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View cartpole_tdlg_cpu_summit_exarl_devel
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Cartpole (Summit) devel-head, nep=100, nst=10 6 PEs per node
Processes(Nodes) Average Learner time
6(1) 149.4078055 124.1062127
12(2) 85.49015947 59.99625203
24(4) 33.86835831 7.538378505
48(8) 34.30913389 7.581754972
96(16) 33.47916579 7.186413113
192(32) 26.65310982 1.02104867
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@limkokhole
limkokhole / mozlz4a.py
Created Aug 14, 2020 — forked from kaefer3000/mozlz4a.py
MozLz4a compression/decompression utility
View mozlz4a.py
#!/usr/bin/env python
#
# Decompressor/compressor for files in Mozilla's "mozLz4" format. Firefox uses this file format to
# compress e. g. bookmark backups (*.jsonlz4).
#
# This file format is in fact just plain LZ4 data with a custom header (magic number [8 bytes] and
# uncompressed file size [4 bytes, little endian]).
#
# This Python 3 script requires the LZ4 bindings for Python, see: https://pypi.python.org/pypi/lz4
#
View DupeFile - 76561198068848003.json
{
"CharacterClass": "UtahAdultS",
"DNA": "",
"Location_Isle_V3": "X=227441.453 Y=-261507.703 Z=-6477.731",
"Rotation_Isle_V3": "P=0.000000 Y=-104.199974 R=0.000000",
"Growth": "1.0",
"Hunger": "255",
"Thirst": "51",
"Stamina": "300",
"Health": "1200",
View SaveFile - 76561198068848003.json
{
"CharacterClass": "UtahAdultS",
"DNA": "",
"Location_Isle_V3": "X=227441.453 Y=-261507.703 Z=-6477.731",
"Rotation_Isle_V3": "P=0.000000 Y=-104.199974 R=0.000000",
"Growth": "1.0",
"Hunger": "255",
"Thirst": "51",
"Stamina": "300",
"Health": "1200",
View xgb.cv_function
def xgboost_cal(f1,f2,gridsearch_params):
''' takes features and do param tuning with xgb.csv '''
min_loss = float("Inf")
best_params = None
for a, b in gridsearch_params:
params[f1] = a
params[f2] = b
View Potential Maintainers
Maintainers:
drewrisinger: python27Packages.osqp, python27Packages.osqp, python38Packages.cvxpy, python37Packages.cvxpy, python38Packages.osqp, python37Packages.osqp, python37Packages.osqp, python38Packages.osqp, python38Packages.cvxpy, python38Packages.osqp, python37Packages.cvxpy, python38Packages.cvxpy, python37Packages.cvxpy, python37Packages.osqp, python37Packages.osqp, python37Packages.cvxpy, python27Packages.osqp, python27Packages.osqp, python38Packages.cvxpy, python38Packages.osqp
View Changed Paths
aarch64-linux python38Packages.qiskit
x86_64-darwin python27Packages.osqp
x86_64-linux python27Packages.osqp
x86_64-linux python37Packages.qasm2image
x86_64-linux python38Packages.qasm2image
aarch64-linux python38Packages.cvxpy
x86_64-linux python37Packages.cvxpy
x86_64-darwin python38Packages.qasm2image
x86_64-linux python37Packages.qiskit
x86_64-darwin python37Packages.qiskit-ignis
@HugsLibRecordKeeper
HugsLibRecordKeeper / output_log.txt
Created Aug 14, 2020
Rimworld output log published using HugsLib
View output_log.txt
This file has been truncated, but you can view the full file.
Log uploaded on Saturday, August 15, 2020, 12:12:04 AM
Loaded mods:
Harmony(brrainz.harmony)[mv:1.0.4.0]: 0Harmony(2.0.2), HarmonyMod(1.0.4)
Core(Ludeon.RimWorld): (no assemblies)
Royalty(Ludeon.RimWorld.Royalty): (no assemblies)
HugsLib(UnlimitedHugs.HugsLib)[ov:8.0.0]: 0Harmony(av:2.0.2,fv:1.2.0.1), HugsLib(av:1.0.0,fv:8.0.0)
Won hair_men(won.hair): (no assemblies)
Red Dragon(Bichang.RedDragon): 0Harmony(av:2.0.2,fv:2.0.0.8), DragonsRangeUnlocker(1.0.7369.21702 [no FileVersionInfo]), IncidentWorker_RedDragon(1.0.0)
Recipe icons(automatic.recipeicons): 0Harmony(2.0.2), RecipeIcons(1.0.379.18)
@stanleychen
stanleychen / SQL-To-DataTable.cs
Created Aug 14, 2020 — forked from hanssens/SQL-To-DataTable.cs
[C#] SQL Query to Databable to CSV
View SQL-To-DataTable.cs
// Simple example for
// 1.) Read a sql server query to datatable;
// 2.) Export it to .csv
class Program
{
static void Main(string[] args)
{
var connectionString = @"data source=bla bla bla";
var selectQuery = "select * from my-table;";
View covid-animation-logs
# Create animation - logs
cases_logs <- covid %>%
filter(location%in%c('China', 'United States', 'South Korea',
'United Kingdom', 'World')) %>%
select(date, total_cases, location, total_cases_per_million) %>%
mutate(log_cases=log10(total_cases_per_million),
location=ifelse(location=='United States','US',ifelse(location=='United Kingdom','UK',ifelse(location=='South Korea','Korea',ifelse(location=='China','China',ifelse(location=='World','World',0)))))
) %>%
ggplot(aes(x=date,
y=log_cases, group=location)) +
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