npinto

In [12]: l = ['a', 'a', 'c', 'c', 'c', 'b']

In [13]: u = np.unique(l)

In [14]: u
Out[14]: 
array(['a', 'b', 'c'], 
      dtype='|S1')

In [15]: np.searchsorted(u, l)

kingjr

import numpy as np
import matplotlib.pyplot as plt
from jr.plot import pretty_plot, plot_eb  # available @ http://github.com/kingjr/jr-tools

# make up data
x = np.linspace(1., 5., 100)
all_data = dict(AG=np.sin(x), V1=np.cos(x), IPS=np.cos(x + 1.5))

# choose color manually
colors = ['r', [.1, 1., .2, 1.], 'b']

yassersouri

I had an issue with how to visualize the ILSVRC mean image. I just wanted to look at it and see how much does it differ from using pixel-wise mean subtraction instead of image-wise mean subtraction.

I assume that you have already downloaded the CaffeNet pretrained and model definition files.

The trick is to initialize two networks, one with mean file set (called net_mean) and the other one without mean file (called net). Then create a fake all 1 image. Use the net_mean to preprocess the fake image for data layer and save the result as fake_pre. Then use the net to deprocess fake_pre for data layer and save it as fake_re. If the two networks net and net_mean were the same then fake_re would be equal to fake, but since we have not set any mean file for net then we can visualize the mean image using 1 - fake_re. Take a look at the code.

The result looks like this:

![ILSVRC mean image](https://gist.github.com/yassersouri/f617bf7eff9172290b4f/raw/863971c47470204234017b91196b5e94a6fe

tkdave

# Installing OpenCV python libs on mac to work with virtualenv
# OpenCV  2.4.3
# Python 2.7.3 installed with brew

# assuming you have virtualenv, pip, and python installed via brew
# assuming $WORKON_HOME is set to something like ~/.virtualenvs

# using homebrew - make sure we're current
brew update

vmg

#include <stdio.h>
#include <stdlib.h>
#include <math.h>
#include <float.h>
                                    int  main
                                    (int argc
         ,char *ac         []){int i, count =                  argc - 1;
         double  *         dvalues=malloc(01-                  01+count*
sizeof(double)+1);         double mi=DBL_MAX,ran=.0,ma         =DBL_MIN,mo;for(i=         00; argc>1
&&i<count;i=i+8-7)         {double val = atof(ac[i+1])         ;if(23&&val<mi)mi=         val;if(val

makinde

#!/bin/bash

if ! hash git &> /dev/null || ! hash php &> /dev/null; then
    echo -e " *****\n ***** INSTALLING GIT AND PHP\n *****"

    if hash apt-get &> /dev/null; then
        sudo apt-get install git-core php5-cli php5-curl
    elif hash port &> /dev/null; then
        sudo port install php5-curl
    elif hash fink &> /dev/null; then

kevinpschaaf

require 'octokit'
require 'csv'
require 'date'

# Description:
# Exports Github issues from one or more repos into CSV file formatted for import into JIRA
# Note: By default, all Github comments will be assigned to the JIRA admin, appended with
# a note indicating the Github user who added the comment (since you may not have JIRA users
# created for all your Github users, especially if it is a public/open-source project:
#

vsoch

from sklearn.externals import joblib
import time
import numpy
import pickle

bigarray = numpy.zeros([190,91,190])
bigarray = bigarray.flatten()


### Saving

SNagappan

##Model

This is an implementation of Facebook's baseline GRU/LSTM model on the bAbI dataset Weston et al. 2015. It includes an interactive demo.

The bAbI dataset contains 20 different question answering tasks.

Model script

The model training script train.py and demo script demo.py are included below.

Instructions

jpantuso

Setup new Mac with OSX Lion from scratch

These commands are good as of 2011-07-27.

Install xcode 4

App store http://itunes.apple.com/us/app/xcode/id448457090?mt=12) The download/install takes awhile so start it first. When it finishes downloading you will still need to run it to complete installation.

iTerm2 http://www.iterm2.com/