0, 0.5],
[0.5, 0.1, 0.4],
[0.5, 0.2, 0.3],
[0.5, 0.3, 0.2],
[0.5, 0.4, 0.1],
[0.5, 0.5, 0.0],
[0.6, 0.0, 0.4],
[0.6, 0.1, 0.3],
[0.6, 0.2, 0.2],
[0.6, 0.3, 0.1],
[0.6, 0.4, 0.0],
[0.7, 0.0, 0.3],
[0.7, 0.1, 0.2],
[0.7, 0.2, 0.1],
[0.7, 0.3, 0.0],
[0.8, 0.0, 0.2],
[0.8, 0.1, 0.1],
[0.8, 0.2, 0.0],
[0.9, 0.0, 0.1],
[0.9, 0.1, 0.0],
[1.0, 0.0, 0.0]]
我想要一个像下面这样的矩阵,3 列,n 行。每行总和为 1。
[[0, 0, 1], [0, 0.1, 0.9], [0.1, 0.1, 0.8], [0.1, 0.2, 0.7] ...]
是否有用于执行此操作的库?
您可以使用 itertools.combinations_with_replacement
从 0.0 到 1.0 之间的 11 个插槽中选择 2 个分区:
from itertools import combinations_with_replacement
[[n / 10 for n in (a, b - a, 10 - b)] for a, b in combinations_with_replacement(range(11), 2)]
这个returns:
[[0.0, 0.0, 1.0],
[0.0, 0.1, 0.9],
[0.0, 0.2, 0.8],
[0.0, 0.3, 0.7],
[0.0, 0.4, 0.6],
[0.0, 0.5, 0.5],
[0.0, 0.6, 0.4],
[0.0, 0.7, 0.3],
[0.0, 0.8, 0.2],
[0.0, 0.9, 0.1],
[0.0, 1.0, 0.0],
[0.1, 0.0, 0.9],
[0.1, 0.1, 0.8],
[0.1, 0.2, 0.7],
[0.1, 0.3, 0.6],
[0.1, 0.4, 0.5],
[0.1, 0.5, 0.4],
[0.1, 0.6, 0.3],
[0.1, 0.7, 0.2],
[0.1, 0.8, 0.1],
[0.1, 0.9, 0.0],
[0.2, 0.0, 0.8],
[0.2, 0.1, 0.7],
[0.2, 0.2, 0.6],
[0.2, 0.3, 0.5],
[0.2, 0.4, 0.4],
[0.2, 0.5, 0.3],
[0.2, 0.6, 0.2],
[0.2, 0.7, 0.1],
[0.2, 0.8, 0.0],
[0.3, 0.0, 0.7],
[0.3, 0.1, 0.6],
[0.3, 0.2, 0.5],
[0.3, 0.3, 0.4],
[0.3, 0.4, 0.3],
[0.3, 0.5, 0.2],
[0.3, 0.6, 0.1],
[0.3, 0.7, 0.0],
[0.4, 0.0, 0.6],
[0.4, 0.1, 0.5],
[0.4, 0.2, 0.4],
[0.4, 0.3, 0.3],
[0.4, 0.4, 0.2],
[0.4, 0.5, 0.1],
[0.4, 0.6, 0.0],
[0.5, 0.0, 0.5],
[0.5, 0.1, 0.4],
[0.5, 0.2, 0.3],
[0.5, 0.3, 0.2],
[0.5, 0.4, 0.1],
[0.5, 0.5, 0.0],
[0.6, 0.0, 0.4],
[0.6, 0.1, 0.3],
[0.6, 0.2, 0.2],
[0.6, 0.3, 0.1],
[0.6, 0.4, 0.0],
[0.7, 0.0, 0.3],
[0.7, 0.1, 0.2],
[0.7, 0.2, 0.1],
[0.7, 0.3, 0.0],
[0.8, 0.0, 0.2],
[0.8, 0.1, 0.1],
[0.8, 0.2, 0.0],
[0.9, 0.0, 0.1],
[0.9, 0.1, 0.0],
[1.0, 0.0, 0.0]]