const fs = require('fs');
const mkdirp = require('mkdirp');
const path = require('path');
const getFileManifestEntries = require('./get-file-manifest-entries');
const errors = require('./errors');
/**
* This method will read an existing service worker file and replace an empty
* precache() call, like: `.precache([])`, and replace the array with
* an array of assets to precache. This allows the service worker
* to efficiently cache assets for offline use.
*
* @param {Object} input
* @param {String} input.swSrc File path and name of the service worker file
* to read and inject the manifest into before writing to `swDest`.
* @param {String} input.swDest The file path and name you wish to writh the
* service worker file to.
* @param {String} input.globDirectory The directory you wish to run the
* `globPatterns` against.
* @param {Array<String>} input.globPatterns Files matching against any of
* these glob patterns will be included in the file manifest.
* @param {String|Array<String>} [input.globIgnores] Files matching against any
* of these glob patterns will be excluded from the file manifest, even if the
* file matches against a `globPatterns` pattern.
* @param {Object<String,Array|String>} [input.templatedUrls]
* If a URL is rendered with templates on the server, its contents may
* depend on multiple files. This maps URLs to an array of file names, or to a
* string value, that uniquely determines the URL's contents.
* @param {String} [input.modifyUrlPrefix] An object of key value pairs
* where URL's starting with the key value will be replaced with the
* corresponding value.
* @param {number} [input.maximumFileSizeToCacheInBytes] This value can be used
* to determine the maximum size of files that will be precached.
*
* Defaults to 2MB.
* @param {RegExp} [input.dontCacheBustUrlsMatching] An optional regex that will
* return a URL string and exclude the revision details for urls matching this
* regex. Useful if you have assets with file revisions in the URL.
* @return {Promise} Resolves once the service worker has been written
* with the injected precache list.
*
* @example <caption>Generate a build manifest of static assets, which could
* then be used with a service worker.</caption>
* const swBuild = require('workbox-build');
*
* swBuild.injectManifest({
* globDirectory: './build/',
* globPatterns: ['**\/*.{html,js,css}'],
* globIgnores: ['admin.html'],
* swSrc: './src/sw.js',
* swDest: './build/sw.js',
* })
* .then(() => {
* console.log('Build Manifest generated.');
* });
*
* @memberof module:workbox-build
*/
const injectManifest = (input) => {
if (!input || typeof input !== 'object' || Array.isArray(input)) {
return Promise.reject(
new Error(errors['invalid-inject-manifest-arg']));
}
const injectionPointRegex = /(\.precache\()\s*\[\s*\]\s*(\))/g;
return getFileManifestEntries(input)
.then((manifestEntries) => {
let swFileContents = fs.readFileSync(input.swSrc, 'utf8');
const injectionResults = swFileContents.match(injectionPointRegex);
if (!injectionResults) {
throw new Error(errors['injection-point-not-found']);
}
if (injectionResults.length > 1) {
throw new Error(errors['multiple-injection-points-found']);
}
const entriesString = JSON.stringify(manifestEntries, null, 2);
swFileContents = swFileContents
.replace(injectionPointRegex, `$1${entriesString}$2`);
return new Promise((resolve, reject) => {
mkdirp(path.dirname(input.swDest), (err) => {
if (err) {
return reject(
new Error(
errors['unable-to-make-injection-directory'] +
` '${err.message}'`
)
);
}
resolve();
});
})
.then(() => {
fs.writeFileSync(input.swDest, swFileContents);
});
});
};
module.exports = injectManifest;


ATOMRegressor
class atom.api.ATOMRegressor(*arrays, y=-1, n_rows=1, test_size=0.2, logger=None, n_jobs=1, warnings=True, verbose=0, random_state=None)
ATOMRegressor is ATOM's wrapper for regression tasks. Use this class to easily apply all data transformations and model management provided by the package on a given dataset. Note that contrary to sklearn's API, an ATOMRegressor instance already contains the dataset on which we want to perform the analysis. Calling a method will automatically apply it on the dataset it contains.
You can predict, plot and call any model from atom. Read more in the user guide.
| Parameters: |
*arrays: sequence of indexables
Dataset containing features and target. Allowed formats are:y: int, str or sequence, optional (default=-1) Target column in X. Ignored if provided throughn_rows: int or float, optional (default=1) test_size: int, float, optional (default=0.2) n_jobs: int, optional (default=1) Number of cores to use for parallel processing.verbose: int, optional (default=0) Verbosity level of the class. Possible values are:warnings: bool or str, optional (default=True) logger: str, Logger or None, optional (default=None) random_state: int or None, optional (default=None) Seed used by the random number generator. If None, the random number generator is the |
Magic methods
The class contains some magic methods to help you access some of its elements faster. Note that methods that apply on the pipeline can return different results per branch.
- __repr__: Prints an overview of atom's branches, models, metric and errors.
- __len__: Returns the length of the pipeline.
- __iter__: Iterate over the pipeline's transformers.
- __contains__: Checks if the provided item is a column in the dataset.
- __getitem__: If int, return the i-th transformer in the pipeline. If str, access a column in the dataset.
Attributes
Data attributes
The dataset can be accessed at any time through multiple attributes, e.g. calling
trainer.train
will return the training set. The data can also be changed through
these attributes, e.g. trainer.test = atom.test.drop(0)
will drop the first row
from the test set. Updating one of the data attributes will automatically update the
rest as well. Changing the branch will also change the response from these attributes
accordingly.
| Attributes: |
pipeline: pd.Series
Series containing all transformers fitted on the data in the current branch. Use this attribute only to access the individual instances. To visualize the pipeline, use the status method from the branch or the plot_pipeline method.feature_importance: list Features ordered by most to least important. This attribute is created after running the feature_selection, plot_permutation_importance or plot_feature_importance methods.dataset: pd.DataFrame Complete dataset in the pipeline.train: pd.DataFrame Training set.test: pd.DataFrame Test set.X: pd.DataFrame Feature set.y: pd.Series Target column.X_train: pd.DataFrame Training features.y_train: pd.Series Training target.X_test: pd.DataFrame Test features.y_test: pd.Series Test target.shape: tuple Dataset's shape: (n_rows x n_columns) or (n_rows, (shape_sample), n_cols) for deep learning datasets.columns: list Names of the columns in the dataset.n_columns: int Number of columns in the dataset.features: list Names of the features in the dataset.n_features: int Number of features in the dataset.target: str Name of the target column.scaled: bool Whether the feature set is scaled. It is considered scaled when it has mean=0 and std=1, or when atom has a scaler in the pipeline.duplicates: int Number of duplicate rows in the dataset.nans: pd.Series Columns with the number of missing values in them.n_nans: int Number of samples containing missing values.numerical: list Names of the numerical features in the dataset.n_numerical: int Number of numerical features in the dataset.categorical: list Names of the categorical features in the dataset.n_categorical: int Number of categorical features in the dataset.outliers: pd.Series Columns in training set with amount of outlier values.n_outliers: int Number of samples in the training set containing outliers. |
Utility attributes
| Attributes: |
missing: list
List of values that are considered "missing" (used by the clean and impute methods). Default values are: "", "?", "None", "NA", "nan", "NaN" and "inf". Note thatmodels: list List of models in the pipeline.metric: str or list Metric(s) used to fit the models.errors: dict Dictionary of the encountered exceptions (if any).winner: model Model subclass that performed best on the test set.results: pd.DataFrame Dataframe of the training results. Columns can include: |
Plot attributes
| Attributes: |
style: str
Plotting style. See seaborn's documentation.palette: str Color palette. See seaborn's documentation.title_fontsize: int Fontsize for the plot's title.label_fontsize: int Fontsize for labels and legends.tick_fontsize: int Fontsize for the ticks along the plot's axes. |
Utility methods
The ATOMRegressor class contains a variety of methods to help you handle the data and inspect the pipeline.
| add | Add a transformer to the current branch. |
| apply | Apply a function to the dataset. |
| automl | Use AutoML to search for an optimized pipeline. |
| canvas | Create a figure with multiple plots. |
| delete | Remove a model from the pipeline. |
| distribution | Get statistics on a column's distribution. |
| drop | Drop columns from the dataset. |
| export_pipeline | Export atom's pipeline to a sklearn's Pipeline object. |
| log | Save information to the logger and print to stdout. |
| report | Get an extensive profile analysis of the data. |
| reset_aesthetics | Reset the plot aesthetics to their default values. |
| reset_predictions | Clear the prediction attributes from all models. |
| save | Save the instance to a pickle file. |
| save_data | Save data to a csv file. |
| scoring | Returns the scores of the models for a specific metric. |
| stacking | Add a Stacking instance to the models in the pipeline. |
| stats | Print out a list of basic statistics on the dataset. |
| status | Get an overview of atom's branches, models and errors. |
| voting | Add a Voting instance to the models in the pipeline. |
method add(transformer, columns=None, train_only=False)
Add a transformer to the current branch. If the transformer is not fitted, it is fitted on the complete training set. Afterwards, the data set is transformed and the transformer is added to atom's pipeline. If the transformer is a sklearn Pipeline, every transformer is merged independently with atom.
Note
If the transformer doesn't return a dataframe, the column naming happens as
follows. If the transformer returns the same number of columns, the names are
kept equal. If the number of columns change, old columns will keep their name
(as long as the column is unchanged) and new columns will receive the name
Feature n
, where n stands for the n-th feature. This means that a transformer
should only transform, add or drop columns, not combinations of these.
Note
If the transformer has a n_jobs
and/or random_state
parameter and it
is left to its default value, it adopts atom's value.
| Parameters: |
transformer: estimator
Transformer to add to the pipeline. Should implement a
columns: int, str, slice, sequence or None, optional (default=None)
Names or indices of the columns in the dataset to transform. If None, transform all columns.train_only: bool, optional (default=False) Whether to apply the transformer only on the train set or on the complete dataset. |
method apply(func, column)
Transform one column in the dataset using a function (can be a lambda). If the provided column is present in the dataset, that same column is transformed. If it's not a column in the dataset, a new column with that name is created. The input of function is the complete dataset as pd.DataFrame.
Note
This approach is preferred over changing the dataset directly
through the property's @setter
since the transformation
is saved to atom's pipeline.
| Parameters: |
func: callable
Function to apply to the dataset.column: int or str Name or index of the column in the dataset to create or transform. |
method automl(**kwargs)
Uses the TPOT package to perform
an automated search of transformers and a final estimator that maximizes
a metric on the dataset. The resulting transformations and estimator are
merged with atom's pipeline. The tpot instance can be accessed through the
tpot
attribute. Read more in the user guide.
| Parameters: |
**kwargs
Keyword arguments for tpot's regressor. |
method canvas(nrows=1, ncols=2, title=None, figsize=None, filename=None, display=True)
This @contextmanager
allows you to draw many plots in one figure. The default
option is to add two plots side by side. See the user guide
for an example use case.
| Parameters: |
nrows: int, optional (default=1)
Number of plots in length.ncols: int, optional (default=2) Number of plots in width.title: str or None, optional (default=None) Plot's title. If None, no title is displayed.figsize: tuple or None, optional (default=None) Figure's size, format as (x, y). If None, adapts size to the number of plots in the canvas.filename: str or None, optional (default=None) Name of the file. If None, the figure is not saved.display: bool, optional (default=True) Whether to render the plot. |
method delete(models=None)
Removes a model from the pipeline. If all models in the pipeline are removed, the metric is reset. Use this method to remove unwanted models or to free some memory before saving the instance.
| Parameters: |
models: str or sequence, optional (default=None)
Name of the models to clear from the pipeline. If None, clear all models. |
method distribution(column=0)
Compute the KS-statistic for various distributions against a column in the dataset. Missing values are ignored.
Tip
Use the plot_distribution method to plot the column's distribution.
| Parameters: |
column: int or str, optional (default=0)
Index or name of the column to get the statistics from. Only numerical columns are accepted. |
| Returns: |
stats: pd.DataFrame
Dataframe with the statistic results. |
method drop(columns)
Drop columns from the dataset.
Note
This approach is preferred over dropping columns from the
dataset directly through the property's @setter
since
the transformation is saved to atom's pipeline.
| Parameters: |
columns: int, str, slice or sequence
Names or indices of the columns to drop. |
method export_pipeline(model=None)
Export atom's pipeline to a sklearn's Pipeline. Optionally, you can add a model as final estimator. If the model needs feature scaling and there is no scaler in the pipeline, a StandardScaler is added. The returned pipeline is already fitted.
| Parameters: |
model: str or None, optional (default=None)
Name of the model to add as a final estimator to the pipeline. If None, no model is added. |
| Returns: |
pipeline: Pipeline
Pipeline in the current branch as a sklearn object. |
method log(msg, level=0)
Write a message to the logger and print it to stdout.
| Parameters: |
msg: str
Message to write to the logger and print to stdout.level: int, optional (default=0) Minimum verbosity level to print the message. |
method report(dataset="dataset", n_rows=None, filename=None)
Create an extensive profile analysis report of the data. The report is rendered
in HTML5 and CSS3. Note that this method can be slow for n_rows
> 10k.
| Parameters: |
dataset: str, optional (default="dataset")
Data set to get the report from.n_rows: int or None, optional (default=None) Number of (randomly picked) rows to process. None for all rows.filename: str or None, optional (default=None) Name to save the file with (as .html). None to not save anything. |
| Returns: |
report: ProfileReport
Created profile object. |
method reset_aesthetics()
Reset the plot aesthetics to their default values.
method reset_predictions()
Clear the prediction attributes from all models.
Use this method to free some memory before saving the trainer.
method save(filename=None, save_data=True)
Save the instance to a pickle file. Remember that the class contains the complete
dataset as attribute, so the file can become large for big datasets! To avoid this,
use save_data=False
.
| Parameters: |
filename: str or None, optional (default=None)
Name to save the file with. None or "auto" to save with the __name__ of the class.save_data: bool, optional (default=True) Whether to save the data as an attribute of the instance. If False, remember to add the data to ATOMLoader when loading the file. |
method save_data(filename=None, dataset="dataset")
Save the data in the current branch to a csv file.
| Parameters: |
filename: str or None, optional (default=None)
Name to save the file with. None or "auto" for default name.dataset: str, optional (default="dataset") Data set to save. |
method scoring(metric=None, dataset="test", **kwargs)
Print all the models' scoring for a specific metric.
| Parameters: |
metric: str or None, optional (default=None)
Name of the metric to calculate. Choose from any of sklearn's regression SCORERS.
If None, returns the models' final results (ignores the
dataset: str, optional (default="test")
Additional keyword arguments for the metric function. |
method stacking(models=None, estimator=None, stack_method="auto", passthrough=False)
Add a Stacking instance to the models in the pipeline.
| Parameters: |
models: sequence or None, optional (default=None)
Models that feed the stacking. If None, it selects all models depending on the current branch.estimator: str, callable or None, optional (default=None) The final estimator, which is used to combine the base estimators. If str, choose from ATOM's predefined models. If None, Ridge is selected.stack_method: str, optional (default="auto") Methods called for each base estimator. If "auto", it will try to invokepassthrough: bool, optional (default=False) When False, only the predictions of estimators are used as training data for the final estimator. When True, the estimator is trained on the predictions as well as the original training data. The passed dataset is scaled if any of the models require scaled features and they are not already. |
method stats()
Print basic information about the dataset.
method status()
Get an overview of the branches, models and errors in the current instance.
This method prints the same information as atom's __repr__ but will also
save it to the logger.
method voting(models=None, weights=None)
Add a Voting instance to the models in the pipeline.
| Parameters: |
models: sequence or None, optional (default=None)
Models that feed the voting. If None, it selects all models depending on the current branch.weights: sequence or None, optional (default=None) Sequence of weights (int or float) to weight the occurrences of predicted class labels (hard voting) or class probabilities before averaging (soft voting). Uses uniform weights if None. |
Data cleaning
ATOMRegressor provides data cleaning methods to scale your features and handle missing values, categorical columns and outliers. Calling on one of them will automatically apply the method on the dataset in the pipeline.
Tip
Use the report method to examine the data and help you determine suitable parameters for the data cleaning methods.
| scale | Scale the dataset. |
| clean | Applies standard data cleaning steps on the dataset. |
| impute | Handle missing values in the dataset. |
| encode | Encode categorical features. |
| prune | Prune outliers from the training set. |
method scale(strategy="standard")
Applies one of sklearn's scalers. Non-numerical columns are ignored (instead
of raising an exception). See the Scaler class.
method clean(prohibited_types=None, strip_categorical=True, maximum_cardinality=True, minimum_cardinality=True, drop_duplicates=False, missing_target=True, encode_target=None)
Applies standard data cleaning steps on the dataset. Use the parameters to choose which transformations to perform. The available steps are:
- Drop columns with prohibited data types.
- Drop categorical columns with maximal cardinality.
- Drop columns with minimum cardinality.
- Strip categorical features from white spaces.
- Drop duplicate rows.
- Drop rows with missing values in the target column.
- Encode the target column.
See Cleaner for a description of the parameters.
method impute(strat_num="drop", strat_cat="drop", min_frac_rows=None, min_frac_cols=None, missing=None)
Impute or remove missing values according to the selected strategy. Also removes
rows and columns with too many missing values. The imputer is fitted only on the
training set to avoid data leakage. Use the missing
attribute to customize what
are considered "missing values". See Imputer for a
description of the parameters. Note that since the Imputer can remove rows from both
the train and test set, the size of the sets may change after the tranformation.
method encode(strategy="LeaveOneOut", max_onehot=10, frac_to_other=None)
Perform encoding of categorical features. The encoding type depends on the number of unique values in the column:
- If n_unique=2, use Label-encoding.
- If 2 < n_unique <= max_onehot, use OneHot-encoding.
- If n_unique > max_onehot, use `strategy`-encoding.
Also replaces classes with low occurrences with the value other
in
order to prevent too high cardinality. Categorical features are defined as
all columns whose dtype.kind not in ifu
. Will raise an error if it encounters
missing values or unknown classes when transforming. The encoder is fitted only
on the training set to avoid data leakage. See Encoder
for a description of the parameters.
method prune(strategy="z-score", method="drop", max_sigma=3, include_target=False, **kwargs)
Prune outliers from the training set. The definition of outlier depends
on the selected strategy and can greatly differ from one each other.
Ignores categorical columns. Only outliers from the training set are pruned
in order to maintain the original distribution of samples in the test
set. Ignores categorical columns. See Pruner
for a description of the parameters.
Feature engineering
To further pre-process the data, you can create new non-linear features transforming the existing ones or, if your dataset is too large, remove features using one of the provided strategies.
| feature_generation | Create new features from combinations of existing ones. |
| feature_selection | Remove features according to the selected strategy. |
method feature_generation(strategy="DFS", n_features=None, generations=20, population=500, operators=None)
Use Deep feature Synthesis or a genetic algorithm to create new combinations
of existing features to capture the non-linear relations between the original
features. See FeatureGenerator for
a description of the parameters. Attributes created by the class are attached to
atom.
method feature_selection(strategy=None, solver=None, n_features=None, max_frac_repeated=1., max_correlation=1., **kwargs)
Remove features according to the selected strategy. Ties between features with equal scores are broken in an unspecified way. Also removes features with too low variance and finds pairs of collinear features based on the Pearson correlation coefficient. For each pair above the specified limit (in terms of absolute value), it removes one of the two. See FeatureSelector for a description of the parameters. Plotting methods and attributes created by the class are attached to atom.
Note
- When strategy="univariate" and solver=None, f_classif is used as default solver.
- When strategy is one of SFM, RFE, RFECV or SFS and the solver is one of
ATOM's predefined models, the
algorithm will automatically select the classifier (no need to add
_class
to the solver). - When strategy is one of SFM, RFE, RFECV or SFS and solver=None, ATOM will use the winning model (if it exists) as solver.
- When strategy is RFECV or SFS, ATOM will use the metric in the pipeline (if it exists) as the scoring parameter (only if not specified).
Training
The training methods are where the models are fitted to the data and their performance is evaluated according to the selected metric. There are three methods to call the three different training approaches in ATOM. All relevant attributes and methods from the training classes are attached to atom for convenience. These include the errors, winner and results attributes, as well as the models, and the prediction and plotting methods.
| run | Fit the models to the data in a direct fashion. |
| successive_halving | Fit the models to the data in a successive halving fashion. |
| train_sizing | Fit the models to the data in a train sizing fashion. |
method run(models, metric=None, greater_is_better=True, needs_proba=False, needs_threshold=False, n_calls=10, n_initial_points=5, est_params=None, bo_params=None, bagging=0)
Runs a DirectRegressor instance.
method successive_halving(models, metric=None, greater_is_better=True, needs_proba=False, needs_threshold=False, skip_runs=0, n_calls=0, n_initial_points=5, est_params=None, bo_params=None, bagging=0)
Runs a SuccessiveHalvingRegressor instance.
method train_sizing(models, metric=None, greater_is_better=True, needs_proba=False, needs_threshold=False, train_sizes=np.linspace(0.2, 1.0, 5), n_calls=0, n_initial_points=5, est_params=None, bo_params=None, bagging=0)
Runs a TrainSizingRegressor instance.
Example
from sklearn.datasets import load_boston
from atom import ATOMRegressor
X, y = load_boston(return_X_y=True)
# Initialize class
atom = ATOMRegressor(X, y, logger="auto", n_jobs=2, verbose=2)
# Apply data cleaning methods
atom.prune(strategy="z-score", method="min_max", max_sigma=2, include_target=True)
# Fit the models to the data
atom.run(
models=["OLS", "BR", "CatB"],
metric="MSE",
n_calls=25,
n_initial_points=10,
bo_params={"cv": 1},
bagging=4,
)
# Analyze the results
print(f"The winning model is: {atom.winner.name}")
print(atom.results)
# Make some plots
atom.plot_errors(figsize=(9, 6), filename="errors.png")
atom.CatB.plot_feature_importance(filename="catboost_feature_importance.png")
# Run an extra model
atom.run(
models="MLP",
metric="MSE",
n_calls=25,
n_initial_points=10,
bo_params={"cv": 1},
bagging=4,
)
# Get the predictions for the best model on new data
predictions = atom.predict(X_new)


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IHT.com Exclusive Luxury Hotel Reservation Service
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An Argentine depositor protesting IMF-backed reforms in front of the Bank Boston in Buenos Aires this week.
AP Photo / Walter Astrada
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A recent briefing given to a top Pentagon advisory board described Saudi Arabia as an enemy of the United States, and recommended that U.S. officials give it an ultimatum to stop backing terrorism or face seizure of its oil fields and its financial assets invested in the United States.
Thomas E. Ricks
|
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Until last year, Argentines were part of the richest, best-educated and most cultured nation in Latin America. The poor here lived with more dignity than their equals anywhere else in the region. Argentina was, as the Argentines liked to say, very civilized. Not anymore.
Anthony Faiola
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The dollar, in retreat for much of this year, is making a tentative comeback, helped by its allure as a bulwark of safety in turbulent times. Though analysts say the danger of a renewed recession in the United States is growing, potential political and financial hazards outside America are piling up even faster.
Eric Pfanner
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Rising voter concern about the future of the United States, driven by an unsteady economy and unrest on Wall Street, is stirring nervousness among some Republicans and lifting confidence among Democratic leaders that their party could capture control of the House in November.
Adam Nagourney
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For countless tourists over two and a half centuries, the Trevi Fountain has been a source of awe and a wellspring of hope, promising another visit to the Eternal City, and a wish fulfilled, to anyone who tosses a coin into its gurgling waters. For Roberto Cercelletta, it has been a lucrative pool of clandestine profit. What tourists gave, Cercelletta took away.
Frank Bruni
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U.S. Stock Quotes
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Capacitated Vehicle Routing Problem¶
This repository contains algorithms to solve the CVRP (Capacitated Vehicle Routing Problem) in C++¶
Algorithms:¶
- Greedy Solution
- Local Search (Run on each vehicle separately; search restricted to the selected vehicle’s initial route) (LS)
- Local Search (LS)
- Tabu Search (TS)
- Genetic Algorithm (GA)
- Simulated Annealing (SA)
- Hybrid Algorithms (Using the solution found using one of the algorithms above as the input/starting point for another) (HA)
To build and run:¶
git clone https://github.com/vss2sn/cvrp.git
cd cvrp
mkdir build
cd build
cmake .. && make -j4
./cvrp
Table of contents:¶
Code Overview:¶
- The code contains
Problem
,Solution
andVehicle
classes. Each algorithm implementation has its own class and inherits theSolution
class. - The problem is setup using the
Problem
class which specifies the number of nodes (centres/dropoff points), maximum demand, number of vehicles, their capacity, the grid range and the type of distribution. The demand for each centre as well as its location is randomly generated. - A base class called
Solution
has been created to store the basic elements of the solution in a user friendly format. This includes avector
of instances of theVehicle
class. - The
Vehicle
class stores the vehicle id, the route it takes, the total capacity, the number of units still left in the vehicle, and the cost associated with the vehicle’s route. The<<
operator is overloaded to show the status of the node and vehicle respectively.PrintVehicleRoute()
prints only the route of the vehicle. - The
Solution
class also contains a virtual method calledSolve()
. Each algorithm class overrides theSolve()
method. - The
Solution
class also contains a method calledPrintSolution(option)
with the an input option (option
) to print vehicles’ statuses or routes in addition to the total cost and validity of the solution.
Documentation:¶
- Documentation can be found on GitHub pages.
- It has been created using
Doxygen
, and pip3 packagesSphinx
(sphinx==1.8.31
),Breathe
(breathe==4.12.0),Exhale
(exhale==0.2.2
),Read the Docs
Sphinx Theme (sphinx_rtd_theme==0.4.3
) andm2r
.
Overview of Algorithm Implementations¶
For a brief overview on the implementations of the algorithms, please refer to this document.
Notes:¶
- The documentation for private functions (such as operators in the
GASolution
class) has been made available to aid understanding. - Custom hybrid algorithms, that involve feeding in the solution of 1 algorithm to another can easily be implemented, as the structure allows the extraction of solution from the algorithm classes. An example is shown at the end of
main.cpp
.
Visualization:¶
This repository (optionally) uses SFML
for visualization. It can be installed using sudo apt install libsfml-dev
.
To run with SFML
, include the header file graphics_utils.hpp
in main.cpp
, and use the function DisplaySolution(<solution>)
, passing in the solution to e displayed, and build the code with cmake .. -DDISPLAY_SOLUTION=ON
, or modify its value in ProjectSpecificSettings.cmake
.
TODOs:¶
- Read in problem from a file.
- Consider adding plot of best solution cost vs time.
- Consider adding savings algorithm
- Consider adding maximum distance constraint
- Consider modifying to allow heterogeneous vehicles.
- Consider adding multiple knapsack solver for initial solution if greedy solution fails, as well as a sanity check to check whether demand exceeds supply.
- Consider adding in a clustering algorithm to break large problems into a
vector
of small problems. - Refactor genetic algorithm to use a struct


Raccoon City Creative Team
Andrew Fields
Andrew enjoys hanging off skyscrapers and cleaning windows. Oh wait, no he doesn’t. That’s why he’s here learning to code. So he never has to scrub bird poop off another building.
Click Here To Make Friends!
Elijah Bruce
Elijah traveled around a bit before making his way to Seattle and Code Fellows. He has dreams of marrying his graphic design training with the skills that Code Fellows teaches so he can build astounding websites for small businesses and non-profits. He also wants to explore all this nature so near to Seattle; maybe even come across a Raccoon or two.
Click Here To Make Friends!
Joyce Liao
Joyce is very happy to work with the team to create Raccoon City. She used to encounter the raccoon family when walking from Seattle Center to the uphill parking. Joyce is aspired to spread hacker sparkles in the eventide of this northwest city.
Click Here To Make Friends!
Katherine Smith
Katherine used to be a veterinary technician. She has worked with dogs, cats, ferrets, rabbits, guinea pigs, chinchillas, hedgehogs, sugar gliders, birds, snakes, iguanas, chameleons, turtles, bearded dragons, horses, pigs, cows, goats, wallabies… But it was this project that gave her the chance to finally work with
!Click Here To Make Friends!


Overview
O-RAN designs an intelligent network controller (RAN Intelligent Controller, RIC) for supporting more efficient and real-time control of the Radio Access Network (RAN). To achieve this goal, O-RAN formulates a series of specifications for its computing architecture and provides optimal designs for different applications and different types of algorithms.
Zero-Shot Learning (ZSL)
In the era of 5G and beyond, O-RAN RIC (Open Radio Access Network RAN Intelligent Controller) faces a daunting challenge: how to train Artificial Intelligence/Machine Learning (AI/ML) models without real datasets. The scarcity of such datasets arises from multifaceted issues including strict privacy guidelines, stringent regulatory constraints, commercial sensitivities, and the multifaceted intricacy of the Radio Access Network (RAN). To navigate this complex landscape, the concept of Zero-Shot Learning (ZSL) emerges as a beacon of hope. ZSL is a machine learning paradigm where a model is trained on one set of classes but is then asked to recognize objects from unseen classes. In the context of O-RAN RIC, this translates to training on one set of network conditions (like URLLC - Ultra Reliable Low Latency Communications, eMBB - enhanced Mobile Broadband, and mMTC - massive Machine Type Communications) and then testing the model under entirely different, previously unseen network scenarios. There are three methods that leverage ZSL to train AI/ML models in O-RAN RIC: 1) O-RAN Simulator: An avant-garde open-source platform that furnishes a realistic RAN simulation environment. This simulator lets a reinforcement learning agent immerse, make decisions, and consequently acquire optimal policies, all while being under the aegis of a controlled environment. 2) O-RAN Digital Twin: This digital counterpart provides a precise virtual depiction of the RAN. It's not just a mirror reflection but a dynamic model that lets agents derive policies which align seamlessly with the real network's idiosyncrasies. 3) Generative Data: Treading a different path, this method crafts synthetic data that mirrors genuine network data. Not only does this preserve the sanctity of privacy, but it also allows the Deep Reinforcement Learning (DRL) agent to imbibe from a plethora of diverse scenarios. In conclusion, using ZSL with these avant-garde methods is revolutionizing the way we approach AI/ML training in O-RAN RIC. Despite the absence of real datasets, these approaches adeptly circumvent issues of privacy, commercial sensitivities, and the labyrinthine complexity intrinsic to RAN, paving the way for a more robust and resilient network future.
Reference


--- title: "The Effort Report Podcast" authors: - "Alvin Thomas" date: '2017-04-27T21:12:14-05:00' tags: - Academia - Podcast comments: yes ---
I wanted to give a quick shoutout to the Effort Report Podcast hosted by Roger Peng and Elizabeth Matsui. Both hosts are faculty members at Johns Hopkins (School of Public Health & School of Medicine) and they discuss issues that any budding academic should consider.
I’ve listened to every podcast (some multiple times) and I’ve even joined the conversation on Twitter.


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It’s often useful to perform the same operation on multiple columns, but copying and pasting is both tedious and error prone:
(If you’re trying to compute mean(a, b, c, d)
for each row, instead see vignette("rowwise")
)
This vignette will introduce you to the across()
function, which lets you rewrite the previous code more succinctly:
We’ll start by discussing the basic usage of across()
, particularly as it applies to summarise()
, and show how to use it with multiple functions. We’ll then show a few uses with other verbs. We’ll finish off with a bit of history, showing why we prefer across()
to our last approach (the _if()
, _at()
and _all()
functions) and how to translate your old code to the new syntax.
across()
has two primary arguments:
The first argument, .cols
, selects the columns you want to operate on. It uses tidy selection (like select()
) so you can pick variables by position, name, and type.
The second argument, .fns
, is a function or list of functions to apply to each column. This can also be a purrr style formula (or list of formulas) like ~ .x / 2
. (This argument is optional, and you can omit it if you just want to get the underlying data; you’ll see that technique used in vignette("rowwise")
.)
Here are a couple of examples of across()
in conjunction with its favourite verb, summarise()
. But you can use across()
with any dplyr verb, as you’ll see a little later.
starwars %>%
summarise(across(where(is.character), ~ length(unique(.x))))
#> # A tibble: 1 x 8
#> name hair_color skin_color eye_color sex gender homeworld species
#> <int> <int> <int> <int> <int> <int> <int> <int>
#> 1 87 13 31 15 5 3 49 38
starwars %>%
group_by(species) %>%
filter(n() > 1) %>%
summarise(across(c(sex, gender, homeworld), ~ length(unique(.x))))
#> # A tibble: 9 x 4
#> species sex gender homeworld
#> <chr> <int> <int> <int>
#> 1 Droid 1 2 3
#> 2 Gungan 1 1 1
#> 3 Human 2 2 16
#> 4 Kaminoan 2 2 1
#> # … with 5 more rows
starwars %>%
group_by(homeworld) %>%
filter(n() > 1) %>%
summarise(across(where(is.numeric), ~ mean(.x, na.rm = TRUE)))
#> # A tibble: 10 x 4
#> homeworld height mass birth_year
#> <chr> <dbl> <dbl> <dbl>
#> 1 Alderaan 176. 64 43
#> 2 Corellia 175 78.5 25
#> 3 Coruscant 174. 50 91
#> 4 Kamino 208. 83.1 31.5
#> # … with 6 more rows
Because across()
is usually used in combination with summarise()
and mutate()
, it doesn’t select grouping variables in order to avoid accidentally modifying them:
df <- data.frame(g = c(1, 1, 2), x = c(-1, 1, 3), y = c(-1, -4, -9))
df %>%
group_by(g) %>%
summarise(across(where(is.numeric), sum))
#> # A tibble: 2 x 3
#> g x y
#> <dbl> <dbl> <dbl>
#> 1 1 0 -5
#> 2 2 3 -9
You can transform each variable with more than one function by supplying a named list of functions or lambda functions in the second argument:
min_max <- list(
min = ~min(.x, na.rm = TRUE),
max = ~max(.x, na.rm = TRUE)
)
starwars %>% summarise(across(where(is.numeric), min_max))
#> # A tibble: 1 x 6
#> height_min height_max mass_min mass_max birth_year_min birth_year_max
#> <int> <int> <dbl> <dbl> <dbl> <dbl>
#> 1 66 264 15 1358 8 896
starwars %>% summarise(across(c(height, mass, birth_year), min_max))
#> # A tibble: 1 x 6
#> height_min height_max mass_min mass_max birth_year_min birth_year_max
#> <int> <int> <dbl> <dbl> <dbl> <dbl>
#> 1 66 264 15 1358 8 896
Control how the names are created with the .names
argument which takes a glue spec:
starwars %>% summarise(across(where(is.numeric), min_max, .names = "{.fn}.{.col}"))
#> # A tibble: 1 x 6
#> min.height max.height min.mass max.mass min.birth_year max.birth_year
#> <int> <int> <dbl> <dbl> <dbl> <dbl>
#> 1 66 264 15 1358 8 896
starwars %>% summarise(across(c(height, mass, birth_year), min_max, .names = "{.fn}.{.col}"))
#> # A tibble: 1 x 6
#> min.height max.height min.mass max.mass min.birth_year max.birth_year
#> <int> <int> <dbl> <dbl> <dbl> <dbl>
#> 1 66 264 15 1358 8 896
If you’d prefer all summaries with the same function to be grouped together, you’ll have to expand the calls yourself:
starwars %>% summarise(
across(c(height, mass, birth_year), ~min(.x, na.rm = TRUE), .names = "min_{.col}"),
across(c(height, mass, birth_year), ~max(.x, na.rm = TRUE), .names = "max_{.col}")
)
#> # A tibble: 1 x 6
#> min_height min_mass min_birth_year max_height max_mass max_birth_year
#> <int> <dbl> <dbl> <int> <dbl> <dbl>
#> 1 66 15 8 264 1358 896
(One day this might become an argument to across()
but we’re not yet sure how it would work.)
We cannot however use where(is.numeric)
in that last case because the second across()
would pick up the variables that were newly created (“min_height”, “min_mass” and “min_birth_year”).
We can work around this by combining both calls to across()
into a single expression that returns a tibble:
starwars %>% summarise(
tibble(
across(where(is.numeric), ~min(.x, na.rm = TRUE), .names = "min_{.col}"),
across(where(is.numeric), ~max(.x, na.rm = TRUE), .names = "max_{.col}")
)
)
#> # A tibble: 1 x 6
#> min_height min_mass min_birth_year max_height max_mass max_birth_year
#> <int> <dbl> <dbl> <int> <dbl> <dbl>
#> 1 66 15 8 264 1358 896
Alternatively we could reorganize results with relocate()
:
If you need to, you can access the name of the “current” column inside by calling cur_column()
. This can be useful if you want to perform some sort of context dependent transformation that’s already encoded in a vector:
Be careful when combining numeric summaries with where(is.numeric)
:
df <- data.frame(x = c(1, 2, 3), y = c(1, 4, 9))
df %>%
summarise(n = n(), across(where(is.numeric), sd))
#> n x y
#> 1 NA 1 4.041452
Here n
becomes NA
because n
is numeric, so the across()
computes its standard deviation, and the standard deviation of 3 (a constant) is NA
. You probably want to compute n()
last to avoid this problem:
Alternatively, you could explicitly exclude n
from the columns to operate on:
Another approach is to combine both the call to n()
and across()
in a single expression that returns a tibble:
So far we’ve focused on the use of across()
with summarise()
, but it works with any other dplyr verb that uses data masking:
Rescale all numeric variables to range 0-1:
For some verbs, like group_by()
, count()
and distinct()
, you can omit the summary functions:
Find all distinct
Count all combinations of variables with a given pattern:
across()
doesn’t work with select()
or rename()
because they already use tidy select syntax; if you want to transform column names with a function, you can use rename_with()
.
We cannot directly use across()
in filter()
because we need an extra step to combine the results. To that end, filter()
has two special purpose companion functions:
if_any()
keeps the rows where the predicate is true for at least one selected column:starwars %>%
filter(if_any(everything(), ~ !is.na(.x)))
#> # A tibble: 87 x 14
#> name height mass hair_color skin_color eye_color birth_year sex gender
#> <chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr> <chr>
#> 1 Luke… 172 77 blond fair blue 19 male mascu…
#> 2 C-3PO 167 75 <NA> gold yellow 112 none mascu…
#> 3 R2-D2 96 32 <NA> white, bl… red 33 none mascu…
#> 4 Dart… 202 136 none white yellow 41.9 male mascu…
#> # … with 83 more rows, and 5 more variables: homeworld <chr>, species <chr>,
#> # films <list>, vehicles <list>, starships <list>
if_all()
keeps the rows where the predicate is true for all selected columns:starwars %>%
filter(if_all(everything(), ~ !is.na(.x)))
#> # A tibble: 29 x 14
#> name height mass hair_color skin_color eye_color birth_year sex gender
#> <chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr> <chr>
#> 1 Luke… 172 77 blond fair blue 19 male mascu…
#> 2 Dart… 202 136 none white yellow 41.9 male mascu…
#> 3 Leia… 150 49 brown light brown 19 fema… femin…
#> 4 Owen… 178 120 brown, gr… light blue 52 male mascu…
#> # … with 25 more rows, and 5 more variables: homeworld <chr>, species <chr>,
#> # films <list>, vehicles <list>, starships <list>
Find all rows where no variable has missing values:
starwars %>% filter(across(everything(), ~ !is.na(.x)))
#> # A tibble: 29 x 14
#> name height mass hair_color skin_color eye_color birth_year sex gender
#> <chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr> <chr>
#> 1 Luke… 172 77 blond fair blue 19 male mascu…
#> 2 Dart… 202 136 none white yellow 41.9 male mascu…
#> 3 Leia… 150 49 brown light brown 19 fema… femin…
#> 4 Owen… 178 120 brown, gr… light blue 52 male mascu…
#> # … with 25 more rows, and 5 more variables: homeworld <chr>, species <chr>,
#> # films <list>, vehicles <list>, starships <list>
_if
, _at
, _all
Prior versions of dplyr allowed you to apply a function to multiple columns in a different way: using functions with _if
, _at
, and _all()
suffixes. These functions solved a pressing need and are used by many people, but are now superseded. That means that they’ll stay around, but won’t receive any new features and will only get critical bug fixes.
across()
?Why did we decide to move away from these functions in favour of across()
?
across()
makes it possible to express useful summaries that were previously impossible:
across()
reduces the number of functions that dplyr needs to provide. This makes dplyr easier for you to use (because there are fewer functions to remember) and easier for us to implement new verbs (since we only need to implement one function, not four).
across()
unifies _if
and _at
semantics so that you can select by position, name, and type, and you can now create compound selections that were previously impossible. For example, you can now transform all numeric columns whose name begins with “x”: across(where(is.numeric) & starts_with("x"))
.
across()
doesn’t need to use vars()
. The _at()
functions are the only place in dplyr where you have to manually quote variable names, which makes them a little weird and hence harder to remember.
across()
?It’s disappointing that we didn’t discover across()
earlier, and instead worked through several false starts (first not realising that it was a common problem, then with the _each()
functions, and most recently with the _if()
/_at()
/_all()
functions). But across()
couldn’t work without three recent discoveries:
You can have a column of a data frame that is itself a data frame. This is something provided by base R, but it’s not very well documented, and it took a while to see that it was useful, not just a theoretical curiosity.
We can use data frames to allow summary functions to return multiple columns.
We can use the absence of an outer name as a convention that you want to unpack a data frame column into individual columns.
Fortunately, it’s generally straightforward to translate your existing code to use across()
:
Strip the _if()
, _at()
and _all()
suffix off the function.
Call across()
. The first argument will be:
_if()
, the old second argument wrapped in where()
._at()
, the old second argument, with the call to vars()
removed._all()
, everything()
.The subsequent arguments can be copied as is.
For example:
df %>% mutate_if(is.numeric, mean, na.rm = TRUE)
# ->
df %>% mutate(across(where(is.numeric), mean, na.rm = TRUE))
df %>% mutate_at(vars(c(x, starts_with("y"))), mean)
# ->
df %>% mutate(across(c(x, starts_with("y")), mean, na.rm = TRUE))
df %>% mutate_all(mean)
# ->
df %>% mutate(across(everything(), mean))
There are a few exceptions to this rule:
rename_*()
and select_*()
follow a different pattern. They already have select semantics, so are generally used in a different way that doesn’t have a direct equivalent with across()
; use the new rename_with()
instead.
Previously, filter_*()
were paired with the all_vars()
and any_vars()
helpers. The new helpers if_any()
and if_all()
can be used inside filter()
to keep rows for which the predicate is true for at least one, or all selected columns:
df <- tibble(x = c("a", "b"), y = c(1, 1), z = c(-1, 1))
# Find all rows where EVERY numeric variable is greater than zero
df %>% filter(if_all(where(is.numeric), ~ .x > 0))
#> # A tibble: 1 x 3
#> x y z
#> <chr> <dbl> <dbl>
#> 1 b 1 1
# Find all rows where ANY numeric variable is greater than zero
df %>% filter(if_any(where(is.numeric), ~ .x > 0))
#> # A tibble: 2 x 3
#> x y z
#> <chr> <dbl> <dbl>
#> 1 a 1 -1
#> 2 b 1 1
When used in a mutate()
, all transformations performed by an across()
are applied at once. This is different to the behaviour of mutate_if()
, mutate_at()
, and mutate_all()
, which apply the transformations one at a time. We expect that you’ll generally find the new behaviour less surprising:


Suppose you have a list of \(n+1\) given point \((x_i, y_i)\) with \(i \in \{0, \dots, n\}\) and \(\forall i,j \in \{0, \dots, n\}: i \neq j \Rightarrow x_i \neq x_j\).
Now you want to find a polynomial \(\displaystyle p(x) = \sum_{i=0}^n a_i \cdot x^i\) that goes through all of the given points. This means:
\(\forall i \in \{0, \dots, n\}: p(x_i) = y_i\)
Existence and uniqueness
Theorem: When you have a list of \(n+1\) points with mutually different \(x_i\) there is exactly one polynom of degree \(\leq n\) that fits those points.
Proof:
You can formulate a linear system of equations:
A matrix of the form of \(A\) is called Vandermonde matrix. When the data points \(x_i\) are mutually different, it is known that the Vandermonde matrix is invertible (source).
This means, the solution \((a_0 \dots a_n)^T\) of this linear equation gives the polynom \(p(x) = \sum_{i=0}^n a_i x^i\)
So the solution exists and is unique \(\blacksquare\)
Straight forward interpolating polynomials
For this algorithm, I'll find the polynomial in its monomial from \(p(x) = \sum_{i=0}^n a_i x^i\). I'll use the matrix \(A\) from section "Uniqueness".
You might want to take a look at my article about Gaussian elimination.
#!/usr/bin/env python
# -*- coding: utf-8 -*-
def pprintGaus(A):
""" Pretty print a n×n matrix with a result vector n×1. """
n = len(A)
for i in range(0, n):
line = ""
for j in range(0, n + 1):
line += str(A[i][j]) + "\t"
if j == n - 1:
line += "| "
print(line)
print("")
def pprintPolynomial(A):
""" Pretty print a polynomial. """
line = ""
for i in range(len(x) - 1, -1, -1):
if x[i] != 0:
if i == 0:
line += "+" + str(x[i])
else:
if x[i] == 1:
line += "+ x^" + str(i) + "\t"
elif x[i] == -1:
line += "- x^" + str(i) + "\t"
else:
line += "+" + str(x[i]) + "·x^" + str(i) + "\t"
print(line)
def gauss(A):
""" Solve a linear sysem of equations given by a n×n matrix
with a result vector n×1. """
n = len(A)
for i in range(0, n):
# Search for maximum in this column
maxEl = abs(A[i][i])
maxRow = i
for k in range(i + 1, n):
if abs(A[k][i]) > maxEl:
maxEl = A[k][i]
maxRow = k
# Swap maximum row with current row (column by column)
for k in range(i, n + 1):
tmp = A[maxRow][k]
A[maxRow][k] = A[i][k]
A[i][k] = tmp
# Make all rows below this one 0 in current column
for k in range(i + 1, n):
c = -A[k][i] / A[i][i]
for j in range(i, n + 1):
if i == j:
A[k][j] = 0
else:
A[k][j] += c * A[i][j]
# Solve equation Ax=b for an upper triangular matrix A
x = [0 for i in range(n)]
for i in range(n - 1, -1, -1):
x[i] = A[i][n] / A[i][i]
for k in range(i - 1, -1, -1):
A[k][n] -= A[k][i] * x[i]
return x
def setGauss(points):
""" Create a system of equations for gaussian elimination from
a set of points. """
n = len(points) - 1
A = [[0 for i in range(n + 2)] for j in range(n + 1)]
for i in range(n + 1):
x = points[i]["x"]
for j in range(n + 1):
A[i][j] = x ** j
A[i][n + 1] = points[i]["y"]
return A
if __name__ == "__main__":
from fractions import Fraction
# Read input data
points = []
points.append({"x": Fraction(-1), "y": Fraction(1)})
points.append({"x": Fraction(1), "y": Fraction(1)})
points.append({"x": Fraction(2), "y": Fraction(2)})
A = setGauss(points)
# Print input
pprintGaus(A)
# Calculate solution
x = gauss(A)
# Print result
pprintPolynomial(x)
It is also interesting to get the value of \(p(x)\) at any given point \(x \in \mathbb{R}\):
def evaluatePolynomial(p, x):
y = 0
xi = 1
for i, a in enumerate(p):
y += a * xi
xi *= x
return y
Time complexity to get the polynomial: \(\frac{1}{3} n^3 + \mathcal{O}(n^2)\) (where \(n\) is the number of points) Space complexity for the polynomial: \(\mathcal{O}(n^2)\) (where \(n\) is the number of points)
Time complexity to evaluate the value of \(p(x)\) for any \(x \in \mathbb{R}\): \(\mathcal{O}(n)\).
Polynomials
\(\displaystyle \mathbb{R}_n[X] := \left \{p:\mathbb{R} \rightarrow \mathbb{R} | p(x) = \sum_{i=0}^n a_i \cdot x^i \text{ with } a_0, \dots, a_n \in \mathbb{R} \right \}\)
So \(\mathbb{R}_n[X]\) are all polynomials with real coefficients and degree not higher than latex]n\(. $\mathbb{R}_n[X]\) forms a vector space for \(n \in \mathbb{N}_0\). The degree of that vector space is \(n+1\).
What does that mean?
This means you can use different bases for polynomials. The base we usually use for \(\mathbb{R}_n[X]\) is \(\{x^0, x^1, x^2, x^3, \dots\}\), but you can switch the base.
Lagrange interpolation
Define \(n+1\) polynomials like this:
\(\displaystyle L_{i}(x) := \prod_{j=0 \atop j \neq i}^n \frac{x-x_j}{x_i - x_j}\)
This means:
\(\displaystyle L_{i}(x_i) := \prod_{j=0 \atop j \neq i}^n \frac{x_i-x_j}{x_i - x_j} = 1\)
and
\(\displaystyle L_{i}(x_p) := \prod_{j=0 \atop j \neq i}^n \frac{x_p-x_j}{x_i - x_j} = 0 \;\; p \in \{0, \dots, n\} \setminus \{i\}\)
So the polynomial \(y_i \cdot L_{i}(x)\) fits the point \((x_i, y_i)\) and is zero for all other points. This implies that \(\displaystyle p(x) = \sum_{i=0}^n y_i \cdot L_i(x)\) is an interpolation of our data points. The degree of \(p(x)\) is not higher than \(n\). You can see that easily when you take a look at the definition of \(p(x)\) and \(L_i(x)\).
The polynomials \(L_i(x)\) form another base for \(\mathbb{R}_n[X]\).
Lagranges way to interpolate polynomials can be implemented like this:
def lagrangeInterpolation(points):
p = []
for i in range(len(points)):
Li = {"y": points[i]["y"], "polynomial": []}
for j in range(len(points)):
if j == i:
continue
Li["polynomial"].append(
{"sub": points[j]["x"], "divisor": points[i]["x"] - points[j]["x"]}
)
p.append(Li)
return p
def evaluateLagrangePolynomial(p, x):
y = 0
for Li in p:
prod = 1
for term in Li["polynomial"]:
prod *= (x - term["sub"]) / term["divisor"]
y += Li["y"] * prod
return y
Time complexity to get the polynomial: \(n^2 + \mathcal{O}(n)\) (where \(n\) is the number of points) Space complexity for the polynomial: \(\mathcal{O}(n^2)\) (where \(n\) is the number of points)
Time complexity to evaluate the value of \(p(x)\) for any \(x \in \mathbb{R}\): \(\mathcal{O}(n^2)\).
Newton interpolation
Define
\(\displaystyle N_i(x) := \prod_{j=0}^{i-1} (x-x_j)\)
So you know that
\(N_i(x) = 0 \Leftrightarrow \exists p \in \{1, \dots, i\}: x = x_{i-p}\)
So your polynomial is
\(\displaystyle p(x) = \sum_{i=0}^n c_i \cdot N_i(x)\)
for the correct \(c_i\). You can do this by solving the following system of equations. Please note that you don't have to store that matrix to get those \(c_i\), divided differences are better.
You can get this lower triangular matrix like this:
def getGaussSystemForNewton(points):
n = len(points) - 1
A = [[0 for i in range(n + 2)] for j in range(n + 1)]
for j in range(0, n + 2):
for i in range(j, n + 1):
if j == 0:
A[i][j] = 1
else:
A[i][j] = A[i][j - 1] * (points[i]["x"] - points[j - 1]["x"])
if j == n + 1:
for i in range(0, n):
A[i][j] = points[i]["y"]
return A
From my previous posts about solving equations of upper triangular matrices and lower unitriangular matrices you know that the space complexity of this is in \(\Theta(n^2)\).
According to Wikipedia, you can use Horner's method to evaluate this Polynom in \(\mathcal{O}(n)\). But I really don't want to implement this today.
Interactive example
- Click to add points.
- Ctrl+Click to remove point.
- You can enter a function that is valid JavaScript in the text field.


Digital Signatures
The image and the text below will become a link to digital signature properties:
Click to open the digital signature properties


Boil water in pot. Open Top Ramen package. Remove spice package from Top Ramen package. Pour ramen noodles into boiling water. Open spice package. Pour spice package into water. By the time you have managed to do that, your noodles are probably done cooking.


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Reflect and Respond
When we’re little, our moms seem like know-it-alls. They are the ones in control. They seem to hold all the cards, and they always know what they’re doing.
On the first night home from the hospital with our first baby, I kicked all the relatives out of the house, imagining our sweet little family of three bonding all alone together. Let’s just say that night convinced me of how much I had to learn.
The surprises definitely didn’t end in the newborn phase. Lately I’m baffled that I thought parenting one tiny baby was so difficult.  Someday I’m sure I will have two teenagers and look back and wonder what all the fuss was about when they were tiny.
Parenting is not for the faint of heart! Running a multimillion-dollar corporation, or a country, is less challenging on some days (not to mention nights) than parenting small children.
Not only do our children start out in the world physically helpless, dependent on us for their needs of nourishment and safety and shelter; they also are a behavioral and social blank slate. They need to be indoctrinated into the family’s culture: the expectations of how family members are to treat one another, behave at the table, speak or wait to be called on, and generally function in this particular group of people. We begin teaching our children about the family’s culture from the first day they are born. Even newborns receive messages about how structured or chaotic, loud or quiet, loving or distant the family’s culture will be. Families teach some of the same lessons, but each of our families is unique in some ways, with its own values, standards, and habits that make it special. Your own family, for example, may have communicated the importance of getting an education, appreciating music or nature, serving the poor, or respecting the wisdom of elders.
Do you have memories of your parents instilling lessons about the behavior and beliefs your family expected of you? Recall one memory and describe it below.


GetCurrentDirectory()
Syntax
Result$ = GetCurrentDirectory()Description
Returns the current directory for the program.
Parameters
None.
Return value
Returns the full path of the current directory. It will end with a directory separator ('\' on Windows and '/' otherwise).
It's very unlikely, but if this function fails, it will return an empty string.
Remarks
All files accesses are relative to this directory, when no absolute path is specified. To change the current directory, use SetCurrentDirectory().
See Also
SetCurrentDirectory(), GetHomeDirectory(), GetTemporaryDirectory()
Supported OS
All


Guest: Jose Antonio, PhD Link
International Society of Sports Nutrition Conference in Clearwater, FL - June 9 -11 2016 Link
Optimizing nutrition for endurance sports
Benefits of caffeine extend to all sports
Beta Alanine Supplementation: One of the best ergogenic aids athletes don’t know about
Optimizing strength and power output
Creatine Monohydrate: “The single most effective sports supplement ever”
The post workout nutrient timing: what’s the truth?
3 Big Myths in Sports Nutrition
“Eating a lot of protein is bad for your kidneys”
“Kids shouldn’t take supplements”
“You need to clean up your diet before taking supplements”
The diet that works the best is the diet you will be compliant with
The Dirty Secret of the Super Lean - they are hungry all the time
National Strength and Conditioning Association Textbooks Link
The Essentials of Sports Nutrition and Supplements Link
yann le meuron Twitter Link
How to perform your best the day of an event?
Jose Antonio, PhD on Twitter Link
International Society for Sports Nutrition Link
13th annual Sports Nutrition Conference in Clearwater, FL - June 9 -11 2016 Link


| Witamol 118 General Information
-
| Synonyms: |
Di-n-C8-C10-alkyl phthalate |
| CAS Registry No.: |
71662-46-9 |
| Chemical Category: |
Phthalates |
Usage and Performance Characteristics
-
| Manufacturer: |
OXENO Olefinchemie GmbH |
| Outstanding Properties: |
High UV & weather resistance, cold flexibility |
| Used for Polymeric Materials: |
PVC |
| Used for Products: |
Roofing, sythetic leather |
Physical Properties
-
| Aggregate State at 25°C: |
Liquid |
| Density, kg/m3: |
967 (20°C) |
| Dynamic Viscosity, cP: |
44 (20°C) |
Optical Properties
-
| Color, Platinum-Cobalt Scale: |
<50 |
Environmental Parameters
-
| Octanol-Water Partition Coeff., log Kow: |
9.03 |
Plasticizers Database (2nd Electronic Edition) Copyright © 2004 by ChemTec Publishing |


Trigonometric Waves
Sine Wave ↓
Canvas is not supported in your browser!!!
Sine (Red), Cos(Blue) and Tan(Green) ↓
Canvas is not supported in your browser!!!
View code on Github


Mini Assignment 3: Shaders (30 Points)
Due Wednesday 10/2/2019 at 11:59PM
Overview
The purpose of this assignment is to familiarize students with the the GLSL programming paradigms, particularly vertex and fragment shaders. This will be the final warmup assignment before the first large scale asssignment on ray tracing.
Getting Started
- Click here to download the repository of skeleton code for this assignment.
- If you haven't already, follow the directions to get the local Python web server working on your computer (click here to see these directions in the last assignment).
Submission Instructions
You will submit all of your fragment shaders to Canvas when you are finished. Please also submit aREADME.txt
file with both submissions with the following information each time:
- Your name
- One of the two statements below
- "I consent to have my art contest submission posted publicly on the class web site. My name/pseudonym for public display is                      .
- "I do not wish to post my art contest submission publicly"
- Approximately how many hours it took you to finish this assignment (I will not judge you for this at all...I am simply using it to gauge if the assignments are too easy or hard)
- Your overall impression of the assignment. Did you love it, hate it, or were you neutral? One word answers are fine, but if you have any suggestions for the future let me know.
- Any other concerns that you have.
General Tips:
- Write your shaders one step at a time! Debugging is very difficult because the only output is a color per pixel. Try to use the colors to help you debug.
- If your shader doesn't compile, drop into the debugging console to see why. Syntax error messages will be printed there with line numbers in the shader.
-
If your shader doesn't update, you may need to do a "hard refresh" and reset your cache. This seems to happen particularly in Google Chrome. The keyboard shortcut for this is
CTRL+SHIFT+R
-
If you get an error like the following
Then it probably means you need to write a number as a decimal. There is no explicit casting, so when GLSL seesERROR: 0:15: '+' : wrong operand types - no operation '+' exists that takes a left-hand operand of type 'const int' and a right operand of type 'mediump float' (or there is no acceptable conversion)
10
, for instance, it assumes it's an integer. So you would need to write10.0
Task 1: Rise N' Shine (10 Points)
Create an animation of a sun that starts on the left hand side of the screen and moves in a semicircular arc across the screen to the right hand side. When it's at the left or right side, the background should be dark. When it reaches its peak at the center of the screen, it should be lighter. When it reaches its minimum at the right hand side of the screen, it should restart on the left. More specifically:
- The uniform variable
uTime
holds the elapsed time since the beginning of the simulation. You should make a circle with sines and cosines of this variable. - The green channel of the output pixel should be
\[ e^{-dR^2/r^2} \]
wheredR
is the distance of the pixel to the center of the disc, andr
is the radius of the disc. - The red channel should be a sine wave that starts at zero and reaches its peak at 1, and then ends at zero before it's reset.
circle.frag
for this task. You do not need to touch the vertex shader or HTML fileGui Tester:
circle.html
- The function for raising
e
to a power in GLSL isexp
-
Be careful when defining vector in GLSL. This is a valid line of code
vec2 x = vec2(1.0, 3.0);
This is not a valid line of code
vec2 x = (1.0, 3.0);
You will get some confusing error messages at compile time if you do the latter. - Since you're only going through a semicircle and it resets each time, you will need to set a variable that's equal to the remainder of the uniform time divided by PI. Since the modulus % operator doesn't exist, you should use the
floor()
function instead.
Screenshots of A Working Implementation
Task 2: Julia Sets (15 Points)
A fractal is an object which is "self-similar," possibly across an infinite amount of rescaling. Roughly, what this means is if we zoom in on the shape, we will see the shape repeated. Or in other words, the shape is made up of itself.
One kind of fractal emerges when we consider the behavior of the following recursive equation\[ z_{n+1} = z_n^2 + c \]
where zn, zn+1, and c are all complex numbers, zn is complex multiplication of zn with itself, c is some constant complex number specified in advance, and z0 is some initial condition specified in advanced. For a fixed c, we can try different initial conditions and see how long it takes them to "escape," or move beyond a certain radius, or "complex modulus" from the origin.Your task will be to implement a real time viewer for Julia sets where you can move around and zoom in and out. You will implement a fragment shader in which every pixel is a different initial condition. You will then need to deal with the following uniforms:
vec2 uCenter:
The center of the viewing window on the complex plane (uCenter.x + i * uCenter.y)-
vec2 uC
: The constant complex number that's added at each iteration. -
float uScale
: The scale of the viewing window -
uEscape:
The escape radius of the equation. We consider a point escaped when \[ \text{real}^2 + \text{imag}^2 > \text{uEscape}^2 \] -
MAX_ITERS
: This is not a uniform per se, but a constant defined at the top of the shader which defines how many iterations you should check before giving up and capping the escape number as MAX_ITERS. vec3 uPows
: A variable that tells us how to convert the escape number into a red, green, or blue channel. Let N be the number of iterations it takes a point to escape, let I beMAX_ITERS
, and let uPows = (a, b, c). Then the RGB values should be \[ R = a^{-N/I}, G = b^{-N/I}, B = c^{-N/I} \]
Note that the equation for the complex number that a fragment location u_position
represents should be uScale*v_position - uCenter
Code to write: You only need to edit fractal.frag
for this task. You do not need to touch the vertex shader or HTML file
Gui Tester: fractal.html
- Complex numbers can be implemented as a
vec2
type, where the x component is the real part and the y component is the imaginary part. Complex addition is then the same as vector addition, but there is a different rule for complex multiplication. In particular, if \[a = x_a + iy_a \] and \[ b = x_b + iy_b \] then \[ ab = (x_ax_b - y_ay_b) + i(x_ay_b + x_by_a) \] - Types are a real killer on this one. For instance, you will be making a for loop to count how many iterations it takes before a point escapes, and you will eventually write a function of this number to a color channel. Because the color channels are expecting floats, though, and because there is no explicit casting, you will need your loop iterator to be a float.
Screenshots of A Working Implementation
Task 3: Art Contest (5 Points)
Do something creative with shaders! There are enough examples in this directory that you can draw from. The winner will receive 5 extra credit points.


Sinus Waves¶
New in version 0.1.
This function generates sinus waves with given period, amplitude and offset. It is also possible to use delay defined in samples to crop the first wave.
Function Documentation¶
-
signalz.generators.sinus.
sinus
(n, period=100, amplitude=2, offset=0, delay=0)[source]¶ Sinus waves generator.
Args:
- n - length of the output data (int) - how many samples will be on output
Kwargs:
- period - length of one sinus wave period (1 / frequency)
- amplitude - difference between minimal and maximal value of waves
- offset - mean value (float)
- delay - how many samples should be croped from first wave (does not influence full length of data)
Returns:
- vector of sinus waves (1d array)


Match Play. Red trails 2-3 to 5. How should Red play 11?
pips Red=113 White=55
(CorrectAnswer= 0/0, ErrorScore= 0)
My apologies for misprinting the question. The dice roll was 11.
I had this position against Ray Kershaw in a London League match a couple of months ago.
I am please to say I found 18/17*, 5/2. This wins more games and gammons than any other play. Making the ace-point is far too weak. Look at how White’s 14 and especially 15 play after 18/17*, 5/2. Any move other than 18/7*, 5/2 is a blunder.
I am pleased to say that I did win the gammon and the match, much to Ray’s chagrin!
I had this position against Ray Kershaw in a London League match a couple of months ago.
I am please to say I found 18/17*, 5/2. This wins more games and gammons than any other play. Making the ace-point is far too weak. Look at how White’s 14 and especially 15 play after 18/17*, 5/2. Any move other than 18/7*, 5/2 is a blunder.
I am pleased to say that I did win the gammon and the match, much to Ray’s chagrin!
1. Cubeful 2-ply 18/17* 5/2 Eq.: +0.554 0.732 0.095 0.004 - 0.268 0.142 0.005 2. Cubeful 2-ply 18/17*/16 18/17 2/1 Eq.: +0.502 (-0.052) 0.718 0.070 0.002 - 0.282 0.067 0.001 3. Cubeful 2-ply 18/17*/15 2/1 Eq.: +0.491 (-0.063) 0.712 0.070 0.002 - 0.288 0.067 0.001 4. Cubeful 2-ply 18/17*/16 5/4 2/1 Eq.: +0.486 (-0.068) 0.712 0.065 0.002 - 0.288 0.064 0.001 5. Cubeful 2-ply 18/17*/16 9/8 2/1 Eq.: +0.480 (-0.074) 0.704 0.075 0.002 - 0.296 0.088 0.002


As this is the first server node provisioned with an index service, please review and set the per-server index RAM quota. This is one time only: when you add further index server nodes, we will not ask you to review this value again.


Danish parliament voting intention - what would an election tomorrow look like?
Like what you see?Buy me a coffee.


|
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java.lang.Object jxl.biff.RecordData jxl.biff.WritableRecordData jxl.biff.FontRecord jxl.write.biff.WritableFontRecord jxl.write.WritableFont
public class WritableFont
A class which is instantiated when the user application wishes to specify the font for a particular cell
| Nested Class Summary | |
|---|---|
static class |
WritableFont.FontName
Static inner class used for classifying the font names |
| Field Summary | |
|---|---|
static WritableFont.FontName |
ARIAL
Objects created with this font name will be rendered within Excel as ARIAL fonts |
static jxl.write.WritableFont.BoldStyle |
BOLD
Indicates that this font should be presented in a BOLD style |
static WritableFont.FontName |
COURIER
Objects created with this font name will be rendered within Excel as COURIER fonts |
static int |
DEFAULT_POINT_SIZE
The default point size for all Fonts |
static jxl.write.WritableFont.BoldStyle |
NO_BOLD
Indicates that this font should not be presented as bold |
static WritableFont.FontName |
TAHOMA
Objects created with this font name will be rendered within Excel as TAHOMA fonts |
static WritableFont.FontName |
TIMES
Objects created with this font name will be rendered within Excel as TIMES fonts |
| Fields inherited from class jxl.biff.FontRecord |
|---|
biff7 |
| Constructor Summary | |
|---|---|
WritableFont(Font f)
Publicly available copy constructor |
|
WritableFont(WritableFont.FontName fn)
Creates a default font, vanilla font of the specified face and with default point size. |
|
WritableFont(WritableFont.FontName fn,
int ps)
Constructs of font of the specified face and of size given by the specified point size |
|
WritableFont(WritableFont.FontName fn,
int ps,
jxl.write.WritableFont.BoldStyle bs)
Creates a font of the specified face, point size and bold style |
|
WritableFont(WritableFont.FontName fn,
int ps,
jxl.write.WritableFont.BoldStyle bs,
boolean italic)
Creates a font of the specified face, point size, bold weight and italicised option. |
|
WritableFont(WritableFont.FontName fn,
int ps,
jxl.write.WritableFont.BoldStyle bs,
boolean it,
UnderlineStyle us)
Creates a font of the specified face, point size, bold weight, italicisation and underline style |
|
WritableFont(WritableFont.FontName fn,
int ps,
jxl.write.WritableFont.BoldStyle bs,
boolean it,
UnderlineStyle us,
Colour c)
Creates a font of the specified face, point size, bold style, italicisation, underline style and colour |
|
WritableFont(WritableFont.FontName fn,
int ps,
jxl.write.WritableFont.BoldStyle bs,
boolean it,
UnderlineStyle us,
Colour c,
ScriptStyle ss)
Creates a font of the specified face, point size, bold style, italicisation, underline style, colour, and script style (superscript/subscript) |
| Method Summary | |
|---|---|
static WritableFont.FontName |
createFont(java.lang.String fontName)
Factory method which creates the specified font name. |
boolean |
isStruckout()
Accessor for the strike-out flag |
void |
setBoldStyle(jxl.write.WritableFont.BoldStyle boldStyle)
Sets the bold style for this font, if the font hasn't been initialized |
void |
setColour(Colour colour)
Sets the colour for this font, if the font hasn't been initialized |
void |
setItalic(boolean italic)
Sets the italic indicator for this font, if the font hasn't been initialized |
void |
setPointSize(int pointSize)
Sets the point size for this font, if the font hasn't been initialized |
void |
setScriptStyle(ScriptStyle scriptStyle)
Sets the script style (eg. |
void |
setStruckout(boolean struckout)
Sets Accessor for the strike-out flag |
void |
setUnderlineStyle(UnderlineStyle us)
Sets the underline style for this font, if the font hasn't been initialized |
| Methods inherited from class jxl.biff.FontRecord |
|---|
equals, getBoldWeight, getColour, getData, getFontIndex, getName, getPointSize, getScriptStyle, getUnderlineStyle, hashCode, initialize, isInitialized, isItalic, uninitialize |
| Methods inherited from class jxl.biff.WritableRecordData |
|---|
getBytes |
| Methods inherited from class java.lang.Object |
|---|
getClass, notify, notifyAll, toString, wait, wait, wait |
| Field Detail |
|---|
public static final WritableFont.FontName ARIAL
public static final WritableFont.FontName TIMES
public static final WritableFont.FontName COURIER
public static final WritableFont.FontName TAHOMA
public static final jxl.write.WritableFont.BoldStyle NO_BOLD
public static final jxl.write.WritableFont.BoldStyle BOLD
public static final int DEFAULT_POINT_SIZE
| Constructor Detail |
|---|
public WritableFont(WritableFont.FontName fn)
fn
- the font namepublic WritableFont(Font f)
f
- the font to copypublic WritableFont(WritableFont.FontName fn, int ps)
ps
- the point sizefn
- the font namepublic WritableFont(WritableFont.FontName fn, int ps, jxl.write.WritableFont.BoldStyle bs)
ps
- the point sizebs
- the bold stylefn
- the font namepublic WritableFont(WritableFont.FontName fn, int ps, jxl.write.WritableFont.BoldStyle bs, boolean italic)
ps
- the point sizebs
- the bold styleitalic
- italic flagfn
- the font namepublic WritableFont(WritableFont.FontName fn, int ps, jxl.write.WritableFont.BoldStyle bs, boolean it, UnderlineStyle us)
ps
- the point sizebs
- the bold styleus
- the underline stylefn
- the font nameit
- italic flagpublic WritableFont(WritableFont.FontName fn, int ps, jxl.write.WritableFont.BoldStyle bs, boolean it, UnderlineStyle us, Colour c)
ps
- the point sizebs
- the bold styleus
- the underline stylefn
- the font nameit
- italic flagc
- the colourpublic WritableFont(WritableFont.FontName fn, int ps, jxl.write.WritableFont.BoldStyle bs, boolean it, UnderlineStyle us, Colour c, ScriptStyle ss)
ps
- the point sizebs
- the bold styleus
- the underline stylefn
- the font nameit
- the italic flagc
- the colourss
- the script style| Method Detail |
|---|
public void setPointSize(int pointSize) throws WriteException
setPointSize
in class jxl.write.biff.WritableFontRecord
pointSize
- the point size
WriteException,
- if this font is already in use elsewhere
WriteException
public void setBoldStyle(jxl.write.WritableFont.BoldStyle boldStyle) throws WriteException
boldStyle
- the bold style
WriteException,
- if this font is already in use elsewhere
WriteException
public void setItalic(boolean italic) throws WriteException
setItalic
in class jxl.write.biff.WritableFontRecord
italic
- the italic flag
WriteException,
- if this font is already in use elsewhere
WriteException
public void setUnderlineStyle(UnderlineStyle us) throws WriteException
us
- the underline style
WriteException,
- if this font is already in use elsewhere
WriteException
public void setColour(Colour colour) throws WriteException
colour
- the colour
WriteException,
- if this font is already in use elsewhere
WriteException
public void setScriptStyle(ScriptStyle scriptStyle) throws WriteException
scriptStyle
- the colour
WriteException,
- if this font is already in use elsewhere
WriteException
public boolean isStruckout()
isStruckout
in interface Font
isStruckout
in class jxl.biff.FontRecord
public void setStruckout(boolean struckout) throws WriteException
setStruckout
in class jxl.write.biff.WritableFontRecord
struckout
- TRUE if this is a struckout font
WriteException,
- if this font is already in use elsewhere
WriteException
public static WritableFont.FontName createFont(java.lang.String fontName)
fontName
- the name of the Excel font
|
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DotNetNuke® Feedback Module v. 06.00.00 -- RELEASE NOTES
Requirements: DotNetNuke version 06.01.02 or later, ASP.Net 3.5 SP1 or later.
Full details on the changes can be found in great detail at http://dnnfeedback.codeplex.com/workitem/list/basic.
This release is primarily focused on delivering DNN 6 compatibility, however it does bring some bug fixes and enhancements as requested through CodePlex.
Bug Fixes
- Corrected alignment of Captcha and other input fields.
- View comments was ignoring the number items per page setting. Corrected.
Enhancements
- Introduce full DNN 6 compatability.
- Added the ability to stop moderation emails being sent to administrators.
- Made the email field configureable as optional.
- Made the message field configureable as optional.
- Made the selection of subject from drop down list mandatory when DDL is used.
- Made the selection of a category from the drop down list configurable as required.
Changes
- Removed help icons and help text on main feedback since they are self explanatory.
- Replaced obsoleted core dnn time function usage, and changed to use UTC date/times.
- Renamed Release notes from .txt to .htm since it is an HTML file.
- Changed Captcha to use DNNCaptcha which utilises RadCaptcha. Also added audo code support.
- Converted the configuration settings to properly 'Typed' fields to reduce errors in usage.
- Converted errors message to standard DNN 6 form patterns.


| G.A.R. 20 |
| ABSENTEE STATEMENT |
|
| Name of Absentee |
Reference to Item No. in the establishment bill |
Designation of vacant Post |
Nature of Absence |
Name of Govt. servant officiating against the vacancy |
Reference to Item No. in the establishment bill |
| Kind |
Period |
From (Fore/After noon) |
To (Fore/After noon) |
| 1 |
2 |
3 |
4 |
5 |
6 |
7 |
8 |
9 |
| |
|
|
|
|
|
|
|
|
|
| Dated |
.................................... |
19 |
................................. |
|
| Signature and Designation of Drawing Officer |
|
|
| Notes: |
| 1. In Column 4 should be stated 'Earned/half pay leave', 'Other duty', |
'Officiating |
......................' |
'in |
|
| transit', |
'transferred to |
........................................' |
'suspended', etc., the date for each being specified as far as |
|
| possible in Columns 6 and 7. |
| 2. The statement should be divided off into sections corresponding to sections in the bill, only
those arrangements affecting one section being shown together. |


Driving results through energized code, alluring design and effective communication planning.
Leading Through Action
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Dexter is an American crime drama mystery television series that aired on Showtime from October 1, 2006, to September 22, 2013. Set in Miami, the series centers on Dexter Morgan (Michael C. Hall), a forensic technician specializing in bloodstain pattern analysis for the fictional Miami Metro Police Department, who leads a secret parallel life as a vigilante serial killer, hunting down murderers who have slipped through the cracks of the justice system.
Orphaned at age three, when his mother was brutally murdered with a chainsaw by drug dealers, Dexter see more...
Dexter Morgan
Debra Morgan
Harry Morgan
Rita Bennet
James Doakes
Maria LaGuerta
Angel Batista
Joseph Quinn
Vince Masuka
Jamie Batista


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Speech
Lord Nash, Schools Minister, addresses the State Boarding Schools’ Association (SBSA) symposium.
Thanks, Roy (Page, State Boarding Schools’ Association Chairman and Headmaster, Royal Grammar School, High Wycombe). It’s a great pleasure to be here and to see the growing appetite and recognition for state boarding that has brought us all here today.
Like you, I’m very keen to capitalise on this interest, so that many more children, especially those from disadvantaged backgrounds, can benefit from state boarding and the unique opportunities it offers.
As you may know, my department has been exploring how we can take this forward with, among others, the State Boarding Schools’ Association (SBSA), which has responded in characteristically positive and constructive style. This can be seen in the support that the association has given to academies with new boarding provision, which I know has been greatly appreciated.
I also had a very useful meeting with Roy, Hilary (Moriarty, National Director, Boarding Schools’ Association) and Melvyn (Roffe, Principal, Wymondham College). What came across most strongly from this meeting, and from the work underway in my department, is that while there’s discussion to be had about how we open up state boarding to more children, there’s little question as to why it’s the right thing to do.
State boarding schools are a key part of the school system and, as such, have an important role to play in improving the prospects of all children, particularly those from poorer backgrounds.
As a south London boy who had no thoughts of boarding until my mother died and I was sent away to a boarding school where I was certainly the only child who had not previously boarded and one of the very few not to have failed to get into Eton, I was regarded as a right pleb - but I fairly quickly learnt to appreciate the experience and I know how much good it did me and how much more trouble I would have probably got into had I gone to school at home.
So my personal experience leads me to be highly supportive of the boarding experience and all the evidence is that it can be very transformative for the right children.
We are keen to do what we can to help the state boarding sector but - and I have to say this - with the context that the cupboard left to this government is decidedly bare of funds.
Being a child growing up in an inner city today can be a pretty tough life with all the problems of gangs and the violence and crime that can bring and with so many of our children having absent fathers. Indeed for some it can be a pretty frightening life - no wonder so many of them look haunted.
Boarding is clearly, not right for every child, vulnerable or otherwise. But where a state boarding school can meet a vulnerable child’s needs by providing stability, strong pastoral care and an environment rich in academic and extra-curricular opportunities, the results can be life-changing.
Last year, 78% of year 11 boarders supported by the charity Buttle UK got 5 good GCSEs - almost as high as the national average and 10% higher than pupils on free school meals, who come from similar backgrounds to their own. This is a significant achievement given that quite a few of these assisted boarders would, otherwise, have been expected to score well below average.
Many of us have, of course, seen the remarkable stories behind the statistics with our own eyes.
There’s the young woman who had been in care since her early teens, but who flourished with the support and structure she got as a day boarder at Wymondham and won a place at Cambridge.
There’s also the pupil at Wellington Academy, who has a parent with alcohol problems, but whose grades and attendance improved after the local authority funded a boarding place.
And there’s, of course, Andrew Adonis, a truly great man, who I am delighted will be here today and who is a living testament to the transformative power of boarding. The local authority that had the wisdom to fund his boarding place, all those years ago, couldn’t have known where it would take him.
As many of you may know, it’s thanks to Andrew that I became a sponsor of Pimlico Academy, where I can think of at least 40 pupils at any one time who might benefit from boarding - something I plan to do something about when I finish this job. I now find myself surprisingly following in Andrew’s footsteps at the department - and I couldn’t have chosen a harder act to follow! Despite their obvious advantages for children from more humble backgrounds, boarding schools are still largely associated with privilege and, all too often, barred to those who might benefit the most from them.
I’m keen to work with you to change this.
Many new and prospective boarding providers and new initiatives, like The Assisted Boarding Network and The Springboard Bursary, have a strong focus on expanding boarding opportunities for poorer children.
We can see this in the good take-up of places by pupils who may not have traditionally boarded at the first 3 academies to offer new boarding provision; Wellington, The Priory and at Harefield, where sponsor David Meller is doing sterling work and himself funding assisted boarding.
And there’s Durand’s bold and ambitious bid to give inner city youngsters a free boarding education and now Eton’s plan’s with Holyport College - so we have a market that is becoming gratifyingly diverse, overturning preconceptions about boarding and dismantling the Berlin wall between the state and private sectors at long last, and I’m delighted to see the independent sector engaging with the state sector on many fronts.
For my own part, I have been delighted to involved over many years with the Eastside Young Leaders’ Academy, which has sent a number of young, black boys to some of our most prestigious public schools. Seven students have gone to Rugby, 1 to Eton, another is at Wellington and others are due to follow them.
It’s difficult to overstate what these kinds of opportunities mean to these young men and their families - opportunities that should, hopefully, become more widely available as local authorities begin to take boarding more seriously as a viable option for disadvantaged children in their care.
Though this is still far from the norm, there are some notably engaged local authorities such as Dudley, where the Virtual School Head convinced senior colleagues to fund boarding provision by arguing it would actually save £1.6 million.
In addition, Buttle UK has reported that more local authorities than ever are engaging with them on boarding, having received inquiries from 94 councils since they launched the Assisted Boarding Network with the Royal National Children’s Foundation last June.
Buttle and the foundation are also undertaking a project to more proactively target pupils suitable for boarding, which is showing real promise, with 30 to 40 local authorities registering an interest.
I want to encourage this interest, get local authorities working much more closely with the boarding sector to find suitable placements for disadvantaged children and work with you to show local authorities case studies and arguments as to why boarding can be more cost efficient.
Of course none of us see state boarding schools as substitutes for children’s homes.
We want a mixed economy that works well for all concerned. Within this, there’s room for more children from challenging backgrounds who often seize boarding opportunities with great enthusiasm, making a terrific contribution to the schools they attend.
It’s very much about matching the right child with the right school, making sure they can bring out the best in each other, which is why it’s so essential that provision meets demand. This is our guiding principle as we consider how to address the funding concerns that I know many of you have.
As I say we live in times of austerity and capital funding is very tight, but my department is keen to support innovative approaches that boost children’s outcomes, and my department is preparing a pack of guidance for schools interested in expanding or creating boarding provision. We would encourage schools and education leaders with something imaginative to offer, especially as regards new academy and free school projects, to get in touch. So, for example, where schools identify a need for new provision, they could consider submitting a free school proposal.
Applications will need to demonstrate demand from children currently in the state system and how they will ensure that the children they want to benefit, whether they’re under-privileged or otherwise, actually do benefit.
State boarding schools can also take advantage of the freedoms which academy status offers, either through the convertor or sponsor route including the use of GAG surpluses.
Many state boarding schools in England have already done this meaning, for example, that they can access money, previously held by the local authority, for building repairs.
I’m also pleased to see that some existing academy chains are being really proactive about developing boarding opportunities. Skegness Grammar School, for example, joined the David Ross Education Trust after becoming an academy and is now working with the trust to open up boarding to a wider intake, including to pupils at other academies. And I hope that the results of the Property Data Survey will result in boarding schools which need urgent maintenance using it.
Boarding is, undoubtedly, one of the biggest success stories of our education system. But while the likes of Eton are internationally renowned, state boarding schools are, as many put it, ‘Education’s best kept secret’.
Well, I’m afraid that we’re going to have to the let the cat out of the bag. The unique education you offer is too valuable to remain unsung and out of reach. Especially for disadvantaged children who can be as good for your schools as you are for them.
Because this desire to extend boarding opportunities - a desire that I know we all share - couldn’t come at a more critical time. More than ever, in an increasingly competitive world, our future rests in the hands of our young people - and in the education they receive.
We all have a role to play in ensuring that this is the very best it can be, so that every child, regardless of background, has the chance to succeed.
Thank you.


Framework11
Framework11 is a web framework built from the ground up for iOS
Test List
Link 1
Link 2
Link 3
Kitchen Sink
Buttons
Link 1
Link 2


Runs
Remotes
Authenticate
Please enter your ssh credentials for
mayall
SSH Authenticate
Secure Remote Connections
Interaction console:
Close
Enter command text and type the "Enter" key:
Command output from remote:


Josef Valvoda
I am a final-year PhD student at the University of Cambridge supervised by Professor Simone Teufel (Cambridge) and Professor Ryan Cotterell (ETH). Before joining the Natural Language and Information Processing group I’ve completed the MPhil in Advanced Computer Science also at the University of Cambridge. Before that, I’ve obtained a Bachelor of Law at the University of Exeter. I’m a member of Rycolab at ETH Zurich and fully funded by the Huaweii Reseach Scholarship.
My PhD work focuses on Aritifical Intelligence and Law. I belive that automation is the key for widening access to justice. I think about what deep learning models can learn, have learned and should learn. I’m on the program committee of the Competition on Legal Information Extraction/Entailment (COLIEE), Natural Legal Language Processing Workshop (NLLP) and the International Workshop on Juris-informatics (JURISIN).
While Legal NLP is the focus of my thesis, I am also very much interested in broader NLP research. I have recently published work on testing the compositional behaviour of neural networks. Finally, I continue to collaborate on developing (probing) methods that could better our understanding of what the neural models learn.
Outside of my work in Cambridge, I’ve been frequenting Japan’s National Institute of Informatics (NII), worked with startups (TechWolf) and interned as AI/ML researcher at Apple.
You can find me on: Twitter, GitHub, LinkedIn or the department website. PDF copy of my CV is here.
selected publications
- TACLOn the Role of Negative Precedent in Legal Outcome PredictionTransactions of the Association for Computational Linguistics 2023
- COLINGBenchmarking Compositionality with Formal LanguagesIn Proceedings of the 29th International Conference on Computational Linguistics 2022
- NAACLWhat About the Precedent: An Information-Theoretic Analysis of Common LawIn Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies 2021
- ACLInformation-Theoretic Probing for Linguistic StructureIn Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, ACL 2020, Online, July 5-10, 2020 2020


Letter sent from the Education Funding Agency to the Association of Employment and Learning Providers in April 2013, about 16 to 19 funding allocations in the 2013 to 2014 academic year and some considerations relating to commercial and charitable providers.


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