There are several ways to create structure
from flatland. Recursion is one of the best.
This
program uses it, with a preliminary step of figuring
out which items are parents.
While the strategy is language-agnostic, every language
has different details of data structures and
building blocks. I've rendered the approach in Python.
NB, about half this code is for display and demonstration purposes, so you can follow along and see how the algorithm is working. For production, fee free to remove those parts.
Oh...and I changed the labels from 'parent' to 'name' and 'children' to 'reports' because I found that more agreeable. You can, of course, choose whatever you like.
from pprint import pprint
from random import shuffle
from collections import defaultdict
import json
def show_val(title, val):
"""
Debugging print helpler.
"""
sep = '-' * len(title)
print "\n{0}\n{1}\n{2}\n".format(sep, title, sep)
pprint(val)
# ORIGINAL NAMES: emp_id, emp_name, mgr_id
# SIMPLIFIED NAMES: eid, name, mid
text = """
323,The Boss,
4444,Manager Joe,323
3,Manager Sally,323
4,Peon Frank,4444
33,Peon Dave,3
5,Peon Jill,4444
6,Peon Rodger,3
7,Peon Ralph,3
233,Clerk Jane,99
99,Supervisor Henri,3
"""
# parse text into lines
lines = [ l.strip() for l in text.strip().splitlines() ]
# construct list of people tuples
people = [ tuple(l.split(',')) for l in lines ]
# for demonstration and testing only, shuffle the results
shuffle(people)
show_val("randomized people", people)
# contstruct list of parents
parents = defaultdict(list)
for p in people:
parents[p[2]].append(p)
show_val("parents", parents)
def buildtree(t=None, parent_eid=''):
"""
Given a parents lookup structure, construct
a data hierarchy.
"""
parent = parents.get(parent_eid, None)
if parent is None:
return t
for eid, name, mid in parent:
report = { 'name': name }
if t is None:
t = report
else:
reports = t.setdefault('reports', [])
reports.append(report)
buildtree(report, eid)
return t
data = buildtree()
show_val("data", data)
show_val("JSON", json.dumps(data))
Running this shows the following output:
-----------------
randomized people
-----------------
[('233', 'Clerk Jane', '99'),
('4444', 'Manager Joe', '323'),
('33', 'Peon Dave', '3'),
('6', 'Peon Rodger', '3'),
('99', 'Supervisor Henri', '3'),
('3', 'Manager Sally', '323'),
('5', 'Peon Jill', '4444'),
('323', 'The Boss', ''),
('4', 'Peon Frank', '4444'),
('7', 'Peon Ralph', '3')]
-------
parents
-------
defaultdict(<type 'list'>, {'99': [('233', 'Clerk Jane', '99')], '323': [('4444', 'Manager Joe', '323'), ('3', 'Manager Sally', '323')], '3': [('33', 'Peon Dave', '3'), ('6', 'Peon Rodger', '3'), ('99', 'Supervisor Henri', '3'), ('7', 'Peon Ralph', '3')], '4444': [('5', 'Peon Jill', '4444'), ('4', 'Peon Frank', '4444')], '': [('323', 'The Boss', '')]})
----
data
----
{'name': 'The Boss',
'reports': [{'name': 'Manager Joe',
'reports': [{'name': 'Peon Jill'}, {'name': 'Peon Frank'}]},
{'name': 'Manager Sally',
'reports': [{'name': 'Peon Dave'},
{'name': 'Peon Rodger'},
{'name': 'Supervisor Henri',
'reports': [{'name': 'Clerk Jane'}]},
{'name': 'Peon Ralph'}]}]}
----
JSON
----
'{"name": "The Boss", "reports": [{"name": "Manager Joe", "reports": [{"name": "Peon Jill"}, {"name": "Peon Frank"}]}, {"name": "Manager Sally", "reports": [{"name": "Peon Dave"}, {"name": "Peon Rodger"}, {"name": "Supervisor Henri", "reports": [{"name": "Clerk Jane"}]}, {"name": "Peon Ralph"}]}]}'
Some preliminaries: It also uses print to help show nested data
structures. We'd normally get data through a database
connection, here we just parse it out of static text.
Finally, while the data you presented is beautifully ordered, with the
bosses at the top and with the lowest employeed id numbers (simplifying the)
problem, I'd like to confirm that the code works in any order. So I've modified
some of the id numbers to reflect a non-sequential allocation, and brought in
random.shuffle to randomize the order of data. You wouldn't do this in
production, but as a part of testing, it increases confidence the logic is
working by design, not accident.
Best Answer
Siri typically doesn't "generate" sentences. She parses what you say and 'recognizes' certain keywords, sure, and for common responses, she will use a template, such as
I found [N] restaurants fairly close to youorI couldn't find [X] in your music, [Username].But most of her responses are canned, depending on her interpretation of your speech, in addition to a random number generator to choose a creative answer to a flippant question. Simply asking Siri "How much wood can a wood chuck chuck?" or "What is the meaning of life?" will generate any of a variety of answers. There are numerous cultural references and jokes built-in (and repeated verbatim) that prove with relative certainty that Siri is not just spontaneously generating most of her text, but pulling it from a database of some sort. It's likely that incoming questions are saved to a central server, where new responses to those questions can be created by Apple employees, allowing Siri to "learn".
Her text-to-speech part is good enough, however, that it sometimes makes it seem as though the answers are being generated...