WATCHER, THE (director: Joe Charbanic; screenwriters: Clay Ayers/David Elliot/based on a story by Darcy Meyers and David Elliot; cinematographer: Michael Chapman; editor: Richard Nord; cast: James Spader (Campbell), Marisa Tomei (Polly), Keanu Reeves (Griffin), Ernie Hudson (FBI Director, Ibby), Chris Ellis (Hollis), Rebakah Louise Smith (Ellie),Yvonne Niami (Lisa), Robert Cicchini (Mitch) Jennifer McShane (Diana), Gina Alexander (Sharon); Runtime: 93; 2000)
Reviewed by Dennis Schwartz
A routine serial killer flick, set in Chicago. Everything seen is what has become a cliché for this genre. There is the sensitive lawman who is traumatized by the killings and his inability to get the killer, and the cat and mouse games that ensue between FBI Agent Joel Campbell (Spader) and the elusive killer, who has the obligatory three names all serial killers seem to have, David Allen Griffin (Reeves). He follows the agent from L.A. to Chicago, where he phones to tell him, it hasn't been the same since you left. I missed you. There is the psychic connection between the killer and the investigating agent, which is depicted here like yin and yang, with each needing the other to exist, and there is the ego maniacal taunting of the killer, coming in the form of sending photos of the next intended victim to Campbell, as he gives the police a day before killing them. The film ends smoothly like those mystery shows on TV do: with a car chase that seems to be obligatory filler for such cop films, a rescue coming just in the nick of time, and the action scenes being well choreographed to fit the pyrotechnical finale. The attempt to bring any psychology into this thriller, never materializes, except in the most banal manner. An inexperienced therapist, played with restraint by Marisa Tomei, doles out inane cheerleading analysis to the burnt-out cop, who has become her patient, concerned that the killer is using him as his excuse to kill. In fact, the killer is more interested in playing mind games with Spader than he is in killing his victims, which convinced me that this film wasn't looking to go into new territory with this 'let's make a profit for the producers' genre film.
This debut feature by the music video director Joe Charbanic is a limited venture, smacking of formula questions and answers to the serial killer's mental state, resulting in a picture that is decidedly painted by-the-numbers.
The film opens under the dark skies of Chicago, where the noise heard is from police sirens and helicopters from above the skyline of Lake Michigan, swooping down on the crime scene. The murder of a young woman takes place in the building where Joel Campbell lives, and he is drawn into the case when he makes contact with the hard-boiled investigating detective who is at the crime scene, Hollis (Ellis). Currently, the agent is on a disability leave, seeing a therapist about his inability to function anymore, taking heavy doses of sleeping pills, self-injecting himself in the stomach with drugs for his migraines, and having nightmares in his unkept apartment about being unable to catch sicko David Allen Griffin during an L.A. foot chase; and, from failing to save the killer's last target in L.A. -- Campbell's married lover -- from a death by fire. The agent gets back on the case when the sicko, seen always dressed in black, sends him by Federal Express a photo of his next victim. The killer's modus operandi is to watch the victim while undetected, get their routine down pat, and then sneak into their apartment, gag them and tie them up, dance with them, touch them without having sex and then strangle them with a piano wire. He makes for an uninteresting sicko, reading his banal lines with an undue need for perfect enunciation, as if he were trying out for an amateur Shakespeare production in some small Midwestern town. Rumor has it, that Reeves expected this role to only be a cameo and when the role was expanded and his pay wasn't, he became unhappy with the studio, as a result refusing to do any publicity work for them on the film.
James Spader as the lead, most of his career a fine actor, has difficulty doing anything with his uninteresting and predictable role, except go with the props he's been handed. He needs reassurance from his shrink that he can catch the killer, but gets further frustrated that he is unsuccessful in saving the lives of two nondescript women, who can't be warned in time of the danger they are in, despite their pictures plastered all over the windy city and on the TV news.
The film covers up its banal story by some pretty slick shooting devices, which include fast-paced editing and jump-cuts, and at times, when taking on the killer's point of view, making the beautiful color go grainy with a hand-held video camera, which is meant to signify that there is some kind of mystery going on. What the sleek looking film accomplishes, is that it makes this hollow film very watchable, looking much like an MTV video.
The film ultimately fails because the story is never drawn out enough to make it spellbinding or tense, the main characters are both too hackneyed to bring something to the table except an exercise in playing a role that goes against their film career typecasting. After about 80 minutes into the film, it breaks completely down in silliness and incredibility, as the killer and the cop - yin and yang - meet face-to-face for the first time in the cemetery where Spader's girlfriend is buried and continue their cat-and-mouse relationship, this time over the pretty therapist, who is held hostage in the same way his former girlfriend was, which makes for a dicey ending to a dicey pic.
REVIEWED ON 9/15/2000 GRADE: C
Dennis Schwartz: "Ozus' World Movie Reviews"
http://www.sover.net/~ozus
<EMAIL_ADDRESS>© ALL RIGHTS RESERVED DENNIS SCHWARTZ
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
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Quadractic Equation Solver
Quadratic Equation Solver
Coefficient for x
2
:
Coefficient for x:
Constant term:
Result
The roots of a quadratic equation
ax
2
+
bx
+
c
= 0 is solved using the following steps:
Step 1 -----
Step 2 -----
Step 3 -----
Step 4 -----
The derivation of this formula can be outlined as follows:
Divide both sides of the equation
ax
2
+
bx
+
c
= 0 by
a
.
Transpose the quantity
c
/
a
to the right side of the equation.
Complete the square by adding
b
2
/ 4
a
2
to both sides of the equation.
Factor the left side and combine the right side.
Extract the square-root of both sides of the equation.
Solve for
x
by transporting the quantity
b
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a
to the right side of the equation.
Combine the right side of the equation to get the quadratic formula.


TABLE OF INTERACTIONS WITH
ALFENTANIL
See also: agonist opioid analgesics
See also: step III opioid analgesics
See also: opioids
See also: sedative medications
See also: substrates at risk of CYP3A4
From the French ANSM Drug Interactions September 2016, p. 9
ALFENTANIL RxNorm: 480 ATC: N01AH02 |
CIMETIDINE RxNorm: 2541 ATC: A02BA01 |
With cimetidine used in doses greater than or equal to 800mg/day: increase of the opioid analgesic's effect of respiratory depression due to decrease of its hepatic metabolism |
Precaution for use Adjust the dosage of the alfentanil when administered with the cimetidine. |
ALFENTANIL RxNorm: 480 ATC: N01AH02 |
DILTIAZEM RxNorm: 3443 ATC: C08DB01 |
Increase of the opioid analgesic's effect of respiratory depression due to decrease of its hepatic metabolism |
Precaution for use Adjust the dosage of the alfentanil when administered with the diltiazem. |
ALFENTANIL RxNorm: 480 ATC: N01AH02 |
ERYTHROMYCIN RxNorm: 4053 ATC: D10AF02 J01FA01 S01AA17 |
Increase of the opioid analgesic's effect of respiratory depression due to decrease of its hepatic metabolism |
Precaution for use Adjust the dosage of the alfentanil when administered with the erythromycin |
ALFENTANIL RxNorm: 480 ATC: N01AH02 |
FLUCONAZOLE RxNorm: 4450 ATC: D01AC15 J02AC01 |
Increase of the opioid analgesic's effect of respiratory depression due to decrease of its hepatic metabolism |
Precaution for use Adjust the dosage of the alfentanil when administered with the fluconazole |
ALFENTANIL RxNorm: 480 ATC: N01AH02 |
STRONG INHIBITORS OF CYP3A4 CLASS CODE: J02-J05 |
Increase of the opioid analgesic's effect of respiratory depression due to decrease of its hepatic metabolism . |
Precaution for use Clinical monitoring and adjustment of the dosage of the opioid analgesic when administered with a strong inhibitor of CYP3A4. |


I'm Dunja! I'm a third year Computer Science student at the University of Waterloo in Waterloo, Canada.
I love all things web development, the outdoors, and self-improvement. I enjoy participating in hackathons and working on meaningful and inventive side projects. I also like board games, podcasts, cooking, and the Oxford comma.
I'm currently trying to build my web development portfolio - shoot me a message if you're in need of a website! Here's another one I created.
This summer, I worked at Square in Kitchener, where I was an Android developer on Cash App . I'd never done any mobile development at that point, so it was a great learning experience.
This past fall, I was a software engineering intern at Accenture in Toronto, Canada where my team and I built a proof of concept for verifying the authenticity of high-value designer goods using blockchain technology.
The winter before that I interned as a full stack developer at Hyalto in Ottawa, Canada.
I did my very first co-op term at LoyaltyOne in Toronto, Canada where I worked as a service desk representative and "have you tried turning it off and on again?" was a large part of my vocabulary.
I am the Vice President of Technology for The GoldenHack, a business hackathon based out of Wilfrid Laurier University in Waterloo, ON.
The hackathon had its inaugural event in October 2019 and it was a huge success. We had over 150 participants who all gave overwhelmingly positive feedback of the event.
This year, I'm leading an interdisciplinary team of 6 in building a new version of the website for the event, and an applicant dashboard and application portal. The whole team is incredibly talented, and I can't wait to see what we accomplish this year.
In my spare time, I strive to be an active and positive member of my community. Last fall, I started volunteering with the Girl Guides of Canada where I helped lead a group of Brownies (adorable 7-8 year olds!) in learning, fun, and yes, cookies. This is us at the local Santa Claus Parade!
While I was working in Toronto in Fall 2019, I also volunteered at St. Alban's Boys and Girls Club for a great program called Raise the Grade where I was a high-school math tutor.
I love to travel! Here are a couple snaps of my latest family trip to Hawaii.
I also love podcasts so much I decided to join one. Listen to me speak about nerdy space stuff on episodes 173, 174, and 175 of The Star Spot.
At the moment, this is my favourite book, this is my favourite movie, and this is my favourite Tiny Desk Concert.


HappyFunTimes Unity Install Helper
Install HappyFunTimes
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Trevor Bedford and Sidney Bell
{{ page.date | date: "%-d %b %Y" }}
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Project to provide a real-time view of the evolving influenza population
in collaboration with Richard Neher
with John Huddleston and Richard Neher
"The future is here, it's just not evenly distributed yet"
— William Gibson
Clade frequencies $X$ derive from the fitnesses $f$ and frequencies $x$ of constituent viruses, such that
$$\hat{X}_v(t+\Delta t) = \sum_{i:v} x_i(t) \, \mathrm{exp}(f_i \, \Delta t)$$
This captures clonal interference between competing lineages
The fitness $f$ of virus $i$ is estimated as
$$\hat{f}_i = \beta^\mathrm{A} \, f_i^\mathrm{A} + \beta^\mathrm{B} \, f_i^\mathrm{B} + \ldots$$
where A, B, etc... are different standardized viral attributes
We learn $\beta$ coefficients from most recent 12 years of H3N2 evolution
Optimal $\beta$ coefficients minimize sum of squared errors between observed clade frequencies and frequency estimated in a 1-year look ahead
Fit to a subset of non-nested clades
| Model | $\beta$ coefficient | Growth correlation | Growth accuracy |
|---|---|---|---|
| Antigenic drift based on HI model (cTiterSub) | 0.33 | 0.29 | 69% |
| Cross-immunity based on epitope mutations (ep_x) | 0.50 | 0.18 | 60% |
| Protein function based on deep mutational scanning (dms) | 0.20 | 0.01 | 57% |
| Clade growth based on local branching index (lbi) | 0.39 | 0.30 | 61% |
Dengue (serotype 2)
Flu (H3N2)
DENV4-I associated with adverse events
Serotypes are genetically distinct
Serotypes are antigenically distinct
Clades are genetically distinct
Are clades antigenically distinct?
Interserotype hypothesis
Full tree hypothesis
PRNT50 titers from monovalent vaccine trials + nonhuman primates
Interserotype model
Full tree model
Dengue
Flu (H3N2)
Clade frequencies $X$ derive from the clade fitness $f_i$, such that
$$\hat{X}_i(t+\Delta t) = X_i(t) \, \mathrm{exp}(f_i \, \Delta t)$$
Estimate fitness as frequency-weighted antigenic distance*
from recently circulating clades
* estimated from the interserotype or full tree antigenic model.
Circulating clades:
Population immunity:
Population susceptibility:
Clade growth:
Interserotype model
Full tree model
Dengue serotype flux
62% of variation explained
5 year windows
Flu clade turnover
53% of variation explained
1 year windows
Similar models of antigenic evolution are effective
for pathogens with very different evolutionary dynamics
Prospective modeling can reveal relative contributions to viral fitness
Bedford Lab:
Alli Black,
Sidney Bell,
John Huddleston,
Barney Potter,
James Hadfield,
Louise Moncla,
Tom Sibley,
Maya Lewinsohn
Influenza: WHO Global Influenza Surveillance Network, GISAID, Richard Neher, John Huddleston, Barney Potter, James Hadfield, Rod Daniels, Boris Shraiman, Colin Russell, Andrew Rambaut, Dave Wentworth, Becky Garten, Jackie Katz, Marta Łuksza, Michael Lässig, Richard Reeve
Dengue: Leah Katzelnick, Molly O'hainle, Richard Neher, Paul Edlefsen, Michal Juraska


I am a computer science student at KAIST. I love programming in C++ and Python. I like
algorithms, optimization, computer networks and functional languages. Occasionally I do experiments
in JS. You can find me on twitter, facebook and linkedIn. Say hello to me!


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This document serves to test various sample HTML elements and there representation in the PDF.
Let's create some basic form elements.


Other Entities Dashboard
Welcome, {{ user.firstName|default('SNAC User') }}, to the Other Entities dashboard! This dashboard provides access to search, add, and manage non-CPF objects in SNAC. Here, you'll find concept (vocabulary) terms, geographic places, and our cache of Resources.
{% if permissions.EditVocabulary %}
{% endif %}
{% if permissions.EditVocabulary %}
{% endif %}
{% if permissions.EditVocabulary %}
{% endif %}
{% if permissions.EditResources %}
{% endif %}
Concept Management
Manage concept vocabularies.
Vocabulary Management
Manage controlled vocabularies.
Geographic Vocabulary Management
Manage geographic vocabulary terms.
Resource Cache Management
Manage the resource cache.


C* Primer - Part 1 of 2
3 Challenges and Cassandra Internals
cqlsh> SELECT * FROM presenters WHERE eventname='CassandraPrimer' AND company='ING'; eventname | company | name | title | area | twitter -----------------+---------+---------------------+--------------+------+----------- CassandraPrimer | ING | Christopher Reedijk | Dev Engineer | NL | @creedijk CassandraPrimer | ING | Gary Stewart | Dev Engineer | NL | @Gaz_GandA (2 rows)
ING Netherlands (@ingnl)
a little bit about us
Gary Stewart
Christopher Reedijk
- Dev Engineer
- Love/hate relationship with C*
- 14 years of experience in IT
- @ING since: 01-01-2012
- Dev Engineer
- Back-end guy
- 10 years of experience in IT
- @ING since: 01-01-2011
3 Challenges
challenge 1 Improve availability
without trading consistency
availability & consistency
Consequences of not being available
source: nu.nl
availability & consistency
Consequences of not being consistent
challenge 2
Aim to be easier scalable
Changes are happening at an increasing pace
easier scalable
Stop focusing on the
expected loadyesterday
source: bradfrostweb.com
easier scalable
Start focusing on the
unexpected load
      today tomorrow   source: bradfrostweb.com
challenge 3
Adopt new ways of thinking
Become the top engineering company
new way of thinking
ING's culture is changing fast.
Waterfall to Scrum to DevOps in less than 2 years
new way of thinking
Aim to approach problems as green field
"Un-learn" principles that don't scale
Use cache correctly!
Reduce locking (transaction)
Cassandra Internals
Objectives
Introduction into Apache Cassandra and Datastax
Learn about the drivers
Learn about partitioning, replication factor and consistency levels
Learn about the network topology
Learn about the write and read path
DataStax and Cassandra
Founded in 2010 by Jonathan Ellis and Matt Pfeil
80% Apache Cassandra code contribution
Offer commercial support for DataStax Enterprise version of Cassandra
DataStax Enterprise integrates Search and Analytics
Head Quarter in San Francisco Bay area
EMEA office opened in March 2013
Native Protocol / DataStax Drivers
CQL3 Only
Fully asynchronous protocol, using Netty
Server notifications
Java, C#, Python, node.js and Ruby, ...
Many policies, including:
TokenAwarePolicy, DowngradeConsistencyRetryPolicy, etc...
Metrics (codahale) included
com.datastax.cassandra cassandra-driver-core 2.0.8
Apache Cassandra
Created by Avinash Lakshman and Prashant Malik at Facebook
Cassandra is
Cassandra is not
- A distributed database
- Highly scalable
- Fault tolerant
- Consistency is tunable
- High throughput
- Network topology aware
- Multi-DC Active-Active
- Written in Java
- A column family
- CQL3
- ACID compliant database
- A relational database
- ANSI SQL compliant
- A document database
Logical Grouping
Keyspace: group of tables
Table: group of data
Replication Factor:
Number of copies of data
Cassandra Replication Strategy
Token Range 0 -> 2127-1 in Ring Formation
Consistent Hashing Algorithm
Default: Murmur3 Partitioner
Replica nodes in clockwise
Gossip protocol between nodes
Replication Factor (RF) = 3
Network Topology Awareness
Tuneable Consistency
Configureable Consistency Level (CL)
per read and write action
Writes
- Any
- One
- Two
- Three
- Local_Quorum
- Quorum
- Each_Quorum
- All
Reads
- One
- Two
- Three
- Local_Quorum
- Quorum
- Each_Quorum
- All
Strong Consistency
CAP – Consistency, Availability, Partition Tolerance
Consistency is achieved with W + R > RF
Quorum = (Integer) RF / 2 + 1
Write Path
Read Path
Summary
- Cassandra is a masterless NoSQL storage engine
- Cassandra's consistency is tuneable per query
- Replication factor is defined per keyspace
- Partitioning is used to determine which node contains the data
- Write path writes to both commitlog and Memtables
- The commitlog is an append-only log, replayed to restore a downed node's Memtables
- Read path is designed to limit i/o as much as possible
- SStables are immutable
Thank you
...see Part 2 of 2: Modelling Basics and 3 Facts
Contact
<EMAIL_ADDRESS>@creedijk
<EMAIL_ADDRESS>@Gaz_GandA
this presentation is created using:
reveal.js


West Fulton High School
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This is my first web page. A new entry in my programming world. Nothing serious, just practicing with the basics. Just because this is my first page, I wanna devote this to my favourite hobby, POETRY!!!!!
Amidst the maze of increasing academics, poetry is one of the major things that helps me find my way out of this. Poetry lets me express my own thoughts freely, no one can interfere with what I pen down!!!!!
I keep running with the rhythm of rain
With absurd alliterations ablazing in brain.....
It's quite fun when you are with the freedom of wordplay, styles and imagination. All this takes me to an entirely different world,away from everything in vicinity with the reality.
Below listed are some of my favourite poets who are the Greatest Of All Time:


{% extends "structure.html" %} {% block general_controls %} {{ super() }}
Computing... (Please don't close page until finished)
Filtering VCF... Please don't close page until finished. The result will appear as a download file


<
bracket
>
is an app that offers fill-in-the-blank coding challenges. For every correct answer, the player will be rewarded 1 point. A leaderboard displays who has the top 10 highest scores. The player may choose from the 5 categories: Recursion, Data Structures, Algorithms, Time Complexity, and Loops.
This is a great tool for interview practice, general understanding, competition, and fun!
The app includes interactive completion of challenges. The styling and coloring is similar to that of Sublime Text.
<
bracket
>
was developed using NativeScript. NativeScript allows for building native cross-platform mobile applications using XML, CSS, and JavaScript.
Feel free to visit the NativeScript website to learn more.
Our developer team consisted of Yossi Rosenstein, Aaron Nussbaum, Becca Lesser, and Leah Bar-On Simmons. Each of us used our expertises to construct the application.
<
bracket
>
To start, if you already have an account, click login. Otherwise, click the register button as seen below and complete all necessary information. From there, you will be redirected to the Choose A Challenge page. If you have logged in within the last few days, the application will automatically keep you logged in and Choose A Challenge will be the first page to show up.
Login (iOS)
Register (iOS)
Login (Android)
Register (Android)
Next, if you'd like to start completing challenges, you should choose one of the categories, which are computer science concepts. The five options are: Recursion, Data Structures, Algorithms, Time Complexity, and Loops. This page can also be accessed via the 'Challenges' tab.
Choose A Challenge (iOS)
Choose A Challenge (Android)
The page you will be shown after choosing a category contains a swipe view of the challenges for that topic. You can scroll left and right through them to see the title and description of each one. You may also use the next and previous buttons to do this. Once you find one you like, click it.
Scroll View (iOS)
Scroll View (Android)
Once on the challenge page, you will notice that there is a comment at the top with a more detailed description of the challenge and a snippet of C++ code that lies below it. Within the snippet, there are little white text boxes. Those must be filled in with the correct values to make the code work. To check if you are correct, click the submit button. Any text boxes that have a red background are incorrect, and any text boxes that have a green background are correct. Capitalization and whitespace are not taken into account when responses are checked. Each correct box rewards you with one point. The total points are equivalent to the number of white boxes. Your scores for each challenge will be recorded. However, the application will only keep your highest score for each challenge.
Challenge Not Complete (iOS)
Challenge Complete (iOS)
Challenge Not Complete (Android)
Challenge Complete (Android)
To see information regarding your account, number of challenges completed/attempted, and total points, you should go to the profile page. This can be reached by clicking on the button in the tab view that says 'Profile'.
Profile (iOS)
Profile (Android)
There is a ranking of the top 10 highest scores under the tab that says 'Leaderboard'. It shows the user's name and number of points for each ranking.
Leaderboard (iOS)
Leaderboard (Android)


- Industrial experience is important for Product based
companies while hiring fresh talent - Students only get theoretical knowledge through lectures.
Experience comes from solving real world problems - No easy way exists for student to work on industry projects
and get hands on work experience


Mobile Project Management
Services Provided
- Content Development
- Conceptual Product Design Development
- Prototype Creation
- Original Writing
- Substantive Editing
- Editorial Services
- Page-for-Page Design
- Cover Design
- Production
- Image Creation
- Image Procurement
- Studio Photography
- Photo Research and Permissions
- Technical Art
- Image Capture and Manipulation
- Project Management
- File Preflight and PDF Release
Product Description
Created non-fiction leveled readers and associated teacher support for 48 titles on science and social studies topics tied to the standards for grades 4-6. Created series using three design formats that were applied in multiple color palettes across all 48 titles to achieve visual variety, and protect the "program look," while reducing the need for high per-book design budgets.


Tufts Health Plan 10 km
Boston MA/USA
Website: www.tuftshealthplan.com/tufts10k
Race Winners
(42 runnings)
08 Oct
2007
32:01.0
09 Oct
2006
32:37.5
10 Oct
2005
32:25.2
11 Oct
2004
32:47.4
13 Oct
2003
32:33.3
14 Oct
2002
31:45.4
08 Oct
2001
32:11
08 Oct
2000
32:46
11 Oct
1999
32:49
12 Oct
1998
31:57
13 Oct
1997
32:46
14 Oct
1996
32:47
09 Oct
1995
32:30
10 Oct
1994
31:39
11 Oct
1993
32:02
12 Oct
1992
32:22
14 Oct
1991
32:12
08 Oct
1990
32:39
09 Oct
1989
31:57
10 Oct
1988
31:38
12 Oct
1987
32:22
13 Oct
1986
31:40
14 Oct
1985
31:49
08 Oct
1984
31:51
10 Oct
1983
31:36
11 Oct
1982
31:42
12 Oct
1981
32:04
13 Oct
1980
32:24
08 Oct
1979
32:47
09 Oct
1978
33:15
1977
34:31
Notes: 1977 was the inaugural year. For 1984 and earlier years, this
race was known as Bonne Bell. The course was first certified in 1986; courses prior to 1986 are considered to have
been about 150 meters short, based on the race time bias (see below).
09 Oct
2017
33:30
Lauren Paquette
(TN/USA)
10 Oct
2016
31:47
Emily Sisson
(RI/USA)
(nc)
12 Oct
2015
31:20.4
Molly Huddle-4
(nc)
13 Oct
2014
31:38.5
Jordan Hasay (OR/USA)
14 Oct
2013
31:32.9
Sentayehu Ejigu (ETH)
08 Oct
2012
32:29.8
Helen Chemutai (KEN)
(nc)
10 Oct
2011
32:46.4
Janet Cherobon (GA/USA)
(nc)
11 Oct
2010
31:59.9
Molly
Huddle-3
(nc)
12 Oct
2009
32:06.6
Molly
Huddle-2
(nc)
13 Oct
2008
32:51.2
Molly Huddle (IN/USA)
(nc)
Deena Kastor (CA/USA) (nc)
Kathryn McGregor-2 (nc)
Kathryn McGregor (MN/USA) (nc)
Marie Davenport (IRL) (nc)
Elva Dryer (NM/USA) (nc)
Marla Runyan (OR/USA) (nc)
Colleen deReuck (CO/USA)
Catherine Ndereba (KEN) (nc)
Libbie Hickman-2 (nc)
Libbie Hickman (USA) (nc)
Gladys Ondeyo-2 (nc)
Gladys Ondeyo (KEN) (nc)
Collette Murphy (USA) (nc)
Elana Meyer (RSA) (nc)
Lynn Jennings-6
Lynn Jennings-5
Lynn Jennings-4
Lynn Jennings-3
Lynn Jennings-2
Anne Hannam (NZL)
Nancy Tinari (CAN)
Ingrid Kristiansen (NOR)
Joan Samuelson-3
Betty Jo Geiger (USA)
Joan Samuelson-2
Anne Audain (NZL)
Jan Merrill (USA)
Patti Catalano (USA)
Margaret Groos (USA)
Joan Samuelson (USA)
Lynn Jennings (USA)
(nc) => national champion
2012 Chelsea Reilly was the national champion (2nd
place, 32:40.9)
2004 Amy Begley was the national champion (3rd place, 33:11.9)
2000 Libbie Hickman was the national champion (2ndplace, 32:47)
1997 Kim Jones was the national champion (2nd place, 32:49)
1996 Anne-Marie Lauck was the national champion (3rd place, 33:16)
1994 Lynn Jennings was the national champion (2nd place, 31:48)
Most Wins
(1977-date)
Women
6
Lynn Jennings (USA)
1977,1989-93
Prize Money
(ADR qualified runners)
Year Women 2016 $42,275 2015 42,200 2014 38,700 2013 42,060 2012 42,550 2011 39,600 2010 38,215 2009 36,925 2008 35,550 2007 34,100 2006 32,500 2005 30,950 2004 28,750 2003 26,675 2002 25,500 2001 21,800 2000 19,700 1999 21,700 1998 19,750 1997 16,600 1996 16,600 1995 16,600 1994 13,200 1993 13,700 1992 13,200 1991 13,200 .... 1989 14,200 1988 14,000 1987 13,500 1986 4,500 1985 4,000 1984 14,450 Sum $787,250
Race Time Bias (sec/km)
Year Women 2017 +3.0 2016 -1.6 2015 -0.2 2014 -3.6 2013 -1.6 2012 -3.0 2011 +1.5 2010 -4.1 2009 -3.5 2008 -0.6 2007 -4.3 2006 +3.1 2005 -2.9 2004 +0.2 2003 +1.3 2002 -2.5 2001 -2.3 2000 -1.6 1999 -0.1 1998 -3.6 1997 -1.7 1996 +0.1 1995 +0.6 1994 -1.8 1993 -1.3 1992 -1.1 1991 -1.1 1990 +0.7 1989 -0.4 1988 +0.0 1987 -0.8 1986 -0.8 Avg -1.1 1985 -2.6 1984 -2.9 1983 -5.2 1982 -3.6 1981 -4.7 1980 -0.6 1979 -6.9 Avg -3.8
Notes: The course was first certified for the 1986 race. This resulted
in a shift of the average race time bias of 27 seconds, suggesting that the course prior to the 1986 certification
was roughly 150 meters short.
Competitive Level
(1990-date)
Year Women 2017 459 2016 590 2015 746 2014 735 2013 623 2012 641 2011 560 2010 580 2009 665 2008 557 2007 655 2006 705 2005 568 2004 560 2003 613 2002 736 2001 528 2000 749 1999 749 1998 784 1997 692 1996 736 1995 790 1994 934 1993 862 1992 823 1991 769 1990 641 Quality Performances
Women
<31:30 1
<32:00 21
<32:30 59
<33:00 142
<33:30 263
<34:00 380
<34:30 508
<35:00 644
Note: Performances for 1985 and earlier are excluded.
Last Updated on 11 Oct 2017
Contributor: Granite State Race Services (3)


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Top
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.simplemarquee({
speed: 100,
cycles: 5,
space: 100,
delayBetweenCycles: 5000,
handleHover: false,
handleResize: false
});
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QR Code Pattern Recognition and Message Extraction
This project is maintained by astorfi
The goal of this project is to successfully detect and reconstruct perfect QR-code pattern and then decode and extract the message and information within. Usually the QR-code images are corrupted, Blurred or at least rotated which make the pattern recognition harder than simple scenarios. The following image, demonstrates a damaged QR-Code:
.
In this situation robust algorithms can effectively recognize specic patterns in the image and reconstruct the main matrix of quick response code. The project has two main part:
In the first part of this project the implemented software, which is developed using MATLAB, successfully recognize the main QR pattern and then extract the QR-Matrix. Here's an example of performing this phase: .
Then by using decoding techniques, the message is extracted from the QR-code. The QR-matrix might be corrupted, i.e. we probably lost some amount of information. According to this scenario, QR-codes are made by Error-Correction Coding which help us through the decoding process. As an example a sample final output depicted in the following image: .
Run Mfiles/MAIN.m file. Two things needs to be considered:
The following code of the MAIN.m file demonstrates the two aforementioned matter.
prompt = {'Please input the version:'};
dlg_title = 'Version';
num_lines = 1;
def = {'2'};
answer = inputdlg(prompt,dlg_title,num_lines,def);
[version, ~] = str2num(answer{1});
module=4*version+17; % Number of modules(General formula=4*(Version Number)+17)
Im = imread('path_to_image'); % Input image.
The reliability is based on two important consideration:
The second item related to the number of errors which Reed-Solomon decoder should correct. If the error is more than the maximum ability of the error correction then the message cannot be correctly and fully decoded. Since this part is not the main goal of this project/course so we don't go deep any further in this area.
There is different scenarios and different approaches to overcome the deficiencies. We investigate them from the easy to the worst case scenario.
Suppose that we have a clear image and straight point of view. This is the easiest scenario. We believe that the software can easily detect the finder patterns and alignment pattern and the QR code reconstruction would be easy. In other scenarios even if the image is blurred or damaged, if we can find this 4-points, then we can perfectly reconstruct the QR code.
The caveat is that what if the simulation finds a wrong alignment pattern or finder patter? The answer is according to the characteristics of the finder patter it is very unlikely to find a wrong finder pattern. The software has been tested for wide variety of images. The problem is that it is likely for software to find a point as the Alignment Pattern while it is not!! The reason that we have add another diagonal ration search for AP candidates is to prevent this phenomenon and by testing different test images we conclude that the developed software is reliable to that even.
Sometimes the software cannot find the AP because there might be no AP in a damaged image or there is but the software cannot be find the Alignment Pattern. The former has no solution for finding AP but for the later we can allow more error in searching for the alignment pattern. This ability is defined in the software.
The caveat is that allowing more error might lead to finding an Alignment Pattern which is not the correct one. The final solution for that which prevent this issue is that if the software cannot detect AP then ignore it and use the last corner of the QR-Code as the fourth point for perspective transformation. But the previous algorithm that we developed for this task was not very precise because if the image is not from straight point of view the if the looking angle is far from 90 even by 20 degree, then the software fail to find the fourth corner of the QR-Code\footnote{Fourth corner refers to the corner which is not connected to any of three finder patterns} pattern. In better words it finds something which is not the correct one. The last algorithm that we implemented for this issue is to find two candidates for the fourth corner and using the average. According to the test images that we used for this algorithm, it is more efficient than the previous one.
If the simulation cannot find more than three finder pattern then it is very difficult to tackle this problem. It may happen in very rare scenarios which the image is very blurred and almost the pattern cannot be scene. The solution is to use different thresholding in order to find and locate the finder patterns and separators. In this scenario we try the previous approach for finding the fourth corner of QR code and try to reconstruct the pattern.
The advantage of finding the fourth corner is that is lots of the scenarios we can have a straight image from the pattern whether blurred and noisy or not. Using this method we don't need to have the Alignment Pattern location. It might be less reliable but if there is not any other choice it can be applied.
The simulation sometimes fails to decode the version 5-Q and 5-H QR code because of the more complex interleaving pattern of the data and error correction code-words. The author is working on modifying the software to successfully decode 5-Q and 5-H QR codes and any contribution is more than welcome for that part or any other parts.
Since this project is only considering the version 1 through 6 then an interesting extend to this project can be modifying software for having correct results for higher versions.
Since the version number should be enter by the software executive man another interesting extend can be developing a software to automatically detect the version of QR-Code which is a practical task and can be well implemented in real life applications.
The other issue to finding the alignment pattern. The software should be adapted such that if there is any Alignment pattern it has the reliability to locate that. Basically our software do the job quite well but testing its reliability in deeper sense can improve its efficiency.
In order to demonstration of precision and authentication of software, different test images have been tested. Note that this project is restricted to QR codes version-1 through version-6.


Implements the JSON, INI, YAML and TOML parser for R setting and writing of configuration file. The functionality of this package is similar to that of package 'config'.
| Version: | 0.3.4 |
| Depends: | R (≥ 3.3.0) |
| Imports: | jsonlite (≥ 1.2), ini (≥ 0.2), yaml (≥ 2.1.3), RcppTOML (≥ 0.1.3), stringr (≥ 1.2.0), utils, glue |
| Suggests: | testthat, knitr, rmarkdown, prettydoc |
| Published: | 2018-11-13 |
| Author: | Jianfeng Li [aut, cre] |
| Maintainer: | Jianfeng Li <lee_jianfeng at sjtu.edu.cn> |
| BugReports: | https://github.com/Miachol/configr/issues |
| License: | MIT + file LICENSE |
| URL: | https://github.com/Miachol/configr |
| NeedsCompilation: | no |
| Materials: | README NEWS ChangeLog |
| CRAN checks: | configr results |
| Reference manual: | configr.pdf |
| Vignettes: |
Usage of configr Configuration file types |
| Package source: | configr_0.3.4.tar.gz |
| Windows binaries: | r-devel: configr_0.3.4.zip, r-release: configr_0.3.4.zip, r-oldrel: configr_0.3.4.zip |
| OS X binaries: | r-release: configr_0.3.4.tgz, r-oldrel: configr_0.3.4.tgz |
| Old sources: | configr archive |
| Reverse imports: | BioInstaller, ngstk |
Please use the canonical form https://CRAN.R-project.org/package=configr to link to this page.


|
QuantumLibrary
|
Exposes methods to manipulate the coroutine context, especially future wait methods. More...
#include <quantum_icoro_context_base.h>
Public Types | |
| using | Ptr = std::shared_ptr< ICoroContextBase > |
| Public Types inherited from Bloomberg::quantum::IContextBase | |
| using | Ptr = std::shared_ptr< IContextBase > |
| Public Types inherited from Bloomberg::quantum::ICoroSync | |
| using | Ptr = std::shared_ptr< ICoroSync > |
Public Member Functions | |
| virtual void | wait (ICoroSync::Ptr sync) const =0 |
| Waits for the future associated with this context to be ready. More... | |
| virtual std::future_status | waitFor (ICoroSync::Ptr sync, std::chrono::milliseconds timeMs) const =0 |
| Waits for the future associated with this context to be ready for a maximum of 'timeMs' milliseconds. More... | |
| virtual void | waitAt (int num, ICoroSync::Ptr sync) const =0 |
| Waits for the future in the 'num-th' continuation context to be ready. More... | |
| virtual std::future_status | waitForAt (int num, ICoroSync::Ptr sync, std::chrono::milliseconds timeMs) const =0 |
| Waits for the future in the 'num-th' continuation context to be ready for a maximum of 'timeMs' milliseconds. More... | |
| virtual void | waitAll (ICoroSync::Ptr sync) const =0 |
| Wait for all the futures in the continuation chain to be ready. More... | |
| Public Member Functions inherited from Bloomberg::quantum::IContextBase | |
| virtual | ~IContextBase ()=default |
| Virtual destructor. More... | |
| virtual bool | valid () const =0 |
| Determines if the future object associated with this context has a valid shared state with the corresponding promise. More... | |
| virtual bool | validAt (int num) const =0 |
| Determines if the future object associated with the 'num'-th continuation context is still valid. More... | |
| virtual int | setException (std::exception_ptr ex)=0 |
| Set an exception in the promise associated with the current IThreadContext or ICoroContext. More... | |
| Public Member Functions inherited from Bloomberg::quantum::ICoroSync | |
| virtual | ~ICoroSync ()=default |
| Default virtual destructor. More... | |
| virtual void | setYieldHandle (Traits::Yield &yield)=0 |
| Sets the underlying boost::coroutine object so that it can be yielded on. More... | |
| virtual Traits::Yield & | getYieldHandle ()=0 |
| Retrieve the underlying boost::coroutine object. More... | |
| virtual void | yield ()=0 |
| Explicitly yields this coroutine context. More... | |
| virtual std::atomic_int & | signal ()=0 |
| Accessor to the underlying synchronization variable. More... | |
| virtual void | sleep (const std::chrono::milliseconds &timeMs)=0 |
| Sleeps the coroutine associated with this context for at least 'timeMs' milliseconds or 'timeUs' microseconds depending on the overload chosen. More... | |
| virtual void | sleep (const std::chrono::microseconds &timeUs)=0 |
Exposes methods to manipulate the coroutine context, especially future wait methods.
| using Bloomberg::quantum::ICoroContextBase::Ptr = std::shared_ptr<ICoroContextBase> |
|
pure virtual |
Waits for the future associated with this context to be ready.
| [in] | sync | Pointer to the coroutine synchronization object. |
Implemented in Bloomberg::quantum::Context< RET >.
|
pure virtual |
Wait for all the futures in the continuation chain to be ready.
| [in] | sync | Pointer to the coroutine synchronization object. |
Implemented in Bloomberg::quantum::Context< RET >.
|
pure virtual |
Waits for the future in the 'num-th' continuation context to be ready.
Allowed range for num is [-1, total_continuations). -1 is equivalent of calling wait() or waitAt(total_continuations-1) on the last context in the chain (i.e. the context which is returned via end()). Position 0 represents the first future in the chain.
| [in] | num | The number indicating which future to wait on. |
| [in] | sync | Pointer to the coroutine synchronization object. |
Implemented in Bloomberg::quantum::Context< RET >.
|
pure virtual |
Waits for the future associated with this context to be ready for a maximum of 'timeMs' milliseconds.
| [in] | sync | Pointer to the coroutine synchronization object. |
| [in] | timeMs | The maximum amount of milliseconds to wait until the future value becomes ready. |
Implemented in Bloomberg::quantum::Context< RET >.
|
pure virtual |
Waits for the future in the 'num-th' continuation context to be ready for a maximum of 'timeMs' milliseconds.
Allowed range for num is [-1, total_continuations). -1 is equivalent of calling wait() or waitAt(total_continuations-1) on the last context in the chain (i.e. the context which is returned via end()). Position 0 represents the first future in the chain.
| [in] | num | The number indicating which future to wait on. |
| [in] | sync | Pointer to the coroutine synchronization object. |
| [in] | timeMs | The maximum amount of milliseconds to wait until the future value becomes ready. |
Implemented in Bloomberg::quantum::Context< RET >.


05 May 1999
Disease Outbreak Reported
An outbreak has been reported in the Watsa zone, in the northeastern part of the country. Clinical features include fever, severe headache, lassitude, gastrointestinal bleeding and agitation, but according to the health care workers who have looked after many of the patients, haemorrhagic features are not the norm. Death has generally occurred 5-6 days after onset. The first cases are believed to have taken place in January 1999, however there are indications that since 1994, there have been multiple small outbreaks of apparently similar illness in the vicinity of Durba, hence the locally-used description of "Durba syndrome". Durba, the site of pit gold-mining operations, is situated about 20 km from Watsa. Up to 3 May, 68 cases have been recorded, with 63 deaths (case-fatality rate 93%). Most of the cases have occurred in gold miners, and a few in the general community. The chief medical officer of the zone was among the fatalities, but it seems that secondary spread to family contacts or health care workers has been a rare event.
Staff from the WHO Regional Office for Africa and a joint team from Médecins sans frontières (Belgium and the Netherlands) have arrived in the area. Clinical samples collected from 5 patients have been sent to a reference laboratory for diagnostic testing and an epidemiological investigation is under way. An isolation unit has been set up in Durba, and local health care personnel are being instructed in preventive and protective measures, as well as in case management. This report is preliminary, and updated information will be provided as soon as it becomes available.


Cyber-Physic Risk Management Ontology in Critical Infrastructures: Overview
back to
ToC
This ontology has the following classes and properties.


Utility classes for JCuda.
Package Specification
This package contains utility classes for JCuda. This includes helper
classes that may be used for benchmarking, unit tests and simple file
operations, as well as classes that simplify different JCuda
programming tasks.


Two Rohingya drug suspects die in alleged shootout with BGB in Bandarban
by bdnews24.comTwo Rohingya refugees with alleged links to crime have been killed in an alleged shootout with Border Guards Bangladesh or BGB in Bandarban’s Naikhyangchhari Upazila.
The dead men have been identified as Md Yasin Ali, 30, and Hossain Ali, 23, both residents of Kutupalong refugee camp in Cox’s Bazar’s Ukhia Upazila.
The incident took place near the Ghumdhum-Tumbru border area around 4am on Sunday, said Sub-Inspector Emon Kanti Chowdhury of Ghumdhum police outpost.
“A gang of criminals opened fire when they were spotted by a BGB patrol unit, forcing the border security personnel to retaliate,” BGB-34 Battalion Commander Col Ali Haider Azad Ahmed said.
“At one point, the criminals fled the scene leaving the bullet-ridden bodies of the two men behind.”
They were members of a drug cartel, said Ahmed, adding that 40,000 yaba tablets, a gun and two bullets were recovered from the scene.
The bodies were taken to Cox’s Bazar Sadar Hospital’s morgue for autopsy, SI Emon Kanti said.
Earlier on Nov 11, a gang of drug smugglers opened fire on two BGB troopers near the Ghumdhum border area. They were later admitted to the Combined Military Hospital (CMH) in Cox’s Bazar’s Ramu, he added.


From scala collection library documentation:
xs.reverseIterator
is the same as
xs.reverse.iterator
but might be more efficient.


created 12/10/99; revised 01/22/00; edited 07/18/02
A boolean value (a true/false
value) picks the branch
of an if
statement or allows a loop to continue.
Sometimes the boolean expression is more complicated
than we have seen so far in these notes.
Often a program must make a decision based on a number of factors.
This chapter will examine this topic and discuss some additional boolean
operators.
&&
and ||
operators.&
and |
operators.&&
operators.||
operators.&&
and &&
operators.&&
and ||
operators.


mcc -o BAT_simulate -W WinMain:BAT_simulate -T link:exe -d 'C:\Users\kkgor\OneDrive\Professional Current DnD\McGill_Docs\BAT_local\Binary_Activity_Thermodynamics_Model\Matlab_runtime\BAT_Model\for_testing' -R '-logfile,BAT_log.txt' -v 'C:\Users\kkgor\OneDrive\Professional Current DnD\McGill_Docs\BAT_local\Binary_Activity_Thermodynamics_Model\Matlab_source_code\Highlevel_programs\Run_VBSBAT_from_input_file_with_timer.m' -a 'C:\Users\kkgor\OneDrive\Professional Current DnD\McGill_Docs\BAT_local\Binary_Activity_Thermodynamics_Model\Matlab_source_code\Lowlevel_programs\matlab_graphing_related\BAT_logo.mat' -a 'C:\Users\kkgor\OneDrive\Professional Current DnD\McGill_Docs\BAT_local\Binary_Activity_Thermodynamics_Model\Matlab_source_code\Lowlevel_programs\matlab_graphing_related\load_bat_logo.m' -r 'C:\Users\kkgor\OneDrive\Professional Current DnD\McGill_Docs\BAT_local\Binary_Activity_Thermodynamics_Model\Matlab_runtime\BAT_Model_resources\icon.ico' Compiler version: 7.0 (R2018b) Dependency analysis by REQUIREMENTS. [Warning: Your deployed application may error out because file or folder paths not present in the deployed environment may be included in your MATLAB startup file. Use the MATLAB function "isdeployed" in your MATLAB startup file to determine the appropriate execution environment when including file and folder paths, and recompile your application. ] Parsing file "C:\Users\kkgor\OneDrive\Professional Current DnD\McGill_Docs\BAT_local\Binary_Activity_Thermodynamics_Model\Matlab_source_code\Highlevel_programs\Run_VBSBAT_from_input_file_with_timer.m" (Referenced from: "Compiler Command Line"). Deleting 9 temporary MEX authorization files. Removing: 'C:\Users\kkgor\AppData\Local\Temp\dbd7-ee65-5226-f31f_17264.auth'. Removing: 'C:\Users\kkgor\AppData\Local\Temp\2684-a050-2480-dad3_17264.auth'. Removing: 'C:\Users\kkgor\AppData\Local\Temp\15df-925a-923b-9616_17264.auth'. Removing: 'C:\Users\kkgor\AppData\Local\Temp\1d0a-caf9-7631-7dd3_17264.auth'. Removing: 'C:\Users\kkgor\AppData\Local\Temp\e21a-e69e-f318-fa13_17264.auth'. Removing: 'C:\Users\kkgor\AppData\Local\Temp\d0ca-5ab2-aaf2-0954_17264.auth'. Removing: 'C:\Users\kkgor\AppData\Local\Temp\3a3b-a6f6-7bf1-7aa2_17264.auth'. Removing: 'C:\Users\kkgor\AppData\Local\Temp\76ce-8da0-eba7-4a06_17264.auth'. Removing: 'C:\Users\kkgor\AppData\Local\Temp\9f99-41e7-8328-8240_17264.auth'. Generating file "C:\Users\kkgor\OneDrive\Professional Current DnD\McGill_Docs\BAT_local\Binary_Activity_Thermodynamics_Model\Matlab_runtime\BAT_Model\for_testing\readme.txt". Packaging... Creating the bundle... Web based installer created at C:\Users\kkgor\OneDrive\Professional Current DnD\McGill_Docs\BAT_local\Binary_Activity_Thermodynamics_Model\Matlab_runtime\BAT_Model\for_redistribution\BAT_installer_v1_web.exe. Packaging complete. Elapsed packaging time was: 1 minutes and 8 seconds.


I am recently persuing B.tech in computer science and engineering from invertis university.I belive in work more then words.I have good leardership skills.I m a good listner as well as a good speaker.I have good confidence and communication skills as well. I have a good knowledge of languages such as c and i m learning python.I like to make strategies for the task given to me. I make sure to give the result of the work given to me in a best way.
Apart from technical skills if talk about soft skills i have good communication skills ,and i m also good at presenting my ideas and cocept in a way second person could understand them.i am kind of workholic reason i want to have good job where i can enjoy and use my skills to represent an organization with my good work and help it to grow furthur in every possible way.I like to work in teams as well
Read moreALL KIND WEBSITE DESIGNING
ALL KIND ADVERTISEMNET OF YOUR PRODUCTS AND ORGANIZATION
ALL KIND APPLICATION DESIGNING
If you liked my profile and want to contact me go through furthur details and reach out to me. You are free to share your views with me through mails about similar intrests.


=== Run information ===
Scheme: weka.classifiers.trees.J48 -C 0.25 -M 2
Relation: pima_diabetes
Instances: 526
Attributes: 9
preg
plas
pres
skin
insu
mass
pedi
age
class
Test mode: evaluate on training data
=== Classifier model (full training set) ===
_$1Problem 9a*_
_$2Problem 9b*_
J48 pruned tree
------------------
_$2plas <= 127*_
| age <= 30: tested_negative (210.0/20.0)
| _$2age > 30*_
| | mass <= 27: tested_negative (23.0/1.0)
| | _$2mass > 27*_
| | | _$2pedi <= 0.514*_
| | | | _$2plas <= 106: _$4tested_negative*_ (37.0/5.0)*_
| | | | plas > 106
| | | | | pres <= 66: tested_positive (7.0/1.0)
| | | | | pres > 66: tested_negative (23.0/8.0)
| | | pedi > 0.514: tested_positive (36.0/13.0)
_$1plas > 127*_
| _$1mass <= 29.8*_
| | _$1plas <= 164*_
| | | pres <= 55: tested_positive (2.0)
| | | _$1pres > 55*_
| | | | _$1age <= 41: _$3tested_negative*_ (19.0)*_
| | | | age > 41
| | | | | age <= 53: tested_positive (3.0)
| | | | | age > 53: tested_negative (11.0/1.0)
| | plas > 164
| | | mass <= 25.8: tested_positive (4.0)
| | | mass > 25.8
| | | | preg <= 5
| | | | | plas <= 186: tested_positive (2.0)
| | | | | plas > 186: tested_negative (2.0)
| | | | preg > 5: tested_negative (2.0)
| mass > 29.8
| | preg <= 7
| | | plas <= 157
| | | | pres <= 86
| | | | | pres <= 60: tested_positive (9.0/1.0)
| | | | | pres > 60
| | | | | | preg <= 2
| | | | | | | mass <= 41.8: tested_negative (16.0)
| | | | | | | mass > 41.8: tested_positive (4.0/1.0)
| | | | | | preg > 2
| | | | | | | pedi <= 0.233: tested_negative (6.0)
| | | | | | | pedi > 0.233
| | | | | | | | pres <= 66: tested_negative (3.0)
| | | | | | | | pres > 66
| | | | | | | | | age <= 41
| | | | | | | | | | age <= 33: tested_positive (4.0)
| | | | | | | | | | age > 33: tested_negative (4.0/1.0)
| | | | | | | | | age > 41: tested_positive (7.0)
| | | | pres > 86: tested_positive (10.0/1.0)
| | | plas > 157: tested_positive (52.0/10.0)
| | preg > 7: tested_positive (30.0/2.0)
Number of Leaves : 25
Size of the tree : 49
Time taken to build model: 0.04 seconds
=== Predictions on training set ===
Predictions for dataset pima_diabetes
First five instances
| inst# | actual | predicted | error | prediction |
| 1 | 2:tested_positive | 2:tested_positive | | 1 |
| 2 | 1:tested_negative | 1:tested_negative | | 0.957 |
| 3 | 2:tested_positive | 2:tested_positive | | 1 |
| 4 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 5 | 2:tested_positive | 2:tested_positive | | 0.889 |
| inst# | actual | predicted | error | prediction |
| 6 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 7 | 2:tested_positive | 1:tested_negative | + | 0.905 |
| 8 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 9 | 2:tested_positive | 2:tested_positive | | 0.808 |
| 10 | 2:tested_positive | 1:tested_negative | + | 0.957 |
| 11 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 12 | 2:tested_positive | 2:tested_positive | | 0.933 |
| 13 | 1:tested_negative | 1:tested_negative | | 0.909 |
| 14 | 2:tested_positive | 2:tested_positive | | 0.808 |
| 15 | 2:tested_positive | 2:tested_positive | | 1 |
| 16 | 2:tested_positive | 1:tested_negative | + | 0.865 |
| 17 | 2:tested_positive | 2:tested_positive | | 0.639 |
| 18 | 2:tested_positive | 1:tested_negative | + | 0.652 |
| 19 | 1:tested_negative | 1:tested_negative | | 0.865 |
| 20 | 2:tested_positive | 2:tested_positive | | 0.639 |
| 21 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 22 | 1:tested_negative | 1:tested_negative | | 0.865 |
| 23 | 2:tested_positive | 2:tested_positive | | 0.808 |
| 24 | 2:tested_positive | 1:tested_negative | + | 0.905 |
| 25 | 2:tested_positive | 2:tested_positive | | 0.933 |
| 26 | 2:tested_positive | 1:tested_negative | + | 0.652 |
| 27 | 2:tested_positive | 2:tested_positive | | 1 |
| 28 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 29 | 1:tested_negative | 1:tested_negative | | 0.909 |
| 30 | 1:tested_negative | 1:tested_negative | | 0.652 |
| 31 | 1:tested_negative | 2:tested_positive | + | 0.639 |
| 32 | 2:tested_positive | 2:tested_positive | | 0.808 |
| 33 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 34 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 35 | 1:tested_negative | 1:tested_negative | | 0.652 |
| 36 | 1:tested_negative | 1:tested_negative | | 0.957 |
| 37 | 1:tested_negative | 2:tested_positive | + | 0.933 |
| 38 | 2:tested_positive | 2:tested_positive | | 0.639 |
| 39 | 2:tested_positive | 1:tested_negative | + | 0.905 |
| 40 | 2:tested_positive | 2:tested_positive | | 0.639 |
| 41 | 1:tested_negative | 2:tested_positive | + | 0.808 |
| 42 | 1:tested_negative | 1:tested_negative | | 0.75 |
| 43 | 1:tested_negative | 1:tested_negative | | 0.957 |
| 44 | 2:tested_positive | 2:tested_positive | | 0.933 |
| 45 | 1:tested_negative | 1:tested_negative | | 1 |
| 46 | 2:tested_positive | 2:tested_positive | | 0.808 |
| 47 | 1:tested_negative | 1:tested_negative | | 1 |
| 48 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 49 | 2:tested_positive | 1:tested_negative | + | 0.865 |
| 50 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 51 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 52 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 53 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 54 | 2:tested_positive | 2:tested_positive | | 0.933 |
| 55 | 1:tested_negative | 1:tested_negative | | 1 |
| 56 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 57 | 2:tested_positive | 2:tested_positive | | 0.808 |
| 58 | 1:tested_negative | 2:tested_positive | + | 0.639 |
| 59 | 1:tested_negative | 1:tested_negative | | 1 |
| 60 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 61 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 62 | 2:tested_positive | 2:tested_positive | | 0.933 |
| 63 | 1:tested_negative | 1:tested_negative | | 0.957 |
| 64 | 1:tested_negative | 1:tested_negative | | 1 |
| 65 | 2:tested_positive | 2:tested_positive | | 0.857 |
| 66 | 1:tested_negative | 1:tested_negative | | 0.865 |
| 67 | 2:tested_positive | 2:tested_positive | | 0.639 |
| 68 | 1:tested_negative | 2:tested_positive | + | 0.639 |
| 69 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 70 | 1:tested_negative | 1:tested_negative | | 1 |
| 71 | 2:tested_positive | 1:tested_negative | + | 0.905 |
| 72 | 1:tested_negative | 1:tested_negative | | 1 |
| 73 | 2:tested_positive | 2:tested_positive | | 0.639 |
| 74 | 1:tested_negative | 1:tested_negative | | 1 |
| 75 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 76 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 77 | 1:tested_negative | 1:tested_negative | | 0.865 |
| 78 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 79 | 2:tested_positive | 2:tested_positive | | 0.889 |
| 80 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 81 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 82 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 83 | 1:tested_negative | 2:tested_positive | + | 0.639 |
| 84 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 85 | 2:tested_positive | 2:tested_positive | | 0.9 |
| 86 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 87 | 1:tested_negative | 1:tested_negative | | 0.865 |
| 88 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 89 | 2:tested_positive | 2:tested_positive | | 0.933 |
| 90 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 91 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 92 | 1:tested_negative | 1:tested_negative | | 0.652 |
| 93 | 1:tested_negative | 1:tested_negative | | 0.865 |
| 94 | 2:tested_positive | 1:tested_negative | + | 0.909 |
| 95 | 1:tested_negative | 1:tested_negative | | 1 |
| 96 | 1:tested_negative | 1:tested_negative | | 0.75 |
| 97 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 98 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 99 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 100 | 2:tested_positive | 1:tested_negative | + | 0.652 |
| 101 | 2:tested_positive | 2:tested_positive | | 0.808 |
| 102 | 1:tested_negative | 1:tested_negative | | 1 |
| 103 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 104 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 105 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 106 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 107 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 108 | 1:tested_negative | 1:tested_negative | | 1 |
| 109 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 110 | 2:tested_positive | 1:tested_negative | + | 0.905 |
| 111 | 2:tested_positive | 2:tested_positive | | 0.808 |
| 112 | 2:tested_positive | 2:tested_positive | | 0.933 |
| 113 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 114 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 115 | 2:tested_positive | 2:tested_positive | | 0.808 |
| 116 | 2:tested_positive | 2:tested_positive | | 0.9 |
| 117 | 2:tested_positive | 1:tested_negative | + | 0.652 |
| 118 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 119 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 120 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 121 | 2:tested_positive | 2:tested_positive | | 0.808 |
| 122 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 123 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 124 | 1:tested_negative | 1:tested_negative | | 0.909 |
| 125 | 2:tested_positive | 1:tested_negative | + | 0.905 |
| 126 | 2:tested_positive | 1:tested_negative | + | 0.905 |
| 127 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 128 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 129 | 2:tested_positive | 1:tested_negative | + | 0.652 |
| 130 | 2:tested_positive | 2:tested_positive | | 0.639 |
| 131 | 2:tested_positive | 2:tested_positive | | 1 |
| 132 | 2:tested_positive | 2:tested_positive | | 0.639 |
| 133 | 2:tested_positive | 2:tested_positive | | 0.808 |
| 134 | 1:tested_negative | 1:tested_negative | | 0.865 |
| 135 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 136 | 1:tested_negative | 2:tested_positive | + | 0.857 |
| 137 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 138 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 139 | 1:tested_negative | 1:tested_negative | | 1 |
| 140 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 141 | 1:tested_negative | 1:tested_negative | | 0.909 |
| 142 | 1:tested_negative | 1:tested_negative | | 0.865 |
| 143 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 144 | 2:tested_positive | 2:tested_positive | | 0.857 |
| 145 | 1:tested_negative | 1:tested_negative | | 1 |
| 146 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 147 | 1:tested_negative | 1:tested_negative | | 0.865 |
| 148 | 1:tested_negative | 2:tested_positive | + | 0.639 |
| 149 | 1:tested_negative | 1:tested_negative | | 1 |
| 150 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 151 | 1:tested_negative | 1:tested_negative | | 1 |
| 152 | 1:tested_negative | 1:tested_negative | | 0.957 |
| 153 | 2:tested_positive | 2:tested_positive | | 0.933 |
| 154 | 1:tested_negative | 1:tested_negative | | 1 |
| 155 | 2:tested_positive | 2:tested_positive | | 0.933 |
| 156 | 2:tested_positive | 2:tested_positive | | 0.9 |
| 157 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 158 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 159 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 160 | 2:tested_positive | 2:tested_positive | | 0.933 |
| 161 | 1:tested_negative | 1:tested_negative | | 1 |
| 162 | 1:tested_negative | 1:tested_negative | | 0.865 |
| 163 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 164 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 165 | 2:tested_positive | 2:tested_positive | | 0.9 |
| 166 | 2:tested_positive | 2:tested_positive | | 0.639 |
| 167 | 1:tested_negative | 1:tested_negative | | 1 |
| 168 | 1:tested_negative | 2:tested_positive | + | 0.639 |
| 169 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 170 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 171 | 2:tested_positive | 1:tested_negative | + | 0.865 |
| 172 | 2:tested_positive | 2:tested_positive | | 1 |
| 173 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 174 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 175 | 1:tested_negative | 1:tested_negative | | 0.865 |
| 176 | 2:tested_positive | 2:tested_positive | | 0.933 |
| 177 | 1:tested_negative | 1:tested_negative | | 0.865 |
| 178 | 2:tested_positive | 2:tested_positive | | 0.9 |
| 179 | 1:tested_negative | 1:tested_negative | | 1 |
| 180 | 2:tested_positive | 1:tested_negative | + | 0.75 |
| 181 | 1:tested_negative | 1:tested_negative | | 0.957 |
| 182 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 183 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 184 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 185 | 1:tested_negative | 1:tested_negative | | 1 |
| 186 | 2:tested_positive | 2:tested_positive | | 0.808 |
| 187 | 2:tested_positive | 2:tested_positive | | 0.933 |
| 188 | 2:tested_positive | 2:tested_positive | | 0.9 |
| 189 | 2:tested_positive | 2:tested_positive | | 0.639 |
| 190 | 2:tested_positive | 2:tested_positive | | 1 |
| 191 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 192 | 1:tested_negative | 1:tested_negative | | 0.652 |
| 193 | 2:tested_positive | 2:tested_positive | | 0.808 |
| 194 | 2:tested_positive | 2:tested_positive | | 0.933 |
| 195 | 1:tested_negative | 1:tested_negative | | 0.957 |
| 196 | 2:tested_positive | 2:tested_positive | | 0.808 |
| 197 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 198 | 2:tested_positive | 1:tested_negative | + | 0.905 |
| 199 | 2:tested_positive | 1:tested_negative | + | 0.905 |
| 200 | 2:tested_positive | 2:tested_positive | | 0.889 |
| 201 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 202 | 1:tested_negative | 1:tested_negative | | 1 |
| 203 | 1:tested_negative | 2:tested_positive | + | 0.639 |
| 204 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 205 | 1:tested_negative | 1:tested_negative | | 0.865 |
| 206 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 207 | 2:tested_positive | 2:tested_positive | | 0.933 |
| 208 | 2:tested_positive | 2:tested_positive | | 0.808 |
| 209 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 210 | 2:tested_positive | 2:tested_positive | | 0.808 |
| 211 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 212 | 1:tested_negative | 2:tested_positive | + | 0.75 |
| 213 | 1:tested_negative | 2:tested_positive | + | 0.808 |
| 214 | 2:tested_positive | 2:tested_positive | | 0.75 |
| 215 | 2:tested_positive | 1:tested_negative | + | 0.652 |
| 216 | 2:tested_positive | 2:tested_positive | | 0.933 |
| 217 | 2:tested_positive | 1:tested_negative | + | 0.905 |
| 218 | 1:tested_negative | 1:tested_negative | | 0.652 |
| 219 | 2:tested_positive | 2:tested_positive | | 0.639 |
| 220 | 2:tested_positive | 2:tested_positive | | 0.857 |
| 221 | 2:tested_positive | 2:tested_positive | | 0.808 |
| 222 | 2:tested_positive | 2:tested_positive | | 0.808 |
| 223 | 1:tested_negative | 1:tested_negative | | 0.957 |
| 224 | 1:tested_negative | 1:tested_negative | | 0.909 |
| 225 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 226 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 227 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 228 | 2:tested_positive | 2:tested_positive | | 0.808 |
| 229 | 1:tested_negative | 2:tested_positive | + | 0.808 |
| 230 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 231 | 2:tested_positive | 2:tested_positive | | 1 |
| 232 | 2:tested_positive | 2:tested_positive | | 1 |
| 233 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 234 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 235 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 236 | 2:tested_positive | 2:tested_positive | | 0.808 |
| 237 | 2:tested_positive | 2:tested_positive | | 0.808 |
| 238 | 2:tested_positive | 2:tested_positive | | 0.808 |
| 239 | 2:tested_positive | 2:tested_positive | | 0.933 |
| 240 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 241 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 242 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 243 | 2:tested_positive | 2:tested_positive | | 1 |
| 244 | 2:tested_positive | 2:tested_positive | | 0.639 |
| 245 | 1:tested_negative | 1:tested_negative | | 1 |
| 246 | 2:tested_positive | 2:tested_positive | | 0.933 |
| 247 | 1:tested_negative | 1:tested_negative | | 0.652 |
| 248 | 1:tested_negative | 2:tested_positive | + | 0.808 |
| 249 | 1:tested_negative | 1:tested_negative | | 0.652 |
| 250 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 251 | 1:tested_negative | 1:tested_negative | | 0.865 |
| 252 | 1:tested_negative | 1:tested_negative | | 1 |
| 253 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 254 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 255 | 2:tested_positive | 2:tested_positive | | 0.639 |
| 256 | 2:tested_positive | 1:tested_negative | + | 0.905 |
| 257 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 258 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 259 | 1:tested_negative | 1:tested_negative | | 1 |
| 260 | 2:tested_positive | 2:tested_positive | | 0.933 |
| 261 | 1:tested_negative | 2:tested_positive | + | 0.808 |
| 262 | 2:tested_positive | 2:tested_positive | | 0.889 |
| 263 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 264 | 1:tested_negative | 1:tested_negative | | 1 |
| 265 | 2:tested_positive | 2:tested_positive | | 0.857 |
| 266 | 1:tested_negative | 2:tested_positive | + | 0.639 |
| 267 | 2:tested_positive | 2:tested_positive | | 0.889 |
| 268 | 1:tested_negative | 1:tested_negative | | 1 |
| 269 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 270 | 2:tested_positive | 2:tested_positive | | 1 |
| 271 | 2:tested_positive | 2:tested_positive | | 0.639 |
| 272 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 273 | 1:tested_negative | 1:tested_negative | | 0.957 |
| 274 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 275 | 1:tested_negative | 1:tested_negative | | 0.865 |
| 276 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 277 | 2:tested_positive | 1:tested_negative | + | 0.905 |
| 278 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 279 | 1:tested_negative | 1:tested_negative | | 0.957 |
| 280 | 1:tested_negative | 1:tested_negative | | 1 |
| 281 | 1:tested_negative | 1:tested_negative | | 1 |
| 282 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 283 | 1:tested_negative | 2:tested_positive | + | 0.9 |
| 284 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 285 | 1:tested_negative | 1:tested_negative | | 0.909 |
| 286 | 2:tested_positive | 1:tested_negative | + | 0.905 |
| 287 | 1:tested_negative | 1:tested_negative | | 0.957 |
| 288 | 1:tested_negative | 1:tested_negative | | 0.957 |
| 289 | 1:tested_negative | 1:tested_negative | | 0.909 |
| 290 | 2:tested_positive | 2:tested_positive | | 0.808 |
| 291 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 292 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 293 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 294 | 2:tested_positive | 2:tested_positive | | 0.889 |
| 295 | 2:tested_positive | 2:tested_positive | | 0.75 |
| 296 | 1:tested_negative | 1:tested_negative | | 1 |
| 297 | 1:tested_negative | 2:tested_positive | + | 0.808 |
| 298 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 299 | 1:tested_negative | 1:tested_negative | | 1 |
| 300 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 301 | 1:tested_negative | 1:tested_negative | | 0.865 |
| 302 | 1:tested_negative | 1:tested_negative | | 0.865 |
| 303 | 2:tested_positive | 2:tested_positive | | 0.639 |
| 304 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 305 | 1:tested_negative | 1:tested_negative | | 1 |
| 306 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 307 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 308 | 2:tested_positive | 2:tested_positive | | 1 |
| 309 | 1:tested_negative | 1:tested_negative | | 1 |
| 310 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 311 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 312 | 2:tested_positive | 2:tested_positive | | 0.639 |
| 313 | 1:tested_negative | 2:tested_positive | + | 0.639 |
| 314 | 1:tested_negative | 1:tested_negative | | 0.865 |
| 315 | 1:tested_negative | 1:tested_negative | | 0.865 |
| 316 | 2:tested_positive | 2:tested_positive | | 0.808 |
| 317 | 1:tested_negative | 1:tested_negative | | 1 |
| 318 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 319 | 1:tested_negative | 1:tested_negative | | 0.957 |
| 320 | 2:tested_positive | 1:tested_negative | + | 0.865 |
| 321 | 1:tested_negative | 1:tested_negative | | 1 |
| 322 | 1:tested_negative | 1:tested_negative | | 0.957 |
| 323 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 324 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 325 | 2:tested_positive | 2:tested_positive | | 0.808 |
| 326 | 2:tested_positive | 2:tested_positive | | 0.933 |
| 327 | 1:tested_negative | 1:tested_negative | | 0.652 |
| 328 | 1:tested_negative | 1:tested_negative | | 0.865 |
| 329 | 1:tested_negative | 1:tested_negative | | 0.909 |
| 330 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 331 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 332 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 333 | 2:tested_positive | 2:tested_positive | | 0.933 |
| 334 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 335 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 336 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 337 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 338 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 339 | 1:tested_negative | 1:tested_negative | | 0.957 |
| 340 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 341 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 342 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 343 | 1:tested_negative | 1:tested_negative | | 0.865 |
| 344 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 345 | 2:tested_positive | 2:tested_positive | | 0.889 |
| 346 | 1:tested_negative | 1:tested_negative | | 0.865 |
| 347 | 1:tested_negative | 1:tested_negative | | 0.957 |
| 348 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 349 | 2:tested_positive | 2:tested_positive | | 0.9 |
| 350 | 2:tested_positive | 2:tested_positive | | 0.639 |
| 351 | 2:tested_positive | 2:tested_positive | | 1 |
| 352 | 2:tested_positive | 2:tested_positive | | 0.639 |
| 353 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 354 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 355 | 2:tested_positive | 2:tested_positive | | 0.933 |
| 356 | 2:tested_positive | 2:tested_positive | | 0.808 |
| 357 | 1:tested_negative | 1:tested_negative | | 1 |
| 358 | 1:tested_negative | 2:tested_positive | + | 0.808 |
| 359 | 1:tested_negative | 1:tested_negative | | 1 |
| 360 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 361 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 362 | 1:tested_negative | 1:tested_negative | | 0.652 |
| 363 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 364 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 365 | 1:tested_negative | 1:tested_negative | | 0.957 |
| 366 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 367 | 1:tested_negative | 1:tested_negative | | 0.652 |
| 368 | 1:tested_negative | 1:tested_negative | | 0.865 |
| 369 | 1:tested_negative | 1:tested_negative | | 0.865 |
| 370 | 2:tested_positive | 1:tested_negative | + | 0.652 |
| 371 | 2:tested_positive | 2:tested_positive | | 0.808 |
| 372 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 373 | 1:tested_negative | 1:tested_negative | | 0.957 |
| 374 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 375 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 376 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 377 | 1:tested_negative | 1:tested_negative | | 0.865 |
| 378 | 1:tested_negative | 1:tested_negative | | 0.75 |
| 379 | 2:tested_positive | 2:tested_positive | | 0.857 |
| 380 | 1:tested_negative | 1:tested_negative | | 0.865 |
| 381 | 1:tested_negative | 1:tested_negative | | 1 |
| 382 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 383 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 384 | 1:tested_negative | 1:tested_negative | | 1 |
| 385 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 386 | 1:tested_negative | 1:tested_negative | | 0.957 |
| 387 | 2:tested_positive | 1:tested_negative | + | 0.905 |
| 388 | 1:tested_negative | 1:tested_negative | | 1 |
| 389 | 2:tested_positive | 2:tested_positive | | 0.808 |
| 390 | 2:tested_positive | 2:tested_positive | | 0.9 |
| 391 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 392 | 1:tested_negative | 1:tested_negative | | 0.957 |
| 393 | 1:tested_negative | 1:tested_negative | | 0.865 |
| 394 | 2:tested_positive | 2:tested_positive | | 0.639 |
| 395 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 396 | 2:tested_positive | 2:tested_positive | | 0.933 |
| 397 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 398 | 2:tested_positive | 2:tested_positive | | 0.808 |
| 399 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 400 | 2:tested_positive | 2:tested_positive | | 0.639 |
| 401 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 402 | 2:tested_positive | 2:tested_positive | | 1 |
| 403 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 404 | 1:tested_negative | 2:tested_positive | + | 0.639 |
| 405 | 2:tested_positive | 2:tested_positive | | 0.808 |
| 406 | 1:tested_negative | 1:tested_negative | | 0.865 |
| 407 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 408 | 2:tested_positive | 2:tested_positive | | 0.808 |
| 409 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 410 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 411 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 412 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 413 | 2:tested_positive | 2:tested_positive | | 1 |
| 414 | 2:tested_positive | 2:tested_positive | | 1 |
| 415 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 416 | 2:tested_positive | 2:tested_positive | | 0.808 |
| 417 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 418 | 1:tested_negative | 1:tested_negative | | 1 |
| 419 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 420 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 421 | 2:tested_positive | 2:tested_positive | | 0.808 |
| 422 | 2:tested_positive | 2:tested_positive | | 0.808 |
| 423 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 424 | 2:tested_positive | 2:tested_positive | | 0.933 |
| 425 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 426 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 427 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 428 | 2:tested_positive | 2:tested_positive | | 0.639 |
| 429 | 2:tested_positive | 1:tested_negative | + | 0.905 |
| 430 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 431 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 432 | 1:tested_negative | 2:tested_positive | + | 0.808 |
| 433 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 434 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 435 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 436 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 437 | 1:tested_negative | 1:tested_negative | | 1 |
| 438 | 1:tested_negative | 1:tested_negative | | 1 |
| 439 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 440 | 2:tested_positive | 2:tested_positive | | 0.639 |
| 441 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 442 | 1:tested_negative | 2:tested_positive | + | 0.639 |
| 443 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 444 | 1:tested_negative | 1:tested_negative | | 1 |
| 445 | 2:tested_positive | 2:tested_positive | | 1 |
| 446 | 2:tested_positive | 2:tested_positive | | 0.808 |
| 447 | 2:tested_positive | 2:tested_positive | | 1 |
| 448 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 449 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 450 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 451 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 452 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 453 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 454 | 2:tested_positive | 2:tested_positive | | 0.889 |
| 455 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 456 | 1:tested_negative | 2:tested_positive | + | 0.639 |
| 457 | 1:tested_negative | 1:tested_negative | | 0.652 |
| 458 | 2:tested_positive | 1:tested_negative | + | 0.905 |
| 459 | 1:tested_negative | 1:tested_negative | | 0.909 |
| 460 | 2:tested_positive | 2:tested_positive | | 0.808 |
| 461 | 2:tested_positive | 2:tested_positive | | 0.933 |
| 462 | 2:tested_positive | 2:tested_positive | | 0.933 |
| 463 | 2:tested_positive | 2:tested_positive | | 0.857 |
| 464 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 465 | 2:tested_positive | 2:tested_positive | | 1 |
| 466 | 2:tested_positive | 1:tested_negative | + | 0.652 |
| 467 | 1:tested_negative | 1:tested_negative | | 0.865 |
| 468 | 1:tested_negative | 2:tested_positive | + | 0.933 |
| 469 | 1:tested_negative | 2:tested_positive | + | 0.808 |
| 470 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 471 | 1:tested_negative | 1:tested_negative | | 0.865 |
| 472 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 473 | 1:tested_negative | 2:tested_positive | + | 0.639 |
| 474 | 2:tested_positive | 2:tested_positive | | 0.808 |
| 475 | 2:tested_positive | 2:tested_positive | | 1 |
| 476 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 477 | 2:tested_positive | 1:tested_negative | + | 0.905 |
| 478 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 479 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 480 | 2:tested_positive | 2:tested_positive | | 0.808 |
| 481 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 482 | 2:tested_positive | 1:tested_negative | + | 0.905 |
| 483 | 1:tested_negative | 1:tested_negative | | 0.909 |
| 484 | 1:tested_negative | 1:tested_negative | | 1 |
| 485 | 1:tested_negative | 1:tested_negative | | 1 |
| 486 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 487 | 1:tested_negative | 1:tested_negative | | 1 |
| 488 | 2:tested_positive | 2:tested_positive | | 0.75 |
| 489 | 1:tested_negative | 1:tested_negative | | 0.957 |
| 490 | 2:tested_positive | 2:tested_positive | | 0.933 |
| 491 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 492 | 2:tested_positive | 2:tested_positive | | 1 |
| 493 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 494 | 2:tested_positive | 2:tested_positive | | 0.9 |
| 495 | 2:tested_positive | 2:tested_positive | | 0.808 |
| 496 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 497 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 498 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 499 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 500 | 2:tested_positive | 2:tested_positive | | 0.639 |
| 501 | 2:tested_positive | 2:tested_positive | | 0.808 |
| 502 | 1:tested_negative | 2:tested_positive | + | 0.889 |
| 503 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 504 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 505 | 2:tested_positive | 1:tested_negative | + | 0.905 |
| 506 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 507 | 2:tested_positive | 2:tested_positive | | 0.933 |
| 508 | 2:tested_positive | 1:tested_negative | + | 0.905 |
| 509 | 1:tested_negative | 2:tested_positive | + | 0.808 |
| 510 | 1:tested_negative | 1:tested_negative | | 0.652 |
| 511 | 2:tested_positive | 2:tested_positive | | 0.933 |
| 512 | 1:tested_negative | 1:tested_negative | | 1 |
| 513 | 1:tested_negative | 1:tested_negative | | 0.865 |
| 514 | 2:tested_positive | 2:tested_positive | | 0.808 |
| 515 | 2:tested_positive | 2:tested_positive | | 0.808 |
| 516 | 1:tested_negative | 1:tested_negative | | 0.957 |
| 517 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 518 | 2:tested_positive | 1:tested_negative | + | 0.865 |
| 519 | 1:tested_negative | 1:tested_negative | | 0.865 |
| 520 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 521 | 2:tested_positive | 2:tested_positive | | 1 |
| 522 | 1:tested_negative | 1:tested_negative | | 0.652 |
| 523 | 1:tested_negative | 1:tested_negative | | 0.652 |
| 524 | 1:tested_negative | 1:tested_negative | | 0.652 |
| 525 | 1:tested_negative | 1:tested_negative | | 0.905 |
| 526 | 1:tested_negative | 1:tested_negative | | 1 |
=== Evaluation on training set ===
Time taken to test model on training data: 0.4 seconds
=== Summary ===
Correctly Classified Instances 461
87.6426%
Incorrectly Classified Instances 65
12.3574 %
Kappa statistic 0.7205
Mean absolute error 0.1973
Root mean squared error 0.3141
Relative absolute error 44.1711 %
Root relative squared error 66.4764 %
Total Number of Instances 526
=== Detailed Accuracy By Class ===
TP Rate FP Rate Precision Recall F-Measure MCC ROC Area PRC Area Class
0.917 0.203 0.899 0.917 0.908 0.721 0.906 0.928 tested_negative
0.797 0.083 0.829 0.797 0.813 0.721 0.906 0.838 tested_positive
Weighted Avg. 0.876 0.163 0.876 0.876 0.876 0.721 0.906 0.898
=== Confusion Matrix ===
a b <-- classified as
320 29
| a = tested_negative
36 141
| b = tested_positive
Information Gain Report
=== Run information ===
Evaluator: weka.attributeSelection.InfoGainAttributeEval
Search: weka.attributeSelection.Ranker -T -1.7976931348623157E308 -N -1
Relation: pima_diabetes
Instances: 526
Attributes: 9
preg
plas
pres
skin
insu
mass
pedi
age
class
Evaluation mode: evaluate on all training data
=== Attribute Selection on all input data ===
Search Method:
Attribute ranking.
Attribute Evaluator (supervised, Class (nominal): 9 class):
Information Gain Ranking Filter
Ranked attributes:
0.1841 2 plas
0.0812 8 age
0.0753 6 mass
0.0578 5 insu
0.0563 1 preg
0.0377 7 pedi
0 3 pres
0 4 skin
Selected attributes: 2,8,6,5,1,7,3,4 : 8


{% extends 'base.html' %} {% block title %} Animal Facts {% endblock %} {% block content %}
Here is your animal fact:
Your animal fact goes here.


Methods
(static) sessionCounter()
The sessionCounter
is used with setTimeout() to repeatedly decrement the session time. Ideally,
we'd look at the start time of the session time.
- Source:
expireSession()
expireSession() expires the session and requests termination of
sessionCounter().
- Source:
startSession()
startSession() starts the 1 second calls of sessionCounter().
- Source:


| Number of Classes | Line Coverage | Mutation Coverage |
|---|---|---|
| 5 | 0% | 0% |
| Name | Line Coverage | Mutation Coverage |
|---|---|---|
| BijectiveNsMap.java | 0% |
0% |
| DOMOutputElement.java | 0% |
0% |
| DOMReaderImpl.java | 0% |
0% |
| DOMWriterImpl.java | 0% |
0% |
| OutputElementBase.java | 0% |
0% |


Orchid Island Capital: In A Coma
by Michael A. Gayed, CFASummary
- Orchid Island Capital (ORC) has paid $10.705 as total dividend per share since its IPO in 2013.
- The company reported a disappointing Q3 2019 and was hit by analyst downgrades.
- For now, it’s a risky risk-off stock.
Compound interest is the eighth wonder of the world. He who understands it, earns it ... he who doesn't ... pays it. ~Albert Einstein
The good news is that Orchid Island Capital (ORC) has been paying a consistent dividend. The bad news is that dividend payouts are diminishing, and the ugly news is that the stock price has been in deep coma since the last few years.
So, should you consider it as a dividend play? Does the stock have potential to bounce up? Should you hold on to it or exit? Here’s my analysis:
A Benign Business Model
ORC invests in RMBS (Residential Mortgage-backed Securities), which is a safe model because the principal and interest are guaranteed by the mortgage associations (Fannie Mae, Freddie Mac and Ginnie Mae).
While this sounds good and secure on paper, the problems with the RBMS business is that it involves borrowing short-term and buying long-term mortgage securities to take advantage of the interest differential (logic: short-term rates are lower, long-term rates are higher).
Now, when the yield curve inverts or flattens, the margins get slaughtered, and this scenario has started playing out over the past few months:
The company’s margins have started dipping and the future seems uncertain, because despite the Fed cutting rates, the yield curve has plunged after every bump up.
Residential Mortgage Interest Rates Outlook 2020
Freddie Mac is of the opinion that mortgage rates will remain muted in 2020. It expects the 30-year fixed-rate mortgage rate to average 3.8% in 2020, which is near the current rates (Dec 2019).
For Q3 2019, ORC suffered a net loss on GAAP basis of $0.14 per share. Earnings per share were $0.18 and $0.32 was lost because of net realized/unrealized gains and losses on financial instruments. We can reasonably expect that the company will report similar figures (per quarter) in 2020.
The Dependence of the Short-Term Money Markets on the Fed
Since Sep 2019, the Fed has been pumping in billions of dollars to keep the short-term money markets afloat.
If it weren’t for the Fed, the short-term money markets would have been in trouble. The ORC management team is of the opinion (in its Q3 2019 earnings call) that “They (FED) are not appearing to take steps like they did during the financial crisis to get very much in front of this. And so going forward, there is still some doubt in terms of just how effective they'll be … I would say that it was very much a tough quarter for mortgage investors, especially if you are levered and dynamically hedged.”
Now what happens if the Fed continues with the daily monetary doses without resolving the issue structurally? The short-term interest rates will rise and ORC’s margins will start becoming thinner, which is not good news for its shareholders.
The Trade War and Economy Issues
The U.S. –China trade war has taken a toll on the economy, and it is uncertain what the endgame could be. There are positive noises one day and saber rattling the next. Plus, the U.S. has threatened tariffs on Latin American and European countries.
The trade war and protectionism is knifing the economy, and it is natural that if economic indicators droop, the residential housing market will get adversely impacted. And if this were to happen, the yield will get inverted and it would take many months to get it out of the inverted-flat zone. This possibility is hanging like a sword over all RMBS REITs.
Summing Up
Orchid Island Capital has been hit by downgrades from ValuEngine and Zacks Investment Research in the recent past, and, any which way you look, you draw a blank. Whether it is the challenges that its business model faces, or the economic outlook, or the trade war, or the fund issues in the short-term money markets – everything sounds negative for the company in 2020.
On a side note, the company issued a public offering of 7M shares in Aug 2019 at $6.55. The stock price today (6 Dec 2019) is $5.62. It says a lot about the investor interest in the stock.
Orchid Island Capital is a risk-off stock that must be bought to earn dividend income in a booming economy. Even in a risk-off situation, the stock will see dull days as the yield curve inverts or flattens during such times.
I’d avoid this stock and consider it as a dividend play when the economy starts to recover.
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Disclosure: I/we have no positions in any stocks mentioned, and no plans to initiate any positions within the next 72 hours. I wrote this article myself, and it expresses my own opinions. I am not receiving compensation for it (other than from Seeking Alpha). I have no business relationship with any company whose stock is mentioned in this article.
Additional disclosure: This writing is for informational purposes only and Lead-Lag Publishing, LLC undertakes no obligation to update this article even if the opinions expressed change. It does not constitute an offer to sell, a solicitation to buy, or a recommendation regarding any securities transaction. It also does not offer to provide advisory or other services in any jurisdiction. The information contained in this writing should not be construed as financial or investment advice on any subject matter. Lead-Lag Publishing, LLC expressly disclaims all liability in respect to actions taken based on any or all of the information on this writing.


Hibernate Validator Brazilian constraints.
This package is part of the public Hibernate Validator API.


Class SQLiteRepository<T1, T2>
Repository base class for SQLite databases.
Implements the
Inheritance
Namespace: GlitchedPolygons.RepositoryPattern.SQLite
Assembly: GlitchedPolygons.RepositoryPattern.SQLite.dll
Syntax
public abstract class SQLiteRepository<T1, T2> : IRepository<T1, T2> where T1 : IEntity<T2>
Type Parameters
| Name | Description |
|---|---|
| T1 | The type of entity stored in the repository. The name of this type should also exactly match the name of the db table! |
| T2 | The type of unique id with which the entities will be identified inside the repo (typically this will be either a |
Constructors
| Improve this Doc View SourceSQLiteRepository(String, String)
Creates a new SQLite repository using a specific connection string.
Declaration
public SQLiteRepository(string connectionString, string tableName = null)
Parameters
| Type | Name | Description |
|---|---|---|
| System.String | connectionString | The sqlite db connection string. Ensure that this is valid! |
| System.String | tableName | Optional custom name for the underlying SQLite database table. If left out, the entity's type name is used. |
Properties
| Improve this Doc View SourceItem[T2]
Gets an entity by its unique identifier.
Declaration
public T1 this[T2 id] { get; }
Parameters
| Type | Name | Description |
|---|---|---|
| T2 | id | The entity's unique identifier. |
Property Value
| Type | Description |
|---|---|
| T1 | The first found GlitchedPolygons.RepositoryPattern.IEntity`1; |
TableName
The name of the underlying SQLite database table where the repository entities are stored.
Declaration
public string TableName { get; }
Property Value
| Type | Description |
|---|---|
| System.String |
Methods
| Improve this Doc View SourceAdd(T1)
Adds the specified entity to the data repository.
You need to ENSURE the uniqueness of the addendum
Declaration
public abstract Task<bool> Add(T1 entity)
Parameters
| Type | Name | Description |
|---|---|---|
| T1 | entity | The entity to add. |
Returns
| Type | Description |
|---|---|
| Task<System.Boolean> | Whether the entity could be added successfully or not. |
AddRange(IEnumerable<T1>)
Adds multiple entities at once.
You need to ENSURE the uniqueness of each added
Declaration
public abstract Task<bool> AddRange(IEnumerable<T1> entities)
Parameters
| Type | Name | Description |
|---|---|---|
| IEnumerable<T1> | entities | The entities to add. |
Returns
| Type | Description |
|---|---|
| Task<System.Boolean> | Whether the entities were added successfully or not. |
Find(Expression<Func<T1, Boolean>>)
Finds all entities according to the specified predicate System.Linq.Expressions.Expression.
WARNING: Can be very slow! For specialized queries, just create a new repository! Derive from this class and create custom SQL queries that return the mapped types that you need.
Declaration
public Task<IEnumerable<T1>> Find(Expression<Func<T1, bool>> predicate)
Parameters
| Type | Name | Description |
|---|---|---|
| Expression<Func<T1, System.Boolean>> | predicate | The search predicate (all entities that match the provided conditions will be added to the query's result). |
Returns
| Type | Description |
|---|---|
| Task<IEnumerable<T1>> | The found entities (System.Collections.Generic.IEnumerable`1). |
Get(T2)
Gets an entity asynchronously by its unique identifier.
Declaration
public Task<T1> Get(T2 id)
Parameters
| Type | Name | Description |
|---|---|---|
| T2 | id | The entity's unique identifier. |
Returns
| Type | Description |
|---|---|
| Task<T1> | The first found GlitchedPolygons.RepositoryPattern.IEntity`1; |
GetAll()
Gets all entities from the repository.
Declaration
public Task<IEnumerable<T1>> GetAll()
Returns
| Type | Description |
|---|---|
| Task<IEnumerable<T1>> | All entities inside the repo. |
OpenConnection()
Opens a
Does not dispose automatically: make sure to wrap your usage into a
Declaration
protected IDbConnection OpenConnection()
Returns
| Type | Description |
|---|---|
| IDbConnection | The opened |
Remove(T1)
Removes the specified entity.
Declaration
public Task<bool> Remove(T1 entity)
Parameters
| Type | Name | Description |
|---|---|---|
| T1 | entity | The entity to remove. |
Returns
| Type | Description |
|---|---|
| Task<System.Boolean> | Whether the entity could be removed successfully or not. |
Remove(T2)
Removes the specified entity.
Declaration
public Task<bool> Remove(T2 id)
Parameters
| Type | Name | Description |
|---|---|---|
| T2 | id | The unique id of the entity to remove. |
Returns
| Type | Description |
|---|---|
| Task<System.Boolean> | Whether the entity could be removed successfully or not. |
RemoveAll()
Removes all entities at once from the repository.
Declaration
public Task<bool> RemoveAll()
Returns
| Type | Description |
|---|---|
| Task<System.Boolean> | Whether the entities were removed successfully or not. If the repository was already empty, |
RemoveRange(Expression<Func<T1, Boolean>>)
Removes all entities that match the specified conditions (via the predicate System.Linq.Expressions.Expression parameter).
WARNING: Can be slow. If you know the entities' id, please use the other RemoveRange overloads!
Declaration
public Task<bool> RemoveRange(Expression<Func<T1, bool>> predicate)
Parameters
| Type | Name | Description |
|---|---|---|
| Expression<Func<T1, System.Boolean>> | predicate | The predicate System.Linq.Expressions.Expression that defines which entities should be removed. |
Returns
| Type | Description |
|---|---|
| Task<System.Boolean> | Whether the entities were removed successfully or not. |
RemoveRange(IEnumerable<T1>)
Removes the range of entities from the repository.
Declaration
public Task<bool> RemoveRange(IEnumerable<T1> entities)
Parameters
| Type | Name | Description |
|---|---|---|
| IEnumerable<T1> | entities | The entities to remove. |
Returns
| Type | Description |
|---|---|
| Task<System.Boolean> | Whether all entities were removed successfully or not. |
RemoveRange(IEnumerable<T2>)
Removes the range of entities from the repository.
Declaration
public Task<bool> RemoveRange(IEnumerable<T2> ids)
Parameters
| Type | Name | Description |
|---|---|---|
| IEnumerable<T2> | ids | The unique ids of the entities to remove. |
Returns
| Type | Description |
|---|---|
| Task<System.Boolean> | Whether all entities were removed successfully or not. |
SingleOrDefault(Expression<Func<T1, Boolean>>)
Gets a single entity from the repo according to the specified predicate condition.
If 0 or >1 entities are found,
null
is returned.WARNING: Can be very slow! For specialized queries, just create a new repository! Derive from this class and create custom SQL queries that return the mapped types that you need.
Declaration
public Task<T1> SingleOrDefault(Expression<Func<T1, bool>> predicate)
Parameters
| Type | Name | Description |
|---|---|---|
| Expression<Func<T1, System.Boolean>> | predicate | The search predicate. |
Returns
| Type | Description |
|---|---|
| Task<T1> | Single found entity; |
Update(T1)
Updates the specified entity.
Declaration
public abstract Task<bool> Update(T1 entity)
Parameters
| Type | Name | Description |
|---|---|---|
| T1 | entity | The entity to update. |
Returns
| Type | Description |
|---|---|
| Task<System.Boolean> | Whether the entity could be updated successfully or not. |


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