CristinaSolana

1. Clone your fork:

git clone git@github.com:YOUR-USERNAME/YOUR-FORKED-REPO.git

2. Add remote from original repository in your forked repository:

cd into/cloned/fork-repo
git remote add upstream git://github.com/ORIGINAL-DEV-USERNAME/REPO-YOU-FORKED-FROM.git
git fetch upstream

whophil

# After Ubuntu 16.04, Systemd becomes the default. 
# It is simpler than https://gist.github.com/Doowon/38910829898a6624ce4ed554f082c4dd

[Unit]
Description=Jupyter Notebook

[Service]
Type=simple
PIDFile=/run/jupyter.pid
ExecStart=/home/phil/Enthought/Canopy_64bit/User/bin/jupyter-notebook --config=/home/phil/.jupyter/jupyter_notebook_config.py

Nikolay-Lysenko

import numpy as np


def xgb_quantile_eval(preds, dmatrix, quantile=0.2):
    """
    Customized evaluational metric that equals
    to quantile regression loss (also known as
    pinball loss).

    Quantile regression is regression that

mrocklin

MMesch

#!/usr/bin/env python
"""
Robust non-linear feature estimation with scikit-learn applied to
the Fourier Transform.
"""

from matplotlib import pyplot as plt
import numpy as np
from sklearn.base import TransformerMixin
from sklearn.pipeline import make_pipeline

MMesch

#!/usr/bin/env python
"""
Robust B-Spline regression with scikit-learn
"""

import matplotlib.pyplot as plt
import numpy as np
import scipy.interpolate as si
from sklearn.base import TransformerMixin
from sklearn.pipeline import make_pipeline

MMesch

#!/usr/bin/env python

"""
ND Bspline basis class for Python
"""

import numpy as np
import scipy.interpolate as si
import matplotlib.pyplot as plt
import itertools

kevinzakka

"""
Create train, valid, test iterators for CIFAR-10 [1].
Easily extended to MNIST, CIFAR-100 and Imagenet.

[1]: https://discuss.pytorch.org/t/feedback-on-pytorch-for-kaggle-competitions/2252/4
"""

import torch
import numpy as np

TomAugspurger

from urllib.parse import urlparse
import lightgbm as lgb
import threading


def _parse_machines(workers, listen_port):
    """From dask worker info to LightGBM mlist"""
    # TODO: Assert that we're using TCP?
    mlist = ['127.0.0.1:{}'.format(listen_port)] + [urlparse(worker).netloc
                                                    for worker in workers]

MMesch

#!/usr/bin/env python

"""Mini implementation of continuous wavelet transform (Morlet wavelet)."""

import numpy as np
import matplotlib.pyplot as plt


def continuous_wavelet_transform(signal, frequencies, time_step=1.0,
                                 wavelet_width=5):