| 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) |
| javascript:(function(e,t,n,r,i,s,o,u){if(!(i=e.jQuery)||n>i.fn.jquery||r(i)){s=t.createElement("script");s.type="text/javascript";s.src="http://ajax.googleapis.com/ajax/libs/jquery/"+n+"/jquery.min.js";s.onload=s.onreadystatechange=function(){if(!o&&(!(u=this.readyState)||u=="loaded"||u=="complete")){r((i=e.jQuery).noConflict(1),o=1);i(s).remove()}};t.documentElement.childNodes[0].appendChild(s)}})(window,document,"1.8.3",function(e,t){var n=e("font[color]").filter(function(){if(parseInt(e(this).text(),10)>0){return true}return false}).parent();var r=e("body").html();n.each(function(){var t=e(this).attr("href").replace("#","");var n=parseInt(t.replace("f",""),10);var i=new RegExp('<a name="'+t+'">([^]*?)[[]');var s=r.match(i);if(s&&s[0]){var o=e(s[0].replace("<br>","\n")).text().replace(n+"]","");o=o.replace("csell_env = 'mud';","").replace("// Begin Y! Store Generated Code","");var u=e('<div class="note" style="background:#FFFFFF;border:3px solid #000;left:0px;top:15px;width:300px;position:absolute;padding:1 |
| # 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 |
Prerequisites:
Software components used:
| """Kernel K-means""" | |
| # Author: Mathieu Blondel <[email protected]> | |
| # License: BSD 3 clause | |
| import numpy as np | |
| from sklearn.base import BaseEstimator, ClusterMixin | |
| from sklearn.metrics.pairwise import pairwise_kernels | |
| from sklearn.utils import check_random_state |
##Assignments Latex Template
###V 0.1
I always wanted some latex template that I could use for assignments. But none of the templates I found online had all the features I wanted. So the natural next step for me was to create one.
###Notes:
XeLaTeX
(by @andrestaltz)
If you prefer to watch video tutorials with live-coding, then check out this series I recorded with the same contents as in this article: Egghead.io - Introduction to Reactive Programming.
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:
| * { | |
| font-size: 12pt; | |
| font-family: monospace; | |
| font-weight: normal; | |
| font-style: normal; | |
| text-decoration: none; | |
| color: black; | |
| cursor: default; | |
| } |
| from sklearn.externals import joblib | |
| import time | |
| import numpy | |
| import pickle | |
| bigarray = numpy.zeros([190,91,190]) | |
| bigarray = bigarray.flatten() | |
| ### Saving |