keuv-grvl

import tensorflow as tf

ReduceMean = lambda axis=1: tf.keras.layers.Lambda(lambda x: tf.reduce_mean(x, axis=axis))

i = tf.keras.layers.Input(shape=(5,15))  # shape: [BATCHSIZE, 5, 15]
m = ReduceMean(i)  # shape: [BATCHSIZE, 15]

# TODO would be nice to have a wrapping function for any TF op with arbitrary

keuv-grvl

# ln -s /path/to/this/.autoenv.sh /path/to/workdir/

if test -f .autoenv_conda_env_name ; then
    TARGET_CONDA_ENV="$(cat .autoenv_conda_env_name)"
    if [[ $TARGET_CONDA_ENV != $CONDA_DEFAULT_ENV ]]; then
        conda activate "$TARGET_CONDA_ENV" 2&>1 2> /dev/null
    fi
else
    CUR_DIR=$(basename $(pwd))
    conda activate "$CUR_DIR-dev" 2> /dev/null  \

keuv-grvl

def perfboxplot(
    setup,
    kernels,
    labels,
    n=3,
    xlabel="Performances",
    equality_check=None,
    save_path=None,
    log_scale=True,
):

keuv-grvl

import numpy as np
import pandas as pd
import seaborn as sbn
from matplotlib import pyplot as plt
from scipy.stats import anderson
from sklearn import datasets
from sklearn.cluster import MiniBatchKMeans
from sklearn.preprocessing import scale, LabelEncoder

# TODO doc, memoization

keuv-grvl

[user]
	name = Keuv Grvl
	email = k***@***m
[core]
    quotepath = false
    editor = vim
    pager = less -F -x2
[init]
    defaultBranch = main
[push]

keuv-grvl

class FItSNE_wrapper:
    """
    Wrapper class for FItSNE `fast_tsne.fast_tsne` v1.1.0.
    It is currently designed to work in a Conda environment with Python 3.7 and FFTW 3.
    
    Install and compile as follow:
    
      $ conda create -n YOUR-ENV python=3.7 fftw=3
      $ source activate YOUR-ENV

keuv-grvl

.PHONY: pdf display clean nuke

PDFREADER :=evince
OUTPUT    :=document.pdf
BUILD     :=build
SRC       :=$(shell find src -name '*.tex' -o -name '*.png')
LMKFLAGS   = -pdf -outdir=$(BUILD)

pdf: $(OUTPUT)

keuv-grvl

#!/usr/bin/env python3

import argparse
import gzip
import logging
import sys
from time import time

from lxml import etree

keuv-grvl

#!/usr/bin/env bash

# usage: toggle-vpn.sh vpn-name

set -e           # this script exits on error
set -o pipefail  # a pipeline returns error code if any command fails
set -u           # unset variables as an error, and immediately exit

command -v nmcli > /dev/null  # ensure nmcli is installed

keuv-grvl

#!/usr/bin/env bash

docker run  \
  --volume $(pwd):/data  \
  --user $(id -u $USER):$(id -g $USER)  \
  --entrypoint /path/to/your_program  \
  CONTAINER_NAME  \
  --input /data/inputfile --output /data/outputfile
  
# Will run '/path/to/your_program --input /data/inputfile --output /data/output' inside the container,