sehugg

#!/usr/bin/env python
from __future__ import print_function
from keras.models import Sequential
from keras.layers import TimeDistributed
from keras.layers.core import Dense, Activation, Dropout, RepeatVector, TimeDistributedDense
from keras.layers.recurrent import LSTM
from keras.utils.data_utils import get_file
import numpy as np
import random,string
import sys

d0c-s4vage

#!/usr/bin/env python
# encoding: utf-8

import imp
import os
import pdb
import pip
import readline
from pip.commands.search import SearchCommand
import sys

parmentf

Inspired by dannyfritz/commit-message-emoji

See also gitmoji.

Commit type	Emoji
Initial commit	🎉 :tada:
Version tag	🔖 :bookmark:
New feature	✨ :sparkles:
Bugfix	🐛 :bug:

dennybritz

# Helper function to plot a decision boundary.
# If you don't fully understand this function don't worry, it just generates the contour plot below.
def plot_decision_boundary(pred_func):
    # Set min and max values and give it some padding
    x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5
    y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5
    h = 0.01
    # Generate a grid of points with distance h between them
    xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
    # Predict the function value for the whole gid

izabera

#!/bin/bash
owner=izabera nick=sedirc network=irc.freenode.net
echo "
# send bot data
1 {
  s/.*/nick $nick/p
  s/.*/user $nick $nick $nick :$nick/p

  s/.*/cat factoids 2>\/dev\/null/e; H
  b

Jim-Holmstroem

#!/usr/bin/env bash

export GIT_FLAGS=$*

OLD_BRANCH="master" # $(git $GIT_FLAGS rev-parse --abbrev-ref HEAD)
echo $OLD_BRANCH
TEMP_BRANCH="deis_build"
git $GIT_FLAGS branch --force $TEMP_BRANCH && git $GIT_FLAGS checkout --force $TEMP_BRANCH
 
git $GIT_FLAGS submodule status | cut -d' ' -f3 | xargs --no-run-if-empty -I{} bash -c "git $GIT_FLAGS config -f .git/config --remove-section submodule.{}" && find -type f -name .git -o -type f -name .gitmodules | xargs --no-run-if-empty rm -f && rm -rf .git/modules/* && rm -f .git/index && git $GIT_FLAGS add -A && git $GIT_FLAGS commit -m"flattened" && git $GIT_FLAGS push deis $TEMP_BRANCH:master --force

jberkus

WITH table_scans as (
    SELECT relid,
        tables.idx_scan + tables.seq_scan as all_scans,
        ( tables.n_tup_ins + tables.n_tup_upd + tables.n_tup_del ) as writes,
                pg_relation_size(relid) as table_size
        FROM pg_stat_user_tables as tables
),
all_writes as (
    SELECT sum(writes) as total_writes
    FROM table_scans

XVilka

THIS GIST WAS MOVED TO TERMSTANDARD/COLORS REPOSITORY.

PLEASE ASK YOUR QUESTIONS OR ADD ANY SUGGESTIONS AS A REPOSITORY ISSUES OR PULL REQUESTS INSTEAD!

jakevdp

Numba Ball Tree

This is a quick attempt at writing a ball tree for nearest neighbor searches using numba. I've included a pure python version, and a version with numba jit decorators. Because class support in numba is not yet complete, all the code is factored out to stand-alone functions in the numba version. The resulting code produced by numba is about ~10 times slower than the cython ball tree in scikit-learn. My guess is that part of this stems from lack of inlining in numba, while the rest is due to some sort of overhead

squioc

from datetime import datetime
import calendar

def epoch_to_iso8601(timestamp):
    """
    epoch_to_iso8601 - convert the unix epoch time into a iso8601 formatted date

    >>> epoch_to_iso8601(1341866722)
    '2012-07-09T22:45:22'
    """