When we plan our trips, we do not always need a detailed route right away. Sometimes we just need to measure the direct distances on the map. What is the distance between two cities? How far are we from the center? Now you can use a simple ruler to answer such questions.
To use the Ruler for Android, just go to the Configure screen menu and enable 'Radius ruler' (Right panel). You can show or hide Compass Ruler. After you enable it, you'll see the circles showing distance on the map. You will also be able to check the distance from your current location to the map center on the widget above.
To check the distance from your location to a point on the map, just touch a spot on the map. You'll see the measurement and the line connecting the two points. The line will stay on screen as long as you are touching it.
To check the distance between two random points, touch them simultaneously and see the information in the same widget. The line will fade by itself after 2 seconds. You can customize your ruler. Press the widget to select one of three modes - the black scale, the gray one and no scale (only ruler). You can also adjust this setting and the way the widget looks in Configure screen - 'Radius ruler' menu.
Please see full video guide below:
To set up the Ruler fo iOS you can follow to Configure screen-> Radius ruler (Right panel), enable it. You'll see the circles showing distance on the map. You will also be able to check the distance from your current location to the map center on the widget above.
To check the distance from your location to a point on the map, just touch a spot on the map. You'll see the measurement and the line connecting the two points. The line will stay on screen as long as you are touching it.
To check the distance between two random points, touch them simultaneously and see the information in the same widget. The line will fade by itself after 2 seconds. You can customize your ruler. Press the widget to select one of three modes - the black scale, the white one and no scale (only ruler).
You can show or hide Compass Ruler: menu -> Configure scree -> Radius ruler -> Settings (Show or Hide compass ruler).
Please see full video guide below:
OsmAnd at Facebook, Twitter, and Reddit!
Join us at our groups of Telegram (EN), (IT), (FR), (DE), (RU), (UA).


This demo demonstrates an issue with Google Chrome versions 8-10 (possibly 11) where if you push a state with data, then do history.back to the initial state, the event.state will contain the pushed states data instead of being null.
Note: The issue requires a clean history list, as such this should always be opened in a new tab/window where there are no prior history items.
Reported by Benjamin Lupton author of History.js


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The jMock project follows strict rules that govern its use of version numbers. The version number of a release indicates how that release is compatible with previous and future releases.
Version numbers have the form major.minor.patch. The major version number identifies the API version. A release that changes the API in a way that breaks backwards compatibility will increment the major version number and reset the minor and patch versions to zero. The minor version number identifies the backwards compatible revision of the API. An API version that adds new elements to the API will have the same major version, increment the minor version and set the patch version to zero. The patch version number identifies revisions that do not change the API. A release that fixes bugs or refactors implementation details without changing the API will have the same minor and major versions as the previous release and increment the patch number.
A hypothetical example:
| 1.0.0 | First release |
| 1.0.1 | Improves Javadoc comments |
| 1.1.0 | Adds new API elements |
| 1.2.0 | Adds new API elements, deprecates some API elements |
| 1.2.1 | Fixes bugs |
| 2.0.0 | Incompatible API changes, removes API elements deprecated by version 1.2.0. |
| 2.1.0 | Adds new API elements |
| etc. | etc. |
Before a new major or minor release, jMock will make release candidate (RC) packages available so that users can test them against their own code. There will be one or more release candidates given the version major.minor.0.RCn, where the major and minor version numbers identify the upcoming release and RC1 identifies the first candidate release, RC2 the second, and so on.
A release candidate does not guarantee backward compatability with new API features introduced by any previous RC of the same upcoming version. A major version RC can change/remove API features introduced in a previous RC for the same major version; a minor version RC can change API features introduced by any previous RC of the same upcoming minor version but guarantees backward compatability with the previous minor version.
During development, the continuous integration process automatically builds snapshot packages. These are not guaranteed to be complete: although the unit tests will all pass the snapshot will probably contain failing acceptance tests that describe planned or requested features that have not yet been implemented. Snapshots are identified by the UTC time at which the continuous integration process built the package. The timestamp has the form YYYYMMDD-hhmmss, where YYYY is the (four-digit) year, MM the month, DD the day, hh the hour, mm the minute and ss the second. The snapshot version is not included in the filename of the snapshot archive itself, but expanding the archive will create a directory named after the snapshot version.
A minor release might deprecate some API features. Deprecated features will not actually be removed until the next major release. A release will never remove API features that have not been deprecated in a previous release.


[§257-8] Matches. (a) The State shall match an amount of up to $100,000 per calendar year for individual development accounts.
(b) Not more than a 2:1 match of state funds to account holder deposits shall be deposited into any individual development account in a given year. [L 1999, c 160, pt of §25]


| Class and Description |
|---|
| Outer
Smart proxies and composite proxies extending their functionalty via calls on
the inner proxies implement this interface.
|
| Partner
Servers and other compound proxies using inner proxies to extend their
functionalty via calls on the inner proxies implement this interface.
|
| RemotePartner
Remote servers and other compound remote proxies using inner proxies to
extend their functionality via calls on the inner proxies implement this
interface.
|
| Class and Description |
|---|
| Outer
Smart proxies and composite proxies extending their functionalty via calls on
the inner proxies implement this interface.
|
| Partner
Servers and other compound proxies using inner proxies to extend their
functionalty via calls on the inner proxies implement this interface.
|
| Partnership
Smart proxies and other proxies containing inner proxies (partners) can
extend their functionalty via calls on partners.
|
| Class and Description |
|---|
| Outer
Smart proxies and composite proxies extending their functionalty via calls on
the inner proxies implement this interface.
|
| Partner
Servers and other compound proxies using inner proxies to extend their
functionalty via calls on the inner proxies implement this interface.
|
| Partnership
Smart proxies and other proxies containing inner proxies (partners) can
extend their functionalty via calls on partners.
|
| Class and Description |
|---|
| Outer
Smart proxies and composite proxies extending their functionalty via calls on
the inner proxies implement this interface.
|
| Partner
Servers and other compound proxies using inner proxies to extend their
functionalty via calls on the inner proxies implement this interface.
|
| Partnership
Smart proxies and other proxies containing inner proxies (partners) can
extend their functionalty via calls on partners.
|
| Class and Description |
|---|
| Outer
Smart proxies and composite proxies extending their functionalty via calls on
the inner proxies implement this interface.
|
| Partner
Servers and other compound proxies using inner proxies to extend their
functionalty via calls on the inner proxies implement this interface.
|
| Partnership
Smart proxies and other proxies containing inner proxies (partners) can
extend their functionalty via calls on partners.
|
| Class and Description |
|---|
| Outer
Smart proxies and composite proxies extending their functionalty via calls on
the inner proxies implement this interface.
|
| Partner
Servers and other compound proxies using inner proxies to extend their
functionalty via calls on the inner proxies implement this interface.
|
| Partnership
Smart proxies and other proxies containing inner proxies (partners) can
extend their functionalty via calls on partners.
|
| Class and Description |
|---|
| Outer
Smart proxies and composite proxies extending their functionalty via calls on
the inner proxies implement this interface.
|
| Partner
Servers and other compound proxies using inner proxies to extend their
functionalty via calls on the inner proxies implement this interface.
|
| Partnership
Smart proxies and other proxies containing inner proxies (partners) can
extend their functionalty via calls on partners.
|
| Class and Description |
|---|
| Outer
Smart proxies and composite proxies extending their functionalty via calls on
the inner proxies implement this interface.
|
| Partner
Servers and other compound proxies using inner proxies to extend their
functionalty via calls on the inner proxies implement this interface.
|
Copyright © 2013 Sorcersoft.com S.A.. All Rights Reserved.


Our service provides a simple elegant way to keep track of bills and how they move across congress and how each member of congress votes on them.
With our service YOU can vote on bills, amendments, keep track of how you voted and even check if you congressmembers voted as you did.
Our service will allow a person to check on other members of congress and see how much they agree with them on various issues using thier past voting history.
Our idea to build a database application that allows people to keep track of their own congress people comes at a time of great political turmoil. So much is reliant on Congress but thier inability to do anything has hurt the country. We hope that our app shows people who is most and least effective at enacting change so that they know who to vote for, and against.
A large amount of data on senate is made available by Sunlight Foundation. They have a public api which includes information about how congress votes and who is up for re-election.
Coolness Engineer
No one Important


CARNEGIE-MELLON UNIVERSITY
College of Humanities and Social Sciences
DISSERTATION
Submitted in Partial Fulfillment of the Requirements For the Degree of _Doctor of Arts in History
Title Municipal Organization for Public Health in
Pittsburgh, 1851-1895
Presented by Jacqueline Karnell Corn
Accepted by the Department of _ History
Director o Losertation LL
See ¢ 47 1G Dy Date
Qe € or, Ta Daa 22,777
Date /
Approved by the Committee on Graduate Degrees
L A - ) 4 Date
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.


| 002:239 Khan | : |
And if you fear (an enemy), perfrom Salat (pray) on foot or riding. And when you are in safety, offer the Salat (prayer) in the manner He has taught you, which you knew not (before). |
| 002:239 Maulana | : |
But if you are in danger (say your prayers) on foot or on horseback. And when you are secure, remember Allah as He has taught you what you knew not. |
| 002:239 Pickthal | : |
And if ye go in fear, then (pray) standing or on horseback. And when ye are again in safety, remember Allah, as He hath taught you that which (heretofore) ye knew not. |
| 002:239 Rashad | : |
Under unusual circumstances, you may pray while walking or riding. Once you are safe, you shall commemorate GOD as He taught you what you never knew. |
| 002:239 Sarwar | : |
In an emergency you may say your prayers while walking or riding; but when you are safe, remember God, as He has taught you what you did not know before. |
| 002:239 Shakir | : |
But if you are in danger, then (say your prayers) on foot or on horseback; and when you are secure, then remember Allah, as. He has taught you what you did not know. |
| 002:239 Sherali | : |
If you are in a state of fear, then say your prayers on foot or riding, but when you are safe, remember ALLAH as HE has taught you that which you did not know. |
| 002:239 Yusufali | : |
If ye fear (an enemy), pray on foot, or riding, (as may be most convenient), but when ye are in security, celebrate Allah's praises in the manner He has taught you, which ye knew not (before). |
Concepts/Themes Covered: أركان الإسلام > الصلاة > أداء الصلاة > الحض عليها Pillars of Islam> The Prayers>Performing the Prayers> The Incitement to Pray
This tool uses eight English parallel translations for the Holy Quran imported from Mr. Clay Smith. You can also visit Qurany Concepts for browsing the Quran themes or topics. If you want to search the Holy Quran for keywords, please visit Qurany Keywords.


Money Play. How should Red play 11?
pips Red=99 White=99
(CorrectAnswer= 0/0, ErrorScore= 0)
This one is fairly straightforward.
The race is close so Red will to block White’s 6s. That makes 13/11(2) mandatory. Note that Red has plenty of spares to play with while White has only a couple of spare 6s. He may be forced off Red’s 5-pt at a time he doesn’t really want to run.
Any play other 13/11(2) is very nearly a blunder.
The race is close so Red will to block White’s 6s. That makes 13/11(2) mandatory. Note that Red has plenty of spares to play with while White has only a couple of spare 6s. He may be forced off Red’s 5-pt at a time he doesn’t really want to run.
Any play other 13/11(2) is very nearly a blunder.
1. Cubeful 2-ply 13/11(2) Eq.: +0.287 0.598 0.019 0.000 - 0.402 0.012 0.000 2. Cubeful 2-ply 8/7(2) 3/2(2) Eq.: +0.247 (-0.040) 0.583 0.026 0.000 - 0.417 0.016 0.000 3. Cubeful 2-ply 13/12(2) 8/7(2) Eq.: +0.238 (-0.049) 0.581 0.021 0.000 - 0.419 0.012 0.000 4. Cubeful 2-ply 8/7(2) 3/1 Eq.: +0.230 (-0.057) 0.579 0.025 0.000 - 0.421 0.017 0.000 5. Cubeful 2-ply 13/12(2) 3/1 Eq.: +0.226 (-0.061) 0.577 0.026 0.000 - 0.423 0.017 0.000


Navigation
index
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|
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|
Formish v1 documentation
»
Overview
Welcome to Formish’s documentation!
¶
Contents:
Introducing Formish
Data Schema
Validatish
The Form and the Widget
Convertish
The widget templates
Using your form
What else is there?
Anything else interesting?
The way forward
A Formish Walkthrough
Introduction - A Simple Form
Introduction - A Slightly More Complex Form
Validation
File Uploads
Sequences
Nested Form Structures
Class Documentation
Using Formish within Restish
Using a simple form withing Restish
Multiple Actions on a Form
Multiple Forms on a Page
Future Developments.. For Discussion
Customising Templates
Filtering Field Emission
Deriving CSS class names from schemaish types and formish widgets
Core formish Modules
Indices and tables
¶
Index
Module Index
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Formish v1 documentation
»


| Modifier and Type | Interface and Description |
|---|---|
interface |
CARuleMiner
Interface for learning class association rules.
|
| Modifier and Type | Class and Description |
|---|---|
class |
AbstractAssociator
Abstract scheme for learning associations.
|
class |
Apriori
Class implementing an Apriori-type algorithm.
|
class |
CheckAssociator
Class for examining the capabilities and finding problems with associators.
|
class |
FilteredAssociator
Class for running an arbitrary associator on data
that has been passed through an arbitrary filter.
|
class |
FPGrowth
Class implementing the FP-growth algorithm for
finding large item sets without candidate generation.
|
class |
SingleAssociatorEnhancer
Abstract utility class for handling settings common to meta associators that
use a single base associator.
|
| Modifier and Type | Class and Description |
|---|---|
class |
BestFirst
BestFirst:
Searches the space of attribute subsets by greedy hillclimbing augmented with a backtracking facility. |
class |
CfsSubsetEval
CfsSubsetEval :
Evaluates the worth of a subset of attributes by considering the individual predictive ability of each feature along with the degree of redundancy between them. |
class |
CheckAttributeSelection
Class for examining the capabilities and finding problems with attribute
selection schemes.
|
class |
ClassifierAttributeEval
ClassifierAttributeEval :
Evaluates the worth of an attribute by using a user-specified classifier. |
class |
ClassifierSubsetEval
Classifier subset evaluator:
Evaluates attribute subsets on training data or a seperate hold out testing set. |
class |
CorrelationAttributeEval
CorrelationAttributeEval :
Evaluates the worth of an attribute by measuring the correlation (Pearson's) between it and the class. |
class |
GainRatioAttributeEval
GainRatioAttributeEval :
Evaluates the worth of an attribute by measuring the gain ratio with respect to the class. |
class |
GreedyStepwise
GreedyStepwise :
Performs a greedy forward or backward search through the space of attribute subsets. |
class |
InfoGainAttributeEval
InfoGainAttributeEval :
Evaluates the worth of an attribute by measuring the information gain with respect to the class. |
class |
OneRAttributeEval
OneRAttributeEval :
Evaluates the worth of an attribute by using the OneR classifier. |
class |
PrincipalComponents
Performs a principal components analysis and
transformation of the data.
|
class |
Ranker
Ranker :
Ranks attributes by their individual evaluations. |
class |
ReliefFAttributeEval
ReliefFAttributeEval :
Evaluates the worth of an attribute by repeatedly sampling an instance and considering the value of the given attribute for the nearest instance of the same and different class. |
class |
SymmetricalUncertAttributeEval
SymmetricalUncertAttributeEval :
Evaluates the worth of an attribute by measuring the symmetrical uncertainty with respect to the class. |
class |
WrapperSubsetEval
WrapperSubsetEval:
Evaluates attribute sets by using a learning scheme. |
| Modifier and Type | Class and Description |
|---|---|
class |
AbstractClassifier
Abstract classifier.
|
class |
BVDecompose
Class for performing a Bias-Variance decomposition on any classifier using the method specified in:
Ron Kohavi, David H. |
class |
BVDecomposeSegCVSub
This class performs Bias-Variance decomposion on any classifier using the sub-sampled cross-validation procedure as specified in (1).
|
class |
CheckClassifier
Class for examining the capabilities and finding problems with classifiers.
|
class |
CheckSource
A simple class for checking the source generated from Classifiers
implementing the
weka.classifiers.Sourcable interface. |
class |
IteratedSingleClassifierEnhancer
Abstract utility class for handling settings common to
meta classifiers that build an ensemble from a single base learner.
|
class |
MultipleClassifiersCombiner
Abstract utility class for handling settings common to
meta classifiers that build an ensemble from multiple classifiers.
|
class |
ParallelIteratedSingleClassifierEnhancer
Abstract utility class for handling settings common to meta classifiers that
build an ensemble in parallel from a single base learner.
|
class |
ParallelMultipleClassifiersCombiner
Abstract utility class for handling settings common to
meta classifiers that build an ensemble in parallel using multiple
classifiers.
|
class |
RandomizableClassifier
Abstract utility class for handling settings common to randomizable
classifiers.
|
class |
RandomizableIteratedSingleClassifierEnhancer
Abstract utility class for handling settings common to randomizable
meta classifiers that build an ensemble from a single base learner.
|
class |
RandomizableMultipleClassifiersCombiner
Abstract utility class for handling settings common to randomizable
meta classifiers that build an ensemble from multiple classifiers based
on a given random number seed.
|
class |
RandomizableParallelIteratedSingleClassifierEnhancer
Abstract utility class for handling settings common to randomizable
meta classifiers that build an ensemble in parallel from a single base
learner.
|
class |
RandomizableParallelMultipleClassifiersCombiner
Abstract utility class for handling settings common to
meta classifiers that build an ensemble in parallel using multiple
classifiers based on a given random number seed.
|
class |
RandomizableSingleClassifierEnhancer
Abstract utility class for handling settings common to randomizable
meta classifiers that build an ensemble from a single base learner.
|
class |
SingleClassifierEnhancer
Abstract utility class for handling settings common to meta
classifiers that use a single base learner.
|
| Modifier and Type | Class and Description |
|---|---|
class |
NaiveBayes
Class for a Naive Bayes classifier using estimator
classes.
|
class |
NaiveBayesMultinomial
Class for building and using a multinomial Naive Bayes classifier.
|
class |
NaiveBayesMultinomialText
Multinomial naive bayes for text data.
|
class |
NaiveBayesMultinomialUpdateable
Class for building and using an updateable multinomial Naive Bayes classifier.
|
class |
NaiveBayesUpdateable
Class for a Naive Bayes classifier using estimator classes.
|
| Modifier and Type | Class and Description |
|---|---|
class |
BayesNetGenerator
Bayes Network learning using various search
algorithms and quality measures.
|
class |
BIFReader
Builds a description of a Bayes Net classifier
stored in XML BIF 0.3 format.
|
class |
EditableBayesNet
Bayes Network learning using various search
algorithms and quality measures.
|
| Modifier and Type | Class and Description |
|---|---|
class |
BayesNetEstimator
BayesNetEstimator is the base class for estimating
the conditional probability tables of a Bayes network once the structure has
been learned.
|
class |
BMAEstimator
BMAEstimator estimates conditional probability
tables of a Bayes network using Bayes Model Averaging (BMA).
|
class |
DiscreteEstimatorBayes
Symbolic probability estimator based on symbol counts and a prior.
|
class |
DiscreteEstimatorFullBayes
Symbolic probability estimator based on symbol counts and a prior.
|
class |
MultiNomialBMAEstimator
Multinomial BMA Estimator.
|
class |
SimpleEstimator
SimpleEstimator is used for estimating the
conditional probability tables of a Bayes network once the structure has been
learned.
|
| Modifier and Type | Class and Description |
|---|---|
class |
SearchAlgorithm
This is the base class for all search algorithms for learning Bayes networks.
|
| Modifier and Type | Class and Description |
|---|---|
class |
CISearchAlgorithm
The CISearchAlgorithm class supports Bayes net structure search algorithms that are based on conditional independence test (as opposed to for example score based of cross validation based search algorithms).
|
class |
ICSSearchAlgorithm
This Bayes Network learning algorithm uses
conditional independence tests to find a skeleton, finds V-nodes and applies
a set of rules to find the directions of the remaining arrows.
|
| Modifier and Type | Class and Description |
|---|---|
class |
FromFile
The FromFile reads the structure of a Bayes net
from a file in BIFF format.
|
| Modifier and Type | Class and Description |
|---|---|
class |
GlobalScoreSearchAlgorithm
This Bayes Network learning algorithm uses cross
validation to estimate classification accuracy.
|
| Modifier and Type | Class and Description |
|---|---|
class |
GeneticSearch
This Bayes Network learning algorithm uses genetic
search for finding a well scoring Bayes network structure.
|
class |
HillClimber
This Bayes Network learning algorithm uses a hill
climbing algorithm adding, deleting and reversing arcs.
|
class |
K2
This Bayes Network learning algorithm uses a hill
climbing algorithm restricted by an order on the variables.
|
class |
LAGDHillClimber
This Bayes Network learning algorithm uses a Look
Ahead Hill Climbing algorithm called LAGD Hill Climbing.
|
class |
LocalScoreSearchAlgorithm
The ScoreBasedSearchAlgorithm class supports Bayes
net structure search algorithms that are based on maximizing scores (as
opposed to for example conditional independence based search algorithms).
|
class |
RepeatedHillClimber
This Bayes Network learning algorithm repeatedly
uses hill climbing starting with a randomly generated network structure and
return the best structure of the various runs.
|
class |
SimulatedAnnealing
This Bayes Network learning algorithm uses the
general purpose search method of simulated annealing to find a well scoring
network structure.
|
class |
TabuSearch
This Bayes Network learning algorithm uses tabu
search for finding a well scoring Bayes network structure.
|
class |
TAN
This Bayes Network learning algorithm determines
the maximum weight spanning tree and returns a Naive Bayes network augmented
with a tree.
|
| Modifier and Type | Class and Description |
|---|---|
class |
AbstractOutput
A superclass for outputting the classifications of a classifier.
|
class |
CSV
Outputs the predictions as CSV.
|
class |
HTML
Outputs the predictions in HTML.
|
class |
Null
Suppresses all output.
|
class |
PlainText
Outputs the predictions in plain text.
|
class |
XML
Outputs the predictions in XML.
|
| Modifier and Type | Class and Description |
|---|---|
class |
GaussianProcesses
* Implements Gaussian processes for regression without hyperparameter-tuning.
|
class |
LinearRegression
Class for using linear regression for prediction.
|
class |
Logistic
Class for building and using a multinomial logistic
regression model with a ridge estimator.
|
class |
MultilayerPerceptron
A Classifier that uses backpropagation to classify
instances.
|
class |
SGD
Implements stochastic gradient descent for learning various linear models (binary class SVM, binary class logistic regression, squared loss, Huber loss and epsilon-insensitive loss linear regression).
|
class |
SGDText
Implements stochastic gradient descent for learning a linear binary class SVM or binary class logistic regression on text data.
|
class |
SimpleLinearRegression
Learns a simple linear regression model.
|
class |
SimpleLogistic
Classifier for building linear logistic regression
models.
|
class |
SMO
Implements John Platt's sequential minimal optimization algorithm for training a support vector classifier.
|
class |
SMOreg
SMOreg implements the support vector machine for regression.
|
class |
VotedPerceptron
Implementation of the voted perceptron algorithm by Freund and Schapire.
|
| Modifier and Type | Class and Description |
|---|---|
class |
CachedKernel
Base class for RBFKernel and PolyKernel that implements a simple LRU.
|
class |
CheckKernel
Class for examining the capabilities and finding problems with kernels.
|
class |
Kernel
Abstract kernel.
|
class |
NormalizedPolyKernel
The normalized polynomial kernel.
|
class |
PolyKernel
The polynomial kernel : K(x, y) = <x, y>^p or
K(x, y) = (<x, y>+1)^p
|
class |
PrecomputedKernelMatrixKernel
This kernel is based on a static kernel matrix that
is read from a file.
|
class |
Puk
The Pearson VII function-based universal kernel.
|
class |
RBFKernel
The RBF kernel : K(x, y) = exp(-gamma*(x-y)^2)
|
class |
RegOptimizer
Base class implementation for learning algorithm of SMOreg
|
class |
RegSMO
Implementation of SMO for support vector regression
as described in :
A.J. |
class |
RegSMOImproved
Learn SVM for regression using SMO with Shevade,
Keerthi, et al. adaption of the stopping criterion.
|
class |
StringKernel
Implementation of the subsequence kernel (SSK) as
described in [1] and of the subsequence kernel with lambda pruning (SSK-LP)
as described in [2].
|
| Modifier and Type | Class and Description |
|---|---|
class |
IBk
K-nearest neighbours classifier.
|
class |
KStar
K* is an instance-based classifier, that is the class of a test instance is based upon the class of those training instances similar to it, as determined by some similarity function.
|
class |
LWL
Locally weighted learning.
|
| Modifier and Type | Class and Description |
|---|---|
class |
ALVOT |
class |
CRplus |
class |
KORAomega |
| Modifier and Type | Class and Description |
|---|---|
class |
AdaBoostM1
Class for boosting a nominal class classifier using
the Adaboost M1 method.
|
class |
AdditiveRegression
Meta classifier that enhances the performance of a regression base classifier.
|
class |
AttributeSelectedClassifier
Dimensionality of training and test data is reduced by attribute selection before being passed on to a classifier.
|
class |
Bagging
Class for bagging a classifier to reduce variance.
|
class |
ClassificationViaRegression
Class for doing classification using regression methods.
|
class |
CostSensitiveClassifier
A metaclassifier that makes its base classifier cost-sensitive.
|
class |
CVParameterSelection
Class for performing parameter selection by cross-validation for any classifier.
|
class |
FilteredClassifier
Class for running an arbitrary classifier on data
that has been passed through an arbitrary filter.
|
class |
IterativeClassifierOptimizer
Chooses the best number of iterations for an IterativeClassifier such as
LogitBoost using cross-validation or a percentage split evaluation
|
class |
LogitBoost
Class for performing additive logistic regression.
|
class |
MultiClassClassifier
A metaclassifier for handling multi-class datasets with 2-class classifiers.
|
class |
MultiClassClassifierUpdateable
A metaclassifier for handling multi-class datasets with 2-class classifiers.
|
class |
MultiScheme
Class for selecting a classifier from among several using cross validation on the training data or the performance on the training data.
|
class |
RandomCommittee
Class for building an ensemble of randomizable base classifiers.
|
class |
RandomizableFilteredClassifier
Class for running an arbitrary classifier on data that has been passed through an arbitrary filter.
|
class |
RandomSubSpace
This method constructs a decision tree based classifier that maintains highest accuracy on training data and improves on generalization accuracy as it grows in complexity.
|
class |
RegressionByDiscretization
A regression scheme that employs any classifier on a copy of the data that has the class attribute (equal-width) discretized.
|
class |
Stacking
Combines several classifiers using the stacking method.
|
class |
Vote
Class for combining classifiers.
|
class |
WeightedInstancesHandlerWrapper
Generic wrapper around any classifier to enable weighted instances support.
|
| Modifier and Type | Class and Description |
|---|---|
class |
InputMappedClassifier
Wrapper classifier that addresses incompatible
training and test data by building a mapping between the training data that a
classifier has been built with and the incoming test instances' structure.
|
class |
SerializedClassifier
A wrapper around a serialized classifier model.
|
| Modifier and Type | Class and Description |
|---|---|
class |
GeneralRegression
Class implementing import of PMML General Regression model.
|
class |
NeuralNetwork
Class implementing import of PMML Neural Network model.
|
class |
PMMLClassifier
Abstract base class for all PMML classifiers.
|
class |
Regression
Class implementing import of PMML Regression model.
|
class |
RuleSetModel
Class implementing import of PMML RuleSetModel.
|
class |
SupportVectorMachineModel
Implements a PMML SupportVectorMachineModel
|
class |
TreeModel
Class implementing import of PMML TreeModel.
|
| Modifier and Type | Class and Description |
|---|---|
class |
DecisionTable
Class for building and using a simple decision
table majority classifier.
|
class |
JRip
This class implements a propositional rule learner,
Repeated Incremental Pruning to Produce Error Reduction (RIPPER), which was
proposed by William W.
|
class |
M5Rules
Generates a decision list for regression problems using separate-and-conquer.
|
class |
OneR
Class for building and using a 1R classifier; in
other words, uses the minimum-error attribute for prediction, discretizing
numeric attributes.
|
class |
PART
Class for generating a PART decision list.
|
class |
ZeroR
Class for building and using a 0-R classifier.
|
| Modifier and Type | Class and Description |
|---|---|
class |
DecisionStump
Class for building and using a decision stump.
|
class |
HoeffdingTree
A Hoeffding tree (VFDT) is an incremental, anytime
decision tree induction algorithm that is capable of learning from massive
data streams, assuming that the distribution generating examples does not
change over time.
|
class |
J48
Class for generating a pruned or unpruned C4.5
decision tree.
|
class |
LMT
Classifier for building 'logistic model trees',
which are classification trees with logistic regression functions at the
leaves.
|
class |
M5P
M5Base.
|
class |
RandomForest
Class for constructing a forest of random trees.
|
class |
RandomTree
Class for constructing a tree that considers K
randomly chosen attributes at each node.
|
class |
REPTree
Fast decision tree learner.
|
| Modifier and Type | Class and Description |
|---|---|
class |
LMTNode
Class for logistic model tree structure.
|
class |
LogisticBase
Base/helper class for building logistic regression models with the LogitBoost
algorithm.
|
| Modifier and Type | Class and Description |
|---|---|
class |
M5Base
M5Base.
|
class |
PreConstructedLinearModel
This class encapsulates a linear regression function.
|
class |
RuleNode
Constructs a node for use in an m5 tree or rule
|
| Modifier and Type | Class and Description |
|---|---|
class |
AbstractClusterer
Abstract clusterer.
|
class |
AbstractDensityBasedClusterer
Abstract clustering model that produces (for each test instance)
an estimate of the membership in each cluster
(ie. a probability distribution).
|
class |
Canopy
Cluster data using the capopy clustering algorithm, which requires just one pass over the data.
|
class |
CheckClusterer
Class for examining the capabilities and finding problems with clusterers.
|
class |
Cobweb
Class implementing the Cobweb and Classit clustering algorithms.
|
class |
EM
Simple EM (expectation maximisation) class.
|
class |
FarthestFirst
Cluster data using the FarthestFirst algorithm.
|
class |
FilteredClusterer
Class for running an arbitrary clusterer on data
that has been passed through an arbitrary filter.
|
class |
HierarchicalClusterer
Hierarchical clustering class.
|
class |
MakeDensityBasedClusterer
Class for wrapping a Clusterer to make it return a
distribution and density.
|
class |
RandomizableClusterer
Abstract utility class for handling settings common to randomizable
clusterers.
|
class |
RandomizableDensityBasedClusterer
Abstract utility class for handling settings common to randomizable
clusterers.
|
class |
RandomizableSingleClustererEnhancer
Abstract utility class for handling settings common to randomizable
clusterers.
|
class |
SimpleKMeans
Cluster data using the k means algorithm.
|
class |
SingleClustererEnhancer
Meta-clusterer for enhancing a base clusterer.
|
| Modifier and Type | Interface and Description |
|---|---|
interface |
DistanceFunction
Interface for any class that can compute and return distances between two
instances.
|
| Modifier and Type | Class and Description |
|---|---|
class |
AllJavadoc
Applies all known Javadoc-derived classes to a source file
|
class |
ChebyshevDistance
Implements the Chebyshev distance.
|
class |
Check
Abstract general class for testing in Weka.
|
class |
CheckGOE
Simple command line checking of classes that are editable in the GOE.
|
class |
CheckOptionHandler
Simple command line checking of classes that implement OptionHandler.
|
class |
CheckScheme
Abstract general class for testing schemes in Weka.
|
class |
DictionaryBuilder
Class for building and maintaining a dictionary of terms.
|
class |
EuclideanDistance
Implementing Euclidean distance (or similarity) function.
|
class |
FilteredDistance
Applies the given filter before calling the given distance function.
|
class |
FindWithCapabilities
Locates all classes with certain capabilities.
|
class |
GlobalInfoJavadoc
Generates Javadoc comments from the class's globalInfo method.
|
class |
HVDM1 |
class |
HVDM2 |
class |
HVDM3 |
class |
Javadoc
Abstract superclass for classes that generate Javadoc comments and replace
the content between certain comment tags.
|
class |
ListOptions
Lists the options of an OptionHandler
|
class |
ManhattanDistance
Implements the Manhattan distance (or Taxicab geometry).
|
class |
MinkowskiDistance
Implementing Minkowski distance (or similarity)
function.
|
class |
NormalizableDistance
Represents the abstract ancestor for normalizable distance functions, like
Euclidean or Manhattan distance.
|
class |
OptionHandlerJavadoc
Generates Javadoc comments from the OptionHandler's options.
|
class |
SimilarityMeasure
Implementing Euclidean distance (or similarity) function.
|
class |
TechnicalInformationHandlerJavadoc
Generates Javadoc comments from the TechnicalInformationHandler's data.
|
class |
TestInstances
Generates artificial datasets for testing.
|
| Modifier and Type | Method and Description |
|---|---|
OptionHandler |
CheckOptionHandler.getOptionHandler()
Get the OptionHandler used in the tests.
|
| Modifier and Type | Method and Description |
|---|---|
void |
CheckOptionHandler.setOptionHandler(OptionHandler value)
Set the OptionHandler to work on..
|
| Modifier and Type | Class and Description |
|---|---|
class |
AbstractFileSaver
Abstract class for Savers that save to a file
Valid options are:
-i input arff file
The input filw in arff format. |
class |
ArffSaver
Writes to a destination in arff text format.
|
class |
C45Saver
Writes to a destination that is in the format used
by the C4.5 algorithm.
|
class |
CSVLoader
Reads a source that is in comma separated format (the default).
|
class |
CSVSaver
Writes to a destination that is in CSV
(comma-separated values) format.
|
class |
DatabaseLoader
Reads Instances from a Database.
|
class |
DatabaseSaver
Writes to a database (tested with MySQL, InstantDB,
HSQLDB).
|
class |
DictionarySaver
Writes a dictionary constructed from string
attributes in incoming instances to a destination.
|
class |
JSONSaver
Writes to a destination that is in JSON format.
|
class |
LibSVMSaver
Writes to a destination that is in libsvm format.
|
class |
MatlabSaver
Writes Matlab ASCII files, in single or double
precision format.
|
class |
SerializedInstancesSaver
Serializes the instances to a file with extension bsi.
|
class |
SVMLightSaver
Writes to a destination that is in svm light
format.
|
class |
TextDirectoryLoader
Loads all text files in a directory and uses the
subdirectory names as class labels.
|
class |
XRFFSaver
Writes to a destination that is in the XML version
of the ARFF format.
|
| Modifier and Type | Class and Description |
|---|---|
class |
BallTree
Class implementing the BallTree/Metric Tree algorithm for nearest neighbour search.
|
class |
CoverTree
Class implementing the CoverTree datastructure.
|
class |
FilteredNeighbourSearch
Applies the given filter before calling the given neighbour search method.
|
class |
KDTree
Class implementing the KDTree search algorithm for nearest neighbour search.
|
class |
LinearNNSearch
Class implementing the brute force search algorithm for nearest neighbour search.
|
class |
NearestNeighbourSearch
Abstract class for nearest neighbour search.
|
| Modifier and Type | Class and Description |
|---|---|
class |
BallSplitter
Abstract class for splitting a ball tree's BallNode.
|
class |
BallTreeConstructor
Abstract class for constructing a BallTree .
|
class |
BottomUpConstructor
The class that constructs a ball tree bottom up.
|
class |
MedianDistanceFromArbitraryPoint
Class that splits a BallNode of a ball tree using
Uhlmann's described method.
|
class |
MiddleOutConstructor
The class that builds a BallTree middle out.
|
class |
PointsClosestToFurthestChildren
Implements the Moore's method to split a node of a
ball tree.
|
class |
TopDownConstructor
The class implementing the TopDown construction
method of ball trees.
|
| Modifier and Type | Class and Description |
|---|---|
class |
KDTreeNodeSplitter
Class that splits up a KDTreeNode.
|
class |
KMeansInpiredMethod
The class that splits a node into two such that the
overall sum of squared distances of points to their centres on both sides of
the (axis-parallel) splitting plane is minimum.
|
class |
MedianOfWidestDimension
The class that splits a KDTree node based on the median value of a dimension in which the node's points have the widest spread.
|
class |
MidPointOfWidestDimension
The class that splits a KDTree node based on the midpoint value of a dimension in which the node's points have the widest spread.
|
class |
SlidingMidPointOfWidestSide
The class that splits a node into two based on the midpoint value of the dimension in which the node's rectangle is widest.
|
| Modifier and Type | Class and Description |
|---|---|
class |
SnowballStemmer
A wrapper class for the Snowball stemmers.
|
| Modifier and Type | Class and Description |
|---|---|
class |
AbstractFileBasedStopwords
Ancestor for file-based stopword schemes.
|
class |
AbstractStopwords
Ancestor for stopwords classes.
|
class |
MultiStopwords
Applies the specified stopwords algorithms one after other.
|
class |
Rainbow
Stopwords list based on Rainbow: http://www.cs.cmu.edu/~mccallum/bow/rainbow/ |
class |
RegExpFromFile
Uses the regular expressions stored in the file for determining whether a word is a stopword (ignored if pointing to a directory).
|
class |
WordsFromFile
Uses the stopwords located in the specified file (ignored _if pointing to a directory).
|
| Modifier and Type | Class and Description |
|---|---|
class |
AlphabeticTokenizer
Alphabetic string tokenizer, tokens are to be
formed only from contiguous alphabetic sequences.
|
class |
CharacterDelimitedTokenizer
Abstract superclass for tokenizers that take characters as delimiters.
|
class |
CharacterNGramTokenizer
Splits a string into an n-gram with min and max
grams.
|
class |
NGramTokenizer
Splits a string into an n-gram with min and max
grams.
|
class |
Tokenizer
A superclass for all tokenizer algorithms.
|
class |
WordTokenizer
A simple tokenizer that is using the
java.util.StringTokenizer class to tokenize the strings.
|
| Modifier and Type | Class and Description |
|---|---|
class |
ClassificationGenerator
Abstract class for data generators for classifiers.
|
class |
ClusterDefinition
Ancestor to all ClusterDefinitions, i.e., subclasses that handle their own
parameters that the cluster generator only passes on.
|
class |
ClusterGenerator
Abstract class for cluster data generators.
|
class |
DataGenerator
Abstract superclass for data generators that generate data for classifiers
and clusterers.
|
class |
RegressionGenerator
Abstract class for data generators for regression classifiers.
|
| Modifier and Type | Class and Description |
|---|---|
class |
Agrawal
Generates a people database and is based on the
paper by Agrawal et al.
|
class |
BayesNet
Generates random instances based on a Bayes network.
|
class |
LED24
This generator produces data for a display with 7
LEDs.
|
class |
RandomRBF
RandomRBF data is generated by first creating a
random set of centers for each class.
|
class |
RDG1
A data generator that produces data randomly by
producing a decision list.
|
| Modifier and Type | Class and Description |
|---|---|
class |
Expression
A data generator for generating y according to a
given expression out of randomly generated x.
|
class |
MexicanHat
A data generator for the simple 'Mexian Hat'
function:
y = sin|x| / |x| In addition to this simple function, the amplitude can be changed and gaussian noise can be added. |
| Modifier and Type | Class and Description |
|---|---|
class |
BIRCHCluster
Cluster data generator designed for the BIRCH
System
Dataset is generated with instances in K clusters. |
class |
SubspaceCluster
A data generator that produces data points in
hyperrectangular subspace clusters.
|
class |
SubspaceClusterDefinition
A single cluster for the SubspaceCluster
datagenerator
|
| Modifier and Type | Class and Description |
|---|---|
class |
CheckEstimator
Class for examining the capabilities and finding problems with estimators.
|
class |
DiscreteEstimator
Simple symbolic probability estimator based on symbol counts.
|
class |
Estimator
Abstract class for all estimators.
|
class |
KernelEstimator
Simple kernel density estimator.
|
class |
MahalanobisEstimator
Simple probability estimator that places a single normal distribution
over the observed values.
|
class |
NormalEstimator
Simple probability estimator that places a single normal distribution over
the observed values.
|
class |
PoissonEstimator
Simple probability estimator that places a single Poisson distribution
over the observed values.
|
class |
UnivariateMixtureEstimator
Simple weighted mixture density estimator.
|
| Modifier and Type | Class and Description |
|---|---|
class |
AveragingResultProducer
Takes the results from a ResultProducer and submits
the average to the result listener.
|
class |
ClassifierSplitEvaluator
A SplitEvaluator that produces results for a
classification scheme on a nominal class attribute.
|
class |
CostSensitiveClassifierSplitEvaluator
SplitEvaluator that produces results for a
classification scheme on a nominal class attribute, including weighted
misclassification costs.
|
class |
CrossValidationResultProducer
Generates for each run, carries out an n-fold
cross-validation, using the set SplitEvaluator to generate some results.
|
class |
CrossValidationSplitResultProducer
Carries out one split of a repeated k-fold
cross-validation, using the set SplitEvaluator to generate some results.
|
class |
CSVResultListener
Takes results from a result producer and assembles
them into comma separated value form.
|
class |
DatabaseResultProducer
Examines a database and extracts out the results
produced by the specified ResultProducer and submits them to the specified
ResultListener.
|
class |
DensityBasedClustererSplitEvaluator
A SplitEvaluator that produces results for a
density based clusterer.
|
class |
Experiment
Holds all the necessary configuration information for a standard type
experiment.
|
class |
ExplicitTestsetResultProducer
Loads the external test set and calls the
appropriate SplitEvaluator to generate some results.
|
class |
InstanceQuery
Convert the results of a database query into instances.
|
class |
InstancesResultListener
Outputs the received results in arff format to a
Writer.
|
class |
LearningRateResultProducer
Tells a sub-ResultProducer to reproduce the current
run for varying sized subsamples of the dataset.
|
class |
PairedCorrectedTTester
Behaves the same as PairedTTester, only it uses the corrected resampled
t-test statistic.
|
class |
PairedTTester
Calculates T-Test statistics on data stored in a set of instances.
|
class |
RandomSplitResultProducer
Generates a single train/test split and calls the
appropriate SplitEvaluator to generate some results.
|
class |
RegressionSplitEvaluator
A SplitEvaluator that produces results for a
classification scheme on a numeric class attribute.
|
class |
RemoteExperiment
Holds all the necessary configuration information for a distributed
experiment.
|
class |
ResultMatrix
This matrix is a container for the datasets and classifier setups and their
statistics.
|
class |
ResultMatrixCSV
Generates the matrix in CSV ('comma-separated values') format.
|
class |
ResultMatrixGnuPlot
Generates output for a data and script file for GnuPlot.
|
class |
ResultMatrixHTML
Generates the matrix output as HTML.
|
class |
ResultMatrixLatex
Generates the matrix output in LaTeX-syntax.
|
class |
ResultMatrixPlainText
Generates the output as plain text (for fixed width
fonts).
|
class |
ResultMatrixSignificance
Only outputs the significance indicators.
|
| Modifier and Type | Class and Description |
|---|---|
class |
AllFilter
A simple instance filter that passes all instances directly
through.
|
class |
Filter
An abstract class for instance filters: objects that take instances as input,
carry out some transformation on the instance and then output the instance.
|
class |
MultiFilter
Applies several filters successively.
|
class |
RenameRelation
A simple filter that allows the relation name of a set of instances to be
altered in various ways.
|
class |
SimpleBatchFilter
This filter is a superclass for simple batch filters.
|
class |
SimpleFilter
This filter contains common behavior of the SimpleBatchFilter and the
SimpleStreamFilter.
|
class |
SimpleStreamFilter
This filter is a superclass for simple stream filters.
|
| Modifier and Type | Class and Description |
|---|---|
class |
AddClassification
A filter for adding the classification, the class
distribution and an error flag to a dataset with a classifier.
|
class |
AttributeSelection
A supervised attribute filter that can be used to
select attributes.
|
class |
ClassConditionalProbabilities
Converts the values of nominal and/or numeric attributes into class conditional probabilities.
|
class |
ClassOrder
Changes the order of the classes so that the class
values are no longer of in the order specified in the header.
|
class |
MergeNominalValues
Merges values of all nominal attributes among the
specified attributes, excluding the class attribute, using the CHAID method,
but without considering re-splitting of merged subsets.
|
class |
PartitionMembership
* A filter that uses a PartitionGenerator to generate partition membership values; filtered instances are composed of these values plus the class attribute (if set in the input data) and rendered as sparse instances.
|
| Modifier and Type | Class and Description |
|---|---|
class |
ClassBalancer
Reweights the instances in the data so that each class has the same total weight.
|
class |
SpreadSubsample
Produces a random subsample of a dataset.
|
class |
StratifiedRemoveFolds
This filter takes a dataset and outputs a specified
fold for cross validation.
|
| Modifier and Type | Class and Description |
|---|---|
class |
AbstractTimeSeries
An abstract instance filter that assumes instances form time-series data and
performs some merging of attribute values in the current instance with
attribute attribute values of some previous (or future) instance.
|
class |
Add
An instance filter that adds a new attribute to the
dataset.
|
class |
AddCluster
A filter that adds a new nominal attribute
representing the cluster assigned to each instance by the specified
clustering algorithm.
|
class |
AddExpression
An instance filter that creates a new attribute by
applying a mathematical expression to existing attributes.
|
class |
AddID
An instance filter that adds an ID attribute to the
dataset.
|
class |
AddNoise
An instance filter that changes a percentage of a
given attribute's values.
|
class |
AddUserFields
A filter that adds new attributes with user
specified type and constant value.
|
class |
AddValues
Adds the labels from the given list to an attribute
if they are missing.
|
class |
CartesianProduct
A filter for performing the Cartesian product of a set of nominal attributes.
|
class |
Center
Centers all numeric attributes in the given dataset to have zero mean (apart from the class attribute, if set).
|
class |
ChangeDateFormat
Changes the date format used by a date attribute.
|
class |
ClassAssigner
Filter that can set and unset the class index.
|
class |
ClusterMembership
A filter that uses a density-based clusterer to
generate cluster membership values; filtered instances are composed of these
values plus the class attribute (if set in the input data).
|
class |
Copy
An instance filter that copies a range of
attributes in the dataset.
|
class |
DateToNumeric
A filter for turning date attributes into numeric ones.
|
class |
Discretize
An instance filter that discretizes a range of
numeric attributes in the dataset into nominal attributes.
|
class |
FirstOrder
This instance filter takes a range of N numeric
attributes and replaces them with N-1 numeric attributes, the values of which
are the difference between consecutive attribute values from the original
instance. eg:
Original attribute values 0.1, 0.2, 0.3, 0.1, 0.3 New attribute values 0.1, 0.1, -0.2, 0.2 The range of attributes used is taken in numeric order. |
class |
FixedDictionaryStringToWordVector
Converts String attributes into a set of attributes
representing word occurrence (depending on the tokenizer) information from
the text contained in the strings.
|
class |
InterquartileRange
A filter for detecting outliers and extreme values
based on interquartile ranges.
|
class |
KernelFilter
Converts the given set of data into
a kernel matrix.
|
class |
MakeIndicator
A filter that creates a new dataset with a Boolean
attribute replacing a nominal attribute.
|
class |
MathExpression
Modify numeric attributes according to a given
mathematical expression.
|
class |
MergeInfrequentNominalValues
Merges all values of the specified nominal attributes that are insufficiently frequent.
|
class |
MergeManyValues
Merges many values of a nominal attribute into one
value.
|
class |
MergeTwoValues
Merges two values of a nominal attribute into one
value.
|
class |
NominalToBinary
Converts all nominal attributes into binary numeric
attributes.
|
class |
NominalToString
Converts a nominal attribute (i.e. set number of
values) to string (i.e. unspecified number of values).
|
class |
Normalize
Normalizes all numeric values in the given dataset
(apart from the class attribute, if set).
|
class |
NumericCleaner
A filter that 'cleanses' the numeric data from
values that are too small, too big or very close to a certain value,
and sets these values to a pre-defined default.
|
class |
NumericToBinary
Converts all numeric attributes into binary
attributes (apart from the class attribute, if set): if the value of the
numeric attribute is exactly zero, the value of the new attribute will be
zero.
|
class |
NumericToDate
A filter for turning numeric attributes into date attributes.
|
class |
NumericToNominal
A filter for turning numeric attributes into
nominal ones.
|
class |
NumericTransform
Transforms numeric attributes using a given
transformation method.
|
class |
Obfuscate
A simple instance filter that renames the relation,
all attribute names and all nominal attribute values.
|
class |
OrdinalToNumeric
An attribute filter that converts ordinal nominal attributes into numeric ones
|
class |
PartitionedMultiFilter
A filter that applies filters on subsets of
attributes and assembles the output into a new dataset.
|
class |
PKIDiscretize
Discretizes numeric attributes using equal
frequency binning and forces the number of bins to be equal to the square root of
the number of values of the numeric attribute.
|
class |
PotentialClassIgnorer
This filter should be extended by other unsupervised attribute filters to
allow processing of the class attribute if that's required.
|
class |
RandomProjection
Reduces the dimensionality of the data by projecting it onto a lower dimensional subspace using a random matrix with columns of unit length.
|
class |
RandomSubset
Chooses a random subset of attributes, either an absolute number or a percentage.
|
class |
Remove
An filter that removes a range of attributes from
the dataset.
|
class |
RemoveByName
Removes attributes based on a regular expression
matched against their names.
|
class |
RemoveType
Removes attributes of a given type.
|
class |
RemoveUseless
This filter removes attributes that do not vary at
all or that vary too much.
|
class |
RenameAttribute
This filter is used for renaming attributes.
|
class |
RenameNominalValues
Renames the values of nominal attributes.
|
class |
Reorder
A filter that generates output with a new order of
the attributes.
|
class |
ReplaceMissingValues
Replaces all missing values for nominal and numeric attributes in a dataset with the modes and means
from the training data.
|
class |
ReplaceMissingWithUserConstant
Replaces all missing values for nominal, string,
numeric and date attributes in the dataset with user-supplied constant
values.
|
class |
ReplaceWithMissingValue
A filter that can be used to introduce missing values in a dataset.
|
class |
SortLabels
A simple filter for sorting the labels of nominal
attributes.
|
class |
Standardize
Standardizes all numeric attributes in the given dataset to have zero mean and unit variance (apart from the class attribute, if set).
|
class |
StringToNominal
Converts a range of string attributes (unspecified
number of values) to nominal (set number of values).
|
class |
StringToWordVector
Converts string attributes into a set of numeric attributes representing word occurrence
information from the text contained in the strings.
|
class |
SwapValues
Swaps two values of a nominal attribute.
|
class |
TimeSeriesDelta
An instance filter that assumes instances form time-series data and replaces attribute values in the current instance with the difference between the current value and the equivalent attribute attribute value of some previous (or future) instance.
|
class |
TimeSeriesTranslate
An instance filter that assumes instances form time-series data and replaces attribute values in the current instance with the equivalent attribute values of some previous (or future) instance.
|
class |
Transpose
Transposes the data: instances become attributes and attributes become instances.
|
| Modifier and Type | Class and Description |
|---|---|
class |
NonSparseToSparse
An instance filter that converts all incoming
instances into sparse format.
|
class |
Randomize
Randomly shuffles the order of instances passed
through it.
|
class |
RemoveDuplicates
Removes all duplicate instances from the first batch of data it receives.
|
class |
RemoveFolds
This filter takes a dataset and outputs a specified
fold for cross validation.
|
class |
RemoveFrequentValues
Determines which values (frequent or infrequent
ones) of an (nominal) attribute are retained and filters the instances
accordingly.
|
class |
RemoveMisclassified
A filter that removes instances which are
incorrectly classified.
|
class |
RemovePercentage
A filter that removes a given percentage of a
dataset.
|
class |
RemoveRange
A filter that removes a given range of instances of
a dataset.
|
class |
RemoveWithValues
Filters instances according to the value of an
attribute.
|
class |
Resample
Produces a random subsample of a dataset using
either sampling with replacement or without replacement.
|
class |
ReservoirSample
Produces a random subsample of a dataset using the
reservoir sampling Algorithm "R" by Vitter.
|
class |
SparseToNonSparse
An instance filter that converts all incoming sparse instances into non-sparse format.
|
class |
SubsetByExpression
Filters instances according to a user-specified expression.
|
| Modifier and Type | Class and Description |
|---|---|
class |
Main
Menu-based GUI for Weka, replacement for the GUIChooser
|
| Modifier and Type | Class and Description |
|---|---|
class |
AbstractPlotInstances
Abstract superclass for generating plottable instances.
|
class |
ClassifierErrorsPlotInstances
A class for generating plottable visualization errors.
|
class |
ClustererAssignmentsPlotInstances
A class for generating plottable cluster assignments.
|
| Modifier and Type | Class and Description |
|---|---|
class |
GroovyScript
Represents a Groovy script.
|
class |
JythonScript
Represents a Jython
script.
|
class |
Script
A simple helper class for loading, saving scripts.
|
Copyright © 2018 University of Waikato, Hamilton, NZ. All Rights Reserved.


Orbit - left mouse / touch: one finger move
Zoom - middle mouse, or mousewheel / touch: two finger spread or squish
Pan - right mouse, or arrow keys / touch: three finger swipe
With controls panel, you can change more than 70 parameters.
Find all my resources in head of js file : Gist, textures, examples, etc :)


Here are a few visualisations below:
This is an image of the spectogram.
This is an image of the waveform.
This is an image of the Mel Frequency Cepstral Coefficients.
This is an image of the chromogram.
I created an anlysis of my piece using jSymbolic which is available here.
You can find the metadata for this piece here.
You can find the sheet music as .musicxml here and as .mei here.
An .mp3 version of the piece is available here.


Tips for Participating In Drake Code Reviews using reviewable.io¶
Table of contents
Introduction¶
Drake code reviews use https://reviewable.io. This page documents some best practices for communicating effectively in Reviewable.
GitHub Integration¶
Avoid using the GitHub UI to comment on code during a review. Reviewable will import comments from GitHub, but cannot reliably match them to lines of diff.
When you enter comments in Reviewable, they are saved as drafts. Use the “publish” button to send them out in a batch. Reviewable will post the comments to the GitHub PR in a single, well-formatted block, generating email to everyone else following the PR.
Every time you push to your GitHub branch under review, Reviewable will snapshot a new diff. Because it maintains an independent diff series, you can rebase freely without corrupting the review history.
Life of a Reviewable Comment¶
All threads in Reviewable must be resolved before you can merge your PR.
The semantics of discussion resolution is more nuanced than GitHub’s default code review tool. We recommend to read this explanation to understand the details.
Before commenting on a line of code, reviewers should check to see if there is already a resolved discussion addressing the same topic. Resolved discussions are indicated by a small white check-mark in a grey circle to the left of the line of code.
Reviewers should click the eye-shaped buttons to indicate that they have reviewed a file. Reviewable will remember the revisions at which the file was reviewed, and mark them with an eye icon in the file history.
Curated Commits¶
Each commit on Drake master should pass all unit tests and lint checks, should be logically cohesive (should not require other commits to make sense), and should have a meaningful commit message.
The label status: curate commits before merging
on a PR indicates that (at
the time the label was added) the commits pushed to the PR do not meet those
criteria, usually because the commits contain “work-in-progress” checkpoints or
scattered “fix review comments” adjustments.
When applied to a PR, this label will prevent a PR with more than one commit from being merged.
The author of a PR has three choices for how to resolve this defect:
- Remove the label and then immediately use the “squash and merge” button to merge the PR, being careful to tweak the the commit message in the github edit box to be a sensible description of the change.
- Locally (rebase and) squash the PR down to a single commit, and force-push that commit into the PR. This will automatically remove the merge impediment: even though the label still exists, it is a no-op when there is only one commit in the PR.
- Locally (rebase and) squash the PR to contain more than one commit, but where each individual commit meets the above recommendations, and then remove the label.


Republicans asked to stop promoting 'politically driven falsehoods' at Trump impeachment hearing
It was the final day of a week of explosive testimony at the impeachment inquiry.
by Associated PressTWO KEY WITNESS in the ongoing US impeachment inquiry have said it was clear that Donald Trump’s personal lawyer Rudy Giuliani was pursuing political investigations of Democrats in Ukraine at the direction of the president.
The testimony of former White House analyst Fiona Hill and State Department official David Holmes appears to undercut Trump’s contention that he only wanted to root out Ukrainian corruption.
The House of Representatives probe focuses on allegations that Trump sought to investigate Joe Biden and his son Hunter – and the discredited idea that Ukraine rather than Russia interfered in the 2016 US election — in return for US military aid and for a White House visit for the new Ukrainian president, Volodymyr Zelensky.
During the final day of the impeachment hearing this week, Holmes told investigators that he understood that Giuliani’s push to investigate Burisma, the gas company where Joe Biden’s son Hunter served, was code for the former vice president and his family.
Hill, meanwhile, warned that Giuliani had been making “explosive” and “incendiary” claims.
“He was clearly pushing forward issues and ideas that would, you know, probably come back to haunt us,” she testified. “I think that’s where we are today.”
She also sternly warned Republicans — and implicitly Trump — to stop pushing the “fictional” narrative that Ukraine interfered in the US election in their defence of Trump in the impeachment probe.
Russian meddling ‘beyond dispute’
Republicans, and Trump himself, have continually claimed that the president was trying to root out corruption in the country.
But Hill said the conclusion by US intelligence agencies that Russia meddled in the election “is beyond dispute”.
She said the assertion by some Republicans that Ukraine interfered in the election “is a fictional narrative that has been perpetrated and propagated by the Russian security services themselves”.
She added: “I refuse to be part of an effort to legitimise an alternative narrative that the Ukrainian government is a US adversary, and that Ukraine – not Russia – attacked us in 2016.”
She also warned that Russia is gearing up to intervene again in next year’s election. “We are running out of time to stop them,” she testified.
“I would ask that you please not promote politically driven falsehoods that so clearly advance Russian interests.”
‘F**ks everything up’
Meanwhile, Holmes testified that he came forward after overhearing Trump ask about “investigations” during a “colourful” phone call with the US Ambassador the the EU, Gordon Sondland, at a Kiev restaurant earlier this year.
He said that he realised his account of what he heard would be relevant to the inquiry.
“Those events potentially bore on the question of whether the president did, in fact, have knowledge that those senior officials were using the levers of our diplomatic power” to push Ukraine to investigate his rivals, he testified.
Holmes also expressed growing concern that Giuliani, as the president’s personal lawyer, orchestrated US policy towards Ukraine outside official diplomatic channels, adding that it was a concern shared by others.
“My recollection is that Ambassador Sondland stated, ‘Every time Rudy gets involved he goes and fucks everything up.’”
Yesterday, the inquiry heard from Sondland that Trump had instructed his top diplomats to work with Giuliani, who was publicly pursuing investigations into Democrats.
Holmes testified that he grew alarmed watching Giuliani make “frequent public statements” which pushed for Ukraine to investigate interference in the 2016 election and issues related the Bidens.
With reporting from Press Association.


{% extends "layout.html" %} {% block head %} {% include "includes/head.html" %} {% endblock %} {% block page_title %} Apprenticeships {% endblock %} {% block content %} {% include "includes/phase_banner_alpha.html" %} {% include "includes/secondary-nav-provider.html" %} {% include "includes/breadcrumbs/register-back.html" %} Training search results There are 2 training standards matching your search for 'mechatronics' Access to the service Choose the training you have agreed Mechatronics engineer Training code: 3246 Level: 4 Mechatronics technician Training code: 9267 Level: 2 Search again {% endblock %}
There are 2 training standards matching your search for 'mechatronics'
Choose the training you have agreed
Training code:
3246
Level:
4
9267
2
Search again


Our pizzas are a combination of many top quality ingredients balanced with all the right spice in just the right proportions carefully baked in extremely hot ovens.


Home
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The capital is no longer used as a commercial port since the Cayenne River entrance and harbour have silted up in recent years, making it unsafe for boats of over 4-foot draft to enter.
The port at Dégrad des Cannes on the Mahury River (just SE of Cayenne) is now the seaport.
It is possible to clear in at the Customs office at the Marina de Degrad des Cannes on the Mahoury River, just south of Cayenne.
The office has quite limited hours (open until 13:00 Mon-Fri, and 15:00-18:00 Tues & Thurs.). They no longer stamp passports.
It may be necessary to visit Immigration at the airport to get the passports stamped.
Last updated September 2014.
Visiting yachts, as well as some local yachts, moor at the marina at Degrad des Cannes which is on the northern shore past the Navel base and the commercial port.
Boats also anchor further up the Mahoury River, off the village of Stouppan. The river is well buoyed.
Last updated September 2014.


Senate accuses public hospitals of issuing fake medical reports
by Sunday Aborisade…asks FG, NMA to check quackery in health sector
Sunday Aborisade, Abuja
The Senate on Tuesday directed the Federal Ministry of Health to set up an efficient and proper mechanism to identify and prevent quackery in public hospitals across the country
The red chamber also urged the Nigerian Medical Association to intensify its effort to check incidences of fraud and quackery in the medical profession.
The lawmakers lamented that the dangerous trend in public hospitals; poses great risks to the lives of Nigerians.
The Senate took the decision after a debate on a motion on the proliferation of fake medical reports emanating from public hospitals in Nigeria sponsored by Senator Uche Ekwunife.
Presenting her motion, Ekwunife noted with discomfort, a recent investigation which revealed the ease with which “authentic” medical reports could be obtained from some public hospitals in Nigeria for a fee.
She said the reports could be obtained without any medical tests whatsoever or even proof of identity.
She expressed worry that the commercialisation of medical reports poses a grave danger to the medical health and safety of Nigerians.
She said the development could result in fatal misdiagnosis of patients or anyone else who relies on the contents of such medical reports.
Ekwunife underscored the dire importance of the medical report as a document which is accorded great weight and importance in the ordinary course of our day-to-day business, including in courts of law.
She added that the medical report contains an individual’s personal medical records and details which are used to ascertain a person’s medical condition, obtain health benefits, certify one’s mental state.
She said the document could also be used to investigate addictions, diagnose treatments, and most importantly, ascertain and form medical opinions.
She expressed regrets that the integrity of the medical report was being compromised and eroded by the realisation that it might not contain the true and accurate medical details of the person named in the report;
She said, “I am aware that the Code of Ethics of the medical profession abhors the illegal issuance of medical certificates and records to patients without conducting all the relevant tests;
“I am aware that guidelines exist in the medical profession which proscribe these conducts, but no effective implementation of these regulations.”


Shower Presentation Engine
Yours Truly, Famous Inc.
Marco
Built on HTML, CSS and JavaScript
Works in all modern browsers
Themes are separated from engine
Fully keyboard accessible
Printable to PDF
Shower ['ʃəuə] noun. A person or thing that shows.


Aesthetically pleasing and Visually Appealing Graphs bringing information to the viewers in a nutshell
This plot shows diseases observed in the patients diagnosed over a period of time
This plot shows medical records stored in secure storage across the year 2020 respective of months
This plot shows diseases spread across gender over a period of time
This plot shows medical records accessed across years as indicating no.of patients diagnosed
This plot shows particular year observation showing medical records details observed across gender respective of months
Copyright © 2021 - All Rights Reserved - Health History
Medical Record with QR System


--- layout: default title: Layouts styles: [main.css, layouts.css] ---
This will always be in the first column.
This will move to the second column along with the third box.
This will continue on to be a third column.
This is the first box.
This is the second, and even greater, box.
This will always be in the first column.
This will move to the second column along with the third box.
This will continue on to be a third column.
This will always be in the first column.
This will move to the second column along with the third box.
This will continue on to be a third column.
This is the first column.
This is the first row in the second column.
This is the second row in the second column.
This is the first box.
This is the second box.


Back To Consumer Group
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---
layout: archive
title: Lean Prover Zulip Chat Archive
permalink: 113488general/39467ProvingAxiomSane.html
---
The archive URL format has changed. Please find this page at:
https://leanprover-community.github.io/archive/stream/113488-general/topic/Proving.20Axiom.20Sane.html


gfx Rounded rectangle
The test should display 2 rectangles: the left one with round corners (radius=50), the right one with square corners (radius=0).
That's all Folks!


Status: passed, June 87Issue: COMPILER-WARNING-STREAM
References: COMPILE (p438), COMPILE-FILE (p439)
Category: CLARIFICATION/CHANGE
Edit history: Version 1 by Pitman 02/27/87
Version 2 at committee meeting 15-Mar-87
Version 3 Masinter 15-Mar-87
Version 4 by Fahlman, incorporate Dribble
Version 5 by Masinter, 29-May-87, revert to Version 3
Version 6 by Masinter, 5-Jun-87, minor formatting
Problem Description:
The description of the COMPILE and COMPILE-FILE functions does not
explicitly permit them to print warnings. If this is to be allowed, it
should be an explicitly expressed part of the contract.
Proposal (COMPILER-WARNING-STREAM:ERROR-OUTPUT)
COMPILE and COMPILE-FILE are permitted to output warnings; warnings
should go to the stream that is the value of *ERROR-OUTPUT*.
Rationale:
Compiler warning output is a widely accepted extension to the
compilation. Warnings that come via the WARN function will go to the
stream that is the value of *ERROR-OUTPUT*.
Current Practice:
Some implementations send compiler warning output to *ERROR-OUTPUT*.
Other implementations send it to *STANDARD-OUTPUT*.
Adoption Cost:
In most cases, the change to the compiler to redirect output is expected to be very slight.
Benefits:
Currently, it is difficult to redirect the output of COMPILE and
COMPILE-FILE because it isn't clear where it's directed.
Conversion Cost:
Most user programs that care are probably already tolerant of both
situations or naively expect that output will go to *ERROR-OUTPUT*. As
such, most users will probably perceive this as a clarification.
Aesthetics:
Most users will probably perceive this change as a simplification
because it will allow the kind of warning that comes from WARN and the
kind of warning that comes from compilation to be conceptually grouped.
Discussion:
This was a problem in adapting MACSYMA to Common Lisp because Macsyma
provides alternate user interfaces to the compiler which it needs to be
able to control.
The committee considered extending the proposal to describe the
interaction with DRIBBLE on the warning output, but found that DRIBBLE
was so underspecified as to make the task impossible. DRIBBLE should be
considered in a separate proposal.
The cleanup committee supports this change as stated.


I'm A Celebrity's Jacqueline Jossa 'to do every Bushtucker Trial' this series
Jacqueline Jossa was left in tears on I'm A Celebrity as she was voted to do the latest Bushtucker Trial by viewers
by Irishmirror.ieI'm A Celebrity viewers think former EastEnders star Jacqueline Jossa will be up for plenty of Bushtucker Trials this series.
A usual format on the ITV jungle show, hosted by Ant and Dec, could mean the Lauren Branning actress does several challenges.
After spotting a pattern in previous series, and given her reaction to the news she'd be doing the next trial, fans now think she will be the next serial trial doer.
Jacqueline is super squeamish, and in both challenges she has done so far she's been left in tears by even the thought of doing them.
On Thursday night, she was left visibly distressed when Ant McPartlin and Dec Donnelly told her the news.
Fans immediately took to Twitter , and tipped her to be voted again and again for the rest of the series.
In the past viewers have seen those campmates who visibly struggle with jungle life forced to do task after task by the public.
Katie Price , Natalie Appleton and Helen Flanagan have all been jungle stars who have done numerous challenges, often back-to-back.
There's always one person facing the most amount of trials, and if fans are correct this year it will be Jacqueline.
Taking to Twitter, fans shared their theory as others admitted they hoped it wasn't going to be the case.
One fan said: "I kinda get the feeling Jacqueline will face most (if not all) bushtucker trials!"
Another tweeted: "Jacqueline crying has just cemented the public voting her for every bush tucker trial from here on out."
A third added: "I hope the public don’t vote Jacqueline Jossa to do all the bad trials."
Meanwhile, a fourth viewer commented: "That'll be Jacqueline doing every trial then."
I'm A Celebrity airs nightly at 9pm on ITV.