| ## Java | |
| sudo apt-get update | |
| sudo apt-get install default-jdk | |
| ## Scala | |
| sudo apt-get remove scala-library scala | |
| sudo wget http://scala-lang.org/files/archive/scala-2.12.1.deb | |
| sudo dpkg -i scala-2.12.1.deb | |
| sudo apt-get update | |
| sudo apt-get install scala |
| ruby '2.7.1' | |
| gem 'rails', github: 'rails/rails' | |
| gem 'tzinfo-data', '>= 1.2016.7' # Don't rely on OSX/Linux timezone data | |
| # Action Text | |
| gem 'actiontext', github: 'basecamp/actiontext', ref: 'okra' | |
| gem 'okra', github: 'basecamp/okra' | |
| # Drivers |
| from sklearn.datasets import load_boston | |
| import pandas as pd | |
| import xgboost as xgb | |
| from tensorboard_logger import configure, log_value | |
| from sklearn.cross_validation import train_test_split | |
| def logspy(env): | |
| log_value("train", env.evaluation_result_list[0][1], step=env.iteration) |
Last updated: 2017-03-18
exiftool -filename -filemodifydate -createdate -r -if '(not $datetimeoriginal) and $filetype eq "JPEG"' .
###Output photos that don't have datetimeoriginal to a CSV### Note this can take a long time if you have a lot of jpgs
| val uri = "http://www.yahoo.com" | |
| val reqEntity = Array[Byte]() | |
| val respEntity = for { | |
| request <- Marshal(reqEntity).to[RequestEntity] | |
| response <- Http().singleRequest(HttpRequest(method = HttpMethods.POST, uri = uri, entity = request)) | |
| entity <- Unmarshal(response.entity).to[ByteString] | |
| } yield entity | |
| val payload = respEntity.andThen { |
| html,body,#wrapper { | |
| width: 100%; | |
| height: 100%; | |
| margin: 0px; | |
| } | |
| .chart { | |
| font-family: Arial, sans-serif; | |
| font-size: 12px; | |
| } |
| """ Poisson-loss Factorization Machines with Numba | |
| Follows the vanilla FM model from: | |
| Steffen Rendle (2012): Factorization Machines with libFM. | |
| In: ACM Trans. Intell. Syst. Technol., 3(3), May. | |
| http://doi.acm.org/10.1145/2168752.2168771 | |
| See also: https://github.com/coreylynch/pyFM | |
| """ |
| (function() { | |
| var url = ["cdn.jsdelivr.net/velocity/1.1.0/velocity.min.js","cdn.jsdelivr.net/velocity/1.1.0/velocity.ui.min.js"]; | |
| for(var i=0; i < url.length; i++){ | |
| var v = document.createElement('script'); v.type = 'text/javascript'; v.async = true; | |
| v.src = ('https:' == document.location.protocol ? 'https://' : 'http://') + url[i]; | |
| var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(v, s); | |
| } | |
| })(); |
The Delaunay triangulation, the dual of Voronoi tesselation, creates a planar, triangular mesh for a given set of points. This example updates the Delaunay triangulation in response to mouse interaction! Colors by Cynthia Brewer; algorithm by Steven Fortune; implementation based on work by Nicolas Garcia Belmonte; interaction inspired by Raymond Hill.
| <!DOCTYPE html> | |
| <meta charset="utf-8"> | |
| <body> | |
| <script src="http://d3js.org/d3.v3.min.js"></script> | |
| <script> | |
| var width = 960, | |
| height = 500; | |
| var colors = [ |