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| Class and Description |
|---|
| Template.UIType
Possible ui elements in HELM Editor application
|
| Class and Description |
|---|
| Template.UIType
Possible ui elements in HELM Editor application
|
| XmlElement
Common interface for simple xml elements
|
| Class and Description |
|---|
| Group |
| Polymer
Representation of Polymer tag in OOP
|
| XmlElement
Common interface for simple xml elements
|
| Class and Description |
|---|
| Polymer
Representation of Polymer tag in OOP
|
| XmlElement
Common interface for simple xml elements
|
| Class and Description |
|---|
| XmlElement
Common interface for simple xml elements
|
| Class and Description |
|---|
| Polymer
Representation of Polymer tag in OOP
|
| Template.UIType
Possible ui elements in HELM Editor application
|
| Class and Description |
|---|
| Group |
| Polymer
Representation of Polymer tag in OOP
|
| ShapedXmlFragment |
| Template.UIType
Possible ui elements in HELM Editor application
|
| XmlElement
Common interface for simple xml elements
|
| XmlFragment
Representative part of DNA
|
| XmlMonomer
Monomer substance representation (have monomer id)
|
| Class and Description |
|---|
| Template
This class included all ui type in HELM Editor instance.
|
Copyright © 2013-2016, The Pistoia Alliance


{% extends "layout.html" %} {% block page_title %} Apprenticeships {% endblock %} {% block content %} {% include "includes/phase_banner_beta.html" %} {% include "includes/breadcrumbs/register-back.html" %} {% include "includes/account-name-number.html" %}Your cohort requests 1 New from employer 4 Need your approval 3 Ready for review 8 With employers {% endblock %}


B&N Invitational!
A FreeSki Event
Founded by
Benjamin Åsberg
& Niklas Lundgren.
It's All up To You!
Vote on the skiers and have
an impact on the results.
Time To Vote!
You will recieve a
notification when voting
has opened for each skier.
The Results!
The results will be announced
after the voting has closed
and the event's winner will
be crowned.


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--- subject: New Confirmation Code --- New email address confirmation code! Please follow this link to activate Your account. Your activation code is \{{activationCode}}
Please follow this link to activate Your account.
Your activation code is \{{activationCode}}


| Book of Mormon |
Annotations |
|
Chapter 9
|
|
|
1 And now I, Moroni, proceed with my record. Therefore,
behold, it came to pass that because of the secret combinations of Akish and
his friends, behold, they did overthrow the kingdom of Omer.
|
|
|
2 Nevertheless, the Lord was merciful unto Omer, and also
to his sons and to his daughters who did not seek his destruction.
|
|
|
3 And the Lord warned Omer in a dream that he should
depart out of the land; wherefore Omer departed out of the land with his
family, and traveled many days, and came over and passed by the hill of Shim,
and came over by the place where the Nephites were destroyed, and from thence
eastward, and came to a place which was called Ablom, by the seashore, and
there he pitched his tent, and also his sons and his daughters, and all his
household, save it were Jared and his family.
|
|
|
4 And it came to pass that Jared was anointed king over
the people, by the hand of wickedness; and he gave unto Akish his daughter to
wife.
|
|
|
5 And it came to pass that Akish sought the life of his
father-in-law; and he applied unto those whom he had sworn by the oath of the
ancients, and they obtained the head of his father-in-law, as he sat upon his
throne, giving audience to his people.
|
|
|
6 For so great had been the spreading of this wicked and
secret society that it had corrupted the hearts of all the people; therefore
Jared was murdered upon his throne, and Akish reigned in his stead.
|
|
|
7 And it came to pass that Akish began to be jealous of
his son, therefore he shut him up in prison, and kept him upon little or no
food until he had suffered death.
|
|
|
8 And now the brother of him that suffered death, (and his
name was Nimrah) was angry with his father because of that which his father
had done unto his brother.
|
|
|
9 And it came to pass that Nimrah gathered together a
small number of men, and fled out of the land, and came over and dwelt with
Omer.
|
|
|
10 And it came to pass that Akish begat other sons, and
they won the hearts of the people, notwithstanding they had sworn unto him to
do all manner of iniquity according to that which he desired.
|
|
|
11 Now the people of Akish were desirous for gain, even as
Akish was desirous for power; wherefore, the sons of Akish did offer them
money, by which means they drew away the more part of the people after them.
|
|
|
12 And there began to be a war between the sons of Akish
and Akish, which lasted for the space of many years, yea, unto the
destruction of nearly all the people of the kingdom, yea, even all, save it
were thirty souls, and they who fled with the house of Omer.
|
|
|
13 Wherefore, Omer was restored again to the land of his
inheritance.
|
|
|
14 And it came to pass that Omer began to be old;
nevertheless, in his old age he begat Emer; and he anointed Emer to be king
to reign in his stead.
|
|
|
15 And after that he had anointed Emer to be king he saw
peace in the land for the space of two years, and he died, having seen
exceedingly many days, which were full of sorrow. And it came to pass that
Emer did reign in his stead, and did fill the steps of his father.
|
|
|
16 And the Lord began again to take the curse from off the
land, and the house of Emer did prosper exceedingly under the reign of Emer;
and in the space of sixty and two years they had become exceedingly strong, insomuch
that they became exceedingly rich—
|
|
|
17 Having all manner of fruit, and of grain, and of silks,
and of fine linen, and of gold, and of silver, and of precious things;
|
Linen and silk did not exist in the pre-Columbian
Americas.
|
|
18 And also all manner of cattle, of oxen, and cows,
and of sheep, and of swine, and of goats, and also many other kinds of
animals which were useful for the food of man.
|
Oxen, cows, domesticated sheep, and domesticated goats didn’t exist in the
pre-Columbian Americas.
I think most consider Peccary or Javelina, native to the Americas, to be
pigs. Peccary are not in the same family as old world pigs, but they are in
the same suborder, Suina.
|
|
19 And they also had horses, and asses, and there were
elephants and cureloms and cumoms; all of which were useful unto man, and
more especially the elephants and cureloms and cumoms.
|
Cureloms and cumoms are unrecognized by the modern world. Some
apologists argue that Joseph used anachronistic animal terms like “horses”
because he was not familiar with the animal referred to on the golden plates,
so he used the names of animals he was familiar with. Cureloms and cumoms show
the weakness of these apologetic arguments though. If God gave Joseph the
specific names of these unknown animals, God could have done the same for the
horses the apologists claim are not really horses. Wouldn’t that be more
congruent with a God who works after the manner of plainness (2 Ne 31:3)?
Also, to argue that Joseph borrowed words to describe
things he didnt recognize makes no sense because the Book of Mormon is
allegedly translated by the power of God, and the process gave each English
word to Joseph to use (Book of Mormon Translation)
There were no horses or asses in the pre-Columbian
Americas. The only elephants that one could possibly think of as being in the
Americas at this time (the rest were extinct) were a small group of mammoths
on Wrangel Island (in the Russian Arctic some 300 miles northwest of the
closest point in Alaska) that went extinct about 3,600 years ago. The Wrangel
Island mammoth population was so small by the time the Jaredites are said to
be in the New World that the mammoths were suffering a “genetic meltdown” (Nature: International Weekly Journal of Science).
|
|
20 And thus the Lord did pour out his blessings upon this
land, which was choice above all other lands; and he commanded that whoso
should possess the land should possess it unto the Lord, or they should be
destroyed when they were ripened in iniquity; for upon such, saith the Lord:
I will pour out the fulness of my wrath.
|
|
|
21 And Emer did execute judgment in righteousness all his
days, and he begat many sons and daughters; and he begat Coriantum, and he
anointed Coriantum to reign in his stead.
|
|
|
22 And after he had anointed Coriantum to reign in his
stead he lived four years, and he saw peace in the land; yea, and he even saw
the Son of Righteousness, and did rejoice and glory in his day; and he died
in peace.
|
|
|
23 And it came to pass that Coriantum did walk in the
steps of his father, and did build many mighty cities, and did administer
that which was good unto his people in all his days. And it came to pass that
he had no children even until he was exceedingly old.
|
|
|
24 And it came to pass that his wife died, being an
hundred and two years old. And it came to pass that Coriantum took to wife,
in his old age, a young maid, and begat sons and daughters; wherefore he
lived until he was an hundred and forty and two years old.
|
Verse 23 tells us that Coriantum walked in the steps of
his righteous father “and did administer that which was good unto his people
in all his days.” But imagine in your mind’s eye this old man taking a young
maid and begetting children with her. What moral construct allows for this?
|
|
25 And it came to pass that he begat Com, and Com reigned
in his stead; and he reigned forty and nine years, and he begat Heth; and he
also begat other sons and daughters.
|
|
|
26 And the people had spread again over all the face of
the land, and there began again to be an exceedingly great wickedness upon
the face of the land, and Heth began to embrace the secret plans again of
old, to destroy his father.
|
|
|
27 And it came to pass that he did dethrone his father,
for he slew him with his own sword; and he did reign in his stead.
|
The only pre-Columbian weapon I can find that was similar
to a sword was the macuahuitl which was made with a plank of wood similar in
shape to a cricket bat with obsidian blades mounted on the edges. It is
similar enough to a sword that it is often referred to as the Aztec sword.
However, the macuahuitl does not seem to fit the Book of
Mormon time frame. “Some groups of Central Mexico, principally in the
transition between the Early and the Late Post-Classic, probably
developed this weapon” (emphasis added, Dr. Marco Antonio Cervera Obregón, “The
macuahuitl: an innovative weapon of the Late Post-Classic in Mesoamerica”,
Arms & Armour, Vol.3, Nov. 2, 2006, p. 146, article from a research journal).
The Post-Classic period is between 900 CE and the Spanish conquest.
|
|
28 And there came prophets in the land again, crying repentance
unto them—that they must prepare the way of the Lord or there should come a
curse upon the face of the land; yea, even there should be a great famine, in
which they should be destroyed if they did not repent.
|
|
|
29 But the people believed not the words of the prophets,
but they cast them out; and some of them they cast into pits and left them to
perish. And it came to pass that they did all these things according to the
commandment of the king, Heth.
|
|
|
30 And it came to pass that there began to be a great
dearth upon the land, and the inhabitants began to be destroyed exceedingly
fast because of the dearth, for there was no rain upon the face of the earth.
|
|
|
31 And there came forth poisonous serpents also
upon the face of the land, and did poison many people. And it came to pass that
their flocks began to flee before the poisonous serpents, towards the
land southward, which was called by the Nephites Zarahemla.
|
How many snakes would it take to drive their flocks like
this?
|
|
32 And it came to pass that there were many of them which
did perish by the way; nevertheless, there were some which fled into the land
southward.
|
|
|
33 And it came to pass that the Lord did cause the
serpents that they should pursue them no more, but that they should hedge up
the way that the people could not pass, that whoso should attempt to pass
might fall by the poisonous serpents.
|
|
|
34 And it came to pass that the people did follow the
course of the beasts, and did devour the carcasses of them which fell by the
way, until they had devoured them all. Now when the people saw that they must
perish they began to repent of their iniquities and cry unto the Lord.
|
|
|
35 And it came to pass that when they had humbled
themselves sufficiently before the Lord he did send rain upon the face of the
earth; and the people began to revive again, and there began to be fruit in
the north countries, and in all the countries round about. And the Lord did
show forth his power unto them in preserving them from famine.
|
|


Yahtzee/Kniffel with node.js json api backend and vue.js javascript framework frontend.
Play locally against friends or against up to 3 bots.
GitHub Respository


Horner believes new rules for 2021 should bring "better racing"
by Tom MasonAston Martin Red Bull Racing Team Principal Christian Horner believes that the new rules for 2021 and beyond will cause for better racing and make the gap between the field shorten.
Several new rules and changes have been set out for 2021 in recent weeks including better-looking cars, fairer finances, fewer upgrades and revises race weekends.
"The concept is interesting and should bring better racing, in which the cars catch up closer to the frontman and easier to overtake," Horner told Speedweek.
"But the whole package is not mature yet, the cars will be completely different. There will still have to be many clarifications in detail.
Currently, the top three teams have a major advantage over the rest with fourth-placed McLaren 245 points behind Red Bull Racing who are in third.
From next season, there will be 22 races on the schedule, but Horner feels increasing this by much more would cause problems.
"This is going to be very tough. Next year with 22 races, we try to make up for it with fewer tests. But at some point, oversaturation threatens. I think 21 is optimum."


◆ isFinished()
| boolean frc.lib.statemachine.ActionGroup.isFinished |
( |
| ) |
|
Determines if the state is ready to be advanced. This can happen forcefully on a timeout or if all action in the group time out individually.
This also handles calling the stop code on actions who have completed independently.
- Returns
- true if all actions in the group have finished or if they should be forcibly timed out
◆ onLoop()
| void frc.lib.statemachine.ActionGroup.onLoop |
( |
| ) |
|
Runs the on-loop code for each action in the group.
◆ onStart()
| void frc.lib.statemachine.ActionGroup.onStart |
( |
| ) |
|
Runs the on-start code for each action in the group. Also records the start time of state
◆ onStop()
| void frc.lib.statemachine.ActionGroup.onStop |
( |
| ) |
|
forcibly terminate all actions within the group
◆ toString()
| String frc.lib.statemachine.ActionGroup.toString |
( |
| ) |
|
gets a string representation of the class names inside the action group
- Returns
- a string of all class names in the group
◆ group
| LinkedList<Action> frc.lib.statemachine.ActionGroup.group |
|
private |
◆ t_Start
| double frc.lib.statemachine.ActionGroup.t_Start |
|
private |
The documentation for this class was generated from the following file:


There are multiple ways to generate reports from the execution of SoS workflows. This tutorial introduces some basic methods but you can certainly be more creative.
You can write a report at the end of the workflow that summarizes the results of previous steps. For example, in the following example, the report
action summarizes previous steps and writes a report to the standard output.
%sandbox
[10]
output: 'a.jpg'
R:
jpeg(${output!r})
cars <- c(1, 3, 6, 4, 9)
barplot(cars)
[20]
output: 'a.txt'
run:
echo "100" > a.txt
[100]
input: 'a.jpg', 'a.txt'
with open('a.txt') as a:
res = a.read()
report:
* Figure
![figure](a.jpg)
* result
${res}
It is a pretty bad idea to write report to standard output because other actions can also write to it. You should therefore use the output
option of the report
action to specify an output file of the report
action.
%sandbox
%preview summary.md
[10]
output: 'a.jpg'
R:
jpeg(${output!r})
cars <- c(1, 3, 6, 4, 9)
barplot(cars)
[20]
output: 'a.txt'
run:
echo "100" > a.txt
[counts: shared='counts']
input: 'a.txt'
with open('a.txt') as a:
counts = a.read()
[100]
input: 'a.jpg'
depends: sos_variable('counts')
report: output='summary.md'
* Figure
![figure](a.jpg)
* result
${counts}
Instead of outputting reports in .md
format and rendering them outside of SoS, you can also render them inside SoS using action pandoc
or Rmarkdown
. For example,
%sandbox
%preview summary.html
[10]
output: 'a.jpg'
R:
jpeg(${output!r})
cars <- c(1, 3, 6, 4, 9)
barplot(cars)
[20]
output: 'a.txt'
run:
echo "100" > a.txt
[counts: shared='counts']
input: 'a.txt'
with open('a.txt') as a:
counts = a.read()
[100]
input: 'a.jpg'
depends: sos_variable('counts')
pandoc: output='summary.html'
* Figure
![figure](a.jpg)
* result
${counts}
If you have a large workflow with several steps or modules, it makes sense to write several reports and join them at last. For example, in the following example, an output file is specified for each report
and a summary step is used at the end to collect and process them.
%sandbox --dir ~/tmp
%preview summary.html
[10]
output: 'a.jpg'
R:
jpeg(${output!r})
cars <- c(1, 3, 6, 4, 9)
barplot(cars)
report: output='figure.md'
* Figure
![figure](${output})
[20]
output: 'a.txt'
counts = 100
run:
echo "${counts}" > a.txt
report: output='result.md'
* result
${counts}
[100]
pandoc: input=['figure.md', 'result.md'], output='summary.html'
Final report
You will notice that both script
and input
are specified to action pandoc
. In this case the script
is put before the content of each input
files, making it a perfect place to write headers and summaries.


Implementation of the Coarsened Exact Matching algorithm.
| Version: | 1.1.19 |
| Depends: | tcltk, lattice |
| Imports: | MatchIt, combinat, randomForest, nlme |
| Suggests: | Amelia (≥ 1.2-0) |
| Published: | 2018-04-12 |
| Author: | Iacus, Stefano M., King, Gary, Porro, Giuseppe |
| Maintainer: | Stefano M. Iacus <stefano.iacus at unimi.it> |
| License: | GPL-2 |
| URL: | http://gking.harvard.edu/cem/ |
| NeedsCompilation: | no |
| Materials: | README |
| CRAN checks: | cem results |
| Reference manual: | cem.pdf |
| Vignettes: |
Coarsened Exact Matching |
| Package source: | cem_1.1.19.tar.gz |
| Windows binaries: | r-devel: cem_1.1.19.zip, r-release: cem_1.1.19.zip, r-oldrel: cem_1.1.19.zip |
| OS X binaries: | r-release: cem_1.1.19.tgz, r-oldrel: cem_1.1.19.tgz |
| Old sources: | cem archive |
| Reverse depends: | mwa |
| Reverse suggests: | MatchIt |
Please use the canonical form https://CRAN.R-project.org/package=cem to link to this page.


Pelajari kelas yang sesuai dengan minat anda dan rasakan hasil dari buah investasi ilmu kedepannya.
Some quick example text to build on the card title and make up the bulk of the card's content.
Pelajari SekarangSome quick example text to build on the card title and make up the bulk of the card's content.
Pelajari SekarangSome quick example text to build on the card title and make up the bulk of the card's content.
Pelajari SekarangSome quick example text to build on the card title and make up the bulk of the card's content.
Pelajari SekarangSome quick build on the card title and make up the bulk of the card's content.
Pelajari SekarangSome quick example text to build on the card title and make up the bulk of the card's content.
Pelajari SekarangSome quick example text to build on the card title and make up the bulk of the card's content.
Pelajari SekarangSome quick example text to build on the card title and make up the bulk of the card's content.
Pelajari SekarangSome quick build on the card title and make up the bulk of the card's content.
Pelajari SekarangSome quick example text to build on the card title and make up the bulk of the card's content.
Pelajari SekarangSome quick example text to build on the card title and make up the bulk of the card's content.
Pelajari SekarangSome quick example text to build on the card title and make up the bulk of the card's content.
Pelajari Sekarang


| DOSE: |
|
insufflated |
Ketamine |
(powder / crystals) |
this is about me and my experiences with ketamine, a.k.a. special k. i had done it for the first time a while back. An ex-boyfriend of mine was dealing it trying to make some money and he would give it to me for free.
in the beginning i would do a bump or two at a time with a key or from a bullet three or four times a day. the first time i had done it i was at this lake near my house in new orleans. i was with two of my guy friends and it was their first time doing it also. it was freezing cold outside and we sat in my friends truck and did two little lines each on my friends truck manual book and with a dollar bill. after we all had our two lines of the ketamine we sat there for a minute and we were saying 'oh yeah I kinda feel it.' i said 'well lets get out of the truck and go for a walk closer to the lake.'
As we got out the truck i realized it felt really different to stand up. we started to walk towards the lake and it felt like we were walking so slow, kind of like slow motion. it was the most beautiful view we had though where we were standing. it was about 5:50 P.M. the sun was setting and the sky was pink and blue swirls with the golden sun at a far distance disappearing. it looked like a painting. i will never forget it i felt so awesome.
i had a great feeling come over me i felt so comfortable as we stood there watching the sky. i imagine the people who saw us were maybe thinking 'what are they on?' the thing is when people ask me how does ketamine feel there is no way to describe it in my point of view. all i can say is numbness. everything is numb and really visual colors and shapes. its very intense.
well so eventually we were doing more and more of it every day since we would get it for free and it felt so good.
see, i didnt know anything about this drug. i still don't know that much to this day. i would just snort it and be happy i didnt care what was in it or what the long term effects were. there was only one time i remember that something strange came out of it. i had a out of body experience i believe they call it.
we were in my garage with the black light on it was freezing cold so me and
about 8 of my friends were all huddled next to each other and my friend had a whole vile of K that he cooked and cut up. we sat there for a hour or so doing the whole thing. i remember the next thing i knew after i hit my 11th rail of k, everything got quiet i couldnt speak. i then remember looking down at myself as if i was on the ceiling looking at myself. i saw all my friends around me talking to each other and my boyfriend at the time was next to me waving his hand in front of my face. there was no sound even though we had the radio on and blaring some techno. i just saw myself, everyone, everything. i snapped out of it it seemed like a long time but when i snapped out it had only been a minute or so that this happened.
my boyfriend at the time gave me some coke to drink cause to my knowledge that kills your k-hole high. in a way i was scared. i never even told any of them about this. they thought i was just too messed up to speak, but that wasnt the case. i continued doing ketamine for a while still doing large doses. we started doing about seventy dollars worth a night between three people. even though most people would have probably stopped after that one occurance, i did not. because it is the best feeling in the world. to me it took away all of my problems , pain , worrys when i was on it.
i just had to learn what my limits were. now i barely ever do it. maybe once every few months. if i could though i would do it everyday of my life. i dont know about the long term affects or anything but i honestly would not care. thats how good it is to me. i dont think it is for everyone though. i think if you like acid and shrooms then you will like ketamine. i remember a few times i mixed cocaine and ketamine that was intense also. i was in a dorm at some college cause my friend was dealing him some k. i would suggest doing that also if you can handle it maybe start out small with a little of each and eventually build up to it. but this drug like all drugs can be dangerous.
i really like this site i get to read everyone else's experiences and i hope some of you will read mine also. you can probablly tell that i am younger not a adult but nowadays sometimes us teens know more because times are changing and things the adults never knew existed are being discovered by us.
~ generation X ~ thanks.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


On Demand Minecraft Server
Server Address: minecraft.wahl-e.name:25565
Mod Pack: Enigmatica 2 Version: 1.68a


Uh oh, Gandalf is blocking the way!Maybe you have a typo in the url? Or you meant to go to a different location? Like...Hobbiton?


Plugs, Sockets & Power Strips
| AWP04L Power Monitoring | Plug | US | |
| DWFeng AWP02L-N | Plug | US | |
| Esicoo YX-WS01 | Plug | US | |
| BSD15 | Plug | US | |
| Gosund WP3 | Plug | US | |
| LC-Tech 1Ch and PZEM-004T | DIY Plug | US | |
| Maxcio W-US003 | Plug | US | |
| Oukitel P1 | Plug | US | |
| Oxaoxe NX-SM800 | Plug | US | |
| Powrui AW-39 | Power Strip | US | |
| SP201 Dual | Plug | US | |
| XS-SSA05 | Plug | US |
Lights
| Electrodragon | LED Controller | US | |
| Feit Electric BR30 65W | Bulb | US | |
| Feit Electric BR30 65W Daylight | Bulb | US | |
| Feit Electric A19 | Bulb | US | |
| Merkury A19 60W | Bulb | US | |
| Merkury A21 75W | Bulb | US | |
| Merkury BR30 65W | Bulb | US | |
| Nexlux | LED Controller | GLOBAL |
Switches & Dimmers
| KuLED KS602S | Switch | US | |
| Martin Jerry 15A | Switch | US | |
| WL-SW01_10 | Relay | GLOBAL |


self.__distance = distance
self.__best_adversarial = None
self.__best_distance = distance(value=np.inf)
self._total_prediction_calls = 0
self._total_gradient_calls = 0
def __init__(
self,
model,
criterion,
original_image,
original_class,
distance=MSE):
self.__model = model


Most Americans Think Trump Committed An Impeachable Offense
by Amelia Thomson-DeVeaux, Laura BronnerAs Democrats press forward with the first phase of their impeachment inquiry, they must try to convince a deeply divided American public that President Trump committed impeachable offenses. And according to our impeachment polling tracker, they might be up against tough odds. Overall support for impeachment has been remarkably steady since October, and Americans’ appetite for impeaching and removing Trump may have even started to plateau.
So we wanted to go one step deeper and understand the state of public opinion beneath those top-line numbers — is there a group of persuadable Americans who are still on the fence about impeachment? And will Americans’ views about what constitutes an impeachable offense shift as Democrats present their case against Trump?
To answer this and more, we’re partnering with Ipsos on a panel survey that will follow the same group of respondents over the next few months, checking in with them at regular intervals to see if their perspective on impeachment has changed. Our first wave of the survey went out last week, as Democrats’ first public hearings unfolded. It revealed that most Americans think Trump has committed an impeachable offense — but Democrats still have some work to do to get the public on board with their case against Trump.
Despite majority support, party splits persist
The initial wave of our survey found that even before articles of impeachment against Trump have been drafted, 56 percent of Americans agree that he has committed an impeachable offense. (We didn’t ask whether respondents believe he should be impeached or removed from office as a result.)
Few Republicans see an impeachable offense
Share of respondents in an Ipsos/FiveThirtyEight poll who said they think Trump has committed an impeachable offense, by party
| All | 56.2% | – |
| Democrats | 88.7 | – |
| Republicans | 18.8 | – |
Unsurprisingly, though, there’s an enormous gulf between Republicans and Democrats. Democrats are nearly unanimous in their belief that Trump has committed an impeachable offense, whereas 4 out of 5 Republicans believe that he hasn’t.1
That doesn’t mean, though, that there isn’t room for public opinion to shift as the hearings move forward. The survey also indicated, crucially, that a significant chunk of Americans are still persuadable when it comes to impeachment. While 42 percent of respondents say they are absolutely certain about their position on impeachment, about a quarter still say they’re either “somewhat” or “not at all certain” about whether Trump committed an impeachable offense.
Within this narrower universe of people who haven’t fully made up their mind, respondents who said they currently don’t believe Trump’s behavior is impeachable were especially likely to say they’re not at all certain about their position. It’s important not to read too much into this, since we’re talking about a relatively small slice of the public2 but it suggests that there’s still room for movement among at least some people who aren’t yet on Democrats’ side.
The public remains divided about crucial facts
In building their case against Trump, Democrats are using a set of key questions that are likely to serve as the foundation for articles of impeachment. In broad strokes, they are:
- Did Trump ask Ukraine to investigate Joe Biden and his son Hunter?
- Did Trump withhold military aid to pressure the Ukrainians into opening an investigation into the Bidens?
- Did the Trump administration try to cover up Trump’s actions regarding Ukraine?
According to our poll with Ipsos, a majority of Americans agree that all three of these things happened and that they’re inappropriate behaviors for a president. But there’s less consensus about whether they’re impeachable offenses. Republicans, in particular, seem unconvinced that any of the three prongs in Democrats’ case against Trump are impeachable offenses.
Partisans disagree on the facts and what they mean
Share of respondents in a FiveThirtyEight/Ipsos poll who believe Trump took certain actions relating to Ukraine and whether, if they did happen, those actions would be impeachable or inappropriate
| Trump asked the government of Ukraine to investigate Joe Biden and his son | ||||||
|---|---|---|---|---|---|---|
| If it happened, it would be … | ||||||
| party | believe it happened | inappropriate | impeachable | |||
| All | 77.3% | – | 63.5% | – | 44.4% | – |
| Democrats | 91.4 | – | 89.0 | – | 71.5 | – |
| Republicans | 64.0 | – | 36.1 | – | 13.8 | – |
| Trump withheld military aid from Ukraine unless it agreed to investigate the Bidens | ||||||
| If it happened, it would be … | ||||||
| party | believe it happened | inappropriate | impeachable | |||
| All | 56.4% | – | 79.6% | – | 57.9% | – |
| Democrats | 84.1 | – | 93.0 | – | 85.3 | – |
| Republicans | 25.8 | – | 67.1 | – | 27.4 | – |
| Trump and his administration attempted to cover up his actions regarding Ukraine | ||||||
| If it happened, it would be … | ||||||
| party | believe it happened | inappropriate | impeachable | |||
| All | 59.4% | – | 86.9% | – | 58.0% | – |
| Democrats | 88.5 | – | 94.7 | – | 84.3 | – |
| Republicans | 26.7 | – | 81.0 | – | 28.8 | – |
But the differences in these responses suggests that while the public mostly views Trump’s conduct as serious, it might still be difficult for Democrats to build a broad public consensus around their articles of impeachment. As the table above shows, respondents were more likely to agree that Trump asked Ukraine to investigate the Bidens than to believe that Trump held up security aid over the investigations, or tried to cover up his actions. That makes sense — the White House’s own summary of Trump’s July call with Ukrainian President Volodymyr Zelensky seems to confirm as much. But while most thought all three of those actions would be inappropriate, a bare majority (51 percent) said that Trump asking Ukraine’s government to investigate the Bidens would not be an impeachable offense.
This pattern is especially noticeable among Republicans. They agree, for the most part, that Trump did ask Ukraine to investigate the Bidens. But they mostly don’t think it’s inappropriate or impeachable for Trump to have done so. And while majorities of Republicans think that it would be inappropriate for Trump to have withheld military aid over the investigations or tried to hide information from Congress, they are much less convinced that either of those things actually happened.
But people with doubts aren’t following closely
This could change as the televised hearings wrap up, but right now, the proceedings are getting a lot of attention. Over half (57 percent) of Americans in our survey said they’re following the impeachment proceedings somewhat or very closely, with Democrats a bit more likely to say they’re following closely.
The problem for Democrats is that the people who haven’t fully made up their minds about impeachment — regardless of their political orientation — don’t seem to be very engaged with the process. Only about 34 percent of respondents who aren’t as certain about their stance on impeachment are following the process somewhat or very closely, compared to 66 percent of respondents who are more certain. So while there are people out there who could still be convinced by the Democrats’ case against Trump, they’re also less likely to be paying attention.
Methodology: All the data presented here come from polling done by Ipsos for FiveThirtyEight, using Ipsos’s KnowledgePanel, a probability-based online panel that is recruited to be representative of the U.S. population. For this study, the same group of respondents is contacted for an interview six times — roughly every two weeks for three months — to track whether and how their answers changed; this is the initial wave of this panel survey, conducted from Nov. 13 to Nov. 18 among a general population sample of adults with an oversample of independents, garnering 2,088 respondents. The study weighting included an adjustment for party identification so that results reflect the general population of U.S. adults. The poll has a margin of error of +/- 2.4 percentage points.


The server module¶
-
class
pyrts.server.
Action
[source]¶ Bases:
object
Class containing the actions accepted by MicroRTS. Variable names indicate their meaning and integer values are used in the JSON serialization.
-
class
pyrts.server.
Direction
[source]¶ Bases:
object
Class containing directions, as accepted by MicroRTS in their action parameters. Variable names indicate their meaning and integer values are used in the JSON serialization.
-
class
pyrts.server.
Server
(player_id)[source]¶ Bases:
object
Python implementation of a MicroRTS server, which communicates with MicroRTS via a network socket by sending actions and receiving states in the JSON format. This class should be used as the base class for other agentes who which to interact with MicroRTS.
-
get_action
(state, gameover)[source]¶ Send a list of actions to MicroRTS, given a state. Override this in the super class.
Be aware that, if gameover = True, the action provided will not be processed by MicroRTS.
-
get_available_actions_by_type_name
(unit_type_table, type_name)[source]¶ Given the unit type and the unit type table, return a complete list of the possible actions that unit can perform
-
get_busy_units
(state)[source]¶ Get a list of the units that are currently busy performing an action
Parameters: state – [description] Returns: [description]
-
get_resource_usage_from_actions
(actions)[source]¶ From a list of actions, sum the cost of the actions
-
get_resource_usage_from_state
(state)[source]¶ How many resources are currently being used to build units
-
get_resources_for_player
(state, for_player=None)[source]¶ Get the number of resources the player currently has available
-
get_unit_type_table
()[source]¶ Returns the unit type table, which describes the environment
Returns: [description]
-
get_valid_action_positions_for_state
(state)[source]¶ Returns a tuple containing the following: invalid_move_positions - a set of all the positions that cannot be moved into valid_harvest_positions - a set of all the resource locations valid_base_positions - a set of the positions of bases on the current players team valid_attack_positions - a set of the positions that can be attacked
These positions can be cross-referenced with possible actions that units can perform, to make sure no invalid actions are sent to the environment
-


The SBA 8(a) Program is a 9 year small business development program. Small businesses who qualify as both economically and socially disadvantage may apply for the Small Business Administration (SBA) 8(a) Certification. This 8(a) Certification should be pursued at the "right time" as it is only valid for 9 years. Additionally the SBA 8(a) Certification is a one-time certification and cannot be applied for again, even under a different business name or different industry. Companies who apply for the certification must meet SBA requirements related to finances (economically disadvantaged) and also prove their social disadvantage.
The SBA 8(a) program can be a godsend for small businesses who qualify. Not only does it put you in a small competition pool of those who qualify for set asides, but also provides networking assisstance in recieving sole-source (non competetively awarded) and joint venture (via mentor protégé progran) solicitations. In addition to all that, those who are certified get access to a free Business Opportunity Specialist to help navigate federal contracting and management and technical assistance.
Why would we offer this service?
Here at the StateRidge Group LLC, our only concern is helping your business grow by ensuring that you find programs and solications that are the best fits for your business. This is why we offer SBA 8(a) Certification assistance even though this could lead future business away from us. Only certain businesses qualify and we will take you through the qualifications with a free consultation, because we want the next generation of small business to enter the government marketplace.


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Release!


{include file="common/header"} {include file="/public/head"} {include file="companyintro/header_img/header_img"} {include file="companyintro/know_lezhu/know_lezhu"} {include file="companyintro/know_lezhupic/know_lezhupic"} {include file="companyintro/company_course/company_course"} {include file="companyintro/company_coursepic/company_coursepic"} {include file="companyintro/software_show_lunbo/software_show_lunbo"} {include file="companyintro/address"} {include file="/public/foot"} {include file="common/footer"}


Smart Line Interface for Web
| duckduckgo | d | DuckDuckGo Web search |
| g | Google Web search | |
| googleimages | gi | Google Images search |
| youtube | y | YouTube search |
| map | m | Google Maps search |
| route | r | Routing in Google Maps |
| flight | f | Flight search in Momondo.com |
| hotel | ho | Hotel search in Booking.com |
| car | Car rental search in Momondo.com | |
| wikipedia | w | Wikipedia search |
| papersearch | ps | Scientific literature search in csxstatic.ist.psu.edu |
| cppdoc | c | STL documentation search in cplusplus.com |
| weather | wt | Weather search in AccuWeather.com |
| history | h | Search in history |


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latte
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If we know what our dream job looks like we can start making it a goal. Write it down and you’ll have more chances to see it through.
I feel so privileged to work in the era of the web. The internet has been such a liberating force and I believe that it’s just the beginning. Some old dinosaurs are dying and making place for the new. I want to be that new.
In the past 6 months I’ve lost my job, co-founded a startup, sold it and joined another startup to work in the world of wines. In 6 months I’ve gone through more jobs than my father did in his entire lifetime.
When you get to experience multiple different workplaces you get to discover things you didn’t think were possible. At StandoutJobs we used group chat as an integral part of our workflow and played video games daily during lunch breaks. This team worked better together than any other team I’ve worked with in my life.
I think anyone on that team will have a hard time settling with old school type work. We’d feel less productive and most likely less happy.
I’ve written down what my ideal job should bring me and shared it with friends and family. I actually went one step further. I made the promise that if I complained about my job I will leave it.
My ideal job is one where I would learn transferable skills, be challenged by the experience and change the old ways of doing things. I will run away from artificial ceilings and red tape because I’m too lucky to live today with a passion for the web.
What’s your ideal job?


?D.
S.
Correli
44940
500864
OF
BAHAMA
ISLANDS
Anethum
graveolens
L.
Long
Island:
waterfront
at
Clarence
Town;
grassy
soil.
April
27,
1975
Herbarium
FAIRCHILD
TROPICAL
GARDEN
01500864
Legend -
Level of confidence that token is an accurately-transcribed word
extremely low
very low
low
undetermined
medium
high
very high


Today at the news stands
Daily Guide
*Papavi Tricks Government
*Otumfuo wades into Nepotism debate
*Referendum in limbo
Daily Graphic
*Govt committed to journalists safety
*Doreen Hammond is best as Graphic journalists dominate 2018 awards
*Ministry supplies furniture to second cycle schools
The Finder
*Bawumia cuts sod for 69.5km road
*Otumfuo urges EC to be impartial
*December 2 tariff increment was agreed in 2015 concession - GPHA
*Doreen Hammond is 2018 GJA Journalist of the year
Business and Financial Times
*Global garment brands tour Dawa Industrial zone.
*NDB must not be politically managed.
*Restrict tilapia imports to protect local industry.
The New Publisher
*Soldier, Two others grabbed for impersonation
*'Galamsey Queen Brothers arrested'
*Funeral in Oti Region turns bloody...As Gunmen kill 4
Goldstreet Business
*114 rural banks recapitalized 30 left with 3 months deadline
*Ghc10.3 billion in domestic debt Securities to be issued last 2 months of 2019
Graphic Sports
*Stay off, GPL title ours Kotoko warns Ashanti Gold
*Agyepong to move King Faisal to Assin Fosu?


Based on the along-path component by Jan Azzati scroll the mousewheel to move the plane spitfire model by Renafox Source code here


[1]
Bathrooms are satisfactory.
[2]
The rooms are clean generally clean.
[3]
The lighting in the bathroom is an issue for many.


ChunkedUploader is responsible for uploading a large file to Dropbox in smaller chunks. This allows large files to be uploaded and makes allows recovery during failure.
Completes a file upload
Args:
to_path: The directory path to upload the file to. If the destination directory does not yet exist, it will be created.
overwrite: Whether to overwrite an existing file at the given path. [default is False] If overwrite is False and a file already exists there, Dropbox will rename the upload to make sure it doesn't overwrite anything. You must check the returned metadata to know what this new name is. This field should only be True if your intent is to potentially clobber changes to a file that you don't know about.
parent_rev: The rev field from the ‘parent’ of this upload. If your intent is to update the file at the given path, you should pass the parent_rev parameter set to the rev value from the most recent metadata you have of the existing file at that path. If the server has a more recent version of the file at the specified path, it will automatically rename your uploaded file, spinning off a conflict. Using this parameter effectively causes the overwrite parameter to be ignored. The file will always be overwritten if you send the most-recent parent_rev, and it will never be overwritten you send a less-recent one.
Returns:
A Hash with the metadata of file just uploaded. For a detailed description of what this call returns, visit: www.dropbox.com/developers/reference/api#metadata
# File build/dropbox-ruby-sdk-1.6.2/lib/dropbox_sdk.rb, line 841 def finish(to_path, overwrite=false, parent_rev=nil) response = @client.commit_chunked_upload(to_path, @upload_id, overwrite, parent_rev) Dropbox::parse_response(response) end
Uploads data from this ChunkedUploader’s file_obj in chunks, until an error occurs. Throws an exception when an error occurs, and can be called again to resume the upload.
Args:
chunk_size: The chunk size for each individual upload. Defaults to 4MB.
# File build/dropbox-ruby-sdk-1.6.2/lib/dropbox_sdk.rb, line 791 def upload(chunk_size=4*1024*1024) last_chunk = nil while @offset < @total_size if not last_chunk last_chunk = @file_obj.read(chunk_size) end resp = {} begin resp = Dropbox::parse_response(@client.partial_chunked_upload(last_chunk, @upload_id, @offset)) last_chunk = nil rescue DropboxError => e resp = JSON.parse(e.http_response.body) raise unless resp.has_key? 'offset' end if resp.has_key? 'offset' and resp['offset'] > @offset @offset += (resp['offset'] - @offset) if resp['offset'] last_chunk = nil end @upload_id = resp['upload_id'] if resp['upload_id'] end end
Generated with the Darkfish Rdoc Generator 2.


This div should have a basis width of: 25%, should be able to grow, and should be able to shrink
This div should have a basis width of: 300px, should NOT be able to grow, and should be NOT able to shrink
This div should have a basis width of: 300px, should be able to grow, and should be able to shrink
This div should have a basis width of: 300px, should be able to grow, and should be able to shrink
This div should have a basis width of: 25%, should be able to grow, and should be able to shrink
This div should have a basis width of: 50%, should be able to grow, and should be able to shrink
This div should have a basis width of: 200px, should NOT be able to grow, and should NOT be able to shrink
This div should have a basis width of: 200px, should be able to grow, and should be able to shrink
This div should have a basis width of: 300px, should NOT be able to grow, and should be able to shrink
This div should have a basis width of: 50%, should be able to grow, and should be able to shrink
This div should have a basis width of: 200px, should NOT be able to grow, and should be able to shrink
This div should have a basis width of: 200px, should be able to grow, and should be NOT able to shrink
This div should have a basis width of: 25%, should be able to grow, should be able to shrink And should be the first Item


Count Islands on a Grid
An island is a connected set of cells that are isolated/separated by a border or empty cells.
Select Test Dataset #:
Load Test Data
Select Algorithm:
Recursion
Iterative
Count Islands
Clear Highlighting
Export Grid Data
Test Data Information:
Rows: [
] |
Columns: [
] |
Expected # of Islands: [
] |
Calculated # of Islands: [
] |


Hotelling v1.0.0 Beta
userInfo: {{currentUser.first_name}} / {{currentUser.no}}
Register
to get a desk!
Your Hotelling
Date/Time
5th fl
9th fl
{{newHotelling.floor.value }}
Today
Clear
Submit
Export to CSV
{{ desktotal }} Desks are available for each day for NYC
{{ group.value }} {{ group.data.length }} desks assigned | {{ desktotal - group.data.length }} desks avaiable
{{hotelling.user.username }}/ {{hotelling.user.no}}
{{stats[hotelling.status].name}}
{{hotelling.floor}}
{{ hotelling.title}}


{{/* gotype: github.com/JustaPenguin/assetto-server-manager.championshipListTemplateVars */}} {{ define "title" }}Championships{{ end }} {{ define "championships-list" }}
| Name | Progress | Number of Events | Number of Classes | Number of Entrants | Actions |
|---|---|---|---|---|---|
| {{ $championship.Name }} | {{ int $championship.Progress }}% | {{ len $championship.Events }} | {{ len $championship.Classes }} | {{ $championship.NumEntrants }} | View Export {{ if WriteAccess }} Delete {{ end }} |


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Charon Upload
This is a demo page for the Charon file upload system. The implementation allows cryptographically managed file upload to predefined cloud repositories for users without accounts at cloud providers. Charon is a service working through APIs.
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Equivalent to selecting cells or changing the active cell. There are three syntax forms of SELECT. Use syntax 1 to select a cell on a worksheet or macro sheet; use one of the other syntax forms to select worksheet or macro sheet objects or chart items.
Syntax 1 Selects cells
Syntax 2 Selects objects on worksheets
Syntax 3 Selects chart objects
Return to index
Equivalent to selecting cells or changing the active cell. There are three syntax forms of SELECT. Use syntax 1 to select a cell on a worksheet or macro sheet; use one of the other syntax forms to select worksheet or macro sheet objects or chart items.
Syntax
SELECT(selection, active_cell)
Selection is the cell or range of cells you want to select. Selection can be a reference to the active worksheet, such as !$A$1:$A$3 or !Sales, or an R1C1-style reference to a cell or range relative to the active cell in the current selection, such as "R[-1]C[-1]:R[1]C[1]". The reference must be in text form. If selection is omitted, the current selection is used.
Active_cell is the cell in selection you want to make the active cell. Active_cell can be a reference to a single cell on the active worksheet, such as !$A$1, or an R1C1-style reference relative to the active cell, such as "R[-1]C[-1]". The reference must be in text form. If active_cell is omitted, SELECT makes the cell in the upper-left corner of selection the active cell.
Remarks
Active_cell must be within selection. If it is not, an error message is displayed and SELECT returns the #VALUE! error value.
If you are recording a macro using relative references, Microsoft Excel records the action using R1C1-style relative references in the form of text.
If you are recording using absolute references, Microsoft Excel records the action using R1C1-style absolute references in the form of text.
You cannot give an external reference to a specific sheet as the selection argument. The sheet on which you want to make a selection must be active when you use SELECT. Use FORMULA.GOTO to make a selection on another sheet in the same workbook or in another workbook.
Tip You can enter data in a cell without selecting the cell by using the reference arguments to the CUT, COPY, or FORMULA functions.
Examples
The following macro formula selects cells C3:E5 on the active worksheet and makes C5 the active cell:
SELECT(!$C$3:$E$5, !$C$5)
If the active cell is C3, the following macro formula selects cells E5:G7 and makes cell F6 the active cell in the selection:
SELECT("R[2]C[2]:R[4]C[4]", "R[1]C[1]")
You can also make multiple nonadjacent selections with SELECT. The following macro formula selects a number of nonadjacent ranges:
SELECT("R1C1, R3C2:R4C3, R8C4:R10C5")
The following sequence of macro formulas moves the active cell right, left, down, and up within the selection, just as TAB, SHIFT+TAB, ENTER, and SHIFT+ENTER do:
SELECT(, "RC[1]")
SELECT(, "RC[-1]")
SELECT(, "R[1]C")
SELECT(, "R[-1]C")
Use SELECT with the OFFSET function to select a new range a specified distance away from the current range. For example, the following macro formula selects a range that is the same size as the current range, one column over:
SELECT(OFFSET(SELECTION(), 0, 1))
Related Functions
ACTIVE.CELL Returns the reference of the active cell
SELECT.SPECIAL Selects a group of cells belonging to a category
SELECTION Returns the reference of the selection
SELECT Syntax 2 Selects objects on worksheets
SELECT Syntax 3 Selects chart objects
Return to index
Equivalent to selecting objects on a chart, worksheet, or macro sheet. There are three syntax forms of SELECT. Use syntax 2 to select an object on which to perform an action; use one of the other syntax forms to select cells on a worksheet or macro sheet or items on a chart.
Syntax
SELECT(object_id_text, replace)
Object_id_text is text that identifies the object to select. Object_id_text can be the name of more than one object. To give the name of more than one object, use the following format:
SELECT("Oval 3, Arc 2, Line 4")
The last item in the object_id_text list will be the active object. The active object is important when moving and sizing a group of objects. A multiple selection of objects is moved and sized relative to the upper-left corner of the active object.
Replace is a logical value that specifies whether previously selected objects are included in the selection. If replace is TRUE or omitted, Microsoft Excel only selects the objects specified by object_id_text; if FALSE, it includes any objects that were previously selected. For example, if a button is selected and a SELECT formula selects an arc and an oval, TRUE leaves only the arc and oval selected, and FALSE includes the button with the arc and oval.
Remarks
Objects can be identified by their object type and number as described in CREATE.OBJECT, or by the unique number that specifies the order of their creation. For example, if the third object you create is an oval, you could use either "oval 3" or "3" as object_id_text.
Examples
The following macro formulas each select a number of objects and specify Arc 2 as the active object:
SELECT("Oval 3, Arc 1, Line 4, Arc 2")
SELECT("3, 1, 4, 2")
Related Functions
FORMAT.MOVE Moves the selected object
FORMAT.SIZE Changes the size of the selected objects
GET.OBJECT Returns information about an object
SELECTION Returns the reference of the selection
SELECT Syntax 1 Selects cells
SELECT Syntax 3 Selects chart objects
Return to index
Selects a chart object as specified by the selection code item_text. There are three syntax forms of SELECT. Use syntax 3 to select a chart item to which you want to apply formatting; use one of the other syntax forms to select cells or objects on a worksheet or macro sheet.
 If m is blank, selects both.
Single_point is a logical value that determines whether to select a single point. Single_point is available only when item_text is "SnPm".
If single_point is TRUE, Microsoft Excel selects a single point.
If single_point is FALSE or omitted, Microsoft Excel selects a single point if there is only one series in the chart or selects the entire series if there is more than one series in the chart.
If you specify single_point when item_text is any value other than "SnPm", SELECT returns an error value.
Examples
SELECT("Chart") selects the entire chart.
SELECT("Dropline 2") selects the droplines of an overlay chart.
SELECT("S1P3", TRUE) selects the third point in the first series.
SELECT("Text S1") selects the series title text of the first series in an area chart.
Related Functions
SELECTION Returns the reference of the selection
SELECT Syntax 1 Selects cells
SELECT Syntax 2 Selects objects on worksheets
Return to index


Dog Breed Classification using Deep Learning
Pets occupy a very special place in our households; in most cases they are considered a member of the family. Every time I see a new dog I wonder what breed that dog is. There are more than 300 unique dog breeds in the world, and many dogs are mixed with multiple breeds. As a result, it is often very difficult to know the breed of a dog. But, can we accurately determine a dog's breed using machine learning?
In many cases, this will be a very difficult challenge because even a trained human would have a hard time distinguishing between some breeds of dogs from an image. For example, the Brittany, Irish Red & White Setter, and Welsh Springer Spaniel look very much alike. Another example of breeds that show only slight differences are American Water Spaniel and Curly-Coated Retriever. To add to the potential for confusion, there are many cases of dogs within the same breed that show fairly significant variations (for example, see the American Staffordshire Terrier below).
In this post, I will demonstrate how to use deep learning to try to automatically identify dog breeds from images. This project was inspired by the Udacity Machine Learning Nanodegree program I participated in.
Step 1: Dataset - Break dataset into Training, Testing, and Validation sets
I use the dog dataset from Udacity ,which includes 133 dog breeds with 8,351 total dog images. The data are split into a training set (80%), a validation set (10%), and a test set (10%). The training set is used to determine the best-fit weights for the model, while the validation set allows me to check that the model complexity is appropriate, which means it won't be overfitting or underfitting. Finally, the test set is used only after deciding on the model parameters, to determine the overall accuracy of my model.
The images below are samples of some of the dogs in my dataset. Note that these images are not uniform; many include multiple dogs, people, and other objects. Although it is easy for us as humans to separate the dogs in these images from everything else, it is not super-trivial to train a Neural Network to do the same. However, with a little work, our model could figure out how!
Step 2: Create a Convolutional Neural Network (CNN) to classify Dog Breeds (from Scratch)
With 133 dog breeds in my sample, random chance says that guessing the correct breed has a probability of less than 1%. Thus, as long as the accuracy is higher than 1%, we can say that our model is useful (although maybe not very useful if it is not much higher than 1% :0)
I am going to use Keras , a python library used for deep learning that is particularly popular because it is easy to get up and running.
Step 2-1. Pre-process the Data (Rescale the images)
We will start by pre-processing the data. Keras requires us to input our images as a 4D array (aka a 4D tensor), with the shape
(n_samples, n_rows, n_columns, n_channels)
where `n_samples` corresponds to the total number of images (or samples), and `n_rows`, `n_columns`, correspond to the pixel number of rows and columns for each image, and `n_channels` corresponds to the number of RGB channels for each image, which is 3.
I have a function that takes as input the path to a color image file and converts that image into the proper 4D tensor format to feed into our CNN. It converts the image information (pixel value) into numpy arrays, and resizes each array to make them all uniformly 224 x 224 pixels.
Then, when split into 3 color channels, each image is represented by a 3D array (224, 224, 3).
Next, an extra dimension is added to the 3D array to allow for multiple images (samples) to be processed. Thus, the images are handled as 4D tensors. The returned tensor for a single image will always have the shape
(1, 224, 224, 3)
I have another function that takes an array of strings, with each string being the path to an image, as input to convert those images into a 4D tensor with the shape
(n_samples, 224, 224, 3)
Finally, I rescale the images by dividing every pixel in every image by 255, which changes the range of each image from 0-255 into 0-1.
Step 2-2. Define the CNN model architecture
Why CNNs?
Regular Neural Networks use vectors for hidden layers, which don't represent the full images well. In particular, the fully connective layers of a regular neural network are less time efficient and easily lead to overfitting. For example, the scales of the images in our sample are a respectable 224 x 224, which means a hidden layer needs to include 224 x 224 x 3 = 150,528 weights. If we want to use multiple layers (as expected), the number of weights would add up quickly.
On the other hand, CNNs are especially powerful when we must train on multi-dimensional data, such as images. CNNs consist of locally connected layers, which use far fewer weights compared to the densly connected layers of a regular neural network. The locally connected layers efficiently prevent overfitting and allow us to easily understand the image data because they naturally handle two dimensional patterns. You can read more details here.
Design of CNN
A CNN consists of multiple layers, which include convolutional, pooling, and some fully connected layers (also used in regular Neural Networks).
(1) Convolutional Layer
A convolutional Layer consists of locally connected nodes, meaning that the nodes are only connected to a small subset of the previous layers' nodes. To build a convolutional layer, we first select a width (column) and height (row) that defines a convolution filter. The filter is a matrix that can have its own characteristic pattern, and each convolutional layer will have the task of searching for its filters pattern in the image. To do this, we simply move the filter horizontally and vertically over the matrix of image pixels, and at each position the convolutional filter returns a numerical result that specifies whether its pattern was seen locally. The image below demonstates how a convolutional layer works. In practice, one will use many convolutional layers, each searching for its own unique pattern in the image, in order to identify complex structures.
(2) Pooling Layer
Recall that a convolutional layer is a stack of feature maps where we have one feature map for each filter. More filters means a larger stack, which means that the dimensionality of our convolutional layers can get quite large. Higher dimensionality means we will need to use more parameters, which can lead to overfitting. Therefore, we need a method to reduce this dimensionality by using pooling layers within a CNN.
Generally, there are two popular choices for types of pooling layers. The first type is a max pooling layer. Max pooling layers take a stack of feature maps as input, and are constructed by finding the maximum value from a subset of pixels in the input layer. The second type of pooling layer is a global average. As the name implies, this pooling layer simply stores the average of all values in the input layer, rather than considering smaller windows. The global average pooling is a more extreme type of dimensionality reduction.
My CNN model architecture
I put three convolutional layers in my CNN, as the figure below shows. The first convolutional layer uses 16 filters, and in general it identifies only very broad patterns. The next convolutional layer has 32 filters, and the third uses 64 filters. These latter layers are used to find more complex shapes and patterns. After each convolutional layer is a max pooling layer used to reduce dimensionality, and after the third max pooling layer is a global average pooling layer. Finally, I use a fully connected layer with a softmax activation function that returns probabilities for each dog breed.
The table below shows the summary of my CNN model.
Note that the number of parameters is large, but not prohibitively large (regular neural networks could easily reach millions of parameters).
Step 2-3. Loss function
After designing the CNN model, we need to specify a loss function so that we can quantify the model accuracy. Since we are constructing a multiclass classifier, we will use categorical cross-entropy loss.
This loss function returns a numerical value that is lower if the model predicts the true label (in this case, the correct dog breed). As with other classification tasks in machine learning, we want to minimize the loss function to train our model to give us the highest accuracy possible. The true labels are one-hot encoded, and each label is a vector with 133 entries. The model outputs a vector having 133 entries, where each entry corresponds to the probability of that dog breed.
For example, let's consider the image with a Welsh Springer Spaniel dog. Our model predicts that there is a Brittany in the image with a 90% propability and Welsh Springer Spaniel in the image with a 10% probability. The categorical cross-entropy loss checks the true label vector (with only Welsh Springer Spaniel selected) against the prediction vector (which has 90% chances of Brittany and with only 10% chance of Welsh Springer Spaniel), and returns a high value for the loss. The model then adjusts the weights, and if the prediction changes to favor the true label more, then the loss function decreases. Eventually, if our model is good enough, we would find at the end of our training that it correctly identifies most dog breeds.
Step 2-4. Train the model
In training the model, I modify the weignts to improve the predictions. I chose to train the model for 5 epochs, and I saved the weights that correspond to the highest validation accuracy. This process took around 20 min on my laptop. Expect it to take more time if you want to train your model with a larger number of epochs.
Step 2-5. Load the model and calculate accuracy on test set
Once the training has finished, I load the saved model weights and calculate the classification accuracy on the test set. The accuracy from my CNN model is around 2.4%, which is just a little better than random guessing, and it's not great.
Step 3: Using Transfer Learning
Why Transfer Learning?
As we have seen from the result above, using a CNN model from scratch does not often lead to a satisfying result. The reason our CNN model was not very good at accurately identifying dog breeds is that it lacked sufficient complexity to identify the wide variety of objects in the images. If your model can't tell the difference between a dog, a person, and a tree, then it certainly will not be very good at distinguishing between breeds of dogs. But, what if we could take the foundation of a sufficiently complex neural network that has already been trained to identify objects like dogs and people, and build off of that existing foundation?
This is the basic idea behind transfer learning, which has proven to be a fruitful approach to developing CNNs to perform complex tasks. We can take a CNN model which has already been trained on image data, and use its foundational architecture to apply to a similar problem that we want to solve. If the model has been trained to identify the right kind of object (in this case, dogs), then it can very easily be repurposed for our classification problem. This will save us a great deal of time and effort, because there's no need to reinvent the wheel every time we need to solve a slightly different problem.
There are existing CNN models that have been pre-trained on ImageNet, a database containing nearly 15 million images depicting a vast array of objects and scenes. The models listed below are available in Keras, and they have all been pre-trained on ImageNet. You can click each link to find out more about how you can apply the model you like.
How does it work?
If you take a CNN that has been pre-trained on the ImageNet database, then you can be reasonably confident that your CNN has already learned how to distinguish between the 1,000 different categories of objects that are represented by ImageNet. Most of those categories are animals, fruits, vegetables, and other everyday objects.
You might now be asking yourself: "If I want to use a CNN for a classification task involving something that is not found in the ImageNet database, is it still relevant or beneficial to use that pre-trained CNN knowledge?" The answer is most definitely yes.
The convolutional filters in a trained CNN are arranged in a hierarchy. The filters in the first layer often only detect edges or blobs of color. The second layer might detect circles, stripes, and rectangles. There early features are very generalized, and are typically useful for analyzing any image in any dataset. The filters in the final convolutional layers are much more specific. If there were birds in the training data set, then there are filters that can detect birds. If there were cars or bicycle, there are filters to detect wheels and so on. We will see that it's then useful to remove the final layers of the pre-trained network that are very specific to a particular training data set, while keeping the earlier layers when we do transfer learning. Then, we simply add one or two more layers and train only those final layers on our particular classification task. This is one way to do transfer learning, but your preferred method will depend on both the size of your dataset and the level of similarity it shares with the ImageNet database.
Note that the transfer learning technique will work well if your data set is relatively small and very similar to ImageNet. Our dog data set is appropriately small and it has significant overlap with a subset of the ImageNet categories, which makes it ideal for this application! :-)
First, we slice off the end of the network (the layers highlighted by black boxes in the figure above) and add a new classification layer with 133 nodes (one for each of our output labels). We then train only the weights in this layer, freezing all the weights in the earlier layers. We can take an image, pass it through the pre-trained model, and stop at the last VGG16/VGG19 max pooling layer. This turns each image into another 3D array, which we can then save as part of a new dataset. Then, when we go to train our network, we will use this new data set as the inputs and our network in Keras will have two layers: an input and an output layer. Thus, our model needs to take as input a 7 x 7 x 512 array for each image. That will result in a lot of parameters, so I also do some dimensionality reduction through a global average pooling layer. The figures below show my CNN architecture when using VGG16 and VGG19 as the pre-trained foundation.
For this project, I used VGG16/19, InceptionV3, and Xception by adding global average pooling layer and fully connected layer with softmax activation functions. Finally, I compiled the models with the same loss function described earlier and I check the accuracies against the test set. Below are the resulting accuracies for each model.
- VGG16 - 45%
- VGG19 - 48%
- InceptionV3 - 80%
- Xception - 84%
Using Xception results in the highest accuracy. Thus, I want to apply the model using Xception to predict dog breed! 84% is not bad, right? :-)
Step 4: Apply the Model to predict dog breed
Mis-predicted dogs
When I apply my model to predict dog breeds, I failed to correctly predict dog breeds for 130 out of 836 test images. The images below show examples of the dogs my model failed to correctly guess. The very left images are the input images, with the correct breed label printed in a white box, while the images to the right show the the dog breeds my model predicted for that dog, sorted by the probabilities assigned by the model. Note that many of the dogs my model fails to accurately predict seem not to be easy to distinguish even with human eyes.
The images show examples of incorrect guesses again, but this time where the prediction probability is less than 0.8. Note that it's even more apparent in these cases that it's really hard for human eyes to find the correct breed.
Based on the fact that my model accuracy is higher than 84%, overall it seems to work pretty well. For example, in the case of the Brittany dogs I mentioned at the very begining, our model predicts the breed correctly for 4 out of 6 test images. This is pretty impressive, considering that their appearance looks very similar to other breeds, such as Welsh Springer Spaniel or Irish Red & White setter. If we have enough GPU memory, I believe I could build an even better model to classify dog breeds after testing with different model architectures. Feel free to train your own model with transfer learning and find an even better model to categorize dog breeds!
How about mixed breeds?
If we test mixed breeds on our model, can it predict at least one of dog breeds for those with mixed breeds? I tested it with several mixed-breed dogs. Out of 7 mixed breeds I used for the testing, only 2 dogs are correctly predicted by the model. The figures below show a subset of the images of mixed breed dogs.
The last figure was correctively predicted. However, the dog (Bulldog + Dalmatian) looks very close to being a pure dalmatian, doesn't it? Thus, I guess it is really hard to predict mixed-breed dogs, although all of the natural breeds for the mixed breeds are trained by my model. In order to categorize mixed-breed dogs, I think we need to put the specific mixed-breed cases as new entries in our training and validation data if we want to do a better job of predicting those mixed-breed cases.
Summary and Future work.
I trained more than 8,000 dog images with 133 different breeds to predict their dog breed automatically
using artificial intelligence.
I designed a CNN model from scratch, and found that the result was not impressive (a little higher than 2%).
Next, I used several different transfer learning models, including VGG, Inception, and Xception,
which finally led to a model with 85% accuracy on the test set with the Xception bottleneck feature.
The result seems to be pretty good, at least similar to (or even better) than human eyes.
There are still problems with categorizing mixed dog breeds, so I believe
we would need to train a model on a wider range of dog breeds, pure and mixed,
in order to build a more generalized neural network to identify the breed(s)
of any dog.
Reference
- Udacity Machine Learning Nanodegree program
- The Lecutre Note of CS231n Convolutional Neural Nerworks for Visual Recognition by Stanford
- Deep Learning: Chap 6. Convolutional Neural Net by Ken'ichi Matsui
- Keras Tutorial in EliteDataScience
- ImageNet: VGGNet, ResNet, Inception, and Xception with Keras by Adrian Rosebrock
- An Intuitive Explanation of Convolutional Neural Networks by ujjwalkarn
- Transfer learning & The art of using Pre-trained Models in Deep Learning by Dishashree Gupta


Welcome to the MIP Deployment pack library
You will find here all the documentation pertinent to the installation of the Medical Information Platform (MIP)
List of all documents
- MIP Deployment Pack (All document packed) (PDF document, 9.2 MB)
- 1-MIP Package Description (PDF document, 1 MB)
- 2-MIP Executive Summary (PDF document, 780 kB)
- 3-MIP Installation and License Agreement (PDF document, 540 kB)
- 4-MIP Ethics and Legal Requirements (PDF document, 589 kB)
- 5-MIP Installation Specification (PDF document, 1.4 MB)
- 6-MIP System Description.pdf (PDF document, 3.8 MB)
- 7-MIP User Manual (PDF document, 1.2 MB)


#include <array>
#include <vector>
Go to the source code of this file.
|
| template<typename T > |
| T | mlib::poly (T x, const T *coeff, int n) |
| | Evaluate a polynomial using Horner's scheme. More...
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| |
| template<typename T , size_t N> |
| T | mlib::poly (T x, std::array< T, N >coeff) |
| | Evaluate a polynomial using Horner's scheme. More...
|
| |
| template<typename T > |
| T | mlib::poly (T x, std::vector< T >coeff) |
| | Evaluate a polynomial using Horner's scheme. More...
|
| |
Polynomial evaluation using Horner's scheme
(c) Mircea Neacsu 2020
◆ poly() [1/3]
template<typename T >
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T |
x, |
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const T * |
coeff, |
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int |
n |
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) |
| |
- Parameters
-
| x | Evaluation point |
| coeff | polynomial coefficients in order from lowest power (coeff[0]) to highest power (coeff[N-1]) |
| n | size of coefficient's array. |
- Returns
- Polynomial value in point x coeff[n-1]*x^(n-1) + coeff[n-2]*x^(n-2) + ... + coeff[1]*x + coeff[0]
◆ poly() [2/3]
template<typename T , size_t N>
| T mlib::poly |
( |
T |
x, |
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std::array< T, N > |
coeff |
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) |
| |
- Parameters
-
| x | Evaluation point |
| coeff | array of polynomial coefficients in order from lowest power (coeff[0]) to highest power (coeff[N-1]) |
- Returns
- Polynomial value in point x coeff[N-1]*x^(N-1) + coeff[N-2]*x^(N-2) + ... + coeff[1]*x + coeff[0]
This template function will generate a new instantiation for each array size.
◆ poly() [3/3]
template<typename T >
| T mlib::poly |
( |
T |
x, |
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std::vector< T > |
coeff |
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- Parameters
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| x | Evaluation point |
| coeff | vector of polynomial coefficients in order from lowest power (coeff[0]) to highest power (coeff[N-1]) |
- Returns
- Polynomial value in point x coeff[N-1]*x^(N-1) + coeff[N-2]*x^(N-2) + ... + coeff[1]*x + coeff[0]


Your files have now been uploaded to the Pachyderm server for use during authoring, and the metadata you entered has been stored in the database.
Return to Media


Why PCB Designing?
We often come across two scenarios where we need PCB designing. Sometimes we need to fix a medium/large sized circuit with various components into a smaller and more compact form. And sometimes we need to use a specific circuit more number of times. In such cases, making a Printed Circuit Board of the required circuit is very useful.
Have a look:
What is Eagle?
Eagle is a PCB Designing Software where you can use custom libraries for using a variety of elements, from common Resistors, Capacitors etc to specific ICs, Microcontrollers. It lets you add the elements you want, design the circuit and the choose the physical connections the way you want on your PCB.
Details Of Commands and usages in making the SCHEMATIC
There are basically two parts of our development in Eagle. First, the Schematic, that shows all the connections(but not the way you physically want on the PCB). Secondly, forming final connections and arranging them in the final physical way we want.
So, in the session, we decided to make a IC 555 circuit.
We will go through the following procedure:
-
Making the Schematic
-
Designing the physical connections
Let us move on to using the software and its commands.
1. Making the Schematic
- add: Used to add any component. Type in the command bar on top
- After performing add, lots of components will appear. Type keywords to search components. Eg. RCL in search box to find Resistor, Capacitor, Inductor.(Take US Package for resistor.) (uF == Polarised, nF == ceramic)
- For NE555N type:add on terminal> search timer > Find 555. Double click>Paste.
- Note:- Whenever IC not found, Google the .lbr file for that IC and copy paste in Eagle list.
- Use Escape to go back to menu.
- For our 555 circuit, we will take a 5mm LED for Output
- Type Supply in search bar to get 5V and Ground
- move: to move a component
- net: make connections. Net adds connection and side by side makes a file(which denotes that there must be a physical connection made for this pair in the second part of our designing) too.
- Make end to end connections and use name command to name the wires/components differently so as to differentiate between connections.
- Giving values: Use value command and then click on component to enter value.
- Smash command: to move around names.(Disassemble components to move individually)
- Search Pin Head for Berg Pins!
- Right click a component to rotate
- Delete command and select the “plus” sign (appearing at centre of component on zooming) denoting the component to delete it.
- Label command to show the labels.
You are expected to be done with the schematic using these commands.
Now Type Board command and click Yes option to go to the next part.
2. Making the Physical Connections
- Air Line: denotes a connection that must be made(shows that these two points ought to be physically connected).
- Tools>Ratnest:Simplifies air wire connections(because the connections change with the physical positions of components and so, they can also overlap)
- Change package: helps to change the type of component.
- Note: - If you made wrong connections and want to change a component, do it in the schematic file (part 1) and not here.
- Menu>Autorouter is used to find the possible physical routing by eagle’s own algorithm. It will return certain best possibilities, based on certain criteria.(Note:- Menu>Autorouter>Uncheck Continuous Option while auto routing again). In case of Single Layer PCBs(we mostly have only single layered ones):Top is Not Applicable.
- We can do manual routing by Route Command.
- Width of the connection is given in Inches, around 0.024 inches is a preferred width(it shouldn’t be very small because not many printing facilities may have that accuracy).
- Change track shape by Right click(Avoid using right angles because of some
- Ripoff command changes a physical connection back to just an air wire.
- It might be useful to create a common ground/power line around the PCB, it sometimes simplifies the complexity of connections.
- Make sure to Save the file in the same folder as the project.
To make final output files for Gerbv:
- File > Cam Processor.
- File > Open > Job == gerb274x.cam
- Process Job In bottom
To see the final files in Gerbv:
- Open the files with following extensions
.brd, .cmp, .plc, .sol, .stc, .sts
. - Using the text command, you can write text anywhere. Use it for specifying stuff to make circuit connection and component placement easier. Add these names for reference by choosing layer tNames and adding text.
- Change board size using move command and moving the boundaries.


Praetorian
Praetorian is a minimalist Crystal authorization system inspired by Pundit. It aims to be both lightweight and dependency-less.
Installation
dependencies:
praetorian:
github: ilanusse/praetorian
How to use
Praetorian, inspired by Pundit, works with policy classes. This shard is not designed to be extra compatible with any framework but rather with flexibility in mind. This is a simple example that allows updating a post if the user is an admin, or if the post is unpublished:
class Post
def policy_class
PostPolicy
end
end
class PostPolicy
include Praetorian::Policy
property user, post
def initialize(user, post)
@user = user
@post = post
end
def update?
user.admin? || !post.published?
end
end
# Somewhere in your code
def update
@post = Post.find(params[:id])
Praetorian.authorize(current_user, @post, :update?) # You can also use .authorise if you're a Brit
# Rest of code flow
end
There are two things to notice here:
- The Post is a class that should obey a certain Policy. We can either write a
policy_class
method to return the policy class name, or Praetorian will assume the policy classname to be#{variable_name}Policy
.
- The Policy class includes
Praetorian::Policy
. This adds default query methods to our policy as defaults that should be overwritten as necessary.
The default query methods defined in Praetorian::Policy
are: index?
, show?
, create?
, new?
, update?
, edit?
, destroy?
.
A Praetorian::NotAuthorizedException
will be raised if the user is not authorized to perform said query on the record.
Ok. So far, pretty simple.
You can set up a simple base class to inherit from:
class ApplicationPolicy
include Praetorian::Policy
property user, object
def initialize(user, object)
@user = user
@object = object
end
end
Including the shard as a module
You can include the shard as a module in your controller base class to avoid the prefix:
class ApplicationController
include Praetorian
end
class PostController < ApplicationController
@post = Post.find(params[:id])
authorize(current_user, @post, :update?) # yay no prefix
end
Using a specific policy class
You can pass an argument to override the policy class if necessary. For example:
def create
@publication = find_publication # assume this method returns any model that behaves like a publication
# @publication.class => Post
Praetorian.authorize(current_user, @publication, :create?, PublicationPolicy)
# Rest of code flow
end
License
Licensed under the MIT license, see the separate LICENSE.txt file.


Use hardware wallets to keep your private keys safe. A hardware wallet is a flash drive like device, designed specifically for storing your keys. These are HD wallets that generate private and public keys via mnemonic phrase or seed words when initialized. Some popular hardware devices that are used and trusted are Ledger Nano S and Trezor. Never keep private keys online, in a mail or in the cloud. There they are most susceptible to getting obtained by bad actors.
If you don’t have a hardware wallet, you can temporarily keep your private keys or seed in an offline flash drive or HDD. These options are only safe as long as they are not in the hands of an attacker. One can also use offline drives that encrypt the data stored on them. This way you can add one more security layer for accessing your account. If there is no possibility to obtain a hardware wallet, the other option is paper wallet. A paper wallet is a piece of paper that prints the pair of private and public keys on it. To make it more secure, you can laminate it and store it in a vault.
Never take a screenshot or picture of your private keys. It becomes easily obtainable by bad actors once they get access to your device. Instead, consider various cold storage solutions.


Bootstrap Responsive Tabs is a jQuery plugin which enables Bootstrap Tabs to behave responsively - resize your browser window to see it in action. By Invent Partners.
The Cathedral and Metropolitical Church of Saint Peter in York, commonly known as York Minster, is the cathedral of York, England, and is one of the largest of its kind in Northern Europe. The minster is the seat of the Archbishop of York, the second-highest office of the Church of England, and is the mother church for the Diocese of York and the Province of York. It is run by a dean and chapter, under the Dean of York. The title "minster" is attributed to churches established in the Anglo-Saxon period as missionary teaching churches, and serves now as an honorific title. Services in the minster are sometimes regarded as on the High Church or Anglo-Catholic end of the Anglican continuum.
York Castle in the city of York, England, is a fortified complex comprising, over the last nine centuries, a sequence of castles, prisons, law courts and other buildings on the south side of the River Foss. The now-ruinous keep of the medieval Norman castle is commonly referred to as Clifford's Tower. Built originally on the orders of William I to dominate the former Viking city of York, the castle suffered a tumultuous early history before developing into a major fortification with extensive water defences. After a major explosion in 1684 rendered the remaining military defences uninhabitable, York Castle continued to be used as a jail and prison until 1929.
The York Museum Gardens are botanic gardens in the centre of York, England, beside the River Ouse. They cover an area of 10 acres (4.0 ha) of the former grounds of St Mary's Abbey, and were created in the 1830s by the Yorkshire Philosophical Society along with the Yorkshire Museum which they contain.
York Dungeon is a tourist attraction in York, England. York Dungeon depicts history of the dungeon using actor led shows, special effects and displays of models and objects.
The York Dungeons reopened in March 2013 after a period of closure due to severe flooding.
The Shambles (official name Shambles) is an old street in York, England, with overhanging timber-framed buildings, some dating back as far as the fourteenth century. It was once known as The Great Flesh Shambles, probably from the Anglo-Saxon Fleshammels (literally 'flesh-shelves'), the word for the shelves that butchers used to display their meat. As recently as 1872 twenty-five butchers' shops were located along the street, but now none remain.
Demo text from Wikipedia, the free encylopedia


Jazz
French Bulldog
$4,300
Posted by Nick Thorn, 3 days ago
Breed Society for Australian Working Kelpie


All Classes
AStar
Chell
DataList
Filehandle
Finder
Grid
JPS
List
Map
MinHeap
Pathfinding
Pile
Queue
Reader
RouteSolver


The title
Summary
the first paragraph
Examples
Example 1
the second paragraph
Example 2
the third paragraph
See also
the fourth paragraph


|
Apache Mesos
|
#include <zookeeper.hpp>
Public Member Functions | |
| ZooKeeper (const std::string &servers, const Duration &sessionTimeout, Watcher *watcher) | |
| instantiate new ZooKeeper client. More... | |
| ~ZooKeeper () | |
| int | getState () |
| get the state of the zookeeper connection. More... | |
| int64_t | getSessionId () |
| get the current session id. More... | |
| Duration | getSessionTimeout () const |
| get the current session timeout. More... | |
| int | authenticate (const std::string &scheme, const std::string &credentials) |
| authenticate synchronously. More... | |
| int | create (const std::string &path, const std::string &data, const ACL_vector &acl, int flags, std::string *result, bool recursive=false) |
| create a node synchronously. More... | |
| int | remove (const std::string &path, int version) |
| delete a node in zookeeper synchronously. More... | |
| int | exists (const std::string &path, bool watch, Stat *stat) |
| checks the existence of a node in zookeeper synchronously. More... | |
| int | get (const std::string &path, bool watch, std::string *result, Stat *stat) |
| gets the data associated with a node synchronously. More... | |
| int | getChildren (const std::string &path, bool watch, std::vector< std::string > *results) |
| lists the children of a node synchronously. More... | |
| int | set (const std::string &path, const std::string &data, int version) |
| sets the data associated with a node. More... | |
| std::string | message (int code) const |
| return a message describing the return code. More... | |
| bool | retryable (int code) |
| returns whether or not the specified return code implies the operation can be retried "as is" (i.e., without needing to change anything). More... | |
Protected Attributes | |
| ZooKeeperProcess * | process |
| ZooKeeper::ZooKeeper | ( | const std::string & | servers, |
| const Duration & | sessionTimeout, | ||
| Watcher * | watcher | ||
| ) |
instantiate new ZooKeeper client.
The constructor initiates a new session, however session establishment is asynchronous, meaning that the session should not be considered established until (and unless) an event of state ZOO_CONNECTED_STATE is received.
| servers | comma-separated host:port pairs, each corresponding to a ZooKeeper server. e.g. "<IP_ADDRESS>:3000,<IP_ADDRESS>:3001" |
| watcher | the instance of Watcher that receives event callbacks. When notifications are triggered the Watcher::process method will be invoked. |
| ZooKeeper::~ZooKeeper | ( | ) |
| int ZooKeeper::authenticate | ( | const std::string & | scheme, |
| const std::string & | credentials | ||
| ) |
authenticate synchronously.
| int ZooKeeper::create | ( | const std::string & | path, |
| const std::string & | data, | ||
| const ACL_vector & | acl, | ||
| int | flags, | ||
| std::string * | result, | ||
| bool | recursive = false |
||
| ) |
create a node synchronously.
This method will create a node in ZooKeeper. A node can only be created if it does not already exists. The Create Flags affect the creation of nodes. If ZOO_EPHEMERAL flag is set, the node will automatically get removed if the client session goes away. If the ZOO_SEQUENCE flag is set, a unique monotonically increasing sequence number is appended to the path name.
| path | The name of the node. Expressed as a file name with slashes separating ancestors of the node. |
| data | The data to be stored in the node. |
| acl | The initial ACL of the node. If null, the ACL of the parent will be used. |
| flags | this parameter can be set to 0 for normal create or an OR of the Create Flags |
| result | A string which will be filled with the path of the new node (this might be different than the supplied path because of the ZOO_SEQUENCE flag). The path string will always be null-terminated. |
| recursive | if true, attempts to create all intermediate znodes as required; note that 'flags' and 'data' will only be applied to the creation of 'basename(path)'. |
| int ZooKeeper::exists | ( | const std::string & | path, |
| bool | watch, | ||
| Stat * | stat | ||
| ) |
checks the existence of a node in zookeeper synchronously.
| path | the name of the node. Expressed as a file name with slashes separating ancestors of the node. |
| watch | if true, a watch will be set at the server to notify the client if the node changes. The watch will be set even if the node does not exist. This allows clients to watch for nodes to appear. |
| stat | the return stat value of the node. |
| int ZooKeeper::get | ( | const std::string & | path, |
| bool | watch, | ||
| std::string * | result, | ||
| Stat * | stat | ||
| ) |
gets the data associated with a node synchronously.
| path | the name of the node. Expressed as a file name with slashes separating ancestors of the node. |
| watch | if nonzero, a watch will be set at the server to notify the client if the node changes. |
| result | the data returned by the server |
| stat | if not nullptr , will hold the value of stat for the path on return. |
| int ZooKeeper::getChildren | ( | const std::string & | path, |
| bool | watch, | ||
| std::vector< std::string > * | results | ||
| ) |
lists the children of a node synchronously.
| path | the name of the node. Expressed as a file name with slashes separating ancestors of the node. |
| watch | if true, a watch will be set at the server to notify the client if the node changes. |
| results | return value of children paths. |
| int64_t ZooKeeper::getSessionId | ( | ) |
get the current session id.
The current session id or 0 if no session is established.
| Duration ZooKeeper::getSessionTimeout | ( | ) | const |
get the current session timeout.
The session timeout requested by the client or the negotiated session timeout after the session is established with ZooKeeper. Note that this might differ from the initial sessionTimeout
specified when this instance was constructed.
| int ZooKeeper::getState | ( | ) |
get the state of the zookeeper connection.
The return value will be one of the State Consts.
| std::string ZooKeeper::message | ( | int | code | ) | const |
return a message describing the return code.
| int ZooKeeper::remove | ( | const std::string & | path, |
| int | version | ||
| ) |
delete a node in zookeeper synchronously.
| path | the name of the node. Expressed as a file name with slashes separating ancestors of the node. |
| version | the expected version of the node. The function will fail if the actual version of the node does not match the expected version. If -1 is used the version check will not take place. |
| bool ZooKeeper::retryable | ( | int | code | ) |
returns whether or not the specified return code implies the operation can be retried "as is" (i.e., without needing to change anything).
| int ZooKeeper::set | ( | const std::string & | path, |
| const std::string & | data, | ||
| int | version | ||
| ) |
sets the data associated with a node.
| path | the name of the node. Expressed as a file name with slashes separating ancestors of the node. |
| data | the data to be written to the node. |
| version | the expected version of the node. The function will fail if the actual version of the node does not match the expected version. If -1 is used the version check will not take place. |
|
protected |