我有一个后台工作,需要将很多项目合并在一起。我想将其拆分为多个“子作业”,每个子作业合并数据的一个子集,然后进行最后遍,将所有“子作业”的输出合并到一起。
一种简单的方法是将数据分成x个元素组。问题在于最后一组可能剩下1个元素,因此它是一个“空位”。我想找到最佳的“ x”,以使各组大致均匀,并且每组中具有最小和最大元素数(例如,不少于10个元素,且不超过20个)。
在Ruby中,有什么好的算法?
这是一些示例输出,最小为10,最大为20。数字代表每个数组中的元素数。
<number of elements in input> => <subgroup 1>, <subgroup 2>, etc.
5 => 5
10 => 10
15 => 15
20 => 20
21 => 10, 11
30 => 15, 15
40 => 20, 20
41 => 13, 14, 14
42 => 14, 14, 14
43 => 14, 14, 15
45 => 15, 15, 15
50 => 16, 17, 17
55 => 18, 18, 19
60 => 20, 20, 20
61 => 15, 15, 15, 16
基本上,我想将数组大致分为偶数个组,但每个组中的元素数应为最小和最大。
答案 0 :(得分:2)
我会这样处理:
# count of original items
count = 61
# max bucket size
max = 20
# decide buckets
groups = (count / max) + (count % max > 0 ? 1 : 0)
# this will be the final result
result = []
# create buckets
groups.times { result.push(0) }
# iterate over original items and distribute them in the buckets
count.times do |n|
result[n % groups] += 1
end
p result
假设count
为61,它将输出16、15、15、15。我已经在代码段本身中解释了每个语句的目的。
答案 1 :(得分:1)
版本略有不同:
def divide(c, max = 20)
groups = (c.to_f / max).ceil
min_count = (c.to_f / groups).floor
[min_count + 1] * (c % min_count) + [min_count] * (groups - c % min_count)
end
答案 2 :(得分:0)
这是我尝试解决的方法:
class ArrayPartition
def self.partition_lengths(length, minimum, maximum)
if length <= maximum
return [length]
end
group_size = maximum
groups = []
while groups.empty? || groups.last < minimum
groups = []
remaining = length
while remaining > group_size
groups << group_size
remaining -= group_size
end
groups << remaining
group_size -= 1
end
# Redistribute evenly
avg_group_size = (length / groups.size.to_f).round
groups = []
remaining = length
while remaining > maximum
groups << avg_group_size
remaining -= avg_group_size
end
groups << remaining
groups.sort
end
end
RSpec测试:
RSpec.describe ArrayPartition do
it 'partitions an array into optimal groups with min and max elements' do
expect(ArrayPartition.partition_lengths(5, 5, 10)).to eq [5]
expect(ArrayPartition.partition_lengths(6, 5, 10)).to eq [6]
expect(ArrayPartition.partition_lengths(7, 5, 10)).to eq [7]
expect(ArrayPartition.partition_lengths(10, 5, 10)).to eq [10]
expect(ArrayPartition.partition_lengths(11, 5, 10)).to eq [5, 6]
expect(ArrayPartition.partition_lengths(12, 5, 10)).to eq [6, 6]
expect(ArrayPartition.partition_lengths(13, 5, 10)).to eq [6, 7]
expect(ArrayPartition.partition_lengths(16, 5, 10)).to eq [8, 8]
expect(ArrayPartition.partition_lengths(20, 5, 10)).to eq [10, 10]
expect(ArrayPartition.partition_lengths(21, 5, 10)).to eq [7, 7, 7]
expect(ArrayPartition.partition_lengths(22, 5, 10)).to eq [7, 7, 8]
expect(ArrayPartition.partition_lengths(5, 10, 20)).to eq [5]
expect(ArrayPartition.partition_lengths(10, 10, 20)).to eq [10]
expect(ArrayPartition.partition_lengths(15, 10, 20)).to eq [15]
expect(ArrayPartition.partition_lengths(20, 10, 20)).to eq [20]
expect(ArrayPartition.partition_lengths(21, 10, 20)).to eq [10, 11]
expect(ArrayPartition.partition_lengths(30, 10, 20)).to eq [15, 15]
expect(ArrayPartition.partition_lengths(40, 10, 20)).to eq [20, 20]
expect(ArrayPartition.partition_lengths(41, 10, 20)).to eq [13, 14, 14]
expect(ArrayPartition.partition_lengths(42, 10, 20)).to eq [14, 14, 14]
expect(ArrayPartition.partition_lengths(43, 10, 20)).to eq [14, 14, 15]
expect(ArrayPartition.partition_lengths(45, 10, 20)).to eq [15, 15, 15]
expect(ArrayPartition.partition_lengths(50, 10, 20)).to eq [16, 17, 17]
expect(ArrayPartition.partition_lengths(55, 10, 20)).to eq [18, 18, 19]
expect(ArrayPartition.partition_lengths(60, 10, 20)).to eq [20, 20, 20]
expect(ArrayPartition.partition_lengths(61, 10, 20)).to eq [15, 15, 15, 16]
end
end