// Hash tables
// 1) hash0 - Perfect hashing (use the value as the hash)
// 2) hash1 - Use hash1() with open addressing (linear probing)
// Compilation options -------
assert inlist(`is_vector', 0, 1)
if (`is_vector') {
loc suffix
}
else {
loc suffix ", ."
}
// ---------------------------
mata:
// Open addressing hash function (linear probing)
// Use this for non-integers (2.5, "Bank A") and big ints (e.g. 2014124233573)
`Factor' __factor_hash1_`is_vector'(
`DataFrame' data,
`Boolean' verbose,
`Integer' dict_size,
`Boolean' sort_levels,
`Integer' max_numkeys,
`Boolean' save_keys)
{
`Factor' F
`Integer' h, num_collisions, j, val
`Integer' obs, start_obs, num_obs, num_vars
`Vector' dict
`Vector' levels // new levels
`Vector' counts
`Vector' p
`DataFrame' keys
`DataRow' key, last_key
`String' msg
num_obs = rows(data)
num_vars = cols(data)
assert(dict_size > 0 & dict_size < .)
assert ((num_vars > 1) + (`is_vector') == 1) // XOR
dict = J(dict_size, 1, 0)
levels = J(num_obs, 1, 0)
keys = J(max_numkeys, num_vars, missingof(data))
counts = J(max_numkeys, 1, 1) // keys are at least present once!
j = 0 // counts the number of levels; at the end j == num_levels
val = J(0, 0, .)
num_collisions = 0
last_key = J(0, 0, missingof(data))
for (obs = 1; obs <= num_obs; obs++) {
key = data[obs`suffix']
// (optional) Speedup when dataset is already sorted
// (at a ~10% cost for when it's not)
if (last_key == key) {
start_obs = obs
do {
obs++
} while (obs <= num_obs ? data[obs`suffix'] == last_key : 0 )
levels[|start_obs \ obs - 1|] = J(obs - start_obs, 1, val)
counts[val] = counts[val] + obs - start_obs
if (obs > num_obs) break
key = data[obs`suffix']
}
// Compute hash and retrieve the level the key is assigned to
h = hash1(key, dict_size)
val = dict[h]
// (new key) The key has not been assigned to a level yet
if (val == 0) {
val = dict[h] = ++j
keys[val`suffix'] = key
}
else if (key == keys[val`suffix']) {
counts[val] = counts[val] + 1
}
// (collision) Another key already points to the same dict slot
else {
// Look up for an empty slot in the dict
// Linear probing, not very sophisticate...
do {
++num_collisions
++h
if (h > dict_size) h = 1
val = dict[h]
if (val == 0) {
dict[h] = val = ++j
keys[val`suffix'] = key
break
}
if (key == keys[val`suffix']) {
counts[val] = counts[val] + 1
break
}
} while (1)
}
levels[obs] = val
last_key = key
} // end for >>>
dict = . // save memory
if (save_keys | sort_levels) keys = keys[| 1 , 1 \ j , . |]
counts = counts[| 1 \ j |]
if (sort_levels & j > 1) {
// bugbug: replace with binsort?
p = order(keys, 1..num_vars) // this is O(K log K) !!!
if (save_keys) keys = keys[p, .] // _collate(keys, p)
counts = counts[p] // _collate(counts, p)
levels = rows(levels) > 1 ? invorder(p)[levels] : 1
}
p = . // save memory
if (verbose) {
msg = "{txt}(%s hash collisions - %4.2f{txt}%%)\n"
printf(msg, strofreal(num_collisions), num_collisions / num_obs * 100)
}
F = Factor()
F.num_levels = j
if (save_keys) swap(F.keys, keys)
swap(F.levels, levels)
swap(F.counts, counts)
return(F)
}
end