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+212 687742334
Lives in Marrakech
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Check out the full documentation of the project here to know how to write your AI player
1. You need to implement two functions. First, the do_move which is responsible to the basic move the AI player is doing in each turn. Second, do_continue_jump_move is the function that will run if a toy is possible to continue and jump over other toys.
2. the picked move must be assigned into the answer var. Note that re-declaration of the answer var (using either 'let' or 'var') will result in error.
3. The code must be in pure JS (don't assume any function given aside the ones in the P5.JS library).
4. If you wish to declare more functions and write complex logic, we suggest just to fork the project from here and do it outside the scope of this play ground.
5. Please try avoiding hacking into the website, this is a Front-End only host, you are able to get the code free of charge here .
game - is the game object with all the properties that are found in the documentation. allPossbileMoves - list of all possible moves the AI can pick from them.
game - is the game object with all the properties that are found in the documentation. toyId - the Id of the toy that currently playing. possibleMoves - list of all possible moves the AI can pick from them.
Note: the default AI player is set to pick a possible move in random for each turn. This is a good place to start with but this is not a good player at all.


Opua is known as the “Gateway to New Zealand” and is located 17 miles from Cape Brett light. This beautiful area is very popular with cruising boats and is the primary arrival port for boats that have crossed the Pacific.
Officials are reported to be efficient, polite and helpful and there is a designated floating Quarantine wharf which makes clearance here very straightforward.
Port Opua is well marked and an easy approach any time of day or night. In addition to Opua, there are three other main townships in the Bay; Paihia 10 min, Russell 15 min and Kerikeri 25 min.
For full details on clearing into and out of New Zealand see Noonsite/New Zealand/Formalities. In particular, pay attention to the new strict controls in place to prevent the entry of Mediterranean fanworm into NZ.
In Opua, the Custom's dock is located at the northern end of the Opua Marina outer breakwater and is clearly marked as a quarantine area - with no access to shore. It is possible to tie to either side so it can handle a large number of yachts. Customs and MAF travel to and from the dock in their work boats.
Opua Marina berths can be arranged from here via VHF 12. Customs will give you a map of the marina upon asking. Be careful shifting your boat within the marina as the tide runs strongly through here. If possible shift at slack water.
At times it may be necessary to contact customs on 0800 428 786. A MAF inspection must be completed immediately on arrival and if additional travel expenses are incurred or this has to be done outside of normal working hours, any costs may be charged to the yacht.
Once entry formalities are completed the crew and vessel are free to travel throughout the country and are not required to report their movements until departure.
Last updated January 2016.
There are two marinas in the Bay of Islands, Opua Marina and Kerikeri Crusing Club Marina, as well as many anchorages and other types of mooring. The many islands in the bay allow shelter to be found from any wind direction and the rivers winding inland provide protection during the more severe winter storms.
Of the various clubs in the Bay of Islands, popular with visiting yachts are the Opua Cruising Club, Opua, and the Russell Boating Club, Matauwhi Bay, Russell. Call on VHF Ch 16, Opua keeps a much better listening watch, working channel 77. Both clubs are open Wednesday, Thursday (in season), Friday and Sunday nights, with meals available. Summer (short term) membership available at both clubs.


Project: Resource-Hub
Project: Resource-Hub Home
Project: Resource-Hub : How-to articles
Created by
Bryan Guner
on Mar 12, 2021
Add how-to article
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4 minutes ago


{% extends 'base.html' %}
{% block title %}{% endblock %}
{% load staticfiles %}
{% block body %}
PICK UP
Order online or call us on<PHONE_NUMBER>0 between 10:00AM -
9:00PM.
We will send you confirmation as soon as we can.
Opening Hours:
Lunch : 11:00 AM - 3:30 PM
Dinner : 6:30 AM - 10:30 PM
HOME DELIVERY
Order online or call us on<PHONE_NUMBER>0 between 10:00AM -
9:00PM.
We will send you confirmation as soon as we can.
{% endblock %}


The Compass Rose universe -- an ancient milieu where places have no names, cities spring forth like bouquets in the desert, gods and dreams walk the scorching sands in the South, ice floats like mirror shards upon the Northern sea, islands that do not exist are found in the East, death chases a thief on the rooftops of a Western city, immortal love spans time, and directions are intertwined into one road we all travel....
You come to this place when you wonder, and sometimes, only when you dream.
Books in the Compass Rose Epos:
Dreams of the Compass Rose, a mythic arabesque "collage novel" of interconnected stories in the vein of The One Thousand and One Nights was originally published in May 2002 by Wildside Press. It garnered critical acclaim and was a 2002 Nebula Awards® Preliminary Ballot nominee for Best Novel.
After many years out of print, the 10th Anniversary Edition of this extraordinary work is available now, in handsome trade hardcover and trade paperback editions.
Also available on Amazon Kindle and various other ebook formats.
A standalone sequel titled Gods of the Compass Rose is forthcoming. The new novel in this milieu will include, as one of its "story-chapters," the 2007 Nebula Awards® Finalist in the Short Story category, "The Story of Love," and "Three Names of the Hidden God," as well as "Niola's Last Stand," appearing in Black Gate Magazine.
Praise for Dreams of the Compass Rose
"...a clever concoction of vignettes and short stories knitted into a morality tale about the temptation of illusion
and the price of truth.... an exotic setting reminiscent of Tanith Lee's Flat Earth series.... The author's
sumptuous language will resonate with Lord Dunsany and Clark Ashton Smith fans.... Nazarian's vital themes and
engaging characters are sure to entertain."
— Publishers
Weekly
"Nazarian's story cycle treads the borderline between the episodic novel and the short-story collection, recalling
the work of contemporary fantasist Charles de Lint, early-twentieth-century fantasist Lord Dunsany, and even,
reaching way back, The Thousand and One Nights....her imagery is rich, vivid, and memorable, not to mention
being remarkable because she realizes it not in her native language, Russian, but in English.... Indeed, this is a
singularly appealing book by a new voice in fantasy."
— Roland Green, ALA
Booklist
"...unusual fantasy novel... although it uses many of the trappings of traditional heroic fantasy, the elements are
assembled in a unique fashion.... Often the conflict is dark and foreboding.... Nazarian's prose is well constructed
and her exotic settings are vivid and realistic. There's almost a fairy tale quality to the book, and it differs
enough from the usual array of usurped thrones and quest stories to warrant your time."
— Science Fiction Chronicle
"An intricate multi-level story... a kind of Aesop's Fables... spoken with a voice from the Far East, hypnotic as the
desert sands."
— Locus
"This is a novel to be read and reread in small doses as pieces of the puzzle are put together."
— The Denver Post
"Vera Nazarian is a fervent prose poet; her first novel, Dreams of the Compass Rose, is a feast of baroque
colour and eloquence echoing and rivalling Lee's remarkable arabesque cycle, the five-volume Tales of the Flat
Earth.... Dreams may well become a landmark in the architectonics of the fantastic.... a fierce and
stylish statement of innovative purpose in a genre that needs shaking up. Fortunately, with Vera Nazarian joining
the reforming crusade of such fantasists as China Miéville, Jeff VanderMeer, Kelly Link, and Jeffrey Ford, an
earthquake seems imminent..."
— Nicholas Gevers, Infinity Plus
"Vera Nazarian is faithful, in her own way, to the storyteller tradition. Her stories have the same sense of
mysteries being revealed, and often producing more mysteries, that myth and folk tales do. Her prose tends toward
the peculiar combination of exotic and generic detailing found in fairy tales. She keeps her vocabulary and her
setting pre-industrial. Her world is filled with timeless lands, endless deserts, unfathomable abysses. Slaves,
kings, nomads, warlords, mages, sea captains, and gods all tangle in quests of conquest and journeys of
discovery."
— Eugene Reynolds, The New York Review of Science
Fiction
"...a sweeping, lyrical style that nevertheless contains touches of earthiness.... It may well be that Vera Nazarian,
like her trickster goddess, Ris, is one of the old masters of fantasy, returned to earth in a new guise. She bears
watching."
— A. L. Sirois, Deep Outside SFFH
"Vera Nazarian has captured a rare and special kind of beauty.... Each story is a gem in itself, a self-contained
modern myth with a certain unique flavor. It's as though the lands of the Thousand and One Nights had collided with
the worlds of L. Frank Baum, producing a story cycle with an Ozian undertone and an Arabian accent. Take one story
at a time, and savor the rich language, the images, and the archetypical characters which populate it. Read them
all, and trace the patterns of man vs god, truth vs illusion, reality vs legend, appearing over and over in
different guises."
— Michael M. Jones, The Green Man
Review
"Nazarian's writing is a banquet for readers who savor fluid language."
—
Charlene Brusso, Black Gate
"It can be said that all stories are dreams, through some are more real than others. It is the measure of a writer to
take a tale of wonder and make it honest, with the marvel of fantasy and the keenness of actuality. So Vera Nazarian
does in her astonishing work Dreams Of The Compass Rose. This master storyteller takes us to a world of
beauty and harshness, love and cruelty, covering all from light to dark."
—
Colleen Cahil, SFRevu.com
"If you took the entire oeuvre of Tanith Lee, melted it down, and shot it up, you'd have the experience of this
book."
— Allison Lonsdale
From the book jacket:
"The colorful strong writing style that Vera has worked on for years has come to full fruition."
— Marion Zimmer Bradley
"Dreams of the Compass Rose is a rich, exotic blend of high romance and high magic of a kind rarely seen since
the classic fantasies of Lord Dunsany, James Branch Cabell and Clark Ashton Smith. This is a work not to be gulped
but savored, but if you do find yourself unable to stop, re-read it slowly enough to taste the truth within the
poetry."
— Diana L. Paxson, author of Hallowed Isle
"I love this book. Dreams of the Compass Rose is a story-cycle in which we keep coming back to the same
characters, except from different viewpoints and different times in their lives. It's set in a land of desert
empires that never was, though it could easily be our world -- far in the future, or deep in the past. Some of the
stories are brutal, some are like dreams. All of them are engaging and resonant, creating a new mythology that feels
so right one might be forgiven for thinking that it's the cultural heritage of some forgotten country or people that
have been lost to history. It reminded me of those wonderful, dream-laden story-cycles that Clark Ashton Smith and
Lord Dunsany were writing around the turn of the last century. Dreams of the Compass Rose has a similar
stately lyricism, a compelling and visionary voice that speaks to the heart of the reader."
— Charles de Lint
"Vera Nazarian's Compass Rose leads the reader on a lyrical journey through time and space, while her compelling and
kaleidoscopic tale of multiple lives intersecting about a common point explores some of human nature's most basic
truths."
— Jane S. Fancher, Author of Dance of the
Rings
"With a glorious richness of language and a use of visual imagery that makes each page like a fireworks display,
Nazarian has produced a novel that might seem at first glance to be straightforward generic fantasy. But it's not.
It's far, far more rewarding than that. What she does in Dreams of the Compass Rose is use both old threads
and new to weave fresh myths -- and vigorous myths at that: there is something almost primal in their power. It is
rare these days to find a high fantasy that is more than a rehashing of tired themes. Dreams of the Compass
Rose is one of those rarities -- and a rarity to be treasured. This book breathes the true stuff of
fantasy."
— John Grant, Co-Editor of The Encyclopedia of
Fantasy


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The most honest and gullible of the imps they work for little to nothing and very strong, rugged and durable
A Wrath Imp trying to pull it's cart out of the earth.
The Wrathful imps like to venture out and fight strong opponents. They can also be fighters for hire and can reside in a village to help protect other imps. They turned their rage and inability to show mercy into a form of justice that they dispense upon the monsters and unjust imps. They are pretty much neutral to everyone, until other imps try to antagonize them; they kick, bite, head-butt, and scratch even until after the other imp tries to flee. They tend to have short tempers but they will warn you first if you are making them irritable. Wrath imps are good at destroying old buildings, being bodyguards and truing rocks into pebbles for decorations. Every punch and kick is premeditated and thrown with skill and precision, however, they have a higher chance of dying due to their inability to back down from conflicts and need medicine from the Prideful imps. Lustful imps can calm down their obsession with violence.
These fruits are spicy and keep Wrath Imps energized.
Wrath Imps wore armor made by the Greed Imps in times of war against the humans.
These swords that the energy of their user and deals more damage the more it is used. Wrath Imps losing themselves in the heat of battle have died and taken many lives with this weapon.
Wrath Imps make their homes out of rocks, trees and other materials readily found in the forest.


Gardaí appealing for information on movements of car after body found in Lucan
by James CoxGardaí are appealing for information on the movements of a Navy Toyota Corolla 05-CW-2225 following the discover of a man's body in a burning vehicle in Lucan.
The body has been removed to the Office of the State Pathologist, Whitehall, Dublin, where a post mortem will be carried out by Locum State Pathologist Dr. Heidi Okkers.
The Navy Toyota was sold on November 13, 2019, and gardaí are appealing to the public for any information on the vehicles movements from this date.
Gardaí are also appealing for any road users who travelled in the area last night and who may have dash cam footage available to contact Gardaí in Lucan Garda Station on 01 666 7300 or the Garda Confidential Line on 1800 666 111.


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During the last two decades there has been a swift and amazing development of Machine Learning techniques and algorithms that impact many areas in not only Science and Technology but also the Humanities, Social Sciences, Medicine, Law, indeed, almost all possible disciplines. The applications are incredibly many, from self-driving cars to solving high-dimensional differential equations or complicated quantum mechanical many-body problems. Machine Learning is perceived by many as one of the main disruptive techniques nowadays.
Statistics, Data science and Machine Learning form important fields of research in modern science. They describe how to learn and make predictions from data, as well as allowing us to extract important correlations about physical process and the underlying laws of motion in large data sets. The latter, big data sets, appear frequently in essentially all disciplines, from the traditional Science, Technology, Mathematics and Engineering fields to Life Science, Law, education research, the Humanities and the Social Sciences.
It has become more and more common to see research projects on big data in for example the Social Sciences where extracting patterns from complicated survey data is one of many research directions. Having a solid grasp of data analysis and machine learning is thus becoming central to scientific computing in many fields, and competences and skills within the fields of machine learning and scientific computing are nowadays strongly requested by many potential employers. The latter cannot be overstated, familiarity with machine learning has almost become a prerequisite for many of the most exciting employment opportunities, whether they are in bioinformatics, life science, physics or finance, in the private or the public sector. This author has had several students or met students who have been hired recently based on their skills and competences in scientific computing and data science, often with marginal knowledge of machine learning.
Machine learning is a subfield of computer science, and is closely related to computational statistics. It evolved from the study of pattern recognition in artificial intelligence (AI) research, and has made contributions to AI tasks like computer vision, natural language processing and speech recognition. Many of the methods we will study are also strongly rooted in basic mathematics and physics research.
Ideally, machine learning represents the science of giving computers the ability to learn without being explicitly programmed. The idea is that there exist generic algorithms which can be used to find patterns in a broad class of data sets without having to write code specifically for each problem. The algorithm will build its own logic based on the data. You should however always keep in mind that machines and algorithms are to a large extent developed by humans. The insights and knowledge we have about a specific system, play a central role when we develop a specific machine learning algorithm.
Machine learning is an extremely rich field, in spite of its young age. The increases we have seen during the last three decades in computational capabilities have been followed by developments of methods and techniques for analyzing and handling large date sets, relying heavily on statistics, computer science and mathematics. The field is rather new and developing rapidly. Popular software packages written in Python for machine learning like Scikit-learn, Tensorflow, PyTorch and Keras, all freely available at their respective GitHub sites, encompass communities of developers in the thousands or more. And the number of code developers and contributors keeps increasing. Not all the algorithms and methods can be given a rigorous mathematical justification, opening up thereby large rooms for experimenting and trial and error and thereby exciting new developments. However, a solid command of linear algebra, multivariate theory, probability theory, statistical data analysis, understanding errors and Monte Carlo methods are central elements in a proper understanding of many of algorithms and methods we will discuss.
These sets of lectures aim at giving you an overview of central aspects of statistical data analysis as well as some of the central algorithms used in machine learning. We will introduce a variety of central algorithms and methods essential for studies of data analysis and machine learning.
Hands-on projects and experimenting with data and algorithms plays a central role in these lectures, and our hope is, through the various projects and exercises, to expose you to fundamental research problems in these fields, with the aim to reproduce state of the art scientific results. You will learn to develop and structure codes for studying these systems, get acquainted with computing facilities and learn to handle large scientific projects. A good scientific and ethical conduct is emphasized throughout the course. More specifically, you will
There are several topics we will cover here, spanning from statistical data analysis and its basic concepts such as expectation values, variance, covariance, correlation functions and errors, via well-known probability distribution functions like the uniform distribution, the binomial distribution, the Poisson distribution and simple and multivariate normal distributions to central elements of Bayesian statistics and modeling. We will also remind the reader about central elements from linear algebra and standard methods based on linear algebra used to optimize (minimize) functions (the family of gradient descent methods) and the Singular-value decomposition and least square methods for parameterizing data.
We will also cover Monte Carlo methods, Markov chains, well-known algorithms for sampling stochastic events like the Metropolis-Hastings and Gibbs sampling methods. An important aspect of all our calculations is a proper estimation of errors. Here we will also discuss famous resampling techniques like the blocking, the bootstrapping and the jackknife methods and the infamous bias-variance tradeoff.
The second part of the material covers several algorithms used in machine learning.
The approaches to machine learning are many, but are often split into two main categories. In supervised learning we know the answer to a problem, and let the computer deduce the logic behind it. On the other hand, unsupervised learning is a method for finding patterns and relationship in data sets without any prior knowledge of the system. Some authours also operate with a third category, namely reinforcement learning. This is a paradigm of learning inspired by behavioral psychology, where learning is achieved by trial-and-error, solely from rewards and punishment.
Another way to categorize machine learning tasks is to consider the desired output of a system. Some of the most common tasks are:
The methods we cover have three main topics in common, irrespective of whether we deal with supervised or unsupervised learning. The first ingredient is normally our data set (which can be subdivided into training and test data), the second item is a model which is normally a function of some parameters. The model reflects our knowledge of the system (or lack thereof). As an example, if we know that our data show a behavior similar to what would be predicted by a polynomial, fitting our data to a polynomial of some degree would then determin our model.
The last ingredient is a so-called cost function which allows us to present an estimate on how good our model is in reproducing the data it is supposed to train.
Here we will build our machine learning approach on elements of the statistical foundation discussed above, with elements from data analysis, stochastic processes etc. We will discuss the following machine learning algorithms
Python plays nowadays a central role in the development of machine learning techniques and tools for data analysis. In particular, seen the wealth of machine learning and data analysis libraries written in Python, easy to use libraries with immediate visualization(and not the least impressive galleries of existing examples), the popularity of the Jupyter notebook framework with the possibility to run R codes or compiled programs written in C++, and much more made our choice of programming language for this series of lectures easy. However, since the focus here is not only on using existing Python libraries such as Scikit-Learn or Tensorflow, but also on developing your own algorithms and codes, we will as far as possible present many of these algorithms either as a Python codes or C++ or Fortran (or other languages) codes.
The reason we also focus on compiled languages like C++ (or Fortran), is that Python is still notoriously slow when we do not utilize highly streamlined computational libraries like Lapack or other numerical libraries written in compiled languages (many of these libraries are written in Fortran). Although a project like Numba holds great promise for speeding up the unrolling of lengthy loops, C++ and Fortran are presently still the performance winners. Numba gives you potentially the power to speed up your applications with high performance functions written directly in Python. In particular, array-oriented and math-heavy Python code can achieve similar performance to C, C++ and Fortran. However, even with these speed-ups, for codes involving heavy Markov Chain Monte Carlo analyses and optimizations of cost functions, C++/C or Fortran codes tend to outperform Python codes.
Presently thus, the community tends to let code written in C++/C or Fortran do the heavy duty numerical number crunching and leave the post-analysis of the data to the above mentioned Python modules or software packages. However, with the developments taking place in for example the Python community, and seen the changes during the last decade, the above situation may change swiftly in the not too distant future.
Many of the examples we discuss in this series of lectures come with existing data files or provide code examples which produce the data to be analyzed. Most of the applications we will discuss deal with small data sets (less than a terabyte of information) and can easily be analyzed and tested on standard off the shelf laptops you find in general stores.
In most of the cases we will study, we will either generate the data to analyze ourselves (both for supervised learning and unsupervised learning) or we will recur again and again to data present in say Scikit-Learn or Tensorflow. Many of the examples we end up dealing with are from a privacy and data protection point of view, rather inoccuous and boring results of numerical calculations. However, this does not hinder us from developing a sound ethical attitude to the data we use, how we analyze the data and how we handle the data.
The most immediate and simplest possible ethical aspects deal with our approach to the scientific process. Nowadays, with version control software like Git and various online repositories like Github, Gitlab etc, we can easily make our codes and data sets we have used, freely and easily accessible to a wider community. This helps us almost automagically in making our science reproducible. The large open-source development communities involved in say Scikit-Learn, Tensorflow, PyTorch and Keras, are all excellent examples of this. The codes can be tested and improved upon continuosly, helping thereby our scientific community at large in developing data analysis and machine learning tools. It is much easier today to gain traction and acceptance for making your science reproducible. From a societal stand, this is an important element since many of the developers are employees of large public institutions like universities and research labs. Our fellow taxpayers do deserve to get something back for their bucks.
However, this more mechanical aspect of the ethics of science (in particular the reproducibility of scientific results) is something which is obvious and everybody should do so as part of the dialectics of science. The fact that many scientists are not willing to share their codes or data is detrimental to the scientific discourse.
Before we proceed, we should add a disclaimer. Even though we may dream of computers developing some kind of higher learning capabilities, at the end (even if the artificial intelligence community keeps touting our ears full of fancy futuristic avenues), it is we, yes you reading these lines, who end up constructing and instructing, via various algorithms, the machine learning approaches. Self-driving cars for example, rely on sofisticated programs which take into account all possible situations a car can encounter. In addition, extensive usage of training data from GPS information, maps etc, are typically fed into the software for self-driving cars. Adding to this various sensors and cameras that feed information to the programs, there are zillions of ethical issues which arise from this.
For self-driving cars, where basically many of the standard machine learning algorithms discussed here enter into the codes, at a certain stage we have to make choices. Yes, we , the lads and lasses who wrote a program for a specific brand of a self-driving car. As an example, all carmakers have as their utmost priority the security of the driver and the accompanying passengers. A famous European carmaker, which is one of the leaders in the market of self-driving cars, had if statements of the following type: suppose there are two obstacles in front of you and you cannot avoid to collide with one of them. One of the obstacles is a monstertruck while the other one is a kindergarten class trying to cross the road. The self-driving car algo would then opt for the hitting the small folks instead of the monstertruck, since the likelihood of surving a collision with our future citizens, is much higher.
This leads to serious ethical aspects. Why should we opt for such an option? Who decides and who is entitled to make such choices? Keep in mind that many of the algorithms you will encounter in this series of lectures or hear about later, are indeed based on simple programming instructions. And you are very likely to be one of the people who may end up writing such a code. Thus, developing a sound ethical attitude to what we do, an approach well beyond the simple mechanistic one of making our science available and reproducible, is much needed. The example of the self-driving cars is just one of infinitely many cases where we have to make choices. When you analyze data on economic inequalities, who guarantees that you are not weighting some data in a particular way, perhaps because you dearly want a specific conclusion which may support your political views? Or what about the recent claims that a famous IT company like Apple has a sexist bias on the their recently launched credit card?
We do not have the answers here, nor will we venture into a deeper discussions of these aspects, but we want you think over these topics in a more overarching way. A statistical data analysis with its dry numbers and graphs meant to guide the eye, does not necessarily reflect the truth, whatever that is. As a scientist, and after a university education, you are supposedly a better citizen, with an improved critical view and understanding of the scientific method, and perhaps some deeper understanding of the ethics of science at large. Use these insights. Be a critical citizen. You owe it to our society.


|
DemoLib
|
Go to the source code of this file.
Functions | |
| TEST (ComponentB, R_001) | |
| Units tests for each requirement associated to Component B. More... | |
| TEST (ComponentB, R_002) | |
| TEST (ComponentB, R_003) | |
| TEST | ( | ComponentB | , |
| R_001 | |||
| ) |
Units tests for each requirement associated to Component B.
Tests requirement ComponentB-001: whether ComponentB presents an inputPort of type InterfaceB
| TEST | ( | ComponentB | , |
| R_002 | |||
| ) |
| TEST | ( | ComponentB | , |
| R_003 | |||
| ) |
Tests requirement ComponentB-003 This requirement is not testable since no side affect of the compute
can be observed via the available public interface
Just to get the build to complete successful.


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We are a small furniture design company that focuses on beautiful rustic appeal with a modern functional twist. We pride ourselves on thinking outside the box to create unique quality pieces of decor that break the mold of those portrayed by large furniture companies.
Drawing inspiration from the old we believe beauty lies in the connections, materials, and adaptive re-use, where the character of the past adapts to our contemporary needs.
All our products are custom made to order and are designed to withstand the wear and tear of day to day use. Pieces differ depending on the availability of materials. To submit custom furniture requests and exact item specifications please contact us through our contact page.
We are a small furniture design company that focuses on beautiful rustic appeal with a modern functional twist. We pride ourselves on thinking outside the box to create unique quality pieces of decor that break the mold of those portrayed by large furniture companies.
Drawing inspiration from the old we believe beauty lies in the connections, materials, and adaptive re-use, where the character of the past adapts to our contemporary needs.
All our products are custom made to order and are designed to withstand the wear and tear of day to day use. Pieces differ depending on the availability of materials. To submit custom furniture requests and exact item specifications please contact us through our contact page.
Building your vision, with detailed, tailored work. We pride ourselves in designing and building any idea imaginable. For years, our architectural experience has given a flawless compliment to our solutions as we build the future. Design, Build, Fabricate.
Lead Designer
Phone: (915) - 253 - 7052.


pepper.responder.conversational module¶
-
class
pepper.responder.conversational.
AffirmationResponder
[source]¶ Bases:
pepper.responder.responder.Responder
-
AFFIRMATION
= ['yes', 'yeah', 'correct', 'alright', 'right', 'great', 'nice', 'sweet', 'true', 'good', 'ok', 'well', 'fair', 'correctamundo', 'splendid', 'indeed', 'superduper', 'wow', 'amazing']¶
-
HAPPY
= ['Nice!', 'Cool!', 'Great!', 'Wow!', 'Superduper!', 'Amazing!', 'I like it!', 'That makes my day!', 'Incredible', 'Mesmerizing']¶
-
requirements
¶
-
respond
(utterance, app)[source]¶ Respond to Utterance
When successful, respond should return a quality score and a callable implementing the response
Parameters: - utterance (Utterance) – Utterance to respond to
- app (Union[requirements]) – Components to Interact with as Response
Returns: response – response quality & responder function
Return type: float, callable
-
type
¶
-
-
class
pepper.responder.conversational.
GoodbyeResponder
[source]¶ Bases:
pepper.responder.responder.Responder
-
GOODBYES
= ['Bye', 'Bye Bye', 'See you', 'See you later', 'Goodbye', 'Good Bye', 'Have a nice day', 'Nice having talked to you']¶
-
goodbye
= 'Nice having talked to you'¶
-
requirements
¶
-
respond
(utterance, app)[source]¶ Respond to Utterance
When successful, respond should return a quality score and a callable implementing the response
Parameters: - utterance (Utterance) – Utterance to respond to
- app (Union[requirements]) – Components to Interact with as Response
Returns: response – response quality & responder function
Return type: float, callable
-
type
¶
-
-
class
pepper.responder.conversational.
GreetingResponder
[source]¶ Bases:
pepper.responder.responder.Responder
-
GREETINGS
= ['Yo', 'Hey!', 'Hello!', 'Hi!', 'Good Day', "How's it going?", 'How are you doing?', "What's up?", "What's new?", "What's going on?", "What's up?", 'Good to see you!', 'Nice to see you!']¶
-
greeting
= 'Nice to see you!'¶
-
requirements
¶
-
respond
(utterance, app)[source]¶ Respond to Utterance
When successful, respond should return a quality score and a callable implementing the response
Parameters: - utterance (Utterance) – Utterance to respond to
- app (Union[requirements]) – Components to Interact with as Response
Returns: response – response quality & responder function
Return type: float, callable
-
type
¶
-
-
class
pepper.responder.conversational.
NegationResponder
[source]¶ Bases:
pepper.responder.responder.Responder
-
NEGATION
= ['no', 'nope', 'incorrect', 'wrong', 'false', 'bad', 'stupid']¶
-
SORRY
= ['Sorry!', 'I am sorry!', 'Forgive me!', 'My apologies!', 'My humble apologies!', 'How unfortunate!', 'My mistake']¶
-
requirements
¶
-
respond
(utterance, app)[source]¶ Respond to Utterance
When successful, respond should return a quality score and a callable implementing the response
Parameters: - utterance (Utterance) – Utterance to respond to
- app (Union[requirements]) – Components to Interact with as Response
Returns: response – response quality & responder function
Return type: float, callable
-
type
¶
-
-
class
pepper.responder.conversational.
ThanksResponder
[source]¶ Bases:
pepper.responder.responder.Responder
-
THANKS
= ['thank you', 'thanks', 'appreciate', 'cheers']¶
-
THANKS_REPLY
= ["You're welcome", 'No problem', 'Glad to be of service', 'Anytime']¶
-
requirements
¶
-
respond
(utterance, app)[source]¶ Respond to Utterance
When successful, respond should return a quality score and a callable implementing the response
Parameters: - utterance (Utterance) – Utterance to respond to
- app (Union[requirements]) – Components to Interact with as Response
Returns: response – response quality & responder function
Return type: float, callable
-
type
¶
-


Set the options and pick something!
Skill Level
Cooldown
Set to zero to never repeat. Reload page to clear memory.
One of my Krav Maga instructors needed it. I make no claims that anyone else will find it useful or correct. Use at your own risk!
The page always remembers the name of every skill it has selected since the page was last reloaded. If you have selected to disallow repeats for x turns, skills chosen up to x turns ago will be locked out. Changing this setting does not affect memory. If you turn it on and disallow the last x turns' worth of skills, and those skills themselves include some repeats, fewer than x skills will be locked out at first.
No. But you can. The page source and Javascript are fully visible, so make your own, Grasshopper.
A couple things. If you're using this on jenniferneff.com, I pay for that site per refresh, not per month the page exists. So if you have your own space to host it on, you can host your own version. As a bonus, you'll be able to tailor it to your own needs.
If you were looking for a tip jar, donate to someone fighting climate change instead.


{% extends "layout.html" %} {% block content %}
Everything is probably totally borked, I know nothing of web development thus this is very much WIP.
good luck, brave soul.


Top Recipes
| Kansar | |
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| Kansar is a sweet dessert dish from the Gujarat in Indian subcontinent, whose main ingredients are cracked wheat and jaggery. |
| Lasaniya Batata | |
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| Lasaniya Batata is garlic flavored potato curry that every hot and spicy food lover must try. Learn how to make hot yet flavorful Gujarati style lasaniya batata |
| Moong Dal Payasam | |
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| Paruppu Payasam is a one of many verities of payasams in south India, especially popular in Kerala and Tamilnadu. |
| Cholia aur Soyabean ka Pulao | |
|---|---|
| A unique combination of fresh green gram and soya bean chunks with aromatic Basmati cooked on dum in sealed. |
| Gulab Jamun | |
|---|---|
| Gulab jamun is a milk-solids -based dessert, similar to a dumpling. It is popular in countries of South Asia such as India, Sri Lanka, Nepal, Pakistan and Bangladesh. |


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{include file="public/navbar"} {block name="left"}{/block} {block name="main"}主内容{/block} {block name="right"}{/block} {include file="public/footer"}


[§342H-2] Administration. The department shall administer this chapter through the director. The director may delegate to any person such power and authority vested in the director by this chapter as the director deems reasonable and proper for the effective administration of this chapter, except the power to make rules. [L 1989, c 212, pt of §4]
Cross References
Regulation of nondegradable solid waste, see §§339-21 to 24.