Brideau

import faker as f
import numpy as np

from numpy.random import default_rng
from pandas import DataFrame
from scipy.stats import truncnorm
from sklearn.metrics import average_precision_score

fake_bad_actor_generator = f.Faker()
rng = default_rng()

Brideau

def precision_at_k(y_true, y_score, k, pos_label=1):
    from sklearn.utils import column_or_1d
    from sklearn.utils.multiclass import type_of_target
    
    y_true_type = type_of_target(y_true)
    if not (y_true_type == "binary"):
        raise ValueError("y_true must be a binary column.")
    
    # Makes this compatible with various array types
    y_true_arr = column_or_1d(y_true)

Brideau

{
  "name": "Testing a Pool",
  "description": "The pool that tests all the pools",
  "ticker": "TEST",
  "homepage": "https://teststakepool.com"
}

Brideau

  // If you need to add additional parameters, as is the case if creating a
  // CSV file, you can use the -lco flag multiple times
  val outPathCsv = Paths.get("temp", "Canada3573Csv", "Canada3573.csv").toAbsolutePath
  val cmdCsv = Array("-f", "CSV", outPathCsv.toString, inPath.toString,
    "-lco", "GEOMETRY=AS_WKT",
    "-lco", "CREATE_CSVT=YES",
    "-lco", "SEPARATOR=SEMICOLON"
  )

  convertUsingOgr2Ogr(outPathCsv, cmdCsv).onComplete{

Brideau

export DYLD_LIBRARY_PATH=/usr/local/opt/gdal2/lib:$DYLD_LIBRARY_PATH

Brideau

Keybase proof

I hereby claim:

• I am Brideau on github.
• I am brideau (https://keybase.io/brideau) on keybase.
• I have a public key whose fingerprint is 3912 9FB2 7C3A 0110 302F 91E4 F19F A065 AC03 9521

To claim this, I am signing this object:

Brideau

A tweak of the D3Kit Circles example to fix some transition bugs it had, and to add, well, a ton of circles.

Brideau

Brideau

An automatically sorting list of words that fit into a hexagon. Words are currently gibberish.

Brideau