Chinese version here.
Below is a list of common React interview questions.
this
in the
constructor?
Children
?
React creates a virtual DOM. When state changes in a component it firstly runs a “diffing” algorithm, which identifies what has changed in the virtual DOM. The second step is reconciliation, where it updates the DOM with the results of diff.
Presentational components are concerned with how things look. They generally receive data and callbacks exclusively via props. These components rarely have their own state, but when they do it generally concerns UI state, as opposed to data state.
Container components are more concerned with how things work. These components provide the data and behavior to presentational or other container components. They define actions and provide these as callbacks to the presentational components. They are also often stateful as they serve as data sources.
Class components allows you to use additional features such as local state and lifecycle hooks. Also, to enable your component to have direct access to your store and thus holds state.
When your component just receives props and renders them to the page, this is a ‘stateless component’, for which a pure function can be used.
State is a data structure that starts with a default value when a Component mounts. It may be mutated across time, mostly as a result of user events.
Props (short for properties) are a Component’s configuration. They are received from above and immutable as far as the Component receiving them is concerned. A Component cannot change its props, but it is responsible for putting together the props of its child Components. Props do not have to just be data - callback functions may be passed in as props.
componentWillMount
(deprecated) - this is most commonly
used for App configuration in your root component.
componentDidMount
- here you want to do all the setup you
couldn’t do without a DOM, and start getting all the data you need. Also
if you want to set up eventListeners etc. this lifecycle hook is a good
place to do that.
componentWillReceiveProps
(deprecated) - this lifecyclye
acts on particular prop changes to trigger state transitions.
shouldComponentUpdate
- if you’re worried about wasted
renders shouldComponentUpdate
is a great place to improve
performance as it allows you to prevent a rerender if component receives
new prop
. shouldComponentUpdate
should always
return a boolean and based on what this is will determine if the
component is rerendered or not.
componentWillUpdate
(deprecated) - rarely used. It can be
used instead of componentWillReceiveProps
on a component
that also has shouldComponentUpdate
(but no access to
previous props).
componentDidUpdate
- also commonly used to update the DOM
in response to prop or state changes.
componentWillUnmount
- enables you can cancel any outgoing
network requests, or remove all event listeners associated with the
component.
Hooks let you use more of React’s features without having to use classes.
The first hook that you will most likely encounter is
useState
. useState
is a Hook that lets you add
React state to function components. It returns an array with a getter and
a setter. The syntax looks like
const [count, setCount] = React.useState(0);
. The equivalent
when using a class component would be.
this.state = {
count: 0,
};
<button onClick={() => this.setState({ count: this.state.count + 1 })}>
Increase Count
</button>;
The next hook you will most likely encounter is useEffect
.
The Effect Hook lets you perform side effects in function components. By
passing an empty array as the second argument to useEffect
is
equivalent to using componentDidMount
. If you pass a value to
the array it will only call the useEffect
function when the
value in the array updates.
componentDidMount
is where an AJAX request should be made in
a React component. This method will be executed when the component
“mounts” (is added to the DOM) for the first time. This method is only
executed once during the component’s life. Importantly, you can’t
guarantee the AJAX request will have resolved before the component mounts.
If it doesn’t, that would mean that you’d be trying to setState on an
unmounted component, which would not work. Making your AJAX request in
componentDidMount
will guarantee that there is a component to
update.
In HTML, form elements such as <input>
,
<textarea>
, and <select>
typically
maintain their own state and update it based on user input. When a user
submits a form the values from the aforementioned elements are sent with
the form. With React it works differently. The component containing the
form will keep track of the value of the input in it’s state and will
re-render the component each time the callback function e.g. onChange
is fired as the state will be updated. An input form element whose value
is controlled by React in this way is called a “controlled component”.
Refs are used to get reference to a DOM node or an instance of a component in React. Good examples of when to use refs are for managing focus/text selection, triggering imperative animations, or integrating with third-party DOM libraries. You should avoid using string refs and inline ref callbacks. Callback refs are advised by React.
A higher-order component is a function that takes a component and returns
a new component. HOC’s allow you to reuse code, logic and bootstrap
abstraction. The most common is probably Redux’s
connect
function. Beyond simply sharing utility libraries and
simple composition, HOCs are the best way to share behavior between React
Components. If you find yourself writing a lot of code in different places
that does the same thing, you may be able to refactor that code into a
reusable HOC.
Exercises
Because this.props
and this.state
may be updated
asynchronously, you should not rely on their values for calculating the
next state.
this
in the constructor?
You can use property initializers to correctly bind callbacks. This is enabled by default in create react app. you can use an arrow function in the callback. The problem here is that a new callback is created each time the component renders.
Returning null from a component’s render method means nothing will be displayed, but it does not affect the firing of the component’s lifecycle methods.
If the amount of times the component re-renders is an issue, there are two
options available. Manually implementing a check in the
shouldComponentUpdate
lifecycle method hook.
shouldComponentUpdate(nextProps, nextState){
// Do some check here
return resultOFCheckAsBoolean
}
Or using React.PureComponent instead of React.Component
React.PureComponent implements shouldComponentUpdate()
with a
shallow prop and state comparison. This enables you to avoid re-rendering
the component with the same props and state.
Keys help React identify which items have changed, are added, or are removed. Keys should be given to the elements inside the array to give the elements a stable identity. The best way to pick a key is to use a string that uniquely identifies a list item among its siblings. Most often you would use IDs from your data as keys. When you don’t have stable IDs for rendered items, you may use the item index as a key as a last resort. It is not recommend to use indexes for keys if the items can reorder, as that would be slow.
A child class constructor cannot make use of this
until
super()
has been called. Also, ES2015 class constructors have
to call super()
if they are subclasses. The reason for
passing props
to super()
is to enable you to
access this.props
in the constructor.
JSX is a syntax extension to JavaScript and comes with the full power of JavaScript. JSX produces React “elements”. You can embed any JavaScript expression in JSX by wrapping it in curly braces. After compilation, JSX expressions become regular JavaScript objects. This means that you can use JSX inside of if statements and for loops, assign it to variables, accept it as arguments, and return it from functions:
Question:
const element = (
<h1 className="greeting">
Hello, world!
</h1>
);
Answer:
const element = React.createElement(
'h1',
{className: 'greeting'},
'Hello, world!'
);
Children
?
In JSX expressions that contain both an opening tag and a closing tag, the
content between those tags is passed to components automatically as a
special prop: props.children
.
There are a number of methods available in the React API to work with this
prop
. These include React.Children.map
,
React.Children.forEach
, React.Children.count
,
React.Children.only
, React.Children.toArray
.
State is similar to props, but it is private and fully controlled by the component. State is essentially an object that holds data and determines how the component renders and behaves.
Until you eject you are unable to configure webpack or babel presets.
The basic idea of redux is that the entire application state is kept in a single store. The store is simply a javascript object. The only way to change the state is by sending actions from your application and then writing reducers for these actions that modify the state. The entire state transition is kept inside reducers and should not have any side-effects.
The store is a javascript object that holds application state. Along with this it also has the following responsibilities:
getState()
;dispatch(action)
;subscribe(listener)
;subscribe(listener)
.
Actions are plain javascript objects. They must have a type indicating the type of action being performed. In essence, actions are payloads of information that send data from your application to your store.
A reducer is simply a pure function that takes the previous state and an action, and returns the next state.
Redux thunk is middleware that allows you to write action creators that return a function instead of an action. The thunk can then be used to delay the dispatch of an action if a certain condition is met. This allows you to handle the asynchronous dispatching of actions.
A pure function is a function that doesn’t depend on and doesn’t modify the states of variables out of its scope. Essentially, this means that a pure function will always return the same result given same parameters.
…..
…..


| No. | Student name | Grade level | Section |
| 1 | Kurt Villacorte | Grade 1 | |
| 2 | Nenet Wong | Grade 1 | |
| 3 | Ged Bart | Grade 2 |
| No. | Name | |
|
|
1 | Jason Deichmann |
|
|
2 | Jayvee Gabriel |
|
|
3 | Alwren Corpuz |
|
|
4 | Charles Cambaliza |
|
|
5 | Mark Santos |
| No. | Name | |
|
|
1 | Nicole Domingo |
|
|
2 | Olie Sandel |
|
|
3 | Kayle Tiu |
| Name | Capacity | |
| Love | 0/30 | |
| Sad | 0/30 | |
| Friends | 0/30 |


- Testando
- Lista
- Com
- Bootstrap
- Testando
- Lista
- Com
- Bootstrap
- Testando
- Lista
- Com
- Bootstrap
- Text is a set of linked words and phrases that allow interpretation
- and convey a message. It is any work written in the original version
- and that constitutes a book or a written document. A text is a
- linguistic unit longer than the sentence.
- Text is a set of linked words and phrases
- and convey a message
- and that constitutes
- linguistic unit longer than the sentence
- Título
- Text is a set of linked words and phrases that allow interpretation and convey a message. It is any work written in the original version and that constitutes a book or a written document. A text is a linguistic unit longer than the sentence.
- Título
- Text is a set of linked words and phrases that allow interpretation and convey a message. It is any work written in the original version and that constitutes a book or a written document. A text is a linguistic unit longer than the sentence.
- Título
- Text is a set of linked words and phrases that allow interpretation and convey a message. It is any work written in the original version and that constitutes a book or a written document. A text is a linguistic unit longer than the sentence.
- Título
- Text is a set of linked words and phrases that allow interpretation and convey a message. It is any work written in the original version and that constitutes a book or a written document. A text is a linguistic unit longer than the sentence.


Lorem ipsum dolor sit amet, consectetur adipisicing elit. Minima maxime quam architecto quo inventore harum ex magni, dicta impedit.
Must make an in-store appointment for custom orders. Made to order leather jackets for men and women; Some custom denim coats; Locally made jewelry.
We carry hats, coats, backpacks, wallets, 10 different lines of leather bags


QFileOpenEvent Class
The QFileOpenEvent class provides an event that will be sent when there is a request to open a file or a URL. More...
| Header: | #include <QFileOpenEvent> |
| qmake: | QT += gui |
| Inherits: | QEvent |
Public Functions
- 6 public functions inherited from QEvent
Additional Inherited Members
- 1 property inherited from QEvent
- 1 static public member inherited from QEvent
- 2 protected variables inherited from QEvent
Detailed Description
The QFileOpenEvent class provides an event that will be sent when there is a request to open a file or a URL.
File open events will be sent to the QApplication::instance() when the operating system requests that a file or URL should be opened. This is a high-level event that can be caused by different user actions depending on the user's desktop environment; for example, double clicking on an file icon in the Finder on macOS.
This event is only used to notify the application of a request. It may be safely ignored.
Note: This class is currently supported for macOS only.
macOS Example
In order to trigger the event on macOS, the application must be configured to let the OS know what kind of file(s) it should react on.
For example, the following Info.plist
file declares that the application can act as a viewer for files with a PNG extension:
<?xml version="1.0" encoding="UTF-8"?> <!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd"> <plist version="1.0"> <dict> <key>CFBundleDocumentTypes</key> <array> <dict> <key>CFBundleTypeExtensions</key> <array> <string>png</string> </array> <key>CFBundleTypeRole</key> <string>Viewer</string> </dict> </array> </dict> </plist>
The following implementation of a QApplication subclass prints the path to the file that was, for example, dropped on the Dock icon of the application.
#include <QApplication> #include <QFileOpenEvent> #include <QtDebug> class MyApplication : public QApplication { public: MyApplication(int &argc, char **argv) : QApplication(argc, argv) { } bool event(QEvent *event) { if (event->type() == QEvent::FileOpen) { QFileOpenEvent *openEvent = static_cast<QFileOpenEvent *>(event); qDebug() << "Open file" << openEvent->file(); } return QApplication::event(event); } };
Member Function Documentation
QString QFileOpenEvent::file() const
Returns the file that is being opened.
bool QFileOpenEvent::openFile(QFile &file, QIODevice::OpenMode flags) const
Opens a QFile on the file referenced by this event in the mode specified by flags. Returns true
if successful; otherwise returns false
.
This is necessary as some files cannot be opened by name, but require specific information stored in this event.
This function was introduced in Qt 4.8.
QUrl QFileOpenEvent::url() const
Returns the url that is being opened.
This function was introduced in Qt 4.6.


Github search is a web application where users are able to search
and retrieve information about users and repositories
Users enter info in either the user form and the repository form
and the application retrieves github user info and repository info on the click of the button


Different from classification, the output variable takes continuous values in regression analysis.
Smile's regression algorithms are in the package
smile.regression
and all algorithms implement the interface
Regression
that has a single method predict
to apply the model
to an instance. For all algorithms, the model can be trained through
the constructor. Meanwhile, each algorithm has a Trainer
companion
class that can hold model hyper-parameters and be applied to multiple training
datasets.
The high-level operators are defined in Scala trait
smile.regression.Operators
and also in the package object of smile.regression
.
In what follows, we will discuss each algorithm, their high-level
Scala API, and examples.
In linear regression, the model specification is that the dependent variable is a linear combination of the parameters. The residual is the difference between the value of the dependent variable predicted by the model, and the true value of the dependent variable. Ordinary least squares obtains parameter estimates that minimize the sum of squared residuals, SSE (also denoted RSS).
The ordinary least squares (OLS) estimator is consistent when the independent variables are exogenous and there is no multicollinearity, and optimal in the class of linear unbiased estimators when the errors are homoscedastic and serially uncorrelated. Under these conditions, the method of OLS provides minimum-variance mean-unbiased estimation when the errors have finite variances.
def ols(x: Array[Array[Double]], y: Array[Double]): OLS
where the parameter x
is a matrix containing the explanatory variables,
and y
is the response values.
There are several different frameworks in which the linear regression model can be cast in order to make the OLS technique applicable. Each of these settings produces the same formulas and same results, the only difference is the interpretation and the assumptions which have to be imposed in order for the method to give meaningful results. The choice of the applicable framework depends mostly on the nature of data at hand, and on the inference task which has to be performed.
Least squares corresponds to the maximum likelihood criterion if the experimental errors have a normal distribution and can also be derived as a method of moments estimator.
In what follows, we apply the ordinary least squares to an artificial data set by Breiman. The predictor attributes are generated independently using the following probabilities:
P(X1 = -1) = P(X1 = 1) = 1/2
P(Xm = -1) = P(Xm = 0) = P(Xm = 1) = 1/3, m = 2,...,10
The value of the response variable Y
is:
Y = 3 + 3X2 + 2X3 + X4 + σ(0,1) if X_1 = 1
Y = -3 + 3X5 + 2X6 + X7 + σ(0,1) if X_1 = -1
It is straightforward to fit an OLS model.
val planes = readArff("data/weka/regression/2dplanes.arff", 10)
val (x, y) = planes.unzipDouble
val model = ols(x, y)
We then can apply the regression model on a new data point.
smile> model.predict(x(0))
res1: Double = 5.073347387304475
smile> y(0)
res2: Double = 7.73906
Once a regression model has been constructed, it may be important to confirm the goodness of fit of the model and the statistical significance of the estimated parameters. Commonly used checks of goodness of fit include the R-squared, analysis of the pattern of residuals and hypothesis testing. Statistical significance can be checked by an F-test of the overall fit, followed by t-tests of individual parameters.
Simply print out the model, we can inspect R-squared, the statistics and p-values of the t-test of parameter significance, and the F-test of goodness-of-fit.
smile> println(model)
Linear Model:
Residuals:
Min 1Q Median 3Q Max
-8.5260 -1.6514 -0.0049 1.6755 7.8116
Coefficients:
Estimate Std. Error t value Pr(>|t|)
Intercept -0.0148 0.0118 -1.2503 0.2112
Var 1 2.9730 0.0118 251.7998 0.0000 ***
Var 2 1.5344 0.0145 105.8468 0.0000 ***
Var 3 1.0357 0.0144 71.7815 0.0000 ***
Var 4 0.5281 0.0145 36.4827 0.0000 ***
Var 5 1.4766 0.0144 102.2472 0.0000 ***
Var 6 1.0044 0.0144 69.5380 0.0000 ***
Var 7 0.5238 0.0145 36.1696 0.0000 ***
Var 8 -0.0011 0.0145 -0.0750 0.9402
Var 9 0.0024 0.0145 0.1649 0.8690
Var 10 -0.0278 0.0145 -1.9239 0.0544 .
---------------------------------------------------------------------
Significance codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.3838 on 40757 degrees of freedom
Multiple R-squared: 0.7056, Adjusted R-squared: 0.7055
F-statistic: 9766.9504 on 10 and 40757 DF, p-value: 0.000
Interpretations of these diagnostic tests rest heavily on the model assumptions. Although examination of the residuals can be used to invalidate a model, the results of a t-test or F-test are sometimes more difficult to interpret if the model's assumptions are violated. For example, if the error term does not have a normal distribution, in small samples the estimated parameters will not follow normal distributions and complicate inference. With relatively large samples, however, a central limit theorem can be invoked such that hypothesis testing may proceed using asymptotic approximations.
Coefficient estimates for multiple linear regression models rely on
the independence of the model terms. When terms are correlated and
the columns of the design matrix X have an approximate linear dependence,
the matrix X'X
becomes close to singular. As a result, the least-squares estimate
becomes highly sensitive to random errors in the observed response Y
,
producing a large variance.
Ridge regression is one method to address these issues. In ridge regression,
the matrix X'X
is perturbed so as to make its determinant appreciably
different from 0.
Ridge regression is a kind of Tikhonov regularization, which is the most commonly used method of regularization of ill-posed problems. Ridge regression shrinks the regression coefficients by imposing a penalty on their size. By allowing a small amount of bias in the estimates, more reasonable coefficients may often be obtained. Often, small amounts of bias lead to dramatic reductions in the variance of the estimated model coefficients.
Another interpretation of ridge regression is available through Bayesian estimation. In this setting the belief that weight should be small is coded into a prior distribution.
def ridge(x: Array[Array[Double]], y: Array[Double], lambda: Double): RidgeRegression
where the parameter x
is a matrix containing the explanatory variables,
y
is the response values, and lambda
is
the shrinkage/regularization parameter. Large lambda
means more shrinkage.
Choosing an appropriate value of lambda
is important, and also difficult.
Longley's macroeconomic regression data has 7 economical variables, observed yearly from 1947 to 1962. It is a well-known example for a highly collinear regression.
val x = Array(
Array(234.289, 235.6, 159.0, 107.608, 1947, 60.323),
Array(259.426, 232.5, 145.6, 108.632, 1948, 61.122),
Array(258.054, 368.2, 161.6, 109.773, 1949, 60.171),
Array(284.599, 335.1, 165.0, 110.929, 1950, 61.187),
Array(328.975, 209.9, 309.9, 112.075, 1951, 63.221),
Array(346.999, 193.2, 359.4, 113.270, 1952, 63.639),
Array(365.385, 187.0, 354.7, 115.094, 1953, 64.989),
Array(363.112, 357.8, 335.0, 116.219, 1954, 63.761),
Array(397.469, 290.4, 304.8, 117.388, 1955, 66.019),
Array(419.180, 282.2, 285.7, 118.734, 1956, 67.857),
Array(442.769, 293.6, 279.8, 120.445, 1957, 68.169),
Array(444.546, 468.1, 263.7, 121.950, 1958, 66.513),
Array(482.704, 381.3, 255.2, 123.366, 1959, 68.655),
Array(502.601, 393.1, 251.4, 125.368, 1960, 69.564),
Array(518.173, 480.6, 257.2, 127.852, 1961, 69.331),
Array(554.894, 400.7, 282.7, 130.081, 1962, 70.551))
val y = Array(83.0, 88.5, 88.2, 89.5, 96.2, 98.1, 99.0, 100.0, 101.2,
104.6, 108.4, 110.8, 112.6, 114.2, 115.7, 116.9)
println(ridge(x, y, 0.0057))
The summary of model is as following.
Ridge Regression:
Residuals:
Min 1Q Median 3Q Max
-2.0691 -0.5736 0.2619 0.4844 1.6328
Coefficients:
Estimate Std. Error t value Pr(>|t|)
Intercept -247.2810 NA NA NA
Var 1 0.1789 7.8561 0.0228 0.9823
Var 2 0.0197 2.0319 0.0097 0.9925
Var 3 0.0066 0.8647 0.0076 0.9941
Var 4 -1.3433 4.2777 -0.3140 0.7607
Var 5 0.2216 9.7525 0.0227 0.9824
Var 6 -0.0575 3.7635 -0.0153 0.9881
---------------------------------------------------------------------
Significance codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.2361 on 9 degrees of freedom
Multiple R-squared: 0.9921, Adjusted R-squared: 0.9869
F-statistic: 189.0534 on 6 and 9 DF, p-value: 6.011e-09
When including an intercept term in the regression, we usually leave
this coefficient unpenalized. Otherwise we could add some constant amount
to the vector y
, and this would not result in the same solution.
If we center the columns of X
, then the intercept estimate
ends up just being the mean of y
.
The penalty term is unfair is the predictor variables are not on the same scale. Therefore, if we know that the variables are not measured in the same units, we typically scale the columns of X (to have sample variance 1), and then we perform ridge regression.
It can be shown that ridge regression doesn’t set coefficients exactly to zero
unless λ = ∞
, in which case they’re all zero.
Hence ridge regression cannot perform variable selection, and even though
it performs well in terms of prediction accuracy, it does poorly in terms
of offering a clear interpretation.
Lasso (least absolute shrinkage and selection operator) regression is a shrinkage and selection method for linear regression. It minimizes the usual sum of squared errors, with a bound on the sum of the absolute values of the coefficients (i.e. L1-regularized). It has connections to soft-thresholding of wavelet coefficients, forward stage-wise regression, and boosting methods.
The Lasso typically yields a sparse solution, of which the parameter vector β has relatively few nonzero coefficients. In contrast, the solution of L2-regularized least squares (i.e. ridge regression) typically has all coefficients nonzero. Because it effectively reduces the number of variables, the Lasso is useful in some contexts.
There is no analytic formula or expression for the optimal solution to the L1-regularized least squares problems. Therefore, its solution must be computed numerically. The objective function in the L1-regularized least squares is convex but not differentiable, so solving it is more of a computational challenge than solving the L2-regularized least squares. The Lasso may be solved using quadratic programming or more general convex optimization methods, as well as by specific algorithms such as the least angle regression algorithm.
def lasso(x: Array[Array[Double]], y: Array[Double], lambda: Double, tol: Double = 1E-3, maxIter: Int = 5000): LASSO
where the parameter x
is a matrix containing the explanatory variables,
y
is the response values,
lambda
is the shrinkage/regularization parameter,
tol
is the tolerance for stopping iterations (relative target duality gap),
and maxIter
is the maximum number of iterations.
For over-determined systems (more instances than variables, commonly in machine learning), we normalize variables with mean 0 and standard deviation 1. For under-determined systems (less instances than variables, e.g. compressed sensing), we assume white noise (i.e. no intercept in the linear model) and do not perform normalization. Note that the solution is not unique in this case.
In what follows, we will apply Lasso to the diabetes data used in the "Least Angle Regression" paper. The basic data has 10 columns standardized to have unit L2 norm in each column and zero mean. The data used in the below example has 64 columns that consists of basic data plus certain interactions.
val diabetes = readCsv("data/regression/diabetes.csv", Some((new NumericAttribute("y"), 0)), header = true)
val (x, y) = diabetes.unzipDouble
println(lasso(x, y, 10))
println(lasso(x, y, 100))
println(lasso(x, y, 500))
With large lambda
values, we can observe that more and more
parameters are regularized towards to 0.
A radial basis function network is an artificial neural network that uses radial basis functions as activation functions. It is a linear combination of radial basis functions. They are used in function approximation, time series prediction, and control.
In its basic form, radial basis function network is in the form
y(x) = Σ wi φ(||x-ci||)
where the approximating function y(x) is represented as a sum of N radial basis functions φ, each associated with a different center ci, and weighted by an appropriate coefficient wi. For distance, one usually chooses Euclidean distance. The weights wi can be estimated using the matrix methods of linear least squares, because the approximating function is linear in the weights.
RBF networks are typically trained by a two-step algorithm.
In the first step, the center vectors ci
of the RBF functions in the hidden layer are chosen.
This step can be performed in several ways; centers can be randomly
sampled from some set of examples, or they can be determined
using k-means clustering. Note that this step is unsupervised.
The second step simply fits a linear model with coefficients wi
to
the hidden layer's outputs with respect to some objective function.
A common objective function, at least for regression/function estimation,
is the least squares function.
A optional third backpropagation step can be performed to fine-tune all of the RBF network's parameters.
def rbfnet[T](x: Array[T], y: Array[Double], distance: Metric[T], rbf: RadialBasisFunction, centers: Array[T]): RBFNetwork[T]
def rbfnet[T](x: Array[T], y: Array[Double], distance: Metric[T], rbf: Array[RadialBasisFunction], centers: Array[T]): RBFNetwork[T]
def nrbfnet[T](x: Array[T], y: Array[Double], distance: Metric[T], rbf: RadialBasisFunction, centers: Array[T]): RBFNetwork[T]
def nrbfnet[T](x: Array[T], y: Array[Double], distance: Metric[T], rbf: Array[RadialBasisFunction], centers: Array[T]): RBFNetwork[T]
The popular choices for φ comprise the Gaussian function and the so called thin plate splines. The advantage of the thin plate splines is that their conditioning is invariant under scalings. Gaussian, multi-quadric and inverse multi-quadric are infinitely smooth and and involve a scale or shape parameter, r0 > 0. Decreasing r0 tends to flatten the basis function. For a given function, the quality of approximation may strongly depend on this parameter. In particular, increasing r0 has the effect of better conditioning (the separation distance of the scaled points increases).
val diabetes = readCsv("data/regression/diabetes.csv", Some((new NumericAttribute("y"), 0)), header=true)
val (data, y) = diabetes.unzipDouble
val x = data.map(_.slice(0, 10)) // use only the primary attributes
cv(x, y, 10) { case (x, y) =>
val centers = new Array[Array[Double]](10)
val basis = gaussrbf(x, centers)
smile.regression.rbfnet(x, y, new EuclideanDistance, basis, centers)
}
Note that we use the fully qualified name smile.regression.rbfnet
in above example.
The reason is that we import both smile.regression._
and
smile.classification._
in the shell. And by Scala's scope rule,
the later imported names shadow the same ones in earlier imported packages.
If you work only on regression problems in a shell session, just import
smile.regression._
again to avoid using the fully qualified names.
A variant on RBF networks is normalized radial basis function (NRBF) networks, in which we require the sum of the basis functions to be unity. NRBF arises more naturally from a Bayesian statistical perspective. However, there is no evidence that either the NRBF method is consistently superior to the RBF method, or vice versa.
Support vector machine can be used as a regression method, maintaining all the main features of the algorithm. In the case of regression, a margin of tolerance ε is set in approximation. The goal of SVR is to find a function that has at most ε deviation from the response variable for all the training data, and at the same time is as flat as possible. In other words, we do not care about errors as long as they are less than ε, but will not accept any deviation larger than this.
Like SVM for classification, nonlinear SVR employs kernel trick for implict mapping. And the model produced by SVR depends only on a subset of the training data, because the cost function ignores any training data close to the model prediction (within the ε threshold).
def svr[T](x: Array[T], y: Array[Double], kernel: MercerKernel[T], eps: Double, C: Double, weight: Array[Double] = null, tol: Double = 1E-3): SVR[T]
where the parameter x
is the training data,
y
is the response variable,
kernel
is the kernel function,
eps
is the loss function error threshold,
C
is the soft margin penalty parameter,
weight
is the optional positive instance weight so that the soft margin penalty
parameter for instance i will be weight(i) * C
,
and tol
the tolerance of convergence test.
cv(x, y, 10) { case (x, y) =>
val centers = new Array[Array[Double]](10)
val basis = gaussrbf(x, centers)
svr(x, y, new GaussianKernel(0.06), 20, 10)
}
Applying SVR to the diabetes data, we observe a smaller RMSE by 10-fold cross validation compared to the RBF networks in previous section. Note that we have not optimized the hyperparameters in both cases.
Similar to decision trees, regression trees can be learned by splitting the training set into subsets based on an attribute value test. This process is repeated on each derived subset in a recursive manner called recursive partitioning. The recursion is completed when the subset at a node all has the same value of the target variable, or when splitting no longer adds value to the predictions.
The algorithms that are used for constructing decision trees usually work top-down by choosing a variable at each step that is the next best variable to use in splitting the set of items. "Best" is defined by how well the variable splits the set into homogeneous subsets that have the same value of the target variable. Different algorithms use different formulae for measuring "best". Used by the CART algorithm, Gini impurity is a measure of how often a randomly chosen element from the set would be incorrectly labeled if it were randomly labeled according to the distribution of labels in the subset. Gini impurity can be computed by summing the probability of each item being chosen times the probability of a mistake in categorizing that item. It reaches its minimum (zero) when all cases in the node fall into a single target category. Information gain is another popular measure, used by the ID3, C4.5 and C5.0 algorithms. Information gain is based on the concept of entropy used in information theory. For categorical variables with different number of levels, however, information gain are biased in favor of those attributes with more levels. Instead, one may employ the information gain ratio, which solves the drawback of information gain.
def cart(x: Array[Array[Double]], y: Array[Double], maxNodes: Int, attributes: Array[Attribute] = null): RegressionTree
where the parameter x
is the training data,
y
is the response variable,
maxNodes
is the maximum number of leaf nodes in the tree as a regularization,
and The optional attributes
is the attribute properties. If not provided, all attributes
are treated as numeric values.
Similar to rbfnet
, we have to use the fully qualified name
smile.regression.randomForest
in the shell for regression.
Applying to the diabetes data, we get RMSE 67.9 for 10-fold cross validation with regression trees of 200 nodes.
cv(x, y, 10) { case (x, y) =>
smile.regression.cart(x, y, 200)
}
Classification and Regression Tree techniques have a number of advantages over many of those alternative techniques.
In most cases, the interpretation of results summarized in a tree is very simple. This simplicity is useful not only for purposes of rapid classification of new observations, but can also often yield a much simpler "model" for explaining why observations are classified or predicted in a particular manner.
Other techniques are usually specialized in analyzing datasets that have only one type of variable.
The final results of using tree methods for classification or regression can be summarized in a series of (usually few) logical if-then conditions (tree nodes). Therefore, there is no implicit assumption that the underlying relationships between the predictor variables and the dependent variable are linear, follow some specific non-linear link function, or that they are even monotonic in nature. Thus, tree methods are particularly well suited for data mining tasks, where there is often little a priori knowledge nor any coherent set of theories or predictions regarding which variables are related and how. In those types of data analytics, tree methods can often reveal simple relationships between just a few variables that could have easily gone unnoticed using other analytic techniques.
One major problem with classification and regression trees is their high variance. Often a small change in the data can result in a very different series of splits, making interpretation somewhat precarious. Besides, decision-tree learners can create over-complex trees that cause over-fitting. Mechanisms such as pruning are necessary to avoid this problem. Another limitation of trees is the lack of smoothness of the prediction surface.
Some techniques such as bagging, boosting, and random forest use more than one decision tree for their analysis.
Random forest is an ensemble classifier that consists of many decision trees and outputs the majority vote of individual trees. The method combines bagging idea and the random selection of features.
Each tree is constructed using the following algorithm:
def randomForest(x: Array[Array[Double]], y: Array[Double], attributes: Array[Attribute] = null, ntrees: Int = 500, maxNodes: Int = -1, nodeSize: Int = 5, mtry: Int = -1, subsample: Double = 1.0): RandomForest
where the parameter x
is the training data,
y
is the response variable,
ntrees
is the number of trees,
nodeSize
is the number of instances in a node below which the tree will
not split, setting nodeSize = 5 generally gives good results,
and maxNodes
is the maximum number of leaf nodes.
The parameter mtry
is the number of input variables to be used to determine the decision
at a node of the tree. The default value p/3 seems to give generally good performance,
where p is the number of variables.
Besides, subsample
is the sampling rate for training tree. The default value
1.0 means sampling with replacement. Otherwise, the value < 1.0 means
sampling without replacement.
The optional attributes
is the attribute properties. If not provided, all attributes
are treated as numeric values.
Similar to rbfnet
, we have to use the fully qualified name
smile.regression.randomForest
in the shell for regression.
Applying to the diabetes data, we get RMSE 56.4 for 10-fold cross validation with 500 regression trees of 200 nodes, which significantly smaller than a single regression tree.
cv(x, y, 10) { case (x, y) =>
smile.regression.randomForest(x, y, maxNodes = 200)
}
The advantages of random forest are:
The disadvantages are
Gradient boosting is typically used with decision trees (especially CART regression trees) of a fixed size as base learners. For this special case Friedman proposes a modification to gradient boosting method which improves the quality of fit of each base learner.
Generic gradient boosting at the t-th step would fit a regression tree to pseudo-residuals. Let J be the number of its leaves. The tree partitions the input space into J disjoint regions and predicts a constant value in each region. The parameter J controls the maximum allowed level of interaction between variables in the model. With J = 2 (decision stumps), no interaction between variables is allowed. With J = 3 the model may include effects of the interaction between up to two variables, and so on. Hastie et al. comment that typically 4 ≤ J ≤ 8 work well for boosting and results are fairly insensitive to the choice of in this range, J = 2 is insufficient for many applications, and J > 10 is unlikely to be required.
Fitting the training set too closely can lead to degradation of the model's generalization ability. Several so-called regularization techniques reduce this over-fitting effect by constraining the fitting procedure. One natural regularization parameter is the number of gradient boosting iterations T (i.e. the number of trees in the model when the base learner is a decision tree). Increasing T reduces the error on training set, but setting it too high may lead to over-fitting. An optimal value of T is often selected by monitoring prediction error on a separate validation data set.
Another regularization approach is the shrinkage which times a parameter η (called the "learning rate") to update term. Empirically it has been found that using small learning rates (such as η < 0.1) yields dramatic improvements in model's generalization ability over gradient boosting without shrinking (η = 1). However, it comes at the price of increasing computational time both during training and prediction: lower learning rate requires more iterations.
def gbm(x: Array[Array[Double]], y: Array[Double], attributes: Array[Attribute] = null, loss: GradientTreeBoost.Loss = GradientTreeBoost.Loss.LeastAbsoluteDeviation, ntrees: Int = 500, maxNodes: Int = 6, shrinkage: Double = 0.05, subsample: Double = 0.7): GradientTreeBoost
where the parameter x
is the training data,
y
is the response variable,
ntrees
is the number of trees,
and maxNodes
is the maximum number of leaf nodes.
The parameter loss
is the loss function for regression. By default, least absolute
deviation is employed for robust regression.
The parameter shrinkage
is the shrinkage parameter in (0, 1] controls the learning rate of procedure.
Besides, subsample
is the sampling rate for training tree. The default value
1.0 means sampling with replacement. Otherwise, the value < 1.0 means
sampling without replacement.
The optional attributes
is the attribute properties. If not provided, all attributes
are treated as numeric values.
Similar to rbfnet
, we have to use the fully qualified name
smile.regression.gbm
in the shell for regression.
Applying to the diabetes data, we get RMSE 58.8 for 10-fold cross validation with 500 iterations. This is comparable to the result of random forest but uses only 6 nodes for each regression tree.
cv(x, y, 10) { case (x, y) =>
smile.regression.gbm(x, y)
}
Soon after the introduction of gradient boosting Friedman proposed a minor modification to the algorithm, motivated by Breiman's bagging method. Specifically, he proposed that at each iteration of the algorithm, a base learner should be fit on a subsample of the training set drawn at random without replacement. Friedman observed a substantial improvement in gradient boosting's accuracy with this modification.
Subsample size is some constant fraction f of the size of the training set. When f = 1, the algorithm is deterministic and identical to the one described above. Smaller values of f introduce randomness into the algorithm and help prevent over-fitting, acting as a kind of regularization. The algorithm also becomes faster, because regression trees have to be fit to smaller datasets at each iteration. Typically, f is set to 0.5, meaning that one half of the training set is used to build each base learner.
Also, like in bagging, sub-sampling allows one to define an out-of-bag estimate of the prediction performance improvement by evaluating predictions on those observations which were not used in the building of the next base learner. Out-of-bag estimates help avoid the need for an independent validation dataset, but often underestimate actual performance improvement and the optimal number of iterations.
Gradient tree boosting implementations often also use regularization by limiting the minimum number of observations in trees' terminal nodes. It's used in the tree building process by ignoring any splits that lead to nodes containing fewer than this number of training set instances. Imposing this limit helps to reduce variance in predictions at leaves.
A Gaussian process is a stochastic process whose realizations consist of random values associated with every point in a range of times (or of space) such that each such random variable has a normal distribution. Moreover, every finite collection of those random variables has a multivariate normal distribution.
A Gaussian process can be used as a prior probability distribution over functions in Bayesian inference. Given any set of N points in the desired domain of your functions, take a multivariate Gaussian whose covariance matrix parameter is the Gram matrix of N points with some desired kernel, and sample from that Gaussian. Inference of continuous values with a Gaussian process prior is known as Gaussian process regression.
The fitting is performed in the reproducing kernel Hilbert space with the "kernel trick". The loss function is squared-error. This also arises as the kriging estimate of a Gaussian random field in spatial statistics.
A significant problem with Gaussian process prediction is that it typically scales as O(n3). For large problems (e.g. n > 10,000) both storing the Gram matrix and solving the associated linear systems are prohibitive on modern workstations. An extensive range of proposals have been suggested to deal with this problem. A popular approach is the reduced-rank Approximations of the Gram Matrix, known as Nystrom approximation. Greedy approximation is another popular approach that uses an active set of training points of size m selected from the training set of size n > m. We assume that it is impossible to search for the optimal subset of size m due to combinatorics. The points in the active set could be selected randomly, but in general we might expect better performance if the points are selected greedily w.r.t. some criterion. Recently, researchers had proposed relaxing the constraint that the inducing variables must be a subset of training/test cases, turning the discrete selection problem into one of continuous optimization.
This function fits a regular Gaussian process model.
def gpr[T](x: Array[T], y: Array[Double], kernel: MercerKernel[T], lambda: Double): GaussianProcessRegression[T]
while the below function fit an approximate model
def gpr[T](x: Array[T], y: Array[Double], t: Array[T], kernel: MercerKernel[T], lambda: Double, nystrom: Boolean = false): GaussianProcessRegression[T]
where the parameter x
is the training data,
y
is the response variable,
kernel
is the Mercer kernel function,
and lambda
is the shrinkage/regularization parameter.
For approximation model, the parameter t
is the inducing input,
which are pre-selected or inducing samples
acting as active set of regressors. In simple case, these can be chosen
randomly from the training set or as the centers of k-means clustering.
cv(x, y, 10) { case (x, y) =>
val t = kmeans(x, 300).centroids
gpr(x, y, t, new GaussianKernel(0.06), 0.01)
}
In the above example, we train the approximate Gaussian Process models with inducing samples by k-means clustering. The hyperparameters are not optimized yet though.


Pratik Patel
I am really passionate about coding and developing new systems that could potentially help many communities around the world. I am driven by challenging and difficult to solve problems. Currently, I am majoring in Computer Science at University of Illinois at Urbana-Champaign. I plan to graduate in the May of 2022. After my graduation, I am planning to work towards my masters in Computer Science alongside working in the industry. I looking for a job that could put my knowledge to test and make me a better computer programmer. I am ready to put my efforts and work as a team to achieve a common team goal.


{% import "bootstrap/wtf.html" as wtf %} {% extends 'main.html' %} {% block content %}
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| PostgreSQL 9.0.10 Documentation | ||||
|---|---|---|---|---|
| Prev | Up | Appendix E. Release Notes | Next |
Release Date: 2011-09-26
This release contains a variety of fixes from 8.2.21. For information about new features in the 8.2 major release, see Section E.72.
The PostgreSQL community will stop releasing updates for the 8.2.X release series in December 2011. Users are encouraged to update to a newer release branch soon.
A dump/restore is not required for those running 8.2.X. However, if you are upgrading from a version earlier than 8.2.14, see the release notes for 8.2.14.
Fix multiple bugs in GiST index page split processing (Heikki Linnakangas)
The probability of occurrence was low, but these could lead to index corruption.
Avoid possibly accessing off the end of memory in ANALYZE (Noah Misch)
This fixes a very-low-probability server crash scenario.
Fix race condition in relcache init file invalidation (Tom Lane)
There was a window wherein a new backend process could read a stale init file but miss the inval messages that would tell it the data is stale. The result would be bizarre failures in catalog accesses, typically "could not read block 0 in file ..." later during startup.
Fix memory leak at end of a GiST index scan (Tom Lane)
Commands that perform many separate GiST index scans, such as verification of a new GiST-based exclusion constraint on a table already containing many rows, could transiently require large amounts of memory due to this leak.
Fix performance problem when constructing a large, lossy bitmap (Tom Lane)
Fix array- and path-creating functions to ensure padding bytes are zeroes (Tom Lane)
This avoids some situations where the planner will think that semantically-equal constants are not equal, resulting in poor optimization.
Work around gcc 4.6.0 bug that breaks WAL replay (Tom Lane)
This could lead to loss of committed transactions after a server crash.
Fix dump bug for VALUES in a view (Tom Lane)
Disallow SELECT FOR UPDATE/SHARE on sequences (Tom Lane)
This operation doesn't work as expected and can lead to failures.
Defend against integer overflow when computing size of a hash table (Tom Lane)
Fix portability bugs in use of credentials control messages for "peer" authentication (Tom Lane)
Fix typo in pg_srand48
seed initialization (Andres Freund)
This led to failure to use all bits of the provided seed. This function
is not used on most platforms (only those without srandom
),
and the potential security exposure from a less-random-than-expected
seed seems minimal in any case.
Avoid integer overflow when the sum of LIMIT and OFFSET values exceeds 2^63 (Heikki Linnakangas)
Add overflow checks to int4 and int8 versions of
generate_series()
(Robert Haas)
Fix trailing-zero removal in to_char()
(Marti Raudsepp)
In a format with FM and no digit positions after the decimal point, zeroes to the left of the decimal point could be removed incorrectly.
Fix pg_size_pretty()
to avoid overflow for inputs close to
2^63 (Tom Lane)
Fix psql's counting of script file line numbers during COPY from a different file (Tom Lane)
Fix pg_restore's direct-to-database mode for standard_conforming_strings (Tom Lane)
pg_restore could emit incorrect commands when restoring directly to a database server from an archive file that had been made with standard_conforming_strings set to on.
Fix write-past-buffer-end and memory leak in libpq's LDAP service lookup code (Albe Laurenz)
In libpq, avoid failures when using nonblocking I/O and an SSL connection (Martin Pihlak, Tom Lane)
Improve libpq's handling of failures during connection startup (Tom Lane)
In particular, the response to a server report of fork()
failure during SSL connection startup is now saner.
Make ecpglib write double values with 15 digits precision (Akira Kurosawa)
Apply upstream fix for blowfish signed-character bug (CVE-2011-2483) (Tom Lane)
contrib/pg_crypto's blowfish encryption code could give wrong results on platforms where char is signed (which is most), leading to encrypted passwords being weaker than they should be.
Fix memory leak in contrib/seg (Heikki Linnakangas)
Fix pgstatindex()
to give consistent results for empty
indexes (Tom Lane)
Allow building with perl 5.14 (Alex Hunsaker)
Update configure script's method for probing existence of system functions (Tom Lane)
The version of autoconf we used in 8.3 and 8.2 could be fooled by compilers that perform link-time optimization.
Fix assorted issues with build and install file paths containing spaces (Tom Lane)
Update time zone data files to tzdata release 2011i for DST law changes in Canada, Egypt, Russia, Samoa, and South Sudan.


Work Life Balance
Untether from work calls and emails
In the previous video, I received requests to discuss how to balance life and work.
With the holiday season coming up, it's especially important to manage your work life to that you can still enjoy the holidays.
Some folks have asked that I share my personal experience with work/life balance.
- My first year, I was not very good. I worked almost all day on Thanksgiving.
- The next few years were also not great, I worked a lot.
- This year, I've taken more personal time, as it has been a difficult year with the pandemic.
Also, it's important to give youself time to enjoy the holidays and not feel tethered to work.
Research has shown that the more we feel tethered to out phone and/or work while at home, the more likely we are to feel tense and unhappy (source).
Other side effects of poor work life balance include:
- Psychological distress
- Family feeling like you're not present
- Feeling stressed and upset. One Thanksgiving I was so preoccupied with work that I felt too busy to preheat the oven to cook the turkey!
Here are some tips that can help!
- Tell yourself that you will not check your phone
- If this isn't possible, check your phone only twice per day.
- Say it out loud to yourself. Get a mirror!

- Put your phone in a different room or hide it so that you don't impulsively check it
- Have a family member hide it for you (you won't know where it is)!

- If you want to start with baby steps first, TURN OFF NOTIFICATIONS
- Airplane mode
- Do not disturb mode
- Deactive alerts for specific apps
- Do some other activities to distract yourself from the urge to check your phone
- Cook turkey. Try a new mashed potato recipe.
- Host video chats or conferences with your friends and family.
- Remember that rarely will an issue or task be so urgent that you have to respond immediately
- Courts are closed on holidays. Clients are not always working.
- You can respond on Black Friday or on Saturday or Sunday.
If you try these tips, you'll feel more relaxed and able to enjoy the holiday
If someone were to ask me today if I regret working on holidays, I would say that I don't, but I'm not sure it made much of a difference in the long run.
- Would have been fine whether I had completed a project on Thanksgiving, or a bit later.
- If you don't untether, you're more likely to burn out and make bad decisions.
- Some firms have recognized this and have policies in place to prevent employees from checking emails or logging in to work systems after hours.
So remember, untether from your phone and work! It is healthy and will make you more productive in the long run.


Freelancer and Indie Game Developer Resume Here!!
Your favourite personal healthcare companion Baymax from Big Hero
Eve from the movie WallE
Medium poly Colt Python model with one round.
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Donut with sprinkles modelled in Blender.
Medium poly model of Buggati Type 45 in maya.
Light Saber modelled and texture in Blender
Light bulbs modelled textured and rendered in Blender
Grand Piano textured and rendered in Blender.Procedural Texture
My Laptop with room stuff in Blender
Modelling+Riggings+Texturing
Advanced Python and C# with a fair bit of skill in C,C++ and Java
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Loading data - data.py¶
This module defines the Dataset
class that is used to load the .csv
files holding the data to be fitted. After the fit is performed it also
stores the initial parameters, the fitted parameters and the fit
estimations.
This module also defines the function load()
used in the
Dataset.load_c()
and Dataset.load_q()
methods to load the .csv files.
-
class
data.
Dataset
(files_c, files_q=None, t_label='t [s]', c_label='C [M]', q_label='Q [C]')¶ Loads and stores kinetics data, also stores fit results and axes labels.
- Parameters
files_c (list) – The path(s) of the concentration vs time files.
files_q (list, optional) – The path(s) of the charge passed vs time files, if this parameter is not set, further fitting will proceed without taking the charge passed in account.
t_label|c_label|q_label (str, optional) – Set the corresponding attributes values. Used to set the x and y axes labels when plotting.
-
df_c|df_c_std|df_c_fit
Store respectively the mean concentration, the concentration standard deviations and the corresponding fit results. Defined by the load_c method or directly at initialization using the files_c parameter.
- Type
pandas.DataFrame
-
df_q|df_q_std|df_q_fit
Store respectively the mean charge passed, the charge passed standard deviations and the corresponding fit results. Defined by the load_q method or directly at initialization using the files_q parameter.
- Type
pandas.DataFrame
-
t_label|c_label|q_label
Labels to be used for the x and y axes when plotting the datasets.
- Type
str
-
fit_results
¶ Stores the results of the fit, for details see the lmfit.minimizer.MinimizerResult documentation: https://lmfit.github.io/lmfit-py/fitting.html
Defined when the fit.fit_dataset() function is run on the Dataset object.
- Type
lmfit.MinimizerResult
-
init_params
¶ Stores the initial parameters for the fit, for details on this object class see: https://lmfit.github.io/lmfit-py/parameters.html
Defined when the fit.fit_dataset() function is run on the Dataset object.
- Type
lmfit.parameter.Parameters
-
names
¶ Species names which concentration evolution over time is tracked. Used in particular to label the data when plotting. Defined in the load_c method.
- Type
list
-
load_c
(files)¶ Loads / processes .csv files holding concentration over time data.
Recommendations for the .csv file formatting: the files headers are formatted in this fashion: “t, species name 1, species name 2…”; first column is time, other columns are concentrations.
An example file can be found here: https://is.gd/GZPZFK
Relies on the load() function from this module, see also this function documentation for more details.
- Parameters
files (list) – Path(s) of the .csv files.
-
load_q
(files)¶ Loads / processes .csv files holding charge passed over time data.
Recommendations for the .csv file formatting: the files headers should be formatted in this fashion: “t,Q”; first column is the time, second column is the charge passed.
An example file can be found here: https://is.gd/0Ma7Ii
Relies on the load() function from this module, see also this function Docstring for more details.
- Parameters
files (list) – Paths of the .csv files.
-
data.
load
(files)¶ Loads and processes data from .csv files, stores it in Dataframes.
The .csv files should have the same headers, the same number of columns and the same number of rows. The header should be held in the first row.
These .csv files are typically generated from raw data files from experiments using custom code to satisfy these requirements.
- Parameters
files (list) – Path(s) to the file(s), each column in each file must have the same number of rows.
- Returns
Averages DataFrame and standard deviation DataFrame.
- Return type
pandas.DataFrame, pandas.DataFrame


You must call this file with a get param to test it. for example: test.html?name=drew
If you do so, then a alert() shows the content of the "name" variable.


Hi folks,
I'm running openenergymonitors in 12 houses now (for electricity use, heat and humidity), but really want to be able to monitor gas. There is a fair few meters out there and I know that there are many people on these forums who are successfully metering their gas. I'm trying to compile a list of meters, what is needed to monitor them in terms of hardware and code, so I can usefully add to the gas metering page.
If anyone has anything to add on any of the following (even if it's what doesn't work), that would be really helpful. I'm a real newbie to arduino and electronics in general but have written a good deal of technical and end-user documentation and I can follow instructions. Ideally the low-power version of emontx makes a good basis as some meters are located outside the house. So here goes.
There is very useful info from National Grid on identifying your gas meter here, but I found images too small, here are ones I've taken recently (click to enlarge).
Eurometer E6
From my understanding the same as the George Wilson E6. Uses ultrasound pulses to measure flow.
I'd assumed we would monitor this with a pulse meter (from LEDs on front), but hear from Marsflyer via Trystan that this won't work. Any thoughts, assuming digital display models don't have a moving magnet we can monitor?
Krom Schroder G4 (BK-G4)
A measurement via diaphragm. National Grid claims not to have purchased these since 2002, but they seem very common (at least in Manchester).
Actaris / Schlumberger G4
Wish there were more of these. Anyone already running a low power version of emontx pulse?
Pre-metric - U6 - the standard
We got lots of these, from George Wilson and other companies as well, but apparently all work in the same way.
U6 with some kind of monitor?
This one is puzzling, it appears to have a british gas branded wireless monitor on it, the householder doesn't know what it is or when it was installed. Any thoughts are where it might be sending?
I've got myself some of the Diodes AH180N hall monitors as recommended on the gas page, I also got hold of some of the A3214EUA-T sensors that Nathan Chantrell has successully used with the Tinytx for gas monitoring but I don't have any of those boards (and a bit scared of pcb ordering if I'm honest).
Sorry for huge post. Any thoughts or pointers?
Mx
Re: Cracking Gas metering
I have the final monitor in your list, and as I run a current cost monitor I used one of their development boards. It has an output to measure the pulses. Am pretty sure a TinyTX would be fine to use. I knew nothing about ordering pcb ordering wither, but have since ordered two lots of TinyTX2's .. I do have quite a few here, so if you want some, contact me with a direct message.
I will likely be moving to TinyTX3 at some point.
Nick
Re: Cracking Gas metering
The Krom Schroder G4 (BK-G4) gass meter can be fitted with a reed switch available at https://www.flukso.net/content/gas-probe. I have ordered one and i'll let you know how i get on :-)
Rick
Re: Cracking Gas metering
Rick, I have the same setup.
Seems to be working okay, its been running since mid Jan running on a 9v battery sending the number of pulses read in the last interval every 15mins.
Re: Cracking Gas metering
I'm also looking to do this but I'm having problems with the code. Anyone willing to share their own code I would be very grateful.
Thanks
Re: Cracking Gas metering
I've got the BK-G4 too (also in Manchester, UK). Looks like the install date was 2004. @ricadelic and @StuntMonkeh - The reed switch setup sounds good. Are there more details on how to get this working with Open Energy Monitor? Would love to have a go. Thanks!
Tom
Re: Cracking Gas metering
not that i have any of the meters...
but from the looks of it i think the Eurometer E6 uses IR coms, hence the 2 "leds"
Re: Cracking Gas metering
I have an actaris, which has an RJ socket on the underside and I gather I can just plug in a pulse counter.
However, it is in an outside cabinet, and we occasionally get unplanned meter reads, and I have a concern that if a meter reader sees a cable hooked into the meter they will hit the panic button and we will get hammered for unauthorised equipment connection. There is no space in front of the meter for a non-invasive counter.
what's people's view on this?
Also what sort of range can the pulses be counted? as my current emontx is about 10 m away.
My emonbase (Nanode) is closer, and I have a Raspberry Pi very close too if I could leverage either of them to count and report the pulses
Thanks
Re: Cracking Gas metering
10 meters is no problem....
but its is the range between emontx and emonbase that counts... so if its in fact only 5 meters absolute no problem
about the meter... anything you have access to without breaking a seal is OK... if they dont want you to access that port they should just install an empty rj45 with a seal so you would have to break it to get access
if you are inside the EU there are actually rules for that they have to allow you to connect a device to the meter, provided that this devices does not interfere with the meter itself...... this is for those remote read meters many companies use these days... it counts for all meters installed after 2008 if not remembering wrong
Re: Cracking Gas metering
The source for this EU rule would be interesting.
There are a few threads about gas and pulse counting. I've tried things myself, and I'm not done with it. I've been discussing this on the forum.
It would be worth centralizing the information.
For instance, create a page on the website, first gather the related thread links, then try to synthesize.
The synthesis is not easy as there are many different situations. For the same reason, it is not easy to create a sketch that suits all needs.
Re: Cracking Gas metering
Like this page? http://openenergymonitor.org/emon/buildingblocks/gas-meter-monitoring
Re: Cracking Gas metering
source in danish: https://www.retsinformation.dk/Forms/R0710.aspx?id=160434&exp=1
a buttom:
"bekendtgørelsen indeholder bestemmelser, der gennemfører dele af Europa-Parlamentets og Rådets direktiv 2009/72/EF af 13. juli 2009 om fælles regler for det indre marked for elektricitet og om ophævelse af direktiv 2003/54/EF, EU-Tidende 2009, nr. L 211, side 55, og dele af Europa-Parlamentets og Rådets direktiv 2012/27/EU af 25. oktober 2012 om energieffektivitet, om ophævelse af direktiv 2009/125/EF og 2010/30/EU samt om ophævelse af direktiv 2004/8/EF og 2006/32/EF, EU-Tidende 2012, nr. L 315, side 1. Bekendtgørelsen har som udkast været notificeret i overensstemmelse med Europa-Parlamentets og Rådets direktiv 98/34/EF (informationsproceduredirektivet), som ændret ved direktiv 98/48/EF"
top line says, roughly that the notice implements parts of EU directive 2009/72/EF dated 13. july 2009 concerning common rules for the inner market of electricity and suspension of eu directive 2003/54/EF........ 2012/27/EU dated 25 october 2012........ 2009/125/EF and 2010/30/EU........2004/8/EF and 2006/32/EF......
the notice also as draft been noticed to be in sompliance with eu directive 98/34/EF changed in directive 98/48/EF
you can substitute EF with EU in the directive numbers...
BUT note install date of your meter comes in to play... if meter is installed before your country implemented this it does not count.... sadly my meter is installed before... the contactless / transistor pulse output is behind a sealed cover, i can get connected to it for the charge of an hours work plus VAT...to make me laugh that one pulses 500 times for each kwh... hte optical does 10000 for each kwh hour.... so makes it easy to pick which one i like....
some meters also talks back to the company on what i belive they call LON network... ie it uses the power lines to talk to a central hub at each transfomer station which then has some equipment to talk over either high voltage lines or fiber net or maybe even phone lines.. who knows on that part....
could be fun if we could tap in to the info that goes back....regadless of what is beeing messured
Re: Cracking Gas metering
not an bad idea... there are so many meters and so many ways to get data out of them....
problem with creating a thread is that it will sooner or later get filled up with info not needed there.... so a kind of thread then the info is copied over to a blog when its agreed the way to read a meter is fast, easy and reliable...
Also this needs some that are willing to help out cracking the nut on each meter type.... we are not all experts, my own weakness is that i can get a heap load of ideas but not always knows how to execute
Re: Cracking Gas metering
ahh think my meter talks back using ISO/IEC 14908-1 (a.k.a. LonWorks). Echelon made that i think they can spit out how to catch the data
Re: Cracking Gas metering
I begun gathering related threads here: http://openenergymonitor.org/emon/buildingblocks/gas-meter-monitoring
I just searched quickly for "gas". Another search with "pulse" will bring a bit more results. Water monitoring threads are linked, just as everything measured by pulses.
Feel free to reorganize / remove useless links / rename links / extract useful info to the page.
Re: Cracking Gas metering
Hi Folks. New member to this forum. Looking for gas meter advice?
I have ITRON SC6 gas meter as pictured below has anyone worked out how to pulse meter for this type of meter?
Many Thanks.
Re: Cracking Gas metering
Schlumberger R5 imperial gas meter info
I have one of these in my house (must have just missed decimalisation) - http://www.flickr.com/photos/gasmetering/4025874437/. Not my photo but mine is the same.
I have been meaning to have a look at the RJ11 port underneath for a while and finally had a tinker on Sunday. The port is under the dial facia, under the BG sticker you can see on the photo.
I plugged in an old modem cable that had pins 2 - 5 broken out and then connected up 2 and 5 and then 3 and 4. Nothing on 2/5 and I had a switch normally open on 3/4.
The switch was closing from the 10 o'clock to just after 12 o'clock positions and looked pretty stable on my little multimeter (I had a couple of AA for the power source). As they say your mileage may vary for how the switch is closed for.
The issue with the meter might be its slow pulse rate. It is apparently 1 pulse per cubic foot of gas. In practice it is around 1 minute between dial rotations/pulses when my boiler is on. I will try and get a pulse counting arduino linked into the meter to see how clean the pulses are of they will need a healthy bit of debouncing.
As the pulses will be quite infrequent, my thoughts at the moment are that I could do with:
a. An incrementing pulse count
b. An on node flow rate calculation - possibly using a cheap RTC module to deal with the issue of the 32b timer not running whilst sleeping
Has anyone tried using building in an RTC yet for pulse counting?
Gary
Re: Cracking Gas metering
Just waiting for my EmonPi Solar PV kit and want to add gas monitoring to the mix. I also have an R5 meter.
From my naive view of the docs on the web-site I need to hack up an RJ11 ro RJ45 cable - not using any other sensors apart from the current clamps and Ac-Ac as supplied on the EmonPi - so that the RJ11 pins 3/4 go through to pins 2 (5V) and 6 (IRQ1/D3) but given the slow nature of the pulse I am not sure this will work.
Has anyone else hooked up an R5 to an emonPi rather than a emonTX?
Ta!
Re: Cracking Gas metering
I'm interested in this, having just noticed the RJ11 port.
1 pulse for 1cuft doesn't sound too bad, as (if my sums are right) that's about 0.3kWh.
I'll probably prototype on something high-level like an rPI or a beaglebone as I've got these lying around. One thought is you could probably use an ESP8266 wifi module to do the whole thing - use the sleep mode and GPIO to wake the module and increment the count. I'm not sure how hard that'd be as I've not touched these modules.
Re: Cracking Gas metering
Sorry should have followed up on my post ages ago
I ended up running a cut up modem cable (RJ11?) to the plug under the dials on my R5 which involved peeling away a sticker. I then have this running into a moteino (lowpowerlabs.com) via one of the spare interrupt pins
There is small circuit involved to add in a pull up resistor, see here
https://harizanov.com/2013/08/door-sensor/
I seem to recall it was a bit temperamental and I needed to redo some soldering - I suspect the very low current means you need to take a bit of care on the joints
The sketch needs a bit of work as the "power" bit is way off but the pulse count/battery bits all seem fine though. The main issue is that if you swap batteries your count restarts. I seem to be getting 1 year out a set of 3 AAs.
For testing I would just use a jumper cable to simulate the dial rotating as it is way too slow and a lot more comfortable doing this in front of the PC than hovering around in a cold garage
Arduino sketch attached hopefully
Good luck
Gary
Re: Cracking Gas metering
Hi, Newbie to the forum.
I've been trying to get my head around gas monitoring to go with my energy monitoring system I've got set up. Currently I use the Currentcost for electricity monitoring but I'm changing over to the emontx v3, and want to get the gas monitoring working. I have a GWI/UGI G4 Gas Meter and so far can't seem to get a magnetic pulse either from Hall effects or reed sensor. I did a video of the fast wheel turning and it looks like the "0" digit has a mirror in it. I'm guessing an IR reflective sensor would do the trick. Has anyone else got experience with the gas meter? I've also spotted in a little circular window a rotating blue encoder looking thing not sure if this is relevant?
I've included a picture of the meter and the zero digit.
Many Thanks
Re: Cracking Gas metering
You could try a magnetic compass around the least significant digit or around that box below the decimal part of the register - that would be more sensitive than a reed and possibly a Hall effect device. If that can't see a magnet, then you're looking at optical - that could be costly in power and pretty much rules out battery operation.
Unless you know the calibration of the other indicator, the register is the only meaningful indication that you can use.
Re: Cracking Gas metering
I use a CNY70 optical sensor with a Schmitt Trigger (ST). You need to adjust the resistors on the ST. Once done, it works perfectly. On the software side (Arduino), the ST triggers an interrupt on the falling edge. On my gas meter, the reflective piece is located on the "6" (type BK-G4).
There are lots of info on Internet around this setup.
Walter
Re: Cracking Gas metering
I've not done it with that particular gas meter but mine is similar and has a mirror in the hole in the '6' so I built a circuit to generate a pulse built around an SG-2BC opto-reflector details here https://github.com/Bra1nK/HomeMonitor/tree/master/Gas%20Meter%20Pulse%20Creator
Re: Cracking Gas metering
Have you seen this link?
http://www.rotwang.co.uk/projects/gasmeter.html
Re: Cracking Gas metering
The link is about capturing the movement of a rotating dial and looking at the pictures posted by chrisghz his meter doesn't appear to have one


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My name is Patrick Kuteesa Kayongo. I am a software developer based in Cape Town, South Africa. Thought I’d create this blog to write down »
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Violence as 'yellow vest' protesters mark one-year anniversary
by EuronewsParis police on Saturday fired tear gas to drive back protesters marking the first anniversary of anti-government "gilets jaunes" ("yellow vest") demonstrations.
On the Place d'Italie in southern Paris, demonstrators, many clad in black and hiding their faces, set bins on fire and hurled projectiles at riot police while building barricades.
An HSBC bank branch was attacked, two cars were overturned, a scooter and car were on fire and police responded with tear gas and a water cannon.
Paris police prefect Didier Lallement cancelled permission for a scheduled demonstration in view of the violence.
"Our response will be very firm.
The authorities said that 147 people had been detained by 20:00 CET.
Clashes also broke out between demonstrators and police near the Porte de Champerret as protesters were preparing to march across town towards Gare d'Austerlitz.
Police also intervened to prevent a few hundred demonstrators from occupying the Paris ring road, according to Reuters TV footage.
The protests, named after the high-visibility jackets worn by demonstrators, erupted in mid-November 2018 over fuel price hikes and the high cost of living. The demonstrations spiralled into a broader movement against President Emmanuel Macron and his economic reforms.
The protests lost strength in recent months, going from tens of thousands of participants to just a few thousand, but the movement's leaders called for people to turn out on Saturday to mark the first anniversary.
At its peak in late 2018, the movement grew to up to 300,000 people.
Protests have been banned near tourists spots such as the Eiffel Tower and many subway stations were closed on Saturday.


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20 Mar 2010
•
prosodylab
Wagner, M. , M. Breen, E. Flemming, Stefanie Shattuck-Hufnagel & E. Gibson (2010). Prosodic Effects of Discourse Salience and Association with Focus. To be presented at Speech prosody.
Three factors that have been argued to influence the prosody of an utterance are (i) which constituents encode discourse-salient information; (ii) which constituents are contrastive and evoke alternatives; and (iii) which constituents interact with the meaning of focus operators such as only (i.e., they ‘associate’ with focus). One challenge for a better understanding of the prosodic effects of these factors has been the difficulty of finding a way to evaluate hypotheses quantitatively, since individual variation in productions is often large enough to wash out experimental effects. In this paper, we apply a methodology introduced in Breen et al. (submitted) which regresses out subject and item variation, uncovering otherwise hidden prosodic patterns, and show how the three factors interact in sentences containing single or multiple foci.
20 Mar 2010
•
conferences
Cornell Workshop on Grammar Induction will bring together researchers from the fields of linguistics, psychology, and computer science who work on issues of learning and learnability in language. Our goal is to facilitate the exchange of ideas between researchers approaching similar problems with the perspectives and methodologies of diverse fields.
Date: 14-May-2010 - 16-May-2010
20 Mar 2010
•
conferences
Sixth North American Phonology Conference:
A celebration of the 51st anniversary of the publication of Morris Halle’s The Sound Pattern of Russian (SPR). We invite papers (on any and all languages–not just Russian!) that address issues raised by the conditions on phonological theory proposed in SPR.
Concordia University, Montreal
April 30-May 2, 2010
Submission deadline: March 26, 2010
09 Mar 2010
•
conferences
•
local
This weekend, mcclue, McGill’s Canadian Conference for Linguistics Undergraduates, is taking place. It is organized by slum, the society of linguistics undergraduates at McGill.
03 Mar 2010
•
conferences
•
local
After this week’s TOM (Toronto-Ottawa-Montreal Semantics Workshop) at McGill, the phonologists will have their say at next week’s MOT (Montreal-Ottawa-Toronto Phonology Workshop) at Carleton.
02 Mar 2010
•
conferences
•
local
More stuff going this week at McGill than just mg3: TOM, the Toronto-Ottawa-Montreal workshop on semantics is taking place this Saturday. Here’s the program: TOM.
01 Mar 2010
•
conferences
•
local
This weekend, the third conference on music and gesture will take place in Montreal this coming Friday and Saturday. Marcin Swoboda and I will be presenting on Encoding emotion: how performers manipulate tempo locally to convey affect.
The conference forms part of the musimars music festival, organized this year under the theme ‘gestural music–musical gesture.’ All throughout this week, there will be concerts and lectures at the Schulich School of Music.
28 Feb 2010
•
prosodylab
Here’s the revised version of our paper on gradient boundary strength and disambiguation, soon to be presented at speech prosody:
Wagner, Michael & Serena Crivellaro: Relative Prosodic Boundary Strength and Prior Bias in Disambiguation
Abstract: Previous research found that the relative rather than the absolute size of prosodic boundaries is crucial in disambiguating attachment ambiguities [1, 2]. Furthermore, relative categorical differences matter whereas merely quantitative ones do not [1]. This paper presents further evidence that relative boundary strength is indeed what is crucial, but, contrary to earlier findings, gradient quantitative differences in boundary rank affect parsing decisions in gradient ways. Furthermore, varying the plausibility of a given reading in a given context shifts the perceptual boundaries between different phrasings such that quantitatively stronger prosodic cues are necessary to counter-act a prior bias against it.
18 Feb 2010
•
conferences
The call for papers for the second conference on linguistic interfaces was posted. The deadline is April 30th. Here’s part of the conference description:
A full description of our knowledge of language must include reference to several different components, each with its own particular properties. These components must interact with each other, and with a lexicon, which we may think of as a system of stored associations between pieces of information pertaining to many of the above components. In recent years, the study of the interaction between these different levels of linguistic knowledge has attracted increasing interest. The nature and extent of the interaction of different linguistic modules is a central question to be addressed by a modern theory of linguistic knowledge. […]
18 Feb 2010
•
conferences
The deadline for TIE 4 in Stockholm was extended: The new deadline is February 22nd.


| Opcode/Instruction | Op/En | 64/32 bit Mode Support | CPUID Feature Flag | Description |
|---|---|---|---|---|
| EVEX.128.66.0F3A.W1 66 /r ib VFPCLASSPD k2 {k1}, xmm2/m128/m64bcst, imm8 | A | V/V | AVX512VL AVX512DQ | Tests the input for the following categories: NaN, +0, -0, +Infinity, -Infinity, denormal, finite negative. The immediate field provides a mask bit for each of these category tests. The masked test results are OR-ed together to form a mask result. |
| EVEX.256.66.0F3A.W1 66 /r ib VFPCLASSPD k2 {k1}, ymm2/m256/m64bcst, imm8 | A | V/V | AVX512VL AVX512DQ | Tests the input for the following categories: NaN, +0, -0, +Infinity, -Infinity, denormal, finite negative. The immediate field provides a mask bit for each of these category tests. The masked test results are OR-ed together to form a mask result. |
| EVEX.512.66.0F3A.W1 66 /r ib VFPCLASSPD k2 {k1}, zmm2/m512/m64bcst, imm8 | A | V/V | AVX512DQ | Tests the input for the following categories: NaN, +0, -0, +Infinity, -Infinity, denormal, finite negative. The immediate field provides a mask bit for each of these category tests. The masked test results are OR-ed together to form a mask result. |
| Op/En | Tuple Type | Operand 1 | Operand 2 | Operand 3 | Operand 4 |
| A | Full | ModRM:reg (w) | ModRM:r/m (r) | NA | NA |
The FPCLASSPD instruction checks the packed double precision floating point values for special categories, specified by the set bits in the imm8 byte. Each set bit in imm8 specifies a category of floating-point values that the input data element is classified against. The classified results of all specified categories of an input value are ORed together to form the final boolean result for the input element. The result of each element is written to the corresponding bit in a mask register k2 according to the writemask k1. Bits [MAX_KL-1:8/4/2] of the destination are cleared.
The classification categories specified by imm8 are shown in Figure 5-13. The classification test for each category is listed in Table 5-13.
| 76543210 | ||||||||
| SNaN | Neg. Finite | Denormal | Neg. INF | +INF | Neg. 0 | +0 | QNaN |
Bits Imm8[0] Imm8[1] Imm8[2] Imm8[3] Imm8[4] Imm8[5] Imm8[6] Imm8[7]
Category QNAN PosZero NegZero PosINF NegINF Denormal Negative SNAN
Classifier Checks for Checks for Checks for Checks for Checks for Checks for Checks for Checks for QNaN +0 0 +INF INF Denormal Negative finite SNaN
The source operand is a ZMM/YMM/XMM register, a 512/256/128-bit memory location, or a 512/256/128-bit vector broadcasted from a 64-bit memory location.
EVEX.vvvv is reserved and must be 1111b otherwise instructions will #UD.
CheckFPClassDP (tsrc[63:0], imm8[7:0]){ //* Start checking the source operand for special type *// NegNum ←tsrc[63]; IF (tsrc[62:52]=07FFh) Then ExpAllOnes←1; FI; IF (tsrc[62:52]=0h) Then ExpAllZeros←1; IF (ExpAllZeros AND MXCSR.DAZ) Then MantAllZeros ← 1; ELSIF (tsrc[51:0]=0h) Then MantAllZeros ← 1; FI; ZeroNumber←ExpAllZeros AND MantAllZeros SignalingBit ← tsrc[51]; sNaN_res←ExpAllOnes AND NOT(MantAllZeros) AND NOT(SignalingBit); // sNaN qNaN_res←ExpAllOnes AND NOT(MantAllZeros) AND SignalingBit; // qNaN Pzero_res←NOT(NegNum) AND ExpAllZeros AND MantAllZeros; // +0 Nzero_res←NegNum AND ExpAllZeros AND MantAllZeros; // -0 PInf_res←NOT(NegNum) AND ExpAllOnes AND MantAllZeros; // +Inf NInf_res←NegNum AND ExpAllOnes AND MantAllZeros; // -Inf Denorm_res←ExpAllZeros AND NOT(MantAllZeros); // denorm FinNeg_res←NegNum AND NOT(ExpAllOnes) AND NOT(ZeroNumber); // -finite bResult = ( imm8[0] AND qNaN_res ) OR (imm8[1] AND Pzero_res ) OR ( imm8[2] AND Nzero_res ) OR ( imm8[3] AND PInf_res ) OR ( imm8[4] AND NInf_res ) OR ( imm8[5] AND Denorm_res ) OR ( imm8[6] AND FinNeg_res ) OR ( imm8[7] AND sNaN_res ); Return bResult; } //* end of CheckFPClassDP() *//
(KL, VL) = (2, 128), (4, 256), (8, 512) FOR j←0 TO KL-1 i←j * 64 IF k1[j] OR *no writemask* THEN IF (EVEX.b == 1) AND (SRC *is memory*) THEN DEST[j]←CheckFPClassDP(SRC1[63:0], imm8[7:0]); ELSE DEST[j]←CheckFPClassDP(SRC1[i+63:i], imm8[7:0]); FI; ELSE DEST[j]←0 ; zeroing-masking only FI; ENDFOR DEST[MAX_KL-1:KL] ← 0
VFPCLASSPD __mmask8 _mm512_fpclass_pd_mask( __m512d a, int c);
VFPCLASSPD __mmask8 _mm512_mask_fpclass_pd_mask( __mmask8 m, __m512d a, int c)
VFPCLASSPD __mmask8 _mm256_fpclass_pd_mask( __m256d a, int c)
VFPCLASSPD __mmask8 _mm256_mask_fpclass_pd_mask( __mmask8 m, __m256d a, int c)
VFPCLASSPD __mmask8 _mm_fpclass_pd_mask( __m128d a, int c)
VFPCLASSPD __mmask8 _mm_mask_fpclass_pd_mask( __mmask8 m, __m128d a, int c)
None
See Exceptions Type E4
| #UD | If EVEX.vvvv != 1111B. |


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| 1 | /* | |
| 2 | * Licensed to the Apache Software Foundation (ASF) under one or more | |
| 3 | * contributor license agreements. See the NOTICE file distributed with | |
| 4 | * this work for additional information regarding copyright ownership. | |
| 5 | * The ASF licenses this file to You under the Apache License, Version 2.0 | |
| 6 | * (the "License"); you may not use this file except in compliance with | |
| 7 | * the License. You may obtain a copy of the License at | |
| 8 | * | |
| 9 | * http://www.apache.org/licenses/LICENSE-2.0 | |
| 10 | * | |
| 11 | * Unless required by applicable law or agreed to in writing, software | |
| 12 | * distributed under the License is distributed on an "AS IS" BASIS, | |
| 13 | * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| 14 | * See the License for the specific language governing permissions and | |
| 15 | * limitations under the License. | |
| 16 | */ | |
| 17 | ||
| 18 | package org.apache.commons.math3.analysis.function; | |
| 19 | ||
| 20 | import org.apache.commons.math3.analysis.FunctionUtils; | |
| 21 | import org.apache.commons.math3.analysis.UnivariateFunction; | |
| 22 | import org.apache.commons.math3.analysis.DifferentiableUnivariateFunction; | |
| 23 | import org.apache.commons.math3.analysis.differentiation.DerivativeStructure; | |
| 24 | import org.apache.commons.math3.analysis.differentiation.UnivariateDifferentiableFunction; | |
| 25 | import org.apache.commons.math3.util.FastMath; | |
| 26 | ||
| 27 | /** | |
| 28 | * Hyperbolic arc-cosine function. | |
| 29 | * | |
| 30 | * @since 3.0 | |
| 31 | * @version $Id: Acosh.java 1383441 2012-09-11 14:56:39Z luc $ | |
| 32 | */ | |
| 33 | public class Acosh implements UnivariateDifferentiableFunction, DifferentiableUnivariateFunction { | |
| 34 | /** {@inheritDoc} */ | |
| 35 | public double value(double x) { | |
| 36 | 1 | return FastMath.acosh(x); |
| 37 | } | |
| 38 | ||
| 39 | /** {@inheritDoc} | |
| 40 | * @deprecated as of 3.1, replaced by {@link #value(DerivativeStructure)} | |
| 41 | */ | |
| 42 | @Deprecated | |
| 43 | public UnivariateFunction derivative() { | |
| 44 | 1 | return FunctionUtils.toDifferentiableUnivariateFunction(this).derivative(); |
| 45 | } | |
| 46 | ||
| 47 | /** {@inheritDoc} | |
| 48 | * @since 3.1 | |
| 49 | */ | |
| 50 | public DerivativeStructure value(final DerivativeStructure t) { | |
| 51 | 1 | return t.acosh(); |
| 52 | } | |
| 53 | ||
| 54 | } | |
Mutations | ||
| 36 |
replaced return of double value with -(x + 1) for org/apache/commons/math3/analysis/function/Acosh::value : NO_COVERAGE |
|
| 44 |
mutated return of Object value for org/apache/commons/math3/analysis/function/Acosh::derivative to ( if (x != null) null else throw new RuntimeException ) : NO_COVERAGE |
|
| 51 |
mutated return of Object value for org/apache/commons/math3/analysis/function/Acosh::value to ( if (x != null) null else throw new RuntimeException ) : NO_COVERAGE |


Our mission is to provide the communities we serve with the finest Gas BBQ Grills that anyone in the nation has to offer.
To this end, we seek out our customer's specific needs and tailor our products and services to meet those needs.
We carry the largest possible selection of grills, conventional and gourmet products. We carry only the highest quality, testing and certifying every product we stock.
We strive to procure merchandise at the lowest possible cost so that we can offer value to our customers and maintain fair prices. We treat each customer like a guest in our home and we strive to exceed our customer's expectations every time they shop with us.
We believe that our employees work to live, not live to work. We believe that each employee’s talents and creativity contribute to making our store successful. We also support employees in their life goals by offering competitive wage and benefit packages and special scheduling whenever possible.
Gas BQQ Grills store is committed to playing an active role in the communities we serve. We support local schools and non-profit groups. Whenever possible, we fill staffing needs from our local neighborhoods.
1234 Somehwere Avenue
Nowhere, AW 13542
USA
(545) 545–5445
Monday – Friday: 5:30 AM – 5:30 PM EST
Saturday: 8:30 AM – 5:00 PM EST
Sunday: CLOSED.
Email<EMAIL_ADDRESS>(545) 545–5445, ext: 101
Email<EMAIL_ADDRESS>(545) 545–5445, ext: 207
Email<EMAIL_ADDRESS>(545) 545–5445, ext: 304


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Two Ring Software exists to create tools that help people.
Software should work within a dharmic model of interaction:
- Quality: deliver software that is safe and maintainable–that means code that is tested, well-factored and documented
- Harmony: we may not be the first or last to touch a codebase–respect the decisions of those that have come before, and try to leave it better than we found it for those who come after
- Duty: our products are meant to help those who use them–seek to understand users' problems and empathize with their needs
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Morality: cultivate respect and compassion between...
- developers: leave ego behind when solving problems and teaching others
- developers and users: shun psychological tricks and exploitative practices
- users (and even nonusers): encourage healthy lifestyles and maintain humanity
Above all, we strive for simplicity and elegance in every aspect of our products, whether in code or user experience.
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Case study
How St Gregory’s Catholic Science College uses weekend detentions to deter poor behaviour.
St Gregory’s Catholic Science College (Harrow) is a larger than average, inner-city London comprehensive for 11- to 18-year-olds. The school has a high proportion of pupils from ethnic minorities and the percentage of pupils eligible for the pupil premium is above average. In October 2013, the school was rated by Ofsted as ‘outstanding’ in behaviour.
This school uses Saturday morning detentions as a sanction for poor behaviour. When this was first introduced they took place approximately every 3 weeks. Now there is a half-termly rota for senior staff to supervise the detentions, but numbers have fallen and Saturday detentions now happen approximately twice a term. This is an effective deterrent because pupils are required to come into school on a non-school day and have the inconvenience of wearing their school uniform on their journey to and from the school on Saturday.
To help manage behaviour, the school ensures that the headteacher and senior staff are visible to pupils and other staff. At a different time every day the headteacher walks around the school and visits each classroom. This visit provides the opportunity for teachers to highlight good work, and if necessary flag inappropriate behaviour. The headteacher is able to manage the behaviour immediately after it occurs.
Published: 25 March 2014