-
Notifications
You must be signed in to change notification settings - Fork 0
/
gen_norm.py
83 lines (65 loc) · 2.55 KB
/
gen_norm.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
import numpy as np
import struct
from scipy.stats import norm, lognorm
import os
# any arbitrary seed value will do, but this one is clearly the best.
np.random.seed(seed=42)
NUM_KEYS = 200_000_000
print("Generating normal data...")
if not os.path.exists("data/normal_200M_uint32"):
print("32 bit...")
keys = np.linspace(0, 1, NUM_KEYS + 2)[1:-1]
# for some reason, the PPF function seems to use quadratic memory
# with the size of its input.
keys = np.array_split(keys, 1000)
keys = [norm.ppf(x) for x in keys]
keys = np.array(keys).flatten()
keys = (keys - np.min(keys)) / (np.max(keys) - np.min(keys))
keys *= 2**32 - 1
keys = keys.astype(np.uint32)
with open("data/normal_200M_uint32", "wb") as f:
f.write(struct.pack("Q", len(keys)))
keys.tofile(f)
if not os.path.exists("data/normal_200M_uint64"):
print("64 bit...")
keys = np.linspace(0, 1, NUM_KEYS + 2)[1:-1]
# for some reason, the PPF function seems to use quadratic memory
# with the size of its input.
keys = np.array_split(keys, 1000)
keys = [norm.ppf(x) for x in keys]
keys = np.array(keys).flatten()
keys = (keys - np.min(keys)) / (np.max(keys) - np.min(keys))
keys *= 2**63 - 1
keys = keys.astype(np.uint64)
with open("data/normal_200M_uint64", "wb") as f:
f.write(struct.pack("Q", len(keys)))
keys.tofile(f)
print("Generating log normal data...")
if not os.path.exists("data/lognormal_200M_uint32"):
print("32 bit...")
keys = np.linspace(0, 1, NUM_KEYS + 2)[1:-1]
# using a sigma of 2 for the 32 bit keys produces WAY too many
# duplicates, so we will deviate from the RMI paper
# and use 1.
keys = np.array_split(keys, 1000)
keys = [lognorm.ppf(x, 1) for x in keys]
keys = np.array(keys).flatten()
keys = (keys - np.min(keys)) / (np.max(keys) - np.min(keys))
keys *= 2**32 - 1
keys = keys.astype(np.uint32)
with open("data/lognormal_200M_uint32", "wb") as f:
f.write(struct.pack("Q", len(keys)))
keys.tofile(f)
if not os.path.exists("data/lognormal_200M_uint64"):
print("64 bit...")
keys = np.linspace(0, 1, NUM_KEYS + 2)[1:-1]
# use a sigma of 2 to match the LIS paper.
keys = np.array_split(keys, 1000)
keys = [lognorm.ppf(x, 2) for x in keys]
keys = np.array(keys).flatten()
keys = (keys - np.min(keys)) / (np.max(keys) - np.min(keys))
keys *= 2**63 - 1
keys = keys.astype(np.uint64)
with open("data/lognormal_200M_uint64", "wb") as f:
f.write(struct.pack("Q", len(keys)))
keys.tofile(f)