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
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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' | |
""" |
THIS GIST WAS MOVED TO TERMSTANDARD/COLORS
REPOSITORY.
PLEASE ASK YOUR QUESTIONS OR ADD ANY SUGGESTIONS AS A REPOSITORY ISSUES OR PULL REQUESTS INSTEAD!
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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 |
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#!/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 |
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#!/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 |
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# 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 |
Inspired by dannyfritz/commit-message-emoji
See also gitmoji.
Commit type | Emoji |
---|---|
Initial commit | 🎉 :tada: |
Version tag | 🔖 :bookmark: |
New feature | ✨ :sparkles: |
Bugfix | 🐛 :bug: |
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#!/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 |
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#!/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 |
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