Info
CPU
Memory
Zram
CPU
Number of Cores
Usage
Kernel Time
User Time
Idle Time
Memory
Total Memory
Used Memory
Total Swap
Used Swap
Zram
Total Used Memory
Original Memory Size
Compressed Memory Size
Compression Ratio


Start location: North Avenue Apartments, Atlanta, GA
Last updated: 10:30
In this group: Piya, Tina, Vicky


Suppose that all the scripts read data either from input file (option -i), or from STDIN if no file is given. Also suppose that they write output into file (option -o) or into STDOUT if no file is given.
/workspace/myproject
using property substitution and executing the sequence of Perl scripts first.pl -i data.txt | second.pl -o result.txt
.
<schedule> <pipedexec workingdir="/workspace/${projectname}"> <exec id="1" command="/usr/local/bin/Perl" args="first.pl -i data.txt" /> <exec id="2" pipefrom="1" command="/usr/local/bin/Perl" args="second.pl -o result.txt" /> </pipedexec> </schedule>
error occurred
message instead of the script's return error code. The sequence is now first.pl -i data.txt | tee >(second.pl -o result.txt) | third.pl -o result.txt
.
<schedule> <pipedexec workingdir="/workspace/${projectname}"> timeout="60" <exec id="1" command="/usr/local/bin/Perl" args="first.pl -i data.txt" /> timeout="10" /> errorstr="error occurred" /> <exec id="2" pipefrom="1" command="/usr/local/bin/Perl" args="second.pl -o results.txt" /> <exec id="3" pipefrom="1" command="/usr/local/bin/Perl" args="third.pl -o result.txt" workingdir="/distribution/${projectname}" /> </pipedexec> </schedule>
<schedule> <pipedexec workingdir="/workspace/${projectname}"> <exec id="1" command="/usr/local/bin/Perl" args="first.pl -i data.txt" /> <exec id="2" pipefrom="1" command="/usr/local/bin/Perl" args="second.pl" /> <exec id="3" pipefrom="2" command="/usr/local/bin/Perl" args="third.pl" /> <exec id="4" pipefrom="3" command="/usr/local/bin/Perl" args="fourth.pl -o 4out.txt" /> <!-- must not start until 4out.txt is ready. Without 'waitfor' it would be started before 4 is finished! --> <exec id="5" pipefrom="2" waitfor="4" command="/usr/local/bin/Perl" args="fiveth.pl -e 4out.txt -o 5out.txt" /> <!-- The same here --> <exec id="6" pipefrom="2" waitfor="5" command="/usr/local/bin/Perl" args="sixth.pl -e 5out.txt" /> <exec id="7" pipefrom="6" command="/usr/local/bin/Perl" args="seventh.pl -o results.txt" /> </pipedexec> </schedule>


I founded a team that built a Python microservice that would perform real-time root cause
analysis/mitigation of incidents and
eliminate the need for an on-call engineer.
Worked on the Azure Cosmos DB, globally distributed NoSQL database. Developed a service that
performed automatic root cause analysis on bugs in the database engine.
Built a special rule langugage and compiler to allow users to automate their root cause
diagnosis logic.


Change the priority of a criteria in the boxes on the left or the impact of an alternative in a colored box, and see the alternatives ranked from left to right based on impact.
You can also click and drag the rows to re-order the criteria, though this doesn't impact ranking.
Interactive version of this evaluation matrix generated by Palo Alto city staff.


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Kenneth Lien is a developer and designer based in San Francisco, CA.
He is interested in real-world applications for machine learning and building robust + scalable systems.


Private probes are the way to reach the deepest, darkest corners of your private networks - or, a way to extend our global footprint. Acts exactly like a public probe, but you control it.
{{ ctrl.apiKey }}
You will only be able to view this key here once, so be sure to copy it and keep it safe.
curl https://app.raintank.io/scripts/collector_install.sh | sudo KEY="asdfasdf7897asdfljadskjads889" bash
git clone https://github.com/raintank/raintank-collector.git
>> https://golang.org/doc/install.
Once you've installed Go, you'll need to configure your workspace like so: https://golang.org/doc/code.html#Workspaces
go get github.com/raintank/raintank-probe
cd raintank-collector && cp $(which raintank-probe) .
npm install
{
"collector": {
"name": "{{ctrl.collector.name}}"
},
"numCPUs": 1,
"serverUrl": "https://controller.raintank.io",
"apiKey": "{{ctrl.apiKey}}",
"probeServerPort": 8284
}
nodejs app.js -c /path/to/config.json


Topics in Applied Computation:
Advanced Practical Data Science
Fall 2020
Pavlos Protopapas
Office Hours: By appointment
Course helpline<EMAIL_ADDRESS>Welcome to AC295: advanced practical data science. The course will be divided into three major topics:
1. How to scale a model from a prototype (often in jupyter notebooks) to the cloud. In this module, we cover virtual environments, containers, and virtual machines before learning about containers and Kubernetes. Along the way, students will be exposed to Dask.
2. How to use existing models for transfer learning. Transfer learning is a machine learning method where a model developed for a task is reused as the starting point for a model on a second task. It is a popular approach in deep learning where pre-trained models are used as the starting point on computer vision and natural language processing tasks. This could be very important, given the vast computing resources required to develop neural network models on these problems and the huge gains that these models can provide. In this part of the course, we will examine various pre-existing models and techniques in transfer learning.
3. In the third part, we will introduce several intuitive visualization tools for investigating and diagnosing models. We will be demonstrating a number of visualization tools ranging from the well established (like saliency maps) to recent examples that have appeared in https://distill.pub.
Lectures (online): Tuesday and Thursday 10:30-11:45am (and possibly depending on timezone of students repeat Tuesday and Thursday from 6:00-7:15pm)
Office Hours: (all times EST) (Office hours begin 09/08)
| TF | Day | Office Hours |
|---|---|---|
| William Palmer | Sunday | 10:00-11:30 AM |
| Shivas Jayaram | Sunday | 8:00 - 9:30 PM |
| Javid Lakha | Monday | 4:30 - 6:00 PM |
| Faras Sadek | Monday | 6:00 - 7:30 PM |
| Hai Bui | Wednesday | 11:00 AM - 12:30 PM |
| Andrea Porelli | Thursday | 1:00 -2:30 PM |
| Rashmi Banthia | Friday | 8:30-10:00 AM |
| Yujiao Chen | Saturday | 11:00 AM - 12:30 PM |


Preparation time
less than 30 mins
Cooking time
30 mins to 1 hour
Serves
Serves 4
An easy chicken casserole recipe should be in every cook's little black book - add your favourite vegetables, herbs or a splash of wine.
Place the chicken breasts, roughly chopped onion, carrot, celery, peppercorns and bay leaf into a large pan and add enough water to cover the ingredients.
Bring the mixture slowly to the boil, then reduce the heat and simmer for 25-30 minutes, or until the chicken is completely cooked through.
Remove the chicken from the pan and set aside. Keep warm. Strain the cooking liquid and reserve 450ml/16fl oz of it. Set aside.
Heat half of the butter in a large separate pan and add the flour. Stir over a low heat until the mixture forms a smooth paste that leaves the sides and base of the pan cleanly. Gradually add the reserved cooking liquid, stirring well after each addition, to make a sauce. Season, to taste, with salt and freshly ground black pepper.
Add the broccoli and water chestnuts to the sauce.
In a bowl, mix the egg yolk with the cream and slowly add this mixture to the sauce. Add a squeeze of lemon juice and stir to combine.
Cut the cooked chicken into bite-sized pieces and return to the sauce. Stir well.
In a separate pan, heat the remaining butter and fry the remaining thinly sliced onion until soft and golden-brown.
To serve, sprinkle the onion over the casserole, garnish with parsley and serve with the boiled baby carrots.
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Knife skills: how to finely chop


This week the challengers have been cast way on a deserted island and must cook their way to civilization!
Name: Chef Pierce
Name: Chef Alyssa


Front-end Developer
Front-end developer with an eye for good-looking, modern, and responsive web designs.
I’m an aspiring nomadic web developer who is fascinated with everything about the web. I’m excited about the process of learning to code under mentorship of experienced developers at Moringa School
I love a challenge and I have a constant urge to learn, discover, and explore.
I am passionate about emerging technologies,self motivated with excellent organizational skills and a team player
I want to master the process of building web sites and increase my knowledge, skills and abilities in:
I am looking forward to working for a web development firm to build amazing websites and apps.
I have undertaken the following Projects in my first month at Moringa School:
This website will give intricate blog details of my favorite pet.It makes use of HTML and CSS for the layout and structure
This website makes use of HTML and CSS to showcase some of the products offered by Kiambu Resort
This website also showcases my HTML and CSS skills practicinsg with the addition of images elements on the pages
Lets chat via email or social media
Email:gilbert.keen@gmail.com


Inside the Rabbit Cave
Since my arrival in Penedès I have been hearing and reading about a winery called Can Rà fols dels Caus (house of rabbit caves). As it is only 10 min. from where I’m staying in Sant Pau d’Ordal, I made a point of making a visit. I was not disappointed.
This large estate contains an eclectic mix of structures dating as far back as Roman times. Indeed, its original purpose was to provide an outpost for Roman travelers making their way between Barcelona and Tarragona. However, amongst all these historic structures its futuristic winery is what really catches the eye. A sleek mixture of stainless steel, concrete, and natural limestone (which is key to their wine’s character) that looks more like a set from a James Bond movie.
Still under construction, the winery’s goal is grape-to-bottle with minimal pumping. It is an awe-inspiring mix of eccentricities (2 ft. thick automatic granite doors and a 20 ft. dia. spherical stone support…that turns!)
with no expense spared common sense. The result of which carries right into the final product. These wines aren’t cheap. They are estate-grown, eclectic, terroir-driven, and inspiring. They are designed to provoke thought and rattle your senses. So, let’s get started.
2011 El Recollàs is their most popular white wine which tips the scales at 30€…and I still bought it. I couldn’t help it. Made from a varietal I’ve never heard of called Incrocio Manzoni (out of Veneto) and French oak aged, this wine has a cornucopia of fruit flavor running the gamut of citrus, stone, and tropical topped off with vanilla and a touch of limestone minerality. It simply makes a good Chardonnay seem one dimensional.
Ok, that was exciting. Now for a rosat. 2013 Gran Caus is their 100% Merlot with about 3% residual sugar. Stop. Before you start running for the hills screaming, ““No! No f#%king White Merlot!”” just read on. These grapes are picked at a slightly lower brix, destemmed, and pumped directly into cold stainless steel tanks where they are gently pumped over for two days worth of extraction prior to press and fermentation. The result is a deep-colored rosat which smells like red vines, cherries, and strawberries, but smacks your palette with a mix of tart cherry, lively acidity, limestone minerality, salinity, thyme, a slight sweetness, and a lengthy finish that fools you into thinking red vs. pink. This wine will truly make you blush ;-)
There is a certain character to red blends I’m finding here in Penedès. The 2006 Can Rà fols dels Caus Pedradura is no exception. There seems to be a certain tobacco/licorice in all the Cabernet Sauvignon-Garnatxa blends I’ve had here. However, this wine also has a little rustic forest floor mixed with its black cherry and raspberry fruit. This 2006 could easily pass as a 2010 in California. Still ruby in color with a hint of brick around the rim. Amazing depth, finish, complexity, and chewy tannins. Yeah, I bought it.
Finally, as a treat, my host David brought out a 1997 Gran Caus, a Bordeaux blend of Cabernet Franc, Cabernet Sauvignon, and Merlot. Even after 17 years this wine was just beginning to show signs of pruning, rich cocoa notes and a bit of the same rustic character of the Pedradura. I was floored by the ageability of these wines.
Some of their wines are available in the U.S. Look for them. You won’t be disappointed.


Hey all, I just had an opportunity to record a conversation with Rene from @world5list. He’s the owner of the YouTube channel World5list, which has 800,000 subscribers.
https://www.youtube.com/user/World5list
He’s one of several people I’ve spoken with recently about how frustrating YouTube has become. Between demonetization of videos, outright censorship, and growing competition it’s becoming less and less attractive for content creators.
Rene has been looking at Patreon and Steemit as two platforms that might offer him a legitimate alternative to YouTube (along with his business partner Angelo who runs TheyWillKillYou with 1,000,000 subscribers).
https://www.patreon.com/theywillkillyou
It seems to me that there are ways to support the central needs of both groups. Steem needs visibility to help grow our active user population and accounts. YouTube personalities need steady income streams to support their work. Pretty sure we can help one another. Steem can help the YouTube channel operators form a community and start shifting their efforts from YouTube centric to Steem first with a Youtube backup.
Steem can support them financially and they can use their reach to help raise awareness across the internet. I’m personally committed to helping this happen.
@world5list has agreed to release exclusive content not only to Patreon, but to Steemit as well, and basically shift to a busines model with a world premier on Steem and Patreon and archive on YouTube. These videos normally cost $20 for one-month early access, but they’re providing it to us here on Steemit for free!
To give you an idea and to make this a little more concrete, here is a link to episode 2 of Escobar Uncovered - an extended interview with Pablo Escobars’ former main hitman; part of a series that Angelo and Rene recorded in Medellin, Colombia. Several trailers and episode one are already publicly accesible on YouTube, but THIS episode is not yet available to the public and I am curious to see how the Steemit community feels about having exclusive access to this type of unique content. Do you think that this would add value to the platform as a whole? Is this something you would support with upvotes to potentially enable them to eventually abandon YouTube all together in favor of Steemit?!
https://www.youtube.com/watch?v=YjES0C64IfM
In the end, these two populations need each other and should help solve one another’s greatest challenges.
I’m hosting a YouTube/Media forum on mspwaves Satuday January 13 at 11am EST (1600 UTC). Hopefully you, some whales, some devs, and some youtube personalities representing millions of subscribers will come join us.
▶ DTube
▶ IPFS
This page is synchronized from the post: ‘@world5list (800k subs) and the The Great Steemification of YouTube in an adpocolypse world’


This article is longer than most. That's because it's more important than most. If you avoid these software mistakes when starting a new project, you will save yourself a lot of grief. And by grief, I mean "money."
My new client was playing the long game. They needed data for analysis.
Lots and lots of data. They had been saving customer data for two years,
waiting for the critical mass at which they could have their data science team
dive into it. I wasn't on this project, but I was doing some work on their
system when I happened to notice some warnings from their database that I was
all too familiar with: Data truncated for column ...
.
No, that wasn't an error; it was a warning. Many of you are familiar with that horror story: MySQL wasn't being run in STRICT or TRADITIONAL mode.. Further investigation revealed that the data being truncated was the key the client was using to link this valuable customer data together. It was impossible to reconstruct. Two years of valuable data had to be thrown away because the client didn't know how to configure MySQL.
This, and many other disasters, can be avoided by taking a little time up front to understand common errors on large projects.
I find it interesting that when I go into a shop running Oracle or MySQL, the database is usually a mess and there's lot of work to fix there. When I go into a shop running PostgreSQL, the database is usually fine. I can't prove this, but I suspect it's because people who really understand databases reach for PostgreSQL due to its obsession with data quality, while people who just use databases reach for MySQL because it's "easy" or Oracle because "the company said so."
As a further data point, when I hire people, there's a small technical test. I've given that test hundreds of times. It was three years before a single developer returned that test with a properly normalized database. There were no "gotchas" and the database itself only required five tables to be normalized correctly. Many of these were excellent developers with a decade or more of experience but nonetheless turned in sloppy databases. It was trivial to demonstrate potential data anomalies in their database. Most software developers don't understand databases.
By running a default configuration of MySQL, you can get invalid dates,
truncated data, NULL
values for division by zero instead of an
error, and so on. MySQL and MariaDB have other issues, but this is the biggest
one. Every one of my clients who has used MySQL and tried to get themselves
into STRICT mode have found their software assumes sloppiness in the
data and can't run with a properly configured database.
Recommendation: if you must use MySQL, ensure you're using one of the recommended modes and have a competent DBA who understands database design and performance. Otherwise, if there's not strong requirement for choice of database, use PostgreSQL. I've heard DBAs complain that it takes more work to administer, but that's because motorcyles require more maintenance than bicycles, but they'll get you there faster.
When dealing with new clients:
Me: "How do you update the database?"
Client: "Run this script. It searches through the SQL directory to find the latest SQL we checked in. It runs that in your database."
Me: "How do you back out the change if there's a problem?"
Client: "Uh, there's hardly ever a problem, but we fix it by hand."
And things go downhill from there. I frequently meet clients with insane database migration strategies. The "dump SQL in a directory" for people to apply is an annoyingly common strategy. There's no clear way to roll it back and, if you're using MySQL or Oracle, if it contains DDL changes, those aren't transaction safe, so they really should be in their own migration, but they're not.
A few clients have an in-house database migration strategy involving numbered migrations. It often looks like this:
...
213-up-add-index-on-address-state.sql
213-down-add-index-on-address-state.sql
214-up-add-customer-notes-table.sql
214-down-add-customer-notes-table.sql
215-up-add-sales-tax-sproc.sql
215-down-add-sales-tax-sproc.sql
That, at least, can allow devs to back out changes (but tricky if your DDL isn't transaction-safe), but it's amazing when you get to migration 215 and you have 30 developers, five of them need to make a datababase change an they're arguing over who gets number 216. Yes, I've seen this happen more than once.

Or there are the migration strategies which require migrations to be written in a particular programming language. Those are often nice, but can't always leverage the strength of the database, often write very poor SQL, and make it hard for other teams not using the language to write migrations.
Recommendation: just use sqitch. It's well-tested, not tied to a particular framework or programming language, let's you write changes in native SQL, declare dependencies, tag changes, and so on.
Oh, and it's free and open source.
This one still surprises me, but many clients are storing datetimes in their database in "local time." Most of the time this means "not UTC." I'll just get the recommendation out of the way now.
Recommendation: whenever possible, store dates and times in the database in UTC.
Many shops started out small and many datetime tools report the local time because if you're in Portland, Oregon, you get confused when it's 1:30 PM but your time function says 8:30 or 9:30 PM.
But it's worse than confusion. I've seen many test suites fail twice a year ... right when DST starts and ends. That costs money!
Or here are some fun problems documented in the Perl DateTime module: Ambiguous and invalid times.
Ambiguous Local Times
Because of Daylight Saving Time, it is possible to specify a local time that is ambiguous. For example, in the US in 2003, the transition from to saving to standard time occurred on October 26, at 02:00:00 local time. The local clock changed from 01:59:59 (saving time) to 01:00:00 (standard time). This means that the hour from 01:00:00 through 01:59:59 actually occurs twice, though the UTC time continues to move forward.
Invalid Local Times
Another problem introduced by Daylight Saving Time is that certain local times just do not exist. For example, in the US in 2003, the transition from standard to saving time occurred on April 6, at the change to 2:00:00 local time. The local clock changes from 01:59:59 (standard time) to 03:00:00 (saving time). This means that there is no 02:00:00 through 02:59:59 on April 6!
Using UTC doesn't make all of your problems go away, but because UTC doesn't use DST, you'll never have invalid or ambiguous times.
Time zone issues seem rather abstract, but as your company grows, these problems become more severe. In particular, if you work with other companies, they will be very unhappy if you cut off their services an hour or two early simply because you got your time zones wrong.
One of my clients in California tried to switch to UTC because they had gone worldwide and local time was causing them headaches. When they realized how much time and money it was going to cost, they switched to Mountain Standard Time (Arizona) because they don't use daylight savings time. My client simply accepted that all of their old dates would forever be an hour or two off.
One of my favorite interactions with a developer was in the Paris office of a client with legitimate "big data" needs. But their NoSQL solution (which I'm not going to name) was awful. It was slow, painful to query, but offered "eventual constistency." There was a different NoSQL solution that seemed like a better fit, including a much better query syntax. However, if you learn one thing as a consultant, let it be this: never assume your client is stupid. If you're wrong, your client gets angry. If you're right, your client gets angry.
I approached a senior dev who was there when the NoSQL decision was made, laid out my findings, and asked "what am I missing? I need help understanding your NoSQL choice."
He replied, "we misread the documentation."
I laughed; he didn't. He wasn't joking. This multi-million dollar business made a very expensive mistake that they couldn't easily back out of because they misread the documentation.
This is not unusual.
Many developers, me amongst them, get excited about new technologies. Either we want to explore new things (that's why we became developers!) or we want to pad our CVs. Neither of these is necessarily in the company's best interest. While I sometimes recommend NoSQL (I love Redis, for example), usually I urge caution. I've seen client after client reach for NoSQL without understanding it. Here's what you need to understand:
Before SQL, everything was NoSQL. That's why we have SQL.
If you're considering a NoSQL solution, here's a handy checklist for you:
Often when I see clients switching from SQL to NoSQL, I find they're trading a known set of problems for an unknown set of problems. How is that good for your business? NoSQL has its place, but given how many developers are don't understand databases, why do we magically assume they're going to understand the byzantine NoSQL solutions available?
If there is any chance that your software will ever need support for multiple languages, investigate building that support in from day one. One of my clients decide to expand from the US to the UK. Easy, right? Hmm, date formats are different. Currency symbol is different. And the subtle spelling differences were "labour"-intensive to fix. It cost them so much money relative to their meager earnings abroad that they canceled the project. They might revisit it someday, but not now.
When looking at internationalization (i18n-getting your code ready for localization) and localization (l10n-the actual translation and other changes necessary), even if you're not going to launch in another language, make a "secret" translation feature. Heck, make it Pig Latin if you want. Even if you get it wrong, it won't really matter because it's not for public consumption. Instead, render everything in Pig Latin and check the software (better, write a spider that can find untranslated strings). One you get over the initial hurdle, it will become second nature to not hard-code strings or date formats everywhere.
Recommendation: Start your i18n work from day one. Trying to change it later is very expensive.
You may have noticed a pattern in the above. Early mistakes are often hard to notice until they turn into expensive mistakes.
The sooner you catch bugs, the less money they cost to fix. When you're dealing with well-known categories of bugs, you have little excuse for them. For open source databases, I can't imagine starting a new project with MySQL instead of PostgreSQL. Of course you need to store times in UTC. Just say "no" to NoSQL until you can prove you need it.
Of the problems I mention above, them can all be fixed in the design phase but are often caught in the maintenance phase where it's literally two orders of magnitude more expensive to fix: every dollar you didn't spend up front becomes $100 dollars later, and that's not counting the money you lost because you didn't fix it.
I'm a huge fan of agile development, but agile shouldn't mean checking your brain at the door and it definitely shouldn't mean "don't think of the big picture." You'll save yourself a lot of money and grief if you take a little more time when you start a new project.
If you have any other "up front" design issues like those above, mention them in the comments below! Or better yet, tell me how many your project has made!
Please leave a comment below!
If you'd like top-notch consulting or training, email me and let's discuss how I can help you. Read my hire me page to learn more about my background.
Copyright © 2018-2023 by Curtis “Ovid” Poe.


Mapbox Studio - A Primer
October 15, 2014 - VCGI
Bill Morris
mapper at
Faraday Inc
Mapbox Studio!
Thanks!
Get in touch:
bill@faraday.io


Preschool
Mission
Antioch Lutheran Preschool offers children of our community a distinctively Christian program, dedicated to excellence in education and rooted in the mission of our congregation, to teach, to grow, and to serve the people of God.
Registration Information
Antioch Lutheran Preschool is accepting registrations for the 2014-2015 school year for the following programs:
- Three-year-old class-Tuesday and Thursday
- Four-year-old class-Monday,Wednesday and Friday
- Book Buddies-Wednesdays: Open to both three- and four- year olds
- Preschool classes begin in September
- Recent Michigan legislation has changed the kindergarten entry age-See Director for information
- Children not required to be toilet trained, but contact Director before registering
- Book Buddies Session 1 begins in October, Session 2 begins in January
For more information download our brochure


home
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Two-Year-Old Girl Killed in Shooting at Her Home Outside St. Louis
by Associated Press(GLASGOW VILLAGE, Mo.) — A 2-year-old girl is dead after being shot inside a home in the latest of several shootings involving children in the St. Louis area this year.
St. Louis County police say they were called Wednesday afternoon to a home in the town of Glasgow Village. The caller believed the child had been electrocuted or shocked by a toy.
Responding officers determined she had sustained a gunshot wound. She was taken to a hospital, where she died.
Authorities identified her Thursday as Zion Israel.
No other details about the shooting have been released. Police say the investigation is ongoing.
About two dozen teenagers and children have died in gun-related incidents this year in the St. Louis area.
Glasgow Village, with 5,400 residents, is in north St. Louis County.


Give your information We will contact you for next Seminer
We will contact you for Next semener which held in end of Month.


New Tottenham boss Jose Mourinho gushes over Spurs transfer target Bruno Fernandes
Bruno Fernandes was a transfer target for Tottenham and Manchester United over the summer but remained at Sporting Lisbon - and Jose Mourinho is a big fan
by James Whaling, https://www.mirror.co.uk/authors/james-whaling/New Tottenham boss Jose Mourinho is a huge fan of Bruno Fernandes - who was strongly linked with a move to Spurs over the summer.
Tottenham appeared to be in a straight fight with Manchester United for the services of Fernandes - but he remained at Sporting where he established himself as one of Europe's hottest properties.
The Portuguese international plundered 31 goals from midfield in just 52 appearances last term and already has eight strikes in 14 games this season.
He is set to be the subject of yet more interest when the transfer window re-opens in January - and Mourinho has spoken of his admiration for the 25-year-old.
“I was surprised by the season of Bruno Fernandes," he told Eleven Sports in the summer.
“It was easy to predict quality, he is a very good player, but scored a very high number of goals for a midfield player.
“It shows that you need to have instinct and qualities to score and he has it all.
“He strikes the ball well, heads well, cool when he is one-on-one with the goalkeeper. He finds space in the penalty area. It’s all there.”
Mourinho has penned a three-and-a-half-year deal in north London after Spurs sacked Mauricio Pochettino on Tuesday night.
It is his first managerial role since being sacked by Manchester United last December.


Microsoft Research License Agreement
Non-Commercial Use Only
Azure Library for Lucene.Net
_____________________________________________________________________
This Microsoft
Research License Agreement, including
all exhibits ("MSR-LA") is a legal agreement between you and
Microsoft Corporation (“Microsoft” or “we”) for the software or data identified
above, which may include source code, and any associated materials, text or
speech files, associated media and "online" or electronic documentation and any updates we
provide in our discretion (together, the "Software").
By installing,
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Baba’s Feta Pasta
Greek Pasta Recipe with sauce made out of feta, stock, cream cheese and other ingredients.
Uses mixed spice for special flavour, and is a great dinner or mid-day meal.
- ⏲ Prep time: 15-30 min
- 🍳 Cook time: 25-30 min
Ingredients
Serves Around 3 People. You can scale up to 4 If you increase most quantities by +¼.
- Medium Sweet Onion (Finely Chopped)
- Virgin olive oil (not extra-virgin)
- White Sugar
- 3 Fresh Medium Garlic Cloves (Chopped Fine)
- 15-20 Kalamon/Kalamata Olives (Cut into thirds)
- Whole small courgette (Cut to small pieces)
- 6 Medium Mushrooms (Cut smallish and small stalk)
- ½ Broccli (Chopped small)
- 8-10 Finely sliced cherry tomatoes
- ½ Block of feta (Washed)
- 1 Chicken stock (Added to ¾ Mug boiling water)
- Overfilled Teaspoon Mixed Spice
- ¾ Bowls fusilli pasta
- Overfilled tablespoon Creme Cheese
- 1/3 Mug whole milk
Directions
Make sure to prepare & chop most ingredients before you start.
- Get a small deepish pan and heat it to low-medium temperature. Add a good amount of virgin olive oil and place one of the small onion pieces into the pan. Wait until it starts to sizzle and add the rest of the onion. Mix well and make sure the temperature is not too high as it can damage ingredients. Optional: You can sprinkle some sugar over the onion to make it sweeter. Then after a few minutes, when the onion has browned/softened, add in the garlic and wait 1-2 minutes, then you can turn off heat and put it to the side for later.
- Get a medium-large sized deepish pan, heat with oil and then add in the olives, courgettes and mushrooms. Sprinkle salt every now and then to enhance taste. After a few minutes when they are soft and are around the same color, then add in the onion and garlic from the smaller pan.
- Wait a couple minutes and then add the broccli and tomato slices to the mix. Wait 2-3 more minutes and then get your rinsed feta and melt it in the pan (chop to speed up).
- Start cooking the pasta.
- When the feta has nearly finished melting, add in the stock to the mix. Turn the pan temperature down slightly and then let it bubble for 5 mins (add a timer). Make sure to add the cover to the pan to keep in the flavor.
- After the 5 mins timer is up, add the overfilled tablespoon creme cheese to the main mix, stir, then re-add lid.
- Wait 2-3 more minutes and then you can test the sauce if you want. If you feel there is some missing, add more mixed spice or herbs such as parsley to it.
- Turn down the main mix again slightly and add the 1/3 Mug of Whole milk to the mix.
- Strain and serve your pasta and cover with the sauce. Enjoy!
Credit
- peepopoggers - github
- Original recipe.
Recipe tags:
greek
feta
pasta
supper


[all packages]
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[class hierarchy]
[index]
public abstract class Constraint extends Printable { public abstract int maximize(int d, Solution s); public abstract int minimize(int d, Solution s); public Constraint(); }
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Across: 1. Lantern-bearer's bare heart is to taint learned elite's leaders (9) 8. see 2-Down 9. Relics of opposition give up - that is second ... (11) 11. ... sign left by network (7) 12. Outside French egg? (5) 13. Simple eyes ring instruments (6) 15. Grounds entrust a diary tract (6) 17. Dislike headwear directions (5) 18. Characters eliminated from (7-Down) argument ... (7) 20. ... they say "No!" to classic misrepresentation? (11) 22. Bad air for rebels (1.1.1.) 23. Strange rule ... I'm cut from fine network (9) Down: 2,8-Across. Exhausted dismissal (3,3) 3. Misquote "CAP" (5) 4. For wild hit, try XXX (6) 5. Intending to keep it, he types a label (7) 6. (11-Down)? No American embracing backward loony arts! (11) 7. Pilot came unstuck embracing (10-Down) centrality (9) 10. Rears litter strangely, living on the ground (11) 11. Without hesitation, proper heretic offers prediction (9) 14. Girl gets over poetic capturer (7) 16. Noting Spring, "Out!" ... (6) 19. ... I call, "Out, flower!" (5) 21. Thank you for Greek character (3)


[1]
compact small size, ease to use; compact small size, ease to use
[2]
Image quality good.; Battery Consumption Defect!
[3]
Manual control, nice grip, great picture,; 20 fps movie, slow flash recharge


Next: Printing Characters, Previous: Printing Complexes, Up: Default Print-Object Methods
The printed representation of a number must not contain escape characters; see Escape Characters and Potential Numbers.


FRAGMENTVC: ANY-TO-ANY VOICE CONVERSION BY END-TO-END EXTRACTING AND FUSING FINE-GRAINED VOICE FRAGMENTS WITH ATTENTION
Abstract: Any-to-any voice conversion aims to convert the voice from and to any speakers even unseen during training, which is much more challenging compared to one-to-one or many-to-many tasks, but much more attractive in real-world scenarios. In this paper we proposed FragmentVC, in which the latent phonetic structure of the utterance from the source speaker is obtained from Wav2Vec 2.0, while the spectral features of the utterance(s) from the target speaker are obtained from log mel-spectrograms. By aligning the hidden structures of the two different feature spaces with a two-stage training process, FragmentVC is able to extract fine-grained voice fragments from the target speaker utterance(s) and fuse them into the desired utterance, all based on the attention mechanism of Transformer as verified with analysis on attention maps, and is accomplished end-to-end. This approach is trained with reconstruction loss only without any disentanglement considerations between content and speaker information and doesn't require parallel data. Objective evaluation based on speaker verification and subjective evaluation with MOS both showed that this approach outperformed SOTA approaches, such as AdaIN-VC and AutoVC.
And the last one is the conversion result generated with the source utterance as source and 10 randomly sampled utterances from the target speaker as target.
© 台大語音實驗室 NTU Speech Lab


Ocean [ay55], ocean, in The Inner Sea
Last updated Turn #136
Ocean [ay55], ocean, in The Inner Sea
------------------------------------------------------------------------
21: Pleasure Sloop [v5n], ship, 30 hulls, 1 rowing port, 90 sails, arrived
21: from Ocean [az55], owner:
21: Dorothy [v1b], Pen, with one worker, 94 sailors
21: Apollo [b5e], Imperial Empire, with 11 workers, 63 lt. xbowmen,
21: accompanied by:
21: Lutra [a4w], Imperial Empire, with 13 paladins, 40 pikemen
21: Hera [g3f], Imperial Empire, with 16 soldiers, 44 pikemen
21: Thor [r4g], Imperial Empire, with 49 pikemen
21: Eos [a2t], Imperial Empire, with ten workers, 48 lt. xbowmen
21: Khaki Blazer [w4j], Imperial Empire, with 17 soldiers,
21: seven pikemen, 35 lt. foot
21: Banian [e7n], Imperial Empire, duke, the High Priest of the
21: Church of Eres [151], with five soldiers, ten angels
21: Dunlop [j5s], Mandor Confederacy, prisoner
22: Pleasure Sloop [v5n], ship, 30 hulls, 1 rowing port, 90 sails,
22: departed for Ocean [ax55].
Routes leaving Ocean:
North, to Ocean [ax55], 6 days
East, to Ocean [ay56], 6 days
South, to Ocean [az55], Crystal Sea, 6 days
West, to Ocean [ay54], 6 days
The province is blanketed in fog.
No one can be seen through the fog.


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Variable selection tools for groups of variables and functional data based on a new grouped variable importance with random forests.
| Version: | 1.1 |
| Depends: | randomForest, wmtsa, fda |
| Published: | 2016-03-17 |
| Author: | Baptiste Gregorutti |
| Maintainer: | Baptiste Gregorutti <baptiste.gregorutti at safety-line.fr> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2.0)] |
| NeedsCompilation: | yes |
| In views: | FunctionalData |
| CRAN checks: | RFgroove results |
| Reference manual: | RFgroove.pdf |
| Package source: | RFgroove_1.1.tar.gz |
| Windows binaries: | r-devel: RFgroove_1.1.zip, r-release: RFgroove_1.1.zip, r-oldrel: RFgroove_1.1.zip |
| OS X binaries: | r-release: RFgroove_1.1.tgz, r-oldrel: RFgroove_1.1.tgz |
| Old sources: | RFgroove archive |
Please use the canonical form https://CRAN.R-project.org/package=RFgroove to link to this page.


r"""Learning and related functionality."""
from itertools import islice
import torch.nn.functional as F
import torch.nn.utils as nn_utils
from .replay import TransitionReplayer
from .model import soft_update
[docs]class Learner():
r"""Base class for all learners.
Your learners should also subclass this class.
"""
def __init__(self):
r"""Constructor method"""
pass
[docs] def learn(self):
r"""Performs a single learning step."""
raise NotImplementedError
[docs]class StackedLearner(Learner):
r"""Implements a learner that delegates to other learners.
:param learners: list of learners
:type learners: list of :class:`rlcc.learn.Learner`
"""
def __init__(self, learners):
r"""Constructor method"""
super(StackedLearner, self).__init__()
self.learners = learners
[docs] def learn(self):
r"""Performs a single learning step."""
for learner in self.learners:
learner.learn()
[docs]class ReplayLearner(Learner):
r"""Implements a learner using a replay buffer.
:param learning_strategy: delegate learning strategy
:type learning_strategy: :class:`rlcc.learn.LearningStrategy`
:param transitions: list of transitions
:type transitions: list of :class:`rlcc.Transition`
:param device: the torch device
:type device: str, optional
:param batch_size: the batch size
:type batch_size: int, optional
:param batches_per_step: the number of batches per step
:type batches_per_step: int, optional
"""
def __init__(self, learning_strategy, transitions, batch_size=128, batches_per_step=1):
r"""Constructor method"""
super(ReplayLearner, self).__init__()
self.learning_strategy = learning_strategy
self.batch_size = batch_size
self.batches_per_step = batches_per_step
self.replayer = TransitionReplayer(transitions, batch_size=batch_size)
[docs] def learn(self):
r"""Performs a single learning step."""
if len(self.replayer)>self.batch_size:
for batch in islice(self.replayer, self.batches_per_step):
self.learning_strategy.step(batch)
[docs]class LearningStrategy():
r"""Base class for all learning strategies.
Your learning strategies should also subclass this class.
"""
def __init__(self):
r"""Constructor method"""
pass
[docs] def step(self, transitions):
r"""Performs a single learning step.
:param transitions: a list of :class:`rlcc.Transition`
:type transitions: :class:`rlcc.Transition`
"""
raise NotImplementedError
[docs]class StackedLearningStrategy(LearningStrategy):
r"""Implements stacked learning strategy
:param strategies: list of learning strategies
:type strategies: list of :class:`rlcc.learn.LearningStrategy`
"""
def __init__(self, strategies):
r"""Constructor method"""
super(StackedLearningStrategy, self).__init__()
self.strategies = strategies
[docs] def step(self, transitions):
r"""Performs a single learning step.
:param transitions: a list of :class:`rlcc.Transition`
:type transitions: :class:`rlcc.Transition`
"""
for strategy in self.strategies:
strategy.step(transitions)
[docs]class DoubleLearningStrategy(LearningStrategy):
r"""Implements a double learning strategy.
:param local: the local network
:type local: :class:`torch.nn.Module`
:param target: the target network
:type target: :class:`torch.nn.Module`
:param optimizer: the optimizer
:type optimizer: :class:`torch.nn.Module`
:param tau: the update weight
:type tau: float, optional
:param clip_norm: the gradient norm maximum
:type clip_norm: float, optional
:param clip_value: the gradient value maximum
:type clip_value: float, optional
"""
def __init__(self,
local,
target,
optimizer,
tau=1e-3,
clip_norm=None,
clip_value=None):
r"""Constructor method"""
super(DoubleLearningStrategy, self).__init__()
self.tau = tau
self.local = local
self.target = target
self.optimizer = optimizer
self.observers = []
self.clip_norm = clip_norm
self.clip_value = clip_value
self.learning_steps = 0
[docs] def step(self, transitions):
r"""Performs a single learning step.
:param transitions: a list of :class:`rlcc.Transition`
:type transitions: :class:`rlcc.Transition`
"""
# Compute actor loss
loss = self.loss(transitions)
# Minimize the loss
self.optimizer.zero_grad()
loss.backward()
# Clip the gradient
if self.clip_norm:
nn_utils.clip_grad_norm_(self.local.parameters(), self.clip_norm)
if self.clip_value:
nn_utils.clip_grad_value_(self.local.parameters(), self.clip_value)
# Optimize
self.optimizer.step()
# Notify the observers
for obs in self.observers:
obs(transitions=transitions, local=self.local, loss=loss, learning_steps=self.learning_steps)
# soft updates
soft_update(self.target, self.local, self.tau)
# update counts
self.learning_steps += 1
[docs] def loss(self, transitions):
r"""Compute the loss of the transitions
:param transitions: a list of :class:`rlcc.Transition`
:type transitions: :class:`rlcc.Transition`
"""
raise NotImplementedError
[docs]class DPGActor(DoubleLearningStrategy):
r"""Implements Deterministic Policy Gradient Actor learner.
:param actor_local: the local network
:type actor_local: :class:`torch.nn.Module`
:param actor_target: the target network
:type actor_target: :class:`torch.nn.Module`
:param actor_optimizer: the optimizer
:type actor_optimizer: :class:`torch.nn.Module`
:param critic: the critic
:type critic: :class:`torch.nn.Module`
:param tau: the update weight
:type tau: float, optional
:param observers: list of observers
:type observers: list of observers, optional
:param clip_norm: the gradient norm maximum
:type clip_norm: float, optional
:param clip_value: the gradient value maximum
:type clip_value: float, optional
"""
def __init__(self,
actor_local,
actor_target,
actor_optimizer,
critic,
tau=1e-3,
clip_norm=None,
clip_value=None):
r"""Constructor method"""
super(DPGActor, self).__init__(
actor_local,
actor_target,
actor_optimizer,
tau=tau,
clip_norm=clip_norm,
clip_value=clip_value)
self.critic = critic
[docs] def loss(self, transitions):
r"""Compute the loss of the transitions
:param transitions: a list of :class:`rlcc.Transition`
:type transitions: :class:`rlcc.Transition`
"""
# Unpack tuples
states, _, _, _, _ = transitions
return -self.critic(states, self.local(states)).mean()
[docs]class DPGCritic(DoubleLearningStrategy):
r"""Implements Deterministic Policy Gradient Critic learner.
:param critic_local: the local network
:type critic_local: :class:`torch.nn.Module`
:param critic_target: the target network
:type critic_target: :class:`torch.nn.Module`
:param critic_optimizer: the optimizer
:type critic_optimizer: :class:`torch.nn.Module`
:param actor: the actor
:type actor: :class:`torch.nn.Module`
:param tau: the update weight
:type tau: float, optional
:param observers: list of observers
:type observers: list of observers, optional
:param clip_norm: the gradient norm maximum
:type clip_norm: float, optional
:param clip_value: the gradient value maximum
:type clip_value: float, optional
"""
def __init__(self,
critic_local,
critic_target,
critic_optimizer,
actor,
gamma=0.99,
tau=1e-3,
clip_norm=None,
clip_value=None):
r"""Constructor method"""
super(DPGCritic, self).__init__(
critic_local,
critic_target,
critic_optimizer,
tau=tau,
clip_norm=clip_norm,
clip_value=clip_value)
self.gamma = gamma
self.actor = actor
[docs] def loss(self, transitions):
r"""Compute the loss of the transitions
:param transitions: a list of :class:`rlcc.Transition`
:type transitions: :class:`rlcc.Transition`
"""
# Unpack tuples
states, actions, rewards, next_states, is_terminals = transitions
# Get predicted next-state actions and Q values from target models
actions_next = self.actor(next_states)
q_targets_next = self.target(next_states, actions_next)
# Compute Q targets for current states
q_targets = rewards + (self.gamma * q_targets_next * (1 - is_terminals))
# Compute critic loss
q_expected = self.local(states, actions)
return F.mse_loss(q_expected, q_targets)


fileName = Files.temporaryFileName();
Return arbitrary name of a file that does not exist and is in a directory where access rights allow to write to this file (useful for temporary output of files).
The created temporary file is not automatically deleted when closed, but needs to be explicitly deleted, e.g. by removeFile(fileName).
Warning: The underlying C implementation of ModelicaInternal_temporaryFileName calls the standard C function tmpnam, which has a race condition security problem in the case another process creates a file with the same fileName just after tmpnam generated the full path name.
fileName = Files.temporaryFileName(); -> fileName is the absolute path name of the temporary file Streams.print(String(System.getPid()), fileName); -> Create the temporary file Warning: Possible race condition on file access Files.removeFile(fileName); -> Explicitly delete the temporary file (after use)
function temporaryFileName extends Modelica.Icons.Function; output String fileName "Full path name of temporary file"; end temporaryFileName;


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Data science hero Roger Peng is the co-host of two different podcasts. For his first podcast, he joined Hilary Parker on a data science podcast called “Not So Standard Deviations”. Their first episode was released on September 16, 2015, and since then they have released a total of 33 episodes. That works out to a rate of 0.061 episodes per day.
But “Not So Standard Deviations” is not Dr. Peng’s only venture in the podcasting world. He has also teamed up with Elizabeth Matsui to create “The Effort Report,” a podcast covering life in academia. The first episode debuted on July 1, 2016. Matsui and Peng have produced 29 episodes, which sets a pace of 0.115 episodes per day.
Even though “NSSD” had a 9 month headstart, “The Effort Report” has been releasing episodes at a much faster rate. We can expect that the number of episodes for “The Effort Report” will someday surpass that of “NSSD.” But when can we expect this momentous historic event to occur? Just for fun, we’ll devote a couple of blog posts to using some basic data science techniques to predict an answer to this burning question.
Getting Some Data
To make a prediction, we’ll need some data to tell us when the podcast episodes have been released. Fortunately, this is easy to obtain from the podcasts’ RSS feeds. We can use R to download and process this data; we just need to make a quick function to download the feeds, parse the XML, and store it in a data.frame
. Below is a function I wrote called rss.to.dataframe
to do the job:
library(XML);
library(dplyr);
# a simple function for converting a list of lists to a data.frame
list.entry.to.dataframe <- function(x) {
data.frame(as.list(x), stringsAsFactors = FALSE)
}
rss.to.dataframe <- function(url) {
# download the RSS data as XML and use XPath to extract "item" elements
xmlDocument <- xmlParse(url, encoding = "UTF-8");
rootNode <- xmlRoot(xmlDocument);
items <- xpathApply(rootNode, "//item");
data <- lapply(items, xmlSApply, xmlValue);
# convert the XML list to a data.frame
df <- do.call(dplyr::bind_rows, lapply(data, list.entry.to.dataframe));
# if the data includes a "pubDate" column, convert that to a date
# and sort the output by that column
if (any(names(df) == "pubDate") == TRUE) {
df$pubDate <- as.POSIXct(df$pubDate, format = "%a, %d %b %Y %T %z");
df <- df[order(df$pubDate), ];
}
# if there is a "duration" column, convert that to a difftime
if (any(names(df) == "duration") == TRUE) {
df$duration <- as.difftime(df$duration, format = "%T");
}
# add a column "n" that increments for each row
df <- cbind(n = 1:nrow(df), df)
podcast <- xpathApply(rootNode, "channel/title", xmlValue);
df$podcast <- podcast[[1]];
return(df);
}
nssd <- rss.to.dataframe("http://feeds.soundcloud.com/users/soundcloud:users:174789515/sounds.rss");
effrep <- rss.to.dataframe("http://effortreport.libsyn.com/rss");
We need to do just a little bit of cleaning on the data. The first item in NSSD’s RSS feed was a sort of teaser for the podcast and is not considered to be an official episode. So I’ll remove it from the data.frame
and re-number the remaining rows so our episode counts will be correct.
# Data Cleaning
# remove the first row from NSSD because it's not really counted as an episode
nssd <- nssd[nssd$title != "Naming The Podcast", ];
nssd$n <- nssd$n - 1;
# For reproducibility in the future, make sure we remove any entries after the date this post was published
as.of.date <- as.POSIXct("2017-03-07 12:00:00");
nssd <- nssd[nssd$pubDate < as.of.date, ];
effrep <- effrep[effrep$pubDate < as.of.date, ];
Great! Now we have two data.frame
objects, one for each podcast. To make our analysis easier, we can combine the two into one. We can also take this as an opportunity to cut out some extraneous columns.
# select the columns we need and then union together the two data frames
columns <- c("podcast", "n", "pubDate", "duration");
episodes <- rbind(nssd[, columns], effrep[ , columns]);
# add a column so we can identify the rows that were actually observed
# (as opposed to the forecast values we will soon be adding)
episodes$type <- "actual";
Making a (Very Basic) Forecast
When conducting a data analysis, I like to take a “start simple” approach. This allows me to quickly study the data and produce some rough results before investing time in a more complex approach. Here I’ll implement that strategy by making a very basic assumption that both podcasts will continue releasing episodes at the same rate. Using this very simple model, we can predict the days on which upcoming episodes will be released.
First, let’s compute the rate at which the podcasts have been released.
# Determine the rate at which podcast episodes are being released
# first, make a simple data frame with the first and last episode of each podcast
first.last <- episodes %>%
group_by(podcast) %>%
summarize(first = min(pubDate), last = max(pubDate), count = max(n)) %>%
arrange(first);
first.last <- data.frame(first.last);
# Compute how many days each podcast has been around and then compute a "days per episode" rate
first.last$days <- with(first.last, as.numeric(last - first));
first.last$rate <- with(first.last, days / (count - 1));
Now let’s extend that trend! I’ll pick an arbitrary future episode of the podcasts… let’s say Episode #45.
Assuming that Dr. Peng produced podcast episodes at a steady rate, on what date should each of the 45 episodes theoretically have been released?
# using each podcast's episode release rate, construct a data set with
# the expected release date of the first 45 episodes
projected <- merge(first.last, 1:45, all = TRUE);
projected$pubDate <- with(projected, first + ((y - 1) * rate * 24 * 60 * 60));
projected$n <- projected$y;
projected$type <- "trend";
projected$duration <- NA;
We now have a data frame called projected
that has the thoretical release dates for 45 episodes of each podcast assuming that production had proceeded at a constant rate.
Answering the Big Question
Now that we have a simple forecast of release dates for future episodes, we can look at those projected release dates to see when “The Effort Report” will surpass “Not So Standard Deviations”. Here’s what we find:
| Episode Number | Not So Standard Deviations | The Effort Report |
|---|---|---|
| 38 | 2017-05-16 | 2017-05-19 |
| 39 | 2017-06-02 | 2017-05-28 |
| 40 | 2017-06-18 | 2017-06-06 |
According to our simple linear forecast, episode #39 of “The Effort Report” will be released on May 28, 2017. That is 5 days before “Not So Standard Deviations” will release its 39th episode. Thus we will consider this to be the “cross-over” point.
Output from “The Effort Report” will surpass “Not So Standard Deviations” on May 28, 2017.
Visualizing the Forecast
As usual, the easiest way to quickly understand the podcast output and our predicted release rate is to plot it out. This sounds like a job for ggplot2
!
Remember that we have two data frames, one with the actual episodes and one with our expected release dates based on our linear forecast. To make our plot easier, we’ll combine those two data frames into one. We can also convert our strings to factors.
# combine our projected episode data with the actual episode data
episodes <- rbind(episodes, projected[ , names(episodes)])
# change data types to be better suited for plotting
episodes$podcast <- factor(episodes$podcast)
episodes$type <- factor(episodes$type)
episodes$pubDate <- as.Date(episodes$pubDate)
# We want to annotate our plot so make a nice label for that
label.text <- paste("Episode", cross.points[cross.index, "Episode Number"], " - ", format(cross.points[cross.index, 3], "%B %e, %Y"));
library(ggplot2);
ggplot() +
geom_line(data = episodes, aes(x = as.Date(pubDate), y = n, color = podcast, linetype = type, size = type)) +
scale_size_manual(values = c(1.2, 0.5)) +
scale_x_date(date_labels = "%b %Y") +
labs(x = "", y = "Number of Episodes Released", title = "Podcast Output Forecast (using simple linear method)") +
geom_point(data = data.frame(x = cross.points[cross.index, 3], y = cross.points[cross.index, "Episode Number"]), aes(x = x, y = y), size=2, color="#00BFC4") +
annotate("text", x = as.Date("2017-02-25"), y = 43, hjust = 1, label = label.text, color="#00BFC4") +
annotate("segment", x = as.Date("2017-03-01"), xend = cross.points[cross.index, 3] - 9, y = 42.75, yend = cross.points[cross.index, "Episode Number"] + 0.25, color = "#00BFC4")
This plot allows us to easily see that the episodes have been released at a consistent rate, and it shows us when we can expect the two trendlines to converge.
Conclusion
The plot reveals that overall Dr. Peng has been fairly consistent with his release schedule. This gives us hope that our simple linear forecast could actually be accurate. Check back next week; I’ll be using the forecast
package on this same data set to try to make a more advanced forecast.
And keep your eye on those podcast RSS feeds. We’ll soon find out how accurate my forecasts really are!


PICTURES: Kirkcaldy gets into the festive spirit at Christmas lights switch on
The countdown to Christmas in Kirkcaldy is under way after the town’s Christmas lights were switched on on Saturday evening.
by Craig SmithFamilies were invited to Charlotte Street for a fun afternoon of entertainment, music and refreshments before the lights on a giant tree outside Society were turned on at 6pm.
It followed the successful reindeer parade which kicked off the festivities last weekend.
Saturday also saw the launch of Kirkcaldy4All’s Christmas Star Trail, which will see 30 stars – some over a metre tall – appearing in shop windows in the town centre, with a map guiding families to them.
The stars have all been decorated by local schools and artists, or the businesses themselves, and shoppers who spot all them will be in with a chnce of winning a £100 shopping voucher.
Bill Harvey, manager at Kirkcaldy4All, said: “This is the third year we have worked with the Mercat Shopping Centre to bring some Christmas cheer to the town and help its local businesses.
“We are grateful to all the town centre businesses and local schools for supporting these initiatives so enthusiastically, and hope that the people of Kirkcaldy will also get behind us and come down to the high street and enjoy what’s on offer in the run-up to Christmas.”
A Candy Cane Cocktail Trail will also be held for the first time for adults. Nine bars and pubs in the town will have the drinks on sale.


The other animals outsmarted you and managed to hide
What do you do now? Your tribe will be so disappointed if you come back with nothing. No pressure, just sayin’


Independence Day is a 1996 American science fiction action film directed and co-written by Roland Emmerich. The film focuses on disparate groups of people who converge in the Nevada desert in the aftermat this is new line.


The set of templates are in a different module not imported by the module lui
, if you want to use it add it to your module
angular.module("myApp", ["lui", "lui.formlytemplates"]);
The basic form of a template
The part about all the things you find in `lui.IField`
The part about how to use classname