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@katylava
katylava / git-selective-merge.md
Last active February 27, 2024 10:18
git selective merge

Update 2022: git checkout -p <other-branch> is basically a shortcut for all this.

FYI This was written in 2010, though I guess people still find it useful at least as of 2021. I haven't had to do it ever again, so if it goes out of date I probably won't know.

Example: You have a branch refactor that is quite different from master. You can't merge all of the commits, or even every hunk in any single commit or master will break, but you have made a lot of improvements there that you would like to bring over to master.

Note: This will not preserve the original change authors. Only use if necessary, or if you don't mind losing that information, or if you are only merging your own work.

@jagregory
jagregory / gist:710671
Created November 22, 2010 21:01
How to move to a fork after cloning
So you've cloned somebody's repo from github, but now you want to fork it and contribute back. Never fear!
Technically, when you fork "origin" should be your fork and "upstream" should be the project you forked; however, if you're willing to break this convention then it's easy.
* Off the top of my head *
1. Fork their repo on Github
2. In your local, add a new remote to your fork; then fetch it, and push your changes up to it
git remote add my-fork git@github...my-fork.git
@jbenet
jbenet / current_utc_time.c
Created July 17, 2011 16:17
work around lack of clock_gettime in os x
/*
author: jbenet
os x, compile with: gcc -o testo test.c
linux, compile with: gcc -o testo test.c -lrt
*/
#include <time.h>
#include <sys/time.h>
#include <stdio.h>
@Robsteranium
Robsteranium / statistics-distributions.js
Created May 11, 2012 20:20 — forked from benrasmusen/statistics-distributions.js
JavaScript library for calculating critical values and upper probabilities of common statistical distributions
/*
* NAME
*
* statistics-distributions.js - JavaScript library for calculating
* critical values and upper probabilities of common statistical
* distributions
*
* SYNOPSIS
*
*
@lttlrck
lttlrck / gist:9628955
Created March 18, 2014 20:34
rename git branch locally and remotely
git branch -m old_branch new_branch # Rename branch locally
git push origin :old_branch # Delete the old branch
git push --set-upstream origin new_branch # Push the new branch, set local branch to track the new remote
class @BaseCtrl
@register: (app, name) ->
name ?= @name || @toString().match(/function\s*(.*?)\(/)?[1]
app.controller name, this
@inject: (annotations...) ->
ANNOTATION_REG = /^(\S+)(\s+as\s+(\w+))?$/
@annotations = _.map annotations, (annotation) ->
@duanefields
duanefields / AngularController.coffee
Last active June 8, 2016 00:55
CoffeeScript Base Classes for AngularJS
module.exports = class AngularController
# register the subclass with angular, module and name are optional
@register: (name, module) ->
module ?= @module || angular.module 'controllers'
name ?= @name || @toString().match(/function\s*(.*?)\(/)?[1]
module.controller name, @
# inject the list of dependencies, as a list of Strings
@inject: (args...) ->
@slaypni
slaypni / xgb.py
Last active September 24, 2021 17:35
A wrapper class of XGBoost for scikit-learn
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import sys
import math
import numpy as np
sys.path.append('xgboost/wrapper/')
import xgboost as xgb
from __future__ import absolute_import
from __future__ import print_function
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation, Flatten
from keras.layers.convolutional import Convolution2D, MaxPooling2D
from keras.utils import np_utils
from scipy.special import expit
import numpy as np
np.set_printoptions(suppress=True)
@baraldilorenzo
baraldilorenzo / readme.md
Last active June 13, 2024 03:07
VGG-16 pre-trained model for Keras

##VGG16 model for Keras

This is the Keras model of the 16-layer network used by the VGG team in the ILSVRC-2014 competition.

It has been obtained by directly converting the Caffe model provived by the authors.

Details about the network architecture can be found in the following arXiv paper:

Very Deep Convolutional Networks for Large-Scale Image Recognition

K. Simonyan, A. Zisserman