How Much Coal was Imported Into Egypt in 1905?
1905 was a prosperous time for Egypt and the Egyptian Gazette has the information to prove it. The Egyptian Gazette contained a great deal of all happenings within and around Egypt. In the year of 1905, The Egyptian Gazette contained information about the political doings of leaders within and around Egypt, news (or gossip) about the royalty in Egypt, and a expansive volume of information about the prices and import/export information of many different scarce resources. This was of interest to people in politics, business owners, and investors. This sparked an initial interest due to the fact that information such as, the prices of resources, or the amount imported or exported were translating to how Egypt’s economy stayed alive at that time.
One resource that was imported to Egypt at that time was coal. Coal had many uses within Egypt, but its main and most important use was for transportation. Many of the boats and trains throughout Egypt used coal to power their engines. This made coal one of the most important resources in Egypt. Interestingly enough, coal was primarily imported to Egypt for the majority of their coal use. Because of that, the amount of coal that was being imported through Egypt was vital as without these imports there would be no use of coal in Egypt. This is what lead the topic of research to become, “How much coal was imported into Egypt in the year 1905?”. This question is important as it gives insight to exactly how much coal was used throughout the seasons and when coal was needed the most. This data could also be transferred to other topics of consideration considering the happenings within and around Egypt at that time.
To do this requires the word “coal” to be searched throughout each and every page. Then after finding those instances of the word’s use, information pertaining to the imports of coal will be recorded. By searching for imports of coal this way rather than using the search term “coal imports” or “imports”, will allow the broadest cases of the use of the word “coal” to be returned from the search term.
The information used will be from newspaper editions digitized by the peers in my Digital Microhistory course. Because of that, the amount of input concerning the import of coal varies from month to month. Also, because there is no input on the amount of coal being imported for the months of July and August, those months will be averaged out using the import amounts from June and September. The import amounts given are on a yearly scale.
In the first month’s issue of The Egyptian Gazette, import data was not available. But instead the finishing amounts of coal imported from January to December of the last year were given. This was due to the year being finished out and a new year beginning. The amount of coal that was being imported through January was not reported on as the number was insignificant until the month of February. But, the amount of coal that was imported into Egypt from January 1, 1904 to December 31, 1904 was 1,057,441 tons. Of the 1,057,441 tons imported, Newcastle exported 240,694 tons, Wales exported 594,774, Yorkshire exported 58,375 tons, and coals imported from all other places amounted to 48,307.
February was the first month that coal import information was given. From January 1,1 1905 to February 2, 1905 32,338 tons of coal were imported in to Egypt. Of the 32,338 tons imported 19,804 tons were imported from Wales, Newcastle exported 5,833 tons, Scotland exported 4,668 tons, and 247 tons were imported from all other places. In the same period last year 60,417 tons of coal were imported into Egypt.
Coal imports had a steeper increase in April with 194,330 tons being imported into Egypt from January 1, 1905 to April 13, 1905. 45,082 tons of the imported coal came from Wales, 45,082 tons of coal were imported from from Newcastle, 22,749 tons were imported from Scotland, 13,901 were imported from Yorkshire, and other places exports 6,644 tons in total. During the same dates in 1904, 324,589 tons of coal were imported into Egypt.
From January 1, 1905 to May 4,1905 coal imports increased to 281,462 tons. Of the 281,462 tons of coal imported, 146,032 were imported from Wales, 68,434 were imported from Newcastle. 36,412 were from Scotland, 16,466 were from Yorkshire, and other places exported 13,118. At the same period in 1904 the amount of coal imported totaled to 369,165 tons.
From the beginning of 1905 to the beginning of June in 1905 Egypt imported 361,183 tons of coal. Wales exported 68,434 tons into Egypt while Newcastle exported 68,434. Scotland exported 45,329 tons, Yorkshire exported 21,221 tons, while all other areas exported 15,243 tons of coal into Egypt. In 1904 during the same time 416,405 tons of coal were imported into Egypt.
Because there is no data input on imported coal in July and August the figures is an average amount based on the subtraction of the June amount of imported coal from the September amount of imported coal.
The average amount of coal imported for the months of July and August averaged at 224,091 tons for the two months total. The average for the year before 1905 calculated out to 188,397. Wales average contribution to the July and August months of coal imports was 111,954 tons, Newcastle’s average was 60,037 tons. Scotland averaged at 21,986 tons, Yorkshire at 17,206 tons, while all other countries averaged 8,214 tons of coal.
The end of September marked the first time in 1905 that the amount of coal imported were larger than the amount of coal imported during the same period of time in 1904. In 1904, from January 1,1904, to September 28, 1904 765,539 tons of coal were imported into Egypt. But from January 1,1905 to September 28,1905 794,370 tons of coal were imported into Egypt. 428,660 tons of coal were imported from Wales, while 185,919 tons were imported from Newcastle. 98,112 tons were imported from Scotland, 52,686 tons were imported from Yorkshire, while all other places contributed 29,953 tons.
Coal imports slowed down by the end of October even though the number of coal imports were still greater than the previous year’s period. From January 1, 1905 to October 26, 1905 884,906 tons of coal were imported into Egypt. In the same period in the previous year coal imports amounted to 875,401. Of the 884,906 tons of coal imported in 1905 473,630 tons were imported from Wales and 211,470 tons were from Newcastle. 103,789 tons of coal were imported from Scotland, while 61,925 tons were imported from Yorkshire. Coal from all other places amounted to 32,784.
November and December saw a steep increase in the import coal in 1905. From January 1,1905 to November 23,1905 974,918 tons of coal were imported into Egypt. 526,310 tons of that coal came from Wales, 221,413 tons came from Newcastle, and 118,203 tons came from Scotland. Yorkshire contributed 72,586 tons of coal to the total amount imported while all other areas exported 36,406 tons of coal to Egypt.
The amount of coal imported fell behind the previous year’s numbers once again in December. In 1905 the total amount of coal imported into Egypt from January 1, 1905 to December 28, 1905 was 1,042,768 tons. The same period in 1904 yielded 1,050,482 tons of imported coal. In the 1905 period, the amount of coal imported from Wales was 572,414 tons. Newcastle exported 227,879 tons of coal to Egypt.
Scotland’s imported coal amounted to 119,294, while Yorkshire’s amounted to 78,636. Every other area exported 44,560 tons of coal into Egypt. This amounts to 1% more coal being imported into Egypt during the same period of time in 1904 to end off the year.
While researching the import of coal into Egypt, it was found that using the broadest search terms possible yielded the most results. This research could be improved by adding qualitative analysis on the events that have happened each month which could have led to an increase or decrease in coal production and import/export amounts. This would give readers a more complete picture of the economy as it regards to coal imports/exports and coal mining. It was also discovered that coal is not generally produced within Egypt. This is why the coal import amounts were so great. This is also why coal proved to be an important resource for Egypt. Without coal many homes within Egypt would not be warmed, Egypt could not transport goods and people in and out, and there would need to be an alternative resource. Coal was one of the most important resources of that time in Egypt.
These figures are important as they can be used to assist many other topics in research concerning Digital Microhistory. These numbers also give a good general indication of the use of coal throughout the seasons in the year. By having these coal import numbers, correlations can easily be made between coal imports and another scholarly topic. The coal economy in Egypt is worth researching and should be continued to form a complete picture of the Egyptian economy.


I am somewhat experienced when it comes to psychoactives. I have done LSD, DOM, DOB, Nitrous, Salvia, and various herbs, entheogens, and inhalants. Diphenhydramine has always kind of struck me as being as being a very interesting drug because of the full-blown delirium. Before this night I had done done it twice recreationally (100mg and 150mg). Me and my friend N had the house all to ourselves last night and we thought it'd be a good chance to trip, so he did ~400mg DXM and I did 350mg DPH.
7:30PM - We both dose, and enjoy a few joints.
8:00PM - Nothing really too noticeable.
8:45PM - I stand up from sitting and immediately fall down. My legs feel like stone and my head feels like a hollow bowling ball. This is the exact same thing that happened when I did DXM for the first time above 300mg. I feel very nauseous, so I go lay down on N's bed. I try to stay calm and not panic, so I called my girlfriend and she talked for a minute or two, and I was feeling better.
8:50PM - My whole mouth is completely dry, but when I try to drink water, it sticks to my throat... it hurts.
9:30PM - I am full blown tripping. The carpet's shadows turn into ants, I see things in the corner of my eye move or melt, the walls are changing colors, dark walls shimmer like electricity, flashes of light, hearing voices, etc. I am SO THIRSTY. I have gone to the bathroom to urinate about 2 times so far this hour. It's ridiculous!
9:34PM - So, I go to the bathroom to urinate and look down at my boxers. The bottom of them morphed into two frogs facing away from each other. I knew I was hallucinating, so I tried to touch it to make them away, but when I touched it, it felt like rubber. I flinched and tried not to startle them. There are freaking rubber frogs in my boxers! I look away at something that caught my attention, and when I looked back, the frogs were gone. It really hurts to pee, man!
9:40PM - I am making absolutely no sense when I talk and it's really aggravating me. My girlfriend calls me, after smoking sativa for the first time, and she is super paranoid. I want to talk to her and calm her down so bad but I kept blabbering out nonsense like 'Read to me, babe.'
9:43PM - I just dozed off and thought I was in my girlfriends room with her, because I was hearing her aunt and mother talking outside extremely distinctly. I woke up only to realize I was still in N's room. So annoying.
9:45PM - I doze off again, staring at the ceiling, where I am watching a movie. Wait, there's no TV in the ceiling! It disappears. Ughhh.
9:50PM - I see a posters on each of the walls, some tilted and some just way off alignment. So, I start complaining to N about how he needs a poster on both sides of his room in equal proportions because it's bothering me. Then I realize there were no posters at all.
10:00PM - N's dog comes in, jumps on my lap, stares at me in the eye, and morphs into my girlfriend's dog. I start saying 'Hi, Bella! How'd you get here?!' and when I went to pet her, it disappeared.
10:05PM - I was wearing pants made out of iPods.
10:10PM - 'Hey, N, Nice moving liberty spikes!  I have short hair.'
10:11PM - I am on the computer looking up cheat codes for a video game. *opens eyes* Wait, what? No! I'm still in a bed. I was moving my hands like I had a mouse and was typing on a blanket.
10:20PM - I've had enough of this mind trickery. I'm going to bed.
10:24PM - Can't sleep, mind is going way too fast and so is my heart.
10:25PM - Hey, it's my friend! Cool he brought some weed over. Let's smoke.
10:26PM - Where did he go? No one is in the room. Did everyone die? Nope, there's N, on the computer, watching Ghost in the Shell. And there's N again, playing guitar on the floor. *Opens eyes* Oh! There's the REAL N, looking at me weird.
10:30PM - WHO KEEPS CALLING MY NAME?!  He yells at me loudly and I feel really bad. We go up front, he follows me out the door, closes it, and locks it. I call him and ask why he did that, and he says he had been asleep the past half hour. I just hallucinated the whole convo and then some.
10:55PM - My mom picks me up, and I prep myself to make sure I don't give away my unsoberness
11:00PM - On the way back from N's house, I saw 3 babies playing in the street. I was about to scream but I remembered that they weren't real and that I would give away my temporary psychosis. I tell my mom a lot of things about what's going on with my life and I tried to act as sober as possible. I got away with it perfectly, but it was so sketchy. We arrived at home around 11:20.
11:20PM - My dog is standing in the main hallway completely still. This is really weird because the slightest noise will rile my dog up into a barking frenzy.  My real dog was greeting my mom at the door and I was wondering how I missed that whole thing.
11:25PM - I go to sleep after much tossing and turning. I kept waking up in the middle of the night whispering to nothing, which was odd because I didn't have any dreams at all(I usually am very good at remembering my dreams).
10:30AM - I woke up and I have a slight hangover. My heart is still a bit fast, and I see things ripped and go crazy in the corner of my eye, hearing beeps and blips, etc.
Overall, Diphenhydramine is definitely a freaking odd drug. I will never do anything like that again, because it was way too intense on my body and there was nothing learned. The visuals are just not worth the load on your body. Stick to the good stuff.
'No benefits from Benedryl.'
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.


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| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |
java.lang.Object uchicago.src.collection.RangeMap
public class RangeMap
A Map whose keys are doubles and whose values are Objects. Objects are
inserted with an implied range, relative to the other objects inserted.
So,
map.put(0, obj1);
map.put(.5, obj2);
map.put(11.2, obj3);
associates obj1 with the range [0, .5) (inclusive of 0 and exclusive of .5),
and obj2 with the range [.5, 11.2) and obj3 with [11.2, pos_infinity).
Getting an object is done with a key as in other types of maps. However,
here the object returned is the object within whose range the key falls.
So,
will return obj1.
map.get(.25);
The data structure used here is redblack tree implemented without removal. The implementation is largely a port of the C++ code in Mark Allen Weiss, _Algorithms, Data Structures, and Problem Solving with C++_.
| Field Summary | |
|---|---|
static int |
BLACK
|
static int |
RED
|
| Constructor Summary | |
|---|---|
RangeMap()
Constructs an empty range map. |
| Method Summary | |
|---|---|
void |
clear()
Removes all the entries in the map. |
java.lang.Object |
get(double key)
Gets the Object within whose range the specified key falls. |
boolean |
isEmpty()
Is this RangeMap empty. |
void |
print()
Prints a representation of this RangeMap to the console. |
void |
print(BinaryNode node)
Prints a representation of this RangeMap to the console, beginning with the specified node. |
void |
put(double key,
java.lang.Object obj)
Puts an object in the map and associates it with a range. |
| Methods inherited from class java.lang.Object |
|---|
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
| Field Detail |
|---|
public static final int BLACK
public static final int RED
| Constructor Detail |
|---|
public RangeMap()
| Method Detail |
|---|
public void clear()
public void print()
public void print(BinaryNode node)
public java.lang.Object get(double key)
key
- the number within the range whose associated object is
returnedpublic void put(double key, java.lang.Object obj)
key
- the inclusive lower bound the range to associate with
the inserted objectobj
- the object to insert and associate with the rangepublic boolean isEmpty()
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First instance of widget
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<@content>
JS FWK npm repository:
LINK
@content>
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<^widgetExample showContent=false>
<@title>
Second instance of widget
@title>
<@content>
This content will be hidden due to attribute showContent being set to false
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^widgetExample>


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--- date: 2013-09-04 15:20:04 author: Colene Chow title: "PhoneGap Day US 2013: BlackBerry Loves the Web" format: html tags: - PhoneGap Day - Community - Event ---
Thank you to everyone who attended PhoneGap Day US 2013 and made it such a huge success!
As we mentioned in our previous PhoneGap Day posts, the audio quality is not what we were hoping for due to some technical glitches. We've been working madly to get it improved which is why we've been delayed in sharing them. Nothing too shabby, but not as good as being at PhoneGap Day in person! So, our apologies in advance but with that, on to the video!
Our next speaker is Ken Wallis from BlackBerry, one of our PhoneGap Day US sponsors, speaking on "BlackBerry loves the Web".


Source code for pymunk.pygame_util
# ----------------------------------------------------------------------------
# pymunk
# Copyright (c) 2007-2012 Victor Blomqvist
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
# ----------------------------------------------------------------------------
"""This submodule contains helper functions to help with quick prototyping
using pymunk together with pygame.
Intended to help with debugging and prototyping, not for actual production use
in a full application. The methods contained in this module is opinionated
about your coordinate system and not in any way optimized.
"""
__version__ = "$Id$"
__docformat__ = "reStructuredText"
__all__ = ["draw_space", "draw_shape", "draw_circle", "draw_poly"
, "draw_segment", "draw_constraint", "to_pygame", "from_pygame"]
import pygame
import pymunk
from pymunk.vec2d import Vec2d
[docs]def draw_space(surface, space):
"""Draw the contents of a pymunk.Space object on a pygame.Surface object
This method currently supports drawing of
* pymunk.Segment
* pymunk.Circle
* pymunk.Poly
* pymunk.Constraint objects
You can control the color of a shape by setting shape.color to the color
you want it drawn in.
>>> my_shape.color = pygame.color.THECOLORS["pink"]
Not all constraints are currently drawn in a very clear way, but all the
different shapes should look fine both as static and dynamic objects.
See pygame_util.demo.py for a full example
:Parameters:
surface : pygame.Surface
Surface that the space will be drawn on
space : pymunk.Space
The contents of this Space will be drawn on the surface.
"""
(width, height) = surface.get_size()
for s in space.shapes:
if not (hasattr(s, "ignore_draw") and s.ignore_draw):
draw_shape(surface, s)
for c in space.constraints:
if not (hasattr(c, "ignore_draw") and c.ignore_draw):
draw_constraint(surface, c)
[docs]def draw_shape(surface, shape):
"""Draw a pymunk.Shape object
See the documentation of draw_space for full details
:Parameters:
surface : pygame.Surface
Surface that the space will be drawn on
shape : pymunk.Shape
The Shape object to draw
"""
if isinstance(shape, pymunk.Circle):
draw_circle(surface, shape)
elif isinstance(shape, pymunk.Segment):
draw_segment(surface, shape)
elif isinstance(shape, pymunk.Poly):
draw_poly(surface, shape)
[docs]def draw_circle(surface, circle):
"""Draw a pymunk.Circle object
See help of draw_space for full details
:Parameters:
surface : pygame.Surface
Surface that the space will be drawn on
shape : pymunk.Circle
The circle shape to draw
"""
circle_center = circle.body.position + circle.offset
p = to_pygame(circle_center, surface)
r = 0
if hasattr(circle, "color"):
color = circle.color
elif circle.body.is_static:
color = pygame.color.THECOLORS["lightgrey"]
r = 1
else:
color = pygame.color.THECOLORS["red"]
pygame.draw.circle(surface, color, p, int(circle.radius), r)
circle_edge = circle_center + Vec2d(circle.radius, 0).rotated(circle.body.angle)
p2 = to_pygame(circle_edge, surface)
line_r = 3 if circle.radius > 20 else 1
pygame.draw.lines(surface, pygame.color.THECOLORS["blue"], False, [p,p2], line_r)
[docs]def draw_poly(surface, poly):
"""Draw a pymunk.Poly object
See help of draw_space for full details
:Parameters:
surface : pygame.Surface
Surface that the space will be drawn on
shape : pymunk.Poly
The poly shape to draw
"""
ps = poly.get_points()
ps = [to_pygame(p, surface) for p in ps]
ps += [ps[0]]
if hasattr(poly, "color"):
color = poly.color
elif poly.body.is_static:
color = pygame.color.THECOLORS["lightgrey"]
else:
color = pygame.color.THECOLORS["green"]
pygame.draw.lines(surface, color, False, ps, 1)
[docs]def draw_segment(surface, segment):
"""Draw a pymunk.Segment object
See help of draw_space for full details
:Parameters:
surface : pygame.Surface
Surface that the space will be drawn on
shape : pymunk.Segment
The segment shape to draw
"""
body = segment.body
pv1 = body.position + segment.a.rotated(body.angle)
pv2 = body.position + segment.b.rotated(body.angle)
p1 = to_pygame(pv1, surface)
p2 = to_pygame(pv2, surface)
if hasattr(segment, "color"):
color = segment.color
elif segment.body.is_static:
color = pygame.color.THECOLORS["lightgrey"]
else:
color = pygame.color.THECOLORS["blue"]
pygame.draw.lines(surface, color, False, [p1,p2], max(int(segment.radius),1))
[docs]def draw_constraint(surface, constraint):
"""Draw a pymunk.Constraint object
See the documentation of draw_space for full details
:Parameters:
surface : pygame.Surface
Surface that the space will be drawn on
shape : pymunk.Shape
The Shape object to draw
"""
if isinstance(constraint, pymunk.GrooveJoint) and hasattr(constraint, "groove_a"):
pv1 = constraint.a.position + constraint.groove_a
pv2 = constraint.a.position + constraint.groove_b
p1 = to_pygame(pv1, surface)
p2 = to_pygame(pv2, surface)
pygame.draw.aalines(surface, pygame.color.THECOLORS["darkgray"], False, [p1,p2])
elif isinstance(constraint, pymunk.PinJoint):
pv1 = constraint.a.position + constraint.anchr1.rotated(constraint.a.angle)
pv2 = constraint.b.position + constraint.anchr2.rotated(constraint.b.angle)
p1 = to_pygame(pv1, surface)
p2 = to_pygame(pv2, surface)
pygame.draw.aalines(surface, pygame.color.THECOLORS["darkgray"], False, [p1,p2])
elif isinstance(constraint, pymunk.GearJoint):
pv1 = constraint.a.position
pv2 = constraint.a.position
p1 = to_pygame(pv1, surface)
p2 = to_pygame(pv2, surface)
pygame.draw.circle(surface, pygame.color.THECOLORS["darkgray"], p1, 3)
pygame.draw.circle(surface, pygame.color.THECOLORS["darkgray"], p2, 3)
elif hasattr(constraint, "anchr1"):
pv1 = constraint.a.position + constraint.anchr1.rotated(constraint.a.angle)
pv2 = constraint.b.position + constraint.anchr2.rotated(constraint.b.angle)
p1 = to_pygame(pv1, surface)
p2 = to_pygame(pv2, surface)
pygame.draw.aalines(surface, pygame.color.THECOLORS["darkgray"], False, [p1,p2])
else:
pv1 = constraint.a.position
pv2 = constraint.b.position
p1 = to_pygame(pv1, surface)
p2 = to_pygame(pv2, surface)
pygame.draw.aalines(surface, pygame.color.THECOLORS["darkgray"], False, [p1,p2])
def get_mouse_pos(surface):
"""Get position of the mouse pointer in pymunk coordinates."""
p = pygame.mouse.get_pos()
return from_pygame(p, surface)
[docs]def to_pygame(p, surface):
"""Convenience method to convert pymunk coordinates to pygame surface
local coordinates
"""
return int(p[0]), surface.get_height()-int(p[1])
[docs]def from_pygame(p, surface):
"""Convenience method to convert pygame surface local coordinates to
pymunk coordinates
"""
return to_pygame(p,surface)


Consultation outcome
Ref: Ofqual/15/5747 PDF , 226KB , 5 pages
If you use assistive technology (such as a screen reader) and need a version of this document in a more accessible format, please email<EMAIL_ADDRESS>. Please tell us what format you need. It will help us if you say what assistive technology you use.
Ref: Ofqual/15/5746 PDF , 422KB , 15 pages
If you use assistive technology (such as a screen reader) and need a version of this document in a more accessible format, please email<EMAIL_ADDRESS>. Please tell us what format you need. It will help us if you say what assistive technology you use.
We have made minor drafting changes to clarify some of our expectations, but have otherwise decided to adopt our consultation proposals in full.
We have now published updated versions of our:
Our rules require all reformed GCE qualifications in Biology, Chemistry or Physics to:
Our guidance explains:
This consultation ran from
to
Proposed regulations and guidance for the design, delivery and award of new biology, chemistry and physics A levels.
Ref: Ofqual/15/5713 PDF , 691KB , 50 pages
If you use assistive technology (such as a screen reader) and need a version of this document in a more accessible format, please email<EMAIL_ADDRESS>. Please tell us what format you need. It will help us if you say what assistive technology you use.
We are seeking views on the additional rules and guidance that we propose to put in place for the new A level qualifications in biology, chemistry and physics which are to be taught in England from September 2015.
These new rules relate to the assessment of A level students’ practical skills and associated requirements on marking, results and certification.
We have already accredited all the new AS and A level qualifications in biology, chemistry and physics which will be taught from September 2015. The specifications for these new qualifications currently include provisional information about the practical skills assessments. We are now consulting on the detailed rules and guidance that we propose to put in place for these assessments.
We are also consulting on the consolidation of the rules and guidance in relation to biology, chemistry and physics into one single set of documents which will apply to all three subjects.
After this consultation, when we have put in place the final rules and guidance, exam boards will make appropriate changes to the practical skills sections of their specifications (as needed). The exam boards will not need to resubmit their amended specifications for accreditation.
Published: 8 June 2015
Updated: 20 July 2015
From: Ofqual


Plugin Information |
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View Polarion Webclient for SVN on the plugin site for more information. |
Polarion Plugin
This plugin integrates the Polarion WebClient for SVN, an open source, web based interface to Subversion.
Configuration simply involves choosing 'Polarion Web Client' as the repository browser when configuring a project to use Subversion as its source code management.
Once configured, the change logs of a build will provide links to the specific Subversion revision as well as the color coded difference views within Polarion WebClient.
Note that JIRA also integrates the Polarion WebClient as a web based viewer of subversion changes related to issues. When coupled with the Jenkins JIRA Plugin this combination provides a very useful integration between source control viewing, issue tracking and continuous integration and builds.
Change History
Version 1.3 (Nov 03, 2011)
- JENKINS-11602
Version 1.2 (Jan 29, 2010)
- Update code for more recent Hudson
Version 1.1 (Sep 5, 2007)
- Added the location option (report)
Attachments:
configuration.tiff (image/tiff)
configuration.png (image/png)
changes.png (image/png)


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{{ pageName }}
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About us The Medieval Theme Park!
About
This theme park was designed for the people who loved Medieval, and was
to experience some of that for themselves.
We offer a thematic local built to feel like the world of Medieval
Europe everywhere you look.
There's a collection of rides based on fantastic moments within the age
of Medieval, fictional or non-fictional, and lives shows of some of
those fan favourites.
Available Jobs
By joining The Medieval Theme Park team you will be part of a dynamic
group of people who are always a part of the magic of this wonderful
location.
It's a friendly working environment that allows for growth for those who
are passionate.
Currently we have no available job positions but check back here for
updates.


My name is Julianne, I grew up in a small town in Central California. Both of my parents still live there, as well as my dog, Minnie!
I moved to New Zealand for University, where I studied Earth Science at the Univeristy of Auckland. I chose to study this because I really enjoyed learning about our planet and its history.
While I was studying I joined a satellite building team called Koios. We are building a 10 x 10 x 10 centimeter satellite called a Cubesat. This was throught the Auckland Programme for Space Systems.
This is when I really became interested in technology and decided that for my career I wanted to be more tech-focused.
This is what brough me to Dev Acacdemy! Dev Academy is a 15 week web developer bootcamp. I joined shortly after I finished my bachelors degree.
So now I currently live in Auckland, New Zealand and am studying to become a web developer!
Check out what I've learned so far by looking at my blogs!


Hadley Wickham has announced to depreceate dplyr::do
in favor of purrr:map
. In a recent post, I have made use of do
, so some commentators informed me about that. In this post, I will show use cases of map
, specifically as a replacement of do
. map
is for lists; read more about lists here.
library(tidyverse)
library(broom)
We will use mtcars
as a sample dataframe (boring, I know, but convenient).
data(mtcars)
Cor
is a function that takes a dataframe as its input
As in the last post, assume we would like to conduct a correlation test. First, let’s start simple using cor
.
mtcars %>%
select_if(is.numeric) %>%
select(1:3) %>% # to make it smaller
cor
#> mpg cyl disp
#> mpg 1.0000000 -0.8521620 -0.8475514
#> cyl -0.8521620 1.0000000 0.9020329
#> disp -0.8475514 0.9020329 1.0000000
Here’s no need for purrr:map
. map
is essentially a looping device, taking a list as input. However, cor
does not takes lists as input. It takes a whole dataframe (consisting of many lists). That’s even more practical than a looping function such as map
.
cor.test
via do
and via map
Now let’s see where map
makes sense. Consider cor.test
from the last post. cor.test
does not accept a dataframe as input, hence the dplyr
logic does not apply well. Instead we have to build a workaround using do
:
mtcars %>%
do(cor.test(.$hp, .$cyl) %>% tidy)
#> estimate statistic p.value parameter conf.low conf.high
#> 1 0.8324475 8.228604 3.477861e-09 30 0.6816016 0.9154223
#> method alternative
#> 1 Pearson's product-moment correlation two.sided
Here we apply the function cor.test
to two columns. Applying functions to columns (ie., lists) works smoothly with map
and friends:
mtcars %>%
select(hp) %>% # take out this line for iteration/loop
map(~cor.test(.x, mtcars$cyl) %>% tidy)
#> $hp
#> estimate statistic p.value parameter conf.low conf.high
#> 1 0.8324475 8.228604 3.477861e-09 30 0.6816016 0.9154223
#> method alternative
#> 1 Pearson's product-moment correlation two.sided
map
applies a function to a list element
So, what does map
do? It applies a function .fun
over all elements of a list .list
:
map(.list, .fun)
.list
must either be a list or a simple vector. mapp
is convenient for iteration as a replacement of “for-loops”:
mtcars %>%
select(hp, cyl, mpg) %>% # only three for the sake of demonstration
map(~cor.test(.x, mtcars$cyl) %>% tidy)
#> $hp
#> estimate statistic p.value parameter conf.low conf.high
#> 1 0.8324475 8.228604 3.477861e-09 30 0.6816016 0.9154223
#> method alternative
#> 1 Pearson's product-moment correlation two.sided
#>
#> $cyl
#> estimate statistic p.value parameter conf.low conf.high
#> 1 1 Inf 0 30 1 1
#> method alternative
#> 1 Pearson's product-moment correlation two.sided
#>
#> $mpg
#> estimate statistic p.value parameter conf.low conf.high
#> 1 -0.852162 -8.919699 6.112687e-10 30 -0.9257694 -0.7163171
#> method alternative
#> 1 Pearson's product-moment correlation two.sided
BTW, it would be nice to combine the tidy output elements ($hp
, $cyl
, $mpg
) to a dataframe:
mtcars %>%
select(hp, cyl, mpg) %>% # only three for the sake of demonstration
map_df(~cor.test(.x, mtcars$cyl) %>% tidy)
#> estimate statistic p.value parameter conf.low conf.high
#> 1 0.8324475 8.228604 3.477861e-09 30 0.6816016 0.9154223
#> 2 1.0000000 Inf 0.000000e+00 30 1.0000000 1.0000000
#> 3 -0.8521620 -8.919699 6.112687e-10 30 -0.9257694 -0.7163171
#> method alternative
#> 1 Pearson's product-moment correlation two.sided
#> 2 Pearson's product-moment correlation two.sided
#> 3 Pearson's product-moment correlation two.sided
map_df
maps the function (that’s what comes after “~”) to each list (ie., column) of mtcars
. If possible, the resulting elements will be row-binded to a dataframe. To make the output of cor.test
nice (ie., tidy) we again use tidy
.
Extract elements from a list using map
Say, we are only interest in the p-value (OMG). How to extract each of the 3 p-values in our example?
mtcars %>%
select(hp, cyl, mpg) %>% # only three for the sake of demonstration
map(~cor.test(.x, mtcars$cyl) %>% tidy) %>%
map("p.value")
#> $hp
#> [1] 3.477861e-09
#>
#> $cyl
#> [1] 0
#>
#> $mpg
#> [1] 6.112687e-10
To extract several elements, say the p-value and r, we can use the [
operator:
mtcars %>%
select(hp, cyl, mpg) %>% # only three for the sake of demonstration
map(~cor.test(.x, mtcars$cyl) %>% tidy) %>%
map(`[`, c("p.value", "statistic"))
#> $hp
#> p.value statistic
#> 1 3.477861e-09 8.228604
#>
#> $cyl
#> p.value statistic
#> 1 0 Inf
#>
#> $mpg
#> p.value statistic
#> 1 6.112687e-10 -8.919699
[
is some kind of “extractor” function; it extracts elements, and returns a list or data frame:
mtcars["hp"] %>% head
#> hp
#> Mazda RX4 110
#> Mazda RX4 Wag 110
#> Datsun 710 93
#> Hornet 4 Drive 110
#> Hornet Sportabout 175
#> Valiant 105
mtcars["hp"] %>% head %>% str
#> 'data.frame': 6 obs. of 1 variable:
#> $ hp: num 110 110 93 110 175 105
x <- list(1, 2, 3)
x[1]
#> [[1]]
#> [1] 1
Maybe more convenient, there is a function called magrittr:extractor
. It’s a wrapper aroung [
:
library(magrittr)
mtcars %>%
select(hp, cyl, mpg) %>% # only three for the sake of demonstration
map(~cor.test(.x, mtcars$cyl) %>% tidy) %>%
map_df(extract, c("p.value", "statistic"))
#> p.value statistic
#> 1 3.477861e-09 8.228604
#> 2 0.000000e+00 Inf
#> 3 6.112687e-10 -8.919699


Logged sizes over time
This plot shows size of a directory corresponding to a machine, at certain dates. (Not really implemented)
Logged sizes over time
This plot shows size of a directory corresponding to a machine, at certain dates. (Not really implemented)
Logged sizes over time


|
3432
|
BIOL
156A
1
|
Project Laboratory in Biotechnology
[
sn
]
See Course Catalog for prerequisites.
Required course for the MS in Biotechnology.
See Course Catalog for Special Notes.
See Catalog for course fee.
Lab held in Bassine 235. Classroom portion meets in Bassine 208.
Instructor's Signature Required.
|
M,W
1:00 PM–5:20 PM
Bassine Biology Building 235
|
Open
Consent Req.
12 /
16
/ 0
|
Sutera, Vincent A
|


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This plugin allows Jenkins agents to be dynamically provisioned on a Kubernetes cluster.
Plugin Information |
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View Kubernetes on the plugin site for more information. |
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{{ define "title" }}{{ .Title }}{{ end }} {{ define "hero-box" }}{{ end }} {{ define "pagestyles" }} {{ end }} {{ define "main" }}
Exhibits in the Boston College University Libraries will advance discovery of library resources by highlighting connections between our collections and academic and community life at BC. The Libraries welcome partnerships with the Boston College community and other institutions, groups, or individuals to create thoughtful and attractive exhibits. Successful exhibit proposals will be in strong alignment with the mission and collections of the Libraries.
Description of space: One exhibit case in Gargan Hall.
Policy for use of space: Exhibit space is used to highlight books in the collection and selected items from the University Archives.
Contact: Laurie Mayville
Description of space: Two cases found outside Gargan Hall.
Policy for use of space: Exhibit space is used to highlight books in the collection and selected items from the University Archives.
Contact: Katherine Fox
Description of space: Burns wall cases (4 cases).
Policy for use of space: Exhibits highlight collections in the Burns Library.
Contact: Katherine Fox
Description of space: One wall with bulletin board and two bookcases (approximately 11 linear feet)
Policy for use of space: This space is designated to feature educational and curriculum materials. Student work may be exhibited with approval of ERC exhibit committee.
Contact: Tiffeni Fontno
Description of space: One horizontal case
Policy of use for the main exhibit case: The space is designated to materials relating to the Lowell Humanities Series.
Contact: Stephen Sturgeon
Description of space: The galley is an open space of 770 sq. ft., with wall space of 385 sq. ft. for displaying flatwork only.
Policy for use of space: Since 2009 Level One Gallery in O'Neill Library has been a space for the BC Community to display their creative work. The Boston College Libraries fully support the Arts here at the university and the gallery encourages the creative spirit of Boston College.
Contact: Scott Britton
Description of space: Gallery space for hanging art, 11’ tall with at total length of approximately 75’. The entire length includes 3 wall bays that are 24’ each with 2 pilasters bump outs at one third intervals. There is existing library furniture in front of the wall that comes to about 3 feet of the lower portion of the wall.
Policy for use of space: Level Three Gallery in O'Neill Library is a space for the BC faculty and alumni to display their creative work. Work should support the educational purpose and curricula of the university.
Contact: Nina Bogdanovsky
Description of space: There are three horizontal cases in the O’Neill Reading Room.
Policy of use of space: Exhibits in the O’Neill Reading Room feature special collections from the John J. Burns Library.
Contact: Katherine Fox
Description of space: There are three horizontal cases against the Back Wall of the O’Neill Reading Room.
Policy of use of space: Policy of use for the main exhibit case: Exhibits in O’Neill are part of the Library’s educational outreach and are specifically created to stimulate interest in, and encourage further study of, the topics showcased in the displays. One case is devoted to campus events, and the remaining are open to exhibits which highlight collections, spotlight interesting research being done on campus, explore current and/or historically important events, or demonstrate cultural trends and their significance for the BC community.
Contact: Scott Britton
Description of space: 5'x4' magnetic whiteboard and a five-feet long credenza on top of which to display items.
Policy for use of space: Display themes need to be relevant to social work matters and consistent with the missions and values of BC and the BC School of Social Work.
Contact: Hannah Ha
Description of space: Approximately 90 linear feet of space along three walls fitted with picture rail on the lower level of the Theology & Ministry Library.
Policy for use of space: Exhibits are sought which are consistent with the Jesuit mission of Boston College and, more specifically, with the mission and values of the School of Theology & Ministry
Contact: Steve Dalton


The Go Doctor is an automated refactoring tool for the Go
programming language. It currently supports:
Try out the Go Doctor using the online demo to the left. Then, install it on your machine. There is a Vim plug-in for the Go Doctor, or it can be run directly from the command line.
Can't find your favorite feature? The Go Doctor is open source, so fork it on GitHub.
- Rename
- Extract Function
- Extract Local Variable
- Toggle var ⇔ :=
- Add Godoc stubs
Try out the Go Doctor using the online demo to the left. Then, install it on your machine. There is a Vim plug-in for the Go Doctor, or it can be run directly from the command line.
Can't find your favorite feature? The Go Doctor is open source, so fork it on GitHub.


|
VA-API
2.6.0.pre1
|
VP8 Encoding Picture Parameter Buffer Structure. More...
#include <va_enc_vp8.h>
Public Attributes | |
| int8_t | loop_filter_level [4] |
| int8_t | ref_lf_delta [4] |
| int8_t | mode_lf_delta [4] |
| uint8_t | sharpness_level |
| uint8_t | clamp_qindex_high |
| uint8_t | clamp_qindex_low |
| uint32_t | va_reserved [VA_PADDING_LOW] |
| Reserved bytes for future use, must be zero. | |
| uint32_t | first_ref: 2 |
| uint32_t | reserved: 16 |
| Reserved for future use, must be zero. | |
| uint32_t | clamping_type: 1 |
| uint32_t | update_mb_segmentation_map: 1 |
| uint32_t | update_segment_feature_data: 1 |
| uint32_t | loop_filter_adj_enable: 1 |
| uint32_t | refresh_entropy_probs: 1 |
| uint32_t | refresh_golden_frame: 1 |
| uint32_t | refresh_alternate_frame: 1 |
| uint32_t | refresh_last: 1 |
| uint32_t | copy_buffer_to_golden: 2 |
| uint32_t | copy_buffer_to_alternate: 2 |
| uint32_t | sign_bias_golden: 1 |
| uint32_t | sign_bias_alternate: 1 |
| uint32_t | mb_no_coeff_skip: 1 |
| uint32_t | forced_lf_adjustment: 1 |
VP8 Encoding Picture Parameter Buffer Structure.
This structure conveys picture level parameters.
| uint8_t VAEncPictureParameterBufferVP8::clamp_qindex_high |
Application supplied maximum clamp value for Qindex used in quantization. Qindex will not be allowed to exceed this value. It has a valid range [0..127] inclusive.
| uint8_t VAEncPictureParameterBufferVP8::clamp_qindex_low |
Application supplied minimum clamp value for Qindex used in quantization. Qindex will not be allowed to be lower than this value. It has a valid range [0..127] inclusive. Condition clamp_qindex_low <= clamp_qindex_high must be guaranteed, otherwise they are ignored.
| uint32_t VAEncPictureParameterBufferVP8::clamping_type |
The following fields correspond to the same VP8 syntax elements in the frame header. 0: clamping of reconstruction pixels is disabled, 1: clamping enabled.
| uint32_t VAEncPictureParameterBufferVP8::copy_buffer_to_alternate |
Determines if the alternate reference is replaced by another reference.
| uint32_t VAEncPictureParameterBufferVP8::copy_buffer_to_golden |
Determines if the golden reference is replaced by another reference.
| uint32_t VAEncPictureParameterBufferVP8::first_ref |
following two flags indicate the reference order LastRef is specified by 01b; GoldRef is specified by 10b; AltRef is specified by 11b; first_ref specifies the reference frame which is searched first. second_ref specifies the reference frame which is searched second if there is.
| uint32_t VAEncPictureParameterBufferVP8::forced_lf_adjustment |
Enforces unconditional per-MB loop filter delta update setting frame header flags mode_ref_lf_delta_update, all mb_mode_delta_update_flag[4], and all ref_frame_delta_update_flag[4] to 1. Since loop filter deltas are not automatically refreshed to default values at key frames, dropped frame with delta update may prevent correct decoding from the next key frame. Encoder application is advised to set this flag to 1 at key frames.
| uint32_t VAEncPictureParameterBufferVP8::loop_filter_adj_enable |
indicates if the MB level loop filter adjustment is enabled for the current frame (0 off, 1 on).
| int8_t VAEncPictureParameterBufferVP8::loop_filter_level[4] |
Contains a list of 4 loop filter level values (updated value if applicable) controlling the deblocking filter strength. Each entry represents a segment. When segmentation is disabled, use entry 0. When loop_filter_level is 0, loop filter shall be disabled.
| uint32_t VAEncPictureParameterBufferVP8::mb_no_coeff_skip |
Enables or disables the skipping of macroblocks containing no non-zero coefficients.
| int8_t VAEncPictureParameterBufferVP8::mode_lf_delta[4] |
Contains a list of 4 delta values for coding mode based MB-level loop filter adjustment. If no update, then set to 0.
| int8_t VAEncPictureParameterBufferVP8::ref_lf_delta[4] |
Contains a list of 4 delta values for reference frame based MB-level loop filter adjustment. If no update, then set to 0.
| uint32_t VAEncPictureParameterBufferVP8::refresh_alternate_frame |
Determines if the current decoded frame refreshes the alternate reference frame.
| uint32_t VAEncPictureParameterBufferVP8::refresh_entropy_probs |
Determines whether updated token probabilities are used only for this frame or until further update. It may be used by application to enable error resilient mode. In this mode probability updates are allowed only at Key Frames.
| uint32_t VAEncPictureParameterBufferVP8::refresh_golden_frame |
Determines if the current decoded frame refreshes the golden frame.
| uint32_t VAEncPictureParameterBufferVP8::refresh_last |
Determines if the current decoded frame refreshes the last frame reference buffer.
| uint8_t VAEncPictureParameterBufferVP8::sharpness_level |
Controls the deblocking filter sensitivity. Corresponds to the same VP8 syntax element in frame header.
| uint32_t VAEncPictureParameterBufferVP8::sign_bias_alternate |
Controls the sign of motion vectors when the alternate frame is referenced.
| uint32_t VAEncPictureParameterBufferVP8::sign_bias_golden |
Controls the sign of motion vectors when the golden frame is referenced.
| uint32_t VAEncPictureParameterBufferVP8::update_mb_segmentation_map |
Determines if the MB segmentation map is updated in the current frame.
| uint32_t VAEncPictureParameterBufferVP8::update_segment_feature_data |
Indicates if the segment feature data is updated in the current frame.


Greytip to Zoho Converter
GreytipHR payroll excel file to Zoho Books vendor bills CSV file converter!
Select GreytipHR Payroll Excel File


What's your favorite color?
Click a button to choose what color you'd like this page to be:
Yellow
purple
These are some of my cats favorite things
favorite toys
yarn
red dot lazer
socks
These are some of my cats favorite activities
favorite activities
sleeping
eating
sleeping
eating
sleeping
eating
Here is a page about cats!


Class: RubyTerraform::Commands::StatePull
- Includes:
- Options::Global
- Defined in:
- lib/ruby_terraform/commands/state_pull.rb
Overview
Wraps the terraform state pull command which pulls the state from its location, upgrades the local copy, and outputs it to stdout.
This command “pulls” the current state and outputs it to stdout. As part of this process, Terraform will upgrade the state format of the local copy to the current version.
The primary use of this is for state stored remotely. This command will still work with local state but is less useful for this.
For options accepted on construction, see Base#initialize.
When executing an instance of StatePull via Base#execute, the following options are supported:
-
:chdir
: the path of a working directory to switch to before executing the given subcommand.
The Base#execute method accepts an optional second parameter which is a map of invocation options. Currently, the only supported option is :environment
which is a map of environment variables to expose during invocation of the command.
Constructor Details
This class inherits a constructor from RubyTerraform::Commands::Base


{{- partial "head.html" . -}}
{{- partial "header.html" . -}}
{{- partial "home/banner.html" . -}} {{- partial "home/pillars.html" . -}} {{- partial "home/partners.html" . -}} {{- partial "home/news.html" . -}} {{- partial "home/case-study.html" . -}} {{- partial "home/home.html" . -}} {{- partial "home/impact.html" . -}}
{{- partial "footer.html" . -}}


Loading map...
Power
Health
[ 1 ]
[ 2 ]
[ 3 ]
[ 4 ]
[ 5 ]
Welcome to Wizard Warz version 0.1 Beta.
Create a new player or login with existing to start playing.
Press 'H' to view key-bindings when in-game. And 'G' for current score board.
Loading (0%)
| Key Binding | Action |
| W | Walk forward |
| D | Walk backward |
| A | Turn left |
| D | Turn right |
| space | Jump |
| B | 1st person view |
| V | 3rd person view |
| G | Score Board |
| 1 | Weapon 1 |
| 2 | Weapon 2 |
| 3 | Weapon 3 |
| 4 | Weapon 4 |
| 5 | Weapon 5 |
| Left mouse button | Shoot |
| # | Player | Level | Kills | Deaths | Experience | Health |
| Ranking | Player | Level | Kills | Deaths | Experience | Health |
Code and parts of the models/textures made by myself. First WebGL game I've made, just for fun. Multiplayer shoot'em up wizard style game!
Three.js client with node.js server. Server-side dynamic terrain generation. Always generates a unique map.
<EMAIL_ADDRESS>or proc_ @ freenode (#three.js).
Use W-A-S-D-space and mouse to move. Click to lock pointer.
Tip: Hold left mouse button to load higher power of the weapon.


--- layout: page title: "Project" ---
Pixel level calibration for LED display.
Feature Vector => Dimension Reduction => Classification => Prediction
Identification and verification using 3D palmprint scans.
Know how instructions are executed in a MIPS CPU.
Java address book application.


The Pythagorean Theorem says that for any right triangle with perpendicular sides \(a\) and \(b\), and long side (hypoteneuse) \(c\), the equation \(a^2 + b^2 = c^2\) holds. We can see that this is true by considering the following diagram. You can drag the diamond to change the shape of the right triangles shown in yellow.
The big squares have the same size and the 4 right triangles that each big square contains also have exactly the same size, so the remaining space in each big square must have the same area. That means the areas of the pink square (\(a^2\)) and the green square (\(b^2\)) must add up to the area of the blue square (\(c^2\)).
Built using Constrain


Sharding-JDBC
Sharding-Proxy
Sharding-Sidecar
Database sharding & Table sharding
Read-write splitting
Distributed primary key
XA transaction
BASE transaction
Dynamic configuration
Fusing & Disabling
Open tracing
Multiple data replication (Planing)
Elastic scaling out (Planing)
Sharding-JDBC is compatible of all the Java ORM frameworks and database connection pools
Sharding-Proxy is compatible of all the heterogeneous languages
Sharding-Sidecar provides cloud native support for Kubernetes/Mesos (planning)


The rules:
- You start with £15,000 to invest.
- There are eight companies to chose from. Each one has unique characteristics.
- If a companies' stonks reach a value of £0 or less, that company is bust, and you may no longer trade those stonks.
- At any time, you may cash out using the very large button at the base of your screen.
- Your score is the combined sum of your cash and the value of your stonks the instant you cash out.


Work Day Scheduler
A simple calendar app for scheduling your work day
Placeholder
9:00 am
10:00 am
11:00 am
12:00 pm
1:00 pm
2:00 pm
3:00 pm
4:00 pm
5:00 pm


3.2. Support Vector Machines¶
Support vector machines (SVMs) are a set of supervised learning methods used for classification, regression and outliers detection.
The advantages of support vector machines are:
- Effective in high dimensional spaces.
- Still effective in cases where number of dimensions is greater than the number of samples.
- Uses a subset of training points in the decision function (called support vectors), so it is also memory efficient.
- Versatile: different Kernel functions can be specified for the decision function. Common kernels are provided, but it is also possible to specify custom kernels.
The disadvantages of support vector machines include:
- If the number of features is much greater than the number of samples, the method is likely to give poor performances.
- SVMs do not directly provide probability estimates, these are calculated using five-fold cross-validation, and thus performance can suffer.
The support vector machines in scikit-learn support both dens (numpy.ndarray and convertible to that by numpy.asarray) and sparse (any scipy.sparse) sample vectors as input. However, to use an SVM to make predictions for sparse data, it must have been fit on such data. For optimal performance, use C-ordered numpy.ndarray (dense) or scipy.sparse.csr_matrix (sparse) with dtype=float64.
In previous versions of scikit-learn, sparse input support existed only in the sklearn.svm.sparse module which duplicated the sklearn.svm interface. This module still exists for backward compatibility, but is deprecated and will be removed in scikit-learn 0.12.
3.2.1. Classification¶
SVC, NuSVC and LinearSVC are classes capable of performing multi-class classification on a dataset.
SVC and NuSVC are similar methods, but accept slightly different sets of parameters and have different mathematical formulations (see section Mathematical formulation). On the other hand, LinearSVC is another implementation of Support Vector Classification for the case of a linear kernel. Note that LinearSVC does not accept keyword ‘kernel’, as this is assumed to be linear. It also lacks some of the members of SVC and NuSVC, like support_.
As other classifiers, SVC, NuSVC and LinearSVC take as input two arrays: an array X of size [n_samples, n_features] holding the training samples, and an array Y of integer values, size [n_samples], holding the class labels for the training samples:
>>> from sklearn import svm
>>> X = [[0, 0], [1, 1]]
>>> Y = [0, 1]
>>> clf = svm.SVC()
>>> clf.fit(X, Y)
SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.5, kernel='rbf', probability=False, scale_C=True, shrinking=True,
tol=0.001)
After being fitted, the model can then be used to predict new values:
>>> clf.predict([[2., 2.]])
array([ 1.])
SVMs decision function depends on some subset of the training data, called the support vectors. Some properties of these support vectors can be found in members support_vectors_, support_ and n_support:
>>> # get support vectors
>>> clf.support_vectors_
array([[ 0., 0.],
[ 1., 1.]])
>>> # get indices of support vectors
>>> clf.support_
array([0, 1]...)
>>> # get number of support vectors for each class
>>> clf.n_support_
array([1, 1]...)
<IP_ADDRESS>. Multi-class classification¶
SVC and NuSVC implement the “one-against-one” approach (Knerr et al., 1990) for multi- class classification. If n_class is the number of classes, then n_class * (n_class - 1)/2 classifiers are constructed and each one trains data from two classes:
>>> X = [[0], [1], [2], [3]]
>>> Y = [0, 1, 2, 3]
>>> clf = svm.SVC()
>>> clf.fit(X, Y)
SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=1.0, kernel='rbf', probability=False, scale_C=True, shrinking=True,
tol=0.001)
>>> dec = clf.decision_function([[1]])
>>> dec.shape[1] # 4 classes: 4*3/2 = 6
6
On the other hand, LinearSVC implements “one-vs-the-rest” multi-class strategy, thus training n_class models. If there are only two classes, only one model is trained:
>>> lin_clf = svm.LinearSVC()
>>> lin_clf.fit(X, Y)
LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True,
intercept_scaling=1, loss='l2', multi_class='ovr', penalty='l2',
scale_C=True, tol=0.0001)
>>> dec = lin_clf.decision_function([[1]])
>>> dec.shape[1]
4
See Mathematical formulation for a complete description of the decision function.
Note that the LinearSVC also implements an alternative multi-class strategy, the so-called multi-class SVM formulated by Crammer and Singer, by using the option “multi_class=’crammer_singer’”. This method is consistent, which is not true for one-vs-rest classification. In practice, on-vs-rest classification is usually preferred, since the results are mostly similar, but the runtime is significantly less.
For “one-vs-rest” LinearSVC the attributes coef_ and intercept_ have the shape [n_class, n_features] and [n_class] respectively. Each row of the coefficients corresponds to one of the n_class many “one-vs-rest” classifiers and simliar for the interecepts, in the order of the “one” class.
In the case of “one-vs-one” SVC, the layout of the attributes is a little more involved. In the case of having a linear kernel, The layout of coef_ and intercept_ is similar to the one described for LinearSVC described above, except that the shape of coef_ is [n_class * (n_class - 1) / 2, corresponding to as many binary classifiers. The order for classes 0 to n is “0 vs 1”, “0 vs 2” , ... “0 vs n”, “1 vs 2”, “1 vs 3”, “1 vs n”, . . . “n-1 vs n”.
The shape of dual_coef_ is [n_class-1, n_SV] with a somewhat hard to grasp layout. The columns correspond to the support vectors involved in any of the n_class * (n_class - 1) / 2 “one-vs-one” classifiers. Each of the support vectors is used in n_class - 1 classifiers. The n_class - 1 entries in each row correspond to the dual coefficients for these classifiers.
This might be made more clear by an example:
Consider a three class problem with with class 0 having 3 support vectors and class 1 and 2 having two support vectors and respectively. For each support vector , there are 2 dual coefficients. Let’s call the coefficient of support vector in the classifier between classes i and k . Then dual_coef_ looks like this:
Coefficients
for SVs
of class 0
Coefficients
for SVs of class 1
Coefficients
for SVs of class 2
<IP_ADDRESS>. Unbalanced problems¶
In problems where it is desired to give more importance to certain classes or certain individual samples keywords class_weight and sample_weight can be used.
SVC (but not NuSVC) implement a keyword class_weight in the fit method. It’s a dictionary of the form {class_label : value}, where value is a floating point number > 0 that sets the parameter C of class class_label to C * value.
SVC, NuSVC, SVR, NuSVR and OneClassSVM implement also weights for individual samples in method fit through keyword sample_weight.
3.2.2. Regression¶
The method of Support Vector Classification can be extended to solve regression problems. This method is called Support Vector Regression.
The model produced by support vector classification (as described above) depends only on a subset of the training data, because the cost function for building the model does not care about training points that lie beyond the margin. Analogously, the model produced by Support Vector Regression depends only on a subset of the training data, because the cost function for building the model ignores any training data close to the model prediction.
There are two flavors of Support Vector Regression: SVR and NuSVR.
As with classification classes, the fit method will take as argument vectors X, y, only that in this case y is expected to have floating point values instead of integer values:
>>> from sklearn import svm
>>> X = [[0, 0], [2, 2]]
>>> y = [0.5, 2.5]
>>> clf = svm.SVR()
>>> clf.fit(X, y)
SVR(C=1.0, cache_size=200, coef0=0.0, degree=3,
epsilon=0.1, gamma=0.5, kernel='rbf', probability=False, scale_C=True,
shrinking=True, tol=0.001)
>>> clf.predict([[1, 1]])
array([ 1.5])
3.2.3. Density estimation, novelty detection¶
One-class SVM is used for novelty detection, that is, given a set of samples, it will detect the soft boundary of that set so as to classify new points as belonging to that set or not. The class that implements this is called OneClassSVM.
In this case, as it is a type of unsupervised learning, the fit method will only take as input an array X, as there are no class labels.
See, section Novelty and Outlier Detection for more details on this usage.
3.2.4. Complexity¶
Support Vector Machines are powerful tools, but their compute and storage requirements increase rapidly with the number of training vectors. The core of an SVM is a quadratic programming problem (QP), separating support vectors from the rest of the training data. The QP solver used by this libsvm-based implementation scales between and depending on how efficiently the libsvm cache is used in practice (dataset dependent). If the data is very sparse should be replaced by the average number of non-zero features in a sample vector.
Also note that for the linear case, the algorithm used in LinearSVC by the liblinear implementation is much more efficient than its libsvm-based SVC counterpart and can scale almost linearly to millions of samples and/or features.
3.2.5. Tips on Practical Use¶
Avoiding data copy: For SVC, SVR, NuSVC and NuSVR, if the data passed to certain methods is not C-ordered contiguous, and double precision, it will be copied before calling the underlying C implementation. You can check whether a give numpy array is C-contiguous by inspecting its flags attribute.
For LinearSVC (and LogisticRegression) any input passed as a numpy array will be copied and converted to the liblinear internal sparse data representation (double precision floats and int32 indices of non-zero components). If you want to fit a large-scale linear classifier without copying a dense numpy C-contiguous double precision array as input we suggest to use the SGDClassifier class instead. The objective function can be configured to be almost the same as the LinearSVC model.
Kernel cache size: For SVC, SVR, nuSVC and NuSVR, the size of the kernel cache has a strong impact on run times for larger problems. If you have enough RAM available, it is recommended to set cache_size to a higher value than the default of 200(MB), such as 500(MB) or 1000(MB).
Setting C: In constrast to the scaling in LibSVM and LibLinear, the C parameter in sklearn.svm is a per sample penalty. Commonly good values for C often are very large (i.e. 10**4) and seldom below 1.
Support Vector Machine algorithms are not scale invariant, so it is highly recommended to scale your data. For example, scale each attribute on the input vector X to [0,1] or [-1,+1], or standardize it to have mean 0 and variance 1. Note that the same scaling must be applied to the test vector to obtain meaningful results. See section Preprocessing data for more details on scaling and normalization.
Parameter nu in NuSVC/OneClassSVM/NuSVR approximates the fraction of training errors and support vectors.
In SVC, if data for classification are unbalanced (e.g. many positive and few negative), set class_weight=’auto’ and/or try different penalty parameters C.
The underlying LinearSVC implementation uses a random number generator to select features when fitting the model. It is thus not uncommon, to have slightly different results for the same input data. If that happens, try with a smaller tol parameter.
Using L1 penalization as provided by LinearSVC(loss=’l2’, penalty=’l1’, dual=False) yields a sparse solution, i.e. only a subset of feature weights is different from zero and contribute to the decision function. Increasing C yields a more complex model (more feature are selected). The C value that yields a “null” model (all weights equal to zero) can be calculated using l1_min_c.
3.2.6. Kernel functions¶
The kernel function can be any of the following:
- linear: .
- polynomial: . d is specified by keyword degree, r by coef0.
- rbf (). is specified by keyword gamma.
- sigmoid (), where r is specified by coef0.
Different kernels are specified by keyword kernel at initialization:
>>> linear_svc = svm.SVC(kernel='linear')
>>> linear_svc.kernel
'linear'
>>> rbf_svc = svm.SVC(kernel='rbf')
>>> rbf_svc.kernel
'rbf'
<IP_ADDRESS>. Custom Kernels¶
You can define your own kernels by either giving the kernel as a python function or by precomputing the Gram matrix.
Classifiers with custom kernels behave the same way as any other classifiers, except that:
- Field support_vectors_ is now empty, only indices of support vectors are stored in support_
- A reference (and not a copy) of the first argument in the fit() method is stored for future reference. If that array changes between the use of fit() and predict() you will have unexpected results.
<IP_ADDRESS>.1. Using python functions as kernels¶
You can also use your own defined kernels by passing a function to the keyword kernel in the constructor.
Your kernel must take as arguments two matrices and return a third matrix.
The following code defines a linear kernel and creates a classifier instance that will use that kernel:
>>> import numpy as np
>>> from sklearn import svm
>>> def my_kernel(x, y):
... return np.dot(x, y.T)
...
>>> clf = svm.SVC(kernel=my_kernel)
Examples:
<IP_ADDRESS>.2. Using the Gram matrix¶
Set kernel=’precomputed’ and pass the Gram matrix instead of X in the fit method. At the moment, the kernel values between all training vectors and the test vectors must be provided.
>>> import numpy as np
>>> from sklearn import svm
>>> X = np.array([[0, 0], [1, 1]])
>>> y = [0, 1]
>>> clf = svm.SVC(kernel='precomputed')
>>> # linear kernel computation
>>> gram = np.dot(X, X.T)
>>> clf.fit(gram, y)
SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.0, kernel='precomputed', probability=False, scale_C=True,
shrinking=True, tol=0.001)
>>> # predict on training examples
>>> clf.predict(gram)
array([ 0., 1.])
3.2.7. Mathematical formulation¶
A support vector machine constructs a hyper-plane or set of hyper-planes in a high or infinite dimensional space, which can be used for classification, regression or other tasks. Intuitively, a good separation is achieved by the hyper-plane that has the largest distance to the nearest training data points of any class (so-called functional margin), since in general the larger the margin the lower the generalization error of the classifier.
<IP_ADDRESS>. SVC¶
Given training vectors , i=1,..., n, in two classes, and a vector such that , SVC solves the following primal problem:
Its dual is
where is the vector of all ones, C > 0 is the upper bound, Q is an n by n positive semidefinite matrix, and is the kernel. Here training vectors are mapped into a higher (maybe infinite) dimensional space by the function .
The decision function is:
Note
In practice to have independent of the number of samples , is scaled by (Replace by in the equations above). It corresponds to the scale_C parameter which is True by default in all estimators since version 0.11.
Note
While SVM models derived from libsvm and liblinear use C as regularization parameter, most other estimators use alpha. The relation between both is .
This parameters can be accessed through the members dual_coef_ which holds the product , support_vectors_ which holds the support vectors, and intercept_ which holds the independent term :
References:
- “Automatic Capacity Tuning of Very Large VC-dimension Classifiers” I Guyon, B Boser, V Vapnik - Advances in neural information processing 1993,
- “Support-vector networks” C. Cortes, V. Vapnik, Machine Leaming, 20, 273-297 (1995)
<IP_ADDRESS>. NuSVC¶
We introduce a new parameter which controls the number of support vectors and training errors. The parameter is an upper bound on the fraction of training errors and a lower bound of the fraction of support vectors.
It can be shown that the nu-SVC formulation is a reparametrization of the C-SVC and therefore mathematically equivalent.


Software Development | Full-stack Development
Ann Arbor, MI | September 2017 – April 2019 (Expected)
B.S.E in Computer Science
GPA: 3.88/4.00
- EECS 281: Data Structures and Algorithms
- EECS 370: Computer Organization
- EECS 376: Foundations of Computer Science
- EECS 388: Introduction to Computer Security
- EECS 398: Computing for Computer Science
- EECS 441: Mobile App Development for Entrepreneurs
- EECS 445: Introduction to Machine Learning
- EECS 482: Introduction to Operation System
- EECS 484: Database Management System
- EECS 485: Web Databases and Information Systems
- STATS 412: Probability and Statistics
Shanghai, China | September 2015 – August 2019 (Expected)
B.S.E in Electrical and Computer Engineering (ECE)
GPA: 3.50/4.00
- EECS 280: Programming & Elem. Data Structures
- EECS 270: Logic Design
- EECS 216: Signals and Systems
- EECS 300: Technical Communication
Ann Arbor, MI | December 2017 – Present
Michigan China Forum
- Directed the planning and execution of forum events (meetings and fundraisers) of 1100+ attendees in Ann Arbor
- Maintained relationships with corporations, school departments and local organizations and expanded media exposure
Ann Arbor, MI | Aug 2017– Aug 2018
SJTU Student & Alumni Association
- Worked with the University of Michigan and industry partners to hold culture immersion programs
- Created networking opportunities for SJTU and international students
- Outreached the local communities and MCity research complex to provide professional development and research resources
Shanghai, China | May 2017 – August 2017
Michigan China Forum Committee
- Planned and organized the events of China Thinks Big project fair, Mission 300, the cocktail party, and three house games
- Managed the video shootings and photography work of speech series and VIP interviews
- Produced the videos for opening (welcome video) and closing (review video) ceremonies
Shanghai, China | June 2016 – August 2017
Joint Institute Student Union
- Planned and organized the 2nd US-Sino cuisine culture festival at Shanghai Jiao Tong University
- Encouraged international and local students to make and share food from their own cultures to enhance friendship
Ann Arbor, MI | September 2017 – Present
Tiger Express
- Published 5 social media posts with 10,000+ views
- Established the visual standard of the online magazine Tiger Express
- Designed and posted the Tiger Express UM Survival Guide
Luxi, Yunnan Province, China | December 2016 – January 2017
Joint Institute Peer Volunteer Group
- Participated in the project of volunteer teaching in Yunnan, southwest border province, China
- Prepared and delivered the maths lectures for two classes in the 7th and 8th grade
- Involved the study of the educational issues in Chinese rural areas
Shanghai, China | May 2017
Michigan Pan Asia Reunion
- Helped the organization and management of the UM alumni reunion in pan Asia region
<EMAIL_ADDRESS>|<PHONE_NUMBER>
ResumeFor more info about me, please check my LinkedIn profile and Github repos.


Thato is ISTQB certified(Advanced Test Analyst) in software testing and has graduated from CTI Pretoria in 2014 with a Bsc in Computer systems. Thato has gained valuable experience in the banking/financial industry working in teams that did functional testing on client banking applications, websites and payment systems. Thato is goal driven and easy to get along with which helps him work great in a team. He is accountable, flexible, his ability to manage time and remain calm in stressful situations will make him an asset in any team. He enjoys a challenge and is always eager to learn. He is inquisitive and pays very close attention to detail.
My duties on the bench are basically up skilling on testing, below are some online courses and tasks I have completed since I was assigned to the bench on the 16th Feb.
Apart from being a Test analyst, I enjoy learning new technologies and widening my skill set. I am currently self learning web development.


|
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| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | |||||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |
java.lang.Object | +--com.milk.objed.BaseWrappedValueEditor
This is a base class which implements ValueEditor
to defer
to some other target editor. Among other things, it provides call-backs
for when events get fired from the target. Also, it implements the
Editable
interface to refer to itself, as opposed to
creating an editor for the innards of the object itself. By default,
most of the Editor
methods just defer to the target, except
for {add,remove}Listener()
, which operate locally to make
the events work out right. The ValueEditor
methods may be
overridden to do special processing on the value.
| Nested Class Summary | |
private class |
BaseWrappedValueEditor.TargetListener
This class is used as a listener on the target, in order to cause events that it sends out to be sent out from this (outer) object too. |
| Field Summary | |
private BaseWrappedValueEditor.TargetListener |
myListener
the listener which listens to the target |
private ListenerList |
myListeners
the list of listeners to this object |
private ValueEditor |
myTarget
the target editor |
| Constructor Summary | |
protected |
BaseWrappedValueEditor(ValueEditor target)
Construct a BaseWrappedValueEditor . |
| Method Summary | |
void |
addListener(java.util.EventListener listener)
Add a listener to this object. |
protected void |
broadcast(BaseEvent event)
Broadcast an event to the listeners of this object. |
java.lang.String |
getDescription()
Get a full human-oriented description for the object this editor is editing. |
Editor |
getEditor()
Get an Editor for this object. |
java.lang.String |
getLabel()
Get a short human-oriented label/description for the object this editor is editing. |
java.lang.Object |
getValue()
Get the current value from this editor. |
boolean |
isMutable()
Return true if this editor is mutable. |
void |
removeListener(java.util.EventListener listener)
Remove a listener from this object that was previously added with addListener() . |
void |
setValue(java.lang.Object value)
Set a new value for the editor. |
java.lang.String |
toString()
Get the string form of this object. |
void |
update()
Ask this editor to update its internal state. |
| Methods inherited from class java.lang.Object |
clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait |
| Field Detail |
private ValueEditor myTarget
private BaseWrappedValueEditor.TargetListener myListener
private ListenerList myListeners
| Constructor Detail |
protected BaseWrappedValueEditor(ValueEditor target)
BaseWrappedValueEditor
.
target
- the target to wrap around| Method Detail |
public java.lang.String toString()
"{ClassName label}"
.
toString
in class java.lang.Object
public java.lang.Object getValue()
super.getValue()
to actually get the underlying value from the target).
getValue
in interface ValueEditor
BadValueException
- thrown if the value is bad in some
waypublic void setValue(java.lang.Object value) throws BadValueException, ImmutableException
super.setValue()
to actually set the underlying value in the target).
setValue
in interface ValueEditor
value
- the new value
BadValueException
- thrown if the value is inappropriate
for this editor
ImmutableException
- thrown if the editor is in fact
immutablepublic final void addListener(java.util.EventListener listener)
addListener
in interface Editor
listener
- the listener to addpublic final void removeListener(java.util.EventListener listener)
addListener()
.
removeListener
in interface Editor
listener
- the listener to removepublic final java.lang.String getLabel()
getLabel
in interface Editor
public final java.lang.String getDescription()
descriptionChanged
event will be sent to any registered
listeners.
getDescription
in interface Editor
public final boolean isMutable()
mutabilityChanged
event will be sent
to any registered listeners.
isMutable
in interface Editor
public final void update()
update
in interface Editor
public final Editor getEditor()
Editor
for this object. In this case, it
returns this
.
getEditor
in interface Editable
Editor
for this objectprotected final void broadcast(BaseEvent event)
event
- the event to broadcast
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| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |


Police watchdog probes death of man hit with stun gun win Mississauga, Ont.
MISSISSAUGA, Ont. — A man has died after an officer used a stun gun on him west of Toronto, police said Wednesday, prompting Ontario's police watchdog to launch an investigation.
by Canadian PressMISSISSAUGA, Ont. — A man has died after an officer used a stun gun on him west of Toronto, police said Wednesday, prompting Ontario's police watchdog to launch an investigation.
Peel Regional Police said they were called to a home in Mississauga, Ont., around 3 a.m. for reports of a suspicious person in the area.
Const. Akhil Mooken said the officers found a man in a backyard.
"Officers located the male and had an interaction, at which point a conducted energy weapon was deployed," Mooken told reporters at the scene.
He said the man was taken to a local hospital, where he died shortly after 4 a.m.
The Special Investigations Unit, a civilian agency that looks into cases where there is a death, injury or allegation of sexual assault involving police, has invoked its mandate.
This is the second stun-gun death in Mississauga in recent months.
The SIU is also investigating the death of a 34-year-old man who died after police used a stun gun on him on Sept. 10.
In that case, the watchdog said Peel police officers responded to a domestic call in the Malton area around 9 p.m.
The SIU said the "officers became involved in an interaction with a man which involved the use of a conducted energy weapon."
The agency said the man stopped breathing, his heart stopped beating and he was later pronounced dead in hospital.
This report by The Canadian Press was first published Nov. 20, 2019.
The Canadian Press


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{% extends 'base.html' %} {% block head %} {% endblock %} {% block content %} $ Loading... ×
This may take a while for large funds.


Submit
Result
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FASTA
Show Top-K aptamers
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Target Protein Sequence (Input Query)
Candidates (Output)


Reverse DNS¶
DNS usually determines an IP address associated with a domain name. Reverse DNS is the opposite process: resolving a domain name from an IP address. This is usually achieved with a domain name pointer.
Get PTR record¶
PTR records refer to a parent device: either a Cloud Server or a Cloud Load Balancer with a public virtual IP address. You must supply a fully formed resource object in order to retrieve either one’s PTR record:
/** @param $parent OpenCloud\DNS\Resource\HasPtrRecordsInterface */
$ptr = $service->ptrRecord(array(
'parent' => $parent
));
So, in the above example, a $parent could be an instance of OpenCloud\Compute\Resource\Server or OpenCloud\LoadBalancer\Resource\LoadBalancer - because they both implement OpenCloud\DNS\Resource\HadPtrRecordsInterface. Please consult the server documentation and load balancer documentation for more detailed usage instructions.
List PTR records¶
/** @param $parent OpenCloud\DNS\Resource\HasPtrRecordsInterface */
$ptrRecords = $service->ptrRecordList($parent);
foreach ($ptrRecords as $ptrRecord) {
}
Please consult the iterator documentation for more information about iterators.
Add PTR record¶
$parent = $computeService->server('foo-server-id');
$ptr = $dnsService->ptrRecord(array(
'parent' => $parent,
'ttl' => 3600,
'name' => 'example.com',
'type' => 'PTR',
'data' => '<IP_ADDRESS>'
));
$ptr->create();
Here is a table that explains the above attributes:
| Name | Description | Required |
|---|---|---|
| type | Specifies the record type as “PTR”. | Yes |
| name | Specifies the name for the domain or subdomain. Must be a valid domain name. | Yes |
| data | The data field for PTR records must be a valid IPv4 or IPv6 IP address. | Yes |
| ttl | If specified, must be greater than 300. Defaults to 3600 if no TTL is specified. | No |
| comment | If included, its length must be less than or equal to 160 characters. | No |
Modify PTR record¶
$ptr->update(array(
'ttl' => $ptr->ttl * 2
));
Delete PTR record¶
$ptr->delete();


Galactica CIC
Character sets
Core game characters
Pegasus Expansion characters
Exodus Expansion characters
Daybreak Expansion characters
Draft Rules
Include Cylon leaders
Allow players to refuse Cylon Leader
Add extra loyalty card into the loyalty deck
Players:
3
4
5
6
7
Hint:
When selecting characters, click on the character's name to open a full card.
Start draft


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{% extends "posts/base.html" %} {% block title %} - Page not found{% endblock %} {% block content %}
Sorry the page you were looking for cannot be found. You may wish to try searching.


SgfView(context: Context, attrs: AttributeSet?)
Draws an interactive Board together with undo/redo buttons and additional informational text. It's a
TextView
, so the text it displays is styled according to the TextView
attributes.
The layout will be as follows: The grid is drawn with an additional padding to account for
pieces at the borders of the board. Below the grid, there are undo and redo buttons, square
shaped, in the size of half the space between two lines on the grid. Directly underneath, the
text is drawn using the android:text*
attributes.
For best results, you should not set the height of the view to an absolute value, use
MATCH_PARENT
or WRAP_CONTENT
instead. Otherwise, the view might be clipped.
If you need more control, you have the following options:
You can use this view to implement any kind of board game you wish, but there are some limitations for games with unusual boards:
VW
property is used is only supported
if the game uses XYPoint as its SgfType.Point implementation; otherwise the whole board will
be visible all the timeVW
property will only cover a square portion of the tileSee Also


PipelineBuilder¶
-
class
statefun_tasks.
PipelineBuilder
(pipeline=None)¶ Builder class for creating pipelines of Flink Tasks
- Parameters
pipeline (optional) – list of initial pipeline entries e.g. from another builder
-
__init__
(pipeline=None)¶ Initialize self. See help(type(self)) for accurate signature.
Methods
__init__
([pipeline])Initialize self.
append_group
(pipelines[, max_parallelism])Appends tasks from another pipeline builder into a new in_parallel group inside this one
append_to
(other)Appends tasks from another pipeline builder into this one
continue_with
(continuation, *args, **kwargs)Adds continuation to the pipeline
finally_do
(finally_action, *args, **kwargs)Adds finally to the pipeline
from_proto
(pipeline_proto)Deserialises the pipeline from protobuf
Returns the initial destination of the pipeline as None - i.e. use the default ingress.
Returns a list of all tasks identifiers (namespace, worker name, id) that make up this pipeline
is_empty
()Tests if the pipeline contains any tasks
send
(fun, *args, **kwargs)Adds a task entry for the given Flink Task and arguments
set
([retry_policy, namespace, worker_name])Sets task properties on the last entry added to the builder
to_pipeline
([serialiser, is_fruitful])Concretises the builder into a pipeline
to_proto
([serialiser])Serialises the pipeline to protobuf
to_task_request
(serialiser)Serialises the pipeline as a TaskRequest with a task type of ‘__builtins.run_pipeline’
validate
()Validates the pipeline
The ID of this pipeline
Attributes
The ID of this pipeline
-
append_group
(pipelines, max_parallelism=None)¶ Appends tasks from another pipeline builder into a new in_parallel group inside this one
- Parameters
other – the other pipeline builder
max_parallelism (option) – the maximum number of tasks to invoke in parallel for this group
- Return type
PipelineBuilder
- Returns
the builder
-
append_to
(other)¶ Appends tasks from another pipeline builder into this one
- Parameters
other (
PipelineBuilder
) – the other pipeline builder- Return type
PipelineBuilder
- Returns
the builder
-
continue_with
(continuation, *args, **kwargs)¶ Adds continuation to the pipeline
- Parameters
continuation – the python function which should be decorated with @tasks.bind()
args – the task args
kwargs – the task kwargs
- Return type
PipelineBuilder
- Returns
the builder
-
finally_do
(finally_action, *args, **kwargs)¶ Adds finally to the pipeline
- Parameters
finally_action – the python function which should be decorated with @tasks.bind()
args – the task args
kwargs – the task kwargs
- Return type
PipelineBuilder
- Returns
the builder
-
static
from_proto
(pipeline_proto)¶ Deserialises the pipeline from protobuf
- Parameters
pipeline_proto (
Pipeline
) – the pipeline as protobuf- Return type
PipelineBuilder
- Returns
Pipeline protobuf message
-
get_destination
()¶ Returns the initial destination of the pipeline as None - i.e. use the default ingress
-
get_tasks
()¶ Returns a list of all tasks identifiers (namespace, worker name, id) that make up this pipeline
- Return type
list
- Returns
list of tuples of namespace, worker name, id for each task
-
property
id
¶ The ID of this pipeline
-
is_empty
()¶ Tests if the pipeline contains any tasks
- Returns
true if empty, false otherwise
-
send
(fun, *args, **kwargs)¶ Adds a task entry for the given Flink Task and arguments
- Parameters
fun – the python function which should be decorated with @tasks.bind()
args – the task args
kwargs – the task kwargs
- Return type
PipelineBuilder
- Returns
the builder
-
set
(retry_policy=None, namespace=None, worker_name=None)¶ Sets task properties on the last entry added to the builder
- Parameters
retry_policy (option) – the task retry policy to use
namespace (option) – the task namespace
worker_name (option) – the task worker_name
- Return type
PipelineBuilder
- Returns
the builder
-
to_pipeline
(serialiser=None, is_fruitful=True)¶ Concretises the builder into a pipeline
- Parameters
serialiser (option) – the serialiser to use such as DefaultSerialiser
is_fruitful (option) – whether this pipeline is fruitful (i.e. returns a result). Default is True
- Returns
a Flink Tasks pipeline
-
to_proto
(serialiser=None)¶ Serialises the pipeline to protobuf
- Parameters
serialiser – the serialiser to use such as DefaultSerialiser
- Return type
Pipeline
- Returns
Pipeline protobuf message
-
to_task_request
(serialiser)¶ Serialises the pipeline as a TaskRequest with a task type of ‘__builtins.run_pipeline’
- Parameters
serialiser – the serialiser to use such as DefaultSerialiser
- Return type
TaskRequest
- Returns
TaskRequest protobuf message
-
validate
()¶ Validates the pipeline
- Return type
PipelineBuilder
- Returns
the builder


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This program accepts a number value and calculates the area of a circle with the value the user inserts.


This webpage allows Foodsby restaurant partners to upload their daily orders and generate a list of the total quantity for each item.
Select 'All Meals' to view receipts / labels for all meals. Right-click in popup and select 'View Page Source'. Copy and paste all the content in the new tab into this input box.
Select 'All Meals' to view receipts / labels for all meals. Right-click in popup and select 'Save As'. Save the page to your device and use the file upload button below.
Photo by Tim Mossholder


Extending¶
Writing new estimators is easy. One way of doing this is by writing a estimator conforming to the sickit-learn protocol, and then wrapping it with ibex.frame()
(see Adapting Estimators). A different way is writing it directly as a pandas
estimator. This might be the only way to go, if the logic of the estimator is pandas
specific. This chapter shows how to write a new estimator from scratch.
Example Transformation¶
Suppose we have a pandas.DataFrame
like this:
>>> import numpy as np
>>> import pandas as pd
>>>
>>> df = pd.DataFrame({'a': [1, 3, 2, 1, 2], 'b': range(5), 'c': range(2, 7)})
>>> df
a b c
0 1 0 2
1 3 1 3
2 2 2 4
3 1 3 5
4 2 4 6
We think that, for each row, the mean values of 'b'
and 'c'
, aggregated by 'a'
, might make a useful feature. In pandas
, we could write this as follows:
>>> df.groupby(df.a).transform(np.mean)
b c
0 1.5 3.5
1 1.0 3.0
2 3.0 5.0
3 1.5 3.5
4 3.0 5.0
We now want write a transformer to do this, in order to use it for more general settings (e.g., cross validation).
Writing A New Transformer Step¶
We can write a (slightly more general) estimator, as follows:
>>> from sklearn import base
>>> import ibex
>>> class GroupbyAggregator(
... base.BaseEstimator, # (1)
... base.TransformerMixin, # (2)
... ibex.FrameMixin): # (3)
...
... def __init__(self, group_col, agg_func=np.mean):
... self._group_col, self._agg_func = group_col, agg_func
...
... def fit(self, X, _=None):
... self.x_columns = X.columns # (4)
... self._agg = X.groupby(df[self._group_col]).apply(self._agg_func)
... return self
...
... def transform(self, X):
... Xt = X[self.x_columns] # (5)
... Xt = pd.merge(
... Xt[[self._group_col]],
... self._agg,
... how='left')
... return Xt[[c for c in Xt.columns if c != self._group_col]]
Note the following general points:
- We subclass
sklearn.base.BaseEstimator
, as this is an estimator. - We subclass
sklearn.base.TransformerMixin
, as, in this case, this is specifically a transformer. - We subclass
ibex.FrameMixin
, as this estimator deals withpandas
entities. - In
fit
, we make sure to setibex.FrameMixin.x_columns
; this will ensure that the transformer will “remember” the columns it should see in further calls. - In
transform
, we first usex_columns
. This will verify the columns ofX
, and also reorder them according to the original order seen infit
(if needed).
The rest is logic specific to this transformer.
- In
__init__
, the group column and aggregation function are stored. - In
fit
,X
is aggregated by the group column according to the aggregation function, and the result is recorded. - In
transform
,X
(which is not necessarily the one used infit
) is left-merged with the aggregation result, and then the relevant columns of the result are returned.
We can now use this as a regular step. If we fit it on df
and transform it on the same df
, we get the result above:
>>> GroupbyAggregator('a').fit(df).transform(df)
b c
0 1.5 3.5
1 1.0 3.0
2 3.0 5.0
3 1.5 3.5
4 3.0 5.0
We can, however, now use it for fitting on one DataFrame
, and transforming another:
>>> try:
... from sklearn.model_selection import train_test_split
... except: # Older sklearn versions
... from ibex.sklearn.cross_validation import train_test_split
>>>
>>> tr, te = train_test_split(df, random_state=3)
>>> GroupbyAggregator('a').fit(tr).transform(te)
b c
0 0... 2...
1 2... 4...


| PERL5303DELTA(1) | Perl Programmers Reference Guide | PERL5303DELTA(1) |
If you are upgrading from an earlier release such as 5.30.1, first read perl5302delta, which describes differences between 5.30.1 and 5.30.2.
The target system needs a sufficient amount of memory to allocate partial expansions of the nested quantifiers prior to the overflow occurring. This requirement is unlikely to be met on 64-bit systems.
Discovered by: ManhND of The Tarantula Team, VinCSS (a member of Vingroup).
Discovered by: Hugo van der Sanden and Slaven Rezic.
Discovered by: Sergey Aleynikov.
Excluding auto-generated files, documentation and release tools, there were approximately 350 lines of changes to 8 .pm, .t, .c and .h files.
Perl continues to flourish into its fourth decade thanks to a vibrant community of users and developers. The following people are known to have contributed the improvements that became Perl 5.30.3:
Chris 'BinGOs' Williams, Hugo van der Sanden, John Lightsey, Karl Williamson, Nicolas R., Sawyer X, Steve Hay.
The list above is almost certainly incomplete as it is automatically generated from version control history. In particular, it does not include the names of the (very much appreciated) contributors who reported issues to the Perl bug tracker.
Many of the changes included in this version originated in the CPAN modules included in Perl's core. We're grateful to the entire CPAN community for helping Perl to flourish.
For a more complete list of all of Perl's historical contributors, please see the AUTHORS file in the Perl source distribution.
If you believe you have an unreported bug, please open an issue at <https://github.com/Perl/perl5/issues>. Be sure to trim your bug down to a tiny but sufficient test case.
If the bug you are reporting has security implications which make it inappropriate to send to a public issue tracker, then see "SECURITY VULNERABILITY CONTACT INFORMATION" in perlsec for details of how to report the issue.
perlthanks
This will send an email to the Perl 5 Porters list with your show of thanks.
The INSTALL file for how to build Perl.
The README file for general stuff.
The Artistic and Copying files for copyright information.
| 2020-05-29 | perl v5.30.3 |


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{% extends 'base.html' %} {% block title %}PCA Error{% endblock title %} {% block content %}
If you see this page, that means you have some problems with your PCA input.
Following is the reason:
{{error_reason}}


This website is a tool to download geocache descriptions and latest logs for printing from geocaching.hu (Note: this site is completely independent of geocaching.com; it has a different (but somewhat overlapping) set of caches and all the content is in Hungarian).
© András Hann 2021 – released under the MIT license. API, GitHub repo.
Thanks to BootsrapFormBuilder.com.


@@include('../includes/head.html')
@@include( '../includes/header.html', { "site": "mew" } )
@@include('../includes/alerts.tpl')
@@include( '../includes/footer.tpl', { "site": "mew" } )
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