layout method
Compute the layout for this render object.
This method is the main entry point for parents to ask their children to update their layout information. The parent passes a constraints object, which informs the child as which layouts are permissible. The child is required to obey the given constraints.
If the parent reads information computed during the child's layout, the parent must pass true for parentUsesSize. In that case, the parent will be marked as needing layout whenever the child is marked as needing layout because the parent's layout information depends on the child's layout information. If the parent uses the default value (false) for parentUsesSize, the child can change its layout information (subject to the given constraints) without informing the parent.
Subclasses should not override layout directly. Instead, they should override performResize and/or performLayout. The layout method delegates the actual work to performResize and performLayout.
The parent's performLayout method should call the layout of all its children unconditionally. It is the layout method's responsibility (as implemented here) to return early if the child does not need to do any work to update its layout information.
Implementation
void layout(Constraints constraints, { bool parentUsesSize: false }) {
assert(constraints != null);
assert(constraints.debugAssertIsValid(
isAppliedConstraint: true,
informationCollector: (StringBuffer information) {
final List<String> stack = StackTrace.current.toString().split('\n');
int targetFrame;
final Pattern layoutFramePattern = new RegExp(r'^#[0-9]+ +RenderObject.layout \(');
for (int i = 0; i < stack.length; i += 1) {
if (layoutFramePattern.matchAsPrefix(stack[i]) != null) {
targetFrame = i + 1;
break;
}
}
if (targetFrame != null && targetFrame < stack.length) {
information.writeln(
'These invalid constraints were provided to $runtimeType\'s layout() '
'function by the following function, which probably computed the '
'invalid constraints in question:'
);
final Pattern targetFramePattern = new RegExp(r'^#[0-9]+ +(.+)$');
final Match targetFrameMatch = targetFramePattern.matchAsPrefix(stack[targetFrame]);
if (targetFrameMatch != null && targetFrameMatch.groupCount > 0) {
information.writeln(' ${targetFrameMatch.group(1)}');
} else {
information.writeln(stack[targetFrame]);
}
}
}
));
assert(!_debugDoingThisResize);
assert(!_debugDoingThisLayout);
RenderObject relayoutBoundary;
if (!parentUsesSize || sizedByParent || constraints.isTight || parent is! RenderObject) {
relayoutBoundary = this;
} else {
final RenderObject parent = this.parent;
relayoutBoundary = parent._relayoutBoundary;
}
assert(() {
_debugCanParentUseSize = parentUsesSize;
return true;
}());
if (!_needsLayout && constraints == _constraints && relayoutBoundary == _relayoutBoundary) {
assert(() {
// in case parentUsesSize changed since the last invocation, set size
// to itself, so it has the right internal debug values.
_debugDoingThisResize = sizedByParent;
_debugDoingThisLayout = !sizedByParent;
final RenderObject debugPreviousActiveLayout = _debugActiveLayout;
_debugActiveLayout = this;
debugResetSize();
_debugActiveLayout = debugPreviousActiveLayout;
_debugDoingThisLayout = false;
_debugDoingThisResize = false;
return true;
}());
return;
}
_constraints = constraints;
_relayoutBoundary = relayoutBoundary;
assert(!_debugMutationsLocked);
assert(!_doingThisLayoutWithCallback);
assert(() {
_debugMutationsLocked = true;
if (debugPrintLayouts)
debugPrint('Laying out (${sizedByParent ? "with separate resize" : "with resize allowed"}) $this');
return true;
}());
if (sizedByParent) {
assert(() { _debugDoingThisResize = true; return true; }());
try {
performResize();
assert(() { debugAssertDoesMeetConstraints(); return true; }());
} catch (e, stack) {
_debugReportException('performResize', e, stack);
}
assert(() { _debugDoingThisResize = false; return true; }());
}
RenderObject debugPreviousActiveLayout;
assert(() {
_debugDoingThisLayout = true;
debugPreviousActiveLayout = _debugActiveLayout;
_debugActiveLayout = this;
return true;
}());
try {
performLayout();
markNeedsSemanticsUpdate();
assert(() { debugAssertDoesMeetConstraints(); return true; }());
} catch (e, stack) {
_debugReportException('performLayout', e, stack);
}
assert(() {
_debugActiveLayout = debugPreviousActiveLayout;
_debugDoingThisLayout = false;
_debugMutationsLocked = false;
return true;
}());
_needsLayout = false;
markNeedsPaint();
}


DEEP CRIMSON By Harvey Karten, Ph.D. Ivania Films Director: Arturo Ripstein Writer: Paz Alicia Garciadiego Cast: Julieta Egurrola, Bianca Florido, Giovanni Florido, Rosa Furman, Daniel Gimenez Cacho, Sherlyn Gonzalez, Veronica Merchant, Regina Orozco, Marisa Paredes, Patricia Reyes Spindola At: Film Forum, 209 W. Houston St. New York City Films and plays that treat murder in comic ways are abundant, a leading example being "Arsenic and Old Lace," in which a couple of dotty old ladies poison lonely widowers to put them out of their misery. "Deep Crimson," a tortilla-y- frijoles remake of the 1970 cult movie "Honeymoon Killers," uses takes a true series of events which occurred in the 1940s and injects an almost surreal take. The picture, which is called an example of the genre "mad-love," makes cartoon figures out of two petty creatures living seedy lives in Mexico and shows how their merger in mayhem melds them into blood siblings. What a duo! Coral Fabre (Regina Orozco) is obese and just not-all-there, while the man she adores, Nicolas Estrella (Daniel Gimenez Cacho) is handsome enough when he wears his hairpiece, generally bemused, and ultimately surprised by the depth of his feeling for the repulsive Coral. The story begins by showing just how randy and forlorn Ms. Fabre must be. A nurse in a seedy institution, she makes advances on an 80-year-old, wheelchair-bound man. Reading the personals column she discovers an ad that describes the guy of her dreams: a Spaniard who comes across as a true gentleman. When he quickly departs after meeting her complaining of a migraine headache, we assume the pain is a ruse to get away from this humongous creature (played by Shirley Stoler in the 1970 American version). But he returns and during their courtship she rifles through his papers and discovers his plan to extort money from the widows he charms. She is pleased. Teaming up, Coral and Nicolas set their sights on other women whose loneliness makes them as desolate and dried-up as the backwater surroundings. Director Arturo Ripstein lingers over each operation of this larcenous duo as they take care of a fidgety woman who claims she is imprisoned by her gypsy husband; a fanatically religious widow who is warned by her cynical, atheistic neighbor that her new love interest is a fraud, and a young, businesslike woman who seems to need only another mechanic to help run her repair shop and to keep the eyes and hands of the local machos away. "Deep Crimson," which also has the Spanish name "Profundo carmesi," takes its title from Coral's favorite color, one which matches the blood which gushes from their victims whose lives are snuffed out not so much for their money but because of Coral's envy. Unable to tolerate the attentions which her man is giving to the victims, she turns her own madness into serial killing and her beau into an accomplice. Hers is the sort of passion which knows no boundaries: she has even given up the two children she loves to satisfy the whims of her sleazy partner in crime. Hairpieces have always been the subject of jokes. In "Deep Crimson," much is made of the value which Nicolas places on his rug--justifiably, though, since he is a great deal more handsome while wearing it. Ripstein unfolds Paz Alicia Garciadiego's screenplay in a straight, narrative style, drawing us into the activities by cleverly alternating the comic with the disturbing. While Coral's plight calls out for some sympathy from the audience, her loathsome obesity prevents us from commiseration. Not so Nicolas, who, despite his history of embezzlement, is a nice-looking soul who believes he is earning the money he misappropriates by providing his lonely women with the solace and company of a handsome escort. "Deep Crimson" is played less tongue-in-cheek than its predecessor, "The Honeymoon Killers," and so could hardly be a candidate for cult status. It is well acted by an ensemble cast particularly Marisa Paredes as the ultra-religious Irene Gallardo and Patricia Reyes Espindola as the Widow Ruelas. Watch for the scene in which Coral, who has had some experience with embalming, props up one of her victims in a chair "until rigor mortis sets in. Not Rated. Running Time: 114 minutes. (C) 1997 Harvey Karten
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


--- layout: page title: About permalink: /implementations/ --- Read about some of the implementations here:


{% if sidebar %}
{% include "main/sidebar.html" %}
{% block scripts %}{% endblock %}
{% endblock %}
{% else %}
{% endif %}
What is it?
In a sea of things that need annotation, you would be lucky to find a Wordfish. We are developing an annotation tool for experts to label things. It is an effort toward collaborative annotation to produce publicly available datasets for research. Wordfish will (eventually) allow for a combination of human- and algorithm- derived annotations of reports. This work is being done by the Langlotzlab at Stanford University.
{% if form %}
{% endif %}


Scratch Games
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PriqueuePriority queues
- empty: priqueue
- insert: key → priqueue → priqueue
- delete_max: priqueue → option (key * priqueue)
- Discrete-event simulations: The highest-priority event is the one whose scheduled time is the earliest. Simulating one event causes new events to be scheduled in the future.
- Sorting: heap sort puts all the elements in a priority queue, then removes them one at a time.
- Computational geometry: algorithms such as convex hull use priority queues.
- Graph algorithms: Dijkstra's algorithm for finding the shortest path uses a priority queue.
- merge: priqueue → priqueue → priqueue
- empty takes constant time
- insert takes constant time
- delete_max takes linear time
- merge takes linear time
Module Signature
This is the "signature" of a correct implementation of priority queues where the keys are natural numbers. Using nat for the key type is a bit silly, since the comparison function Nat.ltb takes linear time in the value of the numbers! But you have already seen in the Extract chapter how to define these kinds of algorithms on key types that have efficient comparisons, so in this chapter (and the Binom chapter) we simply won't worry about the time per comparison.Require Import Perm.
Module Type PRIQUEUE.
Parameter priqueue: Type.
Definition key := nat.
Parameter empty: priqueue.
Parameter insert: key → priqueue → priqueue.
Parameter delete_max: priqueue → option (key * priqueue).
Parameter merge: priqueue → priqueue → priqueue.
Parameter priq: priqueue → Prop.
Parameter Abs: priqueue → list key → Prop.
Axiom can_relate: ∀ p, priq p → ∃ al, Abs p al.
Axiom abs_perm: ∀ p al bl,
priq p → Abs p al → Abs p bl → Permutation al bl.
Axiom empty_priq: priq empty.
Axiom empty_relate: Abs empty nil.
Axiom insert_priq: ∀ k p, priq p → priq (insert k p).
Axiom insert_relate:
∀ p al k, priq p → Abs p al → Abs (insert k p) (k::al).
Axiom delete_max_None_relate:
∀ p, priq p → (Abs p nil ↔ delete_max p = None).
Axiom delete_max_Some_priq:
∀ p q k, priq p → delete_max p = Some(k,q) → priq q.
Axiom delete_max_Some_relate:
∀ (p q: priqueue) k (pl ql: list key), priq p →
Abs p pl →
delete_max p = Some (k,q) →
Abs q ql →
Permutation pl (k::ql) ∧ Forall (ge k) ql.
Axiom merge_priq: ∀ p q, priq p → priq q → priq (merge p q).
Axiom merge_relate:
∀ p q pl ql al,
priq p → priq q →
Abs p pl → Abs q ql → Abs (merge p q) al →
Permutation al (pl++ql).
End PRIQUEUE.
Take some time to consider whether this is the right specification!
As always, if we get the specification wrong, then proofs of
"correctness" are not so useful.
Implementation
Module List_Priqueue <: PRIQUEUE.
Now we are responsible for providing Definitions of all
those Parameters, and proving Theorems for all those Axioms,
so that the values in the Module match the types in
the Module Type. If we try to End List_Priqueue before
everything is provided, we'll get an error. Uncomment the next
line and try it!
(* End List_Priqueue. *)
Some preliminaries
Fixpoint select (i: nat) (l: list nat) : nat * list nat :=
match l with
| nil ⇒ (i, nil)
| h::t ⇒ if i >=? h
then let (j, l') := select i t in (j, h::l')
else let (j,l') := select h t in (j, i::l')
end.
Lemma select_perm: ∀ i l,
let (j,r) := select i l in
Permutation (i::l) (j::r).
Proof. (* Copy your proof from Selection.v, and change one character. *)
intros i l; revert i.
induction l; intros; simpl in *.
(* FILL IN HERE *) Admitted.
☐
let (j,r) := select i l in
Permutation (i::l) (j::r).
Proof. (* Copy your proof from Selection.v, and change one character. *)
intros i l; revert i.
induction l; intros; simpl in *.
(* FILL IN HERE *) Admitted.
Lemma select_biggest_aux:
∀ i al j bl,
Forall (fun x ⇒ j ≥ x) bl →
select i al = (j,bl) →
j ≥ i.
Proof. (* Copy your proof of select_smallest_aux from Selection.v, and edit. *)
(* FILL IN HERE *) Admitted.
Theorem select_biggest:
∀ i al j bl, select i al = (j,bl) →
Forall (fun x ⇒ j ≥ x) bl.
Proof. (* Copy your proof of select_smallest from Selection.v, and edit. *)
intros i al; revert i; induction al; intros; simpl in *.
(* FILL IN HERE *) admit.
bdestruct (i >=? a).
*
destruct (select i al) eqn:?H.
(* FILL IN HERE *) Admitted.
Definition key := nat.
Definition priqueue := list key.
Definition empty : priqueue := nil.
Definition insert (k: key)(p: priqueue) := k::p.
Definition delete_max (p: priqueue) :=
match p with
| i::p' ⇒ Some (select i p')
| nil ⇒ None
end.
Definition merge (p q: priqueue) : priqueue := p++q.
Predicates on priority queues
The representation invariant
Definition priq (p: priqueue) := True.
The abstraction relation is trivial too.
Inductive Abs': priqueue → list key → Prop :=
Abs_intro: ∀ p, Abs' p p.
Definition Abs := Abs'.
Lemma can_relate : ∀ p, priq p → ∃ al, Abs p al.
Proof.
intros. ∃ p; constructor.
Qed.
When the Abs relation says, "priority queue p contains elements al",
it is free to report the elements in any order. It could even relate p
to two different lists al and bl, as long as one is a permutation of
the other.
Lemma abs_perm: ∀ p al bl,
priq p → Abs p al → Abs p bl → Permutation al bl.
Proof.
intros.
inv H0. inv H1. apply Permutation_refl.
Qed.
Lemma empty_priq: priq empty.
Proof. constructor. Qed.
Lemma empty_relate: Abs empty nil.
Proof. constructor. Qed.
Lemma insert_priq: ∀ k p, priq p → priq (insert k p).
Proof. intros; constructor. Qed.
Lemma insert_relate:
∀ p al k, priq p → Abs p al → Abs (insert k p) (k::al).
Proof. intros. unfold insert. inv H0. constructor. Qed.
Lemma delete_max_Some_priq:
∀ p q k, priq p → delete_max p = Some(k,q) → priq q.
Proof. constructor. Qed.
Lemma delete_max_None_relate:
∀ p, priq p →
(Abs p nil ↔ delete_max p = None).
Proof.
(* FILL IN HERE *) Admitted.
Lemma delete_max_Some_relate:
∀ (p q: priqueue) k (pl ql: list key), priq p →
Abs p pl →
delete_max p = Some (k,q) →
Abs q ql →
Permutation pl (k::ql) ∧ Forall (ge k) ql.
Proof.
(* FILL IN HERE *) Admitted.
Lemma merge_priq:
∀ p q, priq p → priq q → priq (merge p q).
Proof. intros. constructor. Qed.
Lemma merge_relate:
∀ p q pl ql al,
priq p → priq q →
Abs p pl → Abs q ql → Abs (merge p q) al →
Permutation al (pl++ql).
Proof.
(* FILL IN HERE *) Admitted.
☐
∀ p, priq p →
(Abs p nil ↔ delete_max p = None).
Proof.
(* FILL IN HERE *) Admitted.
Lemma delete_max_Some_relate:
∀ (p q: priqueue) k (pl ql: list key), priq p →
Abs p pl →
delete_max p = Some (k,q) →
Abs q ql →
Permutation pl (k::ql) ∧ Forall (ge k) ql.
Proof.
(* FILL IN HERE *) Admitted.
Lemma merge_priq:
∀ p q, priq p → priq q → priq (merge p q).
Proof. intros. constructor. Qed.
Lemma merge_relate:
∀ p q pl ql al,
priq p → priq q →
Abs p pl → Abs q ql → Abs (merge p q) al →
Permutation al (pl++ql).
Proof.
(* FILL IN HERE *) Admitted.
End List_Priqueue.


Gets the COM Access or Launch and Activation permissions for the current computer.
Get-ComPermission -Access -Limits [-Identity <String>] [<CommonParameters>]
Get-ComPermission -Access -Default [-Identity <String>] [<CommonParameters>]
Get-ComPermission -LaunchAndActivation -Limits [-Identity <String>] [<CommonParameters>]
Get-ComPermission -LaunchAndActivation -Default [-Identity <String>] [<CommonParameters>]
COM access permissions ared used to "allow default access to application" or "set limits on applications that determine their own permissions". Launch and Activation permissions are used "who is allowed to launch applications or activate objects" and to "set limits on applications that determine their own permissions." Usually, these permissions are viewed and edited by opening dcomcnfg, right-clicking My Computer under Component Services > Computers, choosing Properties, going to the COM Security tab, and clicking Edit Default...
or Edit Limits...
buttons under the Access Permissions or Launch and Activation Permissions sections. This function does all that, but does it much easier, and returns objects you can work with.
These permissions are stored in the registry, under HKLM\Software\Microsoft\Ole
. The default security registry value for Access Permissions is missing/empty until custom permissions are granted. If this is the case, this function will return objects that represent the default security, which was lovingly reverse engineered by gnomes.
Returns Carbon.Security.ComAccessRule
objects, which inherit from `System.Security.AccessControl.AccessRule.
| Name | Type | Description | Required? | Pipeline Input | Default Value |
|---|---|---|---|---|---|
| Access | SwitchParameter | If set, returns permissions for COM Access permissions. |
true | false | False |
| LaunchAndActivation | SwitchParameter | If set, returns permissions for COM Access permissions. |
true | false | False |
| Default | SwitchParameter | Gets default security permissions. |
true | false | False |
| Limits | SwitchParameter | Gets security limit permissions. |
true | false | False |
| Identity | String | The identity whose access rule to return. If not set, all access rules are returned. |
false | false |
Carbon.Security.ComAccessRule.
Get-ComPermission -Access -Default
Gets the COM access default security permissions. Look how easy it is!
Get-ComPermission -LaunchAndActivation -Identity 'Administrators' -Limits
Gets the security limits for COM Launch and Activation permissions for the local administrators group.


The Nokia 7.1 drops to just $210, the lowest price it's ever been
by Hagop KavafianEven though the Nokia 7.1 came out more than a year ago, it still offers excellent value for money. The device was initially selling for $350 when it was released, but you can now get it for just $210, which is $40 less than what it dropped to during the summer.
The Nokia 7.1 features a Snapdragon 636 SoC, 4GB of RAM, a 5.84" 1080p display, a 3,060mAh battery, and 64GB of internal storage, which can be expanded thanks to a microSD card slot.
In our review, we praised the phone's build quality and form-factor but were disappointed by its performance. Nevertheless, this is an exciting deal, especially how low the price is. Some owners have also mentioned the phone has become a bit faster thanks to the latest updates.
If you're interested in getting one, use the link below to purchase a Nokia 7.1 on Amazon while the deal still lasts.
Buy:
Amazon


Modal 1
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This is the content of your Modal #1
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CLERKS A film review by Scott Renshaw Copyright 1994 Scott Renshaw
Starring: Brian O'Halloran, Jeff Anderson, Marilyn Ghiglitotti, Lisa Spoonhauer. Screenplay/Director: Kevin Smith.
For the past few years, filmmakers have been trying to capture what it is that defines the generation which has thus far been difficult even to name. So-called "Generation X" films like SINGLES; BODIES, REST & MOTION; and REALITY BITES have tried, but their target audience seems to have rejected those attempts. At first glance, CLERKS appears to be cut from the same cloth, a bargain basement variation on tales of cynical, over-qualified and under-achieving whiners. But CLERKS is a horse of a different color (black and white, in fact), a sly episodic story with a challenging perspective.
CLERKS examines one day in the life of two 22-year-old New Jersey residents living in the exciting world of retail. Dante (Brian O'Halloran) is called in to work at his job at a convenience store on his day off; Randal (Jeff Anderson) works at the video store next door. While they share a friendship, they have little else in common. Dante hates his job but rejects the entreaties of girlfriend Veronica (Marily Ghigliotti) to return to school, and worries about leaving the store unattended. Randal, meanwhile, shuts down the video store whenever the mood strikes him, is openly hostile to his customers, and goads Dante into taking more chances. Dante also obsesses over his ex-girlfriend Caitilin (Lisa Spoonhauer), whom he has just discovered is engaged.
Made for a paltry $36,000 by writer/director Kevin Smith, CLERKS frequently shows its low-budget roots in performances which can charitably be called amateurish. Jeff Anderson in particular reads his line like a student in a high school production, and his big emotional speeches are pitched at about the same level as his sarcastic banter. Brian O'Halloran has an easy-going and engaging presence, but he's often far too broad in his physical acting. Only Lisa Spoonhauer as the sexy and sharp Caitlin puts much real spark into her performance. Smith's direction is simple, but his insistence on swinging his camera back and forth in two-character scenes also proves distracting. Like GO FISH earlier this year, CLERKS is occasionally just a bit too clunky to be completely endearing.
The episodes which make up CLERKS are of inconsistent quality, but the good ones are very good. A conversation between Dante and Veronica concerning their respective sexual histories is a riot, and Dante and Randal share an illuminating discussion on the moral implications of blowing up the "outside contractors" working on the second Death Star in RETURN OF THE JEDI. The dialogue is filled with ultra-crude observations which almost earned CLERKS an NC-17 rating, but they're caustic and on-target. Randal in particular, who acts both as the devil on Dante's shoulder and as the real voice of reason in CLERKS, gets some marvelous lines, though virtually every one of them is unprintable. Less effective are sequences involving a roller hockey game on the store's roof which simply lasts too long, and a montage of annoying customer types which is a bit is too cutesy.
But CLERKS turns out to be more than just the sum of its parts. In the final fifteen minutes, Smith begins pulling the threads of his narrative together with a surprising message about the twenty- somethings. Dante becomes a character representative of everything that is wrong with his generational peers, wallowing in self-pity and willing to blame everyone but himself for the circumstances of his life, and Randal's brutally honest rebuke is perhaps the film's highlight. Kevin Smith places himself in the awkward position of chastising those who are most likely to be his audience, and it is that daring that makes CLERKS most worthy of admiration; it is not often that personal responsibility is the moral to a story. While he may not have had all the tools at his disposal that a studio filmmaker would have had to tell this story, Kevin Smith did have a story to tell. CLERKS is unquestionably uneven, but it is also a very funny wake-up call to the over-the-counter culture.
On the Renshaw scale of 0 to 10 retail clerks: 7.
-- Scott Renshaw Stanford University Office of the General Counsel
.
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


{type, select, all {All} artwork {Artwork} motif {Motif} artist {Artist} location {Location} genre {Genre} movement {Movement} material {Material} class {Class} }


Choose data structures to export and open the binary STL file to convert to a c++ header file.
Export:
edges
triangles
triangle normals


Detailed Information of Group Name: {{ groupName }} Description: {{ groupInfo.description }} Admin E-Group: {{ groupInfo.admin_group }}
Top Allocations for this Group
Project Allocations for this Group
Group Allocations for this group


Installing Knative v0.5.0 on an OpenShift cluster using the script provided
| This Knative v0.5.0 on OpenShift preview is only available by using the OpenShift 4.0 developer preview. |
Prerequisites
-
You will require a Red Hat Developers login to try this.
-
Using 4.0 clusters with Knative requires visiting try.openshift.com
Installation requires the OpenShift version 4.1.0-rc.0 installer located here. Later versions of the installer are not compatible with the Istio version included with this release.
|
Procedure
-
Login to the cluster using your admin credentials.
oc login <admin-credentials>
-
Clone the
knative-operators
repository.
git clone https://github.com/openshift-cloud-functions/knative-operators
cd knative-operators/
git fetch --tags
git checkout openshift-v0.5.0
-
Navigate to the newly cloned repository and run the
install.sh
script.
./etc/scripts/install.sh
The install.sh script can time-out waiting for the Istio operator to complete, and you may receive an ERROR: Timed out message. If the install.sh script fails to install Istio, run the script again after it times out.
|
-
Once the script starts, you will see the following warning and prompt.
WARNING: This script will attempt to install Istio, Knative, and OLM in your Kubernetes/OpenShift cluster.
If targeting OpenShift, a recent version of 'oc' should be available in your PATH. Otherwise, 'kubectl' will be used.
Pass -q to disable this prompt
Enter to continue or Ctrl-C to exit:
-
Press Enter to continue.


Gallery: Microsoft reveals its other Project JEDI Pinball play, and other concept stuff, as CEO Satya Nadella visits Australia. 25 Nov 2019, 07:11 Mircrosoft is blending AI and ML to port legacy hardware to Azure. The concept project has been dubbed "Cognitive Pinball and was put on display for the visit of CEO Satya Nadella. It's a perfectly running vintage Star Wars pinnie, albeit with a few custom mods. There was no mention of Project JEDI in Nadella's Aussie speech, but the synergies are awesome... If you ever wondered where the concept of the start button spawned from... Here you go... The machine plays itself, and learns as it goes. This is the gaming surface. Which on closer inspection doesn't really date. Upgrades are available. This is the overhead camera that feeds motion data to Azure. With clear instructions. R2 contemplates the upgrade path. Look left for the big hole. Master Yoda likes to get lit. Even if he doesn't look that well. Fully lit. The Force has is rewards. What is it with bad guys and hoodies? Microsoft is also working on AI for trains called Train DNA that ingests data from Sydney's Waratah trains. (The model isn't a Waratah, it's a V Set.) And has built a conceptual model of a city. The data ingetsted from sensors enables "predictive maintenance". Which is aimed at boosting safety and reliability. There was also an Azure powered Juice Bar. It uses recognition of facial expressions. To make binary comparison choices. We couldn't figure out what this workspace was, but suspect it's autonomous transport related. Business Class will never be the same. Though you do get a foot rest. And a retractable work surface. Seating is adequate, but not generous. And there is somewhere to put your bag.


swapi – creates GUI for swapi.dev using gtk+ 3¶
Description about swapi. This program creates GUI for swapi.dev using gtk+ 3. Shows information about people, planets, species, vehicles, starships from The Star Wars API. Can search object and show the list of objects.
warning: unhandled cursor CursorKind.INCLUSION_DIRECTIVE takejson.h
-
char *
unit
¶ unit: String of resource. Depends on button callback functions.
-
char *
search_text
¶ search_text: String of search. Information is taken from search editor.
-
int [6] num
unit: Array of resource’s number.
-
void
output_text
(char **atr_arr, int len, int index)¶ output_text() - Displays text.
- Parameters
atr_arr – array of atributes which this function shows.
len – len of atr_arr.
index – number of resource.
This function displays information about attributes which was taken from JSON file about resource. Information pass to the text widget.
- Context: When information about atribute wasn’t found, this function wright
in widget “none”.
-
void
search_fun
(char **atr_arr, int len)¶ search_fun() - Search and displays text.
- Parameters
atr_arr – array of atributes which this function shows.
len – len of atr_arr.
This function search and displays information about attributes which was taken from JSON file about resource. Information pass to the text widget.
- Context: When name wasn’t found this function wright in widget “Not in <resource>”.
When information about atribute wasn’t found, this function wright in widget “none”.
-
void
btn_people_cl
()¶ btn_people_cl() - Callback function about people.
This function callback output_text() or search_fun(), depends on search status. If search_text = “none” callback output_text() else search_fun(). This fun set unit = “people”.
-
void
btn_planets_cl
()¶ btn_people_cl() - Callback function about planets.
This function callback output_text() or search_fun(), depends on search status. If search_text = “none” callback output_text() else search_fun(). This fun set unit = “planets”.
-
void
btn_species_cl
()¶ btn_people_cl() - Callback function about species.
This function callback output_text() or search_fun(), depends on search status. If search_text = “none” callback output_text() else search_fun(). This fun set unit = “species”.
-
void
btn_vehicles_cl
()¶ btn_people_cl() - callback function about vehicles.
This function callback output_text() or search_fun(), depends on search status. If search_text = “none” callback output_text() else search_fun(). This fun set unit = “vehicles”.
-
void
btn_starships_cl
()¶ btn_people_cl() - Callback function about starships.
This function callback output_text() or search_fun(), depends on search status. If search_text = “none” callback output_text() else search_fun(). This fun set unit = “starships”.
-
void
btn_list_cl
()¶ btn_people_cl() - Callback function, makes list of objects.
This function makes list of objects depends on resource.
-
void
btn_search_cl
()¶ btn_search_cl() - Callback function, makes search.
This function callback button callback function depends on unit.
-
void
btn_right_cl
()¶ btn_right_cl() - Callback function, sets number of resource.
This function callback button callback function and increments number of unit depends on unit.
-
void
btn_left_cl
()¶ btn_left_cl() - Callback function, sets number of resource.
This function callback button callback function and derements number of unit depends on unit.


//# Bitwise AND operator (&) is a binary operator that combines the bits of 2 numbers. It behaves like a logical AND operator. For each bit, if both of them are 1, the result will be 1.


The ambiance at School of Engineering and Technology (SET) is inspiring and conducive for acquisition of knowledge and honing of skills. An unfettered spirit of knowledge and exploration, academic integrity and accountability, and promotion of intellectual excellence makes SET distinct from other engineering institutes.
After completing this programme, graduates of the program are able to:
The hands-on-skills of the students are developed in the laboratories of SET that are well equipped with the latest equipments and the students are trained by performing experiments. The School’s most important priority is to increase its specialized teaching and research space to match the industrial needs.
The School’s 250 faculty members and the professional staff work hard to hold a high standard of excellence. With the strong support of faculty and professional staff, the SET is poised to emerge and also contribute significantly to global challenges.


Kodi 18.5 'Leia' available to download now! [Updated]
by Wayne WilliamsIt’s been a couple of months since the Kodi Foundation released a new version of its hugely popular home theater software.
After spending some time in the pre-release section, Kodi 18.5 is now finally deemed ready for all.
SEE ALSO: Kodi 19 'Matrix' with Python 3 now available to download, but be warned
As you might expect from a point release, there are no new features here, but rather the focus is on squashing bugs and improving stability and performance. The list of changes includes:
Interface/Look-and-Feel
- Estuary and GUI info fixes, including scrollbar behavior, icon names, label changes
- Add dual support for Artist Slideshow 2.x and 3.x
- Fix to always allow 'Black' screen saver
- Fix wrong sort order list for music playlists node
Playback/Display
- Fixes to external subtitle playback
- Fixes to support for archives over UPnP
- Fixes to "queue item" and "play next" for STRM files with Plugin URL
- Fixes to "hide watched" status for videos
- Fixes to resume handling when marking a file as unwatched
PVR
- Fixes to EPG database storage, start/stop of PVR service
- Fix handling of open modal dialogs
- Fixes to commercial skip (EDL) processing
Other/General Fixes
- Multiple Android changes around windowing, secure decoder and SDK versions
- Multiple iOS changes, including support for iPhone 11 and 7th generation iPad, plus fixes for touch input, overlapping drawing surfaces, sandbox checks, notch support.
- MacOS fixes around windowing, where only a portion of the screen was correctly rendered
- Log files improvements around verbosity and security
- Shoutcast improvements
- Various fixes and improvements to add-on settings, package build documentation, build system, advancedsettings.xml, skin/profile changes and many other subsystems
Check out a comparison of 18.4 vs. 18.5 here for a more detailed breakdown.
You can download the latest version of Kodi for Windows, Linux, macOS, Android, and more, from here.
Kodi 18.6 is in development now, and can be downloaded from the pre-release tab on the downloads page.
Image Credit: Andrey_Popov / Shutterstock


From Market and Keyword research to developing relevant content and creatives for your niche or industry. I can help you participate and navigate your customers and prospects through the convoluted buyer journey, cut out the noise from competition and deliver value in the different micromoments, from the consideration to the decision stage.
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Certified and experienced in Google advertisement and also Facebook and Twitter ads, I can help you amplify your brand message and extend your reach using online advertising where majority of your customers and prospects are by, strategically positioning your brand to set objectives, identify audiences, create and manage campaigns and deliver on goals and KPIs.
Spent five years in the University of Benin and graduated with a Bachelors, degree in Chemical Engineering.
I began to work in the Nigerian real estate space with aflliation to companies like; Landwey, Pwan and DNJ.
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Digital Marketer and Junior Front-end web developer.
From the ideation stage to market research and development, I can help you get online and maintain a profitable web presence with an intuitive user experience.


Honey-Glazed Roasted Root Vegetables
- ACTIVE: 25 MIN
- TOTAL TIME: 1 HR 30 MIN
- SERVINGS: 12
- •HEALTHY
- •MAKE-AHEAD
- •VEGETARIAN
The secret to this sweet, slightly tangy dish: the touch of sherry vinegar in the glaze.
- 1 1/4 pounds parsnips, peeled and sliced 1/2 inch thick
- 1 1/4 pounds carrots, peeled and sliced 1/2 inch thick
- One 1 1/4 pound celery root—peeled, quartered and sliced 1/2 inch thick
- 1 1/4 pounds golden beets, peeled and sliced 1/2 inch thick
- 1/2 cup extra-virgin olive oil
- 1/2 cup honey
- 6 thyme sprigs
- Salt and freshly ground pepper
- 2 tablespoons sherry vinegar
- Preheat the oven to 425°. In a large bowl, toss the root vegetables with the oil, honey and thyme and season with salt and pepper. Divide between 2 large, sturdy rimmed baking sheets. Cover with foil and roast for 40 minutes, shifting the pans once, until the vegetables are tender. Remove the foil and roast for 10 minutes longer, until glazed. Return them to the bowl and stir in the vinegar then season with salt and pepper. Serve right away.
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Congratulations to Mei Lin, winner of Top Chef Season 12.
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Newcastle University investigating after student is filmed chanting racial slur
A spokesperson for the university said that some 'disciplinary measures have already been taken'
by Katie CollingsNewcastle University is investigating after a student was filmed on Instagram using racist language.
In the video posted earlier this week, the man can be seen singing down a microphone and shouts the phrases, "I f***ing hate n****s" and another racial slur.
The student is wearing a university hockey club tie and is surrounded by a number of others in the same attire, The Tab reports.
The man can be heard laughing throughout the video which was posted on another student's Instagram story.
The Newcastle University Men's Hockey Club told The Newcastle Tab that they are dealing with the incident.
A Newcastle University spokesperson added: “We are appalled by the language used in this video and while investigations are underway some interim disciplinary measures have already been taken.
“There is no place for racism or racial harassment of any kind at Newcastle University and we are committed to creating a safe and inclusive environment for all our students and staff.”


Probability and Combinatorics
If you've ever played a game of Texas Hold'em poker, you're familiar with the fact that certain hands are worth more than others.
For example,
a flush is better than a straight, and a full house is better than either. Why is this the case?
You may have some intuitive understanding that the more rare a hand is, the higher its rank. Using the ideas discussed here, we will be able to prove this relationship holds.
Addition and Multiplication Principles
Combinatorics is all about
counting. In particular, we often count the number of ways in which we can perform an action or a set of actions.
The
addition and multiplication principles help with this analysis.
- The addition principle (or "rule of sum") states that given two different actions where there are `x` choices for the first one
and `y` choices for the second, there are `x+y` ways of doing either, assuming it is not possible to do both actions simultaneously.
- The multiplication principle (or "rule of product") states that given two different actions where there are `x` choices for the first one
and `y` choices for the second, there are `xy` ways of performing both actions together (in any order).
Consider a person ordering a single meal at an Italian restaurant. The menu has only two items: specialty pizza and salad.
The salad consists only of lettuce and dressing, where there are three choices of lettuce and two types of dressing.
Meanwhile, the pizza comes in one of four different pre-made varieties. How many total meal options are there?
We have two choices, pizza or salad, so we count the number of possible salads and the number of possible pizzas and add them together by the addition principle.
The pizza clearly has only four options, as was given. However, counting the number of salad choices we have requires the multiplication rule.
With `3` choices of lettuce and `2` choices of dressing, we have `2\times3=6` ways of picking one of each to form a salad.
Thus, we obtain a final answer of `4+6=10` total possible meal selections.
Combinations and Permutations
Two fundamental concepts in the field of combinatorics are those of
combinations and
permutations.
A permutation is an ordering of some collection of elements.
The number of possible permutations of a set of `n` distinct elements is simply given by `n!`, where `!` denotes the factorial function.
Suppose we have a set of `n` distinct elements. We want to create one permutation of this set.
We begin by picking the first element of the permutation. There are `n` elements to choose from, so there are `n` ways of doing this.
Then, we select the second element of the permutation. There are now only `n-1` elements remaining, so there are `n-1` ways of doing this.
We repeat until we have exhausted all elements of the original set, leaving only one choice for the last element in the permutation.
By the multiplication principle, there are `n\cdot(n-1)\cdots1=n!` ways of performing this entire task.
Every unique sequence of choices results in a new permutation (and every permutation can be constructed in this manner),
so there are exactly `n!` possible permutations of the original set.
An ordering of `k` elements from a set of `n` elements is called a `k`-permutation. The number of possible `k`-permutations from a set of size `n` is given by
$$P(n, k)={}_nP_k=\frac{n!}{(n-k)!}.$$
Suppose we have a set of `n` distinct elements. We want to create a permutation of size `k` from this set.
We begin by picking the first element of the permutation. There are `n` elements to choose from, so there are `n` ways of doing this.
Then, we select the second element of the permutation. There are now only `n-1` elements remaining, so there are `n-1` ways of doing this.
We repeat for a total of `k` elements, leaving `n-k` elements unselected.
By the multiplication principle, there are `n\cdot(n-1)\cdots(n-k+1)=n!/(n-k)!` ways of performing this entire task.
Alternatively, we can first notice that there are `n!` permutations of all elements in the original set. By extracting the first `k` elements of
any given permutation, we construct a `k`-permutation as desired. However, there are `n-k` elements not selected, and the `k`-permutation is repeated
once for each of their `(n-k)!` possible arrangements. Dividing out the number of repetitions yields `n!/(n-k)!` as before.
A combination is simply a set of elements without respect to order. For instance, the set `\{1, 5, 7\}` is a combination of three odd numbers.
This is
exactly the same as the combinations `\{5, 7, 1\}`, `\{1, 7, 5\}`, etc., since order does not matter.
The number of possible combinations of `k` elements from a set of size `n` is given by
$$C(n, k)={}_nC_k=\binom{n}{k}=\frac{n!}{k!(n-k)!}.$$
Suppose we have a set of `n` distinct elements. We want to create a combination of size `k` from this set.
Note that from any combination of `k` elements, we can rearrange to form `k!` different `k`-permutations which correspond to it.
Since we know that there are `P(n,k)=n!/(n-k)!` total `k`-permutations, there must be `P(n,k)/k!` total `k`-combinations, or
$$\frac{n!}{k!(n-k)!}.$$
Preliminary Probability Definitions
There are a few ideas we need to define immediately. A lot of this may be familiar or intuitive, but it is still important to discuss.
A good source of information can be found
here, summarized below.
Other resources include
Statistics How To and
Wolfram MathWorld,
though the latter can be fairly technical.
- An experiment is an activity with an observable result. (ex. Rolling a die)
- An outcome is a possible result of an experiment. (ex. Rolling a 5)
- The sample space is the set of all possible outcomes of an experiment. (Rolling a 1, 2, 3, 4, 5, or 6)
- An event is a subset of the sample space. (Rolling a 1, 3, or 5)
- An event can include some, none, or all of the events in the sample space.
- The probability of an event is the chance that it occurs.
- If all outcomes in a sample space have the same probability, then the probability of an event `E` occurring is simply
the ratio of the number of outcomes in `E` to the total number of outcomes in the sample space.
- Two events are disjoint / mutually exclusive if they share no outcomes.
- Alternatively, we might say that the intersection of the two events is empty.
- Two events are complementary if they are disjoint and their union is the sample space (so exactly one must occur).
- If the first event is `A`, then the other is the complement of `A`, often denoted `A^c`.
- Two events `A` and `B` are independent if the probability of `A` does not depend on whether `B` occurs, and vice versa.
- If two events are not independent, then they are said to be dependent.
Several of these concepts involve basic set theory like union and intersection, so be sure to understand the meanings of those terms first.
Probability Axioms
There are
three axioms that form the foundation of probability theory, listed below.
- For any event `E`, we have `P(E)\ge0`.
- For a sample space `S`, we have `P(S)=1`.
- For disjoint events `E_1` through `E_n`, we have `P(E_1\cup E_2\cup\cdots\cup E_n)=P(E_1)+P(E_2)+\cdots+P(E_n)`.
These facts should be fairly intuitive. For the first one, we know that nothing should ever have a negative chance of occurring.
For the second, it is clear by definition that if we take the probability that an experiment's outcome is in the entire sample space, we will find this chance to be 100%.
The third should also be fairly obvious. For example, when rolling a fair die, if the chance of rolling a 1 is `1/6` and the chance of rolling a 2 or 3 is `1/3`,
then the chance of rolling a 1, 2, or 3 is `1/6+1/3=1/2`.
There are many important results that can be derived from these axioms, some of which are given below.
Proofs are available on
the associated Wikipedia page.
- For events `A` and `B`, if `A\subseteq B`, then `P(A)\le P(B)`.
- The probability of the empty event `\emptyset` is `P(\emptyset)=0`.
- Note that it is still possible for a nonempty event to have zero probability, as well.
- For any event `A`, the probability of its complement is `P(A^c)=1-P(A)`.
- For any event `A`, its probability must satisfy `0\le P(A)\le1`.
One of the most important results is known as the
inclusion-exclusion principle, which generalizes the third axiom to non-disjoint events.
Namely, we see that $$P(A\cup B)=P(A)+P(B)-P(A\cap B)$$ for any two events `A` and `B`. In essence, we remove the intersection because it is erroneously double-counted otherwise.
When we extend to the union of three or more events, the formulas become far more complicated.
$$\begin{align*}P(A\cup B\cup C)&=P(A)+P(B)+P(C)\\&-P(A\cap B)-P(B\cap C)-P(A\cap C)\\&+P(A\cap B\cap C)\end{align*}$$
$$\begin{align*}P(A\cup B\cup C\cup D)&=P(A)+P(B)+P(C)+P(D)\\
&-P(A\cap B)-P(A\cap C)-P(A\cap D)-P(B\cap C)-P(B\cap D)-P(C\cap D)\\
&+P(A\cap B\cap C)+P(A\cap B\cap D)+P(A\cap C\cap D)+P(B\cap C\cap D)\\
&-P(A\cap B\cap C\cap D)
\end{align*}$$
We proceed as before, adding the individual probabilities and then removing the double-counted pairwise intersections. However, this process removes
the three-way intersections too many times, so we need to add those back. If there are more than three events, then this will result in over-counting of
the four-way intersections, and we must remove one copy of each of those.
Fortunately for us,
it can be proven that this pattern holds in general.
When applying the formula, simply be careful to include all required terms and to have the correct sign on each.
The number of terms can be easily checked - the number of `x`-way intersection terms from a set of `y` events is simply given by `\binom yx`.
Additionally, we see that the total number of terms for a set of `y` events should be `\sum_{i=1}^y\binom yi=2^y-1`.
It should be noted that the inclusion-exclusion principle applies equivalently to set cardinality in place of probability since both are
additive functions
(i.e., the probabilities and cardinalities of disjoint unions are the sums of the components).
Conditional Probability and Bayes' Theorem
A common question to ask is "what is the chance of event `B` occurring given that event `A` occurs?" Questions like these fall into the realm of
conditional probability.
We denote the probability of event `B` given event `A` as `P(B\mid A)`, with a vertical bar in-between. This quantity is defined as follows:
$$P(B\mid A)\overset{\text{def}}{=}\frac{P(A\cap B)}{P(A)}.$$
For example, consider rolling a fair six-sided die. Let event `A` be rolling an odd number, and let `B` be rolling a number less than 4.
It is trivial to find that `P(A)=1/2` and `P(A\cap B)=1/3`, and so we find `P(B\mid A)=(1/3)/(1/2)=2/3` as the chance of rolling less than 4 given that an odd number is rolled.
This makes sense—it is either a 1, 3, or 5 with uniform probability, and exactly two of these three options are less than 4.
Bayes' theorem (or "law," or "rule") takes this definition and expands it a bit. Since `P(A\cap B)=P(B\cap A)` (by commutativity of set intersection), we can write
$$P(A\cap B)=P(A)\cdot P(B\mid A)=P(B)\cdot P(A\mid B).$$
Then, we can simply rearrange to obtain that
$$P(A\mid B)=P(B\mid A)\left(\frac{P(A)}{P(B)}\right).$$
Finally since `P(B)=P(B\cap(A\cup A^c))=P((A\cap B) \cup (A^c\cap B))=P(A\cap B)+P(A^c\cap B)=P(A)\cdot P(B\mid A)+P(A^c)\cdot P(B\mid A^c)`, we can rewrite the fraction as
$$P(A\mid B)=\frac{P(A)\cdot P(B\mid A)}{P(A)\cdot P(B\mid A)+P(A^c)\cdot P(B\mid A^c)}.$$
Note how the numerator term appears in the denominator as well. Note also that `A` and `A^c` are not particularly special; we simply need a set of mutually exclusive
and collectively exhaustive events, and each one will contribute a separate term to the denominator.
Consider an engineer ordering parts for his or her latest project. The engineer orders some gizmos from each of three companies: Advanced Applications (A), Dynamic Dynamos (D), and Mechanical Machines (M).
One half of all gizmos are ordered from A, one third are from D, and the remaining gizmos are ordered from M.
Unfortunately, none of these companies have perfect assembly lines, and each will occasionally produce defective parts.
A randomly selected gizmo produced by A has a `1/100` chance of being defective, whereas the chances of gizmos by D or M being defective are `1/200` and `1/50`, respectively.
If a gizmo is randomly selected from the set of all gizmos ordered and it is found to be defective, what are the odds it was made by Dynamic Dynamos?
From the problem statement, we know that `P(A)=1/2`, `P(D)=1/3`, and `P(M)=1/6`.
Additionally, `P(\text{Defect}\mid A)=1/100`, `P(\text{Defect}\mid D)=1/200`, and `P(\text{Defect}\mid M)=1/50`.
Applying Bayes' theorem yields
$$\begin{align*}
P(D\mid\text{Defect})&=\frac{P(\text{Defect}\cap D)}{P(\text{Defect})}\\
&=\frac{P(\text{Defect}\cap D)}{P(\text{Defect}\cap A)+P(\text{Defect}\cap D)+P(\text{Defect}\cap M)}\\
&=\frac{P(\text{Defect}\mid D)\cdot P(D)}{P(\text{Defect}\mid A)\cdot P(A) + P(\text{Defect}\mid D)\cdot P(D) + P(\text{Defect}\mid M)\cdot P(M)}\\
&=\frac{1/200\cdot 1/3}{1/100\cdot 1/2 + 1/200\cdot 1/3 + 1/50\cdot 1/6}\\
&=\frac{1/600}{3/600 + 1/600 + 2/600}\\
P(D\mid\text{Defect})&=\frac16.
\end{align*}$$
Thus, we find that the probability that a defective gizmo selected at random originated from Dynamic Dynamos is `1/6`.
A more visual way of representing conditional probability is with
probability trees.
A
probability tree is a tree diagram read from left to right where
nodes represent experiments and branches represent disjoint outcomes/events that might occur, each labeled with their respective probabilities.
In order to find the probability of a certain leaf, simply multiply the conditional probabilities along the edges leading from the root to that leaf.
Then, to obtain the probability of some set of leaves, find their individual probabilities and add them together.
(To verify that a tree diagram has been correctly constructed, the sum of all leaf probabilities should always equal one.)
Tree diagrams can be very helpful in certain situations involving dependent events and conditional probability, but their usefulness is limited
by how rapidly they grow in size. The number of leaves in such a tree grows exponentially with height, and this can very quickly become unwieldy.
Independence of Events
Colloquially, two events are independent if they do not impact one another's odds.
Formally, we say that events `A` and `B` are independent if and only if the following condition holds true:
$$P(A\cap B)=P(A)\cdot P(B).$$
If `A` and `B` both have nonzero probability, we can use the definition of conditional probability to obtain these two conditions, each equivalent to the one above:
$$P(A\mid B)=P(A),$$
$$P(B\mid A)=P(B).$$
Notice that if an event does indeed have zero probability, then it is automatically independent of all other events.
Furthermore, if two events of nonzero probability are disjoint, then `P(A\cap B)=0` and they cannot be independent.
Finally, it is possible to find a couple more equivalent conditions (under the assumption `0 < P(A), P(B) < 1`), though these are far less common:
$$P(A\mid B)=P(A\mid B^c),$$
$$P(B\mid A)=P(B\mid A^c).$$
This result is not hard to prove and aligns with our intuition about the notion of event independence.
Application: Card Games
One of the best examples of probability in action is playing card games. Take, for example, the game of Texas Hold'em poker.
If you're not familiar with the game, you can read the rules
here.
In poker,
hands consist of five cards each, taken from a standard deck of fifty-two. Each card has a
suit (hearts, diamonds, spades, clubs)
and a
rank (in ascending order, 2, 3, 4, 5, 6, 7, 8, 9, 10, jack, queen, king, ace). Depending on the exact suit and rank composition of the cards
in one's hand, it will be considered better or worse than (or in rare cases, equal to) another hand. For more information on hand evaluation and ranking,
see
here and
here.
Why are certain hands worth more than others? It is because these hands are provably less common!
We can show this using the probability techniques developed above.
Here's the ordered list of poker hands:
- Royal Flush
- Straight Flush
- Four of a Kind
- Full House
- Flush
- Straight
- Three of a Kind
- Two Pair
- One Pair
- No Pair / High Card
Let's derive the probabilities of each of these hands occurring when five cards are dealt at random from a standard deck.
A
royal flush occurs when we have the ten, jack, queen, king, and ace of a given suit. The ranks are prescribed by definition, but we have four choices for the
suit, so there are four unique royal flushes that are possible. Since there are `\binom{52}{5}` possible hands in total, each with the same chance of occurring,
we obtain a probability of `4/\binom{52}{5}\approx0.000154\%` for dealing a royal flush.
A
straight flush occurs when we have five cards in a row of the same suit, excluding a royal flush.
Notice that each straight flush can be uniquely identified by its lowest card.
(There is only one possibility for a straight flush that starts with the Five of Hearts, for example.) Now, how many possibilities are there for this lowest card?
There have to be four cards after it in sequence, meaning that 9-10-J-Q-K is the highest we can go (since we do not count royal flushes),
and A-2-3-4-5 is the lowest we can go. Since the lowest card ranges from an ace to a nine in any of the four suits, we have exactly 36 possibilities, and as a result, we know there are 36 unique straight flushes.
We proceed to obtain a probability of `36/\binom{52}{5}\approx0.00139\%` for being dealt a straight flush.
A
four of a kind is when we have all four cards of a given rank (along with any other card as the fifth).
We have 13 ways to pick the rank for which we have the four of a kind, followed by 48 ways to pick the remaining card.
Thus, the probability of a four of a kind is given by `13\cdot48/\binom{52}{5}\approx0.02401\%`.
A
full house occurs if we have three cards of one rank (let's call it `A`) and two cards of another rank (let's call it `B`).
We have `P(13,2)=13\cdot12` ways to choose ranks `A` and `B`. Next, we pick the suits.
There are three suits for rank `A`, so we have `\binom43=4` ways of picking them.
Likewise, we have `\binom42=6` ways of picking the two suits for the cards of rank `B`.
Consequently, we find a probability of `13\cdot12\cdot4\cdot6/\binom{52}{5}\approx0.1441\%`.
A
flush occurs if we have five cards of the same suit.
We have 4 ways to pick the suit and `\binom{13}5` ways to pick five ranks within that suit.
However, we have to guarantee that this is not a straight or royal flush as well.
There are ten total possibilities for a straight/royal flush within a given suit, so we simply subtract this from our count.
In the end, we have a probability of `4\cdot\left(\binom{13}5-10\right)/\binom{52}{5}\approx0.1965\%`.
A
straight occurs whenever we have five cards in a row (and do not also have a royal straight or straight flush).
There are 10 ways to pick the ranks, but unlike straight flushes, we now have 4 choices for each card's suit.
This leads to a total of `10\cdot4^5` possibilities, before subtracting the 40 royal and straight flushes.
a probability of `\left(10\cdot4^5-40\right)/\binom{52}{5}\approx0.3925\%`.
A
three of a kind occurs whenever we have three cards of a single rank (which we will call `A`) and two cards of different ranks (`B` and `C`).
There are 13 ranks to choose from for `A`. Then, we have `\binom{12}2` ways to pick ranks `B` and `C` afterwards.
(Note that unlike the full house before, there is a symmetry here.
Ranks `B` and `C` each contribute the same number of cards, one, so the order in which they are chosen does not matter.
Since `A` is associated with a different number of cards, namely three, it has to be chosen separately from `B` and `C`.)
There are `\binom43=4` ways to pick the suits for rank `A` and `4^2` ways to pick the suits for `B` and `C`.
This yields a probability of `13\cdot\binom{12}2\cdot4^3/\binom{52}{5}\approx2.1128\%`.
A
two pair happens whenever we have two cards of one rank (`A`), two cards of another rank (`B`), and one card of a third rank (`C`).
By the same symmetry argument as before, we pick ranks for `A` and `B` together, and we have `\binom{13}2` ways of doing so.
There are 11 choices left for rank `C` afterwards.
There are `\binom42=6` ways each to pick the suits for ranks `A` and `B`, and there are 4 ways to pick the suit for `C`.
This yields a probability of `\binom{13}2\cdot11\cdot6^2\cdot4/\binom{52}{5}\approx4.7539\%`.
A
one pair happens whenever we have two cards of one rank (`A`) and one card each from three other ranks (`B`, `C`, and `D`).
There are 13 choices for rank `A`, after which there are `\binom{12}3` choices for the other ranks.
There are `\binom42=6` ways to pick the suits for rank `A` and there are 4 ways to pick the suits for each of the other three cards.
This generates a probability of `13\cdot\binom{12}3\cdot6\cdot4^3/\binom{52}{5}\approx42.2569\%`.
Finally, it comes down to the
high card if and only if none of the other types of hands occur.
Adding the probabilities above gives a total chance of `49.8823\%`, so the chance of none of those is about `50.1177\%`.
Alternatively, we can directly calculate the odds. There are `\binom{13}5-10` ways to pick the five distinct ranks (and not form a straight),
and there are `4^5-4` ways to pick the five suits (while not giving a flush).
Together, we get a probability of `\left(\binom{13}5-10\right)\left(4^5-4\right)/\binom{52}{5}\approx50.1177\%`, as expected.
All in all, we see that the
probabilities of the poker hands are ordered exactly as we predicted.
Rarer types of hands are worth more, without exception.
How about some non-poker cards questions?
Suppose I perfectly shuffle a standard deck (so that any permutation of the cards is equally likely).
What are the odds that the top half of the deck contains the same set of cards before and after shuffling?
This is a simple counting problem.
If the top and bottom halves of the deck remain the same before and after shuffling, then that means there are `26!` ways to rearrange each half,
so there are `(26!)^2` permutations of the deck that satisfy the condition.
Meanwhile, there are `52!` total distinct permutations of the deck that are possible.
If each rearrangement is equally likely, the probability that the condition is satisfied is `(26!)^2/52!`.
Suppose I perfectly shuffle a standard deck and deal out a game of Klondike solitaire as shown. What is the probability that
all four aces will be face-up at the very start of the game, before any moves are made?
This is just another routine application of the probability principles we have reviewed.
There are seven cards face-up at the beginning, of which four are aces and three are non-aces.
There are `\binom{48}3` ways to select the three non-aces from the deck, and there are `7!` ways to permute these seven cards together.
Then, there are `45!` ways to permute the cards that begin face-down or in the draw pile.
On the other hand, we have `52!` total rearrangements of the deck that are possible.
Therefore, the probability is `\frac{\binom{48}3\cdot7!\cdot45!}{52!}=\frac1{7735}`.
Alternatively, there are `\binom74` ways to pick which of the seven face-up positions will be occupied by aces,
`P(4,4)=4!` ways to arrange the aces, and `P(48,3)=48\cdot47\cdot46` ways to select the non-aces in order.
Then, since there are `P(52,7)` ways to pick the face-up cards in order, we get a probability of
`\frac{\binom74\cdot4!\cdot48\cdot47\cdot46}{P(52,7)}=\frac1{7735}` as before.
Suppose I deal out a game of Klondike solitaire as in the previous example.
However, I can't make any moves with what I see, so I reshuffle and lay out the cards again.
What is the probability that the exact same set of seven cards is face-up (ignoring order)?
There are `7!` ways to permute the seven face-up cards and `45!` ways to permute the rest.
Since there are `52!` possible (equally likely) orderings of the cards in total,
this yields a probability of `\frac{7!\cdot45!}{52!}=\binom{52}{7}^{-1}=\frac1{133,784,560}`.
Alternatively, there are `\binom{52}7` ways to pick the seven face-up cards from the deck while ignoring order.
There is only one of these that will match the face-up set from before the reshuffle, so the chance that that
will occue is `\binom{52}{7}^{-1}` as above.
Application: Disease Testing
Another common application of probability (and Bayes' theorem, in particular) is disease testing.
When there is
an illness spreading throughout the population that
may not present clear symptoms,
it is immensely helpful to have a way to test whether an individual has the disease.
Unfortunately, these kinds of tests are not perfect. Sometimes, an infected individual will be marked disease-free, or vice versa.
To describe these events, here are some terms to know:
- True Positive: Individual has the disease, test indicates they have the disease
- False Positive: Individual does not have the disease, test indicates they have the disease
- True Negative: Individual does not have the disease, test indicates they do not have the disease
- False Negative: Individual has the disease, test indicates they do not have the disease
In short, for each of the above, "true" / "false" tells whether the test result is correct, and "positive" / "negative" simply tells the result of the test.
Although we don't know exactly which tests have incorrect results, we can find the proportion of tests
that will tend to be correct or incorrect under varying circumstances.
That leads us to some more important terms to know:
- Sensitivity / True Positive Rate (TPR): The proportion of infected individuals correctly marked as having the disease
- False Positive Rate (FPR): The proportion of uninfected individuals incorrectly marked as having the disease
- Specificity / True Negative Rate (TNR): The proportion of uninfected individuals correctly marked as not having the disease
- False Negative Rate (FNR): The proportion of infected individuals incorrectly marked as not having the disease
These are all characteristics of the test itself, independent of the number of individuals tested.
On the other hand, there are some terms that do depend on the population or the number of individuals tested.
- Prevalence: The proportion of the general population that is infected with the disease
- Precision / Positive Predictive Value (PPV): The proportion of those marked as having the disease who are actually infected
- Negative Predictive Value (NPV): The proportion of those marked as not having the disease who are actually not infected
- False Discovery Rate (FDR): The proportion of those marked as having the disease who are actually not infected
- False Omission Rate (FOR): The proportion of those marked as not having the disease who are actually infected
Note that if `D+` and `D-` indicate disease-positive and disease-negative while `T+` and `T-` indicate positive and negative test results, respectively,
then TPR is `P(T+\mid D+)`, FPR is `P(T+\mid D-)`, TNR is `P(T-\mid D-)`, and FNR is `P(T-\mid D+)`. Meanwhile, prevalence is `P(D+)`,
PPV is `P(D+\mid T+)`, NPV is `P(D-\mid T-)`, FDR is `P(D-\mid T+)`, and FOR is `P(D+\mid T-)`.
Since a test returns either positive or negative in any case, we can see that `\text{TPR}+\text{FNR}=1` and `\text{FPR}+\text{TNR}=1`.
Additionally, since any individual either has the disease or does not, we have that `\text{PPV}+\text{FDR}=1` and `\text{NPV}+\text{FOR}=1`.
One incredibly useful tool for keeping track of the meanings of these terms is known as a
confusion matrix.
Suppose there is a disease going around, and scientists have devised a test to determine whether a given individual is infected.
The test has a false-positive rate of 0.05 and a false-negative rate of 0.01. Only five percent of the general population has the disease.
Given that a certain person receives a positive test result, what is the likelihood that they truly are infected?
We will first individually calculate the probabilities of a true positive and a false positive when testing a random individual from the general population.
Since the FPR is 0.05 and the prevalence is 0.05, we see that the probability of a false positive is `P(T+\mid D-)\cdot(1-P(D+))=0.05\cdot0.95=0.0475`.
Then, the FNR is 0.01 and the prevalence is 0.05, so we find the probability of a true positive is `(1-P(T-\mid D+))\cdot P(D+)=0.99\cdot0.05=0.0495`.
Thus, the we find the answer to be `P(D+\mid T+)=\frac{P(T+\cap D+)}{P(T+)}=\frac{P(T+\cap D+)}{P(T+\cap D+)+P(T+\cap D-)}=\frac{0.0495}{0.0495+0.0475}\approx0.5103`, or `51.03\%`.


The registration for pgday.eu 2009 is now open!
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The registration fee depends on the type of registration. It must be paid in advance (excluding the pay-at-the-door type) using one of the approved methods. Please verify that your payment has been received within two weeks of paying it.
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We prefer if attendees register as soon as possible, as this makes the planning work much easier. Note that if you register too close to the conference, we cannot guarantee the availability of a conference bag and t-shirt for you!
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mixed pg_delete ( resource $connection , string $table_name , array $assoc_array [, int $options = PGSQL_DML_EXEC ] )
pg_delete() deletes records from a table specified by the keys and values in assoc_array. If options is specified, pg_convert() is applied to assoc_array with the specified options.
Returns TRUE on success or FALSE on failure. Returns string if PGSQL_DML_STRING is passed via options.


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References for S4 classes in R
05 Oct 2013During the last six months I've been working a lot around S4 Classes in R. Compared to the "traditional" S3 class, there is not much information on S4.
Just in case, for those of you who are struggling like me, here's my curated (non-exhaustive) list of pdf references about S4 classes that I've found useful.
- Implementing S4 objects in your package (by Hervé Pagès)
- Object Oriented Programming in R (by Ross Ihaka)
- S4 Classes in 15 pages, more or less (by Robert Gentleman)
- Object Oriented Programming in S-plus (by Robert Gentleman)
- S4 Classes and Methods (by Friedrich Leisch)
- Recent Developments in R (by Paul Murrell)
- S4 Classes and Methods (by Roger Peng)
- Hands on S4 Classes (by Yohan Chalabi)
- Petit Manuel de S4 (by Christophe Genolini, in French)
- A (Not So) Short Introduction to S4 (by Christophe Genolini)
- Construire un Package Classic et S4 (by Christophe Genolini, in French)
- Advanced Programming (by Nicholas Christian)
- S4 System Development in Bioconductor (by Patrick Aboyoun)
- S4 classes and methods (by Martin Morgan and Robert Gentleman)
- Building R Packages (by David Diez)
- The Matrix Package: Programming with S4 classes (by Martin Maechler)
- A step-by-step guide to writing a simple package that uses S4 methods (by Robin Hankin)
As I said, this is not a complete list of pdf resources. However, if you think I'm missing a highly relevant reference, please let me know so the list can be improved.
Happy programming!


Future Evolutions
Gabriel Comte
What are they?
Questions about dual-funding?
Any ideas?
Current situation
Questions splicing?
Ever heard of it?
Draft: BIP 118
Previously SIGHASH_NO_INPUT
Requires a soft fork
... community is not in consensus about it
Questions about eltoo?
Who has heard about Lightning offers?
Questions about BOLT 12?
What could that be?
Questions about blinded paths?
Ever heard of PTLCs?
Questions about PTLCs?
¿Quién sabe?
Payment channels that can spawn other payment channels
Questions about channel factories?
¿Qué son?
Inofficial BOLT 13
All off-chain protocols assume the user remains online and synchronised with the network. To alleviate this assumption, customers can hire a third party watching service ...
Do you see any issues with that scheme?
... do you see any issues with that scheme?
Standard isn't perfectly followed
Standard is a bit outdated
Watchtowers exist
Degree of usage unknown
Questions about watchtowers?
Skipped in talk 'History of Paymnet Channels'


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{% extends "members/base.html" %} {% load members_extras %} {% block content %}
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| View | {{ sub.sub_year }} | {{ sub.active }} | {{ sub.start_date }} | {{ sub.end_date }} | {{ sub.membership_fulldescription }} | {% if sub.has_paid_invoice %} Paid invoice {% elif sub.has_unpaid_invoice %} Unpaid invoice {% elif sub.has_items %} Uninvoiced item {% else %} Not billed {% endif %} |


Clearance is at Sola, which is situated on the SE of the island. There is only a trail to there, so the best way is to sail there if needed.
This anchorage on the West coast of the island and is better protected from the swell than Vureas bay, 7 NM south. It is sheltered from NE & SE tradewinds (but open to infrequent westerlies).
Anchor in black sand in about 40 feet.
Last updated October 2009.
Waterfall Bay Yacht Club - Meet friendly Chief Keroly and his family at the small yacht club.


{% extends "./includes/template.html" %} {% block content %}
Form Start!
You will need the following information to proceed:


BranchContentMetadata
@interface BranchContentMetadata : NSObject
BranchContentMetadata describes properties in your Branch Universal Object.
-
Undocumented
Declaration
Objective-C
@property (nonatomic, strong, nullable) BranchContentSchema contentSchema
Swift
var contentSchema: BranchContentSchema? { get set }
-
Undocumented
Declaration
Objective-C
@property (nonatomic, assign) double quantity
Swift
var quantity: Double { get set }
-
Undocumented
Declaration
Objective-C
@property (nonatomic, strong, nullable) NSDecimalNumber *price
Swift
var price: NSDecimalNumber? { get set }
-
Undocumented
Declaration
Objective-C
@property (nonatomic, strong, nullable) BNCCurrency currency
Swift
var currency: BNCCurrency? { get set }
-
Undocumented
Declaration
Objective-C
@property (nonatomic, strong, nullable) NSString *sku
Swift
var sku: String? { get set }
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Undocumented
Declaration
Objective-C
@property (nonatomic, strong, nullable) NSString *productName
Swift
var productName: String? { get set }
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Declaration
Objective-C
@property (nonatomic, strong, nullable) NSString *productBrand
Swift
var productBrand: String? { get set }
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Undocumented
Declaration
Objective-C
@property (nonatomic, strong, nullable) BNCProductCategory productCategory
Swift
var productCategory: BNCProductCategory? { get set }
-
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Declaration
Objective-C
@property (nonatomic, strong, nullable) NSString *productVariant
Swift
var productVariant: String? { get set }
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Declaration
Objective-C
@property (nonatomic, strong, nullable) BranchCondition condition
Swift
var condition: BranchCondition? { get set }
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Declaration
Objective-C
@property (nonatomic, assign) double ratingAverage
Swift
var ratingAverage: Double { get set }
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Declaration
Objective-C
@property (nonatomic, assign) NSInteger ratingCount
Swift
var ratingCount: Int { get set }
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Declaration
Objective-C
@property (nonatomic, assign) double ratingMax
Swift
var ratingMax: Double { get set }
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Declaration
Objective-C
@property (nonatomic, assign) double rating
Swift
var rating: Double { get set }
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Declaration
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@property (nonatomic, strong, nullable) NSString *addressStreet
Swift
var addressStreet: String? { get set }
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@property (nonatomic, strong, nullable) NSString *addressCity
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var addressCity: String? { get set }
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@property (nonatomic, strong, nullable) NSString *addressRegion
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var addressRegion: String? { get set }
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@property (nonatomic, strong, nullable) NSString *addressCountry
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var addressCountry: String? { get set }
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@property (nonatomic, strong, nullable) NSString *addressPostalCode
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var addressPostalCode: String? { get set }
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@property (nonatomic, assign) double latitude
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var latitude: Double { get set }
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@property (nonatomic, assign) double longitude
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var longitude: Double { get set }
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@property (nonatomic, copy, nonnull) NSMutableArray<NSString*> *imageCaptions
Swift
@NSCopying var imageCaptions: NSMutableArray { get set }
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@property (nonatomic, copy, nonnull) NSMutableDictionary<NSString*, NSString*> *customMetadata
Swift
@NSCopying var customMetadata: NSMutableDictionary { get set }
-
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Objective-C
- (NSDictionary*_Nonnull) dictionary;
Swift
func dictionary() -> [AnyHashable : Any]
-
Undocumented
Declaration
Objective-C
+ (BranchContentMetadata*_Nonnull) contentMetadataWithDictionary:(NSDictionary*_Nullable)dictionary;
Swift
/*not inherited*/ init(dictionary: [AnyHashable : Any]?)


FeatureDataCleaner¶
from eflow.data_pipeline_segments.feature_data_cleaner import FeatureDataCleaner
-
class
FeatureDataCleaner
(segment_id=None, create_file=True)[source]¶ Designed for a multipurpose data cleaner.
-
drop_feature
(df, df_features, feature_name, _add_to_que=True)[source]¶ Drop a feature in the dataframe.
- Args:
- df: pd.Dataframe
Pandas Dataframe
- df_features: DataFrameType from eflow
Organizes feature types into groups.
- feature_name: string
Name of the feature in the datatframe
- _add_to_que: bool
Pushes the function to pipeline segment parent if set to ‘True’.
-
fill_nan_by_distribution
(df, df_features, feature_name, percentile, z_score=None, _add_to_que=True)[source]¶ Fill nan by the distribution of data.
- Args:
- df: pd.Dataframe
Pandas Dataframe
- df_features: DataFrameType from eflow
Organizes feature types into groups.
- feature_name: string
Name of the feature in the datatframe
percentile: float or int
z_score:
- _add_to_que: bool
Pushes the function to pipeline segment parent if set to ‘True’.
-
ignore_feature
(df, df_features, feature_name, _add_to_que=True)[source]¶ Ignore the given feature.
- Args:
- df: pd.Dataframe
Pandas Dataframe
- df_features: DataFrameType from eflow
Organizes feature types into groups.
- feature_name: string
Name of the feature in the datatframe
- _add_to_que: bool
Pushes the function to pipeline segment parent if set to ‘True’.
-
make_nan_assertions
(df, df_features, feature_name, _add_to_que=True)[source]¶ Make nan assertions for boolean features.
- Args:
- df: pd.Dataframe
Pandas Dataframe
- df_features: DataFrameType from eflow
Organizes feature types into groups.
- feature_name: string
Name of the feature in the datatframe
- _add_to_que: bool
Pushes the function to pipeline segment parent if set to ‘True’.
-
remove_nans
(df, df_features, feature_name, _add_to_que=True)[source]¶ Remove rows of data based on the given feature.
- Args:
- df: pd.Dataframe
Pandas Dataframe
- df_features: DataFrameType from eflow
Organizes feature types into groups.
- feature_name: string
Name of the feature in the datatframe
- _add_to_que: bool
Pushes the function to pipeline segment parent if set to ‘True’.
-


Prometheus JSON api exporter scrapes remote JSON from multiple targets.
The exporter get as parameter a json url and convert it to a Prometheus metrics.
The exporter parser support currently this two type of jsons structures.


You could get into this job through an intermediate or advanced apprenticeship in business administration.
Entry requirements
You'll usually need:
- some GCSEs, usually including English and maths, for an intermediate apprenticeship
- 5 GCSEs at grades 9 to 4 (A* to C), usually including English and maths, for an advanced apprenticeship


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Problem Name: ForLoops
Write a for loop to traverse the array and find if ‘Cricket’ exists in the array. Print ‘Cricket exists’ if it is in the array. Otherwise print ‘Does not exist


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JAWBREAKER A film review by Steve Rhodes Copyright 1999 Steve Rhodes RATING (0 TO ****): 1/2
The "Fab 4" of Ronald Reagan High are four stuck-up girls, played by Rose McGowan, Rebecca Gayheart, Julie Benz and Charlotte Roldan, who overdress like 4 Posh Spice wannabes. Their daily ritual is to parade up and down the school halls like royalty, and their constant reflex action is to grab their compacts so they can check their make-up and powder their cheeks. Under no circumstances will they ever let themselves be seen eating in public.
In writer and director Darren Stein's JAWBREAKER, it isn't as funny as it sounds. In fact, it isn't funny at all. Stein has taken the black comedy of a teen movie like HEATHERS and stripped it of any intelligence, wit or charm. His actresses toss off their lines so fast that they don't have time to add any emotion. And all of his female characters have considerably more lipstick than brains.
The setup for the movie has one of the Fab 4, Liz, who is referred to as "the Princess Di of Reagan High," be killed when a prank by the other 3 goes wrong. The Princess Di reference is in bad taste, as is much of the movie. Granted, bad taste can sometimes be funny, but the mere presence of bad taste doesn't guarantee it.
 When they open the trunk later, Liz is dead. They eventually put her blue and bruised body into a sexual position and make it look like Liz was the victim of a brutal rape. These realistic and disgusting scenes of a dead, seminude teen have a pornographic feeling that is hard to forget and will likely turn the stomach of many a viewer.
The film's dialog not only isn't funny, it's trite. One nerdy young girl introduces herself with "My name is Fern Mayo, as in hold the mayo." When Fern discovers what has happened, the Fab 3 make her an offer she can't refuse -- make-up! They cake it on her and welcome her to the clan.
The parents in the movie brag about learning parenting skills from watching Oprah. The teachers fare no better, especially in the ridiculous lines they are given. "Miss Shayne, please cover your bosom," one young teacher, who has been made up to look old and ugly, says without any conviction. "This is a learning institution, not a brothel."
Besides the obvious question of why this movie was ever made, there is one other question that I'd like to ask the director. Why do you have a phone number start with 555 if you going to cover the rest of the number anyway?
In a cameo, Pam Grier plays Detective Vera Cruz, who investigates Liz's murder. As she interrogates the other people, she stares at them like they are all idiots, proving that she is the only one that gets it. I am sure that if the hard nosed Detective Cruz were to see this movie, she'd walk out after about 5 minutes, and you'd be well advised to do the same if you should happen to find yourself in a theater showing it.
JAWBREAKER runs 1:27. It is rated R for violence, gore, profanity and sex. I would advise everyone to avoid it, but if teenagers go they should be older and mature.
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Class: Rumale::Tree::ExtraTreeRegressor
- Inherits:
-
DecisionTreeRegressor
- Object
- Base::Estimator
- BaseDecisionTree
- DecisionTreeRegressor
- Rumale::Tree::ExtraTreeRegressor
- Defined in:
- rumale-tree/lib/rumale/tree/extra_tree_regressor.rb
Overview
ExtraTreeRegressor is a class that implements extra randomized tree for regression.
Reference
-
Geurts, P., Ernst, D., and Wehenkel, L., “Extremely randomized trees,” Machine Learning, vol. 63 (1), pp. 3–42, 2006.
Instance Attribute Summary collapse
-
#feature_importances ⇒ Numo::DFloat
readonly
Return the importance for each feature.
-
#leaf_values ⇒ Numo::DFloat
readonly
Return the values assigned each leaf.
-
#rng ⇒ Random
readonly
Return the random generator for random selection of feature index.
-
#tree ⇒ Node
readonly
Return the learned tree.
Attributes inherited from Base::Estimator
Instance Method Summary collapse
-
#initialize(criterion: 'mse', max_depth: nil, max_leaf_nodes: nil, min_samples_leaf: 1, max_features: nil, random_seed: nil) ⇒ ExtraTreeRegressor
constructor
Create a new regressor with extra randomized tree algorithm.
Methods inherited from DecisionTreeRegressor
Methods included from Base::Regressor
Methods inherited from BaseDecisionTree
Constructor Details
#initialize(criterion: 'mse', max_depth: nil, max_leaf_nodes: nil, min_samples_leaf: 1, max_features: nil, random_seed: nil) ⇒ ExtraTreeRegressor
Create a new regressor with extra randomized tree algorithm.
49 50 51 52 |
# File 'rumale-tree/lib/rumale/tree/extra_tree_regressor.rb', line 49 def initialize(criterion: 'mse', max_depth: nil, max_leaf_nodes: nil, min_samples_leaf: 1, max_features: nil, random_seed: nil) super end |
Instance Attribute Details
#feature_importances ⇒ Numo::DFloat (readonly)
Return the importance for each feature.
23 24 25 |
# File 'rumale-tree/lib/rumale/tree/extra_tree_regressor.rb', line 23 def feature_importances @feature_importances end |
#leaf_values ⇒ Numo::DFloat (readonly)
Return the values assigned each leaf.
35 36 37 |
# File 'rumale-tree/lib/rumale/tree/extra_tree_regressor.rb', line 35 def leaf_values @leaf_values end |
#rng ⇒ Random (readonly)
Return the random generator for random selection of feature index.
31 32 33 |
# File 'rumale-tree/lib/rumale/tree/extra_tree_regressor.rb', line 31 def rng @rng end |
#tree ⇒ Node (readonly)
Return the learned tree.
27 28 29 |
# File 'rumale-tree/lib/rumale/tree/extra_tree_regressor.rb', line 27 def tree @tree end |


Architectural Balance
by Larry Garfield
@Crell
Senior Architect, Palantir.net
Drupal Association Advisor
Web Services & Context Initiative Owner
Co-Author,
Drupal 7 Module Development
,
Packt Publishing
Architectural Gadfly
Nerf Gunslinger
But first, the obligatory car analogy...
Swing axle design
Everything is balance
In an ideal world...
Fast
Flexible
Readable
Infinitely scalable
Perfectly correct
include('pony.php');
You can't always get what you want
Obligatory Venn Diagram
Pick two
Project Management Triad
I want my code/site to be good!
There is no such thing as good.
There is only appropriate.
Good is context-sensitive
Good in software
Engineering axes
Human axes
QA axes
Axes of Good
Engineering axes
Fast to run
Low-memory
Scalability
Modifiability
Extensibility
Human axes
Usability / UX (for end user)
Understandability / DX (for developer)
Learnability (for both)
Expediency
Maintainability
QA axes
Testability
Verifiability
zOMG who cares about this academic crap?
zOMG who cares about this academic crap?
Your client
Your boss
You in 6 months
Software architecture is good if it is good in the ways that matter in this case.
What matters depends on the situation.
Drupal site considerations
Is this site for...
A small church
Low-traffic
Anonymous traffic
Dozens of pages
Mostly static content
Learnability
Expediency
Low-memory
Scalability
Verifiability
A major media network
Hundreds of hits / second
Lots of social networking features
Millions of pages
Constantly changing content
Scalability
Fast-to-run
Verifiability
Expediency
Is this site going to be mostly...
Anonymous
Varnish solves
all
most
Simpler caching strategy
Rarely reach Drupal
Fast-to-run
Low-memory
Scalability
Authenticated
Varnish is no help
Complex caching strategy
Every line of PHP matters
Fast-to-run
Low-memory
Scalability
Expediency
To what extent do I care about mobile?
(0 is not an answer)
Responsive design limits what you can put on a page
Responsive design takes more work
Who is your target audience?
Look at browser stats
Look at
your site's
traffic logs
Is this module...
Site-specific
No integration needs
Limited extensibility
Hard coding is OK!
Expediency
Modifiability
Usability
Extensibility
For Drupal.org
Leverage Views/Rules/Panels?
Assume extensibility
Avoid hard-coding
Extensibility
Scalability
Testability
Understandability
Expediency
Does this site have...
One content editor
No workflow
Train once, use anywhere
All admin access
Usability
Expediency
Learnability
Several editors
Workflow/ACL
Customized editorial process
Unpublished nodes?
Extensibility
Modifiability
Testability
Understandability
Development considerations
Do I want this behavior to be overridable by any module?
(Note: The answer is not always yes!)
Do I want this behavior to be overridable by a specific site?
(Note: The answer is not always yes!)
Do I care if this operation is fast?
(Note: The answer is not always yes!)
 Cost of abstraction
Expediency vs. Modifiability: "Doing it right" takes time
Extensibility vs. Testability: Hooks & bare data structures
Extensibility vs. Understandability: "Wait, what all is happening?" (preprocess functions?)
 Or can you?
Complementary design
Testability and Modifiability both require similar abstraction
Testable code is inherently modular
The easiest way to make something extensible is to make it modular
A well-designed system is rapidly extensible. Expedient later, not now.
Testability and Modifiability are complementary
Complementary design
Performance: seconds / request
Scalability: requests / second
Fewer seconds/request => more requests/second
Better Performance
usually
improves Scalability
Third party code
You want it to be Maintainable...
You want it to be Learnable...
You want it to be reliable (Testable)...
... that makes it Expedient to use!
That's why we're using Symfony
When you try to do it all...
Your job is balance
What did you think?
Locate this session on the DrupalCon Denver website
http://denver2012.drupal.org/program/sessions
Click the “Take the Survey” link.
Thank you


WITNESS LIST
County Affairs Committee
August 22, 2017 - 10:00 AM
Sandra Bland Act Implementation & Opioid Crisis Oversight
On:
Allison, Jim (County Judges and Commissioners Association of Texas)
Bredt, Robert (Texas Medical Board)
Carter, Gina (The Health and Human Services Commission)
Deitch, Michele (Self)
Feehery, Matthew (Self; Memorial Hermann)
Gaines, Sonja (The Health and Human Services Commission)
Garcia, Sonia (DPS)
Hansch, Greg (Self; NATIONAL ALLIANCE on Mental Illness (NAMI) TX)
Joy, Ron (DPS)
Louderback, AJ (Sheriffs' Association of Texas)
Mann, Fatima (Self; Counter Balance: ATX)
Porsa, Esmaeil (Parkland and jail commission)
Vickers, Kim (Texas Commission on Law Enforcement)
Wexler, Sherwin (Police Executive Research Forum)
Wilson, Thomas (Police Executive Research Forum)
Wood, Brandon (Texas Commission on Jail Standards)
1


Minnowbooster has recently released the Community Whitelist program to reduce the abuse of spam content promoting. Only the whitelisted members are eligible to buy high volume of vote from @minnowbooster would be the great solution of letting everyone accessing to the high SP of voting bots.
Whitelist was is the exclusive feature of Smartsteem
@smartsteem is one of the top vote-selling services now with more than 4000 delegators hosted by @therealwolf. I was very fascinated the team’s vision to maintain the cleanliness of the Steem platform by introducing the Smart star whitelisting system. Moderators from the Smartsteem team review applications from the public, approving or rejecting base on the quality of the author. Upon approval, whitelisted authors will be assigned from 1 star to maximum 3 star rating. The higher the rating the higher vote one can buy from the service.
Minnowbooster however, follows the initiative of Smartsteem introducing the whitelist system. The difference is the process is more decentralised. There is no application but invitation. Whitelisted members get to vote on invited users, upon getting 3 votes one admin will be able to approve the whitelist user. There are more benefits to be a whitelisted member of than bigger votes capacity, read the original post for more insight.
Which is the better service to buy votes?
Assuming you are whitelisted in both service, Minnowbooster is going to be the superior choice now.
Smartsteem was my favourite for their high and steady availability of votes capacity. However, due to last price spiking of Steem from $1.5, the demand for vote buying from Smartsteem surged as well. To maintain vote supply, the team has decided to reduce the vote-buying ROI to the range of 180% and 210%
.
Minnowbooster on the other hand, was always in the shortage of vote supply and that was the reason I decided to look for alternative and found Smartsteem. Somehow their vote supply is becoming steady lately and after a several vote-buying from the Minnowbooster, I found out the ROI could be around 220% and 240%.
For the same 1 SDB, Smartsteem would give you a vote at maximum $2.2 vs $2.4 from the Minnowbooster. I’d definitely go for Minnnowbooster unless things changed.
Minnowbooster 比 Smartsteem 更适合买赞，前者的回报高达 220% ~ 240%
而后者则是180% ~ 210%
。
This page is synchronized from the post: ‘Vote buying, Minnowbooster or Smartsteem?’


A big Thanks goes to all of you that support my open-source work. In particular, I'd like to acknowledge CodeStream and Daniel Babiak, Stefan Hagen, Joshua Lückers, jlash13, and Mark McCorkle for sponsoring the Key Promoter X with $4 per month. Additionally, I highly appreciate the support from 9 people who give $2/month and 24 people who committed to $1/month. You are awesome!
If you also appreciate my efforts, then please consider sponsoring me directly on GitHub. And don't forget to star the GitHub repository and leave a rating on the JetBrains plugin site.
Finally, don't be too shy to connect on LinkedIn. I'd like to know in which parts of the world the Key Promoter X is used.
This message will only appear once for each new version. The GitHub Sponsors link can later be found in the plugin description under Settings | Plugins | Key Promoter X.


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