The purpose of the PlotDevice Perception module is to incorporate commonsense knowledge into PlotDevice of how things look, feel, smell and relate to other things. The data is organized in a so-called semantic network (like WordNet). Concepts in a semantic network are connected to each other with semantic relations. For example: red is-property-of rose.
The module has two parts: an online application
where you can add new rules to the network, and a PlotDevice library to retrieve clusters of rules
and analyze them. The library uses the functionality in the Graph
library to find shortest paths and strong concepts in the network. It also contains a
simile search technique as discussed by Dr. Tony Veale and Yanfen Hao in their paper
[1].
The library introduces some advanced programming concepts.
|
Application: http://nodebox.net/perception |
Screenshot from http://nodebox.net/perception.
The application is built to work with the Safari and Firefox browsers.
Concepts in the Perception network are connected to each other with semantic relations. The advantage of using these semantic rules is that they make sense to both people and machines.
There are lots of existing and well-known semantic networks (WordNet and Wikipedia to name
two). Our purpose is not to recreate these - instead, in the Perception network we focus on
the is-property-of relation which describes what something looks or feels like. This
will yield the sort of sensory, emotional, subjective knowledge we want in a visualization tool
like PlotDevice.
The network supports six different relations:
Properties
Everything that has an is-property-of relation to another concept is regarded as a sensory property. Properties are at the heart of the network because we can use them to translate language to visual output (which is the main goal of the Perception module).
At one end we have people using conceptual constraints like bright and trendy colors, at the other end we have machines with technological possibilities like a HSB color model. We can easily define bright in terms of saturation and brightness, and trendy in terms of specific hues (like pink). The Colors library offers a few tools to do that. Hence, bright and trendy are useful sensory go-betweens. When we start tagging other concepts with these properties (e.g bright is-property-of sun) we are basically teaching machines something about aesthetics and the visual world.
If you open up the ‘from property’ window in the web app you’ll notice that some properties
are highlighted. These are properties we think are good candidates to connect to pieces of
PlotDevice code to generate associated visual output.
Context
Sets of related rules are organized in a context, like nature or culture.
Contexts can be used as a filter to narrow down the rules being displayed.
Authorship
When you add your own rules to the network you are encouraged to leave an e-mail address or some other identification code. This way, your own set of rules can be set apart from all the other rules - you can filter for your ID so you only get to see your own data, regardless of what anyone else is saying.
We encourage you to use your e-mail address. If the tool catches on and lots of people are
going to be adding rules, we may want to contact you to learn something about your cultural
background.
Editing
At this point you can only add rules in the online application. If you are planning
on getting your hands dirty you may need to be able to edit existing rules to reorganize or
restructure clusters in the network. If this is the case, contact us and we’ll set you up with
some editing rights.
Where to start?
If you don’t know where to start, here are some good clusters of knowledge in the
network:
| Nature | Culture | Emotion | Media | Graphics |
| animal plant tree landscape sky weather body | state science religion leisure structure | facial expression behavior | book movie genre | color text |
How to start?
Think of a central concept that interests you. Start adding rules to it (and remember to add some is-property-of rules). Then start thinking about the new concepts that were introduced in your rules. You’ll quickly have a good cluster of knowledge.
For example:
The Perception module has a number of robots that help us out with finding new rules. One of the robots is developed at the department of Computer Linguistics at the University of Antwerp (Belgium) and has gathered over two hundred thousand is-property-of rules from various text sources.
|
When you click the suggest link in the Add rule panel you can review them and activate those you think make sense. You can browse the rules starting from a property (e.g. voracious is-property-of predator) or by searching for concepts (e.g. predator has-property voracious). You can switch modes by clicking ‘property’ or ‘concept’ at the top of the widget. Then simply select the rules that make senses and they’ll be activated in the
Perception network. |
Put the perception library folder in the same folder as your script so PlotDevice can find the library. You can also put it in ~/Library/Application Support/PlotDevice/. You’ll also need the Graph library installed. Put it in Application Support as well, or inside the perception library folder.
perception = ximport("perception")
Outside of PlotDevice you can also just do import perception.
The simplest way to retrieve rules from the online Perception database is with the query() command. A list of rules will be returned - all of them involving the given concept, with the given relation, in the given context and/or from the given author.
query(concept, relation=None, context=None, author=None, depth=1)
If the optional depth is 1 the command simply returns all the rules that match. If depth is between 1 and 4, it returns a cluster of interconnected concepts.
This is useful because if there is a wet is-property-of fog rule and a fog is-related-to autumn rule, the command will also retrieve the wet is-property-of autumn rule even though we were only querying for fog rules. This results in much more meaningful data to put in a small ‘fog-graph’.
Note that you can also set the author once in the AUTHOR variable. It will then be used in all queries throughout the library.
perception.AUTHOR =<EMAIL_ADDRESS>Each Rule object in the returned list has the following properties:
Local cache
When you execute a query, the library attempts to connect to the online Perception database. The result is then cached locally so the next time you run the same query it will respond quicker. At some point you may want to empty the local cache:
cache_clear()
Obviously, the next fun thing to do is to cluster related concepts in a small graph (like in the web application). The library builds on the Graph library to accomplish this. The cluster() command returns a graph object that has all of the functionality described in the documentation of the Graph library - that’s the best place to start if you are new to graph networks.
cluster(concept, relation=None, context=None, author=None, depth=2)
So, it’s easy enough to visualize a cluster of rules:
g = perception.cluster("fog") g.distance = 1.2 g.styles.apply() g.solve() g.draw() |
Concept node methods and properties
Nodes (e.g. individual concepts) in the cluster graph have all the standard node properties and methods, and some additional functionality that pertains to the Perception network:
node.is_a(node, direct=True)
node.is_part_of(node. direct=True)
node.is_opposite_of(node, direct=True)
node.is_property_of(node, direct=True)
node.is_related_to(node, direct=True)
node.is_same_as(node, direct=True)
node.has_specific(node, direct=True)
node.has_part(node, direct=True)
node.has_property(node, direct=True)
Each of these methods returns True or False, depending on whether the two nodes are directly with the given relation. If direct is False, intermediary nodes are allowed as long as the relation stays the same. For example:
g = perception.cluster("albatross", depth=3) print g.albatross.is_a("animal", direct=False) >>> True
In this case, True is returned because an albatross is a bird and a bird is an animal. So albatross is an animal. Note that if the cluster query would have been executed with a depth of 2, the animal concept would have been out of reach and False would have been returned. Deeper clusters yield more intelligence, yet slower and more cluttered visualizations.
The following methods return a list of connected nodes by relation type:
node.hyponyms(depth=1) # specific: tree => evergeen, deciduous
node.hypernyms(depth=1) # abstract: tree => organism
node.meronyms(depth=1) # parts: tree => bark, leaf, branch
node.holonyms(depth=1) # group: tree => forest
node.antonyms(depth=1) # opposites: warm <=> cool
node.properties(depth=1) # tree => big, strong, old
node.associations(depth=1) # tree => squirrel, primate
node.objects(depth=1) # illustrate property: strong => horse, tree, ...
With a depth of 1, only directly connected nodes are returned. With a depth of 2 nodes connected to directly connected nodes are also returned, and so on.
Nodes in a cluster graph have one extra property:
Concept cluster methods and properties
A concept cluster graph has all the methods and properties any other graph object has, and some additional semantic functionality:
graph.hyponyms(proper=False, fringe=1) # specify: tree => oak, linden
graph.properties(distance=2) # describe: sun => yellow, red, hot, bright
graph.objects(distance=2) # illustrate: wild => anger, sea, rodeo
The graph.hyponyms() method analyzes the cluster and returns a list of node objects that are concrete examples of the graph’s root. If proper is True, only proper names are returned (e.g. ‘God’ but not ‘god’). The fringe determines how close to the perimeter of the graph the nodes are located. With a higher fringe value (or simply None) nodes can be located deeper in the cluster, nearer to the root.
g = perception.cluster("river", depth=3) print g.hyponyms() >>> [rivulet, brook]
The graph.properties() method analyzes the cluster and returns a list of property nodes relevant to the graph root. The graph.objects() method does the reverse: it returns concrete, tangible nodes if the graph’s root is a property. The distance determines how far away from the root these nodes can be.
At this point you may wonder about the difference between using the properties() method on a single node or using it on a cluster graph. The difference is that the latter is more elaborate: it derives (or infers) additional knowledge from strong nodes surrounding the root. In other words: it finds more relevant nodes. And, nodes in the returned list are sorted by weight. For example:
g = perception.cluster("sun", depth=3) print g.sun.properties() >>> [yellow, red, hot, round, bright] print g.properties() >>> [yellow, red, hot, bright, round, cyan, white, healthy, slow, blue, orange, intense, passionate, exotic, mysterious, dangerous, organic, dry, fast, chaotic, blond, important, warm]
The returned list contains node objects. A simple way to get to a list of strings:
g = perception.cluster("sun", depth=3) print [node.id for node in g.sun.properties()] >>> ["yellow", "red", "hot", "round", "bright"]
A cluster graph object has two additional properties:
Ranges are very different from clusters. While a cluster focuses on the details of one concept, a range is an enumeration of similar concepts. For example: all the trees in Perception, or all the movies in Perception. Such ranges are derived from taxonomies, graphs that show only the is-a relation and in which all nodes come from the same ancestor.
taxonomy(concept, context, author=None, depth=4)
range(type)
The range() command returns a list of concepts that are of the given type. The type is expected to be a singular word, so you query for a movie range instead of a movies range. For example:
print perception.range("movie") >>> ['Conan The Barbarian', 'Dawn of the Dead', 'Star Wars', 'The Matrix']
One special range is the properties range. It contains all the concepts from Perception that fall within the properties context:
print perception.range("properties")[:5] >>> ['abstract', 'agile', 'angry', 'angular', 'annoying']
Often, the rules that define what goes into the same range need to be tweaked a little bit to get the best results. You can do this with the range.append() command:
range.append(name, concept, context, fringe=2, proper=False)
Now what does this do? It tells the range() command that there exists a range with the given name. This range is made up of concepts that are connected to the ancestor concept with is-a relations, in the given context. All of these concepts are located in the given fringe of the taxonomy. If proper is True, they furthermore need to be proper nouns.
Wow, what was all of that! Let’s try to clarify things with an example:
perception.range.append("font", "typeface", "graphics", fringe=2, proper=True) print perception.range("font") >>> ['Arial', 'Beowolf', 'Comic Sans MS', 'Courier', ... ]
What the above rule means is that a font is a concept with a proper name that has typeface as an ancestor. We only look in the graphics context - this avoids ambuigity - and define a rather narrow fringe to avoid that font category names (e.g. ‘Serif’) also end up in the range.
All of the tweaked range rules are stored in the range.rules dictionary. So you can find out what ranges have already been defined by printing out range.rules.keys().
So far we have looked at how to retrieve a lists of rules from the Perception database, construct and analyze a cluster of related rules and retrieve a range of concept siblings. Let’s take it a step further and look at how we can find paths between different, unrelated clusters.
index.shortest_path(concept1, concept2)
index.nearest(root, concepts)
index.sort_by_distance(root, concepts)
The index.shortest_path() command returns a list of concepts describing how to get from concept1 to concept2 in the quickest way. The index.nearest() command returns the concept from the list of concepts that is nearest to the root concept. The index.sort_by_distance() command returns the list of concepts sorted by distance from the root concept.
For example, which is darker: being happy or being sad?
print perception.index.nearest("dark", ["happy", "sad"]) >>> sad
Note that since index itself is a dictionary you can also do index.has_key(), index.keys(), etc.
Now how does this index work on the inside? Obviously, finding a good path between two random concepts takes quite a bit of time and effort. For this reason, the index is cached locally on your machine instead of requesting shortest paths from the online application. Check out the index/ folder inside the library. You’ll notice a ‘properties’ file. This is the cached set we are currently using: it contains all the shortest paths between all of the properties in Perception.
You can also create your own custom sets:
index.build(name, concepts=[], heuristic=None)
This will create a new file with the given name, containing the shortest paths between all
the concepts in the given list. Below is an example script of how to build a new properties
set. Be aware that even for 100-200 concepts it might very well take an hour to build the new
set. You’ll also need an active internet connection.
perception.index.build( "properties", perception.range.properties, perception.cost(None, {"is-property-of":-0.25,"is-opposite-of":10}) )
You may wonder about the heuristic parameter - we’ll get to that in the heuristics chapter below.
To switch between different cached sets, simply change the index name:
perception.index.name = "properties"
The solver combines clusters, ranges and the index to make decisions about the real world.
The solver.find() command will filter and sort the list of given concepts according to
how relevant they are to the given root concept.
solver.find(root, concepts)
For example, we could use it to find a typeface that matches a certain feel:
concepts = perception.range("font") print perception.solver.find("funny", concepts) >>> ['Sauna', 'Helvetica', 'Arial', 'Dolly'] # funny fonts?
concepts = perception.range("font") print perception.solver.find("structured", concepts) >>> ['Verdana', 'Georgia', 'Dolly', 'Arial'] # structured fonts?
concepts = perception.range("color") print perception.solver.find("creepy", concepts) >>> ['black', 'blue', 'azure'] # creepy colors?
concepts = perception.range("emotion") print perception.solver.find("dark", concepts) >>> ['disgust', 'sadness', 'pride', 'fear', 'anger'] # dark emotions?
Note that there is no randomness involved except when the solver needs to make a choice between two exactly equal concepts. The process is entirely dependent on the rules people have added to the Perception database (so obviously, at times you will disagree with the solver’s suggestions). In short, this is what happens behind the scenes:
If you want to use the solver with something else besides properties, you will need to
create your own index. Also, the cluster graph needs a method that returns the concepts you
want. In a hypothetical example, you could redefine the workings of the solver as follows:
def my_analysis(graph): # Our own custom analysis! # It is expected to yield a list of the nodes # we find interesting in the graph. return graph.nodes_by_betweenness()[0:1] perception.graph.add_method("my_analysis", my_analysis) perception.solver.index = "properties" perception.solver.method = "my_analysis"
That said, let’s have a look at pathfinding heuristics used in the library.
When building a cached index, we may want to discourage certain type of relations. For example, a shortest path that has an is-opposite-of in it somewhere is probably not a path that makes much sense. Consider that a tree is ‘organic’, and that ‘artificial’ is the opposite of organic, and that a computer is artificial. This makes the distance between trees and computers very short, but that’s not really a good thing when we want to compare trees to other organic things (which - obviously - computers are not).
We can pass an optional heuristic parameter to the index.build() command. This heuristic (i.e. loosely defined search rule) is a command that takes two node id’s and returns a cost, usually a number between -1.0 and 1.0. Links that cost less are preferred over more expensive ones, even if this means a longer path between two nodes. The Perception library offers a handy heuristic() builder to construct search rules:
heuristic(costs={}, graph=None)
The heuristic() command returns something you can pass to index.build(). The costs parameter is a dictionary of relations linking to costs. This way you can tax individual relations. For example, to build a decent index that ignores is-opposite-of relations and encourages is-property-of relations:
h = perception.heuristic({"is-property-of": -0.25, "is-opposite-of": 10}) perception.index.build( "properties", perception.range("properties"), heuristic=h )
Note that the graph.properties() and graph.objects() methods also have an optional heuristic parameter to tweak the returned results.
With the combination of the online input module, clusters, ranges, customizable indexes and the solver you can start doing anything you want. If you feel like getting your hands dirty, pop open the library’s source code and see what else is possible.
If you require data from the Perception network outside of PlotDevice, that’s possible with the simple query API. You can direct your software to the Perception URL, add some query arguments and retrieve clusters of rules in plain text, JSON or XML format.
http://nodebox.net/perception?[concept]
Arguments:
For example, to get some properties regarding flowers:
http://nodebox.net/perception/?flower&format=txt&relation=is-property-of
This will yield something like the result below:
'feminine', 'is-property-of', 'flower', 'nature', 'author1', '2008-04-17' 'naive', 'is-property-of', 'flower', 'nature', 'author1', '2008-04-17' 'elegant', 'is-property-of', 'flower', 'nature', 'author1', '2008-04-17'
Download the
Dashboard widget to keep track of updates to the Perception module:


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Preparation time
less than 30 mins
Cooking time
10 to 30 mins
Serves
Makes 6
For the filling, wrap one piece of anchovy fillet around the middle of each length of mozzarella. Season, to taste, with salt and freshly ground black pepper.
Open up the flower of one of the courgettes into the palm of your hand, taking care not to tear it. Carefully remove the stamens and pistil from each with a small knife. Insert one piece of the prepared mozzarella inside, then gently twist the petals back around the filling. Repeat the process with the remaining baby courgettes and filling.
Half-fill a large saucepan with sunflower oil and heat to 180C (use a cooking thermometer to check the temperature).
Sift the flour onto a plate or shallow dish and season well with salt and freshly ground black pepper. Set aside.
For the batter, sift the flour and bicarbonate of soda into a mixing bowl. Add the beaten egg and a pinch of salt. Gradually add the sparkling water or lager in a thin stream, whisking continuously with a large whisk until the batter is the consistency of double cream. (NB: Do not overwhisk, as the air will escape from the batter.)
When the oil is hot, dredge the stuffed courgette flowers, one at a time, in the seasoned flour, then dip into the batter until completely covered.
Carefully lower two of the stuffed courgette flowers into the hot oil and fry for 2-3 minutes, turning carefully once or twice, or until crisp and golden-brown. Remove from the pan using a slotted spoon and set aside to drain on kitchen paper. Repeat the process with the remaining stuffed courgette flowers. Keep warm.
To serve, pile the stuffed courgette flowers into the centre of a serving plate, squeeze over the juice from the lemon wedges and season, to taste, with salt.


If you’re interested in becoming a web developer, JavaScript is one of the best coding languages you can learn—but getting familiar with JavaScript basics means using those skills to build JavaScript projects. Need a little help coaxing inspiration to strike? So, I have put together a list of different JavaScript projects you can start working on
Beginner to Intermediate developers and designers who want to become comfortable with both JavaScript fundamentals and working in the DOM without a library. If you already know some basic JavaScript then understanding these will be easy. We learn by application - encountering many new situations while talking about the hows, the whys, and the whats that we use to solve them.
By looking at the source code for each of these simple JavaScript projects we'll see different terms. Like:
Have any questions or any suggestions? I would love to hear from you.


What they really hate is lousy programmers. --Larry Niven and Jerry Pournelle Oath of Fealty [ source]


The friendly chummy boy was feeling crummy, so he decided to act like a dummy just to make his friends laugh. [G|D]
After eating all the gummy bears, the mummy was amazed by the sweet and plummy taste that lingered on her tongue. [G|D]
The cunning rummy player was caught by surprise when he discovered the hidden scummy layer beneath the slummy exterior of his opponent. [G|D]
The little girl was so excited when she found the thingummy tucked away in her tummy that she couldn't help but smile and think of how yummy it would be. [G|D]


MASTER
Section: File Formats (5)Index Return to Main Contents
NAME
master - Postfix master process configuration file formatDESCRIPTION
The Postfix mail system is implemented by small number of (mostly) client commands that are invoked by users, and by a larger number of services that run in the background.Postfix services are implemented by daemon processes. These run in the background under control of the master(8) process. The master.cf configuration file defines how a client program connects to a service, and what daemon program runs when a service is requested. Most daemon processes are short-lived and terminate voluntarily after serving max_use clients, or after inactivity for max_idle or more units of time.
All daemons specified here must speak a Postfix-internal protocol. In order to execute non-Postfix software use the local(8), pipe(8) or spawn(8) services, or run the server under control by inetd(8) or equivalent.
After changing master.cf you must execute "postfix reload" to reload the configuration.
SYNTAX
The general format of the master.cf file is as follows:
- *
- Each logical line defines a single Postfix service. Each service is identified by its name and type as described below. When multiple lines specify the same service name and type, only the last one is remembered. Otherwise, the order of master.cf service definitions does not matter.
- *
- Empty lines and whitespace-only lines are ignored, as are lines whose first non-whitespace character is a `#'.
- *
- A logical line starts with non-whitespace text. A line that starts with whitespace continues a logical line.
Each logical line consists of eight fields separated by whitespace. These are described below in the order as they appear in the master.cf file.
Where applicable a field of "-" requests that the built-in default value be used. For boolean fields specify "y" or "n" to override the default value.
- Service name
- The service name syntax depends on the service type as described next.
- Service type
-
Specify one of the following service types:
-
- inet
-
The service listens on a TCP/IP socket and is accessible
via the network.
The service name is specified as host:port, denoting the host and port on which new connections should be accepted. The host part (and colon) may be omitted. Either host or port may be given in symbolic form (host or service name) or in numeric form (IP address or port number). Host information may be enclosed inside "[]", but this form is not necessary.
Examples: a service named <IP_ADDRESS>:smtp or ::1:smtp receives mail via the loopback interface only; and a service named 10025 accepts connections on TCP port 10025 via all interfaces configured with the inet_interfaces parameter.
Note: with Postfix version 2.2 and later specify "inet_interfaces = loopback-only" in main.cf, instead of hard-coding loopback IP address information in master.cf or in main.cf.
- unix
-
The service listens on a UNIX-domain socket and is accessible
for local clients only.
The service name is a pathname relative to the Postfix queue directory (pathname controlled with the queue_directory configuration parameter in main.cf).
On Solaris systems the unix type is implemented with streams sockets.
- fifo
-
The service listens on a FIFO (named pipe) and is accessible
for local clients only.
The service name is a pathname relative to the Postfix queue directory (pathname controlled with the queue_directory configuration parameter in main.cf).
- pass
-
The service listens on a UNIX-domain socket, receives one
open connection (file descriptor passing) per connection
request, and is accessible to local clients only.
The service name is a pathname relative to the Postfix queue directory (pathname controlled with the queue_directory configuration parameter in main.cf).
This feature is available as of Postfix version 2.5.
-
- Private (default: y)
- Whether or not access is restricted to the mail system. Internet (type inet) services can't be private.
- Unprivileged (default: y)
-
Whether the service runs with root privileges or as the
owner of the Postfix system (the owner name is controlled
by the mail_owner configuration variable in the
main.cf file).
The local(8), pipe(8), spawn(8), and virtual(8) daemons require privileges.
- Chroot (default: y)
-
Whether or not the service runs chrooted to the mail queue
directory (pathname is controlled by the queue_directory
configuration variable in the main.cf file).
Chroot should not be used with the local(8), pipe(8), spawn(8), and virtual(8) daemons. Although the proxymap(8) server can run chrooted, doing so defeats most of the purpose of having that service in the first place.
The files in the examples/chroot-setup subdirectory of the Postfix source archive show set up a Postfix chroot environment on a variety of systems. See also BASIC_CONFIGURATION_README for issues related to running daemons chrooted.
- Wake up time (default: 0)
-
Automatically wake up the named service after the specified
number of seconds. The wake up is implemented by connecting
to the service and sending a wake up request. A ? at the
end of the wake-up time field requests that no wake up
events be sent before the first time a service is used.
Specify 0 for no automatic wake up.
The pickup(8), qmgr(8) and flush(8) daemons require a wake up timer.
- Process limit (default: $default_process_limit)
-
The maximum number of processes that may execute this
service simultaneously. Specify 0 for no process count limit.
NOTE: Some Postfix services must be configured as a single-process service (for example, qmgr(8)) and some services must be configured with no process limit (for example, cleanup(8)). These limits must not be changed.
- Command name + arguments
-
The command to be executed. Characters that are special
to the shell such as ">" or "|" have no special meaning
here, and quotes cannot be used to protect arguments
containing whitespace.
The command name is relative to the Postfix daemon directory (pathname is controlled by the daemon_directory configuration variable).
The command argument syntax for specific commands is specified in the respective daemon manual page.
The following command-line options have the same effect for all daemon programs:
-
- -D
- Run the daemon under control by the command specified with the debugger_command variable in the main.cf configuration file. See DEBUG_README for hints and tips.
- -o name=value
-
Override the named main.cf configuration parameter. The
parameter value can refer to other parameters as $name
etc., just like in main.cf. See postconf(5) for
syntax.
NOTE 1: do not specify whitespace around the "=". In parameter values, either avoid whitespace altogether, use commas instead of spaces, or consider overrides like "-o name=$override_parameter" with $override_parameter set in main.cf.
NOTE 2: Over-zealous use of parameter overrides makes the Postfix configuration hard to understand and maintain. At a certain point, it might be easier to configure multiple instances of Postfix, instead of configuring multiple personalities via master.cf.
- -v
- Increase the verbose logging level. Specify multiple -v options to make a Postfix daemon process increasingly verbose.
-
SEE ALSO
master(8), process manager postconf(5), configuration parameters
README FILES
Use "postconf readme_directory" or "postconf html_directory" to locate this information.
BASIC_CONFIGURATION_README, basic configuration DEBUG_README, Postfix debugging
LICENSE
The Secure Mailer license must be distributed with this software.
AUTHOR(S)
Initial version by Magnus Baeck Lund Institute of Technology Sweden Wietse Venema IBM T.J. Watson Research P.O. Box 704 Yorktown Heights, NY 10598, USA
Index
This document was created by man2html, using the manual pages.
Time: 05:34:00 GMT, December 24, 2015


--- layout: page title: About header-img: "img/about-bg.png" ---
Kristen has worked for over 10 years in the IT industry. During her current sabbatical, she is attending Flatiron School to return to her origins as a web developer, incorporating in-depth database and solutions architecture knowledge acquired in more recent positions.


--- layout: post title: HTML5 Battery API date: 2014-11-27 22:02:00.000000000 +05:30 type: post published: true status: publish categories: - Chrome Externsion - Firefox OS tags: [] meta: blogger_blog: www.technoruler.com blogger_author: VISWAPRASATH KS blogger_cb097c283b9bbfde97b4822e7250157d_permalink: '5317241752921974677' author: login: viswaprasath email<EMAIL_ADDRESS>display_name: Vitchu first_name: '' last_name: '' ---
W3C have recently [ 28th August 2014] introduced Battery API which is compatible with browsers. Previously Mozilla had Battery API for their Firefox OS.
The aim of this experiment is to write a certain line of code so we can use in both Firefox OS and also as Chrome Extension.
Here we need several files
index.html
app.js
manifest.webapp for Firefox OS
manifest.json for Chrome
I would like to explain only the API part
First we have to get whether it is Firefox environment or Chrome.
Then after that we can easily get the battery level, battery charging time, battery dis charging time and level of charge.
battery.charging is used to know whether we are charging.
battery.level is used to know the current battery level.
battery.dischargingTime is used to know how long it will take to discharge.
battery.chargingTime is used to know how long it will take to charge.
Then you can easily load your app to Firefox OS and Chrome extension. The output will be displayed as below
| Chrome Extension Battery API |
| Firefox Battery API |
You can also download the source code from Github and try in your local machine.


Issue 51
Welcome to Haskell Weekly! Haskell is an advanced, purely functional programming language. This is a weekly summary of what’s going on in its community. You can subscribe to the email newsletter or the Atom feed.
Want to contribute to Haskell Weekly? Send us a message on Twitter or open an issue on GitHub.
News from the Haskell community
-
Gathering and classifying all possible optic types gives us a reference point to guide the implementation. In this post I systematically introduce various optic types, using programming language Haskell.
-
The Haskell Cast episode 12: Neil Mitchell on development tools
Neil Mitchell shares with us his enthusiasm for building development tools. We hear the story of how he built Hoogle in order to learn Haskell, why he created the Shake build system and what he hopes to accomplish with it, and how he uses HLint in his own development work.
-
The current state of GHC for mobile development (Android and iOS) via cross compilation is not as smooth as we’d like. We intend to improve the status quo considerably! To kick this off, we have prepared a survey regarding cross compilation with GHC (not just for mobile development) and would like to invite you to help us understand the needs of the community better.
-
GHC as a cross-compiler update
Gentoo’s
dev-lang/ghc-8.2.1_rc1
supports both cross-building and cross-compiling modes! It’s useful for cross-compiling Haskell software and initial porting of GHC itself on a new Gentoo target. -
We can all acknowledge that with liberal application of
void
and similar functions, it’s always possible to rewrite code relying on the generalized version to use the specialized version. The question comes down to that annoying need for throwing invoid
. -
You could take the view (and I suspect that you do) that
regex-<IP_ADDRESS>
was a shoddy release and that I should have taken longer to get the API right, and avoid the confusion and loss of confidence by making all of those releases, and there is a part of me that agrees with you. But is this right? -
What pure functional programming is all about
This is a technical post series about pure functional programming. The intended audience is general programmers who are familiar with closures and some functional programming. We’re going to be seeing how pure functional programming differs from regular “functional programming”, in a significant way.
Package of the week
This week’s package of the week is websockets, a library that allows you to write WebSocket servers and clients.
Send us a message on Twitter to nominate next week’s package!


Hello
- world !
- handsome !
- beautiful !
- you !
To Do:
Limit the times a user can click either up or down per post
Add '...' to the end of the unhidden portion of the description
Sort posts by number of likes
A 'loading' page
Search bar, + functionality
when a user is logged in, disable buttons/links
keep user logged in until they click the logout


The Application Visibility report summarizes the traffic flowing through a Steelhead appliance classified by the application for the time period specified. This report provides application level visibility into layer-7 and shows the application dynamics for pass-through and optimized traffic.You must enable application visibility on the Configure > Networking > Flow Statistics page before the Application Visibility report can gather and display statistics. Application Visibility is enabled by default. For details, see Configuring Flow Statistics.The Application Visibility report includes these statistics for each listed application, traffic direction, and the time period you specify.
Displays the throughput for all traffic flows in bits per second. The minimum sample granularity is 5 minutes.Throughput Peak - Hover the mouse over the data series to display the peak data activity in bits per second. For larger granularity data points, this represents the largest 5 minute average within. For 5 minutes, this is the same as the average.Throughput Average - Hover the mouse over the data series to display the average trended throughput for all traffic flows in kbps. Per-Flow Peak - Hover the mouse over the data series to display the peak trended data activity per traffic flow in bits per second. This is the largest per flow 5-minute bps within a larger sample.Per-Flow Average - Hover the mouse over the data series to display the average trended throughput in all traffic flows of an application in bits per second. This indicates how bandwidth-intensive an application is per user or flow.RiOS calculates the WAN average at each data point by taking the number of bytes transferred, converting that to bits, and then dividing by the granularity.For instance, if the system reports 100 bytes for a data point with a 10-second granularity, RiOS calculates:How much bandwidth is a particular application using? Use the mouse to hover over a specific data point to see what the y values and exact timestamp were in relation to peaks.The Riverbed system reports on performance for periods up to one month. Due to performance and disk space considerations, the display granularity decreases with time passed since the data was sampled. The data is collected at a 5-minute granularity for the entire month.
Select a report time interval of 5 minutes (5m), 1 hour (1h), 1 day (1d), 1 week (1w), All, or type a custom date. All includes statistics for the last 30 days.You can view the newest data and see data points as they are added to the chart dynamically. To display the newest data, click Show newest data. Select an application from the drop-down list. To narrow the search, start typing the first characters in the application name. When the application name and definition appears, select it from the list.Click Update Chart to update the chart without changing the application selection.


Mesto is a project about geolocalization of sites and the equipment related. Designed to help IT organisations to keep track of their equipment and clients in a specific area. One concept driving this project is that updates can be made by any users, in other word the team wants this systems to be some sort of an socially maintain IT management database.
We do not have an install package yet. This project is still in very early stgae. But if you would like to contribute with idea for an install package, join the team on Github.
Everything is on Github at the moment.
Mesto is free software, and is released under the terms MIT License unless otherwise specified.


During the EGU General Assembly 2018, I presented a work on characteristics of strain sensitivity of seismic velocity changes.
In particular I talked about the seismic velocity changes in response to different direction of tidal strain by using ambient noise recorded at active volcanoes in Japan.


Chapter 3 Lab 3: Correlation
If … we choose a group of social phenomena with no antecedent knowledge of the causation or absence of causation among them, then the calculation of correlation coefficients, total or partial, will not advance us a step toward evaluating the importance of the causes at work. —Sir Ronald Fisher
In lecture and in the textbook, we have been discussing the idea of correlation. This is the idea that two things that we measure can be somehow related to one another. For example, your personal happiness, which we could try to measure say with a questionnaire, might be related to other things in your life that we could also measure, such as number of close friends, yearly salary, how much chocolate you have in your bedroom, or how many times you have said the word Nintendo in your life. Some of the relationships that we can measure are meaningful, and might reflect a causal relationship where one thing causes a change in another thing. Some of the relationships are spurious, and do not reflect a causal relationship.
In this lab you will learn how to compute correlations between two variables in software, and then ask some questions about the correlations that you observe.
3.1 General Goals
- Compute Pearson’s r between two variables using software
- Discuss the possible meaning of correlations that you observe
3.1.1 Important Info
We use data from the World Happiness Report. A .csv of the data can be found here: WHR2018.csv
3.2 JAMOVI
In this lab, we will use jamovi to calculate the correlation coefficient. We will focus on the most-commonly used Pearson’s coefficient, r. We will learn how to:
- Produce a correlation matrix
- Produce a scatterplot
Let’s first begin with a short data set we will enter into a new jamovi data spreadsheet. Remember, in order to calculate a correlation, you need to have bivariate data; that is, you must have at least two variables, x and y. You can also have more than two variables, as indicated in the section that follows.
3.2.1 Correlation Coefficient for Bivariate Data: Two Variables
Let’s use the following data set: {x= 1, 3, 2, 5, 4, 6, 5, 8, 9} {y= 6, 5, 8, 7, 9, 7, 8, 10, 13}. Notice there are two variables, x
and y
. Enter these into jamovi and name them appropriately. With your data file open, go up to the top menu and follow these steps:
On the right, jamovi will produce an output table containing the correlation matrix requested. Any variable correlated with itself will result in an r of 1. The Pearson r correlation between variables x and y is 0.765.
In the event that you have more than two variables in your spreadsheet, and would like to evaluate correlations between several variables taken two at a time, you can enter multiple variables into the correlation window and obtain a correlation matrix–a table showing every possible bivariate correlation amongst a group of variables. To illustrate how this is done, let’s add a new variable to our existing spreadsheet: variable z
, {z= 1, 4, 2, 9, 5, 7, 12, 5, 3}. Follow these steps to obtain the correlation matrix:
According to this output: 1. The correlation coefficient between variables x
and y
is .765 2. The correlation coefficient between variables x
and z
is .294 3. The correlation coefficient between variables y
and z
is -.080
3.2.2 Correlation and Scatterplots
To accompany the calculation of the correlation coefficient, the scatterplot is the relevant visualization tool. Let’s use data from The World Happiness Report World Happiness Report. A .csv of the data can be found here: WHR2018.csv
Using this data, let’s answer the following question: does a country’s measure for freedom to make life choices
correlate with that country’s measure for Confidence in national government
? Let’s find the correlation coefficient between these variables first. With your data file open, go up to the top menu and follow these steps to get the correlation matrix and scatter plot. You can keep this scatterplot as it is, or, you can edit it to include a straight line that best fits the data points. This line is known as the best-fitting line as it minimizes the distance from it to all the data. To add the line, just check the option under ‘Regression Line’ for ‘Linear’.
Based on this output, the correlation between Freedom to make life choices
and Confidence in national government
is .408.
This scatterplot is very important. The distance between the line and the data points is indicative of the strength of the correlation coefficient; they are directly related. For example, if the data were more clustered or tighter to the line, the correlation would be stronger. If the data points are more spread out and far from the line, the correlation is weaker.
3.2.3 Practice Problems
Find the correlation between “perceptions of corruption” and “positive affect”. Create a scatterplot to visualize this relationship. What are your conclusions about the relationship between affect and perceived corruption? Is this surprising to you?
What has happened to log GDP (consider this a measure of GDP) in the United States ONLY with time (as the year has increased)? Explain this relationship and provide a scatterplot.
Which country (or countries) have seen a more consistent and strong increase in log GDP over time? Which country (or countries) have seen a decrease over time?


A function definition is a regular binding where the value of the binding is a function.
For example,code defines square to refer to a function that produces the square of a given number.A function is created with an expression that starts with the keyword function . Functions have a set of parameters (in this case, only x) and a body, which contains the statements that are to be executed when the function is called. The function body of a function created this way must always be wrapped in braces, even when it consists of only a single statement.


reasons british people give for not dancing in british period pieces
band names that seemed like a good idea at the time but became tragic
stoner metal band names that might be a bit on the nose


E
H
Labs
Welcome To MyHB Labs
Taste the Awesome 2
Check out our
github
page for examples of our public projects.
Live Dev Projects
Water Taxi V2


The managed forest land in Canada in 2015 includes 225.9 Mha (Table 1) and represents 81% of the total managed land base of the country [ 40]. The total area of unmanaged forest in Canada is 118 Mha and all of this area is in northern regions of the country with limited or no access (Fig. 2). Canada also reported 45.1 Mha of cropland (16.1% of total managed land area) across 10 provinces and 6.6 Mha of managed grassland (2.4%) in Saskatchewan, Alberta and British Columbia. Settlements accounted for 0.9 Mha (0.3%) of the managed land base, and managed wetlands accounted for 0.5 Mha (0.02%). There are 512.4 Mha of unmanaged land in grasslands, wetlands and other lands, predominantly in northern Canada [ 41].
Managed and unmanaged land in Brazil, Canada and the United States (millions of hectares)
| Country | Managed land area | Unmanaged land area | Total land area |
|---|---|---|---|
| Canada | 279.0 | 630.4 | 909.4 |
| Cropland | 45.1 | 0.0 | 45.1 |
| Grassland | 6.6 | Unknown a | Unknown a |
| Forest land | 225.9 | 118.0 | 343.9 |
| Settlements | 0.9 | 0.0 | 0.9 |
| Wetlands | 0.5 | Unknown a | Unknown a |
| Other lands | 0.0 | Unknown a | Unknown a |
| Brazil (observed) | 499.4 | 316.1 | 815.5 |
| Cropland | 68.5 | 0 | 68.5 |
| Grassland | 188.3 | 41.1 | 229.4 |
| Forest land | 235.3 | 258.3 | 493.6 |
| Settlements | 3.9 | 0 | 3.9 |
| Wetlands | 3.4 | 16.1 | 19.5 |
| Other lands | 0 | 0.6 | 0.6 |
| Cloud-covered areas | 36.7 | ||
| United States | 890 | 46 | 936 |
| Cropland | 159.2 | 0 | 159.2 |
| Grassland | 320.6 | 25.8 | 346.4 |
| Forest land | 292.7 | 9.6 | 302.4 |
| Settlements | 50.6 | 0 | 50.6 |
| Wetlands | 43.0 | 0 | 43.0 |
| Other lands | 24.7 | 10.8 | 34.5 |
aThe total area of unmanaged wetland, grassland and other land is 512.4 Mha, but these areas are not disaggregated into the individual categories
Distribution of managed and unmanaged land in Canada. The light gray areas are unmanaged and the darker gray areas are managed
Brazil has approximately 500 million hectares of managed land and 316 million hectares of unmanaged land based on an analysis for 2010 (Table 1) [ 42]. Brazil reports the net emissions for each one of its six biomes (Amazonia—AM; Cerrado—CE; Caatinga—CA; Mata Atlantica—MA; Pampa—PA; Pantanal—PT), with percent coverage of approximately 49, 24, 10, 13, 2 and 2% of the national territory (852, 187, 545 ha) respectively. In 2010, unmanaged native forest land corresponded to approximately 31% in AM; 30% in CE; 48% in CA; 16% in MA, 12% in PA and 54% in PT. Unmanaged native grassland corresponds to approximately 1, 13, 1, 2, 16 and 21% of the total territorial area of AM, CE, CA, MA, PA and PT, respectively. The percent coverage of other land (classified as unmanaged) in the total biome territorial area is near zero in all biomes except for PA, where it is approximately 1%.
Figure 3 shows the distribution of the Conservation Units and Indigenous lands (considered as part of the managed forest land) in the Brazilian territory, where their expansion from the periods 1994 to 2002 and 2002 to 2010 have been observed. The largest expansion occurs in the Amazonia biome, where most of the unmanaged forest land, grassland and wetlands also occurs (46.7%), followed by the Cerrado biome (27.9%). Approximately 36 million ha of land was cloud covered, corresponding to approximately 4% of the national territory [ 42].
Distribution of the Conservation Units and Indigenous Lands in the Brazilian territory, by biome, as represented in the Third National GHG Inventory for 2002 and 2010. Biomes include Amazonia (intermediate green), Caatinga (yellow), Cerrado (orange), Pantanal (brown), Mata Atlantica (darker green), and Pampa (light green). In black, managed areas in 1994; in medium grey (areas created between 1994 and 2002); and light grey (areas created between 2002 and 2010)
The total managed land base in the United States is 890 million hectares, and about 46 million hectares are unmanaged (Table 1, Fig. 4) [ 20]. Managed land includes the majority of the territory in the conterminous 48 states and Hawaii, where much of the land is actively managed for crop production, grazing livestock, timber production, or occurs within settlement areas. Moreover, the majority of the land in these regions is within a 10 km buffer of the road network, railroad corridors or settlements. There are large blocks of land in Alaska that are classified as unmanaged, which are grassland, forest land or wetland. These areas are largely inaccessible, which means that they are outside the 10 km buffer of road networks or other transportation corridors, and have no direct human management or function in which anthropogenic activities are influencing GHG emissions.
Distribution of managed and unmanaged land in the United States. The gray areas are unmanaged and the blue areas are managed


Monroe church feeds seniors Thanksgiving meal
by Reggie WellsMONROE, La. (KNOE) - One Monroe church fed seniors in its community on Saturday. The Greater Realness Cathedral served 70 seniors at the Booker T. Washington Senior Village a thanksgiving meal just in time for the holiday.
Church leaders say the church also has other events in the community as well like bingo nights and bible study.
Saturday’s meal gave them a little bit more, because there's no better way to give than with some "delicious food."
"The church should be a vehicle that not only assembles on Sundays and Wednesdays, but actually has a hand out in the community, showing people by actions that we love you and who Jesus really is," says associate pastor D.J. Banks.
Banks says the church has been feeding and providing Thanksgiving meals for about 10 years now.


There is a bug in Firefox that allows websites and advertisers to persist data in IndexedDB that isn't deleted when you clear your browsering data. This allows trackers to persist unique identifiers in your browser that make it hard to delete. You can manually delete IndexedDB data on a site by site basis, but as far as I know, there is now easy way to clear all IndexedDB data.
The following is a proof of concept that generates a random number and stores it in various data stores. You can then clear your browsing data and see if the random number sticks around.
Your tracking id is unknown
IndexedDB: not found
localStorage: not found
sessionStorage: not found
Generate a new tracking id, then clear your browsing data in Firefox by clicking on 'clear recent history'. You can choose to clear just the last 5 minutes or everything, the effect is the same. Once it is cleared reload the page, and you'll see that your tracker id has persisted in IndexedDB beyond the clear.


Kernel Experiments.
Here you can see the results of the kernel-based algorithms, using the 3-NN score as metric. The used kernels are:
- Linear: Linear kernel.
- Poly-2: Polynomial kernel of degree 2.
- Poly-3: Polynomial kernel of degree 3.
- RBF: Gaussian kernel (radial basis function).
- Laplacian: Laplacian kernel.
See the Scikit-Learn kernel documentation for more details.


OpenLieroX server notifier
Example of the notification. For best results open this page using your phone.
Minimum amount of players
1 player on the server
2 players on the server
3 players on the server
4 players on the server
How often to notify
Notify once per 10 minutes
Notify once per 30 minutes
Notify once per hour
Notify once per 3 hours
Notify once per 6 hours
Notify once per day
Notify once per 3 days
Servers to monitor
Sound / vibration
Sound and vibration on phones


Cloud Vision V1 API - Class Google::Longrunning::Operation (v0.6.4)
Reference documentation and code samples for the Cloud Vision V1 API class Google::Longrunning::Operation.
This resource represents a long-running operation that is the result of a network API call.
Inherits
- Object
Extended By
- Google::Protobuf::MessageExts::ClassMethods
Includes
- Google::Protobuf::MessageExts
Methods
#done
def done() -> ::Boolean
Returns
-
(::Boolean) — If the value is
false
, it means the operation is still in progress. Iftrue
, the operation is completed, and eithererror
orresponse
is available.
#done=
def done=(value) -> ::Boolean
Parameter
-
value (::Boolean) — If the value is
false
, it means the operation is still in progress. Iftrue
, the operation is completed, and eithererror
orresponse
is available.
Returns
-
(::Boolean) — If the value is
false
, it means the operation is still in progress. Iftrue
, the operation is completed, and eithererror
orresponse
is available.
#error
def error() -> ::Google::Rpc::Status
Returns
- (::Google::Rpc::Status) — The error result of the operation in case of failure or cancellation.
#error=
def error=(value) -> ::Google::Rpc::Status
Parameter
- value (::Google::Rpc::Status) — The error result of the operation in case of failure or cancellation.
Returns
- (::Google::Rpc::Status) — The error result of the operation in case of failure or cancellation.
#metadata
def metadata() -> ::Google::Protobuf::Any
Returns
- (::Google::Protobuf::Any) — Service-specific metadata associated with the operation. It typically contains progress information and common metadata such as create time. Some services might not provide such metadata. Any method that returns a long-running operation should document the metadata type, if any.
#metadata=
def metadata=(value) -> ::Google::Protobuf::Any
Parameter
- value (::Google::Protobuf::Any) — Service-specific metadata associated with the operation. It typically contains progress information and common metadata such as create time. Some services might not provide such metadata. Any method that returns a long-running operation should document the metadata type, if any.
Returns
- (::Google::Protobuf::Any) — Service-specific metadata associated with the operation. It typically contains progress information and common metadata such as create time. Some services might not provide such metadata. Any method that returns a long-running operation should document the metadata type, if any.
#name
def name() -> ::String
Returns
-
(::String) — The server-assigned name, which is only unique within the same service that
originally returns it. If you use the default HTTP mapping, the
name
should be a resource name ending withoperations/{unique_id}
.
#name=
def name=(value) -> ::String
Parameter
-
value (::String) — The server-assigned name, which is only unique within the same service that
originally returns it. If you use the default HTTP mapping, the
name
should be a resource name ending withoperations/{unique_id}
.
Returns
-
(::String) — The server-assigned name, which is only unique within the same service that
originally returns it. If you use the default HTTP mapping, the
name
should be a resource name ending withoperations/{unique_id}
.
#response
def response() -> ::Google::Protobuf::Any
Returns
-
(::Google::Protobuf::Any) — The normal response of the operation in case of success. If the original
method returns no data on success, such as
Delete
, the response isgoogle.protobuf.Empty
. If the original method is standardGet
/Create
/Update
, the response should be the resource. For other methods, the response should have the typeXxxResponse
, whereXxx
is the original method name. For example, if the original method name isTakeSnapshot()
, the inferred response type isTakeSnapshotResponse
.
#response=
def response=(value) -> ::Google::Protobuf::Any
Parameter
-
value (::Google::Protobuf::Any) — The normal response of the operation in case of success. If the original
method returns no data on success, such as
Delete
, the response isgoogle.protobuf.Empty
. If the original method is standardGet
/Create
/Update
, the response should be the resource. For other methods, the response should have the typeXxxResponse
, whereXxx
is the original method name. For example, if the original method name isTakeSnapshot()
, the inferred response type isTakeSnapshotResponse
.
Returns
-
(::Google::Protobuf::Any) — The normal response of the operation in case of success. If the original
method returns no data on success, such as
Delete
, the response isgoogle.protobuf.Empty
. If the original method is standardGet
/Create
/Update
, the response should be the resource. For other methods, the response should have the typeXxxResponse
, whereXxx
is the original method name. For example, if the original method name isTakeSnapshot()
, the inferred response type isTakeSnapshotResponse
.


ledger
/
net.corda.v5.ledger.contracts
/
CommandAndState
/
ownableState
ownableState
val
ownableState
:
OwnableState


Tefla: Deep Learning library, a Higher level API for TensorFlow
Tefla is built on top of Tensorflow. It provides higher level access to tensorflow's features.
Tefla features:
. Support for data-sets, data-augmentation
. easy to define complex deep models
. single and multi GPU training
. various prediction fnctions including ensembling of models
. different metrics for performance measurement\
. custom losses
. learning rate schedules, polynomial, step, validation_loss based
TensorFlow Installation
Tefla requires Tensorflow(version >=r0.12)
Tefla Installation
for current version installation:
pip install git+https://github.com/n3011/tefla.git
Examples
Mnist example gives a overview about Tefla usages
def model(is_training, reuse):
common_args = common_layer_args(is_training, reuse)
conv_args = make_args(batch_norm=True, activation=prelu, **common_args)
fc_args = make_args(activation=prelu, **common_args)
logit_args = make_args(activation=None, **common_args)
x = input((None, height, width, 1), **common_args)
x = conv2d(x, 32, name='conv1_1', **conv_args)
x = conv2d(x, 32, name='conv1_2', **conv_args)
x = max_pool(x, name='pool1', **common_args)
x = dropout(x, drop_p=0.25, name='dropout1', **common_args)
x = fully_connected(x, n_output=128, name='fc1', **fc_args)
x = dropout(x, drop_p=0.5, name='dropout2', **common_args)
logits = fully_connected(x, n_output=10, name="logits", **logit_args)
predictions = softmax(logits, name='predictions', **common_args)
return end_points(is_training)
training_cnf = {
'classification': True,
'validation_scores': [('validation accuracy', util.accuracy_wrapper), ('validation kappa', util.kappa_wrapper)],
'num_epochs': 50,
'lr_policy': StepDecayPolicy(
schedule={
0: 0.01,
30: 0.001,
}
)
}
util.init_logging('train.log', file_log_level=logging.INFO, console_log_level=logging.INFO)
trainer = SupervisedTrainer(model, training_cnf, classification=training_cnf['classification'])
trainer.fit(data_set, weights_from=None, start_epoch=1, verbose=1, summary_every=10)


|
|||||||||
| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | ||||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |
java.lang.Object org.catechis.Filter
public class Filter
| Field Summary | |
|---|---|
private java.util.Vector |
log
|
private int |
max_level
Maximum level to match a word at, used by all words associated with the filter passed to the constructor. |
private int |
min_level
Minimum level to match a word at, used by all words associated with the filter passed to the constructor. |
private java.util.Hashtable |
tests
Used to find a certain test. |
private Word |
word
This global Word object is re-used instead off passing it around as and argument during filtering |
private WordFilter |
word_filter
Global filter bean object. |
| Constructor Summary | |
|---|---|
Filter(java.util.Hashtable _tests)
|
|
Filter(WordFilter _word_filter)
Contructor of Filter object with a specific WordFilter bean. |
| Method Summary | |
|---|---|
private boolean |
excludeTimeFilter()
We are assuming the exclude time is entered in milliseconds. |
boolean |
filterWord(Word _word,
int index)
The actual filtering of word objects happens here. |
void |
findTest()
|
private int |
getLevelMax(java.lang.String levels)
Helper method used in getFilteredWords. |
private int |
getLevelMin(java.lang.String levels)
Helper method used in getFilteredWords. |
java.util.Vector |
getLog()
|
private boolean |
indexFilter(int index)
This method checks to see if the word interation number is lower than the start index so that a user can test only words after a certain level. |
private long |
parseMilliseconds(java.lang.String time)
|
private void |
setMinMaxLevels()
This method is necessary because we want all the filter properties to be shared with each word tested instead of getting them each time, and it saves space in the |
private boolean |
wordLevelFilter()
|
| Methods inherited from class java.lang.Object |
|---|
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
| Field Detail |
|---|
private WordFilter word_filter
private Word word
private int min_level
private int max_level
private java.util.Hashtable tests
private java.util.Vector log
| Constructor Detail |
|---|
public Filter(WordFilter _word_filter)
public Filter(java.util.Hashtable _tests)
| Method Detail |
|---|
public boolean filterWord(Word _word, int index)
Here are some of the properties we set in the WordFilter object.
word_filter.setStartIndex(0);
word_filter.setMinMaxRange("0-0");
word_filter.setType("reading");
word_filter.setCategory("october.xml");
If properties have not been set in the filter object, then they are skipped by using a try/catch block within each filter method.
private void setMinMaxLevels()
private boolean indexFilter(int index)
This method wasnt working, so we went back to testing the words by reading and writing separatly.
private boolean wordLevelFilter()
private boolean excludeTimeFilter()
private int getLevelMin(java.lang.String levels)
Helper method used in getFilteredWords.
private int getLevelMax(java.lang.String levels)
Helper method used in getFilteredWords.
private long parseMilliseconds(java.lang.String time)
public void findTest()
public java.util.Vector getLog()
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Embedded Artistry Framework
Embedded Systems C++ Framework
src
os
posix
Classes
|
Namespaces
posix_condition_variable.hpp File Reference
#include "
posix_mutex.hpp
"
#include <cassert>
#include <
rtos/condition_variable.hpp
>
Include dependency graph for posix_condition_variable.hpp:
This graph shows which files directly or indirectly include this file:
Classes
class
os::posix::ConditionVariable
Namespaces
os
FreeRTOS Condition Variable Implementation.
os::posix
POSIX types and definitions for OSX and Linux.
Generated by
1.8.15


Former Australia Captain George Bailey Set to be Australia’s New National Selector
The 37-year-old Bailey, who still plays for Big Bash League (BBL) side Hobart Hurricanes and in the Sheffield Shield for Tasmania, will not be the first active cricketer to be a national selector.
Former limited-overs captain of Australia George Bailey is all set to become the new national selector along side coach Justin Langer and chairman Trevor Hohns.
The 37-year-old Bailey, who still plays for Big Bash League (BBL) side Hobart Hurricanes and in the Sheffield Shield for Tasmania, will not be the first active cricketer to be a national selector.
In the past Don Bradman and Michael Clarke have enjoyed the post while still being the captain of the national team.
Bailey, who has represented Australia in five Tests, 90 ODIs and 30 T20s, is yet to call it quits from the game.
Asked about the necessity of a new selector, Cricket Australia head of national teams Ben Oliver said he was “really pleased with the people who put their hat in the ring for that role”.
“There will be some people who are really disappointed no doubt but they should all take great encouragement and they all have a lot to offer,” he said.
Although it is believed that CA is showing keen interest in Bailey because of his vast knowledge of the white-ball formats as a player, especially with next year’s Twenty20 World Cup at home round the corner, Oliver said the new selector would also be involved in selecting squads for Test matches.
“The panel, and all three, will be responsible for all Australian men’s teams,” Oliver said.
“What we’ve tried to achieve in this recruitment process is adding in some complementary skills to support Trevor and Justin and one of those is a consideration around short-format cricket.”
For breaking news and live news updates, like us on Facebook or follow us on Twitter and Instagram. Read more on Cricket Latest News on India.com.
Story Timeline
- 25 Nov 2019 Former Australia Captain George Bailey Set to be Australia’s New National Selector...
- 25 Nov 2019 After Bangladesh's Annihilation, Umesh Yadav Shares Secret of Success in Test Cricket
- 25 Nov 2019 ‘Stood There, Looked Stupid’: Magnus Carlsen Shares Experience Of Ringing Ceremonial Bell In Day-Night Tes...
- 25 Nov 2019 England Pacer Jofra Archer Reveals He Was Racially Insulted During New Zealand Defeat...
Comments - Join the Discussion


Goal and donation data is loaded from a Google Sheet document identified by SheetID, via the Google Sheets API v4. This application will read the sheet data and convert the data into Goal and Donation object data that can be used for calculations and drawing to the canvas.
Click on the LoadData button to reload the sheet data, recalculate progress, and redraw the canvas. You may be prompted to sign in to Google and allow this app to read your spreadsheet data.
You may use this application as a sandbox seeded with data loaded from a Google Sheet. Any values you change here will only exist in this context. The changes will never be written to the Google Sheet.
Change any of the input values in the Sandbox section to add your temporary data to the calculations and canvas.
Click on the Calculate button to recalculate progress and redraw the canvas.
You may customize the design of the progress bar graphic. Your design changes will be stored in your browser's local storage.
Change any of the input values in the Design section to make your design changes.
Click on the Draw button to redraw the canvas.
Click on the Download button to save the image as a PNG file.
Preview for round-off errors. Check if your donation will fall short (-0.01) or go beyond (+0.01). NOTE: This does not take currency conversions into account.


Vandana Rajesh
One
Two
Three
Four
Favourite Foods
Lasagna
Pasta
Sandwich
Burger
Achievements
Progress in this course (100%)
100%
Progress in the specialization capstone (20%)
20%
Progress in life course (80%)
80%
More About Me
My Childhood
I used to spend most of my evenings playing football with my friends at the beach


--- layout: post title: "My Week in Review" date: 2018-04-15 08:05:00 +0000 tags: image: https://s3.amazonaws.com/audreywatters/2018-04-15-cherryb.jpg imagecredits: https://www.flickr.com/photos/virtualwayfarer/17052087179 ---
Icon credits: The Noun Project


Contains all classes related to the Canvas and visualization of the created game.
The central classes in this package are LevelEditor, which controls the creation of different levels; LayerEditor, which controls the creation of different Entities in each Layer; and Layer, which provides a wrapper for the actual Entities.


{% extends 'base/base_navbar.html' %} {% load static notifications_tags %} {% block content %} Notifications
You Don't Have Any Notifications


Pyopencl, Opencv (cv2 package in python)
Intel Iris
Python (for general functionalities and pyopencl)
C (for kernel code and rewritten mathmetica functions)
In this part corresponding code will be described versus each stage.
# Following are the prepared data for further SIFT computations doubled # image data of doubled size normal # image data of original size halved # image data of halfed size quartered # image data of quartered size pyrlvl1 # Gaussian Pyramid corresponding to double sized image pyrlvl2 # Gaussian Pyramid corresponding to original sized image pyrlvl3 # Gaussian Pyramid corresponding to half sized image pyrlvl4 # Gaussian Pyramid corresponding to quartered sized image diffpyrlvl1 # Difference of Gaussian corresponding to double sized image diffpyrlvl2 # Difference of Gaussian corresponding to original sized image diffpyrlvl3 # Difference of Gaussian corresponding to half sized image diffpyrlvl4 = Difference of Gaussian corresponding to double quartered image
From now on we have prepared all the scales for SIFT stages.
We calculate each extrema for each scale and calculate extrema for each layer within each scale.
# Following is the python code calling opencl functions extrlvl1_gpu, difflvl1_gpu = oclu.createExtraBuffer(extrpyrlvl1, diffpyrlvl1) for i in range(1, 4): oclu.extractMin(extrlvl1_gpu, difflvl1_gpu, threshold, 80,\ diffpyrlvl1.shape[0], diffpyrlvl1.shape[1], diffpyrlvl1.shape[2], i) extrpyrlvl1 = extrlvl1_gpu.get() extrlvl2_gpu, difflvl2_gpu = oclu.createExtraBuffer(extrpyrlvl2, diffpyrlvl2) for i in range(1, 4): oclu.extractMin(extrlvl2_gpu, difflvl2_gpu, threshold, 40,\ diffpyrlvl2.shape[0], diffpyrlvl2.shape[1], diffpyrlvl2.shape[2], i) extrpyrlvl2 = extrlvl2_gpu.get() extrlvl3_gpu, difflvl3_gpu = oclu.createExtraBuffer(extrpyrlvl3, diffpyrlvl3) for i in range(1, 4): oclu.extractMin(extrlvl3_gpu, difflvl3_gpu, threshold, 20,\ diffpyrlvl3.shape[0], diffpyrlvl3.shape[1], diffpyrlvl3.shape[2], i) extrpyrlvl3 = extrlvl3_gpu.get() print "Fourth octave", quartered.shape extrlvl4_gpu, difflvl4_gpu = oclu.createExtraBuffer(extrpyrlvl4, diffpyrlvl4) for i in range(1, 4): oclu.extractMin(extrlvl4_gpu, difflvl4_gpu, threshold, 10,\ diffpyrlvl4.shape[0], diffpyrlvl4.shape[1], diffpyrlvl4.shape[2], i) extrpyrlvl4 = extrlvl4_gpu.get()
In this part we need to solve the following second order derivative equation.
It’s hard to sovle this equation programmatically. So we made the following two approaches: a) Represent derivatives using nearby points. b) Transfer this problem into a least-square linear fitting problem.
We calculate first order derivatives: dx, dy, ds, second order derivatives: dxx, dyy, dss, dxy, dxs, dys Totally 9 derivatives around the candidate key point. dD = [ [dx], [dy], [ds] ] H = [[dxx, dxy, dxs], [dxy, dyy, dys], [dxs, dys, dss]] dD = H * x_hat We apply least-square fitting to get x_hat which specifies the deviation from the candidate key point. We calculate the x_hat from the following matrix calculation.
We simulate the derivative by nearby points. Following is our kernel code in C;
float dx = (diffpyrlvl[getIdx(j, k + 1, i, m, n, l)] - diffpyrlvl[getIdx(j, k - 1, i, m, n, l)]) * 0.5f / 255; float dy = (diffpyrlvl[getIdx(j + 1, k, i, m, n, l)] - diffpyrlvl[getIdx(j - 1, k, i, m, n, l)]) * 0.5f / 255; float ds = (diffpyrlvl[getIdx(j, k, i + 1, m, n, l)] - diffpyrlvl[getIdx(j, k, i - 1, m, n, l)]) * 0.5f / 255; float dxx = (diffpyrlvl[getIdx(j, k + 1, i, m, n, l)] + diffpyrlvl[getIdx(j, k - 1, i, m, n, l)] - 2 * diffpyrlvl[getIdx(j, k, i, m, n, l)]) * 1.0f / 255; float dyy = (diffpyrlvl[getIdx(j + 1, k, i, m, n, l)] + diffpyrlvl[getIdx(j - 1, k, i, m, n, l)] - 2 * diffpyrlvl[getIdx(j, k, i, m, n, l)]) * 1.0f / 255; float dss = (diffpyrlvl[getIdx(j, k, i + 1, m, n, l)] + diffpyrlvl[getIdx(j, k, i - 1, m, n, l)] - 2 * diffpyrlvl[getIdx(j, k, i, m, n, l)]) * 1.0f / 255; float dxy = (diffpyrlvl[getIdx(j + 1, k + 1, i, m, n, l)] - diffpyrlvl[getIdx(j + 1, k - 1, i, m, n, l)] - diffpyrlvl[getIdx(j - 1, k + 1, i, m, n, l)] + diffpyrlvl[getIdx(j - 1, k - 1, i, m, n, l)]) * 0.25f / 255; float dxs = (diffpyrlvl[getIdx(j, k + 1, i + 1, m, n, l)] - diffpyrlvl[getIdx(j, k - 1, i + 1, m, n, l)] - diffpyrlvl[getIdx(j, k + 1, i - 1, m, n, l)] + diffpyrlvl[getIdx(j, k - 1, i - 1, m, n, l)]) * 0.25f / 255; float dys = (diffpyrlvl[getIdx(j + 1, k, i + 1, m, n, l)] - diffpyrlvl[getIdx(j - 1, k, i + 1, m, n, l)] - diffpyrlvl[getIdx(j + 1, k, i - 1, m, n, l)] + diffpyrlvl[getIdx(j - 1, k, i - 1, m, n, l)]) * 0.25f / 255;
We implement our own least-square function in C as following
float det_three(float *M) { float result = 0.0f; //printf("%f", DET_TWO(M[1][1], M[1][2], M[2][1], M[2][2])); result += M[0] * DET_TWO(M[4], M[5], M[7], M[8]); result -= M[1] * DET_TWO(M[3], M[5], M[6], M[8]); result += M[2] * DET_TWO(M[3], M[4], M[6], M[7]); return result; } void inv_matrix(float *INV, float *M) { float det = det_three(M); INV[0] = DET_TWO(M[4], M[5], M[7], M[8]) / det; INV[3] = -1.0f * DET_TWO(M[3], M[5], M[6], M[8]) / det; INV[6] = DET_TWO(M[3], M[4], M[6], M[7]) / det; INV[1] = -1.0f * DET_TWO(M[1], M[2], M[7], M[8]) / det; INV[4] = DET_TWO(M[0], M[2], M[6], M[8]) / det; INV[7] = -1.0f * DET_TWO(M[0], M[1], M[6], M[7]) / det; INV[2] = DET_TWO(M[1], M[2], M[4], M[5]) / det; INV[5] = -1.0f * DET_TWO(M[0], M[1], M[6], M[7]) / det; INV[8] = DET_TWO(M[0], M[1], M[3], M[4]) / det; } void transpose(float *MT, float *M) { for (unsigned int i = 0; i < DIM; i++) for (unsigned int j = 0; j < DIM; j++) MT[i * DIM + j] = M[j * DIM + i]; } void multiplicate(float *C, float *A, float *B, int m, int n, int l) { float grid = 0.0f; unsigned int i, j, k; for (i = 0; i < m; i++) for (j = 0; j < l; j++) { grid = 0.0f; for (k = 0; k < n; k++) grid += A[i * n + k] * B[k * l + j]; C[i * l + j] = grid; } } void least_square(float *C,float *A, float *B) { float T[DIM * DIM], TA[DIM * DIM], INV[DIM * DIM], INVT[DIM * DIM]; transpose(T, A); multiplicate(TA, T, A, DIM, DIM, DIM); inv_matrix(INV, TA); multiplicate(INVT, INV, T, DIM, DIM, DIM); multiplicate(C, INVT, B, DIM, DIM, 1); }
To verify the accuracy, we compare the result with the built-in function: numpy.linalg.lstsq(H, dD) works will for us.
/* We have two Paralism Optimization approaches: */ /* 1) Use Gaussian Window to weight the orientation for each pixel. */ float gaussian_window_pdf( float x, float y, float sigm_x, float sigm_y, float u_x, float u_y) { float result = 1.0 / (2 * M_PI * sigm_x * sigm_y) * exp(-0.5f * ( (x - u_x) * (x - u_x) / (sigm_x * sigm_x) + (y - u_y) * (y - u_y) / (sigm_y * sigm_y) ) ); return result; } /* 2) Calculate the historgram for 8 direction within each window */ float orient_hist[ORIENT_HIST_LEN]; for (int x = -1 * two_sd * 2; x < two_sd * 2 + 1; x++) { int ylim = sqrt((float)(4 * two_sd * two_sd - abs(x) * abs(x))); for (int y = -1 * ylim; y < ylim + 1; y++) { if (j + x < 0 || j + x > m - 1 || k + y < 0 || k + y > n - 1) continue; int idx = getIdx_3d(j + x, k + y, i, m, n, l); weight = magpyrlvl[idx] * gaussian_window_pdf(j + x, k + y, sigm, sigm, u_x, u_y); int oriClip = oripyrlvl[idx]; bin_idx = clipNum(oriClip, 0, 35); orient_hist[bin_idx] += weight; } }
In this strategy, each work item only cache 27 point data from neighboring scale.
In this strategy, each work item takes care of one row of points from neighboring scale.
In this strategy, each work item takes care of one group of points from neighboring scale.


<EMAIL_ADDRESS>+2341234567890
Home: 21 Olugbani street, Off Governor road, By Bayota motel, Ikotun Egbe, Lagos
Office>: Andela EPIC Towers, 235 Ikorodu Road, By Niger Insurance House


If you are still using a 'loop', STOP.
There are better and easier way to do it. Ruby provides various enumerables that can go through all element and yields successive members of the collection.
animal = {}
%w(cat dog rabbit).each_with_index
{ |item, index| animal[item] = index }
animal => {"cat"=>0, "dog"=>1, "rabbit"=>2}
(1..10).find_all { |i| i % 3 == 0 }
=> [3, 6, 9] **returns array
(1..100).find_index
{ |i| i % 5 == 0 and i % 7 == 0 }
=> 34
(1..100).grep 38..44
**returns array=> [38, 39, 40, 41, 42, 43, 44]
(1..6).group_by { |i| i%3 }
**returns hash=>
{0=>[3, 6], 1=>[1, 4], 2=>[2, 5]}
(1..10).each_slice(3) { |a| p a }
**returns array of slice number
[1, 2, 3]
[4, 5, 6]
[7, 8, 9]
[10]
Reduce takes in an array and reduce to a single value.It does this by iterating through a list, keeping and transforming a running total along the way. This running total can be a single value like 0, 3.7, “abcdefg”, a collection ([], {}), or anything else, really. Each iteration starts with the return value of the previous iteration and does something with it.
(5..10).reduce(:+) #=> 45
(5..10).reduce(0){|sum, num| sum+= (num * num + 1)} #=> 361
(5..10).inject(1){|product, n| product * n }
#=> 151200
# inject and reduce are interchangable.
Let’s look at a slightly more complicated case. reduce can be used to implement just about any other collection function, from map to sort to select. Here is a way to emulate select using reduce.
(1..10).reduce([]) do |result, value|
result << value if value > 5
result
end
=> [6, 7, 8, 9, 10]
(1..10).reduce([]) do |result, value|
result << value * value
result
end
=> [1, 4, 9, 25, 36, 49, 64, 81, 100]
longest = %w{ cat sheep bear }.reduce
do |memo, word|
memo.length > word.length ? memo : word
end
puts longest
=> "sheep"
The keyword min, mas and minmax use exactly same method except the return values.
a = %w(albatross dog horse)
a.max { |a, b| a.length <=> b.length }
=> "albatross"
a.max_by { |x| x.length }
=> "albatross"
**If any number is passed, it will return array
a.max_by(2) {|x| x.length }
=> ["albatross", "horse"]
a.minmax { |a, b| a.length <=> b.length }
=> ["dog", "albatross"]
The keyword partition returns two arrays, the first containing the elements of enum for which the block evaluates to true, the second containing the rest.
(1..6).partition { |v| v.even? }
=> [[2, 4, 6], [1, 3, 5]]
%w{apple pear fig}.sort_by { |word| word.length}
=> ["fig", "pear", "apple"]
Awesome methods that you don't need to calculate the result
require 'prime'
Prime.entries 10
=> [2, 3, 5, 7]
More importantly, here is a useful sites.
Ruby Document
Guide to Ruby Collection III
Ruby Explained


Ploror688.github.io
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Winter and January Writing Prompts: Creative Writing. Amazon.com: Rip the Page!: Adventures in Creative Writing. 1, 000 Creative Writing Prompts: Ideas for Blogs, Scripts, Stories and More Bryan Cohen on Amazon.com. FREE shipping on qualifying offers. When you finally. Creative Writing Ideas Teaching Ideas. A number of ideas which can be used as a stimulus for creative writing lessons.
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Creative Writing Prompts – LitBridge.
6th Grade Writing Prompts Essay Topics. Creative Writing Prompts PowerPoint - TES Resources. These helpful creative writing prompts will make the words flow from your fingers and get you in the habit of being able to write Use these 6th grade writing prompts to help your students form opinions, Journal Prompts, Creative Writing Ideas, 6. Write a short story.
25 Creative Writing Prompts Writing Forward. 10 Best Creative Writing Prompts - The Write Practice. Let your sixth-grade students show how imaginative they are, with our most popular creative writing printables. For access to thousands of teaching resources You get better at any skill through practice, and creative writing prompts are a great way to practice writing. Free Creative Writing Prompts #6: Dark, Distrurbing, and Weird.
Ready to get students excited about growth mindset? Well, this collection of writing prompts and doodle desk cards is designed to do just that! You'll Writing Topics Thoughtful Learning. Do you have students that say, "I don't know what to write about!"? Do you need activities for your classroom writing station? These creative and fun writing prompts.
Creative writing prompts for 3rd grade – Simplicity. Creative Writing Prompts, Ideas, Lists, and Resources.
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| Name | Phone | Gender | Blood Group | Street | City | State | Country | |||
|---|---|---|---|---|---|---|---|---|---|---|
| {{ student.name }} | {{ student.phone }} | {{ student.email }} | {{ student.gender }} | {{ student.bloodGroup }} | {{ student.address.street }} | {{ student.address.city }} | {{ student.address.state }} | {{ student.address.country }} |


8.4 Chained Hash Table Example:
Symbol Tables
An important application of hash
tables is the way compilers maintain information about symbols
encountered in a program. Formally, a compiler translates a
program written in one language, a source language such as C, into
another language, which is a set of instructions for the
machine on which the program will run. In order to maintain
information about the symbols in a program, compilers make use
of a data structure called a symbol
table. Symbol tables are
often implemented as hash tables because a compiler must be
able to store and retrieve information about symbols very
quickly.
Several parts of a compiler access the symbol
table during various phases of the compilation process. One
part, the lexical analyzer,
inserts symbols. The lexical analyzer
is the part of a compiler charged with grouping characters
from the source code into meaningful strings, called lexemes. These are
translated into syntactic elements, called tokens, that
are passed on to the parser. The parser performs syntactical
analysis. As the lexical analyzer encounters symbols in its
input stream, it stores information about them into the symbol
table. Two important attributes stored by the lexical analyzer
are a symbol's lexeme and the type of token the lexeme
constitutes (e.g., an identifier or an operator).
The example presented here is a very simple
lexical analyzer that analyzes a string of characters and then
groups the characters into one of two types of tokens: a token
consisting only of digits or a token consisting of something
other than digits alone. For simplicity, we assume that tokens
are separated in the input stream by a single blank. The
lexical analyzer is implemented as a function, lex (see Examples Example
8.4 and Example
8.5), which a parser calls each time it requires another
token.
The function works by first calling the next_token function (whose implementation is not shown) to
get the next blank-delimited string from the input stream
istream. If next_token returns NULL, there are no
more tokens in the input stream. In this case, the function
returns lexit, which tells the parser that
there are no more tokens to be processed. If next_token finds a string, some
simple analysis is performed to determine what type of token
the string represents. Next, the function inserts the lexeme
and token type together as a Symbol structure into the symbol table,
symtbl, and returns the token type to the
parser. The type Symbol is defined in
symbol.h, which is not included in this example.
A chained hash table is a good way to
implement a symbol table because, in addition to being an
efficient way to store and retrieve information, we can use it
to store a virtually unlimited amount of data. This is
important for a compiler since it is
difficult to know how many symbols a program will contain
before lexical analysis.
The runtime complexity of lex is O (1), assuming next_token runs in a constant amount
of time. This is because lex
simply calls chtbl_insert,
which is an O (1)
operation.
Example 8.4.
Header for a Simple Lexical Analyzer /*****************************************************************************
* *
* --------------------------------- lex.h -------------------------------- *
* *
*****************************************************************************/
#ifndef LEX_H
#define LEX_H
#include "chtbl.h"
/*****************************************************************************
* *
* Define the token types recognized by the lexical analyzer. *
* *
*****************************************************************************/
typedef enum Token_ {lexit, error, digit, other} Token;
/*****************************************************************************
* *
* --------------------------- Public Interface --------------------------- *
* *
*****************************************************************************/
Token lex(const char *istream, CHTbl *symtbl);
#endif
Example 8.5.
Implementation of a Simple Lexical Analyzer /*****************************************************************************
* *
* --------------------------------- lex.c -------------------------------- *
* *
*****************************************************************************/
#include <ctype.h>
#include <stdlib.h>
#include <string.h>
#include "chtbl.h"
#include "lex.h"
#include "symbol.h"
/*****************************************************************************
* *
* ---------------------------------- lex --------------------------------- *
* *
*****************************************************************************/
Token lex(const char *istream, CHTbl *symtbl) {
Token token;
Symbol *symbol;
int length,
retval,
i;
/*****************************************************************************
* *
* Allocate space for a symbol. *
* *
*****************************************************************************/
if ((symbol = (Symbol *)malloc(sizeof(Symbol))) == NULL)
return error;
/*****************************************************************************
* *
* Process the next token. *
* *
*****************************************************************************/
if ((symbol->lexeme = next_token(istream)) == NULL) {
/**************************************************************************
* *
* Return that there is no more input. *
* *
**************************************************************************/
free(symbol);
return lexit;
}
else {
/**************************************************************************
* *
* Determine the token type. *
* *
**************************************************************************/
symbol->token = digit;
length = strlen(symbol->lexeme);
for (i = 0; i < length; i++) {
if (!isdigit(symbol->lexeme[i]))
symbol->token = other;
}
memcpy(&token, &symbol->token, sizeof(Token));
/**************************************************************************
* *
* Insert the symbol into the symbol table. *
* *
**************************************************************************/
if ((retval = chtbl_insert(symtbl, symbol)) < 0) {
free(symbol);
return error;
}
else if (retval == 1) {
/***********************************************************************
* *
* The symbol is already in the symbol table. *
* *
***********************************************************************/
free(symbol);
}
}
/*****************************************************************************
* *
* Return the token for the parser. *
* *
*****************************************************************************/
return token ;
}
|


[DONE] Convert the criteria classes to use interfaces. Then, instead of subclassing these in the voting methods, they would implement them. This will make zope.interface a dependency.
[DONE] Copy multi-doctest file test suite code from txULS/soom to here.
[DONE] Add a new docs/methods directory and a file for each voting method.
[IN PROGRESS] Finish implementing the single-winner voting methods.
Implement the Multi-winner voting methods.
Implement the proxy and random voting methods.
Do research on distance-based voting:
- in particular, defining a metric space for candidates based on a set of preferences
- then taking the square root of the sum of the squares, getting the n-dimensional distance
[IN PROGRESS] Add doctests for each voting method.
Add a make target that tests the source dist:
- create a temp dir
- untar the dist there
- run the unit tests and doctests against that directory
- Change uppercase doc files to lower case (with the exceptions of the standard ones like README, TODO, etc.)
- Updated the sphinx configuration file to use ballotbox.meta
- Add sidebar links in the generated docs
- Autogenerate ReST files for API docs
- the logic that drives this should go in ballotbox.util.docs
- add a make target that imports and calls the appropriate functions to generate the API docs


Psychic Game part 2
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[§583A-302] Enforcement under Hague Convention. Under this part a court of this State may enforce an order for the return of the child made under the Hague Convention on the Civil Aspects of International Child Abduction as if it were a child-custody determination. [L 2002, c 124, pt of §2]


Please select difficulty based on your experience level:
| Doggy Name | AM Potty Break | AM Walk | Doggy Demeanor | Kennel |
|---|---|---|---|---|
| 😞 Louie | Yes | Yes | Easy | 1 |
| 😁 Bella | Yes | Yes | Easy | 5 |
| 😞 Baron | Yes | Yes | Hard | 11 |
| 😁 Spot | Yes | Yes | Moderate | 8 |
| 😁 Nancy | Yes | Yes | Easy | 6 |
Please select difficulty based on your experience level:
| Doggy Name | AM Potty Break | AM Walk | Doggy Demeanor | Kennel |
|---|---|---|---|---|
| 😞 Louie | Yes | Yes | Easy | 1 |
| 😁 Bella | Yes | Yes | Easy | 5 |
| 😞 Baron | Yes | Yes | Hard | 11 |
| 😁 Spot | Yes | Yes | Moderate | 8 |
| 😁 Nancy | Yes | Yes | Easy | 6 |


HydroData is designed to help (1) find, (2) get, (3) visualize, and (4) format disparate earth systems data through a core language (R); a common geospatial reference (ESPG:4629) ; and unifying vocabulary built around querying data by an area of interest (AOI). The package supports access to 20+ National/Global data sources.
All functions are designed to work with the AOI package and magrittr pipe operation %>%
allowing successive HydroData calls to be directly chained to an area of interest:
myData = getAOI(clip = list("UCSB", 10, 10)) %>%
findNHD() %>%
findWaterbodies %>%
findNWIS %>%
findNED()
str(myData,max.level = 2)
List of 5
$ AOI :Formal class 'SpatialPolygons' [package "sp"] with 4 slots
$ nhd :Formal class 'SpatialLinesDataFrame' [package "sp"] with 4 slots
$ waterbodies:Formal class 'SpatialPolygonsDataFrame' [package "sp"] with 5 slots
$ nwis :Formal class 'SpatialPointsDataFrame' [package "sp"] with 5 slots
$ NED :Formal class 'RasterLayer' [package "raster"] with 12 slots
- attr(*, "class")= chr "HydroData"
For now, most internal HydroData operations are based of the ‘simple feature (sf)’ library however objects are all returned as Spatial* sp objects. As the community around simple features grows the default output of ‘HydroData’ might change. Until then users can use the to_sf
function to convert a HydroData
object to simple features where appropriate.
HydroData offer in-package tools for generating interactive visualizations of HydroData Spatial* objects
myData.sf = myData %>% to_sf
str(myData.sf,max.level = 1)
List of 5
$ NED :Formal class 'RasterLayer' [package "raster"] with 12 slots
$ AOI :Classes ‘sf’ and 'data.frame': 1 obs. of 1 variable:
..- attr(*, "sf_column")= chr "geometry"
..- attr(*, "agr")= Factor w/ 3 levels "constant","aggregate",..:
.. ..- attr(*, "names")= chr(0)
$ nhd :Classes ‘sf’ and 'data.frame': 100 obs. of 92 variables:
..- attr(*, "sf_column")= chr "geometry"
..- attr(*, "agr")= Factor w/ 3 levels "constant","aggregate",..: NA NA NA NA NA NA NA NA NA NA ...
.. ..- attr(*, "names")= chr [1:91] "id" "ogc_fid" "comid" "fdate" ...
$ waterbodies:Classes ‘sf’ and 'data.frame': 11 obs. of 24 variables:
..- attr(*, "sf_column")= chr "geometry"
..- attr(*, "agr")= Factor w/ 3 levels "constant","aggregate",..: NA NA NA NA NA NA NA NA NA NA ...
.. ..- attr(*, "names")= chr [1:23] "id" "objectid" "comid" "fdate" ...
$ nwis :Classes ‘sf’ and 'data.frame': 3 obs. of 8 variables:
..- attr(*, "sf_column")= chr "geometry"
..- attr(*, "agr")= Factor w/ 3 levels "constant","aggregate",..: NA NA NA NA NA NA NA
.. ..- attr(*, "names")= chr [1:7] "OBJECTID" "feature_id" "site_no" "site_name" ...
HydroData offer in-package tools for generating interactive visualizations of HydroData Spatial* objects
myData %>% explore()
To download and get started with HydroData, install from GitHub using the devtools
packages:
library(devtools)
install_github("mikejohnson51/HydroData")


Police close road near Dunkeld after tree falls in landslip
A country road in Highland Perthshire has been closed after being blocked by a tree which fell in a landslip.
by Ross GardinerThe tree, which fell in the early hours of this morning, is blocking the A822 at Trochry, near Dunkeld.
A police spokesperson said: “Police Scotland would like to advise motorists that there is a road closure on the A822 at Trochry, near to Dunkeld, due to a fallen tree and landslip.
“Diversions are in place and drivers are asked to avoid the area if possible and thanked for their patience.”


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