anomaly score fluctuation

bench.py

import jubatus
from jubatus.common import Datum

import random
import time

cl = jubatus.Anomaly("localhost", 9199, "test", 0)
random.seed(1)
datum_list = []
datum_length = 100
datum_num = 3000
for i in range(datum_num):
    d = Datum()
    for x in range(datum_length):
        d.add_number("{}".format(x), random.random())
    datum_list.append(d)

for i in range(5):
    cl.clear()
    start = time.time()
    for d in datum_list:
        cl.add(d)
    end = time.time()
    print("add time:", end - start)
    print(cl.calc_score(datum_list[-1]))

lof.json

{
  "converter" : {
    "string_filter_types" : {},
    "string_filter_rules" : [],
    "num_filter_types" : {},
    "num_filter_rules" : [],
    "string_types" : {},
    "string_rules" : [
      { "key" : "*", "type" : "str", "sample_weight" : "bin", "global_weight" : "bin" }
    ],
    "num_types" : {},
    "num_rules" : [
      { "key" : "*", "type" : "num" }
    ]
  },
  "parameter" : {
    "nearest_neighbor_num" : 10,
    "reverse_nearest_neighbor_num" : 30,
    "method" : "lsh",
    "parameter" : {
        "hash_num": 64
    }
  },
  "method" : "lof"
}

result.txt

$jubaanomaly -f lof.json&
$ python bench.py
2017-12-11 15:49:34,709 20751 INFO  [anomaly_serv.cpp:246] model cleared: 
add time: 18.31161880493164
1.0249063968658447
2017-12-11 15:49:53,028 20752 INFO  [anomaly_serv.cpp:246] model cleared: 
add time: 17.448886156082153
1.0471123456954956
2017-12-11 15:50:10,488 20752 INFO  [anomaly_serv.cpp:246] model cleared: 
add time: 19.00316023826599
1.0471124649047852
2017-12-11 15:50:29,496 20751 INFO  [anomaly_serv.cpp:246] model cleared: 
add time: 17.5341157913208
1.0249063968658447
2017-12-11 15:50:47,034 20751 INFO  [anomaly_serv.cpp:246] model cleared: 
add time: 17.534404754638672
1.0471123456954956