braz

using MongoDB.Bson;
using MongoDB.Bson.Serialization.Attributes;
using MongoDB.Driver;

BsonSerializer.RegisterSerializer(new DecimalSerializer(BsonType.Decimal128));

var settings = MongoClientSettings.FromConnectionString("");
var client = new MongoClient(settings);

braz

<project xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="http://maven.apache.org/POM/4.0.0"
         xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
    <modelVersion>4.0.0</modelVersion>
    <groupId>com.mdbu</groupId>
    <artifactId>curriculum</artifactId>
    <packaging>jar</packaging>
    <version>1.0-SNAPSHOT</version>
    <dependencies>
        <!-- https://mvnrepository.com/artifact/org.mongodb/mongodb-driver-sync -->
        <dependency>

braz

[program:code-server]
command = code-server workspace /app --user-data-dir /app/user-data --auth none --disable-telemetry
stdout_events_enabled = true
stderr_events_enabled = true

braz

; supervisor config file

[unix_http_server]
file=/var/run/supervisor.sock   ; (the path to the socket file)
chmod=0700                       ; sockef file mode (default 0700)

[supervisord]
logfile=/var/log/supervisor/supervisord.log ; (main log file;default $CWD/supervisord.log)
pidfile=/var/run/supervisord.pid ; (supervisord pidfile;default supervisord.pid)
childlogdir=/var/log/supervisor            ; ('AUTO' child log dir, default $TEMP)

braz

$ ~/.gitconfig

[alias]
janitor = !"git remote prune origin ; git branch -vv | grep ' gone]' | awk '{print $1}' | xargs git branch -D"

Run using
$ git janitor

braz

cycler==0.10.0
dnspython==1.16.0
flatten-json==0.1.7
joblib==0.14.1
kiwisolver==1.1.0
matplotlib==3.1.3
mlxtend==0.17.2
numpy==1.18.1
pandas==1.0.1
pprint==0.1

braz

import datetime
import gridfs
import pickle
import pymongo

from bson.binary import Binary

# Copy and paste the model here from https://gist.github.com/braz/8a65ffc359fd9f208c1e8ca64b7dfac3

model_name = str(type(model))

braz

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn import linear_model

# URL for the Wine Quality Portuguese "Vinho Verde" red wine dataset (UCI Machine Learning Repository)
url = "https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-red.csv"

# download the file
try:

braz

import numpy as np
from sklearn.model_selection import KFold

X = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]

kf = KFold(n_splits=10)
for train, test in kf.split(X):
	print("%s %s" % (train, test))

braz

import urllib
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
 
# URL for the Iris dataset (UCI Machine Learning Repository)
url = "http://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data"

raw_data = urllib.urlopen(url)