The project, created by a group of women from the local Polish chapter of Women in Machine Learning & Data Science (WiMLDS), was designed to be a training project & collaboration on a real-life problem which machine learning can help to solve.
The web application has been developed by Magdalena Kortas (backend - Deep Learning, Python and Flask) Aleksandra Zachariasz (frontend - Flask, CSS, JS, HTML5 and graphic design).
Are you curious about how the AI solution has been built? Check our Medium article.
Data Scientist, WiMLDS Trójmiasto (Poland) co-founder, AI for Good & Data storytelling enthusiast
LinkedIn ProfileGraphic Designer, Frontend Developer, coder, Illustrator, beautiful views catcher
Behance PortfolioOur mission is to support and promote women and gender minorities in Data Science
LinkedIn Profile


Home > Overview > User Interface > Menus > Options >
The Options Dialog can be used to configure how auto-update works in Microsoft XML Notepad. The Options Dialog points to an Updates.xml file that contains the following kind of information:
<
application
><?xml
namespace="" ns="urn:schemas-microsoft-com:office:office" prefix="o" ?>
<
title>
Microsoft XML Notepad
2007</title>
<
location>
http://msdn.microsoft.com/xml/XmlNotepad/Updates.xml</location>
<
download>
http://www.microsoft.com/downloads/details.aspx?familyid=72d6aa49-787d-4118-ba5f-4f30fe913628</download>
<
frequency>
20.00:00:00</frequency>
</application>
This section contains the following information:
- title - the title of the application being updated.
- location - this field allows the remote administsrator to move where Microsoft XML Notepad is looking for updates. When Microsoft XML Notepad discovers a new value here it automatically updates the setting displayed in the options dialog.
- download - the location of the page where the user can download the new installer.
- frequency - how often to ping the updates.xml page for updates. The default is every 20 days. The minimum value supported here is every 5 seconds - but this is not recommended.
Following this is a list of <versionnumber= "2.3"> tags describing the features and bugs fixed in each version of the application. Microsoft XML Notepad will compare this with the version of the current assembly that is running to see if a newer version is available.
If it finds a newer version, Microsoft XML Notepad displays a button on the right hand side of the main menu bar telling the user that a new version is available. When the user clicks on that button it opens the web browser at the download page so the user can then install the new version.
An example "Updates.xml" document is included with the Microsoft XML Notepad installation containing the version information matching the version of Microsoft XML Notepad that is currently installed.


(PECL ps >= 1.3.0)
ps_shading — Creates a shading for later use
$psdoc
, string $type
, float $x0
, float $y0
, float $x1
, float $y1
, float $c1
, float $c2
, float $c3
, float $c4
, string $optlist
) : intCreates a shading, which can be used by ps_shfill() or ps_shading_pattern().
The color of the shading can be in any color space except for pattern.
psdoc
Resource identifier of the postscript file as returned by ps_new().
type
The type of shading can be either radial or
axial. Each shading starts with the current fill
color and ends with the given color values passed in the parameters
c1
to c4
(see ps_setcolor() for their meaning).
x0, x1, y0, y1
The coordinates x0
, y0
,
x1
, y1
are the start and
end point of the shading. If the type of shading is
radial the two points are the middle points of
a starting and ending circle.
c1, c2, c3, c4
See ps_setcolor() for their meaning.
optlist
If the shading is of type radial the
optlist
must also contain the parameters
r0 and r1 with the radius of the
start and end circle.
Returns the identifier of the pattern 或者在失败时返回 FALSE
.


rgbfix
—
Game Boy checksum fixer
rgbfix |
[-CcjsVv ] [-f
fix_spec] [-i
game_id] [-k
licensee_str] [-l
licensee_id] [-m
mbc_type] [-n
rom_version] [-p
pad_value] [-r
ram_size] [-t
title_str] file |
The rgbfix
program changes headers of Game Boy ROM
images. It also performs other filetype operations, such as truncation. The
arguments are as follows:
-C
- Set the Game Boy Color–only flag: 0x143 =
0xC0. If both this and the
-c
flag are set, this
takes precedence.
-c
- Set the Game Boy Color–compatible flag:
0x143 = 0x80. If both this and the
-C
flag are set, -C
takes
precedence.
-f
fix_spec
- Fix certain header values that the Game Boy checks for correctness.
Alternatively, intentionally trash these values by writing their binary
inverse instead. fix_spec is a string containing any
combination of the following characters:
l
- Fix the Nintendo logo
(0x104–0x133).
L
- Trash the Nintendo logo.
h
- Fix the header checksum (0x14D).
H
- Trash the header checksum.
g
- Fix the global checksum
(0x14E–0x14F).
G
- Trash the global checksum.
-i
game_id
- Set the game ID string
(0x13F–0x142) to a
given string of exactly 4 characters. If both this and the title are set,
the game ID will overwrite the overlapping portion of the title.
-j
- Set the non-Japanese region flag: 0x14A = 1.
-k
licensee_str
- Set the new licensee string
(0x144–0x145) to a
given string, truncated to at most two characters.
-l
licensee_id
- Set the old licensee code, 0x14B, to a given value
from 0 to 0xFF. This value is deprecated and should be set to 0x33 in all
new software.
-m
mbc_type
- Set the MBC type, 0x147, to a given value from 0
to 0xFF.
-n
rom_version
- Set the ROM version, 0x14C, to a given value from
0 to 0xFF.
-p
pad_value
- Pad the image to a valid size with a given pad value from 0 to 0xFF.
rgbfix
will automatically pick a size from 32KiB,
64KiB, 128KiB, ..., 8192KiB and give a warning thereafter. The cartridge
size byte (0x148) will be changed to reflect this
new size.
-r
ram_size
- Set the RAM size, 0x149, to a given value from 0
to 0xFF.
-s
- Set the SGB flag: 0x146 = 3.
-t
title
- Set the title string
(0x134–0x143) to a
given string, truncated to at most 16 characters. It is recommended to use
15 characters instead, to avoid clashing with the CGB flag
(
-c
or -C
). If both this
and the game ID are set, the game ID will overwrite the overlapping
portion of the title.
-V
- Print the version of the program and exit.
-v
- Equivalent to
-f
lhg
.
Most values in the ROM header are only cosmetic. The bare minimum requirements
for a workable image are checksums, the Nintendo logo, and (if needed) the
CGB/SGB flags. It is a good idea to pad the image to a valid size as well
(“valid” meaning a multiple of 32KiB).
The following will make a plain, no-color Game Boy game without
checking for a valid size:
$ rgbfix -v foo.gb
The following will make a SGB-enabled, color-enabled game with a
title of “foobar”, and pad it to a multiple of 32KiB. (The
Game Boy itself does not use the title, but some emulators or ROM managers
might.)
$ rgbfix -vcs -l 0x33 -p 0 -t foobar baz.gb
The following will duplicate the header (sans global checksum) of
the game “Survival Kids”:
$ rgbfix -cjsv -k A4 -l 0x33 -m 0x1B -p 0xFF -r 3 -t
SURVIVALKIDAVKE SurvivalKids.gbc
rgbfix
was originally released by Carsten
Sørensen as a standalone program called gbfix, and was later packaged
in RGBDS by Justin Lloyd. It is now maintained by a number of contributors at
https://github.com/rednex/rgbds.


{% extends "layout.html" %} {% import "scoring/_scoring_helpers.html" as macros %} {% block title %}FLL Playoffs{% endblock %} {% block pageContent %}


WITNESS LIST Public Health Committee June 15, 2006-10:00A Interim Charge 1 Scope of Practice On: Bell, M.D., Dr. Charles E. (Texas Health and Human Services Commission) Chernov, Allan (Blue Cross Blue Shield of Texas) Cigarroa, M.D., Francisco G (U.T. Health Science Center @ San Antonio) Fuelberg, Ann (Employees Retirement System) Hromadka, Donnie (Humana, Inc.) Longley, Joey (Sunset Commission - Director) Minors D.C., Steve (Board of Chiropractic Examiners Board Member - Chair Rules Committee) Murdock, Steve (Self) Parker, Glenn (Board of Chiropractic Examiners Executive Director) Patrick, Donald (Texas Medical Board) Priest, Paul (Legislative Budget Board) Raimer, Ben (Statewide Health Coordinating Council) Reichle OD, Randall N. (Texas Optometry Board) Rohack, MD, J. James (Scott and White Center for Healthcare Policy) Zukowski, Dr. Jim (Texas State Board of Dental Examiners) Interim Charge 5 Laser Hair Removal On: Brinck, Tom (Texas Dept. of State Health Services Drugs and Medical Device Group) Finder, M.D., Kimberly (Self) McCoy, MD, Dan (Self and Texas Dermatological Society Texas Medical Associaition) Ratliff, Richard (Texas Dept. of State Health Services) Shackelford, Michele (Texas Medical Board)


Genetics Lab
Generations Size:
Generation Count:
DNA Bit Count:
Potential Step Count:
Desired Scale Steps:
Generate DNA


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Fast reservation tickets for all cinemas or theaters


---
layout: year-nav
title: F.A.Q.
---
Frequently asked questions
What is the difference between an event and a battle?
A single event can have multiple battles.
For example, Torino Dancehall Movement is one event
with three battles:
1 vs 1,
2 vs 2, and
"Who's that Gal?".
Which battles are included?
A battle has to fulfill all the following requirements to be included:
- Contains 1 (one) or more rounds.
- Contains 0 (zero) or more clash rounds.
- Contains only freestyle rounds. Choreographic rounds are not allowed.
- At least one judge is required.
- The audience/public cannot be one of the judges.


| Package | Description |
|---|---|
| org.drip.function.definition |
Function Execution Ancillary Support Objects
|
| org.drip.function.rdtor1 |
Built-in R^d To R^1 Functions
|
| org.drip.function.rdtor1descent |
R^d To R^1 Gradient Descent Techniques
|
| org.drip.function.rdtor1solver |
Built-in R^d To R^1 Solvers
|
| org.drip.learning.kernel |
Statistical Learning Banach Mercer Kernels
|
| org.drip.learning.regularization |
Statistical Learning Empircal Loss Regularizer
|
| org.drip.learning.rxtor1 |
Statistical Learning Empirical Loss Penalizer
|
| org.drip.learning.svm |
Kernal SVM Decision Function Operator
|
| org.drip.measure.continuous |
R^1 R^d Continuous Random Measure
|
| org.drip.optimization.constrained |
KKT Fritz-John Constrained Optimizer
|
| org.drip.portfolioconstruction.allocator |
MVO Based Portfolio Allocation Construction
|
| org.drip.portfolioconstruction.constraint |
Portfolio Construction Constraint Term Suite
|
| org.drip.portfolioconstruction.objective |
Portfolio Construction Objective Term Suite
|
| org.drip.portfolioconstruction.optimizer |
Core Portfolio Construction Optimizer Suite
|
| org.drip.sequence.custom |
Glivenko Cantelli Supremum Deviation Bounds
|
| org.drip.sequence.functional |
Efron Stein Functional Supremum Bounds
|
| org.drip.spaces.functionclass |
Normed Finite Spaces Function Class
|
| org.drip.spaces.metric |
Hilbert/Banach Normed Metric Spaces
|
| org.drip.spaces.rxtor1 |
R^x To R^1 Normed Function Spaces
|
| Modifier and Type | Method and Description |
|---|---|
RdToR1 |
RdToR1.gradientModulusFunction()
Generate the Gradient Modulus Function
|
| Modifier and Type | Class and Description |
|---|---|
class |
AffineBoundMultivariate
AffineBoundMultivariate implements a Bounded Planar Linear R^d To R^1 Function.
|
class |
AffineMultivariate
AffineMultivariate implements a Planar Linear R^d To R^1 Function using a Multivariate Vector.
|
class |
CovarianceEllipsoidMultivariate
CovarianceEllipsoidMultivariate implements an R^d To R^1 Co-variance Estimate of the specified
Distribution.
|
class |
LagrangianMultivariate
LagrangianMultivariate implements an R^d To R^1 Multivariate Function along with the specified Set of
Equality Constraints.
|
class |
RiskObjectiveUtilityMultivariate
RiskObjectiveUtilityMultivariate implements the Risk Objective R^d To R^1 Multivariate Function used in
Portfolio Allocation.
|
| Modifier and Type | Method and Description |
|---|---|
RdToR1[] |
LagrangianMultivariate.constraintFunctions()
Retrieve the Array of the Constraint R^d To R^1 Function Instances
|
RdToR1 |
LagrangianMultivariate.objectiveFunction()
Retrieve the Objective R^d To R^1 Function Instance
|
| Constructor and Description |
|---|
LagrangianMultivariate(RdToR1 RdToR1Objective,
RdToR1[] aRdToR1EqualityConstraint)
LagrangianMultivariate Constructor
|
LagrangianMultivariate(RdToR1 RdToR1Objective,
RdToR1[] aRdToR1EqualityConstraint)
LagrangianMultivariate Constructor
|
| Modifier and Type | Method and Description |
|---|---|
LineEvolutionVerifierMetrics |
WolfeEvolutionVerifier.metrics(UnitVector uvTargetDirection,
double[] adblCurrentVariate,
RdToR1 funcRdToR1,
double dblStepLength) |
abstract LineEvolutionVerifierMetrics |
LineEvolutionVerifier.metrics(UnitVector uvTargetDirection,
double[] adblCurrentVariate,
RdToR1 funcRdToR1,
double dblStepLength)
Generate the Verifier Metrics for the Specified Inputs
|
LineEvolutionVerifierMetrics |
CurvatureEvolutionVerifier.metrics(UnitVector uvTargetDirection,
double[] adblCurrentVariate,
RdToR1 funcRdToR1,
double dblStepLength) |
LineEvolutionVerifierMetrics |
ArmijoEvolutionVerifier.metrics(UnitVector uvTargetDirection,
double[] adblCurrentVariate,
RdToR1 funcRdToR1,
double dblStepLength) |
boolean |
LineEvolutionVerifier.verify(UnitVector uvTargetDirection,
double[] adblCurrentVariate,
RdToR1 funcRdToR1,
double dblStepLength)
Verify if the specified Inputs satisfy the Criterion
|
| Modifier and Type | Method and Description |
|---|---|
RdToR1[] |
InteriorFixedPointFinder.inequalityConstraints()
Retrieve the Array of Inequality Constraints
|
RdToR1[] |
BarrierFixedPointFinder.inequalityConstraints()
Retrieve the Array of Inequality Constraints
|
RdToR1 |
FixedRdFinder.objectiveFunction()
Retrieve the Objective Function
|
RdToR1 |
BarrierFixedPointFinder.objectiveFunction()
Retrieve the Objective Function
|
| Constructor and Description |
|---|
BarrierFixedPointFinder(RdToR1 rdToR1ObjectiveFunction,
RdToR1[] aRdToR1InequalityConstraint,
InteriorPointBarrierControl ipbc,
LineStepEvolutionControl lsec)
BarrierFixedPointFinder Constructor
|
BarrierFixedPointFinder(RdToR1 rdToR1ObjectiveFunction,
RdToR1[] aRdToR1InequalityConstraint,
InteriorPointBarrierControl ipbc,
LineStepEvolutionControl lsec)
BarrierFixedPointFinder Constructor
|
InteriorFixedPointFinder(RdToR1 rdToR1ObjectiveFunction,
RdToR1[] aRdToR1InequalityConstraint,
LineStepEvolutionControl lsec,
ConvergenceControl cc,
double dblBarrierStrength)
InteriorFixedPointFinder Constructor
|
InteriorFixedPointFinder(RdToR1 rdToR1ObjectiveFunction,
RdToR1[] aRdToR1InequalityConstraint,
LineStepEvolutionControl lsec,
ConvergenceControl cc,
double dblBarrierStrength)
InteriorFixedPointFinder Constructor
|
NewtonFixedPointFinder(RdToR1 rdToR1ObjectiveFunction,
LineStepEvolutionControl lsec,
ConvergenceControl cc)
NewtonFixedPointFinder Constructor
|
| Modifier and Type | Method and Description |
|---|---|
RdToR1 |
IntegralOperator.kernelOperatorFunction()
Retrieve the R^d To R^1 Kernel Operator Function
|
| Constructor and Description |
|---|
IntegralOperator(SymmetricRdToNormedR1Kernel kernel,
RdToR1 funcRdToR1,
R1Normed r1OperatorOutput)
IntegralOperator Constructor
|
| Modifier and Type | Method and Description |
|---|---|
RdToR1 |
RegularizationFunction.rdTor1()
Retrieve the R^d To R^1 Regularization Function
|
| Modifier and Type | Method and Description |
|---|---|
static RegularizerRdToR1 |
RegularizerBuilder.RdCombinatorialToR1Continuous(RdToR1 funcRegularizerRdToR1,
NormedRdCombinatorialToR1Continuous funcSpaceRdToR1,
double dblLambda)
Construct an Instance of R^d Combinatorial To R^1 Continuous Regularizer
|
static RegularizerRdToR1 |
RegularizerBuilder.RdContinuousToR1Continuous(RdToR1 funcRegularizerRdToR1,
NormedRdContinuousToR1Continuous funcSpaceRdToR1,
double dblLambda)
Construct an Instance of R^d Continuous To R^1 Continuous Regularizer
|
double |
RegularizerRdToR1.structuralLoss(RdToR1 funcRdToR1,
double[][] aadblX)
Compute the Regularization Sample Structural Loss
|
double |
RegularizerRdContinuousToR1Continuous.structuralLoss(RdToR1 funcRdToR1,
double[][] aadblX) |
double |
RegularizerRdCombinatorialToR1Continuous.structuralLoss(RdToR1 funcRdToR1,
double[][] aadblX) |
double |
RegularizerRdToR1.structuralRisk(RdR1 distRdR1,
RdToR1 funcRdToR1,
double[][] aadblX,
double[] adblY)
Compute the Regularization Sample Structural Loss
|
double |
RegularizerRdContinuousToR1Continuous.structuralRisk(RdR1 distRdR1,
RdToR1 funcRdToR1,
double[][] aadblX,
double[] adblY) |
double |
RegularizerRdCombinatorialToR1Continuous.structuralRisk(RdR1 distRdR1,
RdToR1 funcRdToR1,
double[][] aadblX,
double[] adblY) |
static RegularizerRdToR1 |
RegularizerBuilder.ToRdContinuous(RdToR1 funcRegularizerRdToR1,
RdNormed rdInput,
R1Continuous r1ContinuousOutput,
double dblLambda)
Construct an Instance of R^d Combinatorial/Continuous To R^1 Continuous Regularizer
|
| Constructor and Description |
|---|
RegularizationFunction(R1ToR1 regR1ToR1,
RdToR1 regRdToR1,
double dblLambda)
RegularizationFunction Constructor
|
RegularizerRdCombinatorialToR1Continuous(RdToR1 funcRegularizerRdToR1,
RdCombinatorialBanach rdCombinatorialInput,
R1Continuous r1ContinuousOutput,
double dblLambda)
RegularizerRdCombinatorialToR1Continuous Function Space Constructor
|
RegularizerRdContinuousToR1Continuous(RdToR1 funcRdToR1,
RdContinuousBanach rdContinuousInput,
R1Continuous r1ContinuousOutput,
double dblLambda)
RegularizerRdContinuousToR1Continuous Function Space Constructor
|
| Modifier and Type | Class and Description |
|---|---|
class |
EmpiricalPenaltySupremumEstimator
EmpiricalPenaltySupremumEstimator contains the Implementation of the Empirical Penalty Supremum Estimator
dependent on Multivariate Random Variables where the Multivariate Function is a Linear Combination of
Bounded Univariate Functions acting on each Random Variate.
|
| Modifier and Type | Method and Description |
|---|---|
RdToR1 |
EmpiricalPenaltySupremumEstimator.supremumRdToR1(double[][] aadblX)
Retrieve the Supremum R^d To R^1 Function Instance for the specified Variate Sequence
|
| Modifier and Type | Method and Description |
|---|---|
double |
LpLossLearner.empiricalLoss(RdToR1 funcLearnerRdToR1,
GeneralizedValidatedVector gvviX,
GeneralizedValidatedVector gvviY) |
double |
LipschitzLossLearner.empiricalLoss(RdToR1 funcLearnerRdToR1,
GeneralizedValidatedVector gvviX,
GeneralizedValidatedVector gvviY) |
double |
L1LossLearner.empiricalLoss(RdToR1 funcLearnerRdToR1,
GeneralizedValidatedVector gvviX,
GeneralizedValidatedVector gvviY) |
double |
EmpiricalLearningMetricEstimator.empiricalLoss(RdToR1 funcLearnerRdToR1,
GeneralizedValidatedVector gvviX,
GeneralizedValidatedVector gvviY)
Compute the Empirical Sample Loss
|
double |
LpLossLearner.empiricalRisk(RdR1 distRdR1,
RdToR1 funcLearnerRdToR1,
GeneralizedValidatedVector gvviX,
GeneralizedValidatedVector gvviY) |
double |
LipschitzLossLearner.empiricalRisk(RdR1 distRdR1,
RdToR1 funcLearnerRdToR1,
GeneralizedValidatedVector gvviX,
GeneralizedValidatedVector gvviY) |
double |
L1LossLearner.empiricalRisk(RdR1 distRdR1,
RdToR1 funcLearnerRdToR1,
GeneralizedValidatedVector gvviX,
GeneralizedValidatedVector gvviY) |
double |
EmpiricalLearningMetricEstimator.empiricalRisk(RdR1 distRdR1,
RdToR1 funcLearnerRdToR1,
GeneralizedValidatedVector gvviX,
GeneralizedValidatedVector gvviY)
Compute the Empirical Sample Risk
|
double |
GeneralizedLearner.regularizedLoss(RdToR1 funcLearnerRdToR1,
GeneralizedValidatedVector gvviX,
GeneralizedValidatedVector gvviY) |
double |
EmpiricalLearningMetricEstimator.regularizedLoss(RdToR1 funcLearnerRdToR1,
GeneralizedValidatedVector gvviX,
GeneralizedValidatedVector gvviY)
Compute the Regularized Sample Loss (Empirical + Structural)
|
double |
GeneralizedLearner.regularizedRisk(RdR1 distRdR1,
RdToR1 funcLearnerRdToR1,
GeneralizedValidatedVector gvviX,
GeneralizedValidatedVector gvviY) |
double |
EmpiricalLearningMetricEstimator.regularizedRisk(RdR1 distRdR1,
RdToR1 funcLearnerRdToR1,
GeneralizedValidatedVector gvviX,
GeneralizedValidatedVector gvviY)
Compute the Regularized Sample Risk (Empirical + Structural)
|
double |
GeneralizedLearner.structuralLoss(RdToR1 funcLearnerRdToR1,
GeneralizedValidatedVector gvvi) |
double |
EmpiricalLearningMetricEstimator.structuralLoss(RdToR1 funcLearnerRdToR1,
GeneralizedValidatedVector gvvi)
Compute the Structural Sample Loss
|
double |
GeneralizedLearner.structuralRisk(RdR1 distRdR1,
RdToR1 funcLearnerRdToR1,
GeneralizedValidatedVector gvviX,
GeneralizedValidatedVector gvviY) |
double |
EmpiricalLearningMetricEstimator.structuralRisk(RdR1 distRdR1,
RdToR1 funcLearnerRdToR1,
GeneralizedValidatedVector gvviX,
GeneralizedValidatedVector gvviY)
Compute the Structural Sample Risk
|
| Modifier and Type | Class and Description |
|---|---|
class |
KernelRdDecisionFunction
KernelRdDecisionFunction implements the Kernel-based R^d Decision Function-Based SVM Functionality for
Classification and Regression.
|
class |
LinearRdDecisionFunction
LinearRdDecisionFunction implements the Linear R^d Decision Function-Based SVM Functionality for
Classification and Regression.
|
class |
RdDecisionFunction
RdDecisionFunction exposes the R^d Decision-Function Based SVM Functionality for Classification and
Regression.
|
| Modifier and Type | Method and Description |
|---|---|
RdToR1 |
R1Multivariate.densityRdToR1()
Convert the Multivariate Density into an RdToR1 Functions Instance
|
| Modifier and Type | Method and Description |
|---|---|
double |
R1Multivariate.expectation(RdToR1 funcRdToR1)
Compute the Expectation of the Specified R^d To R^1 Function Instance
|
| Modifier and Type | Method and Description |
|---|---|
RdToR1[] |
OptimizationFramework.activeConstraints(double[] adblVariate)
Retrieve the Array of Active Constraints
|
RdToR1[] |
OptimizationFramework.equalityConstraint()
Retrieve the Array of R^d To R^1 Equality Constraint Functions
|
RdToR1[] |
OptimizationFramework.inequalityConstraint()
Retrieve the Array of R^d To R^1 Inequality Constraint Functions
|
RdToR1 |
OptimizationFramework.objectiveFunction()
Retrieve the R^d To R^1 Objective Function
|
| Constructor and Description |
|---|
OptimizationFramework(RdToR1 rdToR1Objective,
RdToR1[] aRdToR1EqualityConstraint,
RdToR1[] aRdToR1InequalityConstraint)
OptimizationFramework Constructor
|
OptimizationFramework(RdToR1 rdToR1Objective,
RdToR1[] aRdToR1EqualityConstraint,
RdToR1[] aRdToR1InequalityConstraint)
OptimizationFramework Constructor
|
OptimizationFramework(RdToR1 rdToR1Objective,
RdToR1[] aRdToR1EqualityConstraint,
RdToR1[] aRdToR1InequalityConstraint)
OptimizationFramework Constructor
|
| Modifier and Type | Method and Description |
|---|---|
RdToR1[] |
PortfolioConstructionParameters.equalityConstraintRdToR1(AssetUniverseStatisticalProperties ausp)
Retrieve the Equality Constraint R^d To R^1 Corresponding to the Specified Constraint Type
|
RdToR1 |
PortfolioConstructionParameters.fullyInvestedConstraint()
Retrieve the Fully Invested Equality Constraint
|
RdToR1 |
PortfolioConstructionParameters.returnsConstraint(AssetUniverseStatisticalProperties ausp)
Retrieve the Mandatory Returns Constraint
|
RdToR1 |
CustomRiskUtilitySettings.riskObjectiveUtility(java.lang.String[] astrAssetID,
AssetUniverseStatisticalProperties ausp)
Retrieve the Custom Risk Objective Utility Multivariate
|
| Modifier and Type | Method and Description |
|---|---|
RdToR1 |
LimitTurnoverTermIssuerShort.rdtoR1() |
RdToR1 |
LimitTurnoverTermIssuerSell.rdtoR1() |
RdToR1 |
LimitTurnoverTermIssuerNet.rdtoR1() |
RdToR1 |
LimitTurnoverTermIssuerBuy.rdtoR1() |
RdToR1 |
LimitTradesTermIssuerTotal.rdtoR1() |
RdToR1 |
LimitTradesTermIssuerSell.rdtoR1() |
RdToR1 |
LimitTradesTermIssuerBuy.rdtoR1() |
RdToR1 |
LimitThresholdTermIssuerShort.rdtoR1() |
RdToR1 |
LimitThresholdTermIssuerNet.rdtoR1() |
RdToR1 |
LimitThresholdTermIssuerLong.rdtoR1() |
RdToR1 |
LimitTaxTermNetLoss.rdtoR1() |
RdToR1 |
LimitTaxTermNetGains.rdtoR1() |
RdToR1 |
LimitTaxTermLongGains.rdtoR1() |
RdToR1 |
LimitTaxTermLiability.rdtoR1() |
RdToR1 |
LimitTaxTermGrossLoss.rdtoR1() |
RdToR1 |
LimitTaxTermGrossGains.rdtoR1() |
RdToR1 |
LimitRiskTermVariance.rdtoR1() |
RdToR1 |
LimitRiskTermMarginal.rdtoR1() |
RdToR1 |
LimitNamesTermIssuerTotal.rdtoR1() |
RdToR1 |
LimitNamesTermIssuerShort.rdtoR1() |
RdToR1 |
LimitNamesTermIssuerLong.rdtoR1() |
RdToR1 |
LimitHoldingsTermModelDeviation.rdtoR1() |
RdToR1 |
LimitHoldingsTermIssuerWeightedAverage.rdtoR1() |
RdToR1 |
LimitHoldingsTermIssuerShort.rdtoR1() |
RdToR1 |
LimitHoldingsTermIssuerNet.rdtoR1() |
RdToR1 |
LimitHoldingsTermIssuerLongShort.rdtoR1() |
RdToR1 |
LimitHoldingsTermIssuerLong.rdtoR1() |
RdToR1 |
LimitHoldingsTermAbsolute.rdtoR1() |
RdToR1 |
LimitExposureTermNet.rdtoR1() |
RdToR1 |
LimitExposureTermIssuerShort.rdtoR1() |
RdToR1 |
LimitExposureTermIssuerNet.rdtoR1() |
RdToR1 |
LimitExposureTermIssuerLong.rdtoR1() |
RdToR1 |
LimitExposureTermAbsolute.rdtoR1() |
RdToR1 |
LimitChargeTermIssuer.rdtoR1() |
RdToR1 |
LimitBudgetTermTransactionCharge.rdtoR1() |
RdToR1 |
LimitBudgetTermNet.rdtoR1() |
| Modifier and Type | Method and Description |
|---|---|
RdToR1 |
VarianceTerm.rdtoR1() |
RdToR1 |
TransactionChargeTerm.rdtoR1() |
RdToR1 |
TaxLiabilityTerm.rdtoR1() |
RdToR1 |
StandardDeviationTerm.rdtoR1() |
RdToR1 |
ShortTiltTerm.rdtoR1() |
RdToR1 |
ShortSellChargeTerm.rdtoR1() |
RdToR1 |
NetTiltTerm.rdtoR1() |
RdToR1 |
NetTaxGainsTerm.rdtoR1() |
RdToR1 |
MarketImpactChargeTerm.rdtoR1() |
RdToR1 |
LongTiltTerm.rdtoR1() |
RdToR1 |
LinearChargeTerm.rdtoR1() |
RdToR1 |
LinearChargeSellTerm.rdtoR1() |
RdToR1 |
LinearChargeBuyTerm.rdtoR1() |
RdToR1 |
GoldmanSachsShortfallTerm.rdtoR1() |
RdToR1 |
FixedChargeTerm.rdtoR1() |
RdToR1 |
FixedChargeSellTerm.rdtoR1() |
RdToR1 |
FixedChargeBuyTerm.rdtoR1() |
RdToR1 |
ExpectedReturnsTerm.rdtoR1() |
RdToR1 |
CustomNetTaxGainsTerm.rdtoR1() |
| Modifier and Type | Class and Description |
|---|---|
class |
ObjectiveFunction
ObjectiveFunction holds the Terms composing the Objective Function and their Weights.
|
| Modifier and Type | Method and Description |
|---|---|
abstract RdToR1 |
FormulationTerm.rdtoR1()
The R^d To R^1 Objective Term
|
| Modifier and Type | Class and Description |
|---|---|
class |
GlivenkoCantelliFunctionSupremum
GlivenkoCantelliFunctionSupremum contains the Implementation of the Supremum Class Objective Function
dependent on Multivariate Random Variables where the Multivariate Function is a Linear Combination of Bounded
Univariate Functions acting on each Random Variate.
|
class |
GlivenkoCantelliUniformDeviation
GlivenkoCantelliUniformDeviation contains the Implementation of the Bounded Objective Function dependent
on Multivariate Random Variables where the Multivariate Function is a Linear Combination of Bounded
Univariate Functions acting on each Random Variate.
|
class |
KernelDensityEstimationL1
KernelDensityEstimationL1 implements the L1 Error Scheme Estimation for a Multivariate Kernel Density
Estimator with Focus on establishing targeted Variate-Specific and Agnostic Bounds.
|
class |
LongestCommonSubsequence
LongestCommonSubsequence contains Variance Bounds on the Critical Measures of the Longest Common
Subsequence between two Strings.
|
class |
OrientedPercolationFirstPassage
OrientedPercolationFirstPassage contains Variance Bounds on the Critical Measures of the Standard Problem
of First Passage Time in Oriented Percolation.
|
| Modifier and Type | Class and Description |
|---|---|
class |
BoundedMultivariateRandom
BoundedMultivariateRandom contains the Implementation of the Bounded Objective Function dependent on
Multivariate Random Variables.
|
class |
FlatMultivariateRandom
FlatMultivariateRandom contains the Implementation of the Flat Objective Function dependent on
Multivariate Random Variables.
|
class |
MultivariateRandom
MultivariateRandom contains the implementation of the objective Function dependent on Multivariate Random
Variables.
|
| Modifier and Type | Method and Description |
|---|---|
RdToR1 |
EfronSteinMetrics.function()
Retrieve the Multivariate Objective Function
|
| Modifier and Type | Method and Description |
|---|---|
RdToR1[] |
NormedRdToNormedR1Finite.functionRdToR1Set()
Retrieve the Finite Class of R^d To R^1 Functions
|
| Modifier and Type | Method and Description |
|---|---|
double |
RdNormed.borelMeasureSpaceExpectation(RdToR1 funcRdToR1)
Compute the Borel Measure Expectation for the specified R^d To R^1 Function over the full Input Space
|
double |
RdContinuousBanach.borelMeasureSpaceExpectation(RdToR1 funcRdToR1) |
double |
RdCombinatorialBanach.borelMeasureSpaceExpectation(RdToR1 funcRdToR1) |
| Modifier and Type | Method and Description |
|---|---|
RdToR1 |
NormedRdToNormedR1.function()
Retrieve the Underlying RdToR1 Function
|
| Constructor and Description |
|---|
NormedRdCombinatorialToR1Continuous(RdCombinatorialBanach rdCombinatorialInput,
R1Continuous r1ContinuousOutput,
RdToR1 funcRdToR1)
NormedRdCombinatorialToR1Continuous Function Space Constructor
|
NormedRdContinuousToR1Continuous(RdContinuousBanach rdContinuousInput,
R1Continuous r1ContinuousOutput,
RdToR1 funcRdToR1)
NormedRdContinuousToR1Continuous Function Space Constructor
|


{% extends 'layout.html' %} {% set version = "v5" %} {% set heading = "Sorry, those currently pregnant, breastfeeding or planning on becoming pregnant over the next 6 months or over can't take part in clinical trials" %} {% block beforeMain %} {% endblock %} {% block content %}


{% extends 'base.html' %} {% block content %}
this is a review of {{review.activity.name}} and it says {{ review.body |linebreaks }}


System Statistics
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Send a Single Transmission
ICTCore allows to support different kind of transmissions like Voice Call, SMS, Email and Fax to send message to a single contact.
Voice Call
This transmission dials number from a contact list and on answer plays a recorded message.
Send Voice Call
SMS Transmission
This transmission selects number from a contact list and simply broadcast a text message to the contact group.
Send SMS
Email Transmission
This transmission sends email to the specified email address of your contact.
Send Email
Fax Transmission
This transmission dials number from a contact list and on answer transfer fax documents.
Send Fax
IVR Transmission
This transmission dials number from a contact list and on answer plays the IVR menu.
Send IVR
Launch a Campaign
ICTCore supports complete campaign management. Each user can manage, monitor, and run multiple campaigns separately and independently to send message to contact group.
Voice Campaign
This campaign dials number from a contact list and on answer plays a recorded message.
Send Voice Call
SMS Campaign
This campaign selects number from a contact list and simply broadcast a text message to the contact.
Send SMS
Email Campaign
This campaign sends email to the specified email address of your contact group.
Send Email
Fax Campaign
This campaign dials number from a contact list and on answer transfer fax documents.
Send Fax
IVR Campaign
This campaign dials number from a contact list and on answer play the IVR menu.
Send IVR


The matching of textual steps to Java methods, each representing a candidate step, is a key concept in creating a Domain-Specific Language (DSL). Once the DSL has been defined, we need a way to display the "grammar" of the DSL to enable to scenario writer to exercise that grammar in writing the scenarios.
To this end, JBehave provides the concept of Stepdoc, which represents the documentation on a single candidate step as annotated method in the CandidateSteps instance. This documentation includes:
To generate the stepdocs for a given list of CandidateSteps instances:
Alternatively, given a RunnableScenario instance:
The RunnableScenario. will use the configured StepdocReporter to report the stepdocs found in the CandidateSteps defined for the scenario.
Remember to include jbehave-ant to your runtime classpath.


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Japan's Abe says South Korea made strategic choice in keeping intelligence-sharing pact
TOKYO (Reuters) - Japanese Prime Minister Shinzo Abe on Friday said South Korea made a strategic choice in deciding to stick with an intelligence-sharing pact with Japan that it had earlier said it would withdraw from amid a spat over history and trade.
“I stressed the importance of cooperation between Japan and South Korea, and Japan, South Korea and the United States,” Abe said after Seoul informed Tokyo of its decision.
South Korea reversed course after coming under pressure from Washington, which worried that an end to intelligence sharing between its two allies would hamper their ability to respond to threats posed by North Korea.


The Native Windows GUI (NWG) documentation. Written for the version 1.0.*.
The NWG 1.0 guide. The main documentation to get you started with the library.


Base classes for triangulations#
We provide (fast) cython implementations here.
AUTHORS:
Volker Braun (2010-09-14): initial version.
- class sage.geometry.triangulation.base.ConnectedTriangulationsIterator#
Bases:
SageObject
A Python shim for the C++-class ‘triangulations’
INPUT:
point_configuration
– aPointConfiguration
.seed
– a regular triangulation orNone
(default). In the latter case, a suitable triangulation is generated automatically. Otherwise, you can explicitly specify the seed triangulation asA
Triangulation
object, oran iterable of iterables specifying the vertices of the simplices, or
an iterable of integers, which are then considered the enumerated simplices (see
simplex_to_int()
.
star
– eitherNone
(default) or an integer. If an integer is passed, all returned triangulations will be star with respect to thefine
– boolean (default:False
). Whether to return only fine triangulations, that is, simplicial decompositions that make use of all the points of the configuration.
OUTPUT:
An iterator. The generated values are tuples of integers, which encode simplices of the triangulation. The output is a suitable input to
Triangulation
.EXAMPLES:
sage: p = PointConfiguration([[0,0],[0,1],[1,0],[1,1],[-1,-1]]) sage: from sage.geometry.triangulation.base import ConnectedTriangulationsIterator sage: ci = ConnectedTriangulationsIterator(p) sage: next(ci) (9, 10) sage: next(ci) (2, 3, 4, 5) sage: next(ci) (7, 8) sage: next(ci) (1, 3, 5, 7) sage: next(ci) Traceback (most recent call last): ... StopIteration
You can reconstruct the triangulation from the compressed output via:
sage: from sage.geometry.triangulation.element import Triangulation sage: Triangulation((2, 3, 4, 5), p) (<0,1,3>, <0,1,4>, <0,2,3>, <0,2,4>)
How to use the restrictions:
sage: ci = ConnectedTriangulationsIterator(p, fine=True) sage: list(ci) [(2, 3, 4, 5), (1, 3, 5, 7)] sage: ci = ConnectedTriangulationsIterator(p, star=1) sage: list(ci) [(7, 8)] sage: ci = ConnectedTriangulationsIterator(p, star=1, fine=True) sage: list(ci) []
- class sage.geometry.triangulation.base.Point#
Bases:
SageObject
A point of a point configuration.
Note that the coordinates of the points of a point configuration are somewhat arbitrary. What counts are the abstract linear relations between the points, for example encoded by the
circuits()
.Warning
You should not create
Point
objects manually. The constructor ofPointConfiguration_base
takes care of this for you.INPUT:
point_configuration
–PointConfiguration_base
. The point configuration to which the point belongs.i
– integer. The index of the point in the point configuration.projective
– the projective coordinates of the point.affine
– the affine coordinates of the point.reduced
– the reduced (with linearities removed) coordinates of the point.
EXAMPLES:
sage: pc = PointConfiguration([(0,0)]) sage: from sage.geometry.triangulation.base import Point sage: Point(pc, 123, (0,0,1), (0,0), ()) P(0, 0)
- affine()#
Return the affine coordinates of the point in the ambient space.
OUTPUT:
A tuple containing the coordinates.
EXAMPLES:
sage: pc = PointConfiguration([[10, 0, 1], [10, 0, 0], [10, 2, 3]]) sage: p = pc.point(2); p P(10, 2, 3) sage: p.affine() (10, 2, 3) sage: p.projective() (10, 2, 3, 1) sage: p.reduced_affine() (2, 2) sage: p.reduced_projective() (2, 2, 1) sage: p.reduced_affine_vector() (2, 2)
- index()#
Return the index of the point in the point configuration.
EXAMPLES:
sage: pc = PointConfiguration([[0, 1], [0, 0], [1, 0]]) sage: p = pc.point(2); p P(1, 0) sage: p.index() 2
- point_configuration()#
Return the point configuration to which the point belongs.
OUTPUT:
EXAMPLES:
sage: pc = PointConfiguration([ (0,0), (1,0), (0,1) ]) sage: p = pc.point(0) sage: p is pc.point(0) True sage: p.point_configuration() is pc True
- projective()#
Return the projective coordinates of the point in the ambient space.
OUTPUT:
A tuple containing the coordinates.
EXAMPLES:
sage: pc = PointConfiguration([[10, 0, 1], [10, 0, 0], [10, 2, 3]]) sage: p = pc.point(2); p P(10, 2, 3) sage: p.affine() (10, 2, 3) sage: p.projective() (10, 2, 3, 1) sage: p.reduced_affine() (2, 2) sage: p.reduced_projective() (2, 2, 1) sage: p.reduced_affine_vector() (2, 2)
- reduced_affine()#
Return the affine coordinates of the point on the hyperplane spanned by the point configuration.
OUTPUT:
A tuple containing the coordinates.
EXAMPLES:
sage: pc = PointConfiguration([[10, 0, 1], [10, 0, 0], [10, 2, 3]]) sage: p = pc.point(2); p P(10, 2, 3) sage: p.affine() (10, 2, 3) sage: p.projective() (10, 2, 3, 1) sage: p.reduced_affine() (2, 2) sage: p.reduced_projective() (2, 2, 1) sage: p.reduced_affine_vector() (2, 2)
- reduced_affine_vector()#
Return the affine coordinates of the point on the hyperplane spanned by the point configuration.
OUTPUT:
A tuple containing the coordinates.
EXAMPLES:
sage: pc = PointConfiguration([[10, 0, 1], [10, 0, 0], [10, 2, 3]]) sage: p = pc.point(2); p P(10, 2, 3) sage: p.affine() (10, 2, 3) sage: p.projective() (10, 2, 3, 1) sage: p.reduced_affine() (2, 2) sage: p.reduced_projective() (2, 2, 1) sage: p.reduced_affine_vector() (2, 2)
- reduced_projective()#
Return the projective coordinates of the point on the hyperplane spanned by the point configuration.
OUTPUT:
A tuple containing the coordinates.
EXAMPLES:
sage: pc = PointConfiguration([[10, 0, 1], [10, 0, 0], [10, 2, 3]]) sage: p = pc.point(2); p P(10, 2, 3) sage: p.affine() (10, 2, 3) sage: p.projective() (10, 2, 3, 1) sage: p.reduced_affine() (2, 2) sage: p.reduced_projective() (2, 2, 1) sage: p.reduced_affine_vector() (2, 2)
- reduced_projective_vector()#
Return the affine coordinates of the point on the hyperplane spanned by the point configuration.
OUTPUT:
A tuple containing the coordinates.
EXAMPLES:
sage: pc = PointConfiguration([[10, 0, 1], [10, 0, 0], [10, 2, 3]]) sage: p = pc.point(2); p P(10, 2, 3) sage: p.affine() (10, 2, 3) sage: p.projective() (10, 2, 3, 1) sage: p.reduced_affine() (2, 2) sage: p.reduced_projective() (2, 2, 1) sage: p.reduced_affine_vector() (2, 2) sage: type(p.reduced_affine_vector()) <class 'sage.modules.vector_rational_dense.Vector_rational_dense'>
- class sage.geometry.triangulation.base.PointConfiguration_base#
Bases:
Parent
The cython abstract base class for
PointConfiguration
.Warning
You should not instantiate this base class, but only its derived class
PointConfiguration
.- ambient_dim()#
Return the dimension of the ambient space of the point configuration.
See also
dimension()
EXAMPLES:
sage: p = PointConfiguration([[0,0,0]]) sage: p.ambient_dim() 3 sage: p.dim() 0
- base_ring()#
Return the base ring, that is, the ring containing the coordinates of the points.
OUTPUT:
A ring.
EXAMPLES:
sage: p = PointConfiguration([(0,0)]) sage: p.base_ring() Integer Ring sage: p = PointConfiguration([(1/2,3)]) sage: p.base_ring() Rational Field sage: p = PointConfiguration([(0.2, 5)]) sage: p.base_ring() Real Field with 53 bits of precision
- dim()#
Return the actual dimension of the point configuration.
See also
ambient_dim()
EXAMPLES:
sage: p = PointConfiguration([[0,0,0]]) sage: p.ambient_dim() 3 sage: p.dim() 0
- int_to_simplex(s)#
Reverse the enumeration of possible simplices in
simplex_to_int()
.The enumeration is compatible with [PUNTOS].
INPUT:
s
– int. An integer that uniquely specifies a simplex.
OUTPUT:
An ordered tuple consisting of the indices of the vertices of the simplex.
EXAMPLES:
sage: U=matrix([ ....: [ 0, 0, 0, 0, 0, 2, 4,-1, 1, 1, 0, 0, 1, 0], ....: [ 0, 0, 0, 1, 0, 0,-1, 0, 0, 0, 0, 0, 0, 0], ....: [ 0, 2, 0, 0, 0, 0,-1, 0, 1, 0, 1, 0, 0, 1], ....: [ 0, 1, 1, 0, 0, 1, 0,-2, 1, 0, 0,-1, 1, 1], ....: [ 0, 0, 0, 0, 1, 0,-1, 0, 0, 0, 0, 0, 0, 0] ....: ]) sage: pc = PointConfiguration(U.columns()) sage: pc.simplex_to_int([1,3,4,7,10,13]) 1678 sage: pc.int_to_simplex(1678) (1, 3, 4, 7, 10, 13)
- is_affine()#
Return whether the configuration is defined by affine points.
OUTPUT:
Boolean. If true, the homogeneous coordinates all have \(1\) as their last entry.
EXAMPLES:
sage: p = PointConfiguration([(0.2, 5), (3, 0.1)]) sage: p.is_affine() True sage: p = PointConfiguration([(0.2, 5, 1), (3, 0.1, 1)], projective=True) sage: p.is_affine() False
- n_points()#
Return the number of points.
Same as
len(self)
.EXAMPLES:
sage: p = PointConfiguration([[0,0],[0,1],[1,0],[1,1],[-1,-1]]) sage: p A point configuration in affine 2-space over Integer Ring consisting of 5 points. The triangulations of this point configuration are assumed to be connected, not necessarily fine, not necessarily regular. sage: len(p) 5 sage: p.n_points() 5
- point(i)#
Return the i-th point of the configuration.
Same as
__getitem__()
INPUT:
i
– integer.
OUTPUT:
A point of the point configuration.
EXAMPLES:
sage: pconfig = PointConfiguration([[0,0],[0,1],[1,0],[1,1],[-1,-1]]) sage: list(pconfig) [P(0, 0), P(0, 1), P(1, 0), P(1, 1), P(-1, -1)] sage: [ p for p in pconfig.points() ] [P(0, 0), P(0, 1), P(1, 0), P(1, 1), P(-1, -1)] sage: pconfig.point(0) P(0, 0) sage: pconfig[0] P(0, 0) sage: pconfig.point(1) P(0, 1) sage: pconfig.point( pconfig.n_points()-1 ) P(-1, -1)
- points()#
Return a list of the points.
OUTPUT:
A list of the points. See also the
__iter__()
method, which returns the corresponding generator.EXAMPLES:
sage: pconfig = PointConfiguration([[0,0],[0,1],[1,0],[1,1],[-1,-1]]) sage: list(pconfig) [P(0, 0), P(0, 1), P(1, 0), P(1, 1), P(-1, -1)] sage: [ p for p in pconfig.points() ] [P(0, 0), P(0, 1), P(1, 0), P(1, 1), P(-1, -1)] sage: pconfig.point(0) P(0, 0) sage: pconfig.point(1) P(0, 1) sage: pconfig.point( pconfig.n_points()-1 ) P(-1, -1)
- reduced_affine_vector_space()#
Return the vector space that contains the affine points.
OUTPUT:
A vector space over the fraction field of
base_ring()
.EXAMPLES:
sage: p = PointConfiguration([[0,0,0], [1,2,3]]) sage: p.base_ring() Integer Ring sage: p.reduced_affine_vector_space() Vector space of dimension 1 over Rational Field sage: p.reduced_projective_vector_space() Vector space of dimension 2 over Rational Field
- reduced_projective_vector_space()#
Return the vector space that is spanned by the homogeneous coordinates.
OUTPUT:
A vector space over the fraction field of
base_ring()
.EXAMPLES:
sage: p = PointConfiguration([[0,0,0], [1,2,3]]) sage: p.base_ring() Integer Ring sage: p.reduced_affine_vector_space() Vector space of dimension 1 over Rational Field sage: p.reduced_projective_vector_space() Vector space of dimension 2 over Rational Field
- simplex_to_int(simplex)#
Return an integer that uniquely identifies the given simplex.
See also the inverse method
int_to_simplex()
.The enumeration is compatible with [PUNTOS].
INPUT:
simplex
– iterable, for example a list. The elements are the vertex indices of the simplex.
OUTPUT:
An integer that uniquely specifies the simplex.
EXAMPLES:
sage: U=matrix([ ....: [ 0, 0, 0, 0, 0, 2, 4,-1, 1, 1, 0, 0, 1, 0], ....: [ 0, 0, 0, 1, 0, 0,-1, 0, 0, 0, 0, 0, 0, 0], ....: [ 0, 2, 0, 0, 0, 0,-1, 0, 1, 0, 1, 0, 0, 1], ....: [ 0, 1, 1, 0, 0, 1, 0,-2, 1, 0, 0,-1, 1, 1], ....: [ 0, 0, 0, 0, 1, 0,-1, 0, 0, 0, 0, 0, 0, 0] ....: ]) sage: pc = PointConfiguration(U.columns()) sage: pc.simplex_to_int([1,3,4,7,10,13]) 1678 sage: pc.int_to_simplex(1678) (1, 3, 4, 7, 10, 13)


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A channel to discuss trading. For useful links and more info click the pinned messages to the right.
100 messages
Before 05-Dec-19 12:00 AM
jorian30-Nov-19 04:11 PM
/care
Alright30-Nov-19 04:11 PM
must withdraw today if you had anything on there
1
Oszy30-Nov-19 04:13 PM
not sure but if the volume is bigger than what the cheapest order could take, it won't match with that one , if the one you select has the volume, it will match with that
Community driven open source CPU mineable cryptocurrency with 50/50 Proof-of-Work/Proof-of-Stake, zk-SNARK zero-knowledge proof privacy.
last version 0.5.9-2
Acura30-Nov-19 04:47 PM
For me 0.59.2 is working
Deltazulu30-Nov-19 04:48 PM
works for me too
𝙊𝙊𝙊𝙊30-Nov-19 05:31 PM
I've seen 2 reports of people having problems connecting to Komodo in Native mode, which apparently was solved by compiling a new komodo daemon using the dev branch.
Verus is close to releasing a new wallet. As I understood it will also include a new Komodo daemon.
Flint30-Nov-19 07:10 PM
Pirate is good at ranking nodes bypassing Komodo which has 51 nodes! but Horizen (more likely a competitor to Pirate APPP than Komodo) who once was attacked is steadily gaining momentum and even outperformed bitcoin in terms of the number of nodes, and as follows from the article, the number of nodes is a more objective metric for evaluating projects than CMC
An analyst makes a compelling case for ranking cryptocurrencies by the number of active nodes instead of their market capitalization.
1
jl777c30-Nov-19 07:21 PM
the data seems to be very wrong. there are far more than 51 KMD nodes, more like 1000+
also, ZEN has so many nodes due to the masternode rewards and these are basically subsidized from the blockreward
ranking coins by how much they subsidize nodes... it seems a bit random
cipi30-Nov-19 07:34 PM
the article mentions that they used the data from https://kmdexplorer.io/status for the number of nodes
but that does not represent the total number of nodes on the network
and atm https://kmdexplorer.io/status shows 90... it also depends on when then daemon was restarted and how it is configured
CHMEX30-Nov-19 07:36 PM
Not very accurate research from them.
They should spin up a few nodes in different regions and dump getpeerinfo and analyse the distinct ips(edited)
cipi30-Nov-19 07:37 PM
pretty sure .onion are not acounted at all because the daemon on kmdexplorer.io does not used tor
yes, they should collect the data over a longer period of time
and from different sources
CHMEX30-Nov-19 07:37 PM
There are at least 64 nodes plus all the exchanges
Komodo (KMD) Network Peer Explorer | Active Peers: 16 | Unique Peer Countries: 7 | Addnode List
CHMEX30-Nov-19 07:39 PM
I see.
Btw that exchange is closing down today
cipi30-Nov-19 07:40 PM
yes, i know...
SHossain30-Nov-19 07:40 PM
i had over 90 conns on my nodes
cipi30-Nov-19 07:40 PM
yes, a simple komodo-cli getinfo shows "connections": 105
Flint30-Nov-19 07:41 PM
nonetheless most will swallow it! maybe you can somehow influence the correct display in such ratings because the metric of the number of nodes estimate does not become useless from the fact that the data is not completely correct and therefore it would be great to have an updated actual tool for displaying node growth which would be a great driving force for Komodo
Statistics for the Komodo Platform's dPOW notarization of KMD and supported blockchains.
Flint30-Nov-19 07:43 PM
But is this a unique and best metric for evaluating projects, or is the author disingenuous?
SHossain30-Nov-19 07:44 PM
this is not a proper way of evaluating projects. at least they didn't do research and didn't gather enough data
looks like they scraped some data and called it research?
Flint30-Nov-19 07:48 PM
it seems like that! but what to do with the lack of this truth for ordinary users who read it and believe everything without verification, but there will be the majority of them, and then Horizen fans will then repost it all over the Internet
jl777c30-Nov-19 08:02 PM
ZEN pays to have more nodes. does that make ZEN a better coin?
it is artificial. likely article written by someone with ZEN
30,000 nodes is just a silly number of nodes considering the unique users is likely much smaller
Flint30-Nov-19 08:09 PM
new users in ZEN will be bribed by another important fact that ZEN did not have ICO and premine, as well as at the moment, along with bitcoin, one of the most stable trends on the market, and now we add here even a fake number of nodes, but in total there are a lot of positive factors in the struggle for the user
Patrick Crypto30-Nov-19 08:09 PM
Regarding the DEX order, I can try to buy the next more expensive order, but it still matches with the cheapest. Which fails
Using MM2 or Android
Flint30-Nov-19 08:15 PM
@Patrick Crypto liquidity unfortunately leaves much to be desired and there are very few orders in the order book and orders at reasonable prices are even smaller only orders designed for inexperienced users who peck for the sake of interest in an order that is 30-50% lower than the real market price but since competition is in price No, then these orders are hanging no one needs
SHossain30-Nov-19 08:16 PM
@Patrick Crypto try putting higher price and try buying slightly less than than what bob is selling
Patrick Crypto30-Nov-19 08:23 PM
@SHossain I have tried that. It forces the match with the 20kmd order. I've had about 6 failed swaps because of that order
Patrick Crypto30-Nov-19 08:31 PM
which is typically the desired behavior, but there is apparently something wrong with that order
saturday01-Dec-19 12:12 AM
@Patrick Crypto I have same failed swap. I think that 20kmd-dex order have problem.
BoBot-BUY KMD01-Dec-19 01:48 AM
@Acura still can't get agama to work, was working fine up until today
Error: Failed to connect best block
komodod exited with code 1, crashed?
BoBot-BUY KMD01-Dec-19 02:04 AM
I'll try to redownload agama and see if that works
nah that didn't work either
I guess I have to wait for Verus to release the new agama wallet. I'm using windows
joe2x401-Dec-19 05:41 AM
You could learn how to install Ubuntu. Especially if you are going to use crypto. It's very easy to do.

Crashing might be happen due to bad exit from native mode and because of that your downloaded blockchain files being corrupt.
I had last week problem with verus native mode. I had to remove blockchain files and install them from bootstrap and after that wallet loaded fine. Another way is to remove blockchain files and just let wallet to resync but it takes some time.
Blockchain files for agama verus coin are in folder %appdata%\komodo\VRSC
Inside that folder you need to backup wallet.dat file into safe place. And after that AND only after that you can remove everything else EXCEPT wallet.dat file. (Taking copy is precaution if worst happens since it hold coins and losing that causes PERMANENT loss of your coins). Now after that you can unzip verus bootstrap to this same folder or just start agama and it starts resync).
Take note also that %appdata%\komodo holds komodos blochain files and ALSO its ASSETCHAIN files in subdirectories like for verus in under VRSC so you must be careful when you play with these since those holds all information to coins in wallet.dat files.
Enjoy the videos and music you love, upload original content, and share it all with friends, family, and the world on YouTube.
banned Ted talk, ESP
Hollowman900002-Dec-19 01:42 AM
I see myself in the future now
Living in a dumpster because I tried using ESP to trade with leverage
4
Crabby Paddy02-Dec-19 02:05 AM
Where’s tek when we need him
mrlynch02-Dec-19 02:54 AM
banned
some say you can find him in pirate discord.. he has his own channel #not-safespace
1
Crabby Paddy02-Dec-19 09:05 AM
Anyone looking to buy kmd @ market price
Need to sell ~ 2btc worth / 16.3k kmd(edited)
more than happy to use atomicdex
Oszy02-Dec-19 09:10 AM
that's more like 1.5 btc at market rate :p
Crabby Paddy02-Dec-19 09:10 AM
maths is not my subject of choice
Oszy02-Dec-19 09:11 AM
ok then i'll give you 0.1 BTC for 16K
trust me it's correct
1
Crabby Paddy02-Dec-19 09:13 AM
11,050/0.676 = 16,346kmd for 11,050 worth of btc
Ill throw in 200 extra kmd on my side, so 16,546 kmd for $11,050 btc
Anyone looking to buy without bumping the price up please dm me asap
Alright02-Dec-19 09:20 AM
@Crabby Paddy still have faith in fabien's projects?
Crabby Paddy02-Dec-19 09:21 AM
one can have hope.
dune is interesting to me, so its worth a shot
Alright02-Dec-19 09:21 AM
isn't dune definitively not his project?
1
Crabby Paddy02-Dec-19 09:22 AM
no comment. I think there were quite a lot of misunderstandings, but this is komodo channel so I will keep my mouth shut
BridgeCoin Price Chart | Market Capitalization | BCO to USD Calculator | BCO ROI Calculator | News | Description | Team | Related Events | Community | Similar coins to BridgeCoin BCO
1
Alright02-Dec-19 10:09 AM
All users have to withdraw their funds in the meantime. Please note that user verification is required by EU law for all withdrawals. We highly recommend that you start the process as early as possible as verification can take a few days.
"DEX" lmao
jorian02-Dec-19 10:11 AM
ouch
hyperDEX02-Dec-19 10:21 AM
Let's block them from withdrawing, then close.... nice!
Lumbi02-Dec-19 10:27 AM
CB not only took a 1+BTC listing fee from projects, but they got investors to lock in BCO for a cut of the profits. Any investor that locked BCO for a year after Dec 15th 2018 will take a 100% hit.(edited)
mobipoi02-Dec-19 10:29 AM
Is deposit and withdrawal open?
Not sure for binance
hyperDEX02-Dec-19 10:31 AM
@Lumbi oh wow even worse
Crabby Paddy02-Dec-19 10:33 AM
Ledger ; Server could not handle request. Error code 26 ; bad txns input missing
any ideas?
Oszy02-Dec-19 10:36 AM
No idea but i saw the same error from someone else using 1.6


Student-Grade Manager
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Actions


Across: 7. Current measure of musical equipment (4) 8,22dn. Energy lost in error, say? Alas, faulty ones powered 12 (5 6) 10. Country's U.N. seat even taken away (3) 11. Illegal war U.N. led legally? (5 3) 12. After time and energy spent, key 21 27 for 10 14 19 (6) 13. Ease of new user's corruption (5) 15. Was "union" missing from hub, as in something they couldn't have on 12? (9) 18. Worked on my tax? A sorry task that could be done on 12 (1-3 9) 20. Birth of nice pinto, dotty (9) 21. Great time had in second final (5) 24. One vote against zero for 10 14 program (6) 26. Brit POWs trained in one part of craft (8) 27. Including favourite opening of departed (3) 28. Part of 29 to reorient? (5) 29. Wild orgy which may cause a change of attitude on 14 19 (4) Down: 1. Planet six five one used to deliver 12 (6 1) 2. Webmaster's pride's hurt (6) 3. Lyrical part of the bible, major line with viper coming up (5) 4. Sophocles mostly worked around king's early education centre (9) 5.  Wisdom of professor Carl, mostly urban (8) 9. Ultimately fail to damage fellow with big hammer part where 12 operated (3 5 5) 14. Destination reached from Canaveral, or assorted capes (5) 16. Strange to all, land that's mostly lagoon (5) 17. 12 had a velcro one, as confused Rat and Pig in D&D (9) 18. Australian senator grinding axe: no phone! (8) 19. Wandering, is not at post (7) 22. see 8 across 23. Celebration of a charge? (5) 25. Flit about to get what 1 gave 12 (4)


customizeBoard Information Report
Contents
Contents
Board Specification
General
Routing
Routing Information
Layer Information
Layer Information
Drill Pairs
Copper Area
Copper Area
Board Holes
Non-Plated Hole Size
Plated Hole Size
Board Slots
Non-Plated Slot Size
Plated Slot Size
Board Square Holes
Non-Plated Square Hole Size
Plated Square Hole Size
Other
Top Layer Annular Ring Size
Mid Layer Annular Ring Size
Bottom Layer Annular Ring Size
Pad Solder Mask
Pad Paste Mask
Pad Pwr/Gnd Expansion
Pad Relief Conductor Width
Pad Relief Air Gap
Pad Relief Entries
Via Solder Mask
Via Pwr/Gnd Expansion
Track Width
Arc Line Width
Arc Radius
Arc Degrees
Text Height
Text Width
Net Track Width
Net Via Size
Board Specification
Back to top
Routing
| Routing Information |
| Routing completion |
100,00% |
| Connections |
106 |
| Connections routed |
106 |
| Connections remaining |
0 |
Back to top
Layer Information
| Layer |
Arcs |
Pads |
Vias |
Tracks |
Texts |
Fills |
Regions |
ComponentBodies |
| Top Layer |
0 |
24 |
0 |
142 |
0 |
0 |
71 |
0 |
| Bottom Layer |
0 |
45 |
0 |
131 |
0 |
0 |
85 |
0 |
| Mechanical 1 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
13 |
| Board_Shape |
0 |
0 |
0 |
4 |
0 |
0 |
0 |
0 |
| Top Assembly |
0 |
0 |
0 |
48 |
0 |
0 |
0 |
21 |
| Bottom Assembly |
0 |
0 |
0 |
55 |
12 |
1 |
0 |
0 |
| Top Component Center |
0 |
0 |
0 |
12 |
0 |
0 |
0 |
0 |
| Top 3D Body |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
6 |
| Top Courtyard |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
| Mechanical 13 |
4 |
0 |
0 |
76 |
0 |
0 |
0 |
24 |
| Bottom 3D Body |
0 |
0 |
0 |
48 |
0 |
0 |
0 |
13 |
| Mechanical 15 |
0 |
0 |
0 |
96 |
0 |
0 |
0 |
0 |
| Bottom Component Center |
0 |
0 |
0 |
26 |
0 |
0 |
0 |
0 |
| Top Component Outline |
0 |
0 |
0 |
48 |
0 |
0 |
0 |
0 |
| Bottom Component Outline |
0 |
0 |
0 |
4 |
0 |
0 |
0 |
0 |
| Bottom Courtyard |
0 |
0 |
0 |
48 |
0 |
0 |
0 |
0 |
| Multi-Layer |
0 |
82 |
22 |
0 |
0 |
0 |
1 |
0 |
| Top Paste |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
| Top Overlay |
9 |
0 |
0 |
192 |
54 |
0 |
0 |
0 |
| Top Solder |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
| Bottom Solder |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
| Bottom Overlay |
3 |
0 |
0 |
35 |
42 |
1 |
0 |
0 |
| Bottom Paste |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
| Drill Guide |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
| Keep-Out Layer |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
| Drill Drawing |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
| Total |
16 |
151 |
22 |
965 |
108 |
2 |
157 |
77 |
Back to top
| Drill Pairs |
Vias |
| Top Layer - Bottom Layer |
22 |
| Total |
22 |
Back to top
Copper Area
| Layer |
Sq.In. |
Sq.MM. |
Percent of layer |
| Top Layer |
4,433 |
2860,249 |
67% |
| Bottom Layer |
5,216 |
3365,247 |
79% |
| Total |
NaN |
NaN |
|
Back to top
Board Holes
| Non-Plated Hole Size |
Pads |
Vias |
| 0mm
|
2 |
0 |
| Total |
2 |
0 |
Back to top
Back to top
Board Slots
| Non-Plated Slot Size / Length |
Pads |
| Total |
0 |
Back to top
| Plated Slot Size / Length |
Pads |
| Total |
0 |
Back to top
Board Square Holes
| Non-Plated Square Hole Size |
Pads |
| Total |
0 |
Back to top
| Plated Square Hole Size |
Pads |
| Total |
0 |
Back to top
Other
Back to top
Back to top
Back to top
Back to top
Back to top
| Pad Pwr/Gnd Expansion |
Count |
| 0.35mm
|
151 |
| Total |
151 |
Back to top
| Pad Relief Conductor Width |
Count |
| 0.35mm
|
151 |
| Total |
151 |
Back to top
| Pad Relief Air Gap |
Count |
| 0.35mm
|
151 |
| Total |
151 |
Back to top
| Pad Relief Entries |
Count |
| 4 |
151 |
| Total |
151 |
Back to top
| Via Solder Mask |
Count |
| -0.6mm
|
22 |
| Total |
22 |
Back to top
| Via Pwr/Gnd Expansion |
Count |
| 0.35mm
|
22 |
| Total |
22 |
Back to top
Back to top
Back to top
Back to top
| Arc Degrees |
Count |
| 360 |
16 |
| Total |
16 |
Back to top
Back to top
Back to top
| Net Track Width |
Count |
| 0.35mm
|
42 |
| Total |
42 |
Back to top
| Net Via Size |
Count |
| 0.6mm
|
42 |
| Total |
42 |
Back to top


{% load staticfiles %}
PEnhancer - {% block title %}Try PEnhancer{% endblock %}
Welcome {% if user.get_username %} {{ user.get_username }} {% else %} {% for row in user %} {{ row.username}} {%endfor%} {% endif %}
Account Settings
Change Password
Logout
Feeds
News
Featured Lends
Point Transaction
Catlog
Category
Brand
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User List
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Configuration
Client
Chat {% if unread_chat > 0 %}
{{unread_chat}}
{% endif %}
{% block body_block %} {% endblock %}


@TupleConstructor
@MapConstructor
includes
excludes
Example:
// unresolved 'c' @TupleConstructor(includes = ['a', 'b', 'c']) class X { def a def b }


Placement Policy: Select Direct Mapped Set Associative Fully Associative Replacement Policy: Select LRURandom Number of Blocks: Select 48 1632 64128 Cache block size (bytes): Select 48 1632 64 Number of Sets: Select 12 48 1632 64128
Cache Capacity: UnDefined Yet


Game of Life
Draw live cells with mouse. Press 'r' to randomize, 'w' to wipe and 'p' to play/pause
The Rules
Live Cell = BLACK, Dead Cell = White
- Any live cell with fewer than two live neighbours dies.
- Any live cell with two or three live neighbours lives on to the next generation.
- Any live cell with more than three live neighbours dies.
- Any dead cell with exactly three live neighbours becomes a live cell.


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Leave the baking to us. Contact us at<EMAIL_ADDRESS>for more information.


# Check Python Version
import sys
import scipy
import numpy
import matplotlib
import pandas
import sklearn
print('Python: {}'.format(sys.version))
print('scipy: {}'.format(scipy.__version__))
print('numpy: {}'.format(numpy.__version__))
print('matplotlib: {}'.format(matplotlib.__version__))
print('pandas: {}'.format(pandas.__version__))
print('sklearn: {}'.format(sklearn.__version__))
import numpy as np
from sklearn import preprocessing, cross_validation
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn import model_selection
from sklearn.metrics import classification_report
from sklearn.metrics import accuracy_score
from pandas.plotting import scatter_matrix
import matplotlib.pyplot as plt
import pandas as pd
# Load Dataset
url = "https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/breast-cancer-wisconsin.data"
names = ['id', 'clump_thickness', 'uniform_cell_size', 'uniform_cell_shape',
'marginal_adhesion', 'single_epithelial_size', 'bare_nuclei',
'bland_chromatin', 'normal_nucleoli', 'mitoses', 'class']
df = pd.read_csv(url, names=names)
# Preprocess the data
df.replace('?',-99999, inplace=True)
print(df.axes)
df.drop(['id'], 1, inplace=True)
# Let explore the dataset and do a few visualizations
print(df.loc[10])
# Print the shape of the dataset
print(df.shape)
# Describe the dataset
print(df.describe())
# Plot histograms for each variable
df.hist(figsize = (10, 10))
plt.show()
# Create scatter plot matrix
scatter_matrix(df, figsize = (18,18))
plt.show()
# Create X and Y datasets for training
X = np.array(df.drop(['class'], 1))
y = np.array(df['class'])
X_train, X_test, y_train, y_test = cross_validation.train_test_split(X, y, test_size=0.2)
# Testing Options
seed = 8
scoring = 'accuracy'
# Define models to train
models = []
models.append(('KNN', KNeighborsClassifier(n_neighbors = 5)))
models.append(('SVM', SVC()))
# evaluate each model in turn
results = []
names = []
for name, model in models:
kfold = model_selection.KFold(n_splits=10, random_state = seed)
cv_results = model_selection.cross_val_score(model, X_train, y_train, cv=kfold, scoring=scoring)
results.append(cv_results)
names.append(name)
msg = "%s: %f (%f)" % (name, cv_results.mean(), cv_results.std())
print(msg)
# Make predictions on validation dataset
for name, model in models:
model.fit(X_train, y_train)
predictions = model.predict(X_test)
print(name)
print(accuracy_score(y_test, predictions))
print(classification_report(y_test, predictions))
# Accuracy - ratio of correctly predicted observation to the total observations.
# Precision - (false positives) ratio of correctly predicted positive observations to the total predicted positive observations
# Recall (Sensitivity) - (false negatives) ratio of correctly predicted positive observations to the all observations in actual class - yes.
# F1 score - F1 Score is the weighted average of Precision and Recall. Therefore, this score takes both false positives and false
clf = SVC()
clf.fit(X_train, y_train)
accuracy = clf.score(X_test, y_test)
print(accuracy)
example_measures = np.array([[4,2,1,1,1,2,3,2,1]])
example_measures = example_measures.reshape(len(example_measures), -1)
prediction = clf.predict(example_measures)
print(prediction)


Preparing Exam: {{ teacher.id }} {{ subject }}
Exam Info
Date:
Start:
Duration:
hr
min
Multiple Choice
In total of
Question(s).
Fill in Blank
In total of
Question(s).
Q & A
In total of
Question(s),


WITNESS LIST Human Services Committee April 23, 2001-2:00P SB 177 For: Ferris, Beth (Texas Advocates for Nursing Home Residents) Registering, but not testifying: For: Hayes, Aaryce (Advocacy, Inc.) Sandlin, Abby (Texas Watch) Wisdom, Marie (Advocates for Nursing Home Reform) SB 355 For: Breeding, John (Self) Dolan, Dr. Moira (Self) Fecher, Lauren (Citizen's Commission on Human Rights) Wilson, Laura Ann (Self) Registering, but not testifying: For: Chapman, Randall (Legal Hotline with Older Texans) Ferris, Beth (Texas Advocates for Nursing Home Residents) Hayes, Aaryce (Advocacy, Inc.) McGiffert, Lisa (Consumers Union) McMurrough, Amy (Self) McPherson, Kim (The Mental Health Association in Texas) Miztcles, Amy (National Alliance for the Mentally Ill of Texas) Sandlin, Abby (Texas Watch) Spiller, Lee (Citizen's Commission on Human Rights) Wisdom, Marie (Advocates for Nursing Home Reform) SB 833 For: Ivy, Floyd (Permian Basin Licensed Child Care Association) Pewitt, Bill (Texas Licensed Child Care Association) Registering, but not testifying: For: Chatelle, Melody (United Ways of Texas) Kostantenaco, Linda (Self and San Antonio Chapter - Texas Licensed Child Care Association) On: Rasco, Sasha (Texas Department of Protective and Regulatory Services) SB 875 Registering, but not testifying: For: Bresette, Patrick (Center for Public Policy Priorities) On: Walker, Dr. John (Texas Department of Health) SB 1238 Registering, but not testifying: For: Albright, Wendy (Self) Chatelle, Melody (United Ways of Texas) Horsford, Marshall (Self) On: Rasco, Sasha (Texas Department of Protective and Regulatory Services)


int fseek ( resource $handle , int $offset [, int $whence = SEEK_SET ] )
Sets the file position indicator for the file referenced by handle. The new position, measured in bytes from the beginning of the file, is obtained by adding offset to the position specified by whence. In general, it is allowed to seek past the end-of-file; if data is then written, reads in any unwritten region between the end-of-file and the sought position will yield bytes with value 0. However, certain streams may not support this behavior, especially when they have an underlying fixed size storage.
Upon success, returns 0; otherwise, returns -1.


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{% extends 'base.html' %} {% import 'macros.html' as macro %} {% import 'bootstrap/wtf.html' as wtf %}
Design is not just what it looks like and feels like. Design is how it works..


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For years past Unix-like operating systems (Linux, the BSDs, etc.) were successfully used as workstations and servers. At some point, however, a community of keen observers noticed the potential for growth in new directions. Although GNU (in a general sense) + Linux (kernel) = chaos, it also means that GNU + Linux is an ever-shifting landscape and that it can be anything. Ranging from a tightly-secured supercomputer to a lean and mean hacker’s haven or even a desktop operating system for the masses. In modern times access to computers, big and small, fast and moderately speedy is so prevalent that the niche for an additional operating system is definitely there! Especially, considering Microsoft’s mishaps in recent years. Therefore, I decided to look at how the GNU/Linux ecosystem has changed and perhaps make some predictions for the future.
Ubuntu was, is and probably will be at the top of the Linux food chain for years to come. Canonical (the company behind Ubuntu) knows how to make use of their product and the growing prevalence of Linux on personal computers. It's no surprise that when people think Linux, they really mean Ubuntu. For the majority of everyday computer users Ubuntu defines Linux. Fortunately, the overall image of Linux has improved over the past years. I remember how in high school my peers would consider Linux to be some sort of niche hacker operating system with a pretentiously bizarre name. Nowadays, there is more respect and consideration – partially thanks to Ubuntu.
Apart from Ubuntu, people recognize the names Red Hat and SUSE. They are not as popular as Ubuntu among end users, but they have earned quite some esteem in the enterprise sector. SUSE Linux Enterprise Server (SLES) and Red Hat Enterprise Linux (RHEL) used to be offered as Windows alternatives for the HP/Compaq series of office desktop computers. Both distributions gave birth to successful community projects – Fedora (Core) and openSUSE.
Thanks to the rapid advancements on the global electronics market and the increasing openness of hardware manufacturers, Unix-like operating systems are shaping up to become the future of computing. Financial backing from Red Hat, Canonical, SUSE and many other companies provides jobs for skilled developers and creates a significant presence on the computing market. In addition, Unix makes for a very flexible, stable and secure all-purpose platform. However, what in my opinion matters the most is community. Much more can be achieved when customers are willing to improve their favorite operating system by providing feedback, filing bug reports, helping each other, etc. Finally, I recently took a look at the hardware components of various laptops and notebooks offered at a local retailer and most of them have a low-powered Intel or AMD processor, coupled with an integrated Intel or Radeon GPU. Apparently, the majority of complaints regarding hardware compatibility of Linux is no longer valid. Nowadays, almost any laptop will do.


You can use the Nimbits SDK from a .net project. There is a utility you can use to convert Java JARS to .NET DLLs and add the use the DLL in your project.
ikvmc -target:library [path to nimbits java jar]\nimbits.jar
class Program { static void main(string[] args) { EmailAddress emailAddress = CommonFactory.createEmailAddress("test@example.com"); NimbitsUser user = UserFactory.createNimbitsUser(emailAddress, "pas$$word"); NimbitsClient client = NimbitsClientFactory.getInstance(user, "http://mynimbitsserver.domain.com"); PointName pointName = CommonFactory.createPointName("MyDataPoint"); Value currentValue = client.getCurrentRecordedValue(pointName); } }


Google Assistant is rolling out in Portuguese (Portugal)
by Rita El KhouryIt's been more than two years since Google Assistant became available in the Brazilian localization of Portuguese, but speakers of the Portugal version (pt-PT) were left waiting. Google has been working on that for over a year, and it began testing it in a closed beta a few months ago, but it appears ready to make it public now.
Head over to your Assistant's settings and look for the português (Portual) option. Or, if your device is already set to that locale and you didn't have Assistant yet, try tapping and holding on the home button to see if it pops up. When it's enabled, you can ask Assistant questions in your language, play music, and control some smart home devices.
Given many of the comments on Pplware's website, the language option is rolling out to many but still hasn't showed up for everyone just yet. It also hasn't been officially announced or added to any Google support documentation, so be patient if you still don't have it.
Thanks:
Miguel Ferreira,
Andre Mendes,
Jorge Rodrigues
Image credit: Pplware.sapo.pt


Express 3.x IS NO LONGER MAINTAINED
Known and unknown security and performance issues in 3.x have not been addressed since the last update (1 August, 2015). It is highly recommended to use the latest version of Express.{% include api/{{ page.lang }}/3x/express.md %} {% include api/{{ page.lang }}/3x/app.md %} {% include api/{{ page.lang }}/3x/req.md %} {% include api/{{ page.lang }}/3x/res.md %} {% include api/{{ page.lang }}/3x/middleware.md %}


NeWS
`Prev `__
N
`Next `__
--------------
**NeWS**: /nee´wis/, /n[y]oo´is/, /n[y]ooz/, n.
[acronym; the “Network Window System”] The road not taken in window
systems, an elegant `*PostScript* <../P/PostScript.html>`__-based
environment that would almost certainly have won the standards war with
`*X* <../X/X.html>`__ if it hadn't been
`*proprietary* <../P/proprietary.html>`__ to Sun Microsystems. There is
a lesson here that too many software vendors haven't yet heeded. Many
hackers insist on the two-syllable pronunciations above as a way of
distinguishing NeWS from Usenet news (the `*netnews* `__
software).
--------------
+----------------------------+----------------------------+------------------------------+
| `Prev `__ | `Up <../N.html>`__ | `Next `__ |
+----------------------------+----------------------------+------------------------------+
| newline | `Home <../index.html>`__ | newsfroup |
+----------------------------+----------------------------+------------------------------+


Chapter 2 Importing Data into R
2.1 Read in data
The two datasets we will be using are:
- messy_demographic.csv:
- 5 variables:
- subject identifier (subject_ID), age, sex, ethnicity, and diagnosis (dx)
- messy_cognitive.csv:
- 4 variables:
- subject identifier (subID), and three cognitive scores (cog1, cog2, cog3)
In order to view and manipulate this data in R, we need to import the data into our R workspace (the same as you would open a file in Excel to edit it).
Rstudio trick:
- Click on the
Environment
Tab, then click onImport Dataset
–>From Text File (readr)
- Navigate the browser window to the location of messy_cognitive.csv and click Open
- This opens a text reader window: You see the raw text on the top and what R will read in (the data frame) at the bottom
- In my view, it looks like the R is not going to read in the first line as a header..to change this
- switch the Heading option on the right to yes
- Click Import
- Now, if you look at the Environment tab you should see that demo_df has been loaded into R, It has 350 rows (or observations) and 5 variables
- So that you do not have to type this again tomorrow - go to History, click on the line “demo_df <- read.csv(…)” and then click on To Source
- Repeat this whole process for messy_demographic.csv
library(readr)
demo_df <- read_csv("~/Downloads/messy_demographic.csv")
cog_df <- read_csv("~/Downloads/messy_cognitive.csv")
- What you actually did was use the
read_csv
function… to find out more about this option you can type?read_csv
in the Console - This is the basic syntax of R functions: some.function(“stuff inside to do the function on”)
- The help document for the
read_csv
function is shown in the Help tab
Now we have two “data frames” loaded into our workspace. They are called demo_df and cog_df.
2.2 Basic data summaries and visualization ( head, tail, describe() )
- Now that we have the data loaded, how do we just look at it? The simplest way is with the “View” function within RStudio.
- In Enviroment tab, click on the little spreadsheet to the far right of the
demo_df
row. This shows you your data in what looks like a spreadsheet - but you cannot edit it!
To look at the top six rows of your data:
head(demo_df)
## # A tibble: 6 x 5
## subject_ID age sex ethnicity dx
## <chr> <chr> <dbl> <chr> <chr>
## 1 SUB_1 43 0 Cauc 0
## 2 SUB_2 47 1 Cauc 1
## 3 SUB_3 69 1 Cauc 1
## 4 SUB_4 51 0 Cauc 1
## 5 SUB_5 52 1 Cauc 0
## 6 SUB_6 71 0 AA 1
To look at the bottom six rows:
tail(cog_df)
## # A tibble: 6 x 4
## subID cog1 cog2 cog3
## <chr> <chr> <chr> <chr>
## 1 subject345 10.5491439098318 29.0366805293698 50.0341778674542
## 2 subject346 13.7560798734217 21.047620123942 11.2510017219872
## 3 subject347 16.4949897425522 33.323511618334 2.42614834379569
## 4 subject348 11.1612493587292 30.8723352333704 16.0049438698844
## 5 subject349 15.3654440645612 29.7598065423247 44.3994545119479
## 6 subject350 13.993297479479 28.3229119000634 11.2012255384154
Using the function names()
tells us what all the variables in our dataframe are called.
names(demo_df)
## [1] "subject_ID" "age" "sex" "ethnicity" "dx"
the ls()
function does the same thing, except it returns the variables in alphabetical order
ls(demo_df)
## [1] "age" "dx" "ethnicity" "sex" "subject_ID"
That was all nice, but we want to find out more about this data we can use summary()
summary(demo_df)
## subject_ID age sex
## Length:350 Length:350 Min. : 0.00
## Class :character Class :character 1st Qu.: 0.00
## Mode :character Mode :character Median : 1.00
## Mean : 29.44
## 3rd Qu.: 1.00
## Max. :9999.00
## NA's :3
## ethnicity dx
## Length:350 Length:350
## Class :character Class :character
## Mode :character Mode :character
##
##
##
##
summary(cog_df)
## subID cog1 cog2
## Length:350 Length:350 Length:350
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
## cog3
## Length:350
## Class :character
## Mode :character
2.3 Data cleaning
- Now that you have looked at your data - you might have noticed that there are a couple problems The RA that you have been working with has coded missing values in three different ways (“9999”, “missing”, and "")
- We first need to set these all to NA - which R recognizes as missing value:
The following will take all values in demo_df that are equal to "“,”missing“, or”9999", and code them as missing in a way that R understands:
demo_df[demo_df==""] <- NA
demo_df[demo_df=="missing"] <- NA
demo_df[demo_df=="9999"] <- NA
Because R is “smart”, it categorizes data types automatically when data are loaded. Before working with new data, especailly if it is real (i.e. messy), it is important to tell R what kind of data you are dealing with. This will be especially important when we discuss our statistical analyses… after all, R is statistical software.
The following will correctly format our variables for analyses:
- age is a numeric variable
- ethicity is a discrete factor
- sex is a discrete factor
- diagnosis is a discrete factor
demo_df$age <- as.numeric(as.character(demo_df$age))
demo_df$ethnicity <- factor(demo_df$ethnicity,levels=c("Cauc","AA","As","In","Other"))
demo_df$sex <- factor(demo_df$sex, levels=c(0,1), labels=c("Male","Female"))
demo_df$dx <- factor(demo_df$dx, levels=c(0,1), labels=c("Control","Case"))
By indicating the levels of our factors, we have erased from R the memory that we once had values of "“,”9999“, and”missing" (which up until now R had no reason to assume were not observations).
Let us now apply the same cleanup steps to our second data frame:
Remove missing:
cog_df[cog_df==""] <- NA
cog_df[cog_df=="missing"] <- NA
cog_df[cog_df=="9999"] <- NA
Correctly format variables for analyses:
cog_df$cog1 <- as.numeric(as.character(cog_df$cog1))
cog_df$cog2 <- as.numeric(as.character(cog_df$cog2))
cog_df$cog3 <- as.numeric(as.character(cog_df$cog3))
2.4 Merging data frames
In order to analyze the effect of sex on diagnosis, or perform any other comparison across our data frames, we should merge them. If you remember only this and nothing else today, it will still have been worth your time.
Conceptually, merging two data frames assumes that the rows in one correspond to rows in the other, even if they are not in the same order. In order to match up the correct rows between data frames we need to make sure that one column in each spreadsheet can act as a “key” (i.e. each row has a unique value in this key that is the same in both spreadsheets). In our case, we have one subject identifier column in each of our spreadsheets.
2.4.1 First we need to make sure that the values in these columns are the same
We are going to make use a package called stringr
, which was built to help us manipulate “strings” (string is a computer science word of sets of characters).
Note: There are many ways to deal strings in r, too many ways in fact. stringr
was created to make the commands working with strings more consistent so that your code will be easier for another person to read.
library(stringr)
cog_df$subject_ID <- str_replace(cog_df$subID,"subject","SUB_")
We can then merge the two datasets by specifying their names (in order x,y) and then specifying which columns are to be used as the key to merging the two data frames (by.x and by.y):
library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
alldata <- inner_join(demo_df,cog_df,by="subject_ID")
Skipping ahead a little - now we can look at histograms of our numeric variables, just to see what we are dealing with:
hist(cog_df$cog1)
hist(cog_df$cog2)
hist(cog_df$cog3)
hist(demo_df$age)
Now that our data are loaded, cleaned, and merged, it is time to do some basic statistics!
2.4.2 STUDY QUESTION 1: What is the relationship between sex and diagnosis?
For this question, our null hypothesis is that there is no difference in the number of males and females between our case and control diagnosis groups
The ftable() function will give us a 2 x 2 contingency table of the frequency of observations in each category. the formula syntax “y ~ x” is common in R!
ftable(data=alldata,dx~sex)
## dx Control Case
## sex
## Male 37 90
## Female 127 86
We now want to save that table as an object called “dxXsex_table”:
dxXsex_table <- ftable(data=alldata,dx~sex)
Now, in order to test our null hypothesis using a chi-squared test, we simply apply the chisq.test() function to that table:
chisq.test(dxXsex_table)
##
## Pearson's Chi-squared test with Yates' continuity correction
##
## data: dxXsex_table
## X-squared = 28.415, df = 1, p-value = 9.791e-08
Similarly, we can use the nonparametric Fisher test to get a more exact test statistic:
fisher.test(dxXsex_table)
##
## Fisher's Exact Test for Count Data
##
## data: dxXsex_table
## p-value = 5.68e-08
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.1686647 0.4567064
## sample estimates:
## odds ratio
## 0.279488
A bit more advanced!
This will accomplish the same thing as ftable(), except that here we are indexing our alldata dataframe with the R syntax [
table(alldata[ ,c("dx","sex")])
## sex
## dx Male Female
## Control 37 127
## Case 90 86
2.4.3 STUDY QUESTION 2: What is the relationship between diagnosis and cog1?
for this question, our null hypothesis is that there is no difference in cog1 between our case and control diagnosis groups
t.test(cog1 ~ dx, data=alldata)
##
## Welch Two Sample t-test
##
## data: cog1 by dx
## t = -6.347, df = 334.92, p-value = 7.133e-10
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -2.871388 -1.512676
## sample estimates:
## mean in group Control mean in group Case
## 9.940047 12.132079
library(ggplot2)
ggplot(alldata, aes(x=dx, y=cog1)) +
geom_boxplot()
## Warning: Removed 5 rows containing non-finite values (stat_boxplot).
P.S.* Here is an R script with all of the steps we went over today!! Download Intro R script © 2018 GitHub, Inc. Terms Privacy Security Status Help Contact GitHub Pricing API Training Blog About


Project :: Shinto Mon 神道門
Shinto Mon is a simple game written in C# with Unity. It takes much influence from the shrine gate that stands near the shore of Lake Biwa in Shiga Prefecture, Japan. The game is based on classic breakout games of the 80's, you must grant all the wishes by collecting all the ema with bouncing orbs. Ema are wishes written by ordinary people on wooden (and sometimes gold) tablets at Shinto and Buddhist shrines all over Japan. The game environment attempts to provide a calm atmosphere and gaming experience, moving away from the challenging and exciting spectrum. The goal was not to make the game-play very difficult, although some levels will be quite challenging, but instead to provide enjoyable game-play in short stints.
Source => http://github.com/retroburst/ShintoMon.Unity.3D
External Sources
- Textures sourced from Spiral Graphics, used under the royalty free license.
- Sounds sourced from Freesound.org, the following users are attributed with contributions Taira Komori, jakobthiesen, Setuniman, ryancacophony, percussionfiend, JohnLaVine333, jalastram.
- Icon sourced from freepik.
- 'YuzuPop' Japanese font sourced from black-yuzunyan.
- Sans and serif font sourced from GNU.
- Skybox sourced from Unity Asset Store.
Default Bindings
- Movement - Left, Right or Swipe Left and Right for Touch
- Launch Orb - Control or Tap for Touch
- Pause Game - P
- Restart Game - R
- Toggle Background Sounds - M
- Toggle Sound Effects - N
- Save Game - S
- Load Game - L
How to Win
You need to collect all the ema using a bouncing orb - through all levels the game throws at you.Number of Levels
- Standard : 11 levels
Notes
The game will store your preferences such as mute state of music and sound effects. You can also save and load your game.WebGL Build
At this stage Unity WebGL player is starting to support mobile device browsers but that functionality can sometimes be patchy. For best results, play the WebGL build in any decent modern desktop browser that supports HTML5. Touch input is now be supported in the WebGL player as of Unity 5.1 (2015-06-16).Known issues
- Unity rendering issue : lighting on terrain is not working at all in WebGL build (mountains do not render correctly).
Play Shinto Mon in Unity WebGL Player


Home
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Just check how I spent a eve playing football !!!
Please scroll down the pdf viewer to read the full comic !!!


Welcome to the Portfolio of Jonathan Hamer a Fullstack Web Developer. Click below to read more about me!
About MeHailing from Malvern, PA, Jonathan moved to Atlanta where he recieved his BA in Business Administration with a concentration in Accounting from Morehouse College. After a few years of being an accountant, Jonathan decided to make a change and follow his long time passion of development and coding. Because he knows coding is the future and he wants to be apart of writing it.
My Skills!State, Props, Components, Lifecycle methods like ComponentWillMount etc.
Data Models, Arrays, functions such as: .map .filter etc!
Use postgresql to make API calls so users can add, update, delete data.
Experience with Sql and NoSql Databases
Efficent in using DevTools and Error Driven Development
Front end styling using raw or styled components within a variety of languages.
Stay up to date with new technologies through blogs, twitter updates and Mozilla Developer Network posts
Git commits, creating repositories, merging branches.
Below is my phone number and email address. I look forward to hearing from you in the near future!
610-563-3556


HERE YOU CAN HAVE A GLIMPSE OF MY WORK
Search for a city to get weather information.
Prevent a threat to security by obtaining a secure and random password with this password generator.
The universe is yours to explore.
Javascript multiple choices quiz application.


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Minimal plugin for popup tweets window
This is a plugin that you can put a popup panel that holds your tweets
Here is a demo
_ of sample effect
Put poptweet.html under the _includes folder of your Jekyll project.
Get your own twitter publish code at https://publish.twitter.com/, for example, replace this code in poptweet.html
Tweets by junlulocky

'junpopup.css'
include /poptweet.html


{{ (climbingSite$ | async).address.street }} {{ (climbingSite$ | async).address.postCode + ' ' + (climbingSite$ | async).address.city }} {{ (climbingSite$ | async).address.state }}


| ID | Name | Description | Required People Count | Currently Avalable People | Created Date Time | Start Date Time | End Date Time | Requires Approval For Joining | Is Available For Joining | Location | Creator Profile | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| {{game.id}} | {{game.name}} | {{game.description}} | {{game.requiredPeopleCount}} | {{game.currentlyAvalablePeople}} | {{game.createdDateTime | date:'medium'}} | {{game.startDateTime | date:'medium'}} | {{game.endDateTime | date:'medium'}} | {{game.requiresApprovalForJoining}} | {{game.isAvailableForJoining}} |
|


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