S H R I M A T H I . V E T R I
I'm a senior at UC Davis, studying Computer Science and Technology Management.


Lesson Plan Title: Denial of Service from Multiple Logins
Concept / Topic To Teach:
Denial of service attacks are a major issue in web applications. If the end user cannot conduct business or perform the service offered by the web application, then both time and money is wasted.General Goal(s):
This site allows a user to login multiple times. This site has a database connection pool that allows 2 connections. You must obtain a list of valid users and create a total of 3 logins.


This example uses the addEventListener() method to execute a function when a user clicks anywhere in the document.


The following table states whether or not we have received the expected data from our partners this month and the previous month.


{% extends 'emails/base.html' %} {% from 'cronjobs_cern:_lists.html' import render_event_list, render_reservation_list %} {% block subject %}Indico events requiring support for the coming two weeks{% endblock %} {% block header_recipient %}Support Team{% endblock %} {% block body -%}
Please find below the Indico events requiring support for the coming two weeks:
{% for room, events_by_date in events_by_room.iteritems() %}Events in {{ room.name }}:
{{ render_event_list(events_by_date, show_location=false) }} {% endfor %}Committees:
{{ render_event_list(committees_by_date, show_location=false) }} {% for room, reservations_by_date in res_events_by_room.iteritems() %}Events in {{ room.name }}:


The following plot (a) shows the classification results for all experiments based on the (best performing) linear SVM classifier. An alternative ordernig can be seen in (b) where results of the same task are grouped together.
(a)
(b)
Overall average results for each embedding using linear SVM can be seen in the following figure. One can observe that OpenL3 embeddings outperform all other embeddings, and obtain comparable results to the baseline model:
| Table III shows results comparing early fusion with late fusion approaches. Apart from task 6, late fusion performs best in all experiments. Table IV presents a comparison between different OpenL3 embeddings. Environmental embeddings perform constantly worse compared to the ones trained on music. Table V illustrates the results with OpenL3 embeddings with 512 values and larger embeddings with of 6144 values. Apart from Task 1, all tasks show better results with larger embeddings. However, larger embeddings are more computationally expensive. | |
In this section, the confusion matrices with mean file-wise accuracy for all tasks are shown.
| Task 1 - Ensemble Size Classification in Music | Task 2 - Musical Instrument Family Recognition |
| Task 3 - Speech Music Classification | Task 4 - Classification of Operational States in Electric Engines |
| Task 5 - Metal Surface Classification | Task 6 - Plastic Material Classification |
| Task 6 - Plastic Material Classification (Early Fusion) | Task 6 - Plastic Material Classification (Late Fusion) |
Here the baseline model of task 2 (Musical Instrument Family Recognition) can be seen.
The first plot shows the actual model used for this publication. In contrast to the original model (below), each convolutional block has only half of the layers (two of the original four convolutional blocks have been removed). After each block, batch normalization is applied. Dropout has been moved to the end of the model to avoid overfitting. Finally, the Sigmoid activation has been replaced with a softmax activation in the output layer since the application is a single label task.
The plot below describes the original model from Han et. al Y. Han, J. Kim, and K. Lee, “Deep Convolutional Neural Networks for Predominant Instrument Recognition in Polyphonic Music,”IEEE/ACMTransactions on Audio, Speech, and Language Processing, vol. PP, 2016.
The following image shows the baseline model used for the Task 1 and Task 3. It is a CNN with 4 convolutional layers with a 3x3 kernel and a fully connected (dense) layer at the end. Max Pooling and dropout is applied after each block and a softmax activation is used for the output layer. The number of filters doubles in each convolutional block and starts with 16.


|cFFFFD100/tomcats|r to reopen this configuration & information panel
Map Markers (|A:VignetteKill:20:20:-2:-2|a) and Guides:
• Shadowlands (including The Maw & Korthia)
• Uldum & Vale of Eternal Blossoms (patch 8.3.0 Visions of N'Zoth)
• Nazjatar & Mechagon (patch 8.2.0 Rise of Azshara)
• Darkshore (patch 8.1.0 'Battle for Darkshore')
• Arathi Highlands (patch 8.0.1 'Battle for Stromgarde')
Holiday Helpers and Guides:
• Lunar Festival, Love is in the Air, Noblegarden, Children's Week and Hallow's End


--- author: brian.leroux categories: app date: '2009-10-29 09:37:29' link: http://blogs.nitobi.com/brian/2009/10/29/phonegap-training-in-new-york/ permalink: /2009/10/29/phonegap-training-in-new-york/ slug: phonegap-training-in-new-york status: publish title: PhoneGap Training in New York type: post format: html ---
If you're a web or mobile developer interested in building cross-platform mobile apps, then don't miss Nitobi's PhoneGap Training on November 20 in New York-the day after Web 2.0 Expo. In a day, we'll teach you how to build impressive, cross-platform mobile apps with PhoneGap.
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#include <Tests.h>
|
| | BallTest (const Parser &par, const DimensionManager &dim, double mid_x, double mid_y) |
| |
| double | PhiM (double t) const |
| |
| double | PhiP (double t) const |
| |
| double | PsiM (double t) const |
| |
| double | PsiP (double t) const |
| |
| double | u (double x, double y, double t) const override |
| |
| double | v (double x, double y, double t) const override |
| |
| double | h (double x, double y, double t) const override |
| |
| double | b (double x, double y) const override |
| |
| double | w (double x, double y, double t) const |
| |
| double | hu (double x, double y, double t) const |
| |
| double | hv (double x, double y, double t) const |
| |
| bool | IsWet (double x, double y, double t) const |
| |
◆ BallTest()
◆ h()
| double BallTest::h |
( |
double |
x, |
|
|
double |
y, |
|
|
double |
t |
|
) |
| const |
|
inlineoverridevirtual |
◆ PhiM()
| double BallTest::PhiM |
( |
double |
t | ) |
const |
|
inline |
◆ PhiP()
| double BallTest::PhiP |
( |
double |
t | ) |
const |
|
inline |
◆ PsiM()
| double BallTest::PsiM |
( |
double |
t | ) |
const |
|
inline |
◆ PsiP()
| double BallTest::PsiP |
( |
double |
t | ) |
const |
|
inline |
◆ u()
| double BallTest::u |
( |
double |
x, |
|
|
double |
y, |
|
|
double |
t |
|
) |
| const |
|
inlineoverridevirtual |
◆ v()
| double BallTest::v |
( |
double |
x, |
|
|
double |
y, |
|
|
double |
t |
|
) |
| const |
|
inlineoverridevirtual |
◆ m_a1
◆ m_a2
◆ m_a3
◆ m_a4
◆ m_alpha
◆ m_beta
◆ m_lam1
◆ m_lam2
◆ m_u0
◆ m_v0
◆ m_x0
◆ m_y0
The documentation for this class was generated from the following file:


--- layout: post title: ! 'Hidden Gems of Online Learning: x86' date: 2013-11-10 13:25:07.000000000 -08:00 categories: - Hidden Gems - Programming - Resources - Tutorials - x86 tags: - architecture - assembly - intel - low-level - programming - resources - tutorial - tutorials - x86 status: publish type: post published: true meta: _edit_last: '1' dsq_thread_id: '2046407523' author: login: siegedog email<EMAIL_ADDRESS>display_name: siegedog first_name: '' last_name: '' ---
Every now and then, you stumble across amazing learning resources on the web. Some great, extensive tutorials which lie on websites that somehow look deserted. You take a look - it's not spam, the tutorials are there, for free, ready to enlighten you. The only ominous thing about them is the fact that it seems that nobody is using that site. There might be a comment section inhabited by only a couple of lonely comments. The site itself might look like it's ancient, but a closer look reveals posts are all very rich, just sitting there, waiting for someone to learn what they have to teach.
I got this feeling when I stumbled upon Open Security Training.
The site is extremely simple but filled with amazing content at the same time. I was amazed when I found the wealth of information on that website. Everything is free, licensed under Creative-Commons Share Alike license. And I don't mean quick 5 minute how-tos or 15 minute screencasts. These are multiple hour-long, in depth courses, with slides, code samples and additional resources. Grab the slides and click the video links. You find yourself at the beginning of a 10 hour-long playlist on, say, x86. This is just the introduction. You can continue with the intermediate x86 course, an introduction to ARM or an introduction to software exploits. All of them - 6-10 hours long.
There are some other great, well-known sites such as Coursera, Udacity and Khan Academy which are also amazing resources. They are also quite popular, so I won't be talking about them in much detail here, since they don't need the extra advertisement. They are gems, that's for sure, just not hidden ones.
I found myself addicted to Xeno's lectures, and now I'm already cruising through the intermediate x86 course, frantically taking notes, looking at slides and writing x86 assembly code.
The only thing that I believed was a bit of a problem was the absence of homework assignments in the form of programming challenges. I did go through all of the examples, re-wrote them, altered them (e.g. managed to successfully overflow my buffers and hijack my own program, which was a lot of fun), but some more concrete assignments would have been nice.
This was only a problem while going through the main body of the course (the introductory one), since at the very end, the students (myself included) were presented with Carnegie Mellon's binary bomb. And this more than compensated for everything else! I will probably be talking about the bomb (and its numerous variations) in another article, but what this "bomb" basically is, is an executable that's provided with no source (and only a handful of debug symbols) which expects some sort of text input. You give it the right input, and it's defused. Feed it the wrong one and it blows up. And the only way to figure out what input it wants, is to run it with a debugger attached and simply step trough the assembly code, tearing it apart and, essentially, reverse-engineering it. This has been, by far, the best programming challenge I've ever done when it comes to ASM. I've had one A-HA moment after the other, constantly digging through the instructions and figuring out what makes the bomb tick. It was very, very fun. From now on, this will be the number one thing I will recommend to absolutely anyone wishing to really learn x86.
The material is there, up for grabs! What are you waiting for?


THE TIT AND THE MOON A film review by James Berardinelli Copyright 1996 James Berardinelli
RATING (0 TO 10): 6.0 Alternative Scale: **1/2 out of ****
Spain/France, 1994 Running Length: 1:28 MPAA Classification: No MPAA Rating (Sex, nudity, profanity) Theatrical Aspect Ratio: 1.66:1 Shown at the Philadelphia Festival of World Cinema, 5/4/96, 5/9/96, 5/11/96
Cast: Biel Duran, Mathilda May, Gerard Darmon, Miguel Poveda, Albert Folch, Laura Mana Director: Bigas Luna Producer: Xavier Gelin Screenplay: Bigas Luna and Cuca Canals Cinematography: Jose Luis Alcaine Music: Nicola Piovani In Spanish with subtitles
"Could I have your breast?" may not be the most conventional opening line, but, surprisingly enough for young Tete (Biel Duran), it actually works. Seconds later, Estrellita (Mathilda May) has lowered her top, and the kid finds himself eye-to-nipple with the "most beautiful breast" he has ever seen. What's more, when he asks for a drink, Estrellita is more than happy to oblige with a stream of milk. Is this a dream or reality? THE TIT AND THE MOON never makes it clear, but, given the strange way that the plot progresses, I'm inclined to believe the latter.
The title is certainly provocative enough, and, as one might suspect, a lot of breasts are exposed during the ninety-minute running time. Most of them are pretty nice, too, making this Spanish-language release an ogler's delight. The film is intended to be a randy farce, and, when it succeeds, that's what it is. Unfortunately, there are also significant stretches when it's rather dull and silly.
I guess my tastes don't match those of director Bigas Luna. I feel much the same about THE TIT AND THE MOON as I did about his 1992 feature, JAMON, JAMON (although, to be fair, I enjoyed this one a little more). In between, he did something called GOLDEN BALLS, and, while I missed that one, the title indicates it's just another variation on similar themes.
The story, such as it is, takes place in Cantalonia, where Tete works as an anxanta, the boy who climbs to the top of human pyramids. When Tete's mother gives birth to a new son, the older boy suffers from an acute case of envy that his brother has a breast to suckle. One night, Tete begs the moon for a breast of his own. His pleas are soon answered when Estrellita, the "Queen of Stuttgart", and her husband, Fartman Maurice, the "Fire King" (Gerard Darmon), arrive in town to perform at the Cava Park nightclub. But Tete has a rival for Estrellita's affections. Lovesick Miguel (Miguel Poveda), a local electrician, spends all night serenading his beloved with flamenco songs.
For roughly thirty minutes, THE TIT AND THE MOON plays like an offbeat coming-of-age story, and, as such, is absurdly entertaining. Tete is a strange child, but he has several endearing qualities -- curiosity, earnestness, and a kind of naivete. Unfortunately, the film veers way off course before the halfway point, concentrating on a completely uninvolving love triangle featuring Estrellita, the Fartman, and Miguel. For long stretches during the second half, Tete's sole function is to sneak around peeking at half-naked women while providing a wise-beyond-his-years voiceover.
When it comes to tasteless, off-the-wall humor, Luna rivals Benny Hill. Unfortunately, the director is handicapped by a pointlessly foolish storyline. Ultimately, THE TIT AND THE MOON provides exactly what you'd expect from a motion picture with that title.
- James Berardinelli e-mail<EMAIL_ADDRESS>web: http://www.cybernex.net/~berardin
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
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Related links: index of all rec.arts.movies.reviews reviews


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Due Date: 1/26 (Thurs) 11:59 PM PST.
Hard deadline: 1/29 (Sun) 11:59 PM PST with 3 late days. Assignments turned in past this date will be penalized (see the grading page).
In this assignment we will familiarize you with basic concepts of neural networks, word vectors, and their application to sentiment analysis.
Setup
Note: Please be sure you have Python 2.7.x installed on your system. The following instructions should work on Mac or Linux. If you have any trouble getting set up, please come to office hours and the TAs will be happy to help.
Get the code: Download the starter code here and the complementary written problems here.
Written solutions: here. We do not plan to release code solutions online. Come to office hours if you have questions about your code.[Optional] virtual environment: Once you have unzipped the starter code, you might want to create a virtual environment for the project. If you choose not to use a virtual environment, it is up to you to make sure that all dependencies for the code are installed on your machine. To set up a virtual environment, run the following:
cd assignment1
sudo pip install virtualenv # This may already be installed
virtualenv .env # Create a virtual environment
source .env/bin/activate # Activate the virtual environment
pip install -r requirements.txt # Install dependencies
# Work on the assignment for a while ...
deactivate # Exit the virtual environment
Install requirements (without a virtual environment): To install the required packages locally without setting up a virtual environment, run the following:
cd assignment1
pip install -r requirements.txt # Install dependencies
Download data: Once you have the starter code, you will need to download the Stanford Sentiment Treebank dataset and pretrained GloVe vectors. Run the following from the assignment1
directory:
sh get_datasets.sh
Submitting your work
See Piazza for submission instructions. Remember, you MUST follow the instructions and submit your code and writeup.Tasks
There will be four parts to this assignment. Each part has written and code components. The assignment is designed to be completed in order as later sections will leverage solutions to earlier parts. Read the assignment carefully and start early as some parts may take significant time to run.


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| SSW Update - MVC Training for Enterprising Developers PLUS a New Telerik
NerdDinner |
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[the cutting edge ]
G'day Developers!
With ASP.NET MVC in its fourth iteration, it has become a mature and trustworthy technology
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ASP.NET MVC brings writing software for the web back to its roots, and allows developers to
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Got a comment for Adam?
Email
Adam
Adam's
blog
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*NEW* Featured Event: Enterprise MVC Apps
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New training and online sessions!
Enterprise MVC Apps
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Format: 1 day
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The Model View Controller design pattern has been an proven and effective way of structuring
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Brisbane FRI 13 JULY 2012 |
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Melbourne TUE 17 JULY 2012 |
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Sydney WED 18 JULY 2012 |
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Online WED 25 JULY 2012 |
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Enterprise MVC Apps Part II: Making them 500% more Enterprisey
In this course will show you the techniques and tools SSW developers use to make MVC Enterprise
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Brisbane FRI 3 AUGUST 2012 |
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Melbourne TUE 7 AUGUST 2012 |
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Online WED 22 AUGUST 2012 |
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Featured Video: DDD Brisbane – Damian
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Our very own Damian Brady took the stage at the DDD event in Brisbane to talk ASP.NET MVC
Mobile devices like tablets and phones are quickly becoming the primary way people access the web. So
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With ASP.NET MVC, it’s easier than ever to literally put your website in the palm of everyone’s hand.
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what it brings to the table.
Watch
the video on SSWTV
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ASP.NET MVC Webinar - What’s all the fuss?
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Thursday 31st May 2012
11.00am - 12.00pm
Cost: No Charge
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Presented by Eric Phan, Project Manager and Solution Architect
Microsoft are really pushing ASP.NET MVC as the next big web development platform. The last big
change in the web space was the move from classic ASP to
ASP.NET Web Forms and this was a big driver in the adoption of the Microsoft web platform.
In this webinar, find out what all the fuss about ASP.NET MVC is; why developers love it; and why
businesses should pick ASP.NET MVC over ASP.NET Web Forms.
Join SSW’s Eric Phan as he discusses Microsoft’s next big web platform and his experiences with it on
real world projects. There will be lots of time for live questions.
Webinar Objectives
This webinar will provide the attendee with:
- An overview of what MVC is
- The good and the bad of MVC
- Ways of assessing whether ASP.NET Web Forms or MVC is right for your business
- Ample time to ask questions from the presenter
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About Eric
Eric Phan is the ultimate Solution Architect. His wide range of technical skills, and
deep knowledge of certain specialty technologies, mix well with his aimiable manner and
excellent communication skills.
Eric is keen Solution Architect who doesn't mind a challenge. He has an in-depth
knowledge of .NET and the Microsoft Stack. He came from the Java (J2SE & J2ME) world and
values running strong teams, .NET architecture, unit testing and maintainable code via
coding standards.
Eric is always on the cutting edge, using Microsoft's latest technologies before they are
widely adopted. He also is a frequent presenter at usergroups and technical breakfasts
where he shares his experiences using these technologies.
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SharePoint 101 for Business Webinar -
What every business person should know about SharePoint 2010
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Tuesday 5th June 2012
11.00am - 12.00pm
Cost: No Charge
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SharePoint 2010 is a very powerful collaboration tool and one of Microsoft's most popular business
products. Many business people have heard
about SharePoint but struggle to understand exactly how SharePoint can improve their business
processes. Decision makers, who are interested in
using SharePoint, can find it hard to find the right information to help them make informed
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A search on Google will provide a lot of technical information on SharePoint 2010, but this
information can be difficult for a non IT person
to understand. This webinar presents the features and business case to use SharePoint in terms that
all business people can understand.
Webinar Objectives
This webinar will provide the attendee with:
- An broad understanding of the SharePoint feature set
- An in depth understanding of how key features of SharePoint can enhance business processes
- An understanding of the costs, timelines, staff involvement and commitment a business will incur
when implementing SharePoint
- This webinar is presented in layman terms which staff in all departments can understand
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About Marlon
Marlon Marescia has hands on experience with Microsoft technologies like ASP.NET,
SharePoint, Microsoft CRM and Office add-ins.
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SolidQ Training Courses
SSW and SolidQ offer premium public and private SQL Server Training and Business Intelligence
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Choose one of our great courses and sign up today!
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Accelerated SQL Server 2012 Integration Services - 4 Days
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Brisbane 9 JULY 2012 |
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Melbourne 16 JULY 2012 |
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Sydney 6 AUGUST 2012 |
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Microsoft BI Bootcamp: SQL Server 2008 R2 and Office 2010 - 5 Days
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Melbourne 23 JULY 2012 |
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Sydney 30 JULY 2012 |
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Optimization/Performance Tuning and Troubleshooting - 5 Days
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Sydney 20 AUGUST 2012 |
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T-SQL Fundamentals - 4 Days
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Melbourne 30 JULY 2012 |
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Brisbane 6 AUGUST 2012 |
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Sydney 13 AUGUST 2012 |
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3 Day Scrum Training - Intensive Developer Course
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New 3 day format!
Intensive 3-day Professional Scrum Developer course
Cost: $2995 +GST PP
Brisbane, Melbourne, San Francisco, Beijing, Sydney, Perth, Adelaide, Canberra,
Amsterdam, Cologne, Stuttgart/Munich, Zagreb
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Professional Scrum Developer courses teach students how to work in a team, using modern software
engineering practices, to develop
an increment of potentially releasable functionality using a specific technology platform. Students
learn to do so within the Scrum
framework doing iterative incremental development.
Classes are exercise-driven, with students working in self-organizing teams to develop
“done” increments from Product Backlog items.
Course at a Glance
This 3-day Professional Scrum Developer .NET course is a mix of lecture, demonstration, group
discussion, simulation, and hands-on
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Note: Scrum fundamentals are not covered in this 3-day course. If you are not experienced with Scrum
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day course is still available.
Scrum either through working on a Scrum Team or through taking part in a Professional Scrum
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Melbourne 23 MAY 2012 |
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Brisbane 5 JUNE 2012 |
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Canberra 7 AUG 2012 |
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Find out more and Register
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Sydney .NET User Group -
The best place to learn .NET for free in Sydney
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6 Days to go!
Sydney .NET User Group
Wednesday 16th May 2012
5:45pm - 9:00pm
SSW Head Office
Gateway Court, Suite 13
81-91 Military Road, Neutral Bay
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In depth code reviewing with Visual Studio 11
A walk through of how to conduct a thorough code review using the New Visual Studio 11.
With the release of the Visual
Studio 11 Beta, we’ve seen a greatly improved code review experience.
Adam will cover:
- The top things to look out for in VS11
- The new code review tools and functionality
- Reviewing a real world project
ABOUT THE PRESENTER
Adam Cogan is the Chief Architect at SSW, a Microsoft Certified Gold Partner specializing in custom
.NET, SharePoint and CRM solutions (with a splash of Business Intelligence).
At SSW, Adam has been developing custom solutions for businesses across a range of industries such as
Government, engineering, banking, insurance and manufacturing since 1990 for clients such as
Microsoft, Worley Parsons and Aurecon.
He runs the Sydney Dot Net User Group and is one of three Regional Directors for Microsoft.
To see more details and RSVP, please visit our
Facebook .NET Group or our
LinkedIn .NET Group.
|
*NEW* Introducing the SSW and Telerik NerdDinner
|
|
Pizza time!
NerdDinner
Wednesday 16th May 2012
8:10pm - 9:30pm
The Oaks Hotel
118 Military Road, Neutral Bay
|
As a change from sushi at the Sydney .NET User Group, SSW and Telerik invite you to join us for pizza
at The Oaks. A fantastic opportunity for networking with peers and sharing thoughts after the User
Group presentation
Sydney .Net User Group and NerdDinner proudly sponsored by:
|
Canberra .NET User Group -
The best place to learn .NET for free in Canberra
|
|
11 Days to go!
Canberra .NET User Group
Monday 21st May 2012
6:00pm - 9:00pm
Microsoft Canberra Branch
Walter Turnbull Building
Level 2, 44 Sydney Ave
Canberra, Australia
|
In depth code reviewing with Visual Studio 11
A walk through of how to conduct a thorough code review using the New Visual Studio 11.
With the release of the Visual
Studio 11 Beta, we’ve seen a greatly improved code review experience.
Adam will cover:
- The top things to look out for in VS11
- The new code review tools and functionality
- Reviewing a real world project
ABOUT THE PRESENTER
Adam Cogan is the Chief Architect at SSW, a Microsoft Certified Gold Partner specializing in custom
.NET, SharePoint and CRM solutions (with a splash of Business Intelligence).
At SSW, Adam has been developing custom solutions for businesses across a range of industries such as
Government, engineering, banking, insurance and manufacturing since 1990 for clients such as
Microsoft, Worley Parsons and Aurecon.
He runs the Sydney Dot Net User Group and is one of three Regional Directors for Microsoft.
To see more details and RSVP, please visit our
Facebook .NET Group or our
LinkedIn .NET Group.
Canberra .Net User Group proudly sponsored by:
|
Advertise with SSW
If you would like to advertise your product or service with SSW or would like to
find out more about various advertising opportunities, please email your enquiry
to<EMAIL_ADDRESS>or call: +61 2 9953 3000.
|
|
May 2012
Employment
SharePoint Developer
Are you looking to establish a career working with the latest software technology for a large
range of great organisations?
SSW has been the leading Microsoft technology company in Sydney for 21 years. We require an
enthusiastic SharePoint developer to start ASAP.
Read more and Apply NOW...
Senior Designer
SSW is looking for a highly talented front-end Web Designer who is passionate about creating
great standards-based Web 2.0 sites. This role requires
the understanding of how people behave in certain contexts and how various stimuli and
circumstances can affect individual thought processes in social
and professional situations.
You will be working with both creative designers and the development team in various challenging
projects. This role requires someone who is able to
communicate effectively with both disciplines together with creating aesthetically pleasing
design prototypes, including graphic design, site navigation,
and layout of content.
Read more and Apply NOW...
|
|
|


Here at Business, Inc., we offer many services for the busy professional business person.
First among our business services is services for business, which offers excellent solutions for many of the challenges facing the modern business, including business solutions, service solutions, service businesses, and business services, as well as services for business and business for services.
We also pride ourselves on our solutions services, which provide many business advantages for the enterprising entrepreneur. Solutions services include entrepreneurial advantage, service advantage, business advantage, advantage solutions, and enterprising advantages for the modern entrepreneurial solutions.
And last, but not least, we offer our premium advantage solution services for the enterprising business associate. This is our deluxe package and includes everything offered in the first two categories, as well as solution advantages for the advantageous service provider providing business package enterprise for the entrepreneurial solutions. This is a steal at $19,999,999!


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This page has a bunch of sample text and image tricks. All of them can be combined with each other. Don't forget to include the HX-JS tags when you use them, but you only need to include them once per page.
<script src="/static/hx.js" type="text/javascript"></script>
<link rel="stylesheet" type="text/css" href="/static/hx.css" />
You probably noticed the table-of-contents hanging on the right there. That's sometimes useful on long pages like this one, and you can use <script>var hxLocalOptions = {makeTOC: true};</script>
at the top of your page to make that appear if you want.


Additional remarks for MailMergePending/Status
Status of process for this record.
values:
0 (Open): not yet processed.
1 (Processing): Server is processing this record.
2 (Done): processing finished successfully.
-1 (Error): processing finished with error.


--- layout: page title: The Buck - Free Comic permalink: /comic/buck/ description: So a park ranger walks into a bar... --- {% include light-switch.html %}


Basics of Machine Learning Series
Problem Statement
It can so happen that, upon applying learning algorithm to a problem statement, there are unacceptably large errors in the predictions made by a model. There are various options that can possibly increase the performance of a model and in turn accuracy, such as,
- Acquire more training data
- Filter and reduce the number of features
- Increase the number of features
- Adding polynomial features
- Decreasing the regularization parameter, \(\lambda\)
- Increase the regularization parameter, \(\lambda\)
Seeing the number of options it can often be cumbersome to decide which path should one follow. Because it can so happen that sometimes one or more of the listed techniques might not work in a given case and hence would lead to wasted resources. Hence, randomness and coin toss would not be the correct way of picking one of the options.
Machine Learning Diagnostics are tests that help gain insight about what would or would not work with a learning algorithm, and hence give guidance about how to improve the performance. These can take time to implement, but are still worth venturing into during the time of uncertainties.
Overfitting and Train-Test Split
The case of overfitting in a linear regression is easily detectable by looking at the graph of the plot after determining the parameters as shown in Overfitting and Regularization post. But as the number of features increase it becomes increasingly tough to detect overfitting by plotting.
This is where the standard technique of train-test split comes in handy. It takes care of the scenario where the hypothesis has a low error but still is inaccurate due to overfitting. In this method, given a training dataset, it is split into two sets: training set and test set. Typically, the training set has 70% of the data while test set has the remaining 30%.
The training process is then defined as:
- Learn \(\Theta\) and minimize \(J(\Theta)\) on the training set.
- Compute the error, \(J_{test}(\Theta)\) on the test set.
Test set error can be defined as follows:
- For linear regression, mean-squared error (MSE), defined as,
% vectorized implementation to calculate cost function
error = (X * theta - y);
J = 1 / (2*m) * (error' * error)
Note:Complete Code Sample
- For logistic regression, the cross-entropy cost function, defined as,
- Alternative error function, called Misclassification error (0/1 misclassification error) gives the proportion of test data that was misclassified for the logistic regression,
where
Why Train/Validation/Test Splits?
It is seen that the model error on the training data is generally lower than what error it would display for the unseen data. So it would be fair to say that the loss calculations on the training data are not a proper indicator of the accuracy of the model. For this reason the previous section presented the process of train/test split for checking the performance of the model.
Following the similar argument, say we have \(n\) models, having varying candidate hyperparameters (like number of polynomial terms in regression, number of hidden layer and neurons in the neural network) and one is chosen based on the lowest test error it reports after training. Would it be correct to report this error as the indicator of the generalized performance of the selected model?
In general, many practitioners do use the same metrics as model performance, but it is advised against. This is so because, the way the model parameters were fit to the train samples and hence would report lower error on train dataset, similarly the model hyperparameters are fit to the test set and would report a lower error.
To overcome this issue, it is recommended to split the dataset into three parts, namely, train, cross-validation (or validation) and test. Now, train set is used to optimize the model parameters, then cross-validation set is used to select the best model among ones having varying hyperparameters. Finally the generalized performance can be calculated on test dataset which is not seen during training and model selection process. This would be the truly unbiased reporting of model performance metrics. This way the hyperparameters have not been trained using the test set.
Bias vs Variance
Most of the times, if the learning algorithm is not performing well on a dataset, it must be because of a high bias or a high variance problem, which is also sometimes known as underfitting or overfitting problems respectively.
Suppose there is training set for regression split into train, cross validation and test sets. The the training error, \(J_{train}(\Theta)\) and cross-validation error, \(J_{cv}(\Theta)\) can be defined as follows (based on \eqref{1}),
As, the degree of regression is increased the learnt parameters would fit the training data better and better and hence reduce the training error. But it would also lead to progressive overfitting after a certain point until when the cross-validation set also performs better, i.e. the error in cross-validation set would decrease initially until the parameters are fit to generalize, but would see a spike in error when the overfitting happens after a certain point. As a result, the plot of degree of regression vs the training and validation error would look as follows.
So,
- High Bias (Underfitting): both \(J_{train}(\Theta)\) and \(J_{cv}(\Theta)\) are high and \(J_{train}(\Theta) \approx J_{cv}(\Theta)\).
- High Variance (Overfitting): \(J_{train}(\Theta)\) is low, but \(J_{cv}(\Theta)\) is much greater than \(J_{train}(\Theta)\).
Effect of Regularization on Bias/Variance
Given a high order polynomial from previous section, if the regularization term, \(\lambda\) is small, its equivalent to having a regression without regularization which would have a small train error, \(J_{train}(\Theta)\) but would fail to generalize and hence have a high cross-validation error, \(J_{cv}(\Theta)\), i.e. a high variance.
Similarly, for a very high value of \(\lambda\), since all the parameters would be almost zero, both the training and cross-validation errors would be high, i.e. high bias.
Therefore, an optimal value of regularization parameter would balance the tradeoff between the bias-variance and help achieve the ideal model settings.
% plot a validation curve that we can use to select lambda
lambda_vec = [0 0.001 0.003 0.01 0.03 0.1 0.3 1 3 10]';
error_train = zeros(length(lambda_vec), 1);
error_val = zeros(length(lambda_vec), 1);
for i = 1:length(lambda_vec)
lambda = lambda_vec(i);
[theta] = trainLinearReg(X, y, lambda);
error_train(i) = linearRegCostFunction(X, y, theta, 0);
error_val(i) = linearRegCostFunction(Xval, yval, theta, 0);
endfor
Learning Curves
These are the plots of training error and cross-validation errors as a function of number of training data. These plots can give an insight about where the training is suffering from high bias or high variance issues. Fig-3 and Fig-4 show the learning curves for high bias and high variance settings respectively.
In a high bias setting, as the number of training examples increase the training error would increase too since the model is not fitting the data with an appropriate curve. Also since the generalization is bad, after a certain point, the bias would lead to comparable errors in cross-validation and training. This setting suggests that procuring more data is not going to help improve the model because of the biased assumptions made by the model.
In case of high variance, since the order of polynomial is high, the training error will grow slowly but would be well within the desired performance as in the plot above. But the high variance leads to overfitting and hence would cause high cross-validation errors. In this setting, getting more data would help because as the number of training data increases, the model would be forced to learn more generalized parameters that cannot be compensated by a overfit curve. As the plot shows as the number of training data increase, the gap between the training and cross-validation error would close down.
% plot a learning curve
m = size(X, 1);
error_train = zeros(m, 1);
error_val = zeros(m, 1);
for i = 1: m
Xi = X(1:i, :);
yi = y(1:i, :);
[theta] = trainLinearReg(Xi, yi, lambda);
error_train(i) = linearRegCostFunction(Xi, yi, theta, 0);
error_val(i) = linearRegCostFunction(Xval, yval, theta, 0);
endfor
Summarizing Bias and Variance
So, based on the study of bias and variance, the steps mentions in Problem Statement must be used under following settings:
- Acquire more training data fixes high variance
- Filtering and reducing the number of features fixes high variance
- Increasing the number of features fixes high bias
- Adding polynomial features fixes high bias
- Decreasing the regularization parameter, \(\lambda\), fixes high bias
- Increasing the regularization parameter, \(\lambda\), fixes high variance
Back to Neural Networks (NN)
- NN with less hidden units is prone to underfitting or high bias, but is computationally efficient.
- NN with more hidden layers or hidden units is more prone to overfitting or high variance. It is also computationally expensive.
Generally larger nueral networks are used to solve the hard problems of machine learning and the issue of overfitting is solved by choosing and optimal value of regularization parameter, \(\lambda\).
Earlier posts suggested using a single hidden layer as the default. Reading this post, it can be seen that one can use the train-validation split to choose the best combination of number of hidden layers.
Recommendations of Experiments
- Implement a simple learning algorithm as the first draft to test it on the cross-validation data.
- Plot the learning curves to see if there is a high bias or high variance problem and whether increasing training data or working on features is likely to help.
- Manual examination of errors can help find the trends in frequent misclassifications.
- Error AnalysisGet error results in terms of a single numerical value. Otherwise it would be difficult to assess the performance solely on intuition and would take longer time to analyze. For example, if one uses stemming and sees a rise in accuracy then adding the feature is a definite plus. Hence trying different options and strengthening the process by reinforced numerical estimates will speed up the process of keeping or rejecting features.
The error analysis should be done on cross-validation and not on the test dataset, because test set should be the bearer of actual performance of the model and not used to overfit the features being tested.
REFERENCES:
Machine Learning: Coursera - What to try next?
Machine Learning: Coursera - Evaluating a Hypothesis
Machine Learning: Coursera - Model Selection and Train/Validation/Test Splits
Machine Learning: Coursera - Diagnosing Bias and Variance
Machine Learning: Coursera - Regularization vs Bias Variance
Machine Learning: Coursera - Learning Curves
Machine Learning: Coursera - What to Do Next?


WITNESS LIST Public School-S/C on Benefits & Compensation Committee October 23, 2003-10:00A Panel 1 On: Rogers, Kate (HEB) Schacter, John (Milken Family Foundation) Panel 2 For: Casbeer, Ernie (Self and McGregor I.S.D.) On: Harris, Jonathan (Self) Hicks, Robert Todd (Self) Marshall, Susannah Voss (Temple I.S.D.) Payne, Stephanie (Lake Dallas I.S.D.) Panel 3 On: Blair, Cindy (Self and Allen I.S.D. - Kerr Elementary) Estrada-Russell, Celina (Self and Austin I.S.D., J. Frank Dobie Middle School) Hurd, Deryl M. (Self and Houston I.S.D.) Spivey, William F. (Self)


MicroPython CAN API¶
The CAN API is a MicroPython API for sending and receiving CAN frames. It is included in
the rp2
module on the Raspberry Pi Pico and is provided in the pre-built firmware in the CANHack repository (in the file firmware.uf2
),
specifically targeting the Canis Labs CANPico board (see the documentation on Canis CAN boards for more details on this board).
API Classes¶
The CAN API consists of four classes:
The
CAN
class provides control and status of the CAN controllerThe
CANFrame
class encapsulates CAN frames, which are either created in software or by receiving them from the CAN controller hardwareThe
CANID
class describes a CAN ID: every CAN frame has a CAN ID, but many frames can have the same IDThe
CANIDFilter
class describes how a CAN controller should identify and accept CAN frames based on their CAN ID
FIFO transmission¶
The CAN arbitration phase selects the highest priority frame to transmit. A well-designed CAN controller will perform internal arbitration on the same basis: it will enter into bus arbitration its highest priority frame. The drivers of the MCP2518 configure it for a priority queue of 32 frames. But when two frames with the same ID are queued then the order that the hardware chooses to send a frame will typically be arbitrary, and this might not be good enough in some cases. For example, a block message made up from multiple CAN frames must be transmitted in the correct order. To support this the drivers create an additional FIFO priority queue. At any point in time the frame at the head of the FIFO queue will be in the priority queue. Once a FIFO frame is transmitted on the CAN bus, the drivers will copy in the next frame in the FIFO queue, and so on.
Note that the FIFO queue for the MCP2518 is implemented in software: there is a hardware FIFO queue provided by the controller but it suffers from priority inversion with respect to the priority queue.
ID filtering¶
The CAN protocol engine of a CAN controller receives every frame but in most applications the host software
does not need to see all these frames. Instead, ID filters are used to filter out unwanted frames. The
MCP2518 allows 32 ID filters. The matching filter is included in the CANFrame
instance when the
frame is received. ID filters are matched in order, so filter 0 is matched first, down to 31. If no filters
are specified then a default ‘match all frames’ filter is defined.
See CANIDFilter
for more information on creating ID acceptance filters.
Controller specifics¶
MCP2518¶
The Microchip MCP2518 CAN controller has the following hardware specific behaviors:
The number of ID acceptance filters is limited to 32, and key values in the id_filters dictionary must be in the range 0..31.
The number of frames in the transmit priority queue is limited to 32.
The controller will automatically attempt to recover from bus-off (which requires the controller to see 127 idle periods of 11 recessive bits each).
Pending CAN frames will be discarded when entering bus-off.
In addition there are specifics of the MCP2518 driver:
The number of frames in the FIFO queue that feeds into the hardware priority queue is limited to 32.
The free-running timestamp counter is a 32-bit integer counting microseconds.


paint method
- @override
Paints this node to the canvas.
Subclasses, such as Sprite, override this method to do the actual painting of the node. To do custom
drawing override this method and make calls to the canvas
object. All drawing is done in the node's local
coordinate system, relative to the node's position. If you want to make the drawing relative to the node's
bounding box's origin, override NodeWithSize and call the applyTransformForPivot method before making calls for
drawing.
void paint(Canvas canvas) {
canvas.save();
applyTransformForPivot(canvas);
// Do painting here
canvas.restore();
}
Implementation
@override
void paint(Canvas canvas) {
if (points.length < 2) return;
_painter.points = points;
// Calculate colors
List<double> stops = _painter.calculatedTextureStops;
List<Color> colors = <Color>[];
for (int i = 0; i < stops.length; i++) {
double stop = stops[i];
Color color = _colorSequence.colorAtPosition(stop);
if (fadeDuration != null && fadeAfterDelay != null) {
double age = _pointAges[i];
if (age > fadeAfterDelay) {
double fade = 1.0 - (age - fadeAfterDelay) / fadeDuration;
int alpha = (color.alpha * fade).toInt().clamp(0, 255);
color = new Color.fromARGB(alpha, color.red, color.green, color.blue);
}
}
colors.add(color);
}
_painter.colors = colors;
// Calculate widths
List<double> widths = <double>[];
for (int i = 0; i < stops.length; i++) {
double stop = stops[i];
double growth = math.max(widthGrowthSpeed * _pointAges[i], 0.0);
if (widthMode == EffectLineWidthMode.linear) {
double width = minWidth + (maxWidth - minWidth) * stop + growth;
widths.add(width);
} else if (widthMode == EffectLineWidthMode.barrel) {
double width = minWidth + math.sin(stop * math.PI) * (maxWidth - minWidth) + growth;
widths.add(width);
}
}
_painter.widths = widths;
_painter.textureStopOffset = _offset;
_painter.paint(canvas);
}


This game checks if you can remember the clicking pattern you followed previously.
When the game opens, you start the game by pressing a key on the keyboard if you are using a computer or clicking/touching the 'START' button.
When the game starts, any one of the four boxes (green, red, yellow or blue) will blink. This gives you the particular box you are to click to progress to level 2.
When you correctly click this box that blinked, you will see level 2 on top and yet another box will randomly blink. There is a shaking animation of the box you clicked.
 In level 2, you are expected to click the first box that blinked followed by the new one that just blinked. If you get this correctly you will progress to level 3.
In level 3, a new box randomly blinks. Then you click the first box that blinked, followed by the second and the third in that sequence. You have to memorize the pattern.
Whenever you click or touch any box, the box will flash a white light as it shakes. This differenciates it from the blinking of one box at the start of every level.
That is basically what you do in the game. The sounds will also guide you.
SUMMARY
- Your current level tells you how many times you are to click. If your level is 4, you are clicking 4 times to make it to the next level. If you are in level 7, your clicking pattern should also be 7 clicks long and so on.
- The last box that blinked is the last box you are going to click in your pattern or sequence.
Good luck!


Module: ActionView::Helpers::CaptureHelper
| Relationships & Source Files | |
| Extension / Inclusion / Inheritance Descendants | |
|
Included In:
ActionDispatch::DebugView,
::ActionView::Base ,
::ActionView::Helpers ,
AssetTagHelper ,
ActionView::Helpers::DateTimeSelector,
ActionView::Helpers::DebugHelper,
FormHelper ,
FormOptionsHelper ,
FormTagHelper ,
TagHelper ,
ActionView::Helpers::TagHelper::TagBuilder,
ActionView::Helpers::Tags::Base,
ActionView::Helpers::Tags::CheckBox,
ActionView::Helpers::Tags::CollectionCheckBoxes,
ActionView::Helpers::Tags::CollectionRadioButtons,
ActionView::Helpers::Tags::CollectionSelect,
ActionView::Helpers::Tags::ColorField,
ActionView::Helpers::Tags::DateField,
ActionView::Helpers::Tags::DateSelect,
ActionView::Helpers::Tags::DatetimeField,
ActionView::Helpers::Tags::DatetimeLocalField,
ActionView::Helpers::Tags::DatetimeSelect,
ActionView::Helpers::Tags::EmailField,
ActionView::Helpers::Tags::FileField,
ActionView::Helpers::Tags::GroupedCollectionSelect,
ActionView::Helpers::Tags::HiddenField,
ActionView::Helpers::Tags::Label,
ActionView::Helpers::Tags::MonthField,
ActionView::Helpers::Tags::NumberField,
ActionView::Helpers::Tags::PasswordField,
ActionView::Helpers::Tags::RadioButton,
ActionView::Helpers::Tags::RangeField,
ActionView::Helpers::Tags::SearchField,
ActionView::Helpers::Tags::Select,
ActionView::Helpers::Tags::TelField,
ActionView::Helpers::Tags::TextArea,
ActionView::Helpers::Tags::TextField,
ActionView::Helpers::Tags::TimeField,
ActionView::Helpers::Tags::TimeSelect,
ActionView::Helpers::Tags::TimeZoneSelect,
ActionView::Helpers::Tags::UrlField,
ActionView::Helpers::Tags::WeekField,
TextHelper ,
ActionView::Helpers::TranslationHelper,
UrlHelper ,
::ActionView::TestCase ,
::ActionView::TestCase::Behavior
| |
| Defined in: | actionview/lib/action_view/helpers/capture_helper.rb |
Overview
CaptureHelper
exposes methods to let you extract generated markup which can be used in other parts of a template or layout file.
It provides a method to capture blocks into variables through capture and a way to capture a block of markup for use in a layout through content_for.
Instance Method Summary
-
#capture(*args)
The capture method extracts part of a template as a
::String
object. -
#content_for(name, content = nil, options = {}, &block)
Calling
content_for
stores a block of markup in an identifier for later use. -
#content_for?(name) ⇒ Boolean
content_for?
checks whether any content has been captured yet using #content_for. -
#provide(name, content = nil, &block)
The same as #content_for but when used with streaming flushes straight back to the layout.
Instance Method Details
#capture(*args)
The capture method extracts part of a template as a ::String
object. You can then use this object anywhere in your templates, layout, or helpers.
The capture method can be used in ::ERB
templates…
<% @greeting = capture do %>
Welcome to my shiny new web page! The date and time is
<%= Time.now %>
<% end %>
…and Builder (RXML) templates.
@timestamp = capture do
"The current timestamp is #{Time.now}."
end
You can then use that variable anywhere else. For example:

<title><%= @greeting %></title>

<%= @greeting %>

The return of capture is the string generated by the block. For Example:
@greeting # => "Welcome to my shiny new web page! The date and time is 2018-09-06 11:09:16 -0500"
# File 'actionview/lib/action_view/helpers/capture_helper.rb', line 43
def capture(*args) value = nil buffer = with_output_buffer { value = yield(*args) } if (string = buffer.presence || value) && string.is_a?(String) ERB::Util.html_escape string end end
#content_for(name, content = nil, options = {}, &block)
Calling content_for
stores a block of markup in an identifier for later use. In order to access this stored content in other templates, helper modules or the layout, you would pass the identifier as an argument to content_for
.
Note: yield
can still be used to retrieve the stored content, but calling yield
doesn’t work in helper modules, while content_for
does.
<% content_for :not_authorized do %>
alert('You are not authorized to do that!')
<% end %>
You can then use content_for :not_authorized
anywhere in your templates.
<%= content_for :not_authorized if current_user.nil? %>
This is equivalent to:
<%= yield :not_authorized if current_user.nil? %>
content_for
, however, can also be used in helper modules.
module StorageHelper
def stored_content
content_for(:storage) || "Your storage is empty"
end
end
This helper works just like normal helpers.
<%= stored_content %>
You can also use the yield
syntax alongside an existing call to yield
in a layout. For example:
<%# This is the layout %>

<title>My Website</title>
<%= yield :script %>

<%= yield %>

And now, we’ll create a view that has a content_for
call that creates the script
identifier.
<%# This is our view %>
Please login!
<% content_for :script do %>

<% end %>
Then, in another view, you could to do something like this:
<%= link_to 'Logout', action: 'logout', remote: true %>
<% content_for :script do %>
<%= javascript_include_tag :defaults %>
<% end %>
That will place script
tags for your default set of JavaScript files on the page; this technique is useful if you’ll only be using these scripts in a few views.
Note that content_for
concatenates (default) the blocks it is given for a particular identifier in order. For example:
<% content_for :navigation do %>

<%= link_to 'Home', action: 'index' %>

<% end %>
And in another place:
<% content_for :navigation do %>

<%= link_to 'Login', action: 'login' %>

<% end %>
Then, in another template or layout, this code would render both links in order:

<%= content_for :navigation %>

If the flush parameter is true
content_for
replaces the blocks it is given. For example:
<% content_for :navigation do %>

<%= link_to 'Home', action: 'index' %>

<% end %>
<%# Add some other content, or use a different template: %>
<% content_for :navigation, flush: true do %>

<%= link_to 'Login', action: 'login' %>

<% end %>
Then, in another template or layout, this code would render only the last link:

<%= content_for :navigation %>

Lastly, simple content can be passed as a parameter:
<% content_for :script, javascript_include_tag(:defaults) %>
WARNING: content_for
is ignored in caches. So you shouldn’t use it for elements that will be fragment cached.
# File 'actionview/lib/action_view/helpers/capture_helper.rb', line 155
def content_for(name, content = nil, = {}, &block) if content || block_given? if block_given? = content if content content = capture(&block) end if content [:flush] ? @view_flow.set(name, content) : @view_flow.append(name, content) end nil else @view_flow.get(name).presence end end
#content_for?(name) ⇒ Boolean
content_for?
checks whether any content has been captured yet using #content_for. Useful to render parts of your layout differently based on what is in your views.
<%# This is the layout %>

<title>My Website</title>
<%= yield :script %>

<body class="<%= content_for?(:right_col) ? 'two-column' : 'one-column' %>">
<%= yield %>
<%= yield :right_col %>

# File 'actionview/lib/action_view/helpers/capture_helper.rb', line 195
def content_for?(name) @view_flow.get(name).present? end
#provide(name, content = nil, &block)
The same as #content_for but when used with streaming flushes straight back to the layout. In other words, if you want to concatenate several times to the same buffer when rendering a given template, you should use #content_for, if not, use provide
to tell the layout to stop looking for more contents.
# File 'actionview/lib/action_view/helpers/capture_helper.rb', line 175
def provide(name, content = nil, &block) content = capture(&block) if block_given? result = @view_flow.append!(name, content) if content result unless content end


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Preparation time
30 mins to 1 hour
Cooking time
over 2 hours
Serves
Serves 6
Cook the carrots, onion, thyme and garlic in some olive oil over a low heat.
Add the tomatoes and the fish stock.
Season with salt, pepper and a little sugar.
Cover the sauce with a cartouche made from baking paper.
Cook slowly for up to 2 hours. (At this stage, the burrida can be stored in the fridge for 48 hours.)
When you want to serve, reheat the sauce adding the fish and cook for about 5-10 minutes until all the fish is cooked.
Mix the softened butter with the garlic and parsley. Slice the baguette at an angle, grill on both sides and then spread on the parsley and garlic butter. Serve with the fish stew.


--- layout: post title: dinner is ready date: '2014-10-20T07:30:46+02:00' categories: - Sketches - Sketchbooks picture_url: https://film-Storyboards.tumblr.com/post/100495932940/dinner-is-ready ---


Thank You !
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--- layout: archive title: Zulip Chat Archive permalink: /stream/116395-maths/topic/matrix.20updates.html ---
@Kevin Buzzard I worked a bit on matrices on the side. I don't know if you'll be happy with the differences with #334, so I PR'd #2 to your branch on leanprover-community/mathlib
. Let me know what you think.
BTW, to view the diff while ignoring whitespace changes, see https://github.com/leanprover-community/mathlib/pull/2/files?w=1 . There are still a lot of changes. :concerned: But I believe all of the major stuff that was in your original PR is still there.
Cool, so now matrices are even a ring. Nice work Sean!
I agree that the diff looks a bit daunting. But I think that is mostly because of some syntactic changes that don't represent a very big change in mathematics.
I am completely happy with any changes. I am just tired of matrices not being in Lean (as are some of my "clients", as Mario once referred to them).
PS @Johan Commelin Ellen proved they were a ring :P . @Sean Leather I should also add -- thanks a lot!
Yes, all the credit should go to people who worked on it before me. I just build on the shoulders of giants. Also, I think the fintype
idea was due to Johannes and Johan. Since Kevin is happy, I merged the PR.
Somehow Travis is now failing (and it is not a timeout). It didn't receive any output for 10min.
{% endraw %}Last updated: Apr 24 2020 at 19:02 UTC


Simple Cloth Simulation
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from statsmodels.base.elastic_net import RegularizedResults
from statsmodels.stats.regularized_covariance import _calc_nodewise_row, \
_calc_nodewise_weight, _calc_approx_inv_cov
from statsmodels.base.model import LikelihoodModelResults
from statsmodels.regression.linear_model import OLS
import numpy as np
"""
Distributed estimation routines. Currently, we support several
methods of distribution
- sequential, has no extra dependencies
- parallel
- with joblib
A variety of backends are supported through joblib
This allows for different types of clusters besides
standard local clusters. Some examples of
backends supported by joblib are
- dask.distributed
- yarn
- ipyparallel
The framework is very general and allows for a variety of
estimation methods. Currently, these include
- debiased regularized estimation
- simple coefficient averaging (naive)
- regularized
- unregularized
Currently, the default is regularized estimation with debiasing
which follows the methods outlined in
Jason D. Lee, Qiang Liu, Yuekai Sun and Jonathan E. Taylor.
"Communication-Efficient Sparse Regression: A One-Shot Approach."
arXiv:1503.04337. 2015. http://arxiv.org/abs/1503.04337.
There are several variables that are taken from the source paper
for which the interpretation may not be directly clear from the
code, these are mostly used to help form the estimate of the
approximate inverse covariance matrix as part of the
debiasing procedure.
wexog
A weighted design matrix used to perform the node-wise
regression procedure.
nodewise_row
nodewise_row is produced as part of the node-wise regression
procedure used to produce the approximate inverse covariance
matrix. One is produced for each variable using the
LASSO.
nodewise_weight
nodewise_weight is produced using the gamma_hat values for
each p to produce weights to reweight the gamma_hat values which
are ultimately used to form approx_inv_cov.
approx_inv_cov
This is the estimate of the approximate inverse covariance
matrix. This is used to debiase the coefficient average
along with the average gradient. For the OLS case,
approx_inv_cov is an approximation for
n * (X^T X)^{-1}
formed by node-wise regression.
"""
def _est_regularized_naive(mod, pnum, partitions, fit_kwds=None):
"""estimates the regularized fitted parameters.
Parameters
----------
mod : statsmodels model class instance
The model for the current partition.
pnum : scalar
Index of current partition
partitions : scalar
Total number of partitions
fit_kwds : dict-like or None
Keyword arguments to be given to fit_regularized
Returns
-------
An array of the paramters for the regularized fit
"""
if fit_kwds is None:
raise ValueError("_est_regularized_naive currently " +
"requires that fit_kwds not be None.")
return mod.fit_regularized(**fit_kwds).params
def _est_unregularized_naive(mod, pnum, partitions, fit_kwds=None):
"""estimates the unregularized fitted parameters.
Parameters
----------
mod : statsmodels model class instance
The model for the current partition.
pnum : scalar
Index of current partition
partitions : scalar
Total number of partitions
fit_kwds : dict-like or None
Keyword arguments to be given to fit
Returns
-------
An array of the parameters for the fit
"""
if fit_kwds is None:
raise ValueError("_est_unregularized_naive currently " +
"requires that fit_kwds not be None.")
return mod.fit(**fit_kwds).params
def _join_naive(params_l, threshold=0):
"""joins the results from each run of _est_<type>_naive
and returns the mean estimate of the coefficients
Parameters
----------
params_l : list
A list of arrays of coefficients.
threshold : scalar
The threshold at which the coefficients will be cut.
"""
p = len(params_l[0])
partitions = len(params_l)
params_mn = np.zeros(p)
for params in params_l:
params_mn += params
params_mn /= partitions
params_mn[np.abs(params_mn) < threshold] = 0
return params_mn
def _calc_grad(mod, params, alpha, L1_wt, score_kwds):
"""calculates the log-likelihood gradient for the debiasing
Parameters
----------
mod : statsmodels model class instance
The model for the current partition.
params : array-like
The estimated coefficients for the current partition.
alpha : scalar or array-like
The penalty weight. If a scalar, the same penalty weight
applies to all variables in the model. If a vector, it
must have the same length as `params`, and contains a
penalty weight for each coefficient.
L1_wt : scalar
The fraction of the penalty given to the L1 penalty term.
Must be between 0 and 1 (inclusive). If 0, the fit is
a ridge fit, if 1 it is a lasso fit.
score_kwds : dict-like or None
Keyword arguments for the score function.
Returns
-------
An array-like object of the same dimension as params
Notes
-----
In general:
gradient l_k(params)
where k corresponds to the index of the partition
For OLS:
X^T(y - X^T params)
"""
grad = -mod.score(np.asarray(params), **score_kwds)
grad += alpha * (1 - L1_wt)
return grad
def _calc_wdesign_mat(mod, params, hess_kwds):
"""calculates the weighted design matrix necessary to generate
the approximate inverse covariance matrix
Parameters
----------
mod : statsmodels model class instance
The model for the current partition.
params : array-like
The estimated coefficients for the current partition.
hess_kwds : dict-like or None
Keyword arguments for the hessian function.
Returns
-------
An array-like object, updated design matrix, same dimension
as mod.exog
"""
rhess = np.sqrt(mod.hessian_factor(np.asarray(params), **hess_kwds))
return rhess[:, None] * mod.exog
def _est_regularized_debiased(mod, mnum, partitions, fit_kwds=None,
score_kwds=None, hess_kwds=None):
"""estimates the regularized fitted parameters, is the default
estimation_method for class DistributedModel.
Parameters
----------
mod : statsmodels model class instance
The model for the current partition.
mnum : scalar
Index of current partition.
partitions : scalar
Total number of partitions.
fit_kwds : dict-like or None
Keyword arguments to be given to fit_regularized
score_kwds : dict-like or None
Keyword arguments for the score function.
hess_kwds : dict-like or None
Keyword arguments for the Hessian function.
Returns
-------
A tuple of parameters for regularized fit
An array-like object of the fitted parameters, params
An array-like object for the gradient
A list of array like objects for nodewise_row
A list of array like objects for nodewise_weight
"""
score_kwds = {} if score_kwds is None else score_kwds
hess_kwds = {} if hess_kwds is None else hess_kwds
if fit_kwds is None:
raise ValueError("_est_regularized_debiased currently " +
"requires that fit_kwds not be None.")
else:
alpha = fit_kwds["alpha"]
if "L1_wt" in fit_kwds:
L1_wt = fit_kwds["L1_wt"]
else:
L1_wt = 1
nobs, p = mod.exog.shape
p_part = int(np.ceil((1. * p) / partitions))
params = mod.fit_regularized(**fit_kwds).params
grad = _calc_grad(mod, params, alpha, L1_wt, score_kwds) / nobs
wexog = _calc_wdesign_mat(mod, params, hess_kwds)
nodewise_row_l = []
nodewise_weight_l = []
for idx in range(mnum * p_part, min((mnum + 1) * p_part, p)):
nodewise_row = _calc_nodewise_row(wexog, idx, alpha)
nodewise_row_l.append(nodewise_row)
nodewise_weight = _calc_nodewise_weight(wexog, nodewise_row, idx,
alpha)
nodewise_weight_l.append(nodewise_weight)
return params, grad, nodewise_row_l, nodewise_weight_l
def _join_debiased(results_l, threshold=0):
"""joins the results from each run of _est_regularized_debiased
and returns the debiased estimate of the coefficients
Parameters
----------
results_l : list
A list of tuples each one containing the params, grad,
nodewise_row and nodewise_weight values for each partition.
threshold : scalar
The threshold at which the coefficients will be cut.
"""
p = len(results_l[0][0])
partitions = len(results_l)
params_mn = np.zeros(p)
grad_mn = np.zeros(p)
nodewise_row_l = []
nodewise_weight_l = []
for r in results_l:
params_mn += r[0]
grad_mn += r[1]
nodewise_row_l.extend(r[2])
nodewise_weight_l.extend(r[3])
nodewise_row_l = np.array(nodewise_row_l)
nodewise_weight_l = np.array(nodewise_weight_l)
params_mn /= partitions
grad_mn *= -1. / partitions
approx_inv_cov = _calc_approx_inv_cov(nodewise_row_l, nodewise_weight_l)
debiased_params = params_mn + approx_inv_cov.dot(grad_mn)
debiased_params[np.abs(debiased_params) < threshold] = 0
return debiased_params
def _helper_fit_partition(self, pnum, endog, exog, fit_kwds,
init_kwds_e={}):
"""handles the model fitting for each machine. NOTE: this
is primarily handled outside of DistributedModel because
joblib can't handle class methods.
Parameters
----------
self : DistributedModel class instance
An instance of DistributedModel.
pnum : scalar
index of current partition.
endog : array-like
endogenous data for current partition.
exog : array-like
exogenous data for current partition.
fit_kwds : dict-like
Keywords needed for the model fitting.
init_kwds_e : dict-like
Additional init_kwds to add for each partition.
Returns
-------
estimation_method result. For the default,
_est_regularized_debiased, a tuple.
"""
temp_init_kwds = self.init_kwds.copy()
temp_init_kwds.update(init_kwds_e)
model = self.model_class(endog, exog, **temp_init_kwds)
results = self.estimation_method(model, pnum, self.partitions,
fit_kwds=fit_kwds,
**self.estimation_kwds)
return results
[docs]class DistributedModel(object):
__doc__ = """
Distributed model class
Parameters
----------
partitions : scalar
The number of partitions that the data will be split into.
model_class : statsmodels model class
The model class which will be used for estimation. If None
this defaults to OLS.
init_kwds : dict-like or None
Keywords needed for initializing the model, in addition to
endog and exog.
init_kwds_generator : generator or None
Additional keyword generator that produces model init_kwds
that may vary based on data partition. The current usecase
is for WLS and GLS
estimation_method : function or None
The method that performs the estimation for each partition.
If None this defaults to _est_regularized_debiased.
estimation_kwds : dict-like or None
Keywords to be passed to estimation_method.
join_method : function or None
The method used to recombine the results from each partition.
If None this defaults to _join_debiased.
join_kwds : dict-like or None
Keywords to be passed to join_method.
results_class : results class or None
The class of results that should be returned. If None this
defaults to RegularizedResults.
results_kwds : dict-like or None
Keywords to be passed to results class.
Attributes
----------
partitions : scalar
See Parameters.
model_class : statsmodels model class
See Parameters.
init_kwds : dict-like
See Parameters.
init_kwds_generator : generator or None
See Parameters.
estimation_method : function
See Parameters.
estimation_kwds : dict-like
See Parameters.
join_method : function
See Parameters.
join_kwds : dict-like
See Parameters.
results_class : results class
See Parameters.
results_kwds : dict-like
See Parameters.
Examples
--------
Notes
-----
"""
def __init__(self, partitions, model_class=None,
init_kwds=None, estimation_method=None,
estimation_kwds=None, join_method=None, join_kwds=None,
results_class=None, results_kwds=None):
self.partitions = partitions
if model_class is None:
self.model_class = OLS
else:
self.model_class = model_class
if init_kwds is None:
self.init_kwds = {}
else:
self.init_kwds = init_kwds
if estimation_method is None:
self.estimation_method = _est_regularized_debiased
else:
self.estimation_method = estimation_method
if estimation_kwds is None:
self.estimation_kwds = {}
else:
self.estimation_kwds = estimation_kwds
if join_method is None:
self.join_method = _join_debiased
else:
self.join_method = join_method
if join_kwds is None:
self.join_kwds = {}
else:
self.join_kwds = join_kwds
if results_class is None:
self.results_class = RegularizedResults
else:
self.results_class = results_class
if results_kwds is None:
self.results_kwds = {}
else:
self.results_kwds = results_kwds
[docs] def fit(self, data_generator, fit_kwds=None, parallel_method="sequential",
parallel_backend=None, init_kwds_generator=None):
"""Performs the distributed estimation using the corresponding
DistributedModel
Parameters
----------
data_generator : generator
A generator that produces a sequence of tuples where the first
element in the tuple corresponds to an endog array and the
element corresponds to an exog array.
fit_kwds : dict-like or None
Keywords needed for the model fitting.
parallel_method : str
type of distributed estimation to be used, currently
"sequential", "joblib" and "dask" are supported.
parallel_backend : None or joblib parallel_backend object
used to allow support for more complicated backends,
ex: dask.distributed
init_kwds_generator : generator or None
Additional keyword generator that produces model init_kwds
that may vary based on data partition. The current usecase
is for WLS and GLS
Returns
-------
join_method result. For the default, _join_debiased, it returns a
p length array.
"""
if fit_kwds is None:
fit_kwds = {}
if parallel_method == "sequential":
results_l = self.fit_sequential(data_generator, fit_kwds,
init_kwds_generator)
elif parallel_method == "joblib":
results_l = self.fit_joblib(data_generator, fit_kwds,
parallel_backend,
init_kwds_generator)
else:
raise ValueError("parallel_method: %s is currently not supported"
% parallel_method)
params = self.join_method(results_l, **self.join_kwds)
# NOTE that currently, the dummy result model that is initialized
# here does not use any init_kwds from the init_kwds_generator event
# if it is provided. It is possible to imagine an edge case where
# this might be a problem but given that the results model instance
# does not correspond to any data partition this seems reasonable.
res_mod = self.model_class([0], [0], **self.init_kwds)
return self.results_class(res_mod, params, **self.results_kwds)
[docs] def fit_sequential(self, data_generator, fit_kwds,
init_kwds_generator=None):
"""Sequentially performs the distributed estimation using
the corresponding DistributedModel
Parameters
----------
data_generator : generator
A generator that produces a sequence of tuples where the first
element in the tuple corresponds to an endog array and the
element corresponds to an exog array.
fit_kwds : dict-like
Keywords needed for the model fitting.
init_kwds_generator : generator or None
Additional keyword generator that produces model init_kwds
that may vary based on data partition. The current usecase
is for WLS and GLS
Returns
-------
join_method result. For the default, _join_debiased, it returns a
p length array.
"""
results_l = []
if init_kwds_generator is None:
for pnum, (endog, exog) in enumerate(data_generator):
results = _helper_fit_partition(self, pnum, endog, exog,
fit_kwds)
results_l.append(results)
else:
tup_gen = enumerate(zip(data_generator,
init_kwds_generator))
for pnum, ((endog, exog), init_kwds_e) in tup_gen:
results = _helper_fit_partition(self, pnum, endog, exog,
fit_kwds, init_kwds_e)
results_l.append(results)
return results_l
[docs] def fit_joblib(self, data_generator, fit_kwds, parallel_backend,
init_kwds_generator=None):
"""Performs the distributed estimation in parallel using joblib
Parameters
----------
data_generator : generator
A generator that produces a sequence of tuples where the first
element in the tuple corresponds to an endog array and the
element corresponds to an exog array.
fit_kwds : dict-like
Keywords needed for the model fitting.
parallel_backend : None or joblib parallel_backend object
used to allow support for more complicated backends,
ex: dask.distributed
init_kwds_generator : generator or None
Additional keyword generator that produces model init_kwds
that may vary based on data partition. The current usecase
is for WLS and GLS
Returns
-------
join_method result. For the default, _join_debiased, it returns a
p length array.
"""
from statsmodels.tools.parallel import parallel_func
par, f, n_jobs = parallel_func(_helper_fit_partition, self.partitions)
if parallel_backend is None and init_kwds_generator is None:
results_l = par(f(self, pnum, endog, exog, fit_kwds)
for pnum, (endog, exog)
in enumerate(data_generator))
elif parallel_backend is not None and init_kwds_generator is None:
with parallel_backend:
results_l = par(f(self, pnum, endog, exog, fit_kwds)
for pnum, (endog, exog)
in enumerate(data_generator))
elif parallel_backend is None and init_kwds_generator is not None:
tup_gen = enumerate(zip(data_generator, init_kwds_generator))
results_l = par(f(self, pnum, endog, exog, fit_kwds, init_kwds)
for pnum, ((endog, exog), init_kwds)
in tup_gen)
elif parallel_backend is not None and init_kwds_generator is not None:
tup_gen = enumerate(zip(data_generator, init_kwds_generator))
with parallel_backend:
results_l = par(f(self, pnum, endog, exog, fit_kwds, init_kwds)
for pnum, ((endog, exog), init_kwds)
in tup_gen)
return results_l
[docs]class DistributedResults(LikelihoodModelResults):
"""
Class to contain model results
Parameters
----------
model : class instance
class instance for model used for distributed data,
this particular instance uses fake data and is really
only to allow use of methods like predict.
params : array
parameter estimates from the fit model.
"""
def __init__(self, model, params):
super(DistributedResults, self).__init__(model, params)
[docs] def predict(self, exog, *args, **kwargs):
"""Calls self.model.predict for the provided exog. See
Results.predict.
Parameters
----------
exog : array-like NOT optional
The values for which we want to predict, unlike standard
predict this is NOT optional since the data in self.model
is fake.
args, kwargs :
Some models can take additional arguments or keywords, see the
predict method of the model for the details.
Returns
-------
prediction : ndarray, pandas.Series or pandas.DataFrame
See self.model.predict
"""
return self.model.predict(self.params, exog, *args, **kwargs)


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In most cases, the adoption process takes less than an hour. Make sure to give yourself extra time to stroll through our adoption area and meet several different animals. This way, you’re sure to find the pet who is the best fit for you.
What are the steps involved in the adoption process?
After you’ve found a pet you’re interested in adopting, let a member of the staff know.
We’ll verify your ID and proof of address (if different than your ID).
We’ll have a brief counseling session to learn more about your household, your lifestyle, and your ability to love and care for a pet.
Then we'll answer any questions or concerns you may have.
What happens if I have an issue and can't keep the pet?
We will take back any adopted pet if the adoption does not work out for any reason.


The CFITSIO library should be tested by building and running the testprog.c program that is included with the release. On Unix systems, type:
% make testprog % testprog > testprog.lis % diff testprog.lis testprog.out % cmp testprog.fit testprog.stdOn VMS systems, (assuming cc is the name of the C compiler command), type:
$ cc testprog.c $ link testprog, cfitsio/lib, alpha$library:vaxcrtl/lib $ run testprogThe test program should produce a FITS file called `testprog.fit' that is identical to the `testprog.std' FITS file included with this release. The diagnostic messages (which were piped to the file testprog.lis in the Unix example) should be identical to the listing contained in the file testprog.out. The 'diff' and 'cmp' commands shown above should not report any differences in the files. (There may be some minor format differences, such as the presence or absence of leading zeros, or 3 digit exponents in numbers, which can be ignored).
The Fortran wrappers in CFITSIO may be tested with the testf77 program on Unix systems with:
% f77 -o testf77 testf77.f -L. -lcfitsio -lnsl -lsocket or % f77 -f -o testf77 testf77.f -L. -lcfitsio (under SUN O/S) or % f77 -o testf77 testf77.f -Wl,-L. -lcfitsio -lm -lnsl -lsocket (HP/UX) % testf77 > testf77.lis % diff testf77.lis testf77.out % cmp testf77.fit testf77.stdOn machines running SUN O/S, Fortran programs must be compiled with the '-f' option to force double precision variables to be aligned on 8-byte boundarys to make the fortran-declared variables compatible with C. A similar compiler option may be required on other platforms. Failing to use this option may cause the program to crash on FITSIO routines that read or write double precision variables.
Also note that on some systems, the output listing of the testf77 program may differ slightly from the testf77.std template, if leading zeros are not printed by default before the decimal point when using F format.
A few other utility programs are included with CFITSIO; the first four of this programs can be compiled an linked by typing `make program_name' where `program_name' is the actual name of the program:
speed - measures the maximum throughput (in MB per second) for writing and reading FITS files with CFITSIO. listhead - lists all the header keywords in any FITS file fitscopy - copies any FITS file (especially useful in conjunction with the CFITSIO's extended input filename syntax). cookbook - a sample program that performs common read and write operations on a FITS file. iter_a, iter_b, iter_c - examples of the CFITSIO iterator routine


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Text based programming in primary schools?
Feb 19, 2014
Logo, Small Basic and Python are all accessible languages for the top end of primary. Good folk like Phil Bagge and Dr Chips have been very successful in teaching Python with primary pupils, and Logo has a long, noble heritage in primary. Personally, I’d be reluctant to do text-based programming with a primary class for a few reasons:
- I’m more interested in teaching computational thinking than coding, and ‘graphical’ programming languages seem to do that well – algorithmic ideas translate into code blocks quite directly, for example. Remember that computing in the new curriculum is meant to be more about knowledge and understanding than skills per se.
- Syntax – it takes a while to master this for any programming language, even those listed above, and I worry that mastering the syntax gets in the way of the thinking. YMMV, but do have a listen to these pupils comments on programming Raspberry Pis in Python. I suspect that much of a teacher’s time in class would be spent dealing with syntax errors (not always easy to spot), unless they fall back to a worksheet led approach, about which I might have other concerns. On the other hand, great reinforcement for the importance of spelling, punctuation and grammar!
- Progression – as they have to teach a text based language in secondary, I’d rather leave it to them so they have something fresh – I’d like to avoid too many pupils reporting ‘we did this when we were in primary school’ experience that happened a lot with the old ICT and was one of Ofsted’s criticisms of secondary ICT back in 2011. I fear we’ll have plenty of that with Scratch, but better to avoid it with (eg) Python too, if we can.
- In terms of CPD, we’ve our work cut out equipping teachers with the subject knowledge and confidence to teach programming in Kodu and Scratch, as well as the crucial CS hinterland, without also expecting them to learn a text-based language.
If you are going to introduce primary pupils to text based programming, I think I’d want to do things which wouldn’t be possible in Scratch, Kodu et al: at the risk of being far too ambitious, big data, machine learning and natural language processing all could form the basis of some interesting projects.
It’s also worth bearing in mind that Excel (and other spreadsheets) offers another way in to text based programming, albeit via flat, functional programming. Far from ‘creaking into obsolescence’, I still see Excel as a great tool to develop many aspects of computational thinking, as does Terry Freedman.
I think there are good arguments for extra-curricular programming clubs to move on to text-based programming, particularly if the adult:child ratio is somewhat better than 1:30.
I’d also want to do some HTML (and perhaps CSS) work (coding, not programming) – which would give some introduction to the skills of working with a formal language without simultaneously having to cope with the cognitively more demanding challenges of programming. There’s also some coverage for HTML work on the KS2 programme of study, under both the ‘services such as the world wide web’ and ‘design and create content’ sub-bullets. Oh, and it’s a really, really useful skill.
(Written originally as a forum post for the CAS community, free registration required)
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string
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Color description.
Day of the week.
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This is a function defined on a global enumeration.


As part of the curriculum, pre-final year students of B.Tech and Dual Degree undergo summer internships in various leading organizations. Students from other programs also take up internships to add to their educational experience. Such stipendiary industrial activity provides students with adequate exposure in tackling real life problems encountered in the working of an industrial entity. Students are also regularly exposed to the latest industrial practices through several industry visits. Many second year students also gain valuable experience through internships in the industry. Students also pursue internships and projects in various national and international academic insititutions out of their own volition.
Any Company interested in offering Internships can contact the Placement Office or the Faculty-In-Charge for the respective departments.


Mara
Discussion
This application commands a registered Telegram bot to interface with individuals for chronic pain tracking. The bot is here.
This primarily relies on the fantastic telegram_bot gem built by the wonderful @eljojo.
Ruby on Rails
This application requires:
-
Ruby 2.4.4
-
Rails 5.2.0
General Notes
MaraBot does not currently persist or 'remember' conversation state and history, although this is likely to change in the future.
The Bot is run as a background job with Sidekiq/Redis, and is initialized in the Procfile like so:
web: rake mara:start && bundle exec rails server -p
$PORT
To work with MaraBot in development,
you fire it up with rake mara:reset
(kills any active bots and
starts a new one). If you are going from scratch, this will likely involve
booting your redis-server
, then sidekiq
, then
rake mara:reset
.
This application uses the Interactor design pattern (implemented with the interactor gem) to process bot interactions.
Documentation and Support
Full documentation for this application is available here. In development,
update the documentation files with rake rdoc
.
Key technologies:
-
DB: Postgres
-
Front-end:
-
Bootstrap
-
JS
-
ERB
-
Turbolinks
-
Coverage: Coveralls/simplecov
-
Code quality:
-
Rubocop
-
Reek
-
Code Climate
-
Background jobs:
-
Sidekiq
-
Redis (with Heroku Redis)
-
Weather tracking: Wunderground Gem
-
Notable
Misc
Contributing
See CONTRIBUTORS.md
Credits
License
Copyright 2018 Nick Schwaderer
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
 IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.


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This is Spinal Tap
Note that the access key combination is specified in the title attribute (mouse over).


We require permissions to access tabs, idle state, and tab contents to work, and we don't store or keep anything.