如果我有4D Tensor且Tensor包含实数
请告诉我如何在张量中找到大于零的最小正数
例如我有:
[ 0 -1 -3
6 5 0
0.3 0.6 0.9]
这里,大于零的最小正数是0.3。
答案 0 :(得分:2)
一种可能性:
t = { 0, -1, -3,
6, 5, 0,
0.3, 0.6, 0.9 }
temp = {}
for _,n in ipairs(t) do
if n > 0 then --keep only positives
temp[#temp+1] = n
end
end
table.sort(temp) --sorting them will bring the smallest first
print('Answer',temp[1])
更新:要找到最低值出现的位置,请将上述内容修改为:
t = { 0, -1, -3,
6, 5, 0,
0.3, 0.6, 0.9 }
temp = {}
for i,n in ipairs(t) do
if n > 0 then --keep only positives
temp[#temp+1] = { n = n, p = i}
end
end
table.sort(temp,function(a,b) return a.n < b.n end) --sorting them will bring the smallest first
print('Answer '.. temp[1].n ..' at position '.. temp[1].p)
答案 1 :(得分:2)
另一种可能性:
t = { 0, -1, -3,
6, 5, 0,
0.3, 0.6, 0.9 }
for _,n in ipairs(t) do
if n > 0 then --only for positives
if ans == nil then
ans = n --first positive assumed lowest
else
if n < ans then ans = n end --if a lower value is found, replaces previous one
end
end
end
print('Answer',ans)
更新:要找到最低值出现的位置,请将上述内容修改为:
t = { 0, -1, -3,
6, 5, 0,
0.3, 0.6, 0.9 }
for i,n in ipairs(t) do
if n > 0 then --only for positives
if ans == nil then
ans = n --first positive assumed lowest
pos = i
else
if n < ans then --if a lower value is found, replaces previous one
ans = n
pos = i --keep position
end
end
end
end
print('Answer '.. ans ..' at position '.. pos)
答案 2 :(得分:2)
t = torch.Tensor({{0, -1, -3}, {6, 5, 0}, {0.3, 0.6, 0.9}})
minpos = torch.min(t[t:gt(0)])
0.3
如何获取所需元素的索引:
1)创建面具
mask = t:eq(minpos)
0 0 0
0 0 0
1 0 0
[torch.ByteTensor of size 3x3]
2)以某种方式获取掩码的非零元素的索引。例如,使用此功能:
function indexesOf(mask)
local lin_indices = torch.linspace(1, mask:nElement(), mask:nElement())[mask]
if lin_indices:nElement() == 0 then return nil end
local sp_indices = torch.LongTensor(mask:nDimension(), lin_indices:nElement())
sp_indices[1] = lin_indices - 1
local divisor = mask:nElement()
for d = 1, mask:nDimension() - 1 do
divisor = divisor / mask:size(d)
local fdiv = torch.div(sp_indices[d], divisor)
sp_indices[d + 1] = sp_indices[d] - fdiv * divisor
sp_indices[d] = fdiv
end
return sp_indices:t() + 1
end
indexes = indexesOf(mask)
3 1
[torch.LongTensor of size 1x2]