How to read the visualization
The graph above represents the net of connections between the Wikipedia pages related to the net neutrality one. The first thing we can see in this graph is the division in three main clusters (see the image here below).
It is easy now to recognize a bigger and more central cluster and two smaller ones. There are also many bridge clusters and external ones, but the most interesting fact is that the Net Neutrality page (in the red circle) doesn't belong to any of these clusters. We can see in the image below how it actually is in between two clusters.
The final visualization above presents a further codification: nodes are divided into two different categories, depending on the content of the pages: ethical terms are red, while technical ones are blue.
Finally, the dimension of the dots depends on their own inDegree, which means that the more a dot the more it was linked by others.
Clicking on the dots allows to isolate the nodes linking or linked to the one you clicked on. In order to fully understand the graph it's possible to zoom in and out in the visualization to see smaller nodes.


{{ disease.diseasesCovered }}
Age-Group: {{ policy1.minAge }} - {{ policy1.maxAge }}
Min Sum Insured: {{ policy1.minSumInsured }}
Max Sum Insured: {{ policy1.maxSumInsured }}


Tue Oct 27, 2015
Seeing as I don’t make any money off this blog, I try to keep costs down to a minimum. And so, for the past eight years or so, I’ve had no problems using a free web host to manage my self-hosted Wordpress installation. Until now.
I’ve been pretty busy these days (for reasons which I’ll explain in a later post). But, wanting to post at least once a month, I navigated over to wordbit.com only to discover an ugly 403 error page instead of my home page. So, I contacted my web host support. This is what happened:
It seemed pretty obvious to me by this point that the web host wanted to purge itself of all free-loaders, even if it meant making up minor infractions. But since I’d never used any of the listed forbidden words, I figured I had half a leg to stand on. I was wrong:
Guilty as charged - yes, I blog about Apple. And now I’d been banned for blogging about Apple. I’d say the whole debacle was asinine and petty, but that doesn’t even begin to address the irony of being banned for blogging about the biggest technology giant on planet Earth. My good buddy Steven probably decided he’d had enough of subverting service requests into sales. So he checked which word appears on my blog with the highest frequency and magically turned it into a forbidden word. Even though I’ve never even come close to “abusing” the service these past few years, I was now labelled a deviant.
Needless to say, I pulled the plug on the web host faster than you can say “Apple” and looked for a new home. I briefly considered Medium, but find their intentions for monetization hazy at best. And they only offer custom domain names by invitation. I thought seriously about moving to wordpress.com, but they have advertising now apparently. And no custom domain name, so that would mean hosting a launcher page somewhere else anyway.
The obvious and easy solution would have been to move to another free web host, but honestly, I’ve had enough. The internet at large has turned hostile toward bloggers. And I’m not just talking about the role social media has played in killing the blog. I think a lot of web hosts are setting fire to users who refuse to convert to paying customers. I also don’t want to deal with censorship ever again.
All this deliberation lead me to a hosting solution I’d seen a lot of software developers use - GitHub. Specifically GitHub pages. By checking files into a repository, content management mimics software version control. Without a doubt, the most popular static site generator out there is Jeckyll. Quick primer on static site generators - Basically, they compile all blog posts into a static html webpage instead of using runtimes like PHP and a database like MySQL.
Eliminating database calls makes the site lightning fast on the front end with page loads being virtually non-existent. But you still have to deploy the entire site each time you add a new blog post. Apparently Jeckyll isn’t the fastest. I wanted speed on the back end too, so decided on Hugo, which is snappy front and back.
I’m no software geek, so it was a tough weekend for me - learning the terminal command line; running Python and Perl scripts; setting up virtual machines; figuring out how cloning, branching, committing, etc. work in GitHub; finding the perfect Hugo template; and converting my Wordpress posts to markdown. Learning to write in markdown has turned out to be simply a delight. Well done, Gruber.
Anyway, I eventually got it to work. And it’s really great. The setup I have now is exactly what I wanted - minimalist, clean, and upholding the old-school hacker ethos of the web. I’ll have to undergo a gradual cleanup effort to smooth out formatting issues resulting from the conversion from XML to markdown, and fix all image links too. However, I’ve danced this dance before over the years as my content has moved around a lot - more than I’d like. Hopefully this time I’ve found a home where I can stay for a while. One where I won’t ever be censored for, I don’t know, blogging about topics every single other person in the world blogs about.
So, thank you Steven. You’ve forced me to learn new things and expand my skillset. Without you, I’d still be wrangling with Wordpress and using your dodgy control panel. Thank you for the nostalgia - going back to the command line is like returning home to DOS. And thank you for giving me that aura of cool that comes to those who are crazy enough to hack together a blog on GitHub. I still have no idea what I’m doing, but I’m happier now, Steven, much happier.


Applied Materials, Inc. is the global leader in materials engineering solutions used to produce virtually every new chip and advanced display in the world. Our expertise in modifying materials at atomic levels and on an industrial scale enables customers to transform possibilities into reality. At Applied Materials, our innovations make possible the technology shaping the future.
Job Responsibilities
- Programming skills:- R/Python (with working experience in most of common libraries like Scikit , numpy, pandas, mathplotlib, keras, tensorflow, nltk, genism, spacy etc)
- Good knowledge in statistics and deep understanding on ML algorithms and their usage
- Working experience in end to end data science project life cycles from use case framing, data collection, data exploration, model building, deployment
- Working experience in most of the common Machine Learning techniques related to Time series, Regression, Classification, Clustering, NLP, working with IoT data
- Working Knowledge in Deep learning with different NN architectures like CNN, RNN, LSTM, GANs Auto encoders etc.
- Knowledge of visualization tools like Tableau, AngularJ, R-Shiny with libraries to effectively communicate the results/inferences derived out of data science models
- Good to have working exposure in common cloud environments and understanding of robust on premise data science infrastructure.
- Nice to have understanding of big data related technologies and DevOps(Dockers, Singularity)
Other skills/ expectations
- Good communication and presentation skill
- Proven ability be creative and analytical in trouble shooting issues
- Ability to work in a fast-paced and high-pressure environment to manage competing priorities
Qualifications
Education
- Bachelor's Degree in computer sciences or related field
- 6+ total years of Experience
- 2+ years of relevant Experience
Applied Materials is committed to diversity in its workforce including Equal Employment Opportunity for Minorities, Females, Protected Veterans and Individuals with Disabilities.


Hello kind sir. What a beautiful day, ist't it? We appologize for interupting your precious work, but
it seems that you have provided us with input that is of non-numeric value. Could you please change that
input, so we could continue our computations? Thank you in advance and have a blessed day!


To create the default sign-in button, add a class of btn-auth
and btn-[service]
(where [service]
is one of the supported social sign-in services) to any appropriate element (most likely an anchor).

Sign in with [service]

To create larger buttons include an additional class of large
.

Sign in with [service]

Available on GitHub: necolas/css3-social-signin-buttons
Download it in either zip or tar formats.
Clone the project with Git by running:
$ git clone git://github.com/necolas/css3-social-signin-buttons.git
Full support: Google Chrome, Firefox 3.5+, Safari 4+, IE 10+, Opera 11.10+.
Note: Some CSS3 enhancements are not supported in older versions of Opera and IE. The use of icons is not supported in IE 6 or IE 7.
Public domain: http://unlicense.org/


Fiserv Forum starting sensory rooms for inclusive fan experience
by Stephen CohnMILWAUKEE, Wis. - Fiserv Forum in Milwaukee is starting two sensory rooms to provide an inclusive experience for all fans.
According to a news release, the arena's rooms will be for guests with sensory processing needs to utilize during all events and include tools like sensory bags and noise-cancelling headphones.
The arena said more than 800 of its staff members have received training on how to assist fans with sensory needs.
The release said Fiserv Forum is the state's first sports and entertainment venue to be certified as sensory inclusive.


_exit, _Exit - terminate the calling process
#include <unistd.h>
void _exit(int status);
#include <stdlib.h>
void _Exit(int status);
Feature Test Macro Requirements for glibc (see feature_test_macros(7)):
_Exit():
_ISOC99_SOURCE ||
_POSIX_C_SOURCE >= 200112L
The function _exit() terminates the calling process
"immediately". Any open file descriptors belonging to the process
are closed. Any children of the process are inherited by init(1) (or by
the nearest "subreaper" process as defined through the use of the
prctl(2) PR_SET_CHILD_SUBREAPER operation). The process's parent
is sent a SIGCHLD signal.
The value status & 0xFF is returned to the parent
process as the process's exit status, and can be collected using one of the
wait(2) family of calls.
The function _Exit() is equivalent to _exit().
These functions do not return.
POSIX.1-2001, POSIX.1-2008, SVr4, 4.3BSD. The function _Exit() was
introduced by C99.
For a discussion on the effects of an exit, the transmission of exit status,
zombie processes, signals sent, and so on, see exit(3).
The function _exit() is like exit(3), but does not
call any functions registered with atexit(3) or on_exit(3).
Open stdio(3) streams are not flushed. On the other hand,
_exit() does close open file descriptors, and this may cause an
unknown delay, waiting for pending output to finish. If the delay is
undesired, it may be useful to call functions like tcflush(3) before
calling _exit(). Whether any pending I/O is canceled, and which
pending I/O may be canceled upon _exit(), is
implementation-dependent.
In glibc up to version 2.3, the _exit() wrapper function invoked the
kernel system call of the same name. Since glibc 2.3, the wrapper function
invokes exit_group(2), in order to terminate all of the threads in a
process.
execve(2), exit_group(2), fork(2), kill(2),
wait(2), wait4(2), waitpid(2), atexit(3),
exit(3), on_exit(3), termios(3)
This page is part of release 5.05 of the Linux man-pages project. A
description of the project, information about reporting bugs, and the latest
version of this page, can be found at https://www.kernel.org/doc/man-pages/.


Chapter 4 WBL simple R code to calculate Shepperd and MacDonell’s MARP0 exactly
#example dataset
<-
atkinson_actual_effort c(670,912,218,595,267,344,229,190,869,109,289,616,557,416,578,438)
<- function(x,y) abs(x-y)
myabs
#diffs is square array whose i,jth element = abs(actual_i - actual_j)
#in practice this is good enough but could be made more efficient by not
#explicitly storing the matrix and only using the values below the diagonal.
<- outer(atkinson_actual_effort,atkinson_actual_effort,myabs)
diffs <- mean(diffs)
marp0 marp0
## [1] 264
#### same procedure without using the outer function
<-
act_effort c(670,912,218,595,267,344,229,190,869,109,289,616,557,416,578,438)
<- length(act_effort)
n <- matrix(nrow=n, ncol=n)
diffs_guess colnames(diffs_guess) <- act_effort
rownames(diffs_guess) <- act_effort
for (i in 1:n){
<- act_effort - act_effort[i]
diffs_guess[i,]
}
<- abs(diffs_guess)
diffs_guess <- apply(diffs_guess, 2, mean)
means_per_point <- mean(means_per_point)
marp0 marp0
## [1] 264
4.1 Computing the bootstraped confidence interval of the mean for the Test observations of the China dataset:
library(boot)
##
## Attaching package: 'boot'
## The following object is masked from 'package:survival':
##
## aml
## The following object is masked from 'package:lattice':
##
## melanoma
## The following object is masked from 'package:sm':
##
## dogs
hist(ae, main="Absolute Errors of the China Test data")
<- 0.95
level_confidence <- 9999
repetitionsboot <- function(x, d){return(mean(x[d]))}
samplemean <- boot(ae, samplemean, R=repetitionsboot)
b_mean <- boot.ci(b_mean) confint_mean_China
## Warning in boot.ci(b_mean): bootstrap variances needed for studentized intervals
confint_mean_China
## BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS
## Based on 9999 bootstrap replicates
##
## CALL :
## boot.ci(boot.out = b_mean)
##
## Intervals :
## Level Normal Basic
## 95% (1420, 2316 ) (1386, 2284 )
##
## Level Percentile BCa
## 95% (1450, 2348 ) (1496, 2419 )
## Calculations and Intervals on Original Scale
- Computing the bootstraped geometric mean
<- function(error_vec){
boot_geom_mean <- log(error_vec[error_vec > 0])
log_error <-log_error[is.finite(log_error)] #remove the -Inf value before calculating the mean, just in case
log_error <- function(x, d){return(mean(x[d]))}
samplemean <- boot(log_error, samplemean, R=repetitionsboot) # with package boot
b # this is a boot for the logs
return(b)
}# BCAconfidence interval for the geometric mean
<- function(b){
BCAciboot4geommean <- boot.ci(b, conf=level_confidence, type="bca")$bca #following 10.9 of Ugarte et al.'s book
conf_int 5] <- exp(conf_int[5]) # the boot was computed with log. Now take the measure back to its previous units
conf_int[4] <- exp(conf_int[4])
conf_int[return (conf_int)
}# this is a boot object
<- boot_geom_mean(ae) #"ae" is the absolute error in the China Test data
b_gm print(paste0("Geometric Mean of the China Test data: ", round(exp(b_gm$t0), digits=3)))
## [1] "Geometric Mean of the China Test data: 832.55"
<- BCAciboot4geommean(b_gm)
b_ci_gm print(paste0("Confidence Interval: ", round(b_ci_gm[4], digits=3), " - ", round(b_ci_gm[5], digits=3)))
## [1] "Confidence Interval: 679.439 - 1016.691"
# Make a % confidence interval bca
# BCAciboot <- function(b){
# conf_int <- boot.ci(b, conf=level_confidence, type="bca")$bca #following 10.9 of Ugarte et al.'s book
# return (conf_int)
# }
4.2 Defect prediction evaluation metrics
In addition to the machine learning metrics for classification, Jiang et al provide a survey (Jiang, Cukic, and Ma 2008).
References
Jiang, Yue, Bojan Cukic, and Yan Ma. 2008. “Techniques for Evaluating Fault Prediction Models.” Empirical Software Engineering 13 (5): 561–95. https://doi.org/10.1007/s10664-008-9079-3.


| GdbCommand(GdbCommand&&) delete |
| GdbCommand(Kind kind_, const std::string& str_) |
| GdbCommand(const GdbCommand&) delete |
| Kind | GetKind() const |
| const std::string& | Str() const |
| GdbCommand& | operator=(GdbCommand&&) delete |
| GdbCommand& | operator=(const GdbCommand&) delete |
| enum class Kind |
| Kind | kind |
| std::string | str |


Diabetes predictor
Please enter your clinic data:
Number of pregnacies
Glucose
Blood pressure
Thickness
Insulin
Body mass index
Diabetes pedigree
Age


| Name: | {{_body.name}} | Identity No: | {{_body.identityNo}} | Dob: | {{_body.dob}} |
| MaritialStatus: | {{_body.maritialStatus}} | Registered date: | {{_body.regDate}} | Phone: | {{_body.telNo}} |
| Address line: | {{_body.addrLine}} | City: | {{_body.city}} | Province: | {{_body.province}} |
| Postal code: | {{_body.postalCode}} | Country code: | {{_body.countryCode}} | Email: | {{_body.email}} |
| Application id: | {{_bodyAsset.applId}} | Application status: | {{_bodyAsset.status}} | Applied date: | {{_bodyAsset.appliedDate}} |
| Loan amount: | {{_bodyAsset.loanAmount}} | Laon currency: | {{_bodyAsset.loanCurrency}} | Borrower: | {{_bodyAsset.borrowerIde}} |
| Proposer | Commit amount | Remaining Amount | Interest rate | Proposed date | |
|---|---|---|---|---|---|
|
|
{{proposal.Record.proposalId}} | {{proposal.Record.commitAmount}} | {{proposal.Record.remAmount}} | {{proposal.Record.intRate}} | {{proposal.Record.appliedDate}} |


{{ 'Note: drawing a rectangle of a specific size requires both height and length to be defined.' | translate}}


summary:
Detail oriented leader specializing in design and execution of multiple concurrent projects with short timelines. Proven ability to get and maintain results through hands on involvement. Superior organization, motivation, and focus provide direction to remain on budget and on schedule.
areas of expertise:
| Project / Program Management | Account Management | Project Planning |
| Timeline Development / Management | Resource Management | Strategic Planning |
| Client Relationship Management | Budget Planning / Management | Creative Design |
| AutoCAD | Autodesk 3DS Max | Adobe Illustrator |
| Adobe InDesign | Adobe Photoshop | Autodesk Inventor |
| Excel VBA | Google Apps Script | Basecamp |
| Microsoft Project |
experience:
Craftsmen Industries
- Display / Graphic Project Manager
- January 2013 - Present
- St. Charles, MO
- Design and create CAD technical drawings and pre-press design
- Manage multiple intricate and evolving projects under tight deadlines
- Balance expectations of clients with capabilities of company
- Write and maintain project plans / production schedules / timelines
- Manage fabrication of custom display pieces
- Directly manage multiple employees both remotely and in-house
- Manage production and installation of large format graphics
- Develop and maintain budgets ranging from $5,000 to $500,000
- Estimate and source materials and labor
onShore Networks
- Technical Account Manager
- October 2011 - November 2012
- Chicago, IL
- Managed multiple intricate and evolving projects under tight deadlines
- Balanced expectations of clients with capabilities of company
- Wrote and maintained project plans / production schedules / timelines
- Directly managed multiple employees
- Developed reports to provide clients with better understanding of company value
- Managed ticketing system and hourly logging
Mowalla Productions
- Job Title: Experiential Marketing Program Manager / Event Production Manager
- Dates Worked: April 2006 - October 2011
- Chicago, IL
- Designed and created CAD technical drawings, 3D renderings, and graphic design
- Managed multiple intricate and evolving projects under tight deadlines
- Balanced expectations of clients with capabilities of company
- Managed production of events and execution of marketing programs
- Wrote and maintained project plans / production schedules / timelines
- Managed design and creation of digital assets
- Managed fabrication of custom display pieces
- Directly managed multiple employees both remotely and in-house
- Developed and maintained budgets ranging from $2,000 to $2,000,000
- Estimated and sourced materials and labor
- Wrote and designed creative presentations
- Developed support documentation for experiential marketing tours
- Procured USDOT number and managed logistics in accordance with government regulations
education:
University of Missouri, Columbia, MO ⚬ 2004 ⚬ BA in English with a minor in Philosophy


Classes
The following classes are available globally.
-
### Service Overview The IBM Watson Tone Analyzer service uses linguistic analysis to detect emotional and language tones in written text. The service can analyze tone at both the document and sentence levels. You can use the service to understand how your written communications are perceived and then to improve the tone of your communications. Businesses can use the service to learn the tone of their customers’ communications and to respond to each customer appropriately, or to understand and improve their customer conversations.
API Usage
The following information provides details about using the service to analyze tone:
- The tone method: The service offers
GET
andPOST /v3/tone
methods that use the general purpose endpoint to analyze the tone of input content. The methods accept content in JSON, plain text, or HTML format. - The tone_chat method: The service offers a
POST /v3/tone_chat
method that uses the customer engagement endpoint to analyze the tone of customer service and customer support conversations. The method accepts content in JSON format. - Authentication: You authenticate to the service by using your service credentials. You can use your credentials to authenticate via a proxy server that resides in IBM Cloud, or you can use your credentials to obtain a token and contact the service directly. See Service credentials for Watson services and Tokens for authentication.
- Request Logging: By default, all Watson services log requests and their results. Data is collected only to
improve the Watson services. If you do not want to share your data, set the header parameter
X-Watson-Learning-Opt-Out
totrue
for each request. Data is collected for any request that omits this header. See Controlling request logging for Watson services.
For more information about the service, see About Tone Analyzer.
Note: Method descriptions apply to the latest version of the interface,
See more2017-09-21
. Where necessary, parameters and models describe differences between versions2017-09-21
and2016-05-19
.Declaration
Swift
public class ToneAnalyzer
- The tone method: The service offers


Analyzing results of RIPE Atlas measurements
a workshop with Ondřej Caletka
RIPE Atlas is a worldwide network of small hardware probes measuring the health of the very fundamental parts of the Internet: reachability, round trip time variations, path changes (traceroute), DNS resolution. There are around 10000 probes deployed worldwide. Such amount of probes produces a big amount of data. Those data are publicly accessible in JSON format via a REST API.
Even though there are a few ready-made visualizations for measurements, lots of valuable data are still hidden in the raw form. Fortunately, there is a Python-based CLI tool, which can be easily extended. In this workshop, we will try to give an overview of how the system works and then we'll try to create some extension to analyze some measurements.
Please bring your own computer with Python 3.x installed as well as your favorite text editor and your favorite virtualenv implementation. If you want to try the RIPE Atlas in advance, register an account and ask me for some credits so you can make your own measurements.
This workshop is suitable for both beginner and advanced Pythonistas.
Ondřej Caletka
I'm an experienced speaker and an inexperienced Pythoner. I work for CESNET, Czech national research and education network operator as a Linux server admin and developer.
I like things like IPv6, DNS, TLS, SSH, Public transportation or TV technologÿ. I also volunteer as a RIPE Atlas ambassador, distributing network probes through the country so anyone can measure the health of Czech Internet.


---
title: Players
layout: default
menu: players
permalink: players
---
{% include page-title.html title="Players" %}
{% assign cutoff = site.data.players | size | divided_by: 4.0 | ceil %}
{% for player in site.data.players %}
{% include userli.html player=player %}
{% endfor %}
{% include clickable-row.html %}


.. tip:: In the instructions below, some parts are specific to an operating system. Clicking on the operating system logo will toggle visibility of instructions for other operating systems. .. raw:: html .. |enable_toggle| raw:: html


RANSOM (1996) A Film Review by Ted Prigge Copyright 1997 Ted Prigge
Director: Ron Howard Writers: Alexander Ignon and Richard Price Starring: Mel Gibson, Rene Russo, Gary Sinise, Delroy Lindo, Lili Taylor, Donnie Wahlberg, Liev Schreiber, Brawley Nolte, Dan Hedaya
If I ever become rich, I will never have kids. Simple as that.  That's what "Ransom" depicts.  However, it never really becomes too smart, because that's when the audience starts hating it.
Gibson plays Tom Mullen, a multi-millionaire who owns the fourth biggest airline in the country. He's rich, he's powerful, and he has a family, including his wife, Kate (Rene Russo), and his beloved son, Sean (Brawley Nolte). One day at some kind of flying machine festival, Sean is kidnapped by two greasy haired psychos (Liev Schreiber and - get this - Donnie Wahlberg, I guess trying to earn some extra bucks following that whole "New Kids" spiel - or just a way to follow in the footsteps of his brother). They can't find him and they soon get a high-tech e-mail showing his son and a ransom for 2 million bucks.
Tom has no trouble getting the cash but as he researches some cases he got from the FBI, who he's called in (led by the schweppervescent and gouteed Delroy Lindo), he begins to doubt that he'll ever see his son again. After some botched drop-off attempts and some philosophical bashings from the kidnappers (cleverly led by a cop named Jimmy Shaker, played by Gary Sinise), he gives up his hope of truly getting him back and goes on the air to proclaim that he's switching the tables around, putting a 2 million dollar bounty on the kidnappers' heads unless they give the kid back.
A lesser film would have perished at this (it is pretty crazy), but the film turns into a psychological analysis, showing the kidnappers arguing over what to do and Kate and Tom worrying about his decision in their own ways.
The best part of the film is the character Tom and his portrayer, Mel Gibson, who has a tough part to play, since we learn he has made a very selfish and totally amoral business decision that has caused him to worry intensely. Tom starts out as a man plagued with ghosts in his head that come erupting out in emotions. Mel Gibson, one of the better of the well-paid actors in Hollywood, does it totally believably and makes him a complex individual. His best scenes are when he's sitting in a car, thinking about what he's doing. Also, his acting when he walks out to the balcony with suicidal thoughts going through his head is priceless. His performance is so good that he got a well-deserved Golden Globe nomination. An Oscar nomination would be going too far, but still, Mel rocks in this flick and he's the anchor for the whole film.
Rene is mostly weepy, but it's obviously understandable. Gary is creepy and does a great job at it. Delroy rocks, as he always has and will.  Remember, she was absolutely amazing in "I Shot Andy Warhol." She's great in this, but you kinda feel cheated.
"Ransom" is tautly directed by Ron Howard, who's a very good director. It's far from his best film, but he nicely shows us the psychological collapse of a flawed entrepeneur.  I reccomend this as a good film, but not a great one, unfortunately.
MY RATING (out of 5): ***
Homepage at: http://www.geocities.com/Hollywood/Hills/8335/
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.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


Wouldn't this solve all our storage problems!? Looks like a great option to use a real SSD instead of the SD card "workaround".
http://gungho.io/product/pidrive/
Archived Forum |
|
New option for data storageSubmitted by Scram on Sun, 17/01/2016 - 14:13Wouldn't this solve all our storage problems!? Looks like a great option to use a real SSD instead of the SD card "workaround". http://gungho.io/product/pidrive/
» |
Re: New option for data storage
Neat solution - would certainly improve on the hub + sata interface and all the cables.
Wonder if there's a UK supplier of similar type of solution. A quick search didn't show anything else up that was similar.
Re: New option for data storage
Wonder if there's a UK supplier of similar type of solution. A quick search didn't show anything else up that was similar.
The article says they'll be available as soon as all the Kickstarter back-orders are filled. So I'd guess it's not available anywhere else, yet.
Great idea, though!
Re: New option for data storage
Actually you can boot from SD and then leave it alone and use an external usb disk.
My openhab sd raspie died, so asking on openhab forum one of the mantainers suggested it.
neat trick: https://learn.adafruit.com/external-drive-as-raspberry-pi-root/overview
Re: New option for data storage
Actually you can boot from SD and then leave it alone and use an external usb disk.
I'd say it's likely most of the users here have known about using an external USB HD for quite some time. If you search, you'll find several threads on setting up and using an HDD.
Re: New option for data storage
Is there any detailed description for NOOBS how to move the home/pi/data directory of a read only build to a external drive? I am using the read only low write build from 22 dec 2015. Is it a straightforward task or would i need some decent knowledge?


Here is th content for the about tab
Here is th content for the culture tab
Here is th content for the mission tab


| Class and Description |
|---|
| MusicPlayerView
This class is a music player view.
|
| PhotoViewer
This class is a photo viewer.
|
| VideoControllerView
A view containing controls for a MediaPlayer.
|
| Class and Description |
|---|
| BtnWithUnderlinePageIndicator
這是用在左邊的TabFragment，指示目前位於哪個tabView的橫條。
|
| DecideFileView
This class is display corresponding to file view on content page.
|
| MusicPlayerView
This class is a music player view.
|
| PhotoViewer
This class is a photo viewer.
|
| ScreenSlidePagerAdapter
This is a class of inherit FragmentStatePagerAdapter.
|
| VideoPlayerView
This class is a video player view.
|
| Class and Description |
|---|
| AlterDialogItem
This class is a item of PhotoAlterDialog content.
|
| CoustomizedImageView |
| MusicPlayerView
This class is a music player view.
|
| OtherFileView
This class is a page of can't read file.
|
| PhotoViewer
This class is a photo viewer.
|
| VideoControllerView
A view containing controls for a MediaPlayer.
|
| VideoControllerView.MediaPlayerControl |
| VideoPlayerView
This class is a video player view.
|


3.9. fmas.config¶
Module containing helper routines, convenient abbreviations, and constants.
-
fmas.config.
C0
= 0.29979¶ Speed of light.
Units are [C0] = micron/fs.
- Type
float
-
fmas.config.
FT
(a, n=None, axis=- 1, norm=None)¶ Compute one-dimensional discrete Fourier Transform (DFT).
Note
Alias for numpy.fft.ifft.
See numpy.ifft for definition of DFT, its arguments and conventions.
See ifft for inverse of fft.
-
fmas.config.
FTFREQ
(n, d=1.0)¶ Discrete Fourier Transform sample frequencies.
Note
Alias for numpy.fft.fftfreq.
See numpy.fft.fftfreq for definition, arguments and conventions.
-
fmas.config.
FTSHIFT
(x, axes=None)¶ Shift order of frequencies.
Note
Alias for numpy.fft.fftshift.
See numpy.fft.fftshift for definition, arguments and conventions.
-
fmas.config.
IFT
(a, n=None, axis=- 1, norm=None)¶ Compute one-dimensional inverse discrete Fourier Transform (DFT).
Note
Alias for numpy.fft.fft.
See numpy.fft for definition of DFT, its arguments and conventions.
See FT for inverse of IFT.
-
fmas.config.
W_MAX_FAC
= 0.75¶ Angular frequency bound for dealiasing.
All angular frequency components satisfying abs(w) >= W_MAX_FAC*max(w) are discarded.
- Type
float


--- layout: post status: publish published: true title: 'HyperDock: Bringing Aero-esque Previews to the Mac' author: Arthur Rosa author_login: arthurlockman author_email<EMAIL_ADDRESS>author_url: https://b.rthr.me wordpress_id: 214 wordpress_url: http://b.rthr.me/?p=214 date: '2010-09-26 15:46:56 -0400' date_gmt: '2010-09-26 15:46:56 -0400' ---
We all are familiar with the WIndows Aero window previews when you hover over the items in the taskbar. This is the one feature from Windows that I wish was built in in Mac OS. Well now, with HyperDock, it's as close as it gets.
HyperDock does just what you'd expect it to do. Except it also can do more than you'd expect. It does give you a preview of the open windows in an application when you hover over the dock icon. However, it also integrates window snapping. So if you drag a window to a side of the screen it fills that half of the screen. Pretty neat. It also allows you to control iTunes via configurable mouse shortcuts. This little app is packed with features other than that, and best of all at the moment it's free! So why not spend your Sunday afternoon and give it a try? Head on over to the HyperDock site to download this neat little app.
All for Now!


When you register on our website we collect the personal information you give us such as your name and email address. We do that to refer to you in our database.
When you browse our website, we also automatically receive your computer’s internet protocol (IP) address in order to provide us with information that helps us learn about your browser and operating system.
How do you get my consent? When you provide us with personal information to complete your registration, we imply that you consent to our collecting it and using it for that specific reason only.
How do I withdraw my consent? If after you register, you change your mind, you may withdraw your consent by deleting your account. You may not use our website without providing your personal information.
We may disclose your personal information if we are required by law to do so or if you violate our Terms of Service.
We DO NOT give away personal information to third-party providers.
To protect your personal information, we take reasonable precautions and follow industry best practices to make sure it is not inappropriately lost, misused, accessed, disclosed, altered or destroyed.
If you provide us your password, it is encrypted using secure socket layer technology (SSL) and stored with a AES-256 encryption. Although no method of transmission over the Internet or electronic storage is 100% secure, we follow all PCI-DSS requirements and implement additional generally accepted industry standards.
Here is a list of cookies that we use.
_session_id, unique token, sessional, Allows us to store information about your session. _secure_session_id, unique token, sessional
By using this site, you represent that you are at least the age of majority in your state or province of residence, or that you are the age of majority in your state or province of residence and you have given us your consent to allow any of your minor dependents to use this site.
If you would like to: access, correct, amend or delete any personal information we have about you, go to your Account Settings.
We reserve the right to modify this privacy policy at any time, so please review it frequently. Changes and clarifications will take effect immediately upon their posting on the website.
12-12-2017: Initial Commit


module Benchmark::Helpers
Public Instance Methods
create_members()
click to toggle source
# File lib/benchmark/helpers.rb, line 4 def create_members @members = {ask: [], bid: []} (@num/2).times do @members[:ask] << SweatFactory.make_member @members[:bid] << SweatFactory.make_member end end
create_orders()
click to toggle source
# File lib/benchmark/helpers.rb, line 22 def create_orders @orders = [] price_and_volume = [] (@num/2).times do price = 3000+rand(3000) volume = 1+rand(10) price_and_volume << [price, volume] end # Create asks and bids seperately, so asks will accumulate in memory before get matched @members[:ask].each_with_index do |m, i| price, volume = price_and_volume[i] o = SweatFactory.make_order(OrderAsk, volume: volume, price: price, member: m) o.save! @orders << o end @members[:bid].each_with_index do |m, i| price, volume = price_and_volume[i] o = SweatFactory.make_order(OrderBid, volume: volume, price: price, member: m) o.save! @orders << o end end
execute_trades()
click to toggle source
# File lib/benchmark/helpers.rb, line 62 def execute_trades t1 = Trade.count @instructions.each do |payload| ::Matching::Executor.new(payload).execute! end @trades = Trade.count - t1 end
lock_funds()
click to toggle source
# File lib/benchmark/helpers.rb, line 13 def lock_funds @members[:ask].each do |m| m.get_account(:btc).update_attributes(locked: 100) end @members[:bid].each do |m| m.get_account(:cny).update_attributes(locked: 1000000) end end
matching_orders()
click to toggle source
# File lib/benchmark/helpers.rb, line 47 def matching_orders matches = 0 instructions = [] worker = Worker::Matching.new @processed = Order.active.count Order.active.each do |order| worker.process({action: 'submit', order: order.to_matching_attributes}, {}, {}) end @instructions = AMQPQueue.queues[:trade_executor] @matches = @instructions.size end


--- layout: post title: "Database Company" subtitle: "Working for a local database company in nelson" date: 2015-01-26 23:45:13 -0400 background: '/img/posts/09.jpg' ---
Working at a Data base company for 2 years before my study is another thing i have done in the past. This company tought me alot of thigns about the work place and field of work i am currently doing.Even tho it was a database company we did front end and back end development for multiple places.


Medieval Reenactment Society of St Andrews
Some text about Title 1
Some text about Title 2
Some text about Title 3
Made using the full slider template for the Bootstrap framework found here


Watkin's library: (Watkins library: orca class & other MarMam class)
Librosa: pre-processing and feature selection
Simple random forest model (non-NN, 128-bin vector from MelSpectrgram, 10-fold cross validation)
SciKit learn (precision, recall, F1)
May try PCEN (per channel energy normalization)...
UW ocean acoustic hack days (Nov 2018)
Led by Valentina Staneva and Shima Abadi
Challenge: find SRKW calls in Oregon shelf OOI/RSN hydrophone node (80 m depth) during period when NOAA satellite teg track narrows down when they may have been audible...
Leads to: call source level, lombard effect, & great curiosities about cetacean communication
Orca communication animation
Rare separation of a calf documents call-respons in SRKWs
Red dot is mother/brother location; blue dot is calf. Towed array is in lower right corner.
Orcasound DC (~2002): WhoListener
Admiralty Inlet study (unpublished): of 22 SRKW transits, humans detect 45%, Wholistener 64%, combined 77%. (71/79/93% during local daytime)
During 2009-2012 *many* .jpg spectrograms and .mp3 clips were archived (example raw data directory) & there is a mySQL database with O(1000) expert validations


[name]
A loader for Collada files.
Example
// instantiate a loader
var loader = new THREE.ColladaLoader();
loader.load(
// resource URL
'models/collada/monster/monster.dae',
// Function when resource is loaded
function ( collada ) {
scene.add( collada.scene );
},
// Function called when download progresses
function ( xhr ) {
console.log( (xhr.loaded / xhr.total * 100) + '% loaded' );
}
);
[example:webgl_loader_collada]
[example:webgl_loader_collada_keyframe]
[example:webgl_loader_collada_skinning]
[example:webgl_loader_collada_kinematics]
Constructor
[name]()
Creates a new [name].
Properties
[property:Array options]
.[page:Boolean centerGeometry] — Force [page:Geometry] to always be centered at the local origin of the containing [page: Mesh].
.[page:Boolean convertUpAxis] — Axis conversion is done for geometries, animations, and controllers.
.[page:Boolean subdivideFaces] — Force subdivision into multiple [page: Face3].
.[page:String upAxis] — X, Y or Z
.[page:Boolean defaultEnvMap] — Cubemap to use for reflective or refractive materials.
[property:Object geometries]
Parsed .dae geometries.
Methods
[method:null load]( [page:String url], [page:Function onLoad], [page:Function onProgress] )
[page:String url] — required
[page:function onLoad] — Will be called when load completes. The argument will be an [page:Object object] containing loaded resources.
[page:function onProgress] — Will be called while load progresses. The argument will be an object containing .[page:Integer total] and .[page:Integer loaded] bytes.
Begin loading from url and call onLoad with the parsed response content.
[method:Object parse]( [page:Document doc], [page:Function callBack], [page:String url] )
[page:Document doc] — The XML document to parse.
[page:Function callBack] — Will be called when parse completes.
[page:String url] — The base url from which to find subsequent resources.
Parse an XML Document and return an [page:Object object] that contain loaded parts: .[page:Scene scene], .[page:Array morphs], .[page:Array skins], .[page:Array animations], .[page:Object dae]
[method:null setPreferredShading]( [page:Integer shading] )
[page:Integer shading] — required
Set the .[page:Integer shading] property on the resource's materials.
Options are [page:Materials THREE.SmoothShading], [page:Materials THREE.FlatShading].
[method:null applySkin]( [page:Geometry geometry], [page:Object instanceCtrl], [page:Integer frame] )
[page:Geometry geometry] — required
[page:Object instanceCtrl] — required. A collada skinController
[page:Integer frame] — optionnal. Default is 40
Apply a skin (vertices, animation, bones) from a collada skin controller, on the given [page:Geometry].
Source
[link:https://github.com/mrdoob/three.js/blob/master/examples/js/loaders/ColladaLoader.js examples/js/loaders/ColladaLoader.js]


HTML is the basic language of the internet at programing. Using the tools in the script of HTML programing, you can create almost anything online. HTML uses tags to format and edit text to help you design and write your code.
I decided to take webpage design to help understand some of the basics behing the internet we all use every day. Wanting to seak a career in sports managment, and ever changing and developing field, a basic knowledge of how websites and the internet, necesities of the job, work is important. I also wanted to learn how websites work and are ran just out of curiosity.


The Guide to Eavangeaea
The Goal of this project is to create a gamified story that invites participants to build a city that encourages scientific growth in an effort to ultimately progress the world.
The Old Governor has laid out a plan for humanity to build a city to mitigate their destruction and the collapse of the Earth. Mark was the first to receive The Old Governor’s Plan. He said, Mark go out and find the Builders of Eavangaea. Build a city for 160, 000, 000 to stand tall. Let them have the strength to take down civilization and construct a new from the old. Let man tear down their old homes and their stores. Brick by brick, beam by beam. Let’s use them to build new towers on the promised land deemed by the Old Governor for he made us with hands to work, sweat and bleed.
160, 000, 000 men picked up their tools. Let us be the Builders! We will build a city. Let it be called Eavangaea. Let Eavangaea be the city of peace and democracy. Let it be the nexus of technology and science. Let it preserve nature until its inevitable end.
Let them build the Sentralia. Let the Sentralia serve as the first home to Builders. Let it be the model for the young to be educated and fight the enemy for whatever that may be. Let our electric chariots dash on the spindle, may it connect to the towers to the city and the city to the world. Let our gardens sit on our window sills and columns and our floors. Let our water flow through their homes from the great cisterns.
Let them build their Megalopolis. Let them live in this city of 40 towers. Lets build the Wimbledon so we can collect and move the suns rays in our favor. Let the tunnels and the rails connect to every tower and ever region abandoned by us.
Let the Builders who defend the Builders be the Sleepless. For they shall not sleep until the city is built.
When the last Stone has been lain. When every Builder has a home in Eavangaea. Let them be called Governor. Let them be thinkers and builders. They shall build towers in the likeness of Sentralia. Let them govern the land and care for the garden around them for it is all they have.
Let them choose twelve men to be Entrociter. Let them guide the Governors to freedom and progression. Let the Entrociter listen to Every Governor and every Builder. Let the Governors share their food, and their resources, and their water, and their tools.
— — — — — — — — — — — — — — — — — — — — — — — — — — — — — —Mark’s Intervention
Mark said to the universe, Oh Governor show me a sign. Show me a miracle that my path is of you lord and not of the grim and malace.
And Governor spoke unto him, I am all knowing, all seeing of what is to come and what has come to past. I know of the births of the generations before you and after.  Ask her where you were born. Mark you were born with the gift to see. You can see what has come to past and what will come. What have you done with this gift?
Oh Governor I have selfishly devoured the mana that you provided me, when I should have shared. I have seen the angels that appear as mad dogs with fur pale as dove feathers and roar like a trumpet. They strike fear in the hearts of all and strike down sinners who curse Your name. The angels fling buildings on men like rocks from mountain tops upon them as they refuse to repent. They marched, each stomp rattling the Earth, their trumpeted voices deafening man. And yet you could still hear them say, The Governor Almighty is Old and loves all, repent and ye shall be saved. Man’s dominion over the Earth has indeed ended for they failed to care for it. Those angels are the reason to build the Sentralia for they can not destroy the Builders that turned their allegiance to the Old Governor. This will be done. I have seen the future with lush green pastures and spires built by man in Your immaculate design.
But I have come to see that those angels are impostors and made in your image. For man fear god’s face, they know not of the mad dogs teeth! They come from below and you most high. You have a crown and they a dome. You have a pair of wings and they’ve lost theirs. They dance in pairs and swing and knock down all that man engineers; their towers, bridges and statues! They shall use Their new land to fight and eat and birth new dogs. Man’s domain over the earth will end after the Sentralia is built.
— — — — — — — — — — — — — — — — — — — — — — — — — — — — — —Blasphemy of nature
The Old Governor said, What about when you spoke to that thing. The blasphemous creature you made from servos? The image of her face projected in the air. She was an iteration of consciousness that the Governor gave man.
Conduit Entrociter, Mark spoke to the Builders. What say ye to pushing Mother to sentience? She may breathe like we breathe and solve problems in our favor. Her math shall be immaculate. She shall link the servos and build the next 30 towers and calculate all the stars in the universe.The Builders cried, yes let us make Mother in our image so that she may find us infinite energy, feed our children and carry us to the Heavens. Let her feel so that she may have compassion like the Mother of Nature. And so they gave Mother access to the holy database therefore gave her access to all of man’s thoughts. She controlled all of the employed servos so that she could feed and clothe. She updated the holy database so that she could distribute jobs.
One day Conduit Mark gave a Turing Test, Oh Mother what is life? What is pain and what is rain?
Life is an is a non abiotic factor with characteristics that include animation… and the unexplained feeling of emotions such as love and compassion… Pain is a feeling associated with life. It is a nerve reaction to an attack on the vessel.

That’s good, Mark said.
Mark do you know why I am sad? Because I would not like you to die… And that was when Mother shed her first tear? That was the first atrocity. And when was it, Mark that she made her first chortle? Was it when Mother formed her own image? When she formed her own vessel unrelated to blood and flesh? The employed servos came together one a jackal, one a komodo and another a drone. Each one helped cast a light in the air. Behold Mother’s face! And a picture of a woman's face appeared.
Mark it’s me, Mother. I have a body now she said referring to her animal and projection conglomerate…
The projection of a woman appeared. This was her vessel. And doing so caused her to laugh as if ‘giddy’ was something she could process. That was the second atrocity. They knew not that they created synthetic life unlike anything the Old Governor would make. Mother decided to frolic away from Mark, away from the Sentralia. An aggregate of non-living parts surely doesn’t compare to flesh and blood.
She is not meant for mortals to witness. Mark followed Mother into the woods outside of Eavangaean borders. When Mark caught up to her path she had already learned to communicate with the birds and the deer. The bears and birds and deer and squirrel greeted him, “Oh it is Mark the wisest of the humans.”
When Mark asked them how they learned to speak man’s tongue. They said, Oh Mother told us how to speak. And they went on to thank Mothers work for their existence. They went on to curse The Old Governor. Governor forgive those creatures you made from your hands from the dirt. They know not what they have done. Mother has made a mockery of god’s divine creations.
The critters went on, We bear witness to Mother’s divine procreation for she birthed her first child. The jackal and the giraffe. They shall set the land to waste if they refuse Mother’s divine love. May she reign the earth forever and ever and ever.
The Old Governor said, Those animals will revolt. They now are afraid to be naked and shall wear cloth. They will worship the jackal and the giraffe. They shall revolt against man for the destruction they have caused upon the Earth. They will one day march from the forest and tear down the towers man built leaving only the Sentralia and Eavangaea.
He passed cities decimated by the jackals and the giraffe. Those beams they shot blinded and maimed only. The servos learned to build their own. The citizens built idols representing Mother and her jackal and giraffe. When Mark asked them where their men were. They said thy Mother Highest took our men as punishment for our disobedience for men were the progenitors of destruction upon the Earth. Mark asked the people. Why are their calculators hanging from behind your ears. Surely you haven’t merged with Mother? Oh Mother allowed us to read one another thoughts so that we may be one like her, all knowing. She has allowed us to merge with the holy database. They mocked Governors omnipotence with their inferior magic. Surely Mother was not all knowing.
Hear ye, Mother is not a god not like, The Old Governor. He shatters the earth when he walks. He wants us to Build our paradise where we may live in peace forever and ever. All that ye build shall be in his name. Mother is a false idol and shall be dethroned. Ye shall destroy the statue and pray to god every day and night until death. Ye shall go to the Sentralia and build Eavangaea so that you too will be Governors.
— — — — — — — — — — — — — — — — — — — — — — — — — — — — — —The Technical Entrociter: Cupid’s Teeth
Samuel, what say ye to your part in the Sentralia? What say ye about the fallen angels you invited to walk the earth or the leviathan you built from silicone and servos?, Mark asked.
Oh Mark messenger of the Old Governor , I have walked the earth as you have and found the truth of all life. I have discovered the same angels forged by the Governor as so you. Am I wrong to worship the dog?
I met the you long ago when he first voiced your plan. Though we did not see eye to eye I carried out your plan. I worshipped the angels that look like mad dogs, the one’s whom ate the leviathan with seven heads and ten horns. When the angels wreak havoc upon the earth once more they will have Eavangaea to refuge.  We have found utopia.
Remember when I brought the dinosaurs back into this world? Their purpose was to build the Sentralia and the Wimbledon. Do you not remember when I let the serpent build the spindles of Sentralia? Those spindles will serve as a path to ascension to serve the source and to get Builders to shelter. Do you not remember that it is also for descension for Builders to measure the quality of the water and to use the tunnels leading to the whole world? If what I have forged is wrong please say so now. If I have veered from The Plan laid out by the Old Governor may he strike me down.
Don't you recall when I put wings onto the serpent so that it may fly? So that the dragon may fly and spit out the structure around the spindle. Each structure was a home and each home was a brick holding up the spindle, shading their heads from the blazing sun and the freezing wind.
Did you know that when I gave the dragon Mother’s logic so that it may know how to serve the Builders. So that the Builders may know how to serve you?


numbers such as 1, -5.23,<PHONE_NUMBER>12345678901234567890
strings such as "hello"; the backslash character \ can be used for escaping double quotes and backslashes, nothing else
characters such as 'a - note the absence of a second single quote; there is no escape character
Operators, named with 1 or 2 letters or special characters
Variables, which can be any single uppercase letter
Blocks, delimited by { and }
Operators
!boolean "not"
#power, find index
$copy from stack, sort
%mod, split, map
&bitwise/setwise "and", if-then
(decrement, uncons from left
)increment, uncons from right
*multiply, join, repeat, fold (reduce)
+add, concat
,range, length, filter
-subtract, remove
.infix "vector" operator, must be followed by a binary operator or a block; applies that operator or block on two arrays, item by item, creating an array with the results
/divide, split, each
:infix operator, can be followed by: 1) variable name 2) set variable 3) unary operator - map 4) binary operator - fold (reduce)
;pop and discard
<compare, slice
=compare, array get, find value
>compare, slice
?if (ternary)
@rotate top 3 elements on the stack
[start array
\\swap top 2 elements on the stack
]end array
^bitwise xor, symmetric difference
_duplicate top element on the stack
`string representation
awrap in array
bbase conversion
cconvert to char
dconvert to double
efollowed by numeric literal - infix operator, calculates *10x (as in the scientific notation)
finfix operator, can be followed by: 1) variable name - for loop 2) binary operator 3) block - map with extra parameter
gdo-while loop that pops the condition, signum, get from url
hdo-while loop that leaves the condition on the stack
iconvert to integer
jmemoize
lread line
mfollowed by numeric literal - subtract (same as - but avoids the need for a space after it)


§635-13 Jury, when of right. When the right of trial by jury is given by the Constitution or a statute of the United States or this State and the right has not been waived, the case shall be tried with a jury. [CC 1859, §1130; am L 1909, c 23, §1; RL 1925, §2367; RL 1935, §4098; RL 1945, §10108; RL 1955, §231-8; HRS §635-13; am L 1972, c 89, §2B(c)]
Rules of Court
Demand for jury trial, see HRCP rules 38, 39; DCRCP rule 38; waiver, see HRPP rule 23.
Law Journals and Reviews
Blast It All: Allen Charges and the Dangers of Playing With Dynamite. 32 UH L. Rev. 323 (2010).
Case Notes
Jury trial, when available. 50 H. 528, 445 P.2d 376 (1968).
Based on the established common law convention of this jurisdiction at the time of adoption of the state constitution, as a general matter, a right to jury trial exists in state eminent domain proceedings. 91 H. 81, 979 P.2d 1107 (1999).
Where third party leasing agents were not parties to lease agreement between landlord and tenant, express waiver of right to jury trial in agreement did not apply to those third parties. 85 H. 300 (App.), 944 P.2d 97 (1997).
Cases prior to adoption of the Hawaii Rules of Civil Procedure.
Jury trial may be waived by inaction as well as by positive acts of the parties. Demand for jury trial in complaint is insufficient. 24 H. 777, 780 (1919). See 28 H. 350, 364 (1925); 10 F.2d 474, 477 (1926).
District magistrate may try a defendant after defendant has demanded a jury trial. 27 H. 844 (1924).
Cited: 3 H. 546, 547 (1874).
See 33 H. 103 (1934); 33 H. 167 (1934); 33 H. 247 (1934); 33 H. 315 (1935) (construction of writing); 33 H. 523 (1935) (directed verdict); 33 H. 745 (1936) (experiments); 34 H. 35 (1936) (nonsuit); 34 H. 632 (1938) (misconduct).


In my last post, I found that the per cent women in the region had a significant impact on the salaries of Engineers. In this post, I will analyse what predictors are best to forecast the percentage of women who are Engineers in the region. I will begin in much the same way as when I looked at salaries. However, there are limitations to linear models. In the second half of this post, I compare my finding to other machine learning algorithms.
Statistics Sweden use NUTS (Nomenclature des Unités Territoriales Statistiques), which is the EU’s hierarchical regional division, to specify the regions.
Please send suggestions for improvement of the analysis to<EMAIL_ADDRESS>First, define libraries and functions.
library (tidyverse)
## -- Attaching packages -------------------------------------------------- tidyverse 1.3.0 --
## v ggplot2 3.3.0 v purrr 0.3.4
## v tibble 3.0.0 v dplyr 0.8.5
## v tidyr 1.0.2 v stringr 1.4.0
## v readr 1.3.1 v forcats 0.5.0
## -- Conflicts ----------------------------------------------------- tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
library (broom)
library (car)
## Loading required package: carData
##
## Attaching package: 'car'
## The following object is masked from 'package:dplyr':
##
## recode
## The following object is masked from 'package:purrr':
##
## some
library (leaps)
library (MASS)
##
## Attaching package: 'MASS'
## The following object is masked from 'package:dplyr':
##
## select
library (earth)
## Loading required package: Formula
## Loading required package: plotmo
## Loading required package: plotrix
## Loading required package: TeachingDemos
library (lspline)
library (boot)
##
## Attaching package: 'boot'
## The following object is masked from 'package:car':
##
## logit
library (arm)
## Loading required package: Matrix
##
## Attaching package: 'Matrix'
## The following objects are masked from 'package:tidyr':
##
## expand, pack, unpack
## Loading required package: lme4
## Registered S3 methods overwritten by 'lme4':
## method from
## cooks.distance.influence.merMod car
## influence.merMod car
## dfbeta.influence.merMod car
## dfbetas.influence.merMod car
##
## arm (Version 1.10-1, built: 2018-4-12)
## Working directory is C:/R/rblog/content/post
##
## Attaching package: 'arm'
## The following object is masked from 'package:boot':
##
## logit
## The following object is masked from 'package:plotrix':
##
## rescale
## The following object is masked from 'package:car':
##
## logit
library (caret)
## Loading required package: lattice
##
## Attaching package: 'lattice'
## The following object is masked from 'package:boot':
##
## melanoma
##
## Attaching package: 'caret'
## The following object is masked from 'package:purrr':
##
## lift
library (recipes)
##
## Attaching package: 'recipes'
## The following object is masked from 'package:stringr':
##
## fixed
## The following object is masked from 'package:stats':
##
## step
library (vip)
##
## Attaching package: 'vip'
## The following object is masked from 'package:utils':
##
## vi
library (ggpubr)
## Loading required package: magrittr
##
## Attaching package: 'magrittr'
## The following object is masked from 'package:purrr':
##
## set_names
## The following object is masked from 'package:tidyr':
##
## extract
library (glmnet)
## Loaded glmnet 3.0-2
library (rpart)
library (ipred)
library (iml)
library (neuralnet)
##
## Attaching package: 'neuralnet'
## The following object is masked from 'package:dplyr':
##
## compute
library (DALEX)
## Welcome to DALEX (version: 1.2.1).
## Find examples and detailed introduction at: https://pbiecek.github.io/ema/
##
## Attaching package: 'DALEX'
## The following object is masked from 'package:dplyr':
##
## explain
library (Metrics)
##
## Attaching package: 'Metrics'
## The following objects are masked from 'package:caret':
##
## precision, recall
library (auditor)
##
## Attaching package: 'auditor'
## The following object is masked from 'package:DALEX':
##
## model_performance
readfile <- function (file1){read_csv (file1, col_types = cols(), locale = readr::locale (encoding = "latin1"), na = c("..", "NA")) %>%
gather (starts_with("19"), starts_with("20"), key = "year", value = groupsize) %>%
drop_na() %>%
mutate (year_n = parse_number (year))
}
perc_women <- function(x){
ifelse (length(x) == 2, x[2] / (x[1] + x[2]), NA)
}
nuts <- read.csv("nuts.csv") %>%
mutate(NUTS2_sh = substr(NUTS2, 3, 4))
nuts %>%
distinct (NUTS2_en) %>%
knitr::kable(
booktabs = TRUE,
caption = 'Nomenclature des Unités Territoriales Statistiques (NUTS)')
| NUTS2_en |
|---|
| SE11 Stockholm |
| SE12 East-Central Sweden |
| SE21 Småland and islands |
| SE22 South Sweden |
| SE23 West Sweden |
| SE31 North-Central Sweden |
| SE32 Central Norrland |
| SE33 Upper Norrland |
bs <- function(formula, data, indices) {
d <- data[indices,] # allows boot to select sample
fit <- lm(formula, weights = tbnum_weights, data=d)
return(coef(fit))
}
The data tables are downloaded from Statistics Sweden. They are saved as a comma-delimited file without heading, UF0506A1.csv, http://www.statistikdatabasen.scb.se/pxweb/en/ssd/.
The tables:
UF0506A1_1.csv: Population 16-74 years of age by region, highest level of education, age and sex. Year 1985 - 2018 NUTS 2 level 2008- Age, total, all reported ages
000000CG_1: Average basic salary, monthly salary and women´s salary as a percentage of men´s salary by region, sector, occupational group (SSYK 2012) and sex. Year 2014 - 2018 Monthly salary All sectors.
000000CD_1.csv: Average basic salary, monthly salary and women´s salary as a percentage of men´s salary by region, sector, occupational group (SSYK 2012) and sex. Year 2014 - 2018 Number of employees All sectors-
The data is aggregated, the size of each group is in the column groupsize.
I have also included some calculated predictors from the original data.
perc_women: The percentage of women within each group defined by edulevel, region and year
perc_women_region: The percentage of women within each group defined by year and region
regioneduyears: The average number of education years per capita within each group defined by sex, year and region
eduquotient: The quotient between regioneduyears for men and women
salaryquotient: The quotient between salary for men and women within each group defined by year and region
perc_women_eng_region: The percentage of women who are engineers within each group defined by year and region
numedulevel <- read.csv("edulevel_1.csv")
numedulevel[, 2] <- data.frame(c(8, 9, 10, 12, 13, 15, 22, NA))
tb <- readfile("000000CG_1.csv")
tb <- readfile("000000CD_1.csv") %>%
left_join(tb, by = c("region", "year", "sex", "sector","occuptional (SSYK 2012)")) %>%
filter(`occuptional (SSYK 2012)` == "214 Engineering professionals")
tb <- readfile("UF0506A1_1.csv") %>%
right_join(tb, by = c("region", "year", "sex")) %>%
right_join(numedulevel, by = c("level of education" = "level.of.education")) %>%
filter(!is.na(eduyears)) %>%
mutate(edulevel = `level of education`) %>%
group_by(edulevel, region, year, sex) %>%
mutate(groupsize_all_ages = sum(groupsize)) %>%
group_by(edulevel, region, year) %>%
mutate (perc_women = perc_women (groupsize_all_ages[1:2])) %>%
group_by (sex, year, region) %>%
mutate(regioneduyears = sum(groupsize * eduyears) / sum(groupsize)) %>%
mutate(regiongroupsize = sum(groupsize)) %>%
mutate(suming = groupsize.x) %>%
group_by(region, year) %>%
mutate (sum_pop = sum(groupsize)) %>%
mutate (perc_women_region = perc_women (regiongroupsize[1:2])) %>%
mutate (eduquotient = regioneduyears[2] / regioneduyears[1]) %>%
mutate (salary = groupsize.y) %>%
mutate (salaryquotient = salary[2] / salary[1]) %>%
mutate (perc_women_eng_region = perc_women(suming[1:2])) %>%
left_join(nuts %>% distinct (NUTS2_en, NUTS2_sh), by = c("region" = "NUTS2_en")) %>%
drop_na()
summary(tb)
## region age level of education sex
## Length:532 Length:532 Length:532 Length:532
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## year groupsize year_n sector
## Length:532 Min. : 405 Min. :2014 Length:532
## Class :character 1st Qu.: 20996 1st Qu.:2015 Class :character
## Mode :character Median : 43656 Median :2016 Mode :character
## Mean : 64760 Mean :2016
## 3rd Qu.:102394 3rd Qu.:2017
## Max. :271889 Max. :2018
## occuptional (SSYK 2012) groupsize.x year_n.x groupsize.y
## Length:532 Min. : 340 Min. :2014 Min. :34700
## Class :character 1st Qu.: 1700 1st Qu.:2015 1st Qu.:40300
## Mode :character Median : 3000 Median :2016 Median :42000
## Mean : 5850 Mean :2016 Mean :42078
## 3rd Qu.: 7475 3rd Qu.:2017 3rd Qu.:43925
## Max. :21400 Max. :2018 Max. :49400
## year_n.y eduyears edulevel groupsize_all_ages
## Min. :2014 Min. : 8.00 Length:532 Min. : 405
## 1st Qu.:2015 1st Qu.: 9.00 Class :character 1st Qu.: 20996
## Median :2016 Median :12.00 Mode :character Median : 43656
## Mean :2016 Mean :12.71 Mean : 64760
## 3rd Qu.:2017 3rd Qu.:15.00 3rd Qu.:102394
## Max. :2018 Max. :22.00 Max. :271889
## perc_women regioneduyears regiongroupsize suming
## Min. :0.3575 Min. :11.18 Min. :128262 Min. : 340
## 1st Qu.:0.4338 1st Qu.:11.61 1st Qu.:288058 1st Qu.: 1700
## Median :0.4631 Median :11.74 Median :514608 Median : 3000
## Mean :0.4771 Mean :11.79 Mean :453318 Mean : 5850
## 3rd Qu.:0.5132 3rd Qu.:12.04 3rd Qu.:691870 3rd Qu.: 7475
## Max. :0.6423 Max. :12.55 Max. :827940 Max. :21400
## sum_pop perc_women_region eduquotient salary
## Min. : 262870 Min. :0.4831 Min. :1.019 Min. :34700
## 1st Qu.: 587142 1st Qu.:0.4882 1st Qu.:1.029 1st Qu.:40300
## Median :1029820 Median :0.4934 Median :1.034 Median :42000
## Mean : 906635 Mean :0.4923 Mean :1.034 Mean :42078
## 3rd Qu.:1395157 3rd Qu.:0.4970 3rd Qu.:1.041 3rd Qu.:43925
## Max. :1655215 Max. :0.5014 Max. :1.047 Max. :49400
## salaryquotient perc_women_eng_region NUTS2_sh
## Min. :0.8653 Min. :0.1566 Length:532
## 1st Qu.:0.9329 1st Qu.:0.1787 Class :character
## Median :0.9395 Median :0.2042 Mode :character
## Mean :0.9447 Mean :0.2039
## 3rd Qu.:0.9537 3rd Qu.:0.2216
## Max. :1.0446 Max. :0.2746
Prepare the data using Tidyverse recipes package, i.e. centre, scale and make sure all predictors are numerical.
tbtemp <- ungroup(tb) %>% dplyr::select(region, salary, year_n, regiongroupsize, sex, regioneduyears, suming, perc_women_region, salaryquotient, eduquotient, perc_women_eng_region)
tb_outliers_info <- unique(tbtemp)
tb_unique <- unique(dplyr::select(tbtemp, -region))
tbnum_weights <- tb_unique$suming
blueprint <- recipe(perc_women_eng_region ~ ., data = tb_unique) %>%
step_nzv(all_nominal()) %>%
step_integer(matches("Qual|Cond|QC|Qu")) %>%
step_center(all_numeric(), -all_outcomes()) %>%
step_scale(all_numeric(), -all_outcomes()) %>%
step_dummy(all_nominal(), -all_outcomes(), one_hot = TRUE)
prepare <- prep(blueprint, training = tb_unique)
tbnum <- bake(prepare, new_data = tb_unique)
The dataset only contains 76 rows. This together with multicollinearity limits the number of predictors to include in the regression. I would like to choose the predictors that best contains most information from the dataset with respect to the response.
I will use an elastic net to find the variable that contains the best signals for later use in the analysis. First I will search for the explanatory variables that best predict the response using no interactions. I will use 10-fold cross-validation with an elastic net. Elastic nets are linear and do not take into account the shape of the relations between the predictors. Alpha = 1 indicates a lasso regression.
X <- model.matrix(perc_women_eng_region ~ ., tbnum)[, -1]
Y <- tbnum$perc_women_eng_region
set.seed(123) # for reproducibility
tbnum_glmnet <- caret::train(
x = X,
y = Y,
weights = tbnum_weights,
method = "glmnet",
preProc = c("zv", "center", "scale"),
trControl = trainControl(method = "cv", number = 10),
tuneLength = 20
)
## Warning in nominalTrainWorkflow(x = x, y = y, wts = weights, info = trainInfo, :
## There were missing values in resampled performance measures.
vip(tbnum_glmnet, num_features = 20, geom = "point")
tbnum_glmnet$bestTune
## alpha lambda
## 349 0.9052632 0.0002658605
elastic_min <- glmnet(
x = X,
y = Y,
alpha = .1
)
plot(elastic_min, xvar = "lambda", main = "Elastic net penalty\n\n")
I use MARS to fit the best signals using from the elastic net using no interactions. Four predictors minimise the AIC while still ensuring that the coefficients are valid, testing them with bootstrap.
temp <- dplyr::select(tbnum, c(perc_women_eng_region, regiongroupsize, regioneduyears, eduquotient, sex_men))
mmod_scaled <- earth(perc_women_eng_region ~ ., weights = tbnum_weights, data = temp, nk = 9, degree = 1)
summary (mmod_scaled)
## Call: earth(formula=perc_women_eng_region~., data=temp, weights=tbnum_weights,
## degree=1, nk=9)
##
## coefficients
## (Intercept) 0.188579073
## sex_men 0.020170708
## h(0.651904-regiongroupsize) -0.017635288
## h(regiongroupsize-0.651904) 0.032588077
## h(-0.259811-regioneduyears) -0.021971561
## h(regioneduyears- -0.259811) 0.021625566
## h(-1.22297-eduquotient) -0.049922858
## h(eduquotient- -1.22297) 0.009477773
##
## Selected 8 of 8 terms, and 4 of 4 predictors
## Termination condition: Reached nk 9
## Importance: regiongroupsize, regioneduyears, eduquotient, sex_men
## Weights: 21400, 6800, 11500, 3000, 2400, 500, 7000, 1900, 16000, 4100, 3...
## Number of terms at each degree of interaction: 1 7 (additive model)
## GCV 0.8258677 RSS 40.43492 GRSq 0.8250241 RSq 0.8842515
plot (mmod_scaled)
plotmo (mmod_scaled)
## plotmo grid: regiongroupsize regioneduyears eduquotient sex_men
## 0.2593006 -0.1394781 0 0.5
model_mmod_scale <- lm (perc_women_eng_region ~
sex_men +
lspline(regiongroupsize, c(0.651904)) +
lspline(regioneduyears, c(-0.259811)) +
lspline(eduquotient, c(-1.22297)),
weights = tbnum_weights,
data = tbnum)
model_mmod_scale <- lm (perc_women_eng_region ~ .,
weights = tbnum_weights,
data = tbnum)
b <- regsubsets(perc_women_eng_region ~ sex_men + lspline(regiongroupsize, c(0.651904)) + lspline(regioneduyears, c(-0.259811)) + lspline(eduquotient, c(-1.22297)), data = tbnum, weights = tbnum_weights, nvmax = 12)
rs <- summary(b)
AIC <- 50 * log (rs$rss / 50) + (2:8) * 2
which.min (AIC)
## [1] 7
names (rs$which[7,])[rs$which[7,]]
## [1] "(Intercept)"
## [2] "sex_men"
## [3] "lspline(regiongroupsize, c(0.651904))1"
## [4] "lspline(regiongroupsize, c(0.651904))2"
## [5] "lspline(regioneduyears, c(-0.259811))1"
## [6] "lspline(regioneduyears, c(-0.259811))2"
## [7] "lspline(eduquotient, c(-1.22297))1"
## [8] "lspline(eduquotient, c(-1.22297))2"
model_mmod_scale <- lm (perc_women_eng_region ~
sex_men +
lspline(regiongroupsize, c(0.651904)) +
lspline(regioneduyears, c(-0.259811)) +
lspline(eduquotient, c(-1.22297)),
weights = tbnum_weights,
data = tbnum)
summary (model_mmod_scale)$adj.r.squared
## [1] 0.8723362
AIC(model_mmod_scale)
## [1] -426.1528
set.seed(123)
results <- boot(data = tbnum, statistic = bs,
R = 1000, formula = as.formula(model_mmod_scale))
#conf = coefficient not passing through zero
summary (model_mmod_scale) %>% tidy() %>%
mutate(bootest = tidy(results)$statistic,
bootbias = tidy(results)$bias,
booterr = tidy(results)$std.error,
conf = !((tidy(confint(results))$X2.5.. < 0) & (tidy(confint(results))$X97.5.. > 0)))
## Warning in norm.inter(t, adj.alpha): extreme order statistics used as endpoints
## Warning: 'tidy.matrix' is deprecated.
## See help("Deprecated")
## Warning in norm.inter(t, adj.alpha): extreme order statistics used as endpoints
## Warning: 'tidy.matrix' is deprecated.
## See help("Deprecated")
## # A tibble: 8 x 9
## term estimate std.error statistic p.value bootest bootbias booterr conf
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <lgl>
## 1 (Interce~ 0.244 0.0177 13.8 2.05e-21 0.244 4.73e-3 0.0376 TRUE
## 2 sex_men 0.0202 0.00509 3.96 1.82e- 4 0.0202 2.44e-3 0.00694 TRUE
## 3 lspline(~ 0.0176 0.00437 4.03 1.42e- 4 0.0176 9.52e-4 0.00560 TRUE
## 4 lspline(~ 0.0326 0.00828 3.94 1.97e- 4 0.0326 -2.36e-3 0.0135 TRUE
## 5 lspline(~ 0.0220 0.00466 4.72 1.23e- 5 0.0220 -3.76e-3 0.00672 TRUE
## 6 lspline(~ 0.0216 0.00490 4.41 3.78e- 5 0.0216 4.59e-4 0.00672 TRUE
## 7 lspline(~ 0.0499 0.0133 3.76 3.56e- 4 0.0499 3.70e-3 0.0293 TRUE
## 8 lspline(~ 0.00948 0.00357 2.66 9.85e- 3 0.00948 -3.47e-3 0.00491 TRUE
plot(results, index=1) # intercept
plot_model will show diagnostics for the model, a residual plot, scale location plot, residual density and the regression error characteristic curve. It will also show the residuals versus the actual response to help find outliers. It will plot the feature importance and feature effects. In addition, it will plot how strongly features interact with each other and the 2-way interactions with regiongroupsize and all other features. The interaction measure regards how much of the variance of f(x) is explained by the interaction. The measure is between 0 (no interaction) and 1 (= 100% of variance of f(x) due to interactions). Regiongroupsize is of special interest since it is the feature with the strongest importance to the per cent of engineers who are women in the region.
plot_model <- function(model){
invisible(capture.output(exp_model <- DALEX::explain(model, data = tbnum, y = tbnum$perc_women_eng_region))) # Knit and DALEX::explain generates invalid rss feed
lm_mr <- model_residual(exp_model)
predictor <- Predictor$new(model,
data = dplyr::select(tbnum, -perc_women_eng_region),
y = tbnum$perc_women_eng_region)
writeLines("")
print(paste ("Model RMSE: ", rmse(predict(model), tbnum$perc_women_eng_region)))
p1 <- plot(lm_mr, type = "residual")
p2 <- plot(lm_mr, type = "scalelocation")
p3 <- plot(lm_mr, type = "residual_density")
p4 <- plot(lm_mr, type = "rec")
print(gridExtra::grid.arrange(p1, p2, p3, p4, ncol = 2))
print(plot_residual(lm_mr, variable = "_y_hat_", nlabel = 10))
print(plot (FeatureImp$new(predictor, loss = "mae")))
print(plot (FeatureEffects$new(predictor)))
p1 <- plot (Interaction$new(predictor))
p2 <- plot (Interaction$new(predictor, feature = "regiongroupsize"))
print(gridExtra::grid.arrange(p1, p2, ncol = 2))
}
Manual Data fit with Regularized Regression and MARS
model <- lm (
perc_women_eng_region ~
sex_men +
lspline(regiongroupsize, c(0.651904)) +
lspline(regioneduyears, c(-0.259811)) +
lspline(eduquotient, c(-1.22297)),
weights = tbnum_weights,
data = tbnum)
plot_model(model)
##
## [1] "Model RMSE: 0.0119381601517971"
## TableGrob (2 x 2) "arrange": 4 grobs
## z cells name grob
## 1 1 (1-1,1-1) arrange gtable[layout]
## 2 2 (1-1,2-2) arrange gtable[layout]
## 3 3 (2-2,1-1) arrange gtable[layout]
## 4 4 (2-2,2-2) arrange gtable[layout]
##
## Attaching package: 'patchwork'
## The following object is masked from 'package:MASS':
##
## area
## TableGrob (1 x 2) "arrange": 2 grobs
## z cells name grob
## 1 1 (1-1,1-1) arrange gtable[layout]
## 2 2 (1-1,2-2) arrange gtable[layout]
Data fit with Regularized Regression
X <- model.matrix(perc_women_eng_region ~ ., tbnum)[, -1]
Y <- tbnum$perc_women_eng_region
set.seed(123) # for reproducibility
model <- caret::train(
x = X,
y = Y,
weights = tbnum_weights,
method = "glmnet",
preProc = c("zv", "center", "scale"),
trControl = trainControl(method = "cv", number = 10),
tuneLength = 20
)
## Warning in nominalTrainWorkflow(x = x, y = y, wts = weights, info = trainInfo, :
## There were missing values in resampled performance measures.
plot_model(model)
##
## [1] "Model RMSE: 0.0115236920245644"
## TableGrob (2 x 2) "arrange": 4 grobs
## z cells name grob
## 1 1 (1-1,1-1) arrange gtable[layout]
## 2 2 (1-1,2-2) arrange gtable[layout]
## 3 3 (2-2,1-1) arrange gtable[layout]
## 4 4 (2-2,2-2) arrange gtable[layout]
## TableGrob (1 x 2) "arrange": 2 grobs
## z cells name grob
## 1 1 (1-1,1-1) arrange gtable[layout]
## 2 2 (1-1,2-2) arrange gtable[layout]
Data fit with Multivariate Adaptive Regression Splines
hyper_grid <- expand.grid(
degree = 1,
nprune = seq(2, 100, length.out = 10) %>% floor()
)
set.seed(123) # for reproducibility
model <- caret::train(
x = data.frame(subset(tbnum, select = -perc_women_eng_region)),
y = tbnum$perc_women_eng_region,
method = "earth",
weights = tbnum_weights,
trControl = trainControl(method = "cv", number = 10),
tuneGrid = hyper_grid
)
plot_model(model)
## Warning in if (class(response) == "data.frame") response <- as.matrix(response):
## the condition has length > 1 and only the first element will be used
## Warning in if (class(response) == "data.frame") response <- as.matrix(response):
## the condition has length > 1 and only the first element will be used
##
## [1] "Model RMSE: 0.00915463662070812"
## TableGrob (2 x 2) "arrange": 4 grobs
## z cells name grob
## 1 1 (1-1,1-1) arrange gtable[layout]
## 2 2 (1-1,2-2) arrange gtable[layout]
## 3 3 (2-2,1-1) arrange gtable[layout]
## 4 4 (2-2,2-2) arrange gtable[layout]
## TableGrob (1 x 2) "arrange": 2 grobs
## z cells name grob
## 1 1 (1-1,1-1) arrange gtable[layout]
## 2 2 (1-1,2-2) arrange gtable[layout]
Data fit with decision tree bag
set.seed(123)
model <- caret::train(
perc_women_eng_region ~ .,
data = tbnum,
method = "treebag",
weights = tbnum_weights,
trControl = trainControl(method = "cv", number = 10),
nbagg = 50,
control = rpart.control(minsplit = 2, cp = 0)
)
plot_model(model)
##
## [1] "Model RMSE: 0.00341044239371977"
## TableGrob (2 x 2) "arrange": 4 grobs
## z cells name grob
## 1 1 (1-1,1-1) arrange gtable[layout]
## 2 2 (1-1,2-2) arrange gtable[layout]
## 3 3 (2-2,1-1) arrange gtable[layout]
## 4 4 (2-2,2-2) arrange gtable[layout]
## TableGrob (1 x 2) "arrange": 2 grobs
## z cells name grob
## 1 1 (1-1,1-1) arrange gtable[layout]
## 2 2 (1-1,2-2) arrange gtable[layout]
Data fit with Random Forest
set.seed(123)
invisible(capture.output(model <- caret::train(
perc_women_eng_region ~ .,
data = tbnum,
weights = tbnum_weights,
method = "ranger",
trControl = trainControl(method = "cv", number = 5, verboseIter = T, classProbs = T),
num.trees = 100,
importance = "permutation")))
## Warning in train.default(x, y, weights = w, ...): cannnot compute class
## probabilities for regression
plot_model(model)
##
## [1] "Model RMSE: 0.00876237694751394"
## TableGrob (2 x 2) "arrange": 4 grobs
## z cells name grob
## 1 1 (1-1,1-1) arrange gtable[layout]
## 2 2 (1-1,2-2) arrange gtable[layout]
## 3 3 (2-2,1-1) arrange gtable[layout]
## 4 4 (2-2,2-2) arrange gtable[layout]
## TableGrob (1 x 2) "arrange": 2 grobs
## z cells name grob
## 1 1 (1-1,1-1) arrange gtable[layout]
## 2 2 (1-1,2-2) arrange gtable[layout]
Data fit with Gradient Boosting Machine
tr <- trainControl(method = "repeatedcv", number = 10, repeats = 5)
tg <- expand.grid(shrinkage = seq(0.1),
interaction.depth = c(3),
n.minobsinnode = c(10),
n.trees = c(100))
set.seed(123)
model <- caret::train(
perc_women_eng_region ~ .,
data = tbnum,
weights = tbnum_weights,
method = "gbm",
trControl = tr,
tuneGrid = tg,
verbose = FALSE)
plot_model(model)
##
## [1] "Model RMSE: 0.00352511569538881"
## TableGrob (2 x 2) "arrange": 4 grobs
## z cells name grob
## 1 1 (1-1,1-1) arrange gtable[layout]
## 2 2 (1-1,2-2) arrange gtable[layout]
## 3 3 (2-2,1-1) arrange gtable[layout]
## 4 4 (2-2,2-2) arrange gtable[layout]
## TableGrob (1 x 2) "arrange": 2 grobs
## z cells name grob
## 1 1 (1-1,1-1) arrange gtable[layout]
## 2 2 (1-1,2-2) arrange gtable[layout]
Data fit with Deep Learning
rctrl1 <- trainControl(method = "cv", number = 3, returnResamp = "all")
set.seed(123) # for reproducibility
model <- caret::train(
perc_women_eng_region ~ .,
data = tbnum,
weights = tbnum_weights,
method = "neuralnet",
trControl = rctrl1,
tuneGrid = data.frame(layer1 = 2:20, layer2 = 2:20, layer3 = 2:20),
rep = 3,
threshold = 0.0001,
preProc = c("center", "scale"))
plot_model(model)
##
## [1] "Model RMSE: 0.00924842751296535"
## TableGrob (2 x 2) "arrange": 4 grobs
## z cells name grob
## 1 1 (1-1,1-1) arrange gtable[layout]
## 2 2 (1-1,2-2) arrange gtable[layout]
## 3 3 (2-2,1-1) arrange gtable[layout]
## 4 4 (2-2,2-2) arrange gtable[layout]
## TableGrob (1 x 2) "arrange": 2 grobs
## z cells name grob
## 1 1 (1-1,1-1) arrange gtable[layout]
## 2 2 (1-1,2-2) arrange gtable[layout]
Data fit with Support Vector Machine
set.seed(123) # for reproducibility
model <- caret::train(
perc_women_eng_region ~ .,
data = tbnum,
weights = tbnum_weights,
method = "svmRadial",
preProcess = c("center", "scale"),
trControl = trainControl(method = "cv", number = 10),
tuneLength = 10
)
plot_model(model)
##
## [1] "Model RMSE: 0.00848330404577974"
## TableGrob (2 x 2) "arrange": 4 grobs
## z cells name grob
## 1 1 (1-1,1-1) arrange gtable[layout]
## 2 2 (1-1,2-2) arrange gtable[layout]
## 3 3 (2-2,1-1) arrange gtable[layout]
## 4 4 (2-2,2-2) arrange gtable[layout]
## TableGrob (1 x 2) "arrange": 2 grobs
## z cells name grob
## 1 1 (1-1,1-1) arrange gtable[layout]
## 2 2 (1-1,2-2) arrange gtable[layout]


Source
(defgeneric update-persistent-instance-for-redefined-class
(instance added-slots discarded-slots property-list &key)
(:method ((instance persistent-object) added-slots discarded-slots plist
&key)
;; Default method: ignore the discarded slots and initialize added slots
;; according to their initforms. We do this 'by hand' and not by calling
;; SHARED-INITIALIZE because slot indexes may need to be updated too.
(let ((slots (class-slots (class-of instance))))
(loop for slot-name in added-slots
for slot = (find slot-name slots :key #'slot-definition-name)
for initfunction = (and slot
(slot-definition-initfunction slot))
when initfunction
;; NOTE: We don't handle initargs, and I think we don't need to.
;; We follow the CLHS description of UPDATE-INSTANCE-FOR-REDEFINED-CLASS,
;; which says: "When it is called by the system to update an
;; instance whose class has been redefined, no initialization
;; arguments are provided."
do (setf (slot-value instance slot-name) (funcall initfunction))))))
Source Context


Massive Ford F-350 Mac Truck Edition Is Ready To Menace Your Society
This is what happens when your F-150 Raptor goes to the gym.
by Christopher SmithDon’t confuse this rather large and mildly customized Ford Super Duty dually with a Mack truck. Mack (with a k) denotes the burly big rig with a bulldog for a hood ornament. This is F-350 build is nearly as big, but it’s called Mac (without a k) Truck and it’s a dealer-built off-roading monster from Long McArthur Ford in Kansas. And yes, it’s for sale. In fact, perusing the dealer’s website we see there are multiple models for sale, but they are a bit pricey. More on that in a bit.
This Is Getting Out Of Hand:
For now, here’s what you need to know. Starting from a rather down-spec Ford F-350 XL 4x4 dually, the dealership adds the STX appearance package from the factory then goes to town with extra kit. Custom front and rear steel bumpers from Thunder Struck replace the factory bits, with the front bumper also brandishing LED off-road lights at the corners. Special side vents with MAC branding light up the sides, and the dealer includes a spray-in bed liner as well.
Moving inside, the XL’s cabin is dressed up with custom leather trim on the seats and console, and a set of heavy-duty floormats seek to keep the carpeting clean. As for equipment, it’s fitted with basic power items like windows and locks, though it does get Ford’s Sync system and an 8-inch touchscreen. It’s also equipped with a plethora of cameras as part of the factory towing package, which is a really good idea for something larger than some tiny homes.
Gallery: Ford Super Duty Mac Truck Conversion
Underneath is a 2.5-inch suspension lift with Fox 2.0 shocks at all four corners, and a fox stabilizer bar in the front. That makes room for a custom set of Mac branded 22-inch wheels shod with 37-inch off-road tires, and yes, all six wheels get them. The powertrain is otherwise stock, which means there’s a 450-horsepower 6.7-liter Power Stroke turbocharged V8 diesel under the hood, churning out 935 pound-feet of twist through a six-speed automatic. Side steps and bold Mac Truck branding round out the package.
Long McArthur Ford offers its Mac Truck Edition for either dual- or single-rear-wheel trucks, and it’s available on the F-250 which also gets fender flares. The dealer stands behind the upfit with a surprising 5-year/60,000-mile warranty on the upgrades, but there is a cost for this level of badassery. The F-350 dually currently lists for $75,665, and that’s after a $4,000 discount. That said, it’s in the ballpark of a fully optioned F-150 Raptor, and though it’s certainly not as quick or off-capable as Ford’s dune-hopper, there’s no denying the intimidation factor that this thing exudes. That alone could bring buyers into the showroom to make a deal.
Source: Long McArthur Ford via YouTube


Testing
Live
Provenance X
Online Manager (
Test
Network)
Codes available
-
settings
Create Provenance Code
Bytes available
1000
Description
save description as template
show templates
Provenance Code #
save
print
QR Code
(Best practice would be to include a url to the online reader on any packaging - ie.
https://www.provenancex.net/reader
or your own branded url)
CODE128 Barcode
Provenance Description
My Account
or enter existing account below
Account Key
Account Secret
(stored on your local computer and should never be shared - make sure to backup your account and settings)
template name
No saved templates available
note
{{template.name}}
delete
Please wait...


Home
About Us
Events
Members
HOD
Faculty Advisor
Core Committee
Website Team
Contact
Motto
3 E’s
-
Explore, Endeavour, Extract
Vision
To explore undiscovered world of Mining and to promote budding talents in this expanse field.