This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| x=["SyBPtQfAZ","H1S8UE-Rb","S1sRrN-CW","Syt0r4bRZ","HkPCrEZ0Z","rJ5C67-C-","H1T2hmZAb","Hymt27b0Z", | |
| "HJ5AUm-CZ","r1nzLmWAb","HkGJUXb0-","SkERSm-0-","BJlrSmbAZ","HJXyS7bRb","SyhRVm-Rb","SkwAEQbAb", | |
| "B1mvVm-C-","S1TgE7WR-","H1DkN7ZCZ","SJ71VXZAZ","ryk77mbRZ","HJIhGXWCZ","BJInMmWC-","H1I3M7Z0b", | |
| "Bk-ofQZRb","SJx9GQb0-","BJoBfQ-0b","SJyVzQ-C-","HJNGGmZ0Z","H1kMMmb0-","HkGbzX-AW","rJIgf7bAZ", | |
| "SyCyMm-0W","r1ayG7WRZ","H1Nyf7W0Z","HkCvZXbC-","ByED-X-0W","ByuI-mW0W","H1BHbmWCZ","SkqV-XZRZ", | |
| "rk07ZXZRb","HJCXZQbAZ","H1bbbXZC-","rkaqxm-0b","S1XolQbRW","B1TYxm-0-","Bkftl7ZCW","SyBBgXWAZ", | |
| "SkrHeXbCW","S1ANxQW0b","ByOExmWAb","By4Nxm-CW","r1l4eQW0Z","B12QlQWRW","ry831QWAb","B1EGg7ZCb", | |
| "HyMTkQZAb","rJ6iJmWCW","rkZB1XbRZ","HJnQJXbC-","Sy3fJXbA-","HJ8W1Q-0Z","HknbyQbC-","BkrsAzWAb", | |
| "ryH20GbRW","r1HhRfWRZ","B1KFAGWAZ","Byht0GbRZ","B1hYRMbCW","S1q_Cz-Cb","BJ7d0fW0b","HyydRMZC-", | |
| "SyZI0GWCZ","rJSr0GZR-","ryZERzWCZ","rkeZRGbRW","ryazCMbR-","Hyig0zb0Z","H11lAfbCW","HkXWCMbRW", |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| sudo su | |
| # Java | |
| yum -y install java-1.8.0-openjdk-devel | |
| # Build Esentials (minimal) | |
| yum -y install gcc gcc-c++ kernel-devel make automake autoconf swig git unzip libtool binutils | |
| # Extra Packages for Enterprise Linux (EPEL) (for pip, zeromq3) | |
| yum -y install epel-release |
anonymous
/ bayesian_neural_network.ipynb
Created
June 7, 2016 08:05
Sorry, something went wrong. Reload?
Sorry, we cannot display this file.
Sorry, this file is invalid so it cannot be displayed.
awjuliani
/ Deep Layer Visualization.ipynb
Last active
January 28, 2021 10:18
Sorry, something went wrong. Reload?
Sorry, we cannot display this file.
Sorry, this file is invalid so it cannot be displayed.
waleking
/ SparkGibbsLDA.scala
Last active
January 31, 2020 11:15
We implement gibbs sampling for LDA by Spark. This version performs much better than alpha version, and now can handle 3196204 words, 100 topics, 1000 sample iterations on server in 161.7 minutes. To solve the long time consuming in collect() process in alpha version, we utilize the cache() method as line 261 and line 262. We also solve a pile o…
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| package topic | |
| import spark.broadcast._ | |
| import spark.SparkContext | |
| import spark.SparkContext._ | |
| import spark.RDD | |
| import spark.storage.StorageLevel | |
| import scala.util.Random | |
| import scala.math.{ sqrt, log, pow, abs, exp, min, max } | |
| import scala.collection.mutable.HashMap |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| import spark.SparkContext | |
| import SparkContext._ | |
| /** | |
| * A port of [[http://blog.echen.me/2012/02/09/movie-recommendations-and-more-via-mapreduce-and-scalding/]] | |
| * to Spark. | |
| * Uses movie ratings data from MovieLens 100k dataset found at [[http://www.grouplens.org/node/73]] | |
| */ | |
| object MovieSimilarities { |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| import numpy as np | |
| from scipy import linalg, optimize | |
| MAX_ITER = 100 | |
| def group_lasso(X, y, alpha, groups, max_iter=MAX_ITER, rtol=1e-6, | |
| verbose=False): | |
| """ | |
| Linear least-squares with l2/l1 regularization solver. |