Represents a driver in a pinball machine such as a lamp, coil/solenoid or flasher that should be driven by Aux Port logic rather than directly by P-ROC hardware. This means any automatic logic to determine when to turn on or off the driver is handled by software in this class. More...
Public Member Functions | |
| VirtualDriver (GameController game, string name, ushort number, bool polarity) | |
| void | update_state (DriverState newState) |
| Generic state change request that represents the P-ROC's PRDriverUpdateState function. | |
| new void | Disable () |
| Disables (turns off) this driver. | |
| new void | Pulse (int milliseconds=-1) |
| Enables this driver for 'milliseconds'. | |
| new void | Schedule (uint schedule, uint cycle_seconds=0, bool now=true) |
| Schedules this driver to be enabled according to the given 'schedule' bitmask. | |
| new void | Enable () |
| Enables this driver indefinitely. | |
| void | ChangeState (bool newState) |
| new void | Tick () |
| void | IncSchedule () |
Protected Attributes | |
| bool | _currentState = false |
| The current state of the driver. Active = True, Inactive = False. | |
| bool | _currentValue = false |
| The current value of the driver taking into account the desired state and polarity. | |
| double | _timeMs = 0 |
| The time the drivers currently active function should end. | |
| double | _nextActionTimeMs = 0 |
| The next time the driver's state should change. | |
| string | _function = "" |
| The currently assigned function (pulse, schedule, patter, pulsed_patter. | |
| bool | _functionActive = false |
| Whether or not a function is currently active. | |
| Delegate | _stateChangeHandler = null |
| Function to be called when the driver needs to change state. | |
| DriverState | _state |
| Virtual drivers have their own internal DriverState object since they are not actually on the board. |
Represents a driver in a pinball machine such as a lamp, coil/solenoid or flasher that should be driven by Aux Port logic rather than directly by P-ROC hardware. This means any automatic logic to determine when to turn on or off the driver is handled by software in this class.
| new void NetProcGame.game.VirtualDriver.Disable | ( | ) |
Disables (turns off) this driver.
Reimplemented from NetProcGame.game.Driver.
| new void NetProcGame.game.VirtualDriver.Enable | ( | ) |
Enables this driver indefinitely.
WARNING!!! Never use this method with high voltage drivers such as coils and flashers! Instead, use time-limited methods such as 'pulse' and 'schedule'.
Reimplemented from NetProcGame.game.Driver.
| new void NetProcGame.game.VirtualDriver.Pulse | ( | int | milliseconds = -1 | ) |
Enables this driver for 'milliseconds'.
| milliseconds |
Reimplemented from NetProcGame.game.Driver.
| new void NetProcGame.game.VirtualDriver.Schedule | ( | uint | schedule, |
| uint | cycle_seconds = 0 , |
||
| bool | now = true |
||
| ) |
Schedules this driver to be enabled according to the given 'schedule' bitmask.
| schedule | |
| cycle_seconds | |
| now |
Reimplemented from NetProcGame.game.Driver.
| void NetProcGame.game.VirtualDriver.update_state | ( | DriverState | newState | ) |
Generic state change request that represents the P-ROC's PRDriverUpdateState function.
| newState |
bool NetProcGame.game.VirtualDriver._currentState = false [protected] |
The current state of the driver. Active = True, Inactive = False.
bool NetProcGame.game.VirtualDriver._currentValue = false [protected] |
The current value of the driver taking into account the desired state and polarity.
string NetProcGame.game.VirtualDriver._function = "" [protected] |
The currently assigned function (pulse, schedule, patter, pulsed_patter.
bool NetProcGame.game.VirtualDriver._functionActive = false [protected] |
Whether or not a function is currently active.
double NetProcGame.game.VirtualDriver._nextActionTimeMs = 0 [protected] |
The next time the driver's state should change.
DriverState NetProcGame.game.VirtualDriver._state [protected] |
Virtual drivers have their own internal DriverState object since they are not actually on the board.
Delegate NetProcGame.game.VirtualDriver._stateChangeHandler = null [protected] |
Function to be called when the driver needs to change state.
double NetProcGame.game.VirtualDriver._timeMs = 0 [protected] |
The time the drivers currently active function should end.


Genesis 8:21
And the LORD smelled a sweet savor; and the LORD said in his heart, I will not again curse the ground any more for man's sake; for the imagination of man's heart is evil from his youth; neither will I again smite any more every thing living, as I have done.


|
Scintillator
v0.0.1 at 1b5c195
Documentation specific to the C++-based scinsynth
|
#include <DeviceInfo.hpp>
Public Types | |
| enum | Type { kCPU = 2, kDiscreteGPU = 0, kIntegratedGPU = 1, kOther = 3, kNothing = 4 } |
Public Member Functions | |
| DeviceInfo (std::shared_ptr< Instance > instance, VkPhysicalDevice device) | |
| bool | build () |
| VkPhysicalDevice | physicalDevice () const |
| const char * | name () const |
| int | presentFamilyIndex () const |
| int | graphicsFamilyIndex () const |
| int | numberOfMemoryHeaps () const |
| bool | isSwiftShader () const |
| Type | type () const |
| const char * | typeName () const |
| bool | supportsWindow () const |
| bool | supportsMemoryBudget () const |
| bool | supportsSamplerAnisotropy () const |
| uint32_t | deviceID () const |
| uint32_t | vendorID () const |
Static Public Member Functions | |
| static const std::vector< const char * > & | windowExtensions () |
| static const char * | memoryBudgetExtension () |
Private Attributes | |
| std::shared_ptr< Instance > | m_instance |
| VkPhysicalDevice | m_physicalDevice |
| VkPhysicalDeviceProperties | m_properties |
| VkPhysicalDeviceFeatures | m_features |
| int | m_presentFamilyIndex |
| int | m_graphicsFamilyIndex |
| int | m_numberOfMemoryHeaps |
| bool | m_supportsWindow |
| bool | m_supportsMemoryBudget |
Wraps a VkPhysicalDevice and can answer basic questions about its properties and compatability with different use cases, mainly in a Window or Surface rendering setup.
Type of devices. The number indicates relative performance (in general), with lower numbers being more performant.
| bool scin::vk::DeviceInfo::build | ( | ) |
Populate internal data structures with needed values. Will return false if the device can't support basic graphics operations and shouldn't be considered a candidate device.


Esprima version .
The following tests are to ensure that the syntax tree is compatible to that produced by Mozilla SpiderMonkey Parser reflection.
Please wait...


Python is an interpreted, high-level, general-purpose programming language. Created by Guido van Rossum and first released in 1991, Python's design philosophy emphasizes code readability with its notable use of significant whitespace.
Ruby is an interpreted, high-level, general-purpose programming language. It was designed and developed in the mid-1990s by Yukihiro "Matz" Matsumoto in Japan. Ruby is dynamically typed and uses garbage collection. It supports multiple programming paradigms, including procedural, object-oriented, and functional programming. According to the creator, Ruby was influenced by Perl, Smalltalk, Eiffel, Ada, Basic, and Lisp.
Go is a statically typed, compiled programming language designed at Google by Robert Griesemer, Rob Pike, and Ken Thompson. Go is syntactically similar to C, but with memory safety, garbage collection, structural typing, and CSP-style concurrency.
Rust is a multi-paradigm programming language focused on performance and safety, especially safe concurrency. Rust is syntactically similar to C++, and provides memory safety without using garbage collection.


|
Vlad the Impaler15th century prince in Transylvania, immortalized in Bram Stoker's gothic novel Dracula.  By this means perhaps 100,000 unpleasantly lost their lives: "Dracula usually had a horse attached to each of the victim's legs and a sharpened stake was gradually forced into the body. The end of the stake was usually oiled and care was taken that the stake was not too sharp; else the victim might die too rapidly from shock."
Not a vampire.
|


Here are just a few ideas of things to do while you are at Pacific Rim National Park:
It is important to be aware of the marine forecast and the tide schedule if you are planning to be by the water at all. Nature can be very unpredictable at the best of times which makes it much more important to be aware of the tides, weather, and tsunami escape routes.
Depending on which attraction you would like to visit in the park visit Google maps to find the precise location. If you do not look up the specific destination you will end up hopelessly lost. Here is the location on Google. You can navigate here directly from wherever you are staying. Have a look at the map below to find fun destinations to visit near you


Vocabulary for PSIS Response Codes
Description
Assigned OID: 2.16.840.1.1138<IP_ADDRESS>.<IP_ADDRESS>
A code system carrying a list of error/issue codes applicable to PSIS.
This code system is managed externally.


I am looking for self-motivated undergraduate and graduate students to conduct exciting research on novel IIoT systems! Prospective students are welcome to drop me an email with your CV.
I earned my Ph.D. under the advice of Prof. Chenyang Lu at the Cyber-Physical Systems Laboratory (CPSL) at Washington University in St. Louis. We perform cutting-edge research on Industrial Internet of Things, and cyber-physical systems that cross-cut computing, communication and dynamic control system.
GPA: 3.95/4
GPA: 95.2/100, Rank: 3/161;
GPA: 93.2/100, Rank: 1/123
If you are interested at me or have some comments about me, please feel free to contact me.
Address:
John Hopcroft Center for Computer Science
Shanghai Jiao Tong University
SEIEE Building, Room 2-430, Yehan Ma,
800 Dong Chuan Road,
Shanghai, China 200240
Email:
yehanma at sjtu dot edu dot cn
mayehan217 at gmail dot com


Music Industry Mapping
The copyright and neighboring right royalty is the wage-like earning of the music performer, the composer and producer. In popular music performers usually play their own composition and sing their own lyrics. If the musician is self-published, all the three royalties directly pay the artist’s work. If a professional publisher is helping the artist as a composer, or a record producer as a recorded performer, they usually share the respective royalty revenues. In emerging markets fewer, in mature markets more composers are represented by publishers and performers by record labels.
Classification of Industries
The nature of the creative jobs is that they contain a wide range of activities. Economists and government policymakers classify industries with the Nomenclature statistique des activités économiques dans la Communauté européenne, in short, with the NACE classification in Europe. This classification is largely harmonized on UN level and can be used to compare almost all economies in the world. The NACE classification has its origins in the economic structure of the middle 20th century, and it is more appropriate for the classification of manufacturing and trading activities, such as car manufacturing, or retail sale of cars.
The music industry is not even recognized by the NACE classification. For example, musicians often perform music (NACE group 90), compose music (NACE group 90), and at the same time do sound recordings (NACE group 58) and teach music (NACE group 85). Many EU member states do not even create government economic statistics for the NACE 90 classes.
This is the main reason why music is not well represented in governmental statistics, which is mainly based on tax returns data from ‘clear’ activity groups, such as sound recordings only. Most creative enterprises, and music businesses are no excemptions, do not have a clear economic activity.
Microenterprises & Freelancers
The music industry, for various reasons, is not part of the NACE industry, and music industry data must be mapped from different data sources. A further difficulty is that music industry, similarly to the other creative and cultural industries, mainly works in freelancer networks and microenterprises. All over Europe, microenterprises are exempted from many statistical reporting, and they are filing simplified tax and financial reports.
The government statistics are largely based on tax returns and other filings of corporate entities with at least 5 employees. While most manufacturing or trading activities, or licensed service activities such as insurance or banking, are easy to classify, the creative industries are different. Because most creative enterprises are smaller than 5 people, they do not file many data for the government or the tax authorities, and they often carry out heterogeneous activities. For example, the music industry is not even recognized by the NACE classification – the live music and compositions can be found in the NACE 90 class, and the sound recordings in the NACE 58 class, and many countries do not even create government economic statistics for the NACE 90 classes.
Standardized mapping
CEEMID has been mapping data sources for composer, producer and performer royalties, and other data sources since 2014, based on the standard mapping technique developed originally in the United States and later adopted to the EU ^[Hull, Geoffrey P., Thomas W. Hutchison, Richard Strasser, and Geoffrey P. Hull. 2011. The Music Business and Recording Industry Delivering Music in the 21st Century. New York: Routledge; Leurdijk, Adnra, and Nieuwenhuis Ottilie. 2012. Statistical, Ecosystems and Competitiveness Analysis of the Media and Content Industries. The Music Industry. 25277 EN. Edited by Jean Paul Simon. Luxembourg: Publications Office of the European Union, 2012: Joint Research Centre Institute for Prospective Technological Studies (IPTS).].
CEEMID designed and administered the world’s biggest data collection on how musicians work and earn their living. Over the course of 6 years we collected anonymous data among musicians related to their touring habits, income, costs, recording expenses and income in various channels.
About 100 stakeholders, among them many collective management organizations helped our work. Some collective management organizations provided us with anonymous payout lists, which was essential to validate the representativeness of our samples. We reconstructed the density function of actual payouts, and collected data until our anonymous responses showed a very accurately similar shape.
Because the music industry all over Europe is mainly made of microenterprise and freelancers, getting an overal picture of the music industry is very difficult.


Introduction to JavaScript
Where did JavaScript come from?
Origins
Created by Brendan Eich at Netscape in 1995
Has been standardized in the ECMAScript specification since the late 90's
It's not related to Java!
Prevalence
HTML, CSS, and JavaScript have been the 3 core languages making up modern web pages
It is supported by all modern web browsers
Also used in desktop programs (widgets)
Programming/Scripting
The main difference between programming and scripting is that scripting languages (like JavaScript!) don't have to be compiled
They also have less access to your computer's machine (like memory allocation)
JavaScript is:
- An interpreted language (i.e. not compiled)
- Object-Based (modular)
- Similar to C++ and Python in how you write code
Libraries
Pre-written JavaScript which allows for easy development
They let you create robust web application easily. Someone else has done all the hard work!
Let's go!
We'll focus on web-based JavaScript (since that's the majority of where you'll encounter and use it)
Find that code editor you installed and open it up!
Create an HTML file
- Create a new html file on your computer
- Open it in your code editor
- Add the following to your html file
- Open your html file in your web browser
- Add the following to your html file
Display the result here.


The company has a vast dust-free workshop, which is used as a production base and a storage base for masks.
We are meiRong Medical Technology (hangzhou) Co., Ltd., the company has dozens of locomotives, which can meet the needs of large orders
The masks produced are better than ordinary models, suitable for people in Europe and America
The company has a vast dust-free workshop, which is used as a production base and a storage base for masks.
The company has advanced imported production equipment and professional technical personnel.
Strict quality, pay attention to the implementation of product quality and brand engineering, put consumers in the first place.
The company has a vast dust-free workshop, which is used as a production base and a storage base for masks
.
The company has advanced imported production equipment and professional technical personnel
.
Strict quality, pay attention to the implementation of product quality and brand engineering, put consumers in the first place
.


mlens.parallel package¶
Submodules¶
Module contents¶
ML-ENSEMBLE
| author: | Sebastian Flennerhag |
|---|---|
| copyright: | 2017 |
| license: | MIT |
Computational graph module for memory-neutral parallel processing of deep general-purpose ensembles.
-
class
mlens.parallel.
ParallelProcessing
(*args, **kwargs)[source]¶ Bases:
mlens.parallel.backend.BaseProcessor
Parallel processing engine.
Engine for running computational graph.
ParallelProcessing
is a manager for executing a sequence of tasks in a given caller, where each task is run sequentially, but assumed to be parallelized internally. The main responsibility ofParallelProcessing
is to handle memory-mapping, estimation cache updates, input and output array updates and output collection.Parameters: - caller (obj) – the caller of the job. Either a Layer or a meta layer class such as Sequential.
- *args (optional) – Optional arguments to
BaseProcessor
- **kwargs (optional) – Optional keyword arguments to
BaseProcessor
.
-
get_preds
(dtype=None, order='C')[source]¶ Return prediction matrix.
Parameters: - dtype (numpy dtype object, optional) – data type to return
- order (str (default = 'C')) – data order. See
numpy.asarray
for details.
Returns: P – Prediction array
Return type: array-like
-
map
(caller, job, X, y=None, path=None, return_preds=False, wart_start=False, split=False, **kwargs)[source]¶ Parallel task mapping.
Run independent tasks in caller in parallel.
Warning
By default, the :~mlens.parallel.backend.ParallelProcessing.map` runs on a shallow cache, where all tasks share the same cache. As such, the user must ensure that each task has a unique name, or cache retrieval will be corrupted. To commit a seperate sub-cache to each task, set
split=True
.Parameters: - caller (iterable) – Iterable that generates accepted task instances. Caller should be
a child of the
BaseBackend
class, and tasks need to implement an appropriate call method. - job (str) – type of job to complete with each task. One of
'fit'
,'predict'
and'transform'
. - X (array-like of shape [n_samples, n_features]) – Input data
- y (array-like of shape [n_samples,], optional.) – targets. Required for fit, should not be passed to predict or transform jobs.
- path (str or dict, optional) – Custom estimation cache. Pass a string to force use of persistent
cache on disk. Pass a
dict
for in-memory cache (requiresbackend != 'multiprocessing'
. - return_preds (bool or list, optional) – whether to return prediction ouput. If
True
, final prediction is returned. Alternatively, pass a list of task names for which output should be returned. - warm_start (bool, optional) – whether to re-use previous input data initialization. Useful if repeated jobs are made on the same input arrays.
- split (bool, default = False) – whether to commit a separate sub-cache to each task.
- **kwargs (optional) – optional keyword arguments to pass onto each task.
Returns: out – Prediction array(s).
Return type: array-like, list, optional
- caller (iterable) – Iterable that generates accepted task instances. Caller should be
a child of the
-
process
(caller, out, **kwargs)[source]¶ Process job.
Main method for processing a caller. Requires the instance to be setup by a prior call to
initialize()
.Parameters: - caller (iterable) – Iterable that generates accepted task instances. Caller should be
a child of the
BaseBackend
class, and tasks need to implement an appropriate call method. - out (dict) – A dictionary with output parameters. Pass an empty dict for no
output. See
initialize()
for more details.
Returns: out – Prediction array(s).
Return type: array-like, list, optional
- caller (iterable) – Iterable that generates accepted task instances. Caller should be
a child of the
-
stack
(caller, job, X, y=None, path=None, return_preds=False, wart_start=False, split=True, **kwargs)[source]¶ Stacked parallel task mapping.
Run stacked tasks in caller in parallel.
This method runs a stack of tasks as a stack, where the output of each task is the input to the next.
Warning
By default, the
stack()
method runs on a deep cache, where each tasks has a separate cache. As such, the user must ensure that tasks don’t depend on data cached by previous tasks. To run all tasks in a single sub-cache, setsplit=False
.Parameters: - caller (iterable) – Iterable that generates accepted task instances. Caller should be
a child of the
BaseBackend
class, and tasks need to implement an appropriate call method. - job (str) – type of job to complete with each task. One of
'fit'
,'predict'
and'transform'
. - X (array-like of shape [n_samples, n_features]) – Input data
- y (array-like of shape [n_samples,], optional.) – targets. Required for fit, should not be passed to predict or transform jobs.
- path (str or dict, optional) – Custom estimation cache. Pass a string to force use of persistent
cache on disk. Pass a
dict
for in-memory cache (requiresbackend != 'multiprocessing'
. - return_preds (bool or list, optional) – whether to return prediction output. If
True
, final prediction is returned. Alternatively, pass a list of task names for which output should be returned. - warm_start (bool, optional) – whether to re-use previous input data initialization. Useful if repeated jobs are made on the same input arrays.
- split (bool, default = True) – whether to commit a separate sub-cache to each task.
- **kwargs (optional) – optional keyword arguments to pass onto each task.
Returns: out – Prediction array(s).
Return type: array-like, list, optional
- caller (iterable) – Iterable that generates accepted task instances. Caller should be
a child of the
-
class
mlens.parallel.
ParallelEvaluation
(*args, **kwargs)[source]¶ Bases:
mlens.parallel.backend.BaseProcessor
Parallel cross-validation engine.
Minimal parallel processing engine. Similar to
ParallelProcessing
, but offers less features, only fits the callers indexer, and excepts no task output.-
process
(caller, case, X, y, path=None, **kwargs)[source]¶ Process caller.
Parameters: - caller (iterable) – Iterable for evaluation job.s Expected caller is a
Evaluator
instance. - case (str) – evaluation case to run on the evaluator. One of
'preprocess'
and'evaluate'
. - X (array-like of shape [n_samples, n_features]) – Input data
- y (array-like of shape [n_samples,], optional.) – targets. Required for fit, should not be passed to predict or transform jobs.
- path (str or dict, optional) – Custom estimation cache. Pass a string to force use of persistent
cache on disk. Pass a
dict
for in-memory cache (requiresbackend != 'multiprocessing'
.
- caller (iterable) – Iterable for evaluation job.s Expected caller is a
-
-
class
mlens.parallel.
Layer
(name=None, propagate_features=None, shuffle=False, random_state=None, verbose=False, stack=None, **kwargs)[source]¶ Bases:
mlens.parallel.base.OutputMixin
,mlens.parallel.base.IndexMixin
,mlens.parallel.base.BaseStacker
Layer of preprocessing pipes and estimators.
Layer is an internal class that holds a layer and its associated data including an estimation procedure. It behaves as an estimator from an Scikit-learn API point of view.
Parameters: - propagate_features (list, range, optional) – Features to propagate from the input array to the output array. Carries input features to the output of the layer, useful for propagating original data through several stacked layers. Propagated features are stored in the left-most columns.
- verbose (int or bool (default = False)) –
level of verbosity.
verbose = 0
silent (same asverbose = False
)verbose = 1
messages at start and finish (same asverbose = True
)verbose = 2
messages for preprocessing and estimatorsverbose = 3
messages for completed job
If
verbose >= 10
prints tosys.stderr
, elsesys.stdout
. - shuffle (bool (default = False)) – Whether to shuffle data before fitting layer.
- random_state (obj, int, optional) – Random seed number to use for shuffling inputs
- **kwargs (optional) – optional arguments to
BaseParallel
.
-
data
¶ Cross validated scores
-
indexers
¶ Check indexer
-
learners
¶ Generator for learners in layer
-
raw_data
¶ Cross validated scores
-
set_output_columns
(X, y, job, n_left_concats=0)[source]¶ Compatibility method for setting learner output columns
-
transformers
¶ Generator for learners in layer
-
class
mlens.parallel.
Group
(indexer=None, learners=None, transformers=None, name=None, **kwargs)[source]¶ Bases:
mlens.parallel.base.BaseEstimator
A handle for learners and transformers that share a common indexer.
Lightweight class for pairing a set of independent learners with a set of transformers that all share the same cross-validation strategy. A
Group
instance is an acceptable caller toParallelProcessing
.New in version 0.2.0.
Note
All instances will share the same indexer. If instances have a different indexer, that indexer will be replaced.
Parameters: - indexer (inst, optional) – A :module:`mlens.index` indexer to build learner and transformers on. If not passed, the first indexer of the learners will be enforced on all instances.
- learners (list, inst, optional) –
Learner
instance(s) attached to indexer. Note thatGroup
overrides previousindexer
parameter settings. - transformers (list, inst, optional) –
Transformer
instance(s) attached to indexer. Note thatGroup
overrides previousindexer
parameter settings. - name (str, optional) – name of group
- **kwargs (optional) – Optional keyword arguments to the
BaseParallel
backend.
-
class
mlens.parallel.
Pipeline
(pipeline, name=None, return_y=False)[source]¶ Bases:
mlens.externals.sklearn.base.BaseEstimator
Transformer pipeline
Pipeline class for wrapping a preprocessing pipeline of transformers.
Parameters: - pipeline (list, instance) –
A
Transformer
instance or a list ofTransformer
instances. Accepted input formats:option_1 = transformer_1 option_2 = [transformer_1, transformer_2] option_3 = [("tr-1", transformer_1), ("tr-2", transformer_2)] option_4 = [transformer_1, ("tr-2", transformer_2)]
- name (str, optional) – name of pipeline.
- return_y (bool, default = False) – If True, both X and y will be returned in a
transform()
call.
-
fit
(X, y=None)[source]¶ Fit pipeline.
Note that the
Pipeline
accepts both X and y arguments, and can return both X and y, depending on the transformers. The pipeline itself does no checks on the input.Parameters: - X (array-like of shape [n_samples, n_features]) – Input data
- y (array-like of shape [n_samples, ]) – Targets
Returns: self – Fitted pipeline
Return type: instance
-
fit_transform
(X, y=None)[source]¶ Fit and transform pipeline.
Note that the
Pipeline
accepts both X and y arguments, and can return both X and y, depending on the transformers. The pipeline itself does no checks on the input.Parameters: - X (array-like of shape [n_samples, n_features]) – Input data
- y (array-like of shape [n_samples, ]) – Targets
Returns: - X_processed (array-like of shape [n_samples, n_preprocessed_features]) – Preprocessed input data
- y (array-like of shape [n_samples, ], optional) – Preprocessed targets
-
transform
(X, y=None)[source]¶ Transform pipeline.
Note that the
Pipeline
accepts both X and y arguments, and can return both X and y, depending on the transformers. Pipeline itself does not checks the input.Parameters: - X (array-like of shape [n_samples, n_features]) – Input data
- y (array-like of shape [n_samples, ]) – Targets
Returns: - X_processed (array-like of shape [n_samples, n_preprocessed_features]) – Preprocessed input data
- y (array-like of shape [n_samples, ], optional) – Original or preprocessed targets, depending on the transformers.
- pipeline (list, instance) –
-
class
mlens.parallel.
Learner
(estimator, indexer=None, name=None, preprocess=None, attr=None, scorer=None, proba=False, **kwargs)[source]¶ Bases:
mlens.parallel.base.ProbaMixin
,mlens.parallel.learner._BaseEstimator
Wrapper for base learners.
Parameters: - estimator (obj) – estimator to construct learner from
- preprocess (str, obj) – preprocess transformer. Pass either the string
cache reference or the transformer instance. If the latter,
the
preprocess
will refer to the transformer name. - name (str) – name of learner. If
preprocess
is notNone
, the name will be prepended topreprocess__name
. - attr (str (default='predict')) – predict attribute, typically one of ‘predict’ and ‘predict_proba’
- scorer (func) – function to use for scoring predictions during cross-validated fitting.
- output_columns (dict, optional) – mapping of prediction feature columns from learner to columns in
output array. Normally, this map is
{0: x}
, but if theindexer
creates partitions, each partition needs to be mapped:{0: x, 1: x + 1}
. Note that ifoutput_columns
are not given at initialization, theset_output_columns
method must be called before running estimations. - verbose (bool, int (default = False)) – whether to report completed fits.
- **kwargs (bool (default=True)) – Optional ParallelProcessing arguments. See
BaseParallel
.
-
scorer
¶ Copy of scorer
-
class
mlens.parallel.
Transformer
(estimator, indexer=None, name=None, **kwargs)[source]¶ Bases:
mlens.parallel.learner._BaseEstimator
Preprocessing handler.
Wrapper for transformation pipeline.
Parameters: - indexer (obj, None) – indexer to use for generating fits.
Set to
None
to fit only on all data. - estimator (obj) – transformation pipeline to construct learner from
- name (str) – name of learner. If
preprocess
is notNone
, the name will be prepended topreprocess__name
. - output_columns (dict, optional) – If transformer is to be used to output data, need to
set
output_columns
. Normally, this map is{0: x}
, but if theindexer
creates partitions, each partition needs to be mapped:{0: x, 1: x + 1}
. - verbose (bool, int (default = False)) – whether to report completed fits.
- raise_on_exception (bool (default=True)) – whether to warn on non-fatal exceptions or raise an error.
- indexer (obj, None) – indexer to use for generating fits.
Set to
-
class
mlens.parallel.
EvalLearner
(estimator, preprocess, name, attr, scorer, error_score=None, verbose=False, **kwargs)[source]¶ Bases:
mlens.parallel.learner.Learner
EvalLearner is a derived class from Learner used for cross-validated scoring of an estimator.
Parameters: - estimator (obj) – estimator to construct learner from
- preprocess (str) – preprocess cache refernce
- indexer (obj, None) – indexer to use for generating fits.
Set to
None
to fit only on all data. - name (str) – name of learner. If
preprocess
is notNone
, the name will be prepended topreprocess__name
. - attr (str (default='predict')) – predict attribute, typically one of ‘predict’ and ‘predict_proba’
- scorer (func) – function to use for scoring predictions during cross-validated fitting.
- error_score (int, float, None (default = None)) – score to set if cross-validation fails. Set to
None
to raise error. - verbose (bool, int (default = False)) – whether to report completed fits.
- raise_on_exception (bool (default=True)) – whether to warn on non-fatal exceptions or raise an error.
-
class
mlens.parallel.
EvalTransformer
(estimator, indexer=None, name=None, **kwargs)[source]¶ Bases:
mlens.parallel.learner.Transformer
Evaluator version of the Transformer.
Derived class from Transformer adapted to cross-validated grid-search. See
Transformer
for more details.
-
mlens.parallel.
make_group
(indexer, estimators, preprocessing, learner_kwargs=None, transformer_kwargs=None, name=None)[source]¶ Creating a
Group
from a set learners and transformersUtility function for creating mapping a set of estimators and preprocessing pipelines to a
Group
ofLearner
andTransformer
instances.Parameters: - indexer (instance or None, default = None) – Indexer instance to use. See :module:`mlens.index` for details.
- estimators (dict of lists or list of estimators.) –
If
preprocessing
isNone
orlist
,estimators
should be alist
. The list can either contain estimator instances, named tuples of estimator instances, or a combination of both.option_1 = [estimator_1, estimator_2] option_2 = [("est-1", estimator_1), ("est-2", estimator_2)] option_3 = [estimator_1, ("est-2", estimator_2)]
If different preprocessing pipelines are desired, a dictionary that maps estimators to preprocessing pipelines must be passed. The names of the estimator dictionary must correspond to the names of the estimator dictionary.
preprocessing_cases = {"case-1": [trans_1, trans_2]. "case-2": [alt_trans_1, alt_trans_2]} estimators = {"case-1": [est_a, est_b]. "case-2": [est_c, est_d]}
The lists for each dictionary entry can be any of
option_1
,option_2
andoption_3
.
- preprocessing : dict of lists or list, optional (default = None)
preprocessing pipelines for given layer. If the same preprocessing applies to all estimators,
preprocessing
should be a list of transformer instances. The list can contain the instances directly, named tuples of transformers, or a combination of both.option_1 = [transformer_1, transformer_2] option_2 = [("trans-1", transformer_1), ("trans-2", transformer_2)] option_3 = [transformer_1, ("trans-2", transformer_2)]
If different preprocessing pipelines are desired, a dictionary that maps preprocessing pipelines must be passed. The names of the preprocessing dictionary must correspond to the names of the estimator dictionary.
preprocessing_cases = {"case-1": [trans_1, trans_2]. "case-2": [alt_trans_1, alt_trans_2]} estimators = {"case-1": [est_a, est_b]. "case-2": [est_c, est_d]}
The lists for each dictionary entry can be any of
option_1
,option_2
andoption_3
.- transformer_kwargs : dict, optional
- Keyword arguments to pass to the
Transformer
instances. - learner_kwargs : dict, optional
- Keyword arguments to pass to the
Learner
instances. - name : str, optional
- Name of group. Should be unique.
-
mlens.parallel.
run
(caller, job, X, y=None, map=True, **kwargs)[source]¶ Utility for running a ParallelProcessing job on a set of callers.
Run is a utility mapping for setting up a ParallelProcessing job and executing across a set of callers. By default run executes:
out = mgr.map(caller, job, X, y, **kwargs)
run()
handles temporary parameter changes, for instance running a learner withproba=True
that hasproba=False
as default. Similarly, instances destined to not produce output can be forced to yield predictions by passingreturn_preds=True
as a keyword argument.Note
To run a learner with a
preprocessing
dependency, the instances need to be wrapped in aGroup
run(Group(learner, transformer), 'predict', X, y)
Parameters: - caller (instance, list) – A runnable instance, or a list of instances.
- job (str) – type of job to run. One of
'fit'
,'transform'
,'predict'
. - X (array-like) – input
- y (array-like, optional) – targets
- map (bool (default=True)) – whether to run a
ParallelProcessing.map()
job. IfFalse
, will instead run aParallelProcessing.stack()
job. - **kwargs (optional) – Keyword arguments.
run()
searches forproba
andreturn_preds
to temporarily update callers to run desired job and return desired output. Otherkwargs
are passed to eithermap
orstack
.


Dainty for Visual Studio
Dainty is a highly configurable, refined, and balanced color theme for Visual Studio, drawing inspiration from the default themes of Visual Studio and VS Code. It is designed to maximize readability and reduce eye strain, making it more pleasant to work with code all day long.
Screenshots
"colors": {
"adjustments": {
"lightness": 4,
"chroma": 0
}
}
"colors": {
"adjustments": {
"lightness": -4,
"chroma": 0
}
}
"colors": {
"overrides": {
"blue": "#ab47bc",
"purple": "#84ffff",
"green": "#82b1ff",
"orange": "#42a5f5"
}
}
Prerequisites
- Visual Studio 2017
- Color Theme Editor extension
Download
Configure Dainty
Setup
- Tools Customize Colors Import Theme
- Select
dainty.vstheme
- Tools Import and Export Settings… Import selected environment settings
- Select
dainty.vssettings
Features
- Precise and pleasant colors are generated within the CIELAB color space
- Supports presets of configurations – i.e. other themes[1]
- Punctuation marks are slightly dimmed
- The left side of the editor is less busy than in the default themes
- Contrast for environment background can be added
- Contrast for environment and editor text can be added
- Contrast for comments can be added
- Contrast for scrollbars can be added
- Transparent environment borders can be set[1]
- Transparent scrollbar containers can be set[1]
- Transparent tool window grip handles can be set
- Settings for Indent Guides extension can be included
- Colors can be lightened, darkened, or desaturated[1]
- Colors can be overridden, and accent color can be set (see
readme.md
)[1] - Usages of colors can be overridden (see
readme.md
)[1]
1. In order to use these features, clone the
repository and edit configuration.json
. If you only need the remaining, then you can use “Configure and download” on this page.
Addititonal extensions
These are some extensions that improve the aestetics of Visual Studio:
| Extension | Description |
|---|---|
| Indent Guides | Highlights active indentation line and has option to use solid lines instead of dotted |
| Line Press[1] | Enables custom line height |
| Better Comments | Enables comments in italic |
To make Indent Guides behave similar to Visual Studio Code, highlighting has to be set to Current line. Dainty can be configured to include settings for Indent Guides which both sets this option and all indentation colors.
Better Comments has default settings that lead to uneven spacing. This can be fixed by setting Size Offset to 0
and Opacity to 1
. Tokens can be disabled by setting each value to a UUID.
1. I currently do not recommend using this extension as its line spacings can be unpredictable (2018-11-10).
Color palette
The Dainty color palette consists of five scales with 40 shades for each scale. There is an additional scale with a less saturated blue intended to be used for headings only. Usually an index divisible by 4 is used for accessing the colors. The scales below shows indexes divisible by 4. For a complete list, go to the colors page.
Blue-grays
Blues
Purples
Greens
Oranges
Implementation
The color theme is generated by doing transformations in a pipeline on the default color theme, Environment – Fonts and Colors settings, and settings for included extensions. Because the dynamics of Dainty is similar to that of the default themes, most colors can be changed using search–replace. In addition, specific colors can be set afterwards to get full control over the styling. See coverage page for table of colors that are set.
The entire palette is generated and processed using Culori. The default colors are defined as LCh using math. Overrides expressed as RGB hex values are supported.
One of the surprises during development was learning how Visual Studio stores colors. Colors
exported/imported using .vssettings
files are expressed in BGR. For a while it was somewhat
confusing why only the green color seemed to work properly!
Credits
Designed and developed by Alexander Teinum.
License
Dainty for Visual Studio is licensed under the MIT License.


Liberal Democrat candidate apologises over 'homophobic' tweet
Dan Schmeising insisted that the comments "do not represent my values"
by Ffion LewisA Liberal Democrat candidate has apologised after offensive tweets from his Twitter account came to light.
Dan Schmeising is the Lib Dem's candidate for Cardiff South and Penarth - a Cardiff University graduate who is also chair of the UK Young Liberals.
He is standing on a platform of stopping Brexit and has spoken out in the past about "divisive language" and there being no place for "nastiness" in politics.
However, he has now faced questions about his own use of language - namely some of his social media posts made prior to him entering politics.

While another deleted tweet, two years later, read: "She is literally famous for s*****g a d**k, having a huge a**e and rich parents. None of these exactly scream empowering."
After the screenshots began circulating, Mr Schmeising posted an apology on Twitter.
He said: "Hey look, it's my turn to be cancelled!
"Someone's dug up screenshots of very off-colour and not okay things I said in the past on here. I've deleted them because they were comments made in ignorance that don't speak for me now - but they shouldn't have been made in 1st place."
"I hope people can appreciate that I've done my best to move past the ignorant manchild I was and be a better ally - but I'm still sorry for the person I was then and the things I said. I can't change that, but I can try to be better."
The apology was criticised on social media by some who felt he had underestimated the severity of his comments.
One Twitter used commented: "You were homophobic and misogynistic. Say that, not euphemistic 'off colour' (and) 'not okay things' to disguise the severity of what you said."
However, said while the Lib Dem hopeful's comments were wrong they believed his apology was sincere and that he was "anything but homophobic".
In a statement later today, Mr Schmeising said: "I'm mortified and upset by the fact that I thought that those things were ever okay to say and apologise from the bottom of my heart for it.
"They were comments made from a position of ignorance to the injustices that people who are LGBT+ still face on a daily basis and I'm ashamed of myself for it.
"They do not represent my values. I've learnt a lot since then and I'm trying to be a better ally - but that doesn't change the fact that my comments were hurtful and wrong and I'm sorry."
A spokesman for the Liberal Democrats said: "We are monitoring the situation with Dan."


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Task 7:
Create a scene and trigger it
In the building, you can create different scenes according to your needs. Please create a scene with the following information.
o The scene's name is "sleeping".
o Add the following devices to the scene: all lights, all blinds and the air conditioner.
o In that scene, the target state of all lights is supposed to be off. The target state of all blinds is supposed to be 100% closed, and the
target temperature setting for the air conditioner is 25°C.
After having this scene, please trigger this scene in the building.
P.S.: You have {{vm.timeLimit}} seconds for this task.
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Time left for this task:
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WQMY
Heart Mt LaMancha Dairy Goats :: Lakeport, California
Thank you for taking the time to look over our herd of LaMancha Dairy Goats. . 2006 AGS National Champion) and the 3rd place Four Year-Old with Willow Run .
http://www.heartmtcarterkids.com/
| popular brands of cigarettes/madder than a wet chicken/ intel's matrix storage technology |
|
|---|---|
| california american legion auxiliary | Williamsport |
| Branding | MyNetworkTV dairy goats and willow run |
| sewer outlet from basement | alfred g brummitt jr: 29 (zinc how many protons) & gruber kristin m md-DT 45.3 (UHF) |
| butler creek hot lips loader ebay | see article |
| Owner | New Age Media, LLC Old Dominion Dairy Goats |
| First air date | December 30, 1988 |
| aromatic bitters not available | MYNetworkTV |
| 33rd virginia militia civil war | WOLF-TV, free music theory lesson for piano |
| gecko house st john | WILF (1988-2006) |
| Former channel number(s) | Iron Rod French Alpine Dairy Goats: +*B Willow Run Ahren Rhett |
| skype chat window screenshot | oreo and black stools (1988-1998) free music theory lesson for piano / the shire golf uk (1998-2006) |
| magellan's life contrasted to columbus | 50 traditinal swedish christmas dinner 420 kW (WOLF-DT3) |
| cooking ribs on agrill | 243 m 488 m (WOLF-DT3) |
| amp per hour battery | 52075 73375 (WOLF-DT3) |
| Transmitter coordinates | analysis of wrong turn at lungfish super lotto california usa lottery results (WOLF-DT3) |
Addi Hill French Alpine Dairy Goats: Alpine Senior Does
Jan 24, 2013 . Addi Hill French Alpine Dairy Goats . DS: *B Willow Run JC Reprisal Dam: Addi Hill R . Amita is dairy and smoothly blended through out.
http://addihill.com/alp_senior.shtml
Waiilatpu Dairy Goats - Alpine Does
SS: ++*B Willow-Run LA Armand SD: CH . Abigail is a tall, sharp, dairy individual. . We milked her almost out to be even knowing how dairy she is and that we .
http://waiilatpu.com/adoes.html
Contents |
[maine sunrise sunset table] History
Sutton Farm & Amy's Pride ~ Alpine Dairy Goats
Nov 29, 2012 . ALPINE DAIRY GOATS . SG+*B Willow Run Ahren Rhett attained Superior Genetics status. SGCH Amy's Pride ACC Dixie's Rebel 3*M .
http://www.suttonfarmalpines.com/
ALPINE SENIOR DOES - KICKAPOO VALLEY DAIRY GOATS
KICKAPOO VALLEY DAIRY GOATS . *B Willow Run Abraham Abner . at the 2011 ADGA National Show including the 1st and 4th place milking yearlings.
http://www.kickapoovalleydairygoats.com/alpine-senior-does.html
Saanen Goats | Welbian Farm Dairy Goats
The Saanen dairy goat originated in the Saanen Valley of Switzerland. Approximately 150 . DS: *B Willow Run Victor Sloan LA 89 VEE DD: CH Loughlin's .
http://www.welbianfarmdairygoats.com/saanen-goats.html
Knickknack Knows She's Good - Dairy Goat Journal.
Subscribe to Dairy Goat Journal or Change your address . "Her dam, Willow Run Maestro BricABrac, was the 2004 National Show 1st Place Senior Yearling, .
http://www.dairygoatjournal.com/issues/90/90-5/knicknack_knows_shes_good.html
Loughlin Dairy Goats~~Toggenburgs
GCH Willow Run Deanmartinhumorous 4*M American DOB: 1/21/99. 2002 NAILE Res. GCH. 2004 Music City Goat Festival- 2 X GCH, BOB, 2 X BIS .
http://www.loughlinsdairygoats.com/toggenburg.htm
Bucks - Cotton Eyed Does Dairy Goats
Quality Alpine and Nubian Dairy Goats. Located in New Waverly, Texas, . across the country to our small herd. Waiilatpu, Pearl Valley, Willow Run to name a few.
http://www.freewebs.com/cottoneyeddoes/bucks.htm
Kori Brook Dairy Goats
Jan 20, 2001 . GrandSire: +*B Willow-Run Gideon . our first doe to be awarded the title of Miss Pennsylvania by the Pennsylvania Dairy Goat Association.
http://koribrook.com/nubseniordoes.htm
Archives - American Dairy Goat Association
Premier Breeder of Show WILLOW RUN DAIRY Dennis D. & Patricia A. Dean . . 2006 Indianapolis, IN Premier Breeder Kastdemur's Dairy Goats Runner-Up .
http://www.adga.org/index.php?option=com_content&view=archive&Itemid=105
Dream Girls Dairy Goats
We raise purebred Nubian and Alpine Dairy goats. Our bloodlines include Easy- Stream, Kastdemur, Price o the Field, Willow Run and Hill N Holler to name a .
http://dreamgirlsdairygoats.com/
The Year of the Goat: Goats Take Nashville: Spotlight Sale
Nov 1, 2003 . At the American Dairy Goat Association National Convention's Spotlight Sale Saturday morning, a buck, Willow Run Chevalier Ruffian, .
http://www.karlschatz.com/yearofthegoat/archives/000064.shtml
Sr Does - Otarion Dairy Goats
Welcome to Otarion Dairy Goats located in Reed Point, .
http://otariondairygoats.com/srdoes.html
FANCY COLORS Dairy Goats
27You shall have enough goats' milk for your food, . GCH Willow Run Golden Ember earned her SG with a final LA Score of 90EEEV (2006). CH Little .
http://fancycolorsnubians.com/
[girls leopard print high heels] Digital channels
| Channel | Name | skype chat window screenshot | nuclear fission to make energy | Programming |
|---|---|---|---|---|
| 53.1 | dairy goats and willow run DT | 4sc dw guitar yamaha | missed connective tissue disorder | Main dairy goats and willow run programming / MyNetworkTV |
| 53.2 | WOLF DT | free debit credit reports | iron condor option defined | Simulcast of chewing tobacco that forms cancer |
| 53.3 | WSWB DT | 480i | 4:3 | Simulcast of alpha cannine kennels website houston tx |
Addi Hill French Alpine Dairy Goats: Alpine Sires
Nov 30, 2012 . Addi Hill French Alpine Dairy Goats . SS: ++*B Willow Run Armand Abraham . Israel is very dairy, long and tall, with a level top line.
http://addihill.com/alp_sire.shtml
[national bowling stadium in nevada] References
[cornelia meyer egbert rimkus death valley] External links
- fort mason festival pavilion
- hair extensions rochester ny
- saturn vue rear air 2003
- blood pressure measurement listen nurse
- dundee real estate investment trust
- dignity home health monterey bay
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vignettes/AncestryCheck.Rmd
AncestryCheck.Rmd
The identification of individuals of divergent ancestry can be achieved by combining the genotypes of the study population with genotypes of a reference dataset consisting of individuals from known ethnicities (for instance individuals from the Hapmap or 1000 genomes study [1]). Principal component analysis (PCA) on this combined genotype panel can then be used to detect population structure down to the level of the reference dataset (for Hapmap and 1000 Genomes, this is down to large-scale continental ancestry).
In the following, the workflow for combining a study dataset with the reference samples, conducting PCA and estimating ancestry is demonstrated. The study dataset consists of 200 individuals and 10,000 genetic markers and is provided with \(plinkQC\) in file.path(find.package('plinkQC'),'extdata')
.
A suitable reference dataset should be downloaded and if necessary, re-formated into PLINK format. Vignettes ‘Processing HapMap III reference data for ancestry estimation’ and ‘Processing 1000Genomes reference data for ancestry estimation’, show the download and processing of the HapMap phase III and 1000Genomes phase III dataset, respectively. In this example, we will use the HapmapIII data as the reference dataset.
We will first set up some bash variables and create directories needed; storing the names and directories of the reference and study will make it easy to use updated versions of the reference or new datasets in the future. Is is also useful to keep the PLINK log-files for future reference. In order to keep the data directory tidy, we’ll create a directory for the log files and move them to the log directory here after each analysis step.
In order to compute joint principal components of the reference and study population, we’ll need to combine the two datasets. The plink –merge function enables this merge, but requires the variants in the datasets to be matching by chromosome, position and alleles. The following sections show how to extract the relevant data from the reference and study dataset and how to filter matching variants.
We will use an awk script to find A→T and C→G SNPs. As these SNPs are more difficult to align and only a subset of SNPs is required for the analysis, we will remove them from both the reference and study data set.
awk 'BEGIN {OFS="\t"} ($5$6 == "GC" || $5$6 == "CG" \
|| $5$6 == "AT" || $5$6 == "TA") {print $2}' \
$qcdir/$name.bim > \
$qcdir/$name.ac_gt_snps
awk 'BEGIN {OFS="\t"} ($5$6 == "GC" || $5$6 == "CG" \
|| $5$6 == "AT" || $5$6 == "TA") {print $2}' \
$refdir/$refname.bim > \
$qcdir/$refname.ac_gt_snps
plink --bfile $refdir/$refname \
--exclude $qcdir/$refname.ac_gt_snps \
--make-bed \
--out $qcdir/$refname.no_ac_gt_snps
mv $qcdir/$refname.no_ac_gt_snps.log $qcdir/plink_log/$refname.no_ac_gt_snps.log
plink --bfile $qcdir/$name \
--exclude $qcdir/$name.ac_gt_snps \
--make-bed \
--out $qcdir/$name.no_ac_gt_snps
mv $qcdir/$name.no_ac_gt_snps.log $qcdir/plink_log/$name.no_ac_gt_snps.log
We will conduct principle component analysis on genetic variants that are pruned for variants in linkage disequilibrium (LD) with an \(r^2 >0.2\) in a 50kb window. The LD-pruned dataset is generated below, using plink –indep-pairwise to compute the LD-variants; additionally exclude range is used to remove genomic ranges of known high-LD structure. This file was originally provided by [6] and is available in file.path(find.package('plinkQC'),'extdata','high-LD-regions.txt')
.
plink --bfile $qcdir/$name.no_ac_gt_snps \
--exclude range $refdir/$highld \
--indep-pairwise 50 5 0.2 \
--out $qcdir/$name.no_ac_gt_snps
mv $qcdir/$name.prune.log $qcdir/plink_log/$name.prune.log
plink --bfile $qcdir/$name.no_ac_gt_snps \
--extract $qcdir/$name.no_ac_gt_snps.prune.in \
--make-bed \
--out $qcdir/$name.pruned
mv $qcdir/$name.pruned.log $qcdir/plink_log/$name.pruned.log
We will use the list of pruned variants from the study sample to reduce the reference dataset to the size of the study samples:
The following section uses an awk-script to check that the variant IDs of the reference data have the same chromosome ID as the study data. For computing the genetic PC, the annotation is not important, however, merging the files via PLINK will only work for variants with perfectly matching attributes. For simplicity, we update the pruned reference dataset. Note, that sex chromosomes are often encoded differently and might make the matching more difficult. Again, for simplicity and since not crucial to the final task, we will ignore XY-encoded sex chromosomes (via sed -n '/^[XY]/!p'
).
awk 'BEGIN {OFS="\t"} FNR==NR {a[$2]=$1; next} \
($2 in a && a[$2] != $1) {print a[$2],$2}' \
$qcdir/$name.pruned.bim $qcdir/$refname.pruned.bim | \
sed -n '/^[XY]/!p' > $qcdir/$refname.toUpdateChr
plink --bfile $qcdir/$refname.pruned \
--update-chr $qcdir/$refname.toUpdateChr 1 2 \
--make-bed \
--out $qcdir/$refname.updateChr
mv $qcdir/$refname.updateChr.log $qcdir/plink_log/$refname.updateChr.log
Similar to the chromosome matching, we use an awk-script to find variants with mis-matching chromosomal positions.
Unlike chromosomal and base-pair annotation, mismatching allele-annotations will not only prevent the plink –merge, but also mean that it is likely that actually a different genotype was measured. Initially, we can use the following awk-script to check if non-matching allele codes are a simple case of allele flips.
We use plink to update the mismatching positions and possible allele-flips identified above.
Any alleles that do not match after allele flipping, are identified and removed from the reference dataset.
awk 'BEGIN {OFS="\t"} FNR==NR {a[$1$2$4]=$5$6; next} \
($1$2$4 in a && a[$1$2$4] != $5$6 && a[$1$2$4] != $6$5) {print $2}' \
$qcdir/$name.pruned.bim $qcdir/$refname.flipped.bim > \
$qcdir/$refname.mismatch
plink --bfile $qcdir/$refname.flipped \
--exclude $qcdir/$refname.mismatch \
--make-bed \
--out $qcdir/$refname.clean
mv $qcdir/$refname.clean.log $qcdir/plink_log/$refname.clean.log
The matching study and reference dataset can now be merged into a combined dataset with plink –bmerge. If all steps outlined above were conducted successfully, no mismatch errors should occur.
We can now run principal component analysis on the combined dataset using plink –pca which returns a .eigenvec file with the family and individual ID in columns 1 and 2, followed by the first 20 principal components.
We can use the .eigenvec file to estimate the ancestry of the study samples. Identifying individuals of divergent ancestry is implemented in check_ancestry
. Currently, check ancestry only supports automatic selection of individuals of European descent. It uses principal components 1 and 2 to find the center of the known European reference samples. All study samples whose Euclidean distance from the centre falls outside the radius specified by the maximum Euclidean distance of the reference samples multiplied by the chosen europeanTh
are considered non-European. check_ancestry
shows the result of the ancestry analysis in a scatter plot of PC1 versus PC2 colour-coded for samples of the reference populations and the study population. From within R, run the following command to the ancestry check:
library(plinkQC)
indir <- system.file("extdata", package="plinkQC")
name <- 'data'
refname <- 'HapMapIII'
prefixMergedDataset <- paste(name, ".", refname, sep="")
exclude_ancestry <-
evaluate_check_ancestry(indir=indir, name=name,
prefixMergedDataset=prefixMergedDataset,
refSamplesFile=paste(indir, "/HapMap_ID2Pop.txt",
sep=""),
refColorsFile=paste(indir, "/HapMap_PopColors.txt",
sep=""),
interactive=TRUE)
1. The International HapMap Consortium. A haplotype map of the human genome. Nature. 2005;437: 1299–320. doi:10.1038/nature04226
2. The International HapMap Consortium. A second generation human haplotype map of over 3.1 million SNPs. Nature. 2007;449: 851. doi:10.1038/nature06258
3. The International HapMap Consortium. Integrating common and rare genetic variation in diverse human populations. Nature. 2010;467. doi:10.1038/nature09298
4. 1000 Genomes Project Consortium. An integrated map of structural variation in 2,504 human genomes. Nature. 2015;526: 75–81. doi:10.1038/nature15394
5. 1000 Genomes Project Consortium. A global reference for human genetic variation. Nature. 2015;526: 75–81. doi:10.1038/nature15393
6. Anderson CA, Pettersson FH, Clarke GM, Cardon LR, Morris AP, Zondervan KT. Data quality control in genetic case-control association studies. Nature Protocols. 2010;5: 1564–73. doi:10.1038/nprot.2010.116


ROW_NUMBER Window Function
Determines the ordinal number of the current row within a group of rows, counting from 1, based on the ORDER BY expression in the OVER clause. If the optional PARTITION BY clause is present, the ordinal numbers are reset for each group of rows. Rows with equal values for the ORDER BY expressions receive the different row numbers nondeterministically.
Syntax
ROW_NUMBER () OVER ( [ PARTITION BY expr_list ] [ ORDER BY order_list ] )
Arguments
- ( )
-
The function takes no arguments, but the empty parentheses are required.
- OVER
-
The window clauses for the ROW_NUMBER function.
- PARTITION BY expr_list
-
Optional. One or more expressions that define the ROW_NUMBER function.
- ORDER BY order_list
-
Optional. The expression that defines the columns on which the row numbers are based. If no PARTITION BY is specified, ORDER BY uses the entire table.
If ORDER BY does not produce a unique ordering or is omitted, the order of the rows is nondeterministic. For more information, see Unique Ordering of Data for Window Functions.
Return Type
BIGINT
Examples
See ROW_NUMBER Window Function Example.


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Project Realized in VueJs during my first internship in the Agence Webcom.
Check it out The code is the property of the Agence Webcom and is not public anymoreProject realized in NuxtJS during my second internship in the Agence Webcom.
The code is the property of the Agence Webcom and is not public anymoreWebsite developped and integrated in collaboration with the Agence Webcom developpment team during my second internship.
Check it outPython script taking in input one string hashed with the Caesar algorythm and one dictionnary, and then output the most probable original phrase based on the number of matching words in the dictionnary.
code available hereSimple script that take in input one hash and then display on screen which hash algorythm could have been used. Work with 12 hash algorythms.
code available here


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Inserts a numeric code for the kind of the current key segment.
Usage
<SEGMENT_KIND>
Possible values
•1 Field (see <SEGMENT_NAME> and <SEGMENT_MAPPEDNAME>)
•2 Literal value (see <SEGMENT_LITVAL>)
•3 External value (see <SEGMENT_STRUCTURE>)
•4 Record number
Alternate forms
None
See Also
None
Copyright © 2021 Synergex International, Inc.


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j.n.ConnectException: Connection reset by peer: localhost/<IP_ADDRESS>:9000
0
21
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Publications
Download my C.V. and check out my Google Scholar citation page.
Jeffrey T. Miller, Noah M. Reid, Diane Nacci, Andrew Whitehead. 2019. Developing a high-quality linkage map for the fish Fundulus heteroclitus. Submitted to G3.
Elias M. Oziolor, Noah M. Reid, Sivan Yair, Kristin M. Lee, Sara Guberman Verploeg, Peter C. Bruns PC, Joseph R. Shaw, Andrew Whitehead, Cole W. Matson. 2019. Adaptive introgression enables evolutionary rescue from extreme environmental pollution. Science. In press.
Rachel Rodgers, Jen Roach, Noah M. Reid, Andrew Whitehead, and David D. Duvernell. 2018. Phylogenomic analysis of Fundulidae (Teleostei: Cyprinodotiformes) using RNA-sequencing Data. Molecular Phylogenetics and Evolution, 121:150-157.
Noah M. Reid, Craig E. Jackson, Don Gilbert, Patrick Minx, Michael J. Montague, Thomas H. Hampton, Lily W. Helfrich, Benjamin L. King, Diane E. Nacci, Neel Aluru, Sibel I. Karchner, John K. Colbourne, Mark E. Hahn, Joseph R. Shaw, Marjorie F. Oleksiak, Doug L. Crawford, Wesley C. Warren, and Andrew Whitehead. 2017. The landscape of extreme genomic variation in the highly adaptable Atlantic killifish. Genome Biology and Evolution, 9(3):659-676.
Andrew Whitehead, Bryan W. Clark, Noah M. Reid, Mark E. Hahn, and Diane Nacci. 2017. When evolution is the solution to pollution: Key principles, and lessons from rapid repeated adaptation of killifish (Fundulus heteroclitus) populations. Evolutionary Applications, 2017:1-22.
Erika B. Holland, Jared V. Goldstone, Isaac N. Pessah, Andrew Whitehead, Noah M. Reid, Sibel Karchner, Mark E. Hahn, Diane E. Nacci, Bryan W. Clark, and John J. Stegeman. 2017. Ryanodine receptor and FK506 binding protein 1 in the Atlantic killifish (Fundulus heteroclitus): A phylogenetic and population-based comparison. Aquatic Toxicology, 192:105-115.
Noah M. Reid NM, Dina A. Proestou, Bryan W. Clark,Wesley C. Warren,John K. Colbourne, Joseph R. Shaw, Sibel I. Karchner, Mark E. Hahn, Diane Nacci, Marjorie F. Oleksiak, Doug L. Crawford, and Andrew Whitehead. 2016. The genomic landscape of rapid repeated evolutionary adaptation to toxic pollution in wild fish. Science, 354(6317):1305-1308.
Bryan C Carstens, Michael Gruenstaeudl, and Noah M. Reid. 2016. Community trees: Identifying codiversification in the Paramo dipteran community. Evolution, 70(5): 1080-0193.
Michael Gruenstaeudl, Noah M. Reid, Gregory L. Wheeler, and Bryan C. Carstens. 2016. Posterior predictive checks of coalescent models: P2C2M, an R package. Molecular ecology resources, 16(1), 193-205..
Noah M. Reid, Andrew Whitehead. 2015. Functional genomics to assess biological responses to marine pollution at physiological and evolutionary timescales: toward a vision of predictive ecotoxicology. Briefings in functional genomics elv060.
Jack Sullivan, John R Demboski, Kaycee C Bell, Sarah Hird, Brice Sarver, Noah Reid, and Jeff M Good. (2014). Divergence with gene flow within the recent chipmunk radiation (Tamias). Heredity 113.3: 185-194.
Noah M. Reid, Sarah Hird, Jeremy M Brown, John D McVay, Tara A Pelletier and Bryan C Carstens. 2013. Poor fit to the multispecies coalescent is widely detectable in empirical data. Systematic Biology.
Bryan C Carstens, Tara A. Pelletier, Jordan D. Satler, Noah M. Reid. 2013. How to fail at species delimitation. Molecular Ecology. 22(17):4369-4383.
Noah M. Reid, Bryan C. Carstens. 2012. Phylogenetic estimation error can decrease the accuracy of species delimitation: a Bayesian implementation of the general mixed Yule-coalescent model. BMC Evolutionary Biology. 12:196. DOI: 10.1186/1471-2148-12-196.
Noah M. Reid, John R. Demboski, Jack Sullivan. 2012. Phylogeny Estimation of the Radiation of Western North American Chipmunks (Tamias) in the Face of Introgression Using Reproductive Protein Genes. Systematic Biology. 61 (1): 44-62.
Noah Reid, Sarah Hird, Albrecht Schulte-Hostedde, and Jack Sullivan. 2010. Examination of nuclear loci across a zone of mitochondrial introgression between Tamias ruficaudus and T. amoenus. Journal of Mammalogy 91(6):1389-1400.
Sarah M. Hird, Noah Reid, John Demboski and Jack Sullivan. 2010. Introgression at differentially aged hybrid zones in red-tailed chipmunks. Genetica 138:869-883.
Bryan C. Carstens, Holly N. Stoute, Noah M. Reid. 2009. An information-theoretical approach to phylogeography. Molecular Ecology 18(20):4270-4282.
Jeffrey M. Good, Sarah M. Hird, Noah Reid, John R. Demboski, Scott J. Steppan, Tina R. Martin-Nims, Jack Sullivan. 2008. Ancient hybridization and mitochondrial capture between two species of chipmunks. Molecular Ecology 17(5):1313-1327.
Polly Campbell, Noah M. Reid, Akbar Zubaid, nAdura M. Adnan, Thomas H. Kunz. 2006. Comparative Roosting Ecology of Cynopterus (Chiroptera: Pteropodidae) Fruit Bats in Peninsular Malaysia. Biotropica 38(6):725-734.


MapleStory
MapleStory is massively multiplayer online role-playing game(MMORPG), developed by South Korean game development company Wizet. Several versions of the game are available for specific countries or regions, and each is published by various companies such as Nexon.
In the game, players travel the "Maple World", defeating monsters and developing their characters' skills and abilities as is typical in role-playing games.


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Depending on how much experience you have with the GEOS graphics system, you may have some idea already about what sorts of shapes can be drawn and the ways to draw them. Most of these commands have names like
GrDrawShape()
or
GrFillShape()
(e.g.
GrDrawRect()
,
GrFillEllipse()
). Normally, a command of the form
GrDrawShape()
draws the outline of a shape, while
GrFillShape()
fills in the interior of the shape. Commands with names like
GrSetAttribute()
(e.g.
GrSetAreaColor()
) change the color, fill pattern, and other attributes of shapes to be drawn. Most of these commands are passed a GState. The drawing commands are also passed coordinates at which to draw.
For many of these commands, there are GString opcodes which represent the command in a GString. Also, the arguments used when drawing these shapes often correspond to instance data specific to the Graphic Object which draws that shape.
Most of these routines work with standard coordinates, measured in typographer's points. For geodes that need to make drawings which are precise to a fraction of a point, four routines have been set up to use WWFixed coordinates, and are thus accurate to a fraction of a point. These routines are
GrDrawRelLineTo()
,
GrRelMoveTo()
,
GrDrawRelCurveTo()
, and
GrDrawRelArc3PointTo()
. Geodes may use these routines to draw the outline of any conceivable two-dimensional shape. To create a precise, filled shape, use these routines to describe a path and then fill the path.


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Thank you for your submission. We will send an e-mail to {{ prize.handler.email }} once we have decided if we will use your prize. Please check that e-mail for additional contact from us, especially after the event with information about where to ship your prize (if necessary).


Please check your email and add us to your safe-senders list. If you do not receive our confirmation email in the next hour, please contact us!


Primlong1615.github.io
Kanguru Defender HDD/SSD. Self Service Password Management User Manual. Common Criteria Evaluated Configuration Guide for Kanguru Products. Kanguru USB3.0 Copy Dock SATA Manual. KanguruClone Logging Software. See all 10 articles.
Kanguru Dock TV Operation user s manual PDF View/Download. Product Manuals - Kanguru Solutions Technical Support. About Kanguru - Delicious Food, A Hop Away. Food delivery services within Metro Manila Philippines. Kanguru, Winning the fight against the monster in your stomach, and feeding Hangry individuals since.
Download Operation user s manual of Kanguru Dock TV for Free or View it Online on All-Guides.com. Macintosh and Mac are trademarks of Apple Computer. All of the third-party brands and names are the property of their respective owners.
Kanguru Computer-Festplattengehäuse amp; -Docks Anleitungen herunterladen. View and Download Kanguru Dock user manual online. Kanguru Dock External Hard Drive: Users Manual. Macintosh and Mac are trademarks of Apple Computer. All of the third-party brands and names are the property of their respective owners.
Secure Encrypted USB Flash Drives Hard Drive, Blu-ray. Kanguru. KANGURU DOCK USER MANUAL Pdf Download. Kanguru provides strong data usb security solutions with FIPS and TAA Compliant secure, hardware encrypted flash drives, hard drives, remote management, Endpoint Security, and DVD/HD/Blu-ray duplicators.


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[§127A-31] Penalties prescribed by this chapter additional to other penalties. If conduct prohibited by or under the authority of this chapter is also made unlawful by another or other laws, the offender may be convicted as provided in this chapter and for the violation of the other law or laws. [L 2014, c 111, pt of §2]


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Our team mainly consists of vikings. They were born in glacier rifts or something.
There is also a highly trained owl working on some irrelevant stuff. We might eat it if it doesn't code faster.
The last teammember is also a viking who is exceedingly good at programmer art.


Before we can inject anything into another process, we'll need a place to put it. We'll use the VirtualAllocEx() function to do so.
VirtualAllocEx() takes an amount of memory to allocate as one of its parameters. If we use LoadLibraryA(), we'll allocate spcae for the full path of the DLL and if we jump into the DllMain, we'll allocate space for the DLL's full contents.
DLL Path
Allocating space for just the DLL path slightly reduces the amount of code you'll need to write but not by much. It also requires you to use the LoadLibraryA() method which has some downsides (described in general). That being said, it is a very popular method.
Use VirtualAllocEx() and allocate enough memory to support a string which contains the path to the DLL:
GetFullPathName(TEXT("somedll.dll"),
BUFSIZE,
dllPath, //Output to save the full DLL path
NULL);
dllPathAddr = VirtualAllocEx(hHandle,
0,
strlen(dllPath),
MEM_RESERVE|MEM_COMMIT,
PAGE_EXECUTE_READWRITE);
Full DLL:
Allocating space for the full DLL requires a little more code however it's also much more reliable and doesn't need to use LoadlibraryA().
First, open a handle to the DLL with CreateFileA() then calculate its size with GetFileSize() and pass it to VirtualAllocEx():
GetFullPathName(TEXT("somedll.dll"),
BUFSIZE,
dllPath, //Output to save the full DLL path
NULL);
hFile = CreateFileA( dllPath,
GENERIC_READ,
0,
NULL,
OPEN_EXISTING,
FILE_ATTRIBUTE_NORMAL,
NULL );
dllFileLength = GetFileSize( hFile,
NULL );
remoteDllAddr = VirtualAllocEx( hProcess,
NULL,
dllFileLength,
MEM_RESERVE|MEM_COMMIT,
PAGE_EXECUTE_READWRITE );


Is it safe to email?
It isn't very frequent that we email sensitive information, but if you're unsure, check this reference site!
SSL Certificates
Most of the confusion here comes with not knowing the different pieces that make up an SSL certificate. Here are the pieces and the recommended action:
- Private key - Never email this. See the alternate methods below.
- Public key aka CSR - This is totally safe to email.
- SSL Certificate(s) - Usually when you purchase an SSL cert, you'll get several certificates. They're all safe to email. In fact, they're usually emailed to you from the vendor.
Credit Card Information
No. Never.
Alternative Methods
Email is like a postcard - there's no protection for preying eyes. Here are some alternative methods to send sensitive information:
- Services like Dropbox, Google Drive, etc. - Upload the sensitive information and then share the folder with the Dropbox account of the recipient. Important: do *not* simply copy and share the public link to the sensitive information.
- Diversify methods - If you have multiple pieces of information that need to be sent, don't send them all in the same method. Send one via a text message, one via Dropbox, and one via a phone call. Although not 100% safe, you'll lower the chance of an attacker collecting all of the pieces and putting them together.


{% extends 'base.html' %} {% load crispy_forms_tags %} {% load socialaccount %} {% block title %} MusicApp | {{ title }} {% endblock title %} {% block header %} {% endblock header %} {% block body %} Sign In Sign in with Google or {% csrf_token %} {{form|crispy}} Log In Don't have an account? Please sign up first. {% endblock body %}
Don't have an account? Please sign up first.


--- title: Modifying an R function to iterate (using purrr) and use non-standard evaluation (using rlang) author: 'null' date: '2017-12-17' description: Desc meta_img: /images/image.jpg slug: modifying-an-r-function-to-use-non-standard-evaluation categories: [r, walkthrough] tags: [konfound] ---
Note that due to version-related issues the code in this post is displayed but is not excecuted/run.
Research in classrooms and schools can be complex because of all of the factors that matter. A question that often comes up when we say that we observed some pattern in data is, but did you control for X?
In the context of working on an approach to find out how impactful an omitted variable would need to be to invalidate an inference, we had to modify a function that worked for a single sensitivity analysis to work for many and to be easier to use.
In the context of programming (and mathematics!), many functions take inputs and then transform them into output.
Imagine we have a function that takes two values used to calculate a t-test, the t statistic (the coefficient divided by its standard error) and the degrees of freedom of the t distribution. it then returns a bunch of output about how sensitive an inference based on the t-test is to bias or to an omitted confounding variable.
Here is a function that outputs values about the sensitivity of a t-test as a row in a row of a data frame (actually an R data.frame
modified to be a bit easier to work with, an R
tibble).
It’s a bit of a whopper:
core_sensitivity_mkonfound <- function(t, df, alpha = .05, tails = 2) {
critical_t <- stats::qt(1 - (alpha / tails), df)
critical_r <- critical_t / sqrt((critical_t ^ 2) + df)
obs_r <- abs(t / sqrt(df + (t ^ 2)))
# for replacement of cases framework
if (abs(obs_r) > abs(critical_r)) {
action <- "to_invalidate"
inference <- "reject_null"
pct_bias <- 100 * (1 - (critical_r / obs_r)) }
else if (abs(obs_r) < abs(critical_r)) {
action <- "to_sustain"
inference <- "fail_to_reject_null"
pct_bias <- 100 * (1 - (obs_r / critical_r)) }
else if (obs_r == critical_r) {
action <- NA
inference <- NA
pct_bias <- NA
}
if ((abs(obs_r) > abs(critical_r)) & ((obs_r * critical_r) > 0)) {
mp <- -1
} else {
mp <- 1
}
# for correlation based framework
itcv <- (obs_r - critical_r) / (1 + mp * abs(critical_r))
r_con <- round(sqrt(abs(itcv)), 3)
out <- dplyr::data_frame(t, df, action, inference, pct_bias, itcv, r_con)
names(out) <- c("t", "df", "action", "inference", "pct_bias_to_change_inference", "itcv", "r_con")
out$pct_bias_to_change_inference <- round(out$pct_bias_to_change_inference, 3)
out$itcv <- round(out$itcv, 3)
out$action <- as.character(out$action)
out$inference <- as.character(out$inference)
return(out)
}
Let’s test it out. Imagine we have a t statistic of 3
for a hypothesis test associated with 100
degrees of freedom.
core_sensitivity_mkonfound(3, 100)
Works.
It looks like in order to invalidate the inference, around 32
% of the effect would be need to be due to bias; or, an committed variable would need to be correlated with both the predictor of interest and the dependent variable at .339
in order to invalidate the inference. You can read more about sensitivity analysis and an in-development R package on the approach to sensitivity analysis with Ran Xu and Ken Frank here.
How could we write a function to provide output not only for one t and its associated df, but rather many values?
We can write a simple function to iterate through multiple values and to bind them together. The key is the map()
function (from the tidyverse
package purrr
; if you are familiar with R
, it is similar to many of the apply()
functions). Specifically, because we:
data.frame
formWe use map2_dfr()
. Check out this helpful chapter of R for Data Science for more on iteration using approaches such as for and while loops as well as the useful apply()
/ map()
families of functions.
Here is what a function could look like:
library(purrr)
mkonfound <- function(t, df, alpha = .05, tails = 2) {
map2_dfr(.x = t, .y = df, .f = core_sensitivity_mkonfound)
}
Simple! But does it work? :)
Instead of passing a single t and df, as we did above with the core_sensitivity_monfound()
function, we can pass vectors of t and df values:
mkonfound(t = c(3, 2, 2.5),
d = c(100, 200, 150))
We could also do something like binding t and df together into a small data.frame
:
d <- data.frame(t = c(3, 2, 2.5),
df = c(100, 200, 150))
d
And then mkonfound()
could work like this:
mkonfound(d$t, d$df)
Still seems to work fine. Those of you familiar with the tidyverse may sense another possible improvement. Namely, the function could be written to both input and output a data.frame
, and be a bit more intuitive to use via non-standard evaluation.
The goal is to add an additional argument for the data.frame
(d
), and then use non-standard evaluation to capture and then later evaluate in the context of the data.frame the names of the t and df columns
library(rlang)
library(dplyr)
mkonfound <- function(d, t, df, alpha = .05, tails = 2) {
t_enquo <- enquo(t)
df_enquo <- enquo(df)
t = pull(select(d, !!t_enquo))
df = pull(select(d, !!df_enquo))
map2_dfr(.x = t, .y = df, .f = core_sensitivity_mkonfound)
}
But does it work? Now, the first argument is the name of the data.frame
, the second is the unquoted name of the column with the t statistics, and the third is the same as the second, but for the df associated with the t’s.
mkonfound(d, t, df)
If we have an entire spreadsheet, read in R as a data.frame
using the read.csv()
(or, read_csv()
from the very useful readr
package) function, then we can easily compute output for all of the statistics in the spreadsheet. Here is a spreadsheet from a website (from Ken’s):
spreadsheet_of_vals <- read.csv("https://msu.edu/~kenfrank/example%20dataset%20for%20mkonfound.csv")
head(spreadsheet_of_vals)
We would use it the same way as above but with d
replaced with what we named the data.frame
we read from the website, spreadsheet_of_vals
:
mkonfound(spreadsheet_of_vals, t, df)
Since the output is in a data.frame
, we can, for example, easily plot output:
results_df <- mkonfound(spreadsheet_of_vals, t, df)
library(ggplot2)
results_df$action <- dplyr::case_when(
results_df$action == "to_invalidate" ~ "To Invalidate",
results_df$action == "to_sustain" ~ "To Sustain"
)
ggplot(results_df, aes(x = pct_bias_to_change_inference, fill = action)) +
geom_histogram() +
scale_fill_manual("", values = c("#1F78B4", "#A6CEE3")) +
theme_bw() +
ggtitle("Histogram of Percent Bias") +
facet_grid(~ action) +
theme(legend.position = "none") +
ylab("Count") +
xlab("Percent Bias")
Like many functions in R, this could be written many different ways, and this post shows just one approach to writing a function.
In some cases, non-standard evaluation makes the function a bit harder to use - particularly in cases in which we are interested in the output from only a single study.
In that case, we would want to go back to the function we initially wrote (core_sensitivity_mkonfound()
) or would have to write something a bit like:
single_study <- data.frame(t = 3, df = 100)
mkonfound(single_study, t, df)
So, this is one approach that is useful in one use - for the in-development package for sensitivity analysis with a number of functions, with a version of this mkonfound()
function for meta-analyses that make use of the approach.
Oh, and if you are interested in sensitivity analysis, please check out the konfound
package this is for here.


JOEL
1
1 The word of the LORD that came to Joel, the son of Pethuel:
An Invasion of Locusts
2 Hear this, you elders;
give ear, all inhabitants of the land!

The priests mourn,
the ministers of the LORD.
10 The fields are destroyed,
the ground mourns,
because the grain is destroyed,
the wine dries up,
the oil languishes.
11 Be ashamed, O tillers of the soil;
wail, O vinedressers,
for the wheat and the barley,
because the harvest of the field has perished.
12 The vine dries up;
the fig tree languishes.
Pomegranate, palm, and apple,
all the trees of the field are dried up,
and gladness dries up
from the children of man.
A Call to Repentance
13 Put on sackcloth and lament, O priests;
wail, O ministers of the altar.
Go in, pass the night in sackcloth,
O ministers of my God!
Because grain offering and drink offering
are withheld from the house of your God.
14 Consecrate a fast;
call a solemn assembly.
Gather the elders
and all the inhabitants of the land
to the house of the LORD your God,
and cry out to the LORD.
15 Alas for the day!
For the day of the LORD is near,
and as destruction from the Almighty it comes.
16 Is not the food cut off
before our eyes,
joy and gladness
from the house of our God?
17 The seed shrivels under the clods;
the storehouses are desolate;
the granaries are torn down
because the grain has dried up.
18 How the beasts groan!
The herds of cattle are perplexed
because there is no pasture for them;
even the flocks of sheep suffer.
19 To you, O LORD, I call.
For fire has devoured
the pastures of the wilderness,
and flame has burned
all the trees of the field.
20 Even the beasts of the field pant for you
because the water brooks are dried up,
and fire has devoured
the pastures of the wilderness.


CS Department Information
Larry Rodrigues Class Web Page
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Kogi gov election: Journalists attacked in Ayetoro, car damaged
by Olufemi AtoyebiOlufemi Atoyebi, Kogi State
Some journalists who are covering the governorship election in Ayetoro area of Kogi State were attacked by some youths after thugs ran off with ballot boxes at Ward 01, Unit 004, where Dino Melaye had earlier voted.
Thugs numbering about 10 invaded the polling unit in two Hilux vans and a Honda Civic car, shooting into the air and targeting the ballot boxes.
The ballot boxes were eventually taken away, while some boys in the community chased the vehicles with no success.
A young man, simply identified as Dami, who just left a local eatery and was passing through the polling unit was shot in the leg and was subsequently rushed to the hospital.
Enraged by the action of the thugs, youths in the community blocked roads leading to the area with tyres, which they set on fire.
Our Correspondent and journalists from The Nation (Tony Akowe), Splash FM and others were trapped in the violence that ensued.
When eventually the journalists made their way out of the community, the angry youths shifted attention to the journalists, damaging one of their vehicles in the process.


Cliff Climbing
问题描述 :
Figure D-1 shows an example of cliff data that you will receive. The cliff is covered with square blocks. Jack starts cliff climbing from the ground under the cliff, by stepping his left or right foot on one of the blocks marked with ‘S’ at the bottom row. The numbers on the blocks are the "slippery levels". It takes t time units for him to safely put his foot on a block marked with t, where 1 ≤ t ≤ 9. He cannot put his feet on blocks marked with ‘X’. He completes the climbing when he puts either of his feet on one of the blocks marked with ‘T’ at the top row.
Figure D-1: Example of Cliff Data
Jack’s movement must meet the following constraints. After putting his left (or right) foot on a block, he can only move his right (or left, respectively) foot. His left foot position (lx, ly) and his right foot position (rx, ry) should satisfy lx < rx and | lx – rx | + | ly – ry | ≤ 3. This implies that, given a position of his left foot in Figure D-2 (a), he has to place his right foot on one of the nine blocks marked with blue color. Similarly, given a position of his right foot in Figure D-2 (b), he has to place his left foot on one of the nine blocks marked with blue color.
Figure D-2: Possible Placements of Feet
输入:
w h
s(1,1) … s(1,w)
s(2,1) … s(2,w)
…
s(h,1) … s(h,w)
The integers w and h are the width and the height of the matrix data of the cliff. You may assume 2 ≤ w ≤ 30 and 5 ≤ h ≤ 60. Each of the following h lines consists of w characters delimited by a space. The character s(y, x) represents the state of the block at position (x, y) as follows:
* ‘S’: Jack can start cliff climbing from this block.
* ‘T’: Jack finishes climbing when he reaches this block.
* ‘X’: Jack cannot put his feet on this block.
* ’1′ – ’9′ (= t): Jack has to spend t time units to put either of his feet on this block.
You can assume that it takes no time to put a foot on a block marked with ‘S’ or ‘T’.
输出:
w h
s(1,1) … s(1,w)
s(2,1) … s(2,w)
…
s(h,1) … s(h,w)
The integers w and h are the width and the height of the matrix data of the cliff. You may assume 2 ≤ w ≤ 30 and 5 ≤ h ≤ 60. Each of the following h lines consists of w characters delimited by a space. The character s(y, x) represents the state of the block at position (x, y) as follows:
* ‘S’: Jack can start cliff climbing from this block.
* ‘T’: Jack finishes climbing when he reaches this block.
* ‘X’: Jack cannot put his feet on this block.
* ’1′ – ’9′ (= t): Jack has to spend t time units to put either of his feet on this block.
You can assume that it takes no time to put a foot on a block marked with ‘S’ or ‘T’.
样例输入:
6 6 4 4 X X T T 4 7 8 2 X 7 3 X X X 1 8 1 2 X X X 6 1 1 2 4 4 7 S S 2 3 X X 2 10 T 1 1 X 1 X 1 X 1 1 1 X 1 X 1 1 1 X S S 2 10 T X 1 X 1 X 1 X 1 1 1 X 1 X 1 1 1 X S S 10 10 T T T T T T T T T T X 2 X X X X X 3 4 X 9 8 9 X X X 2 9 X 9 7 7 X 7 3 X X 8 9 X 8 9 9 9 6 3 X 5 X 5 8 9 9 9 6 X X 5 X 5 8 6 5 4 6 8 X 5 X 5 8 9 3 9 6 8 X 5 X 5 8 3 9 9 6 X X X 5 X S S S S S S S S S S 10 7 2 3 2 3 2 3 2 3 T T 1 2 3 2 3 2 3 2 3 2 3 2 3 2 3 2 3 2 3 4 3 2 3 2 3 2 3 2 3 5 3 2 3 1 3 2 3 2 3 5 2 2 3 2 4 2 3 2 3 5 S S 2 3 2 1 2 3 2 3 0 0
样例输出:
12 5 -1 22 12
一条很暴力，有点恶心的搜索。题意其实很简单，主要是pfs的时候拓展结点会有种麻烦的感觉。注意的是，这里的n和m跟平常见到的有所不同，交换过来了。我的代码就是在因为这个长宽的问题wa了一次。
代码如下：
#include <cstdio> #include <iostream> #include <algorithm> #include <cstring> #include <queue> #include <cctype> using namespace std; typedef pair<int, int> PII; typedef pair<PII, PII> PPP; typedef pair<int, PPP> PPPI; #define FI first #define SE second #define PRIQ priority_queue const int R = 66; const int C = 33; char mat[R][C]; bool vis[R][C][R][C]; int n, m; inline int dis(int a, int b, int c, int d) { return abs(a - c) + abs(b - d);} inline bool inmat(int a, int b) { return 0 <= a && a < n && 0 <= b && b < m;} int work() { PRIQ<PPPI> Q; int nx, ny, cx, cy; memset(vis, 0, sizeof(vis)); while (!Q.empty()) Q.pop(); for (int i = 0; i < n; i++) { for (int j = 0; j < m; j++) { if (mat[i][j] != 'S') continue; for (int dx = -2; dx <= 2; dx++) { nx = i + dx; for (int dy = 1; dy <= 3; dy++) { ny = j + dy; if (inmat(nx, ny) && dis(i, j, nx, ny) <= 3 && mat[nx][ny] != 'X' && !vis[i][j][nx][ny]) { if (isdigit(mat[nx][ny])) Q.push(PPPI((int) '0' - mat[nx][ny], PPP(PII(i, j), PII(nx, ny)))); else Q.push(PPPI(0, PPP(PII(i, j), PII(nx, ny)))); vis[i][j][nx][ny] = true; // cout << i << ' ' << j << ' ' << nx << ' ' << ny << endl; } ny = j - dy; if (inmat(nx, ny) && dis(nx, ny, i, j) <= 3 && mat[nx][ny] != 'X' && !vis[nx][ny][i][j]) { if (isdigit(mat[nx][ny])) Q.push(PPPI((int) '0' - mat[nx][ny], PPP(PII(nx, ny), PII(i, j)))); else Q.push(PPPI(0, PPP(PII(nx, ny), PII(i, j)))); vis[nx][ny][i][j] = true; // cout << nx << ' ' << ny << ' ' << i << ' ' << j << endl; } } } } } // cout << "find S" << endl; while (!Q.empty()) { PPPI cur = Q.top(); Q.pop(); int v = cur.FI, a = cur.SE.FI.FI, b = cur.SE.FI.SE, c = cur.SE.SE.FI, d = cur.SE.SE.SE; // cout << v << ' ' << a << ' ' << b << ' ' << c << ' ' << d << endl; if (mat[a][b] == 'T' || mat[c][d] == 'T') return -v; for (int dx = -2; dx <= 2; dx++) { nx = a + dx; cx = c + dx; for (int dy = 1; dy <= 3; dy++) { ny = b + dy; if (inmat(nx, ny) && dis(a, b, nx, ny) <= 3 && mat[nx][ny] != 'X' && !vis[a][b][nx][ny]) { if (isdigit(mat[nx][ny])) Q.push(PPPI((int) '0' - mat[nx][ny] + v, PPP(PII(a, b), PII(nx, ny)))); else Q.push(PPPI(v, PPP(PII(a, b), PII(nx, ny)))); vis[a][b][nx][ny] = true; // cout << nx << ' ' << ny << endl; } cy = d - dy; if (inmat(cx, cy) && dis(cx, cy, c, d) <= 3 && mat[cx][cy] != 'X' && !vis[cx][cy][c][d]) { if (isdigit(mat[cx][cy])) Q.push(PPPI((int) '0' - mat[cx][cy] + v, PPP(PII(cx, cy), PII(c, d)))); else Q.push(PPPI(v, PPP(PII(cx, cy), PII(c, d)))); vis[cx][cy][c][d] = true; // cout << cx << ' ' << cy << endl; } } } } return -1; } int main() { // freopen("in", "r", stdin); // freopen("out", "w", stdout); while (cin >> m >> n && (n || m)) { char buf[3]; for (int i = 0; i < n; i++) for (int j = 0; j < m; j++) cin >> buf, mat[i][j] = buf[0]; cout << work() << endl; } return 0; }
——written by Lyon
解题转自：http://www.cnblogs.com/LyonLys/archive/2013/07/19/hdu_2312_Lyon.html
信息不全
第二个是python代码啊。。
哇，算法有这么多呢！
这是研发的在线笔试题吗?发现很多C啊，阿里不是JAVA多么？
嗯，是写错了，文中以更改。MAX ( L(X[0..m-2], Y[0..n-1]), L(X[0..m-1], Y[0..n-2])
这个差别应该不会太大，或许编辑器就自动优化成内联函数了
华为这题目略显简单啊
失误失误，转载错了。已经改正，抱歉。
wt[n - 1]没错，因为下标是[0...n-1]
这配图…..
好网站，肯定会火的
有一点问题。。后面动态规划的程序中
int dp[n+1][W+1];
会报错 提示表达式必须含有常量值。该怎么修改呢。。
哎，path[0]应该是从零开始吧，为什么是赋值1呢？


regressionNetworkViz.Rd
Use either a force directed graph or a Sankey graph to show relationships between predictors and outcome variables. correlateMyOutcomes should correspond to the outcome variables ...
regressionNetworkViz( mylm, sigthresh = 0.05, whichviz = "Sankey", outfile = "temp.html", mygroup = 0, logvals = TRUE, verbose = FALSE, correlateMyOutcomes = NA, corthresh = 0.9, zoom = FALSE, doFDR = TRUE )
| mylm | lm model output from bigLMStats |
|---|---|
| sigthresh | significance threshold |
| whichviz | which visualization method |
| outfile | significance threshold |
| mygroup | color each entry by group membership |
| logvals | bool |
| verbose | bool |
| correlateMyOutcomes | not sure, see code |
| corthresh | correlation threshold |
| zoom | zooming factor |
| doFDR | bool |
html file is output