Question

我有一个总共4950个值的文件，如：

0.012345678912345678

我使用以下方式阅读文件：

a = numpy.genfromtxt(file_name, dtype=str, delimiter=',') # a.shape = (4950L, 1L) #dtype=str as I don't want to compromise accuracy
#say a == ['0.000000000000000001', -'0.000000000000000002', ...., '0.000000000004950']

我想要获得的是获得大小为b的矩阵(100L, 100L)，其中：{/ p>

上三角值用numpy数组中的值填充＆＃39; a＆＃39;。
下三角值用numpy数组中的值填充＆＃39; a＆＃39;但乘以-1。
对角线仅由零组成。

示例（准确性很重要）：

array = ['1','2','-3','-5','6','-7'] # In reality the data is up to 18 decimal places.

final_matrix = [
               ['0','1','2','-3'],
               ['-1',0,'-5','6'],
               ['-2','5','0','-7'],
               ['3','-6','7','0']
               ]

实现这一目标的最有效方法是什么？

Answer 1

不确定它是否是最有效的方式，但这似乎非常有效。

import numpy

# create some random data for testing
sz = 100
a  = numpy.random.random(sz*sz/2 - sz/2).astype('S50')

# convert back to float for a test on minus signs,
# as it would be done if a is read as string values
amins = numpy.where(a.astype(float) <= 0, "", "-")

# get the values without minus signs
aplus = numpy.char.lstrip(a, "-")

# addup to negated string values
aminus = numpy.char.add(amins, aplus)

# create an empty matrix
m = numpy.zeros(shape=(sz,sz), dtype='S51')
# ids of the upper triangle
u_ids = numpy.triu_indices(sz,1)
# set upper values
m[u_ids] = a
# switch coordinates to set lower values
m[u_ids[1],u_ids[0]] = aminus
# fill diag with zeros
numpy.fill_diagonal(m, numpy.zeros(sz).astype('S51'))


print m

从1D numpy数组中获取此类矩阵的最有效方法是什么？

1 个答案: