Package com.dragonboatrace.tools
Class Race
- java.lang.Object
-
- com.dragonboatrace.tools.Race
-
public class Race extends java.lang.Object
Represents a Race.
-
-
Field Summary
Fields Modifier and Type Field Description private com.badlogic.gdx.graphics.Texture
barrier
The separator between each lane.private java.util.ArrayList<ComputerBoat>
computerBoats
The list of boats in the race, not including the player.private int
length
The length of the race.private Boat
player
The players boat.private FinishLine
theFinish
The finish line.private float
timer
The timer for the race.
-
Method Summary
All Methods Instance Methods Concrete Methods Modifier and Type Method Description void
dispose()
boolean
equals(java.lang.Object obj)
java.util.Set<java.lang.Float>
findDuplicates(java.util.ArrayList<java.lang.Float> list)
Find any duplicates in an arraylist of floats.void
getLeaderBoard(DragonBoatRace game)
Generate the leaderboard from the race that just occurred and then show the next round screen.Boat
getPlayer()
Get the players boat.void
render(com.badlogic.gdx.graphics.g2d.SpriteBatch batch)
Render the boats in the race and the player boat.void
restore()
void
save()
void
update(float deltaTime, DragonBoatRace game)
Update the race in respects to the amount of time passed since the last frame.
-
-
-
Field Detail
-
length
private final int length
The length of the race.
-
computerBoats
private final java.util.ArrayList<ComputerBoat> computerBoats
The list of boats in the race, not including the player.
-
player
private final Boat player
The players boat.
-
theFinish
private final FinishLine theFinish
The finish line.
-
barrier
private final com.badlogic.gdx.graphics.Texture barrier
The separator between each lane.
-
timer
private float timer
The timer for the race.
-
-
Method Detail
-
update
public void update(float deltaTime, DragonBoatRace game)
Update the race in respects to the amount of time passed since the last frame.- Parameters:
deltaTime
- The time since the last frame.game
- The instance of the game.
-
render
public void render(com.badlogic.gdx.graphics.g2d.SpriteBatch batch)
Render the boats in the race and the player boat.- Parameters:
batch
- The SpriteBatch to be added to.
-
getLeaderBoard
public void getLeaderBoard(DragonBoatRace game)
Generate the leaderboard from the race that just occurred and then show the next round screen.- Parameters:
game
- The instance of the game.
-
findDuplicates
public java.util.Set<java.lang.Float> findDuplicates(java.util.ArrayList<java.lang.Float> list)
Find any duplicates in an arraylist of floats.- Parameters:
list
- AnArrayList
of floats to be combed through.- Returns:
- An
Set
of type float containing unique values.
-
getPlayer
public Boat getPlayer()
Get the players boat.- Returns:
- A
Boat
representing the players boat.
-
dispose
public void dispose()
-
restore
public void restore()
-
save
public void save()
-
equals
public boolean equals(java.lang.Object obj)
- Overrides:
equals
in classjava.lang.Object
-
-


Thundurus is a Pokemon team building website. you can view pokemon without a login, but create an account and you can save and editmultiple teams!


Erica Kait Creative
Miriam + Mark
Wooden Shoe Tulip Farm - Woodburn, OR
Wedding - July 2017
Miriam and Mark got to know each other while living abroad. When they first met both were convinced that the timing just wasn't right for them to be together and so they waited, not 100% sure if the other shared their feelings. To make their long story short, eventually, they realized how much they both wanted to be with the other and started dating. After their engagement, I Skyped Miriam while they were still overseas to talk about their vision for their wedding day. Miriam is my cousin so I've known her for as long as I can remember, but the first time I met Mark in-person was on the morning of their wedding. I was so blown away by how well their fun-loving personalities compliment each other and what a joy they were to be around as a couple.
I got to spend a huge chunk of their wedding day with them - literally sunrise to sundown. I remember asking if they were sure they wanted to do sunrise photos, even a 7 hour wedding day can feel long to a bride and groom, but they were committed and if they were tired at the end of the day they did a great job of faking it! These two were so ecstatic to tie the knot nothing was slowing them down! Their wedding day was full of big, goofy families, intimate moments, singing, bare toes, laughter, and so much love.


BIOSDEVNAME
Section: User Commands (1)Updated: Nov 28, 2010
Index Return to Main Contents
NAME
biosdevname - give BIOS-given name of a deviceSYNOPSIS
biosdevname [options] [args]...DESCRIPTION
biosdevname takes a kernel device name as an argument, and returns the BIOS-given name it "should" be.OPTIONS
- -i, --interface
- Treat [args] as ethernet devs
- -d, --debug
- Enable debugging
- --policy [physical|all_ethN]
- --prefix [string]
- string use for embedded NICs in the physical policy (default=em)
- --nopirq
- Do not use $PIR table for mapping PCI device to slot. Some BIOS have incorrect values.
- --smbios [x.y]
- Require minimum SMBIOS version x.y
POLICIES
The physical policy is the current default. However, when invoking biosdevname in udev rules, one should always specify the policy you want, as the default has changed over time.
The physical policy uses the following scheme:
- em<port>
- for embedded NICs
- p<slot>#<port>_<virtual instance>
-
for cards in PCI slots
- The
-
all_ethN
policy makes a best guess at what the device order should be, with
embedded devices first, PCI cards in ascending slot order, and ports
in ascending PCI bus/device/function order breadth-first. However,
this policy
does not work
if your PCI devices are hot-plugged or hot-pluggable, including the
virtual functions on an SR-IOV device. In a hot-plug scenario, each separate
udev instance will be invoked in parallel, while the device tree is
still being populated with new devices. Each udev instance will see a
different PCI tree, and thus cannot provide consistent enumeration.
Use of this policy should be limited to only scenarios where all PCI
devices are present at boot (cold-plug).
EXIT CODES
Returns 0 on success, with BIOS-suggested name printed to stdout.Returns 1 on provided device name lookup failure.
Returns 2 if system BIOS does not provide naming information. biosdevname requires system BIOS to provide naming information, either via SMBIOS or sysfs files.
Returns 3 if not run as root but requires root privileges.
Returns 4 if running in a virtual machine.
SEE ALSO
http://linux.dell.com/wiki/index.php/Oss/libnetdevname
http://linux.dell.com/files/biosdevname/
git://linux.dell.com/biosdevname.git
RELATED PROGRAMS
The dmidecode package contains two tools useful for debugging BIOS features that biosdevname uses, specifically dmidecode to read the SMBIOS Type 9 and Type 41 tables, and biosdecode to read the PCI IRQ Routing Table. Please include the output of each of these programs in any bug reports.
AUTHOR
biosdevname was written by Matt Domsch<EMAIL_ADDRESS>manual page was written by Rudy Gevaert<EMAIL_ADDRESS>for the Debian project (but may be used by others).
Index
This document was created by man2html, using the manual pages.
Time: 05:29:02 GMT, December 24, 2015


In this auction, the jackpot of {{ Constants.jackpot }} points goes to the highest bidder. The second highest bidder still has to pay the amount of their bid.


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--- layout: default search: exclude title: Homepage --- Communote Open Communication for Teams Communicate Structure Search & Find Network Download Communote Important features Open Source and free Rich text editor for status updates, notes and memos Spaces for groups and teams Access control to ensure privacy Fulltext search and filtering Hashtags, @mentions and direct messages Activity streams including discussion threads Multiple languages (en, de, es, it, fr) User- and usergroup management with LDAP integration API for access, integration and automation Plugin interface to extend the functionality and more Get started! Download & Install Explore Communote Get involved Need professional help? ... check out Communardo for enterprise support, hosting, development or consulting services.
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| 1 | /* | |
| 2 | * Licensed to the Apache Software Foundation (ASF) under one or more | |
| 3 | * contributor license agreements. See the NOTICE file distributed with | |
| 4 | * this work for additional information regarding copyright ownership. | |
| 5 | * The ASF licenses this file to You under the Apache License, Version 2.0 | |
| 6 | * (the "License"); you may not use this file except in compliance with | |
| 7 | * the License. You may obtain a copy of the License at | |
| 8 | * | |
| 9 | * http://www.apache.org/licenses/LICENSE-2.0 | |
| 10 | * | |
| 11 | * Unless required by applicable law or agreed to in writing, software | |
| 12 | * distributed under the License is distributed on an "AS IS" BASIS, | |
| 13 | * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| 14 | * See the License for the specific language governing permissions and | |
| 15 | * limitations under the License. | |
| 16 | */ | |
| 17 | ||
| 18 | package org.apache.commons.math3.analysis.function; | |
| 19 | ||
| 20 | import org.apache.commons.math3.analysis.FunctionUtils; | |
| 21 | import org.apache.commons.math3.analysis.UnivariateFunction; | |
| 22 | import org.apache.commons.math3.analysis.DifferentiableUnivariateFunction; | |
| 23 | import org.apache.commons.math3.analysis.differentiation.DerivativeStructure; | |
| 24 | import org.apache.commons.math3.analysis.differentiation.UnivariateDifferentiableFunction; | |
| 25 | import org.apache.commons.math3.util.FastMath; | |
| 26 | ||
| 27 | /** | |
| 28 | * Base 10 logarithm function. | |
| 29 | * | |
| 30 | * @since 3.0 | |
| 31 | * @version $Id: Log10.java 1383441 2012-09-11 14:56:39Z luc $ | |
| 32 | */ | |
| 33 | public class Log10 implements UnivariateDifferentiableFunction, DifferentiableUnivariateFunction { | |
| 34 | ||
| 35 | /** {@inheritDoc} */ | |
| 36 | public double value(double x) { | |
| 37 | 1 | return FastMath.log10(x); |
| 38 | } | |
| 39 | ||
| 40 | /** {@inheritDoc} | |
| 41 | * @deprecated as of 3.1, replaced by {@link #value(DerivativeStructure)} | |
| 42 | */ | |
| 43 | @Deprecated | |
| 44 | public UnivariateFunction derivative() { | |
| 45 | 1 | return FunctionUtils.toDifferentiableUnivariateFunction(this).derivative(); |
| 46 | } | |
| 47 | ||
| 48 | /** {@inheritDoc} | |
| 49 | * @since 3.1 | |
| 50 | */ | |
| 51 | public DerivativeStructure value(final DerivativeStructure t) { | |
| 52 | 1 | return t.log10(); |
| 53 | } | |
| 54 | ||
| 55 | } | |
Mutations | ||
| 37 |
replaced return of double value with -(x + 1) for org/apache/commons/math3/analysis/function/Log10::value : SURVIVED |
|
| 45 |
mutated return of Object value for org/apache/commons/math3/analysis/function/Log10::derivative to ( if (x != null) null else throw new RuntimeException ) : NO_COVERAGE |
|
| 52 |
mutated return of Object value for org/apache/commons/math3/analysis/function/Log10::value to ( if (x != null) null else throw new RuntimeException ) : NO_COVERAGE |


| isoacf {sandwich} | R Documentation |
Autocorrelation function (forced to be decreasing by isotonic regression).
isoacf(x, lagmax = NULL, weave1 = FALSE)
x |
numeric vector. |
lagmax |
numeric. The maximal lag of the autocorrelations. |
weave1 |
logical. If set to |
isoacf
computes the autocorrelation function (ACF)
of x
enforcing the ACF to be decreasing by isotonic regression.
See also Robertson et al. (1988).
isoacf
returns a numeric vector containing the ACF.
Lumley T & Heagerty P (1999). “Weighted Empirical Adaptive Variance Estimators for Correlated Data Regression.” Journal of the Royal Statistical Society B, 61, 459–477.
Robertson T, Wright FT, Dykstra RL (1988). Order Restricted Statistical Inference. John Wiley and Sons, New York.
x <- filter(rnorm(100), 0.9, "recursive") isoacf(x) acf(x, plot = FALSE)$acf


This page lets you set up a quiz about wildflowers that you can take. You can tailor it to your particular preferences, or just hit "Create a Quiz"
and take a general test. There are also quiz programs for trees and birds.
I started this project back in 1984, believe it or not, and am doing it purely for fun.
If you like this program enough to feel like showing your appreciation, please consider
becoming a member or
giving a tax-deductible charitable contribution
to the Cornell Plantations.
I have no affiliation with this fine institution; rather, I was married many years ago
in their beautiful herb garden.


Boolean Formulas#
Formulas consist of the operators &
, |
, ~
, ^
, ->
, <->
,
corresponding to and
, or
, not
, xor
, if...then
, if and
only if
. Operators can be applied to variables that consist of a leading
letter and trailing underscores and alphanumerics. Parentheses may be used
to explicitly show order of operation.
EXAMPLES:
Create boolean formulas and combine them with
ifthen()
method:
sage: import sage.logic.propcalc as propcalc
sage: f = propcalc.formula("a&((b|c)^a->c)<->b")
sage: g = propcalc.formula("boolean<->algebra")
sage: (f&~g).ifthen(f)
((a&((b|c)^a->c)<->b)&(~(boolean<->algebra)))->(a&((b|c)^a->c)<->b)
We can create a truth table from a formula:
sage: f.truthtable()
a b c value
False False False True
False False True True
False True False False
False True True False
True False False True
True False True False
True True False True
True True True True
sage: f.truthtable(end=3)
a b c value
False False False True
False False True True
False True False False
sage: f.truthtable(start=4)
a b c value
True False False True
True False True False
True True False True
True True True True
sage: propcalc.formula("a").truthtable()
a value
False False
True True
Now we can evaluate the formula for a given set of inputs:
sage: f.evaluate({'a':True, 'b':False, 'c':True})
False
sage: f.evaluate({'a':False, 'b':False, 'c':True})
True
And we can convert a boolean formula to conjunctive normal form:
sage: f.convert_cnf_table()
sage: f
(a|~b|c)&(a|~b|~c)&(~a|b|~c)
sage: f.convert_cnf_recur()
sage: f
(a|~b|c)&(a|~b|~c)&(~a|b|~c)
Or determine if an expression is satisfiable, a contradiction, or a tautology:
sage: f = propcalc.formula("a|b")
sage: f.is_satisfiable()
True
sage: f = f & ~f
sage: f.is_satisfiable()
False
sage: f.is_contradiction()
True
sage: f = f | ~f
sage: f.is_tautology()
True
The equality operator compares semantic equivalence:
sage: f = propcalc.formula("(a|b)&c")
sage: g = propcalc.formula("c&(b|a)")
sage: f == g
True
sage: g = propcalc.formula("a|b&c")
sage: f == g
False
It is an error to create a formula with bad syntax:
sage: propcalc.formula("")
Traceback (most recent call last):
...
SyntaxError: malformed statement
sage: propcalc.formula("a&b~(c|(d)")
Traceback (most recent call last):
...
SyntaxError: malformed statement
sage: propcalc.formula("a&&b")
Traceback (most recent call last):
...
SyntaxError: malformed statement
sage: propcalc.formula("a&b a")
Traceback (most recent call last):
...
SyntaxError: malformed statement
It is also an error to not abide by the naming conventions:
sage: propcalc.formula("~a&9b")
Traceback (most recent call last):
...
NameError: invalid variable name 9b: identifiers must begin with a letter and contain only alphanumerics and underscores
AUTHORS:
Chris Gorecki (2006): initial version
Paul Scurek (2013-08-03): added polish_notation, full_tree, updated docstring formatting
Paul Scurek (2013-08-08): added
implies()
- class sage.logic.boolformula.BooleanFormula(exp, tree, vo)#
Bases:
object
Boolean formulas.
INPUT:
self
– calling objectexp
– a string; this contains the boolean expression to be manipulatedtree
– a list; this contains the parse tree of the expression.vo
– a list; this contains the variables in the expression, in the order that they appear; each variable only occurs once in the list
- add_statement(other, op)#
Combine two formulas with the given operator.
INPUT:
other
– instance ofBooleanFormula
; this is the formula on the right of the operatorop
– a string; this is the operator used to combine the two formulas
OUTPUT:
The result as an instance of
BooleanFormula
.EXAMPLES:
This example shows how to create a new formula from two others:
sage: import sage.logic.propcalc as propcalc sage: s = propcalc.formula("a&b") sage: f = propcalc.formula("c^d") sage: s.add_statement(f, '|') (a&b)|(c^d) sage: s.add_statement(f, '->') (a&b)->(c^d)
- convert_cnf()#
Convert boolean formula to conjunctive normal form.
OUTPUT:
An instance of
BooleanFormula
in conjunctive normal form.EXAMPLES:
This example illustrates how to convert a formula to cnf:
sage: import sage.logic.propcalc as propcalc sage: s = propcalc.formula("a ^ b <-> c") sage: s.convert_cnf() sage: s (a|b|~c)&(a|~b|c)&(~a|b|c)&(~a|~b|~c)
We now show that
convert_cnf()
andconvert_cnf_table()
are aliases:sage: t = propcalc.formula("a ^ b <-> c") sage: t.convert_cnf_table(); t (a|b|~c)&(a|~b|c)&(~a|b|c)&(~a|~b|~c) sage: t == s True
Note
This method creates the cnf parse tree by examining the logic table of the formula. Creating the table requires \(O(2^n)\) time where \(n\) is the number of variables in the formula.
- convert_cnf_recur()#
Convert boolean formula to conjunctive normal form.
OUTPUT:
An instance of
BooleanFormula
in conjunctive normal form.EXAMPLES:
This example hows how to convert a formula to conjunctive normal form:
sage: import sage.logic.propcalc as propcalc sage: s = propcalc.formula("a^b<->c") sage: s.convert_cnf_recur() sage: s (~a|a|c)&(~b|a|c)&(~a|b|c)&(~b|b|c)&(~c|a|b)&(~c|~a|~b)
Note
This function works by applying a set of rules that are guaranteed to convert the formula. Worst case the converted expression has an \(O(2^n)\) increase in size (and time as well), but if the formula is already in CNF (or close to) it is only \(O(n)\).
This function can require an exponential blow up in space from the original expression. This in turn can require large amounts of time. Unless a formula is already in (or close to) being in cnf
convert_cnf()
is typically preferred, but results can vary.
- convert_cnf_table()#
Convert boolean formula to conjunctive normal form.
OUTPUT:
An instance of
BooleanFormula
in conjunctive normal form.EXAMPLES:
This example illustrates how to convert a formula to cnf:
sage: import sage.logic.propcalc as propcalc sage: s = propcalc.formula("a ^ b <-> c") sage: s.convert_cnf() sage: s (a|b|~c)&(a|~b|c)&(~a|b|c)&(~a|~b|~c)
We now show that
convert_cnf()
andconvert_cnf_table()
are aliases:sage: t = propcalc.formula("a ^ b <-> c") sage: t.convert_cnf_table(); t (a|b|~c)&(a|~b|c)&(~a|b|c)&(~a|~b|~c) sage: t == s True
Note
This method creates the cnf parse tree by examining the logic table of the formula. Creating the table requires \(O(2^n)\) time where \(n\) is the number of variables in the formula.
- convert_expression()#
Convert the string representation of a formula to conjunctive normal form.
EXAMPLES:
sage: import sage.logic.propcalc as propcalc sage: s = propcalc.formula("a^b<->c") sage: s.convert_expression(); s a^b<->c
- convert_opt(tree)#
Convert a parse tree to the tuple form used by
bool_opt()
.INPUT:
tree
– a list; this is a branch of a parse tree and can only contain the ‘&’, ‘|’ and ‘~’ operators along with variables
OUTPUT:
A 3-tuple.
EXAMPLES:
This example illustrates the conversion of a formula into its corresponding tuple:
sage: import sage.logic.propcalc as propcalc, sage.logic.logicparser as logicparser sage: s = propcalc.formula("a&(b|~c)") sage: tree = ['&', 'a', ['|', 'b', ['~', 'c', None]]] sage: logicparser.apply_func(tree, s.convert_opt) ('and', ('prop', 'a'), ('or', ('prop', 'b'), ('not', ('prop', 'c'))))
Note
This function only works on one branch of the parse tree. To apply the function to every branch of a parse tree, pass the function as an argument in
apply_func()
inlogicparser
.
- dist_not(tree)#
Distribute ‘~’ operators over ‘&’ and ‘|’ operators.
INPUT:
tree
a list; this represents a branch of a parse tree
OUTPUT:
A new list.
EXAMPLES:
This example illustrates the distribution of ‘~’ over ‘&’:
sage: import sage.logic.propcalc as propcalc, sage.logic.logicparser as logicparser sage: s = propcalc.formula("~(a&b)") sage: tree = ['~', ['&', 'a', 'b'], None] sage: logicparser.apply_func(tree, s.dist_not) #long time ['|', ['~', 'a', None], ['~', 'b', None]]
Note
This function only operates on a single branch of a parse tree. To apply the function to an entire parse tree, pass the function as an argument to
apply_func()
inlogicparser
.
- dist_ors(tree)#
Distribute ‘|’ over ‘&’.
INPUT:
tree
– a list; this represents a branch of a parse tree
OUTPUT:
A new list.
EXAMPLES:
This example illustrates the distribution of ‘|’ over ‘&’:
sage: import sage.logic.propcalc as propcalc, sage.logic.logicparser as logicparser sage: s = propcalc.formula("(a&b)|(a&c)") sage: tree = ['|', ['&', 'a', 'b'], ['&', 'a', 'c']] sage: logicparser.apply_func(tree, s.dist_ors) #long time ['&', ['&', ['|', 'a', 'a'], ['|', 'b', 'a']], ['&', ['|', 'a', 'c'], ['|', 'b', 'c']]]
Note
This function only operates on a single branch of a parse tree. To apply the function to an entire parse tree, pass the function as an argument to
apply_func()
inlogicparser
.
- equivalent(other)#
Determine if two formulas are semantically equivalent.
INPUT:
self
– calling objectother
– instance of BooleanFormula class.
OUTPUT:
A boolean value to be determined as follows:
True - if the two formulas are logically equivalent
False - if the two formulas are not logically equivalent
EXAMPLES:
This example shows how to check for logical equivalence:
sage: import sage.logic.propcalc as propcalc sage: f = propcalc.formula("(a|b)&c") sage: g = propcalc.formula("c&(a|b)") sage: f.equivalent(g) True sage: g = propcalc.formula("a|b&c") sage: f.equivalent(g) False
- evaluate(var_values)#
Evaluate a formula for the given input values.
INPUT:
var_values
– a dictionary; this contains the pairs of variables and their boolean values.
OUTPUT:
The result of the evaluation as a boolean.
EXAMPLES:
This example illustrates the evaluation of a boolean formula:
sage: import sage.logic.propcalc as propcalc sage: f = propcalc.formula("a&b|c") sage: f.evaluate({'a':False, 'b':False, 'c':True}) True sage: f.evaluate({'a':True, 'b':False, 'c':False}) False
- full_tree()#
Return a full syntax parse tree of the calling formula.
OUTPUT:
The full syntax parse tree as a nested list
EXAMPLES:
This example shows how to find the full syntax parse tree of a formula:
sage: import sage.logic.propcalc as propcalc sage: s = propcalc.formula("a->(b&c)") sage: s.full_tree() ['->', 'a', ['&', 'b', 'c']] sage: t = propcalc.formula("a & ((~b | c) ^ a -> c) <-> ~b") sage: t.full_tree() ['<->', ['&', 'a', ['->', ['^', ['|', ['~', 'b'], 'c'], 'a'], 'c']], ['~', 'b']] sage: f = propcalc.formula("~~(a&~b)") sage: f.full_tree() ['~', ['~', ['&', 'a', ['~', 'b']]]]
Note
This function is used by other functions in the logic module that perform syntactic operations on a boolean formula.
AUTHORS:
Paul Scurek (2013-08-03)
- get_bit(x, c)#
Determine if bit
c
of the numberx
is 1.INPUT:
x
– an integer; this is the number from which to take the bitc
– an integer; this is the but number to be taken, where 0 is the low order bit
OUTPUT:
A boolean to be determined as follows:
True
if bitc
ofx
is 1.False
if bit c of x is not 1.
EXAMPLES:
This example illustrates the use of
get_bit()
:sage: import sage.logic.propcalc as propcalc sage: s = propcalc.formula("a&b") sage: s.get_bit(2, 1) True sage: s.get_bit(8, 0) False
It is not an error to have a bit out of range:
sage: s.get_bit(64, 7) False
Nor is it an error to use a negative number:
sage: s.get_bit(-1, 3) False sage: s.get_bit(64, -1) True sage: s.get_bit(64, -2) False
Note
The 0 bit is the low order bit. Errors should be handled gracefully by a return of
False
, and negative numbersx
always returnFalse
while a negativec
will index from the high order bit.
- get_next_op(str)#
Return the next operator in a string.
INPUT:
str
– a string; this contains a logical expression
OUTPUT:
The next operator as a string.
EXAMPLES:
This example illustrates how to find the next operator in a formula:
sage: import sage.logic.propcalc as propcalc sage: s = propcalc.formula("f&p") sage: s.get_next_op("abra|cadabra") '|'
Note
The parameter
str
is not necessarily the string representation of the calling object.
- iff(other)#
Combine two formulas with the
<->
operator.INPUT:
other
– a boolean formula; this is the formula on the right side of the operator
OUTPUT:
A boolean formula of the form
self <-> other
.EXAMPLES:
This example illustrates how to combine two formulas with ‘<->’:
sage: import sage.logic.propcalc as propcalc sage: s = propcalc.formula("a&b") sage: f = propcalc.formula("c^d") sage: s.iff(f) (a&b)<->(c^d)
- ifthen(other)#
Combine two formulas with the
->
operator.INPUT:
other
– a boolean formula; this is the formula on the right side of the operator
OUTPUT:
A boolean formula of the form
self -> other
.EXAMPLES:
This example illustrates how to combine two formulas with ‘->’:
sage: import sage.logic.propcalc as propcalc sage: s = propcalc.formula("a&b") sage: f = propcalc.formula("c^d") sage: s.ifthen(f) (a&b)->(c^d)
- implies(other)#
Determine if calling formula implies other formula.
INPUT:
self
– calling objectother
– instance ofBooleanFormula
OUTPUT:
A boolean value to be determined as follows:
True
- ifself
impliesother
False
- ifself does not imply ``other
EXAMPLES:
This example illustrates determining if one formula implies another:
sage: import sage.logic.propcalc as propcalc sage: f = propcalc.formula("a<->b") sage: g = propcalc.formula("b->a") sage: f.implies(g) True
sage: h = propcalc.formula("a->(a|~b)") sage: i = propcalc.formula("a") sage: h.implies(i) False
AUTHORS:
Paul Scurek (2013-08-08)
- is_consequence(*hypotheses)#
Determine if
self
(the desired conclusion) is a logical consequence of the hypotheses. The function callis_consequence(conclusion, *hypotheses)
is a synonym forconclusion.is_consequence(*hypotheses)
.INPUT:
*hypotheses
– instances ofBooleanFormula
OUTPUT:
A boolean value to be determined as follows:
True
- ifself
(the desired conclusion) is a logical consequence of the set of hypothesesFalse
- ifself
(the desired conclusion) is not a logical consequence of the set of hypotheses
EXAMPLES:
sage: from sage.logic.propcalc import formula sage: formula("a | b").is_consequence(formula("b")) True sage: formula("a & b").is_consequence(formula("b")) False sage: formula("b").is_consequence(formula("a"), formula("a -> b")) True sage: formula("b -> a").is_consequence(formula("a -> b")) False sage: formula("~b -> ~a").is_consequence(formula("a -> b")) True
sage: f, g, h = propcalc.get_formulas("a & ~b", "c -> b", "c | e") sage: propcalc.formula("a & e").is_consequence(f, g, h) True sage: i = propcalc.formula("a & ~e") sage: i.is_consequence(f, g, h) False sage: from sage.logic.boolformula import is_consequence sage: is_consequence(i, f, g, h) False sage: is_consequence(propcalc.formula("((p <-> q) & r) -> ~c"), f, g, h) True
Only a tautology is a logical consequence of an empty set of formulas:
sage: propcalc.formula("a | ~a").is_consequence() True sage: propcalc.formula("a | b").is_consequence() False
AUTHORS:
Paul Scurek (2013-08-12)
- is_contradiction()#
Determine if the formula is always
False
.OUTPUT:
A boolean value to be determined as follows:
True
if the formula is a contradiction.False
if the formula is not a contradiction.
EXAMPLES:
This example illustrates how to check if a formula is a contradiction.
sage: import sage.logic.propcalc as propcalc sage: f = propcalc.formula("a&~a") sage: f.is_contradiction() True sage: f = propcalc.formula("a|~a") sage: f.is_contradiction() False sage: f = propcalc.formula("a|b") sage: f.is_contradiction() False
- is_satisfiable()#
Determine if the formula is
True
for some assignment of values.OUTPUT:
A boolean value to be determined as follows:
True
if there is an assignment of values that makes the formulaTrue
.False
if the formula cannot be madeTrue
by any assignment of values.
EXAMPLES:
This example illustrates how to check a formula for satisfiability:
sage: import sage.logic.propcalc as propcalc sage: f = propcalc.formula("a|b") sage: f.is_satisfiable() True sage: g = f & (~f) sage: g.is_satisfiable() False
- is_tautology()#
Determine if the formula is always
True
.OUTPUT:
A boolean value to be determined as follows:
True
if the formula is a tautology.False
if the formula is not a tautology.
EXAMPLES:
This example illustrates how to check if a formula is a tautology:
sage: import sage.logic.propcalc as propcalc sage: f = propcalc.formula("a|~a") sage: f.is_tautology() True sage: f = propcalc.formula("a&~a") sage: f.is_tautology() False sage: f = propcalc.formula("a&b") sage: f.is_tautology() False
- length()#
Return the length of
self
.OUTPUT:
The length of the Boolean formula. This is the number of operators plus the number of variables (counting multiplicity). Parentheses are ignored.
EXAMPLES:
sage: import sage.logic.propcalc as propcalc sage: s = propcalc.formula("a") sage: s.length() 1 sage: s = propcalc.formula("(a)") sage: s.length() 1 sage: s = propcalc.formula("~a") sage: s.length() 2 sage: s = propcalc.formula("a -> b") sage: s.length() 3 sage: s = propcalc.formula("alpha -> beta") sage: s.length() 3 sage: s = propcalc.formula("a -> a") sage: s.length() 3 sage: s = propcalc.formula("~(a -> b)") sage: s.length() 4 sage: s = propcalc.formula("((a&b)|(a&c))->~d") sage: s.length() 10
- polish_notation()#
Convert the calling boolean formula into polish notation.
OUTPUT:
A string representation of the formula in polish notation.
EXAMPLES:
This example illustrates converting a formula to polish notation:
sage: import sage.logic.propcalc as propcalc sage: f = propcalc.formula("~~a|(c->b)") sage: f.polish_notation() '|~~a->cb' sage: g = propcalc.formula("(a|~b)->c") sage: g.polish_notation() '->|a~bc'
AUTHORS:
Paul Scurek (2013-08-03)
- reduce_op(tree)#
Convert if-and-only-if, if-then, and xor operations to operations only involving and/or operations.
INPUT:
tree
– a list; this represents a branch of a parse tree
OUTPUT:
A new list with no ‘^’, ‘->’, or ‘<->’ as first element of list.
EXAMPLES:
This example illustrates the use of
reduce_op()
withapply_func()
:sage: import sage.logic.propcalc as propcalc, sage.logic.logicparser as logicparser sage: s = propcalc.formula("a->b^c") sage: tree = ['->', 'a', ['^', 'b', 'c']] sage: logicparser.apply_func(tree, s.reduce_op) ['|', ['~', 'a', None], ['&', ['|', 'b', 'c'], ['~', ['&', 'b', 'c'], None]]]
Note
This function only operates on a single branch of a parse tree. To apply the function to an entire parse tree, pass the function as an argument to
apply_func()
inlogicparser
.
- satformat()#
Return the satformat representation of a boolean formula.
OUTPUT:
The satformat of the formula as a string.
EXAMPLES:
This example illustrates how to find the satformat of a formula:
sage: import sage.logic.propcalc as propcalc sage: f = propcalc.formula("a&((b|c)^a->c)<->b") sage: f.convert_cnf() sage: f (a|~b|c)&(a|~b|~c)&(~a|b|~c) sage: f.satformat() 'p cnf 3 0\n1 -2 3 0 1 -2 -3 \n0 -1 2 -3'
Note
See www.cs.ubc.ca/~hoos/SATLIB/Benchmarks/SAT/satformat.ps for a description of satformat.
If the instance of boolean formula has not been converted to CNF form by a call to
convert_cnf()
orconvert_cnf_recur()
, thensatformat()
will callconvert_cnf()
. Please see the notes forconvert_cnf()
andconvert_cnf_recur()
for performance issues.
- to_infix(tree)#
Convert a parse tree from prefix to infix form.
INPUT:
tree
– a list; this represents a branch of a parse tree
OUTPUT:
A new list.
EXAMPLES:
This example shows how to convert a parse tree from prefix to infix form:
sage: import sage.logic.propcalc as propcalc, sage.logic.logicparser as logicparser sage: s = propcalc.formula("(a&b)|(a&c)") sage: tree = ['|', ['&', 'a', 'b'], ['&', 'a', 'c']] sage: logicparser.apply_func(tree, s.to_infix) [['a', '&', 'b'], '|', ['a', '&', 'c']]
Note
This function only operates on a single branch of a parse tree. To apply the function to an entire parse tree, pass the function as an argument to
apply_func()
inlogicparser
.
- tree()#
Return the parse tree of this boolean expression.
OUTPUT:
The parse tree as a nested list
EXAMPLES:
This example illustrates how to find the parse tree of a boolean formula:
sage: import sage.logic.propcalc as propcalc sage: s = propcalc.formula("man -> monkey & human") sage: s.tree() ['->', 'man', ['&', 'monkey', 'human']]
sage: f = propcalc.formula("a & ((~b | c) ^ a -> c) <-> ~b") sage: f.tree() ['<->', ['&', 'a', ['->', ['^', ['|', ['~', 'b', None], 'c'], 'a'], 'c']], ['~', 'b', None]]
Note
This function is used by other functions in the logic module that perform semantic operations on a boolean formula.
- truthtable(start=0, end=-1)#
Return a truth table for the calling formula.
INPUT:
start
– (default: 0) an integer; this is the first row of the truth table to be createdend
– (default: -1) an integer; this is the last row of the truth table to be created
OUTPUT:
The truth table as a 2-D array
EXAMPLES:
This example illustrates the creation of a truth table:
sage: import sage.logic.propcalc as propcalc sage: s = propcalc.formula("a&b|~(c|a)") sage: s.truthtable() a b c value False False False True False False True False False True False True False True True False True False False False True False True False True True False True True True True True
We can now create a truthtable of rows 1 to 4, inclusive:
sage: s.truthtable(1, 5) a b c value False False True False False True False True False True True False True False False False
Note
Each row of the table corresponds to a binary number, with each variable associated to a column of the number, and taking on a true value if that column has a value of 1. Please see the logictable module for details. The function returns a table that start inclusive and end exclusive so
truthtable(0, 2)
will include row 0, but not row 2.When sent with no start or end parameters, this is an exponential time function requiring \(O(2^n)\) time, where \(n\) is the number of variables in the expression.
- sage.logic.boolformula.is_consequence(self, *hypotheses)#
Determine if
self
(the desired conclusion) is a logical consequence of the hypotheses. The function callis_consequence(conclusion, *hypotheses)
is a synonym forconclusion.is_consequence(*hypotheses)
.INPUT:
*hypotheses
– instances ofBooleanFormula
OUTPUT:
A boolean value to be determined as follows:
True
- ifself
(the desired conclusion) is a logical consequence of the set of hypothesesFalse
- ifself
(the desired conclusion) is not a logical consequence of the set of hypotheses
EXAMPLES:
sage: from sage.logic.propcalc import formula sage: formula("a | b").is_consequence(formula("b")) True sage: formula("a & b").is_consequence(formula("b")) False sage: formula("b").is_consequence(formula("a"), formula("a -> b")) True sage: formula("b -> a").is_consequence(formula("a -> b")) False sage: formula("~b -> ~a").is_consequence(formula("a -> b")) True
sage: f, g, h = propcalc.get_formulas("a & ~b", "c -> b", "c | e") sage: propcalc.formula("a & e").is_consequence(f, g, h) True sage: i = propcalc.formula("a & ~e") sage: i.is_consequence(f, g, h) False sage: from sage.logic.boolformula import is_consequence sage: is_consequence(i, f, g, h) False sage: is_consequence(propcalc.formula("((p <-> q) & r) -> ~c"), f, g, h) True
Only a tautology is a logical consequence of an empty set of formulas:
sage: propcalc.formula("a | ~a").is_consequence() True sage: propcalc.formula("a | b").is_consequence() False
AUTHORS:
Paul Scurek (2013-08-12)


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Middle school math teacher turned software engineer


> Great! I always get nervous no matter how often we say that it can't be our fault ;-) Well this time it was close!  Filip > Christian > > -- > Christian Schulte, www.it.kth.se/~cschulte/ >


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value.m
Summary: Compute expected (discounted) cumulative cost for a given (set of) initial state distributions
function [J, dJdp] = value(p, m0, S0, dynmodel, policy, plant, cost, H)
Input arguments:
p policy parameters chosen by minimize policy policy structure .fcn function which implements the policy .p parameters passed to the policy m0 matrix (D by k) of initial state means S0 covariance matrix (D by D) for initial state dynmodel dynamics model structure plant plant structure cost cost function structure .fcn function handle to the cost .gamma discount factor H length of prediction horizon
Output arguments:
J expected cumulative (discounted) cost dJdp (optional) derivative of J wrt the policy parameters
Copyright (C) 2008-2013 by Marc Deisenroth, Andrew McHutchon, Joe Hall, and Carl Edward Rasmussen.
Last modification: 2013-03-21
Contents
High-Level Steps
- Compute distribution of next state
- Compute corresponding expected immediate cost (discounted)
- At end of prediction horizon: sum all immediate costs up
function [J, dJdp] = value(p, m0, S0, dynmodel, policy, plant, cost, H)
Code
policy.p = p; % overwrite policy.p with new parameters from minimize p = unwrap(policy.p); dp = 0*p; m = m0; S = S0; L = zeros(1,H); if nargout <= 1 % no derivatives required for t = 1:H % for all time steps in horizon [m, S] = plant.prop(m, S, plant, dynmodel, policy); % get next state L(t) = cost.gamma^t.*cost.fcn(cost, m, S); % expected discounted cost end else % otherwise, get derivatives dmOdp = zeros([size(m0,1), length(p)]); dSOdp = zeros([size(m0,1)*size(m0,1), length(p)]); for t = 1:H % for all time steps in horizon [m, S, dmdmO, dSdmO, dmdSO, dSdSO, dmdp, dSdp] = ... plant.prop(m, S, plant, dynmodel, policy); % get next state dmdp = dmdmO*dmOdp + dmdSO*dSOdp + dmdp; dSdp = dSdmO*dmOdp + dSdSO*dSOdp + dSdp; [L(t), dLdm, dLdS] = cost.fcn(cost, m, S); % predictive cost L(t) = cost.gamma^t*L(t); % discount dp = dp + cost.gamma^t*( dLdm(:)'*dmdp + dLdS(:)'*dSdp )'; dmOdp = dmdp; dSOdp = dSdp; % bookkeeping end end J = sum(L); dJdp = rewrap(policy.p, dp);


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Placeholder types¶
The actual relative path will be inserted in the part of the keyword such as
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.
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{RELPATH} |
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This is a relative path notation used in HTML. |
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The path will be relative to Root.
The root will be the work folder you have open in VSCode.
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|
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|
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A delimiter will be inserted at the beginning of the file name. |
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lately.js
Type in characters. The predictor finds causal relations among them and predicts the next character.


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WITNESS LIST Criminal Justice Committee April 6, 2005 - 1:30P SB 90 FOR: Gaylor, Tom Deputy Executive Director (Texas Municipal Police Association), Austin, TX Rodriguez, Ana Public Policy Specialist (Texas Council on Family Violence), Austin, TX Registering, but not testifying: For: Norvell, Chad Chief Deputy (Fort Bend County Precinct 4 Constable), Sugarland, TX Riddering, Hannah President (Texas National Organization for Women), Austin, TX Rolater, John Assistant District Attorney (Bill Hill, Dallas County Criminal District Attorney), Dallas, TX Waters, Jane Assistant District Attorney (Harris County District Attorney's Office), Houston, TX Weeks, David Walker County District Attorney (Self), Huntsville, TX Against: Getting, Roy (Texas Father Alliance), Irving, TX SB 91 FOR: Gaylor, Tom Deputy Executive Director (Texas Municipal Police Association), Austin, TX Rodriguez, Ana (Texas Council on Family Violence), Austin, TX Registering, but not testifying: For: Norvell, Chad Chief Deputy (Fort Bend County Precinct 4 Constable), Sugarland, TX Riddering, Hannah President (Texas National Organization for Women), Austin, TX Rolater, John Assistant District Attorney (Bill Hill, Dallas County Criminal District Attorney), Dallas, TX Waters, Jane Assistant District Attorney (Harris County District Attorney's Office), Houston, TX Weeks, David Walker County District Attorney (Self), Huntsville, TX SB 326 FOR: Renaud, Todd Assistant Vice President (Credit Union Legislation Coalition), San Antonio, TX Riddering, Hannah President (Texas National Organization for Women), Austin, TX Registering, but not testifying: For: Stegall, Melodie Executive Director (Credit Union Legislative Coalition), Austin, TX Wise, Andrew Microsoft (Gov't Affairs), Austin SB 327 Registering, but not testifying: For: Riddering, Hannah President (Texas National Organization for Women), Austin, TX Stegall, Melodie Executive Director (Credit Union Legislative Coalition), Austin, TX Wise, Andrew (Gov't Affairs) SB 337 FOR: Clements, Dianne Director (Justice For All), Houston, TX Davis, Grace Lead Trainer (Internet Probation and Parole Control), King of Prussia, PA Gray, Terra (True.com), DeSoto, TX Hubbarth, William 'Rusty' Vice President (Justice For All), Austin, TX Hubbarth, William 'Rusty' Vice President (Justice For All), Austin, TX AGAINST: Cardillo, John President/CEO (Sentinel Tech), Miami, FL Dye, Marshall (Match.com), Richardson, TX Registering, but not testifying: For: Clark, Mark (Houston Police Officer's Association), Houston, TX Riddering, Hannah President (Texas National Organization for Women), Austin, TX White, Glenn President (Dallas Police Association), Dallas, TX Against: Camacho, Chrissy Legislative Director (American Electronics Association), Austin, TX Hines, James Senior Manager (MCI), Austin, TX Hughes, Lisa Consultant (Yahoo), Austin, TX Paul, Julie Executive Director (Technet Texas), Austin, TX Sullivan, Ray (Ebay Inc), Austin, TX Wise, Andrew Microsoft (Gov't Affairs), Austin SB 548 FOR: Dershowitz, Hanna Liebman Consulting Attorney (Texas Appleseed), Austin, TX Tyler, Jared Deputy Director (Texas Innocence Project), Houston, TX AGAINST: Hubbarth, William 'Rusty' Vice President (Justice For All), Austin, TX Registering, but not testifying: For: Davis, A.W. Criminal District Attorney (Self), Newton, TX Hampton, Keith Legislative Director (Texas Criminal Defense Lawyer's Association), Austin, TX Keilen, Andrea Deputy Director (Texas Defender Service), Austin, TX Smith, Joe R. District Attorney (Criminal District Attorney, Tyler County), Woodville, TX Against: Aleshire, Mary Film Producer Series (Self), Austin, TX Clements, Dianne Director (Justice For All), Houston, TX SB 560 FOR: Gill, Raman Attorney (Texas Appleseed), Austin, TX Hampton, Keith Legislative Director (Texas Criminal Defense Lawyer's Association), Austin, TX Keilen, Andrea Deputy Director (Texas Defender Service), Austin, TX ON: Weeks, David Walker County District Attorney (Self), Huntsville, TX Registering, but not testifying: Against: Davis, A.W. Criminal District Attorney (Self), Newton, TX Smith, Joe R. District Attorney (Criminal District Attorney, Tyler County), Woodville, TX SB 756 Registering, but not testifying: For: Davis, A.W. Criminal District Attorney (Self), Newton, TX Rolater, John Assistant District Attorney (Bill Hill), Dallas, TX Smith, Joe R. District Attorney (Criminal District Attorney, Tyler County), Woodville, TX Weeks, David Walker County District Attorney (Self), Huntsville, TX On: Edmonds, Shannon Attorney (TDCAA), Austin, TX SB 893 FOR: Hampton, Keith Legislative Director (Texas Criminal Defense Lawyers Association), Austin, TX AGAINST: Weeks, David Walker County District Attorney (Self), Huntsville, TX ON: Edmonds, Shannon Attorney (TDCAA), Austin, TX Registering, but not testifying: Against: Cotton, Troy Assistant District Attorney (Harris County District Attorney's Office), Houston, TX Davis, A.W Newton County District Attorney (Self), Newton, TX K. English, Laurie District Attorney (112th District Attorney's Office), Ozona, TX Smith, Joe R District Attorney (Criminal District Attorney, Tyler County), Woodville, TX SB 938 FOR: Dixon, Eston Adayr (Self), Dallas, TX Iles, Leighton Director (Fort Bend County Community Supervision and Corrections), Rosenberg, TX Woodard, Gary (Norma Lucille Alexander), Garland, TX ON: Creuzot, John (Self), Dallas, TX Randall, Vicki Executive Director (The Ministry Advisory Council), Houston, TX Smith, Bradley Senior District Judge (Self), Richmond, TX White, Bonita Director (Texas Department of Criminal Justice), Austin, TX Registering, but not testifying: For: Del Llano, Ann (ACLU), Austin, TX Dixon, Mona (Self), Dallas, TX Yanez-Correa, Ana Legislative Liason for the Southwest (National LULAC), Austin, TX SB 1006 FOR: Carr, Brent Judge (Tarrant County Mental Health Court), Fort Worth, TX Registering, but not testifying: For: Del Llano, Ann (ACLU), Austin, TX Rolater, John Assistant District Attorney (Bill Hill), Dallas, TX SB 1263 AGAINST: Barnard M.D., Dr. Jeffery Cheif Medical Examiner and Director (Southwest Institute of Forensic Science), Dallas, TX ON: Vasquez, Michael Program Manager (Texas Conference of Urban Counties), Austin, TX Registering, but not testifying: For: Dershowitz, Hanna Liebman Consulting Attorney (Innocence project), New York, NY Jones, James Captain (Houston Police Department), Houston, TX On: Allison, William P President (Texas Center for Actual Innocence), Austin, TX Johnson, Glen (Crime Laboratory Services), Austin, TX Johnson, Pat Manager, Field Crime Labs (Department of Public Safety), Austin, TX Sullivan, Dr. Charlotte Special Assistant to the Chancellor (Texas State University System), Austin, TX Urbanovsky, Ron Director, Crime Lab (Texas Department of Public Safety), Austin, TX SB 1303 FOR: Gaylor, Tom Deputy Executive Director (Texas Municipal Police Association), Austin, TX Mican, Randy Chief of Police (Schulenburg Police Department), Schulenburg, TX Nehls, Troy E Constable (Fort Bend County Precinct 4 Constable), Sugarland, TX Registering, but not testifying: For: Landry, Mitch Deputy Executive Director (Texas Municipal Police Association), Austin, TX McGinnis, Larry Legislative Consultant (Department of Public Safety Officer's Association), Austin, TX Rolater, John Assistant District Attorney (Bill Hill), Dallas, TX


class: center, middle, title-slide count: false # Embeddings
.bold[Marc Lelarge] --- # Tip of the week: Dataloading -- count: false ## Dataset class `torch.utils.data.Dataset` is an abstract class representing a dataset. Your custom dataset should inherit `Dataset` and override the following methods: - `__len__` so that `len(dataset)` returns the size of the dataset. - `__getitem__` to support the indexing such that `dataset[i]` can be used to get ith sample -- count: false ## Iterating through the dataset with `Dataloader` By using a simple `for` loop to iterate over the data, we are missing out on: - Batching the data - Shuffling the data - Load the data in parallel using multiprocessing workers. `torch.utils.data.DataLoader` is an iterator which provides all these features. --- ## Examples (1) In the first lesson, we created two datasets, one for the training and one for the validation: ``` from torchvision import transforms,datasets normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) vgg_format = transforms.Compose([transforms.CenterCrop(224), transforms.ToTensor(), normalize]) dsets = {x: datasets.ImageFolder(os.path.join(data_dir, x), vgg_format) for x in ['train', 'valid']} ``` Hence `len(dsets['train'])` returns `23000`, i.e. the number of images in the training set, and more precisely, the number of files located at `data_dir/train/`. Recall that `data_dir/train/` (and similarly `data_dir/valid/`) is split in two folders: `cats/` and `dogs/`, you can check that each of these folders have `11500` images with: `ls | wc -l` You can recover the classes with `dsets['train'].classes` which returns `['cats', 'dogs']` These are features of the `torchvision.datasets.ImageFolder` Module. -- count: false More importantly, what returns `dsets['train'][0]`? -- count: false Answer: a tuple containing a tensor and a label. --- ## Examples (1) To obtain a dataloader for the training set: ``` train_loader = torch.utils.data.DataLoader(dsets['train'], batch_size=64, shuffle=True, num_workers=6) ``` -- count: false Then, you can use it as follows: ``` for input, label in train_loader: output = model(input) loss = loss_fn(output, label) optimizer.zero_grad() loss.backward() optimizer.step() ....``` --- ## Examples (2) In the first lesson, we first precomputed features and converted them as `numpy` arrays in order to store them. You first need to load the features and the labels: ``` feat_train = load_array(os.path.join(data_dir_colab,'vgg16','feat_train.bc')) labels_train = load_array(os.path.join(data_dir_colab,'vgg16','lbs_train.bc')) ``` and then, you can create a list for your dataset as follows: ``` train_dataset = [[torch.from_numpy(f).type(torch.float),torch.tensor(l).type(torch.double)] for (f,l) in zip(feat_train,labels_train)] ``` A list has a buil-in function `len()` and a `__getitem__()` method, hence this is a valid dataset for PyTorch. -- count: false To create a dataloader, it is as simple as: ``` train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=128, shuffle=True) ``` --- ## Examples (3) Today, we will make our own dataloader from scratch using a Python iterator: ``` def minibatch(batch_size, *tensors): if len(tensors) == 1: tensor = tensors[0] for i in range(0, len(tensor), batch_size): yield tensor[i:i + batch_size] else: for i in range(0, len(tensors[0]), batch_size): yield tuple(x[i:i + batch_size] for x in tensors) ``` -- count: false We will use it as follows: ``` for (minibatch_num, (batch_user, batch_item, batch_rating)) in enumerate(minibatch(batch_size, user_ids_tensor, item_ids_tensor, ratings_tensor)): ... ``` where the user and item ids are obtained from `numpy` integers and ratings from `numpy` floats as follows: ``` user_ids_tensor = torch.from_numpy(users) item_ids_tensor = torch.from_numpy(items) ratings_tensor = torch.from_numpy(ratings) ``` --- ## To know more about dataloading have a look at the related [PyTorch tutorial](https://pytorch.org/tutorials/beginner/data_loading_tutorial.html) -- count: false ## Now: how to deal with symbolic data like ids? --- # One-hot encoding Encoding colors: .center.width-40[![](images/one-hot.jpg)] Why not simply use: blue = 0, red = 1 and so on? What are the Pros/Cons of such a representation? -- count: false - Each axis has a meaning - Sparse, discrete representation with large dimension (size of the vocabulary). - Symbols are equidistant from each other with euclidean distance $\sqrt{2}$ for large vocabulary. --- # Embeddings Idea: project in $\mathbb{R}^d$ with $d$ much smaller than the size $n$ of the vocabulary! -- count: false More formaly, let $W\in \mathbb{R}^{n\times d}$, we define $$ \text{embedding}(x) = \text{onehot}(x) W $$ $W$ is typically randomly initialized, then tuned by backprop. $W$ are trainable parameters of the model. -- count: false - Continuous and dense representation. - Can represent a huge vocabulary in low dimension. - Axis have no meaning a priori but once trained, embedding metric can capture semantic distance. --- # Example: embeddings for movies in recommender systems .center.width-50[![](images/Pca_viz.png)] PCA of embeddings for movies learned in this [practical](https://github.com/mlelarge/dataflowr/blob/master/CEA_EDF_INRIA/03_collaborative_filtering_empty_colab.ipynb) .citation.tiny[source Fast.ai] --- ## Embeddings in PyTorch [Sparse layers](https://pytorch.org/docs/master/nn.html#embedding) in PyTorch: `torch.nn.Embedding(num_embeddings, embedding_dim)` Example: creating embeddings for users ``` embedding_dim = 3 embedding_user = nn.Embedding(total_user_id, embedding_dim) input = torch.LongTensor([[1,2,4,5],[4,3,2,0]]) embedding_user(input) ``` -- count: false returns ``` tensor([[[-1.7219, 0.4682, 0.5729], [ 0.0657, -0.7046, -0.4397], [ 1.1512, 1.3380, -1.0272], [ 1.4405, 1.2945, 1.0051]], [[ 1.1512, 1.3380, -1.0272], [-0.6698, -2.1828, -0.1695], [ 0.0657, -0.7046, -0.4397], [-0.7966, -1.6769, -0.7454]]]) ``` -- count: false So now is time to play with embeddings! --- class: end-slide, center count: false The end.


| AbstractParty |
Information about the individual and / or organization of the party.
|
| DefaultAddress |
Location of the responsible individual or organization.
|
| DefaultCitation |
Standardized resource reference.
|
| DefaultCitationDate |
Reference date and event used to describe it.
|
| DefaultContact |
Information required to enable contact with the responsible person and/or organization.
|
| DefaultIndividual |
Information about the party if the party is an individual.
|
| DefaultOnlineResource |
Information about on-line sources from which the dataset, specification, or
community profile name and extended metadata elements can be obtained.
|
| DefaultOrganisation |
Information about the party if the party is an organization.
|
| DefaultResponsibility |
Information about the party and their role.
|
| DefaultResponsibleParty |
Identification of, and means of communication with, person(s) and
organizations associated with the dataset.
|
| DefaultSeries |
Information about the series, or aggregate dataset, to which a dataset belongs.
|
| DefaultTelephone |
Telephone numbers for contacting the responsible individual or organization.
|


@(message: String) @Main("Home") {
After hitting the 'Students' tab in the Nav-Bar, it will take you to a page that displays a table of Students currently in the database.
Click the 'Add Student' button to add your own Student Profile.
You will be re-directed to a new page where you can fill out your details.
After hitting the 'Textbooks' tab in the Nav-Bar, it will take you to a page that displays a table of Textbooks currently in our database.
Click the 'Add Textbook' button to add Textbook that is not in our database.
You will be re-directed to a new page where you can fill out the textbook details.
The 'Marketplace' tab will offer three choices: Buying, Selling, and Matches.
In the 'Buying' and 'Selling' pages, it will display a table of Buy-Offers and Sell-offers on textbooks made by other students.
To add you own offer, click the 'Add Offer' button.
You will be re-directed to a new page where you can fill out your offer details.
The 'Matches' page will display the Buy-Offers and Sell-Offers of a selected Student.
To find matches, select a Student in the and click 'Find Matches'.
Here is an example of what the output table might look like.


Advanced exercises¶
The number of genes that undergoes through alternative splicing is substantially higher than invertebrates or other metazoans. Moreover, the most extensive set of regulated AS events are associated with neuronal tissues and are particularly relevant during the brain’s development. Therefore, you and your bioinformatician friend were excited to try out Whippet with some of the available data at ENCODE.. You decided that it would be interesting to process RNA-seq samples from midbrain to detect alternative splicing events between E10.5 and E14.5. To this end, your bioinformatician friend wrote the following script in bash:
#!/bin/bash
wget https://www.encodeproject.org/files/ENCFF099UUS/@@download/ENCFF099UUS.fastq.gz -O midbrain_E10.5_rep1_L01.fastq.gz
wget https://www.encodeproject.org/files/ENCFF059FUK/@@download/ENCFF059FUK.fastq.gz -O midbrain_E10.5_rep1_L02.fastq.gz
wget https://www.encodeproject.org/files/ENCFF052VGB/@@download/ENCFF052VGB.fastq.gz -O midbrain_E10.5_rep2_L01.fastq.gz
wget https://www.encodeproject.org/files/ENCFF819ZTA/@@download/ENCFF819ZTA.fastq.gz -O midbrain_E10.5_rep2_L02.fastq.gz
wget https://www.encodeproject.org/files/ENCFF156BSL/@@download/ENCFF156BSL.fastq.gz -O midbrain_E14.5_rep1_L01.fastq.gz
wget https://www.encodeproject.org/files/ENCFF421QJA/@@download/ENCFF421QJA.fastq.gz -O midbrain_E14.5_rep1_L02.fastq.gz
wget https://www.encodeproject.org/files/ENCFF499UQZ/@@download/ENCFF499UQZ.fastq.gz -O midbrain_E14.5_rep2_L01.fastq.gz
wget https://www.encodeproject.org/files/ENCFF093YSD/@@download/ENCFF093YSD.fastq.gz -O midbrain_E14.5_rep2_L02.fastq.gz
wget https://hgdownload.soe.ucsc.edu/goldenPath/mm10/bigZips/mm10.fa.gz -O mm10.fa.gz
wget https://hgdownload.soe.ucsc.edu/goldenPath/mm10/bigZips/genes/mm10.ensGene.gtf.gz -O mm10.ensGene.gtf.gz
julia /home/gp7/miniconda/envs/julia_0.6.1/share/julia/site/v0.6/Whippet/bin/whippet-index.jl --fasta mm10.fa.gz --gtf mm10.ensGene.gtf.gz --index mm10.index
julia /home/gp7/miniconda/envs/julia_0.6.1/share/julia/site/v0.6/Whippet/bin/whippet-quant.jl <( cat midbrain_E10.5_rep1*.fastq.gz) --force-gz -x mm10.index.jls -o midbrain_E10.5_rep1
julia /home/gp7/miniconda/envs/julia_0.6.1/share/julia/site/v0.6/Whippet/bin/whippet-quant.jl <( cat midbrain_E10.5_rep2*.fastq.gz) --force-gz -x mm10.index.jls -o midbrain_E10.5_rep2
julia /home/gp7/miniconda/envs/julia_0.6.1/share/julia/site/v0.6/Whippet/bin/whippet-quant.jl <( cat midbrain_E14.5_rep1*.fastq.gz) --force-gz -x mm10.index.jls -o midbrain_E14.5_rep1
julia /home/gp7/miniconda/envs/julia_0.6.1/share/julia/site/v0.6/Whippet/bin/whippet-quant.jl <( cat midbrain_E14.5_rep2*.fastq.gz) --force-gz -x mm10.index.jls -o midbrain_E14.5_rep2
However, as most of bioinformaticians, he was a bit lazy to finish the job and he asked you to do it instead! He sent you the .psi.gz
he obtained. You can find these files at Data/Advanced/
.
Note
As a general rule rep
indicates biological replicates and L
technical replicates, which are samples that were extracted from the same animal but sequenced on different runs. Thus, on this script technical replicates that correspond to the same biological sample are pooled during the quantification step (check --force-gz
parameter on Whippet’s documentation)
FINAL CHALLENGE
Take back control of this splicing project and finish it up by taking the following steps:
Run the last necessary command to detect the alternatively spliced events associated to the embryonic development of mouse midbrain (between E10.5 and E14.E)
Use your bash skills to calculate how many differentially spliced nodes you detected between these conditions. How many of each splicing node type did you detect?
Run the
Data/Advanced/download_bam.sh
script to get at least oneBAM
file representing each embryonic stage and use these to generate sashimi plots. Find some examples to show in your presentation.Generate a non-redundant list of genes with highly variable Core Exon nodes (CE) that has an absolute
DeltaPsi
value of at least 0.3 andProbability
greater than 0.9. Use this list to perform gene ontology analyses and protein-protein interaction networks analyses with STRING (if you reach this point please seek the attention of the instructor in charge of this project).


Q99. Recover Binary Search Tree
You are given the root of a binary search tree (BST), where exactly two nodes of the tree were swapped by mistake. Recover the tree without changing its structure.
Follow up: A solution using O(n) space is pretty straight forward. Could you devise a constant space solution?
Example 1:
Input: root = [1,3,null,null,2]
Output: [3,1,null,null,2]
Explanation: 3 cannot be a left child of 1 because 3 > 1. Swapping 1 and 3 makes the BST valid.
Example 2:
Input: root = [3,1,4,null,null,2]
Output: [2,1,4,null,null,3]
Explanation: 2 cannot be in the right subtree of 3 because 2 < 3. Swapping 2 and 3 makes the BST valid.
Constraints:
The number of nodes in the tree is in the range [2, 1000].
-231 <= Node.val <= 231 - 1
Here I focus on the solution with $O(N)$ space complexity, which definitely falls into the category of “need to know”.
The whole point is about performing the in-order traverse of the BST.
If the BST is valid, the in-order traverse will result in an ascending order sequence.
Whereas, if the BST is not valid, the resulting sequence won’t be ascending and as stated in the question, it should be fixed by a simple swap.
For instance, if we have 0, 1, 4, 3, 2, 5, 6
, we can easily see that 4
and 2
are swapped.
This means that suppose we have the sequence, we could identify the pair to swap by extracting all the pairs with wrong order and extract the first and the last number of these pairs.
So, all the point of this question is to have in-order traverse in mind clearly. To have it briefly reviewed, the in-order traverse procedure is:
- Maintain a stack of node to visit.
- At a current node, push itself to the to-visit stack and go to its left child until there exists no more left child.
- Go to the top of the stack, visit that node and set its right child as current node.
The Python submission is.
# Definition for a binary tree node.
# class TreeNode:
# def __init__(self, val=0, left=None, right=None):
# self.val = val
# self.left = left
# self.right = right
class Solution:
def recoverTree(self, root: TreeNode) -> None:
"""
Do not return anything, modify root in-place instead.
"""
# do in order traverse
left_most = TreeNode(val=-float('inf'))
to_visit = []
prev = left_most
mistake = []
curr = root
while curr is not None or len(to_visit) > 0:
while curr is not None:
to_visit.append(curr)
curr = curr.left
node = to_visit.pop()
# print(node.val)
if node.val < prev.val:
mistake.append(prev)
mistake.append(node)
prev = node
curr = node.right
tmp = mistake[0].val
mistake[0].val = mistake[-1].val
mistake[-1].val = tmp
return root
NOTE: the $O(1)$ space complexity solution is about improving the in-order traverse. This question is related to Q94. Binary Tree Inorder Traversal which is discussed in this post.


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Sep 28 OKC - Jr. VB.Net Developer! - (Oklahoma City, Oklahoma)
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Duration
15 minutes
Goals
The primary goal of this lab will be to create a settings page for our weather app by designing custom static cells with the Xamarin Storyboard Designer.
Required assets
The provided Exercise 4 contains a begin project and a completed solution that you can use to check your work. Please make sure you have this folder before you begin.
Challenge
Here are the high-level steps you will need to perform. Use these to complete the exercise and refer to the more detailed step-by-step instructions to fill in any necessary details. In this exercise you will recreate the first five cells of the Settings App for the iPhone to get a feel for how you would create static cells in Storyboard Designer.
Our basic steps will be:
- Open the Begin solution provided in the Exercise 4 folder.
- Working in the MainStoryboard.storyboard file, select the table view.
- Change the table view to display one section of static cells with a dark gray cell separator.
- Change the section header to read "Settings" and create 5 rows of cells.
- Add the missing cells to the UItableView from the Widget tab of the properties pane.
- For each cell add, style and position the following UIImageViews, UILabels and UIControllers from the toolbox to each of the cells:
- Replace the image and title placeholders with final content
Steps
Below are the step-by-step instructions to implement the exercise.
Inspect the starter solution
- Open the Begin solution provided in the Exercise 4 folder.
- Open the MainStoryboard.storyboard file and select the table view (not the controller).
UITableViews
come with dynamic prototype cells by default, to change this you need to select the table view and change the content type in the properties pane to display one section of static cells with a dark gray cell separator.
- Next, we will change some of the section properties. To do that we must first select the section on the storyboard and then change the properties of the table view section in the properties pane and change the header to "Settings" and number of rows to "5"
- For each cell we are going to add UILabels, UIImageViews and controls.
- The first three cells can be styled with the default cell styles.
- The bottom two cells are custom and we need to add the child UIViews to these cells:
- First, select the cell.
- Drag and position the UI element from the toolbox onto the cell.
- To layout the elements more precisely you can select the element and specify the x and y coordinates in the layout tab of the properties pane.
- An accessory is optional in a custom cell.
- We can add them by selecting the cell and then, in the widget tab of the properties pane, select the accessory you would like to use.
- Now that the UI elements are placed we will customize them by providing new images and custom titles.
- To change the image select the UIImageView and select the icon you want to use from the image dropdown in the properties pane
- To change the title and color of the UI label, select the label and change change them in the properties pane.
- Once all of your images and label titles are changed your UITableView should look something like this:
- In order to interact with our cells in the code behind we need to set unique identifiers for each cell. For the custom cells we also name the child controls.
Summary
In the exercise you designed a table view with custom static cells in the Xamarin iOS Designer.


Search Food
Remove the Restaurant
Are you sure you want to delete {{restaurant.name}}?
Back to Catalog


{{ partial "header.html" . }} {{ .Title }} The page you were looking for doesn't exist {{ partial "footer.html" . }}
The page you were looking for doesn't exist


f2javaws Command Line
f2javaws - Java Web Start launcher command
f2javaws [run-options] <jnlp>
f2javaws [control-options]
f2[run-options]
Command-line run-options. run-options may be in any order. For a discussion of the various run-options, see RUN-OPTIONS below.
f2<jnlp>
This can be either the path of, or the Uniform Resource Locater (URL) of the JNLP (Java Network Launching Protocol) file.
f2[control-options]
Command-line control-options. control-options may be in any order. For a discussion of the various control-options, see CONTROL-OPTIONS below.
The f2javaws command launches Java Web Start, which is the reference implementation of the Java Network Launching Protocol (JNLP). Java Web Start launches Java applications/applets hosted on a network.
If a JNLP file is specified, f2javaws will launch the Java application/applet specified in the JNLP file.
The f2javaws launcher has a set of options that are supported in the current release. However, the options may be removed in a future release.
f2-offline
Run Java Web Start in offline mode.
f2-Xnosplash
Do not display the initial splash screen.
f2-open <arguments>
If specified, replaces the arguments in the jnlp file with f2-open <arguments>.
f2-print <arguments>
If specified, replaces the arguments in the jnlp file with f2-print <arguments>.
f2-online
Use online mode (default behavior).
f2-wait
If specified, the f2javaws process will not exit until the application exits. This option does not function as described on Windows platforms.
f2-verbose
Display additional output.
f2-J<option>
Supply options to the VM.
f2-system
Run the application from the system cache only.
f2-viewer
Show the Cache Viewer in the Java Control Panel.
f2-clearcache
Remove all non-installed applications from the cache.
f2-userConfig <property name>
Clear the specified deployment property.
f2-userConfig <property name> <property value>
Set the specified deployment property to the specified value.
f2-uninstall
Remove all applications from the cache.
f2-uninstall <jnlp>
Remove the application from the cache.
f2-import [import-options] <jnlp>
Import the application to the cache.
f2-silent
Import silently (with no user interface).
f2-system
Import application to the system cache.
f2-codebase <url>
Retrieve resources from the given codebase.
f2-shortcut
Install shortcuts as if user allowed prompt. This option has no effect unless f2-silent option is also used.
f2-association
Install associations as if user allowed prompt. This option has no effect unless f2-silent option is also used.
For information about the user and system cache and deployment.properties files, see
f2System- and User-Level Properties @
http://docs.oracle.com/javase/7/docs/technotes/guides/deployment/deployment-guide/properties.html.
For more information about Java Web Start, see
f2Java Web Start @
http://docs.oracle.com/javase/7/docs/technotes/guides/javaws/index.html.


Striker Diedhiou back in contention for Bristol City
Diedhiou has not played for City for a month.
Striker Famara Diedhiou returns to the Bristol City squad ahead of their Sky Bet Championship clash with Nottingham Forest at Ashton Gate.
Diedhiou is available after serving a three-match ban following his straight red card in the 2-1 victory at home to Charlton on October 23, in which he scored the equalising goal.
Defensive midfielder Korey Smith is close to a comeback after seven months out recovering from a broken bone in his foot and ligament damage, while full-back Jack Hunt is poised to return from a hamstring problem.
Defender Jay DaSilva and striker Benik Afobe remain sidelined as they fight their way back to full fitness from fracturing a tibia and anterior cruciate ligament injuries respectively.
Forest head coach Sabri Lamouchi expects to have experienced defender Michael Dawson available for the trip to Ashton Gate.
Dawson has been sidelined with a calf injury since the end of September but is set to return to the squad.
Carl Jenkinson is also close to a comeback following an ankle injury which has kept him out since the end of August.
Yohan Benalouane and Alfa Semedo have returned to training following hip and back problems respectively but Saturday’s game has come too soon for the pair.


[This was posted to soc.culture.iranian and is reposted here by permission of the author. -Moderator.]
NOT WITHOUT MY DAUGHTER A film review by Kerdar Copyright 1991 Kerdar
I've seen the movie, and I've read the "Reader's Digest" version of the book which truncates the book from 400 to about 250 pages.
Regarding the book: Reading through the passages of the book, one can see the author's "resentment towards Iranians" (as Reza Taheri says). But this is understandable. You keep somebody by force in a strange country, subject her to beating, imprison her in the house, and still want her to be unbiased in evaluating Iranians?
You can see this kind of resentment in many people with some degree or another. In Pakistan, for instance, the Iranian refugees dislike Pakistanis very much. The reason is because they are having a hard time away from home over there. This resentment of Betty Mahmoody goes as far as describing Iranian restrooms as "holes in the ground."
The movie, however, is more "even-handed" (though not quite). The movie starts with a beautiful scenery of a green forest by a small lake. The birds are singing and the atmosphere is peaceful and relaxed. By the lake there is a summer house.... If you have not read the book but know the movie is about Iran, you are led to believe the scene is in Iran. Then you see the title "Alpha, Michigan, 1984...." Anyway, I shouldn't spoil the movie for those who want to see it.
I found that part of the movie which was supposed to show Iran quite accurate with exception of some technical points. Right after getting out of "Mehr Abad" airport, Moody is surrounded by his Hezbollahi family, and at their arrival at the house the family sacrifices a sheep for them, a common practice in Iran. Later in some other scene a car belonging to revolutionary guards stops and four people, two men and two women jump out of the car with machine guns swearing at Betty for not covering all of her hair. This was perhaps the most realistic scene in the movie.
Some parts of the movie is exaggerated. For example, in one scene they showed an old crumbling building which was supposed to be Hosseyie Ershad. I have seen the mosque with its marble floor and beautiful Persian carpets. That old building did not at all resemble Hosseynie Ershad. The other exaggeration was the sheep on the streets of Tehran. The viewer could see them up to the end of the movie. A big portion of the movie is concentrated on Betty's escape; therefore, it shows some Iranian heroes who help her escape. One other thing about the film: the guy who was playing the husband was not Iranian, and it was sort of funny for Iranian audience to see him speak Persian.
Dave Elliott, our fellow netter, writes about the movie: "The culture is presented as simply being very different than American culture, not better or worse." What is Iranian culture? Do we have a homogeneous entity as Iranian culture? I don't think so. First we have diverse ethnic groups such as Turks, Kurds, Turkmans, Baluchies, Arabs, Lors, etc. Each group has its own culture which might be similar to others in some aspects, yet it might be different from the rest in other aspects.
Second, in any ethnic group or even cities we have (what I may call) sub_cultures. For example, in Tehran the culture of southern part of the city is far different from that of the northern residents. Yet another category might be the difference between religious and non-religious people in the same city. A marriage between a religious and a non-religious party especially after the revolution could be disastrous.
 Then we see on the net, "I have not seen the movie and most probably will not either" and, "I am very happy that this movie, NOT WITHOUT MY DAUGHTER, might function as a tool to increase our awareness and sensitivity toward the dirty face of subtle and yet real vilification of all Iranians along a same line as Arabs, Hispanics, and other ethnic groups. We must be aware of these movies, T.V. programs."
I'm afraid the solution is not "not seeing the movie," but this movie might function as a tool to increase our awareness of the dirty parts to *our culture(s)* (yes, they exist). Unfortunately there are thousands of Iranian women who are beaten by their husbands, and have, unlike Mrs. Mahmoody, nowhere to go. Let us have some self-criticism of our culture(s) and ourselves. Let us not get so defensive and let us admit the dark parts of our culture(s) and try to overcome and evolve them for the benefit of the next Iranian generations.
[BTW: The account "fekr nevisandeh" translates to "the thought of the writer" and is an anonymous posting facility. -Moderator]
.
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
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Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
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Related links: index of all rec.arts.movies.reviews reviews


Cypherpunk Labs Hidden Service Website.
This is a simple layout because the site on the clearnet does not do anything but provide discriptions. This is essentially going to list a product with an address in the currency. You may then choose the amount to send based on the current price that we use on clear net site. We could add in here but we will get to it eventually. This is a demonstration the service can run without any site. You can find these details in the github as well. https://github.com/CypherpunkLabs/HiddenServicesWebsite
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Beer Money - 85M75bn8rddCiWWQfwb3NGATv82cQ94G3YKUjHPhAYFLXdZvR7idN9z1RAANVW6kV3WCZqGgCpHeiLXALb7CUzA91GtLXwp
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Once you have made an order check out the Twitter feed for information: https://twitter.com/CypherPunkLabs
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Turkey says American IS suspect stuck at border with Greece will be transferred to the US
by EndPlayANKARA, Turkey - ANKARA, Turkey (AP) - Turkey says American IS suspect stuck at border with Greece will be transferred to the US.
ANKARA, Turkey - ANKARA, Turkey (AP) - Turkey says American IS suspect stuck at border with Greece will be transferred to the US.


WolfTrack
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Luakan National High School
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I understand that all information I provide in this form may be used by the Department of Education and I consent to such with the assurance that my personal details will be kept confidetial.
Signature over Printed Name of the Student
Date


#{forgetful.email}
Password Reminder for Ernescliff Linux/Printer account
Dear #{forgetful.name}:
Somebody has requested that the password for "#{forgetful.username}" be sent to the owner's email address.
Your account's password is '#{forgetful.password}'.
If you did not request a password reminder, you can just ignore this request,
although it might mean that somebody is trying to access your account illicitly.
You can change your password by visiting this site (from inside):
http://srv:8080/usermgmt/home.seam.
If you need further assistance, please contact the
administrator, via admin@ernescliff.ca.


Don’t expect to learn too much at the moment—we’re focused on building the platform and gathering some initial data about what people already know. Over time there will be more learning experiments designed to actually teach new things. We’re laying a foundation first.


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Sorry, we couldn’t find what you were looking for.
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Rip Van winkle
Many years ago, at the foothills of the Kaatskill (Kat-skill) mountains, was a little village. In the village lived a simple, good-natured fellow named Rip Van Winkle. He was a kind neighbour, ready to help anyone.
Many years ago, at the foothills of the Kaatskill (Kat-skill) mountains, was a little village. In the village lived a simple, good-natured fellow named Rip Van Winkle. He was a kind neighbour, ready to help anyone.
As Rip and his companion reached them, they stopped playing and stared at Rip with a fixed gaze. Rip was really frightened. His companion emptied the contents of the barrel into glasses and made Rip drink it.


This week during DBC Phase 0, I learned and explored the command line and version control. Version control is very beneficial to developers for many reasons. Version control software allows you to perform baisc tasks, such as creating, saving, and editing a document, but doesn't stop there. VCS allows for several developers to work on the same project and even the same file at the same time without overwriting anyone else's work! This prevents errors and possible destruction of files, which is essential to avoid. In addition, version control software is able to track changes from all developers, so everyone can see who edited the file, where it was edited, and what was edited.
Git is very helpful when it comes to tracking changes. Git uses the command commit, which bundles the changes you have made and allows you to add a message to see a quick explanation on what you have done/changed. Git also saves revision history, so you are able to easily see a log of the changes that you have made within the particular project. Changes that are tracked can range from typos, bug fixes, or even adding a whole new page to your project. Finally, tracking changes helps you understand the evolution of your project.
Git is important for developers when working individually, but is not exactly beneficial for a group of developers. That is where GitHub comes in. The difference between Git and GitHub is simply the difference of local and remote storage. GitHub acts as a remote branch for developers to store his or her work, so that it can be accessed remotely, anywhere and by anyone. GitHub creates a copy of your original file and is an open source environment for other developers to edit, revise, and add to your work.
Learn about Git!
Learn about CSS Concepts!
Learn about the difference between Arrays and Hashes!
Learn about Enumerable Methods!
Learn about Ruby Classes!
Learn about JavaScript!
Learn about Ruby and Ruby on Rails!
Read about what it was like for me to create my own site.


obvious.data
Interfaces
Data
DataSet
Edge
Forest
Graph
Network
Node
Schema
Table
Tree
Tuple
Classes
DataFactory
Enums
Graph.EdgeType


@(organizations: Seq[models.organization.Organization])(implicit user: models.user.User)
@mainContent("List of Organizations") {
Welcome to the list of Organizations available on the CalcTutor
Here is the list of organizations
Click on a organization for more info (or to join a course in that organization if you have the code)
@if(organizations.isEmpty) {
There are currently no organizations
} else {
@for(organization <- organizations) {- @tag.organizationLink(organization)
}
}
}


Provides example classes and interfaces for demonstrating Netty,
Jersey
and CDI all working together.
@author Laird Nelson
@see microBean™ Jersey Netty Integration
@see microBean™ Jersey Netty CDI Integration
@see microBean™ Jakarta RESTful Web Services CDI
Integration


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SystemC
2.3.3
Accellera SystemC proof-of-concept library
|
#include <sysc/utils/sc_vector.h>
Public Types | |
| typedef std::random_access_iterator_tag | iterator_category |
| typedef std::ptrdiff_t | difference_type |
| typedef access_policy::type | value_type |
| typedef value_type & | reference |
| typedef value_type * | pointer |
Public Member Functions | |
| sc_vector_iter () | |
| template<typename It > | |
| sc_vector_iter (const It &it, SC_ENABLE_IF_((sc_meta::is_more_const< element_type, typename It::policy::element_type >))) | |
| this_type & | operator++ () |
| this_type & | operator-- () |
| this_type | operator++ (int) |
| this_type | operator-- (int) |
| this_type | operator+ (difference_type n) const |
| this_type | operator- (difference_type n) const |
| this_type & | operator+= (difference_type n) |
| this_type & | operator-= (difference_type n) |
| bool | operator== (const const_direct_iterator &that) const |
| bool | operator!= (const const_direct_iterator &that) const |
| bool | operator<= (const const_direct_iterator &that) const |
| bool | operator>= (const const_direct_iterator &that) const |
| bool | operator< (const const_direct_iterator &that) const |
| bool | operator> (const const_direct_iterator &that) const |
| reference | operator* () const |
| pointer | operator-> () const |
| reference | operator[] (difference_type n) const |
| difference_type | operator- (const_direct_iterator const &that) const |
Friends | |
| class | sc_vector< plain_type > |
| template<typename , typename > | |
| class | sc_vector_assembly |
| template<typename , typename > | |
| class | sc_vector_iter |
Definition at line 114 of file sc_vector.h.
| typedef std::ptrdiff_t sc_core::sc_vector_iter< ElementType, AccessPolicy >::difference_type |
Definition at line 335 of file sc_vector.h.
| typedef std::random_access_iterator_tag sc_core::sc_vector_iter< ElementType, AccessPolicy >::iterator_category |
Definition at line 334 of file sc_vector.h.
| typedef value_type* sc_core::sc_vector_iter< ElementType, AccessPolicy >::pointer |
Definition at line 338 of file sc_vector.h.
| typedef value_type& sc_core::sc_vector_iter< ElementType, AccessPolicy >::reference |
Definition at line 337 of file sc_vector.h.
| typedef access_policy::type sc_core::sc_vector_iter< ElementType, AccessPolicy >::value_type |
Definition at line 336 of file sc_vector.h.
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inline |
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inline |
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friend |
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friend |
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friend |
Definition at line 304 of file sc_vector.h.


|
The Multi-Schema Validator(MSV) is a JavaTM technology tool to validate XML documents against a variety of XML schemata. It supports RELAX Namespace, RELAX Core, RELAX NG, TREX, XML DTDs, and a subset of W3C XML Schema Part 1.
Most of the time, you just need plain simple validation and nothing else. If so, you can use JARV, an implementation-independent, simple and easy interface set.
More information about JARV is available here.
If you have existing code that works with JAXP and you don't want to change it, then you should have a look at the JAXP-masquerading feature of MSV.
If you need even more control over the validation process (for example, you are developing a schema-related tool), then you may want to understand the native API of MSV. For more information, read this document.
The current Java API of MSV Core Validator is available here.


Operating Systems Scheduling Demonstration
First Come First Serve
Shortest Job First
Round Robin
Add Another
Process Name
Burst Time
Arrival Time
P1
Simulate
Quantum Time