我认为这将是一个有趣的小玩具项目，所以我写了一个小技巧来从标准输入读取像你这样的输入文件，递归地计算和格式化输出，并吐出看起来有点像你的输出，但是嵌套格式，如下所示：
Cluster 0:
Brucella(2)
melitensis(1)
Brucellaceae(1)
neotomae(1)
Brucellaceae(1)
Streptomyces(1)
neotomae(1)
Brucellaceae(1)
Cluster 1:
Streptomyces(2)
geysiriensis(1)
Streptomycetaceae(1)
minutiscleroticus(1)
Streptomycetaceae(1)
Cluster 2:
Mycobacterium(1)
phocaicum(1)
Mycobacteriaceae(1)
Cluster 7:
Mycobacterium(2)
gastri(1)
Mycobacteriaceae(1)
kansasii(1)
Mycobacteriaceae(1)
Cluster 9:
Hyphomicrobium(2)
facile(2)
Hyphomicrobiaceae(2)
Cluster 10:
Streptomyces(2)
niger(1)
Streptomycetaceae(1)
olivaceiscleroticus(1)
Streptomycetaceae(1)
我还在我的表中添加了一些垃圾数据，这样我就可以在集群 0 中看到一个额外的条目，与其他两个分开...这里的想法是您应该能够看到*“集群”条目然后是属、种、科的嵌套、缩进条目……我希望它也不难扩展到更深的树。
# Sys for stdio stuff
import sys
# re for the re.split -- this can go if you find another way to parse your data
import re
# A global (shame on me) for storing the data we're going to parse from stdin
data = []
# read lines from standard input until it's empty (end-of-file)
for line in sys.stdin:
# Split lines on spaces (gobbling multiple spaces for robustness)
# and trim whitespace off the beginning and end of input (strip)
entry = re.split("\s+", line.strip())
# Throw the array into my global data array, it'll look like this:
# [ "0", "Brucella", "melitensis", "Brucellaceae" ]
# A lot of this code assumes that the first field is an integer, what
# you call "cluster" in your problem description
data.append(entry)
# Sort, first key is expected to be an integer, and we want a numerical
# sort rather than a string sort, so convert to int, then sort by
# each subsequent column. The lamba is a function that returns a tuple
# of keys we care about for each item
data.sort(key=lambda item: (int(item[0]), item[1], item[2], item[3]))
# Our recursive function -- we're basically going to treat "data" as a tree,
# even though it's not.
# parameters:
# start - an integer telling us what line to begin working from so we needn't
# walk the whole tree each time to figure out where we are.
# super - An array that captures where we are in the search. This array
# will have more elements in it as we deepen our traversal of the "tree"
# Initially, it will be []
# In the next ply of the tree, it will be [ '0' ]
# Then something like [ '0', 'Brucella' ] and so on.
# data - The global data structure -- this never mutates after the sort above,
# I could have just used the global directly
def groupedReport(start, super, data):
# Figure out what ply we're on in our depth-first traversal of the tree
depth = len(super)
# Count entries in the super class, starting from "start" index in the array:
count = 0
# For the few records in the data file that match our "super" exactly, we count
# occurrences.
if depth != 0:
for i in range(start, len(data)):
if (data[i][0:depth] == data[start][0:depth]):
count = count + 1
else:
break; # We can stop counting as soon as a match fails,
# because of the way our input data is sorted
else:
count = len(data)
# At depth == 1, we're reporting about clusters -- this is the only piece of
# the algorithm that's not truly abstract, and it's only for presentation
if (depth == 1):
sys.stdout.write("Cluster " + super[0] + ":\n")
elif (depth > 0):
# Every other depth: indent with 4 spaces for every ply of depth, then
# output the unique field we just counted, and its count
sys.stdout.write((' ' * ((depth - 1) * 4)) +
data[start][depth - 1] + '(' + str(count) + ')\n')
# Recursion: we're going to figure out a new depth and a new "super"
# and then call ourselves again. We break out on depth == 4 because
# of one other assumption (I lied before about the abstract thing) I'm
# making about our input data here. This could
# be made more robust/flexible without a lot of work
subsuper = None
substart = start
for i in range(start, start + count):
record = data[i] # The original record from our data
newdepth = depth + 1
if (newdepth > 4): break;
# array splice creates a new copy
newsuper = record[0:newdepth]
if newsuper != subsuper:
# Recursion here!
groupedReport(substart, newsuper, data)
# Track our new "subsuper" for subsequent comparisons
# as we loop through matches
subsuper = newsuper
# Track position in the data for next recursion, so we can start on
# the right line
substart = substart + 1
# First call to groupedReport starts the recursion
groupedReport(0, [], data)
如果您将我的 Python 代码制作成“classifier.py”之类的文件，那么您可以像这样通过它运行您的 input.txt 文件（或您所称的任何文件）：
cat input.txt | python classifier.py
如果您愿意的话，递归的大部分魔力都是使用数组切片实现的，并且很大程度上依赖于比较数组切片的能力，以及我可以使用我的排序例程对输入数据进行有意义的排序这一事实。如果大小写不一致可能导致不匹配，您可能希望将输入数据转换为全小写。