Ghost in the Cel - Ghost in the Shell Review Director: Mamoru Oshii Reviewed by: Foong Ngai Hoe (Yukino Yoshi) ================================
When Ghost in the Shell (GitS) was released in 1995, it was probably one of the most eagerly-awaited full-length anime (Japanese animation) films since Katsuhiro Otomo's 1988 cyberpunk hit, Akira. Jointly financed by Manga Video, Kodansha, and Bandai Visuals, GitS became the first anime project to receive financial support outside Japan, and was subsequently launched simultaneously on both sides of the Pacific - in Japanese and English. Based on Shirow Masamune's manga (comics) series, the film was directed by Mamoru Oshii, one of Japan's top anime directors.
Major Motoko Kusanagi is an elite officer in the Section 9 security force, and along with fellow-cyborg Bateau and the mostly human Togusa, she is ordered to track down an illusive computer-criminal known only as the Puppet Master, a hacker who can hack into the minds of cyborgs. But the conspiracy unravels after Kusangi realises that the Puppet Master is in fact a renegade program belonging to one of the ministries which claims it's born "in a sea of information". As the ministries try to out-maneuver each other in a political game to retrieve the Puppet Master, the latter now turns its attention to Kusanagi. When the Major begins to question her own existence as a cyborg, the Puppet Master offers her a merger which will bring about a new life. This is where the story derives its name from - the 'ghost' being the soul a cyborg feels inside his or her shell (body).
Masamune's GitS is part political thriller, part social satire that access society in the digital age. Those familiar with his comics are quick to acknowledge the footnotes he attaches to his stories - some of them taking up chapters even. GitS is a difficult manga to read, made easier only by Masamune's brand of black humour.
GitS the movie isn't so much an Oshii's adaptation of Masamune's comics, but rather the director's own vision of it. Like in his Patlabor movies, Oshii brings to GitS a shade of noir that's become his trademark - tight camera angles, heavy shadow outlines, depressing colours, and a story that plods ponderously along.
Which is precisely the problem, depending on whether you like Oshii's style of direction or not. Too much of GitS involves either Kusanagi soul-searching, or long spells of dialogue off-screen while audiences are left staring at the backdrop. To be fair, Oshii's unique directional style does give many of the scenes a kind of poetry, but a less-appreciative audience might end up squirming uncomfortably in their seats. However, when the action does take place, it's often intense and gripping in ways many Hollywood action films aren't. Blood splatters across the screen, heads get blown off, but the violence is so well integrated into the action they don't seem too gratuitous.
The immense detail in the animation is breathtaking (even by today's standards) - from the dark and moody backdrops of Hong Kong in the 21st century, to the gunfights that punctuate the film. Kenji Kawaii's etheral score also lends to GitS the meditative feel the story needs.
In short, GitS is a technically brilliant and stylish animation, with a thought-provoking story you'd not find in western animation. Unfortunately, it's also a film too entrenched in its genre for its own good - with a plot so murky you might need a second viewing to fully understand it.
I'm not saying GitS is a poor production - on the contrary, it's a marvellous aural and visual treat. Yet as art often does, GitS will win many admirers but not converts. Neither is it likely to convince the west that Japanese animation's a lot more than robots, babes, and sex. And when you consider the amount of money poured into making this film a blockbuster, that's self-indulgence.
All said, GitS is still worth the watch if you've always wondered how Terminator 2 or Johnny Mnemonic might have turned out if animated. Without any doubt this a great work of art - it's how entertaining it is as a film that's open to debate.
Rating: 3.5/5
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Related links: index of all rec.arts.movies.reviews reviews


Your ingratitude has no place in our industry – KOD fires Kelvyn Boy
by MyNewsGHRadio personality, Kofi Okeyere Darko known popularly as KOD has told Kelvyn Boy that his ingratitude has no place in the entertainment industry.
He made this known in a comment section of a post made by George Britton which was warning Kelvyn Boy to slow down on his crusade against his boss through his public commentary.
KOD said “He needs to be taught that this industry doesn’t appreciate ingratitude”.
Source:MyNewsGh.com/Ayeh Offei-Akoto/2019


Natural Language Processing Expert - San Jose, CA
Location : San Jose, CA
Experience : 3+ years
Required Skills :
- Hands-on NLP & ML (machine leaning) is must and must have worked with NLP packages such as Stanford NLP, NLPTK and other open-source libraries.
- Familiar with concepts such as named entity extraction, topic modelling, relationships between text structures.
- Data modelling.
- Data acquisition technologies.
- Text mining.
- Visualization.
- Experience with proprietary NLP technologies is an added bonus.
Job Responsibilities :
- He/she should be able to develop NLP based tools and technologies for acquiring, parsing, interpreting and visualizing unstructured data.
- Should be able to code hands-on (65%) and provide guidance & manage junior developers(35%).
- Should possess excellent communication skills.
- Should be able to interact with client.
We are always looking out for talented individuals who are ambitious, who love challenges and who have a passion to exceed through innovation!
Optra HEALTH participates in campus hiring programs and also conducts a number of off-campus initiatives. In addition to campus, we also hire experienced professionals from the industry at various mid-level and senior positions.
Candidates can send their updated profile at hr@optrasystems.com


| probe {pbdMPI} | R Documentation |
The functions call MPI probe functions.
probe(rank.source = .pbd_env$SPMD.CT$rank.source, tag = .pbd_env$SPMD.CT$tag, comm = .pbd_env$SPMD.CT$comm, status = .pbd_env$SPMD.CT$status) iprobe(rank.source = .pbd_env$SPMD.CT$rank.source, tag = .pbd_env$SPMD.CT$tag, comm = .pbd_env$SPMD.CT$comm, status = .pbd_env$SPMD.CT$status)
rank.source |
a source rank where an object sent from. |
tag |
a tag number. |
comm |
a communicator number. |
status |
a status number. |
These functions are for internal functions. Potentially, they set/get probe for receiving data.
An invisible state of MPI call is returned.
Wei-Chen Chen<EMAIL_ADDRESS>George Ostrouchov, Drew Schmidt, Pragneshkumar Patel, and Hao Yu.
Programming with Big Data in R Website: http://r-pbd.org/
## Not run: ### See source code of spmd.recv.default() for an example. ## End(Not run)


Enter an Order ID. It will generate a filtered file and automatically download.
General Regex Searcher
Enter a regex statement with brackets. Not recommended for Debenhams API log files.
This searcher takes in regex and then searches through the files uploaded and produces a file containing these search results. You can specify what you want to return by adding brackets. For example: "Hello (World)" will search for "Hello World"s, and return a list of World. A more sophisticated example would be: "Hello ([A-Za-z])" which will search for "Hello [NAME]" e.g. "Hello Dolan", and then return a list of names.


Uses of Class
com.google.zxing.ResultPoint
Package
Description
-
Uses of ResultPoint in com.google.zxing
Modifier and TypeMethodDescriptionvoid
Result.addResultPoints
(ResultPoint[] newPoints) static float
ResultPoint.distance
(ResultPoint pattern1, ResultPoint pattern2) void
ResultPointCallback.foundPossibleResultPoint
(ResultPoint point) static void
ResultPoint.orderBestPatterns
(ResultPoint[] patterns) Orders an array of three ResultPoints in an order [A,B,C] such that AB is less than AC and BC is less than AC, and the angle between BC and BA is less than 180 degrees.ModifierConstructorDescriptionResult
(String text, byte[] rawBytes, int numBits, ResultPoint[] resultPoints, BarcodeFormat format, long timestamp) Result
(String text, byte[] rawBytes, ResultPoint[] resultPoints, BarcodeFormat format) Result
(String text, byte[] rawBytes, ResultPoint[] resultPoints, BarcodeFormat format, long timestamp) -
Uses of ResultPoint in com.google.zxing.aztec
ModifierConstructorDescriptionAztecDetectorResult
(BitMatrix bits, ResultPoint[] points, boolean compact, int nbDatablocks, int nbLayers) AztecDetectorResult
(BitMatrix bits, ResultPoint[] points, boolean compact, int nbDatablocks, int nbLayers, int errorsCorrected) -
Uses of ResultPoint in com.google.zxing.common
-
Uses of ResultPoint in com.google.zxing.common.detector
-
Uses of ResultPoint in com.google.zxing.oned.rss
-
Uses of ResultPoint in com.google.zxing.pdf417.decoder
Modifier and TypeMethodDescriptionstatic DecoderResult
PDF417ScanningDecoder.decode
(BitMatrix image, ResultPoint imageTopLeft, ResultPoint imageBottomLeft, ResultPoint imageTopRight, ResultPoint imageBottomRight, int minCodewordWidth, int maxCodewordWidth) -
Uses of ResultPoint in com.google.zxing.qrcode.decoder
Modifier and TypeMethodDescriptionvoid
QRCodeDecoderMetaData.applyMirroredCorrection
(ResultPoint[] points) Apply the result points' order correction due to mirroring. -
Uses of ResultPoint in com.google.zxing.qrcode.detector
Modifier and TypeClassDescriptionfinal class
Encapsulates an alignment pattern, which are the smaller square patterns found in all but the simplest QR Codes.final class
Encapsulates a finder pattern, which are the three square patterns found in the corners of QR Codes.Modifier and TypeMethodDescriptionprotected final float
Detector.calculateModuleSize
(ResultPoint topLeft, ResultPoint topRight, ResultPoint bottomLeft) Computes an average estimated module size based on estimated derived from the positions of the three finder patterns.


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Definition
Average growing degree-days above 5°C for wheat
Method of calculation
Sum of average daily GDD from GDD2Start to STOPWheat.
Daily GDD = Tmean -5.0 where Tmean is the average mean daily air temperature (°C);
If Tmean is < 5.0, Daily GDD=0.0.
If STOPWheat ‘-99’, i.e.later than GDD2Stop (estimated average date of first fall frost), then accumulation is stopped on GDD2Stop.


Advanced Breeding: Making The Most Of Every Resource
by Bayer ContributorBy BayerPlants have clever ways of defending themselves.
In fact, the smell of freshly cut grass is not only a sign of spring, but a remarkable example of evolution. Your lawn is actually sending out a distress signal in the form of this unmistakable aroma. The grass assumes the lawnmower blade is an insect feeding on the plant and responds by releasing a scent that will attract beneficial predatory insects to deter the pest.A
In many regards, “plant breeding” is the study of special abilities like this. Some plants are exceptional at withstanding drought, resisting insects, fighting disease, or contending with invasive weeds. Plant breeders search the natural world in hopes of finding varieties that have adapted unique and remarkable methods of survival. This natural evolution of plants then informs new generations of crops.
FINDING AND SHARING POSITIVE TRAITS
Plants have a much greater awareness of their surroundings than many originally thought. For example, when an insect lands on a variety of Peruvian tomatoes, tiny hairs on the vine release a sugar that deters the pest from feeding or laying eggs on the plant.
Plant breeders have started to cross these wild tomato plants with other varieties, helping them to inherit this defense. Plant breeders are also experimenting with how the texture of leaves can make landing on the plant more difficult for insects. Mint, basil, and tomato varieties are all being explored by plant scientists for their ability to deter insects in this manner.B,C
PLANTS THAT TELL STORIES
By crossing over a dozen different varieties of cassava (a starchy root commonly eaten in Africa), plant breeders have created several disease-resistant lines of this important crop.
Known as “systemic acquired resistance,” breeders study the immunity response of many generations of plants. In a sense, their immunology tells a story — breeders look deep into the genetics of these plants to learn how past generations have adapted to a pathogen. They then use this as a roadmap to help new varieties inherit this characteristic.D,E
A WEEDY CONFLICT OVER TERRITORY
Many plants have adapted by not giving a single inch to the competition. Crops that can grow close together and prevent sunlight from reaching the soil create a natural defense from invasive weeds. After observing varieties of soy that spread over the soil, plant breeders determined these varieties could create a helpful mechanism for suppressing weeds.
By creating a canopy to block the sunlight, certain soy varieties can discourage weed growth and deny invasive pests the resources needed to grow.
GROWING CORN TO COMBAT DROUGHT
Over decades of research, plant breeders have discovered a number of characteristics that can help a crop withstand periods without rainfall. One key area of progress has centered around varieties of maize with deep and robust root systems.
Researchers selected this characteristic for breeding to help the crop reach further into the soil to acquire water. The increasingly erratic weather patterns brought on by climate change make drought-tolerant and insect-resistant traits even more essential for farmers around the globe.F
At Bayer, we’re collaborating with programs like the TELA Maize Project to create drought-tolerant and insect-protected maize varieties in Sub-Saharan Africa. By providing research, technical expertise, and both drought-tolerant and insect-resistant traits on a royalty-free basis, we can enhance food security for farmers and their families.
“We recognize that smallholder farmers can have a big impact in eradicating hunger in ways that are sustainable for the environment and our communities,” states Mike Graham, Head of Plant Breeding for the Crop Science division of Bayer, “Thanks to the tremendous efforts and achievements of these public-private partnerships, we’re making progress to sustainably deliver seeds that can help improve harvests, reduce hunger, and improve economic conditions.”
EVOLVING OUR IDEAS, INSPIRED BY NATURE
For millions of years, the natural world has adapted many remarkable and effective methods of survival. Plant breeders set out to discover these special traits and use this knowledge to help farmers make the most of every resource. Using inspiration found in the ecosystem, plant breeders across modern agriculture are adapting plants to manage insects, drought, weeds, and disease in new and more effective ways.


{{ define "title"}}Create First User {{end}}
{{ define "loginform" }} {{end}}
{{block "pagedescription" .}}
Create First Admin User
{{end}}
{{define "content"}}
{{if .IsTokenSet}}
Welcome! Since the database is empty, you have been redirected here to create the first user.
This user will have administrative privileges. If you are supposed to be here, then you will
know what the Admin Token is.
{{else}}
If you are seeing this then there is a problem with your configuration.
You are here because there are no users in the database and the system assumes you want to create a new
administrative user. The problem is that there is no AdminToken set. This token serves the purpose of
letting us know that you actually are the administrator and not just some random person who happened
to jump the gun and log in early.
The first step is to make sure that you either have the $GGEROOT environment variable set. If you know
how to do this, great. If not, don't worry. The hard coded default value is '/home/USER/config/gge', where USER
is the name of the user who owns the process. This should be a reasonable default for most users. If not, google
around a bit to find the instructions for either setting an env variable, or passing one to the process at startup time.
The file gge.json should be sitting in the directory indicated by $GGEROOT. Reference
config/example.config.json
to see what the file should look like. AdminToken should be set to a string value. This is the valye that you will
give the form to create your new admin user. Once the administrative user is created, it is advisable to reset the value
of AdminToken to an empty string ("") in the gge.json file. It will only ever be checked when accessing this page.
Remember, you will need to restart the GGE process for this change to register. The configuration file is only loaded
when the process first starts. Once you make the needed corrections and restart the program you may refresh this page.


Datepicker appears on focus - click in the field. The text is in English and the date format is US (mm/dd/yyyy).
Datepicker appears on focus or icon click. It shows days in other months and the week of the year.
The datepicker can be localized for a large number of languages. Here is the French version.
The date can appear in a number of different formats with the default being 'mm/dd/yyyy'. This example shows the full format with day and month names.
Pick a date range by selecting the start and end date. Several months are shown at once.
The datepicker can also appear inline and notify the page when selected.
The datepicker can do even more. Check out the full demonstration on the datepicker home page.


|
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| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | |||||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |
java.lang.Object org.hecl.Thing
public class Thing
The Thing
class is what Hecl revolves around. "Things"
can be of several types, include strings, integers, lists, hash
tables.
| Field Summary | |
|---|---|
protected boolean |
copy
|
boolean |
global
|
protected boolean |
literal
literal is used to indicate Things which come
directly from the parser and thus should not be changed. |
| Constructor Summary | |
|---|---|
Thing(RealThing realthing)
Creates a new Thing instance from an internal
representation. |
|
Thing(java.lang.String s)
Creates a new Thing instance from a string. |
|
Thing(java.lang.StringBuffer s)
Creates a new Thing instance from a string buffer. |
| Method Summary | |
|---|---|
Thing |
deepcopy()
deepcopy copies the thing, its value, and any elements the
value might contain. |
static Thing |
emptyThing()
|
boolean |
equals(java.lang.Object obj)
The equals method overrides the object equals
method, using either the two objects, in the case of two
ObjectThings, or the two string representations. |
RealThing |
getVal()
getVal fetches the internal value of the Thing. |
int |
hashCode()
The hashCode method overrides the object hashCode
method, using the hash of the string representation. |
boolean |
isLiteral()
|
static boolean |
isTrue(Thing thing)
isTrue is a convenience function that lets us know if the
result of a calculation is true or false. |
void |
makeref(Thing newval)
makeref sets the 'this' Thing to be a reference to the
newval that was passed to it. |
void |
setCopyVal(RealThing realthing)
|
Thing |
setLiteral()
The setLiteral method sets the thing in question
to be a literal Thing, and returns it. |
void |
setVal(RealThing realthing)
setVal sets the internal representation of the Thing. |
java.lang.String |
toString()
toString returns the String value of a Thing. |
| Methods inherited from class java.lang.Object |
|---|
clone, finalize, getClass, notify, notifyAll, wait, wait, wait |
| Field Detail |
|---|
protected boolean copy
protected boolean literal
literal
is used to indicate Things which come
directly from the parser and thus should not be changed.
public boolean global
| Constructor Detail |
|---|
public Thing(java.lang.String s)
Thing
instance from a string.
s
- a String
valuepublic Thing(java.lang.StringBuffer s)
Thing
instance from a string buffer.
s
- a StringBuffer
valuepublic Thing(RealThing realthing)
Thing
instance from an internal
representation.
realthing
- a RealThing
value| Method Detail |
|---|
public void setVal(RealThing realthing)
setVal
sets the internal representation of the Thing.
realthing
- a RealThing
valuepublic void setCopyVal(RealThing realthing)
public RealThing getVal()
getVal
fetches the internal value of the Thing.
RealThing
valuepublic static final Thing emptyThing()
public static boolean isTrue(Thing thing) throws HeclException
isTrue
is a convenience function that lets us know if the
result of a calculation is true or false.
thing
- a Thing
value
boolean
value
HeclException
- if an error occurspublic void makeref(Thing newval)
makeref
sets the 'this' Thing to be a reference to the
newval that was passed to it.
newval
- a Thing
value.public java.lang.String toString()
toString
returns the String value of a Thing.
toString
in class java.lang.Object
String
valuepublic int hashCode()
hashCode
method overrides the object hashCode
method, using the hash of the string representation.
hashCode
in class java.lang.Object
int
valuepublic boolean equals(java.lang.Object obj)
equals
method overrides the object equals
method, using either the two objects, in the case of two
ObjectThings, or the two string representations.
equals
in class java.lang.Object
obj
- an Object
value
boolean
valuepublic Thing setLiteral()
setLiteral
method sets the thing in question
to be a literal Thing, and returns it.
Thing
valuepublic boolean isLiteral()
public Thing deepcopy() throws HeclException
deepcopy
copies the thing, its value, and any elements the
value might contain.
Thing
value
HeclException
|
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| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | |||||||||
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New Ethiopian Music 2019 -Tsegaye Eshetu – And Enhun
borkena
New Ethiopian Music 2019 – Tsegaye Eshetu – And enhun
Video : embedded from Hope Entertainment Youtube channel
Cover photo : screenshot from video
Join the conversation. Like borkena on Facebook and get Ethiopian News updates regularly. As well, you may get Ethiopia News by following us on twitter @zborkena


Angular Material Design Buttons With Progress indicators
Scroll down to see it in action!
Component
[options]
[active]
The active option can be manipulated separately to be able to use observables variables inside of the angular template syntax (e.g. in usage with ngrx/store selectors) instead of having to subscribe and unsubscribe to the active state in the component.
[disabled]
The disabled option can be manipulated separately to be able to use observables variables inside of the angular template syntax (e.g. in usage with ngrx/store selectors) instead of having to subscribe and unsubscribe to the disabled state in the component.
Global Options
Optionally pass default `MatProgressButtonOptions` in `forRoot` in side your app.modlue.ts for each button as an array.
[options] will override Global Options provided in `forRoot`
Component
[options]
[active]
The active option can be manipulated separately to be able to use observables variables inside of the angular template syntax (e.g. in usage with ngrx/store selectors) instead of having to subscribe and unsubscribe to the active state in the component.
[disabled]
The disabled option can be manipulated separately to be able to use observables variables inside of the angular template syntax (e.g. in usage with ngrx/store selectors) instead of having to subscribe and unsubscribe to the disabled state in the component.
Global Options
Optionally pass default `MatProgressButtonOptions` in `forRoot` in side your app.modlue.ts for each button as an array.
[options] will override Global Options provided in `forRoot`


Lipreading Video Interface
Configure Session
Multiple Choice:
Shuffle order of videos:
See without caption first in training:
Show results after session:
Allow replays:
Number of times to repeat each video:
Select Videos:
Select All
Train
Practice
Test
Pause
Cancel
Progress:
HTML5 Video Element Not Supported
HTML5 Video Element Not Supported
Answer:
Submit [enter]
Skip
Replay
Continue
Complete Session
Participant ID / Name:
Save Results and Continue
Please enter a participant ID to download results.


Set admist the monsoon of 2018 , this blog contains entries and pictures that help you get a glimpse of our journey, our tour , to the hustling Capital of India, Delhi. A place steeped so rich with history and culture , a hotspot of Indian civilization for centuries , the house of modern marvels and the face of modern India, Delhi. We went to discover it all and much more in this trip of ours, our very own Safar. And this is our blog.


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GrMesh::GrMesh::STriCollision Struct Reference
triangle collision data.
More...
#include <GrMesh.h>
List of all members.
Detailed Description
triangle collision data.
Definition at line 58 of file GrMesh.h.
Constructor & Destructor Documentation
| GrMesh::GrMesh::STriCollision::STriCollision |
( |
|
) |
[inline] |
Member Data Documentation
| MVec3 GrMesh::GrMesh::STriCollision::hitLoc |
| MVec3 GrMesh::GrMesh::STriCollision::hitNormal |
| unsigned int GrMesh::GrMesh::STriCollision::triIdx |
| float GrMesh::GrMesh::STriCollision::u |
The documentation for this struct was generated from the following file:
- C:/Bootstrap/ProjectBX/Documentation/Code/Engine/Graphics/GrMesh.h


Briefly explain why we need to be able to have less than 24-bit color and why this makes for a problem.
Generally, what do we need to do to adaptively transform 24-bit color values to 8-bit ones?
May not be able to handle such large file sizes or not have 24-bit displays.
The colors will be somewhat wrong, however.
We need to cluster color pixels so as to best use the bits available to be as accurate as possible for the colors in an image.
Exercise 2
Suppose we decide to quantize an 8-bit grayscale image down to just 2 bits of accuracy. What is the simplest way to do so? What ranges of byte values in the original image are mapped to what quantized values?
Just use the first 2 bits in the grayscale value.
I.e., any values in the ranges
0000 0000 to 0011 1111
0100 0000 to 0111 1111
1000 0000 to 1011 1111
1100 0000 to 1111 1111
are mapped into 4 representative grayscale values.
In decimal, these ranges are:
0 to (2ˆ6-1)
2ˆ6 to (2ˆ7-1)
2ˆ7 to 2ˆ7 + (2ˆ6-1)
2ˆ7 + 2ˆ6 to (2ˆ8-1)
i.e.,
0 to 63
64 to 127
128 to 191
192 to 255
Then reconstruction values should be taken as the middle
of these ranges; i.e.,
32
96
160
224
Exercise 3
Suppose we have available 24 bits per pixel for a color image. However, we notice that humans are more sensitive to R and G than to B — in fact, 1.5 times more sensitive to R or G than to B.


New to WiCS? Here's a quick guide on how to keep tabs on events, become a member, and more:
Welcome to WiCSJoin us on Fridays at 4:30pm (Malone 107) to hang out with friends and discuss our topic of the week. Snacks and nail polish provided!
Starting in September 2018, we'll be featuring one outstanding WiCS member per month on our Facebook page.
Nominate a WiCS member here.
Attend 3 events to become an official member of WiCS.
To remain an active member of WiCS, attend at least 2 events each semester.


“An Introduction to a revolutionary technique in the field of agriculture that’s going to change the idea of farming to whole new level…!!”
Well “IoT” in agriculture uses robots, drones, remote sensors and computer imaging combined with continuously progressing machine learning and analytical tools for monitoring crops, surveying and mapping the fields and provide data to farmers for rational farm management plans to save both time and money.
But why “IoT” in Agriculture?
IoT is transforming the everyday objects that surrounds us into an ecosystem of information that will enrich our lives. From speakers to parking spaces to our homes IoT is bringing a revolution everywhere. Smart homes and other connected products will not be aimed at home life only they are also going to have a major effect in the industrial growth too.
1. ‘Climate’ plays a huge part:
Climate plays a very critical role for farming. And having improper knowledge about climate heavily deteriorates the quantity and quality of the crop production. But IoT solutions enable you to know the real-time weather conditions. Sensors are placed inside and outside of the agriculture fields. They collect data from the environment which is used to choose the right crops which can grow and sustain in the particular climatic conditions. The main target of the sensors in this field is to eliminate the need of physical presence during disturbing climate conditions.
2. Smart Farming:
Precision Agriculture/Precision Farming is one of the most famous applications of IoT in Agriculture. It makes the farming practice more precise and controlled by realizing smart farming applications such as livestock monitoring, vehicle tracking, field observation, and inventory monitoring. The goal of precision farming is to analyse the data, generated via sensors, to react accordingly. Precision Farming helps farmers to generate data with the help of sensors and analyse that information to take intelligent and quick decisions.
3. Greenhouses can also be ‘Smart’:
To make greenhouses smart, IoT has enabled weather stations to automatically adjust the climate conditions according to a particular set of instructions. Adoption of IoT in Greenhouses has eliminated the human intervention, thus making entire process cost-effective and increasing accuracy at the same time. For example, using solar-powered IoT sensors builds modern and inexpensive greenhouses. Also, Automatic and smart irrigation is carried out with the help of IoT.
4. Analysing Data (Cloud Based):
The conventional database system does not have enough storage for the data collected from the IoT sensors. Cloud based data storage and an end-to-end IoT Platform plays an important role in the smart agriculture system. These systems are estimated to play an important role such that better activities can be performed. In the IoT world, sensors are the primary source of collecting data on a large scale. The data is analysed and transformed to meaningful information using analytics tools. With the help of the IoT devices, you can know the real-time status of the crops by capturing the data from sensors.
5. ‘Drones’ in Agriculture:
Technological advancements have almost revolutionized the agricultural operations and the introduction of agricultural drones is the trending disruption. The Ground and Aerial drones are used for assessment of crop health, crop monitoring, planting, crop spraying, and field analysis. With proper strategy and planning based on real-time data, drone technology has given a high rise and makeover to the agriculture industry. Drones with thermal or multispectral sensors identify the areas that require changes in irrigation.
So how does “IoT” gets in Agriculture?
“All These Tech Talks are Making my Brain Swell” -----By Everyone
There are numerous examples of leveraging IoT technologies in agriculture from versatile data analytics and management systems to futuristic robot pollinators including climate, agricultural sensors and smart greenhouses. For example, Agricultural sensors can help providing data that monitors and optimizes crops as well as adapt changing environmental factors. We can use INT G01 sensor to measure the apparent dielectric constant of the soil and Soil PH Sensor INT-PH1 is used in soil PH detection. Optical sensors like Vishay uses light to measure soil properties by being placed on conveyances or aerial platforms. Mechanical sensors measure soil compaction. For measuring the aerial condition in the area, air temperature sensors, interior temperature sensors, outlet duct and evaporator temperature sensors, pressure sensors, humidity sensors and sunload sensors can be used. Climate can be unpredictable, but by collecting and analysing environmental data throughout a cycle of time period can make the work a whole lot lesser. Agricultural Weather Stations are self-contained units that are placed at various locations throughout growing fields. These stations have a combination of sensors appropriate for the local crops and climate. Data is measured and recorded at predetermined intervals and is compiled and sent wirelessly to a central data logger.
Now the most basic question remains is… “will IoT farming be affordable ??”
According to Second International Conference on Intelligent Computing and Control Systems (ICICCS) in 2018 it was stated that each year many crops go waste due to a lack of optimal climatic conditions to support crop growth. Losses to the tune of over 11 billion dollars are reported each year in India alone. After this “sensors” sound a bit cheaper. Moreover, it uses the cheapest sources of energies that are available currently that is Solar PV and onshore wind.
All the above facts are the “pros” of IoT Farming…so what about the “cons”
The smart agriculture needs availability of internet continuously. Rural part of most of the developing countries do not fulfil this requirement. Moreover, internet connection is slower. The smart farming-based equipment’s require farmers to understand and learn the use of technology. This is major challenge in adopting smart agriculture farming at large scale across the countries.
Last but Not Least…A few things to consider before your smart farming solution:
1| Hardware
To build an IoT solution for agriculture, you need to choose the sensors for your device. Your choice will depend on the types of information you want to collect and the purpose of your solution. In any case, the quality of your sensors is crucial to the success of your product, it will depend on the accuracy of the collected data and its consistency.
2| The Brain
Analysis of the data should be at the core of every smart agriculture solution. The collected data itself will be of little help if you cannot make sense of it. Thus, you need to have powerful data analytics capabilities and apply predictive algorithms and machine learning in order to obtain actionable insights based on the collected data.
3| Maintenance
Maintenance of your hardware is a challenge that is of primary importance for IoT products in agriculture, as the sensors are typically used in the field and can be easily damaged. Thus, you need to make sure your hardware is durable and easy to maintain. Otherwise, you will need to replace your sensors more often than you would like.
4| Mobility
Smart farming applications should be tailored for use in the field. A farm manager should be able to access the information on site or remotely via a smartphone or desktop computer. Plus, each connected device should be autonomous and have enough wireless range to communicate with the other devices and send data to the central server.
5| Security
To ensure that your smart farming application performs well, you need a solid internal infrastructure.  Failing to properly secure your system only increases the likeliness of someone breaking into it, stealing your data, or even taking control of your autonomous devices (can be drones or even tractors).
#Just a fact:
It is estimated over 30 billion devices could be connected to the Internet of Things by 2020. In recent times a “Robot” named “Row-Bot” is developed which is dedicated to increasing the efficiency of nitrogen fertilizer use in production agriculture.
Sources / References [If Any] : Internet


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PSALMS 61
A psalm written by David for the choir director, to be accompanied by musical instruments.
1 God, listen to me
and answer my prayer.
2 While I am discouraged and far from my home,
I am calling out to you.
Lead me to a place that will be like a high rock
on top of which I will be safe.
3 You have been my refuge;
you have been like a strong tower
in which my enemies cannot attack me.
4 Allow me to live close to your sacred tent all during my life!
Allow me to be safe as a little bird is safe under its mother's wings.
5 God, you heard me when I solemnly promised to give offerings to you;
you have given to me the blessings that belong to those who have an awesome respect for you.
6 I am the king of Israel;
please allow me to live and rule for many years,
and allow my descendants to rule also.
7 Allow us to rule forever while you observe us;
watch over us while you faithfully love us and do for us what you promised.
8 If you do that, I will always sing to praise you
while I offer to you each day the sacrifices that I promised to give to you.


Ocean [aj00], ocean, in Crystal Sea
Last updated Turn #146
Ocean [aj00], ocean, in Crystal Sea
------------------------------------------------------------------------
2: The Savvy Aardvark [ha5r], ship, 2 hulls, 1 rowing port, 6 sails,
2: 27% damaged, arrived from Ocean [ak00], owner:
2: Phantasm [n4b], Imperial Empire, duke, with two workers,
2: eight sailors
3: The Savvy Aardvark [ha5r], ship, 2 hulls, 1 rowing port, 6 sails,
3: 27% damaged, departed for Ocean [ai00].
Routes leaving Ocean:
North, to Ocean [ai00], 6 days
East, to Ocean [aj01], 6 days
South, to Ocean [ak00], 6 days
West, to Ocean [aj86], 6 days


This is my mom, Sally. She has the sweetest heart; and, she also happens to be extremely beautiful on the outside, as well. With her big brown eyes and long eyelashes, she will say the cutest, most random things that are so funny; she doesn't even do it on purpose.
Sally loves to have fun. When driving in the car, she'll be the first to sway back and forth, do a little dance move (i.e., the cutest dance move, ever), and sing every word to the song. She always gets super excited when particular songs come on; one of them is "Sugar" by Maroon Five. Decades ago, she would use the cigarette lighter as a microphone, which I especially enjoyed --that, and she would sometimes push the break pedal during the beats of a song, though that was a once-in-a-blue-moon thrill (special occasion).
Ever since I was little, I've been her #1 fan. She has taught me how, and what it means to love, unconditionally; giving unconditional love is one of the things she does best. This girl is all about love. In fact, when I was little, her favorite thing was a heart. During every occasion, she would ask for something (as long as it was in the shape of heart). She would also find hearts, everywhere. One time, she found a bagel that was shaped in a heart, and she encouraged me to save it; it sat on my desk for the longest time. She taught me this -to always look for hearts.
Sally is not only the greatest mom, but she is my best friend. During times I find myself feeling unlucky for various reasons in life, I always know that I am truly the luckiest person, when it comes to having her as my mom.


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Click on the canvas to toggle a cell. Shift+click to place a pulsar, Ctrl+click to place a glider.


"'He who says he can and he who says he can't are both usually right " - Confucius. Neuroplascity is the idea that the brain can learn and form and strengthen new connections as opposed to being static and unchanging. For years the idea of intelligence has been "either you've got it or you don't". Some innate attribute that some people are born with, or not... Going through life, studies have shown that people who believe that they arn't very intelligent for whatever reason will not as readily take on new challenges and therfore new opportunities. Neuroplacity says that those people instead of being static with either having intelligence or not, can actually grow and learn in any skill they put their mind too. The studies have shown that new information is recieved as real physical "real-estate" in the brain that looks like a small ball, and that when called upon will be strengthened with proteins between the trees that are calling them. The more that this new information is used, the more the new cell will resemble a tree and be connected to other cells within the brain, creating a network of understanding. People who understand the science of the brain can understand that although a task or a new skill might be a big task to learn, it is possible with enough practise!
If i can remember that my brain needs practise, and repitition to solidy new ideas and information, i think it will remind me to give myself the patience that i need when i don't understand. Often if i'm learning something new, i want to understand it quickly, and if i don't , sometimes i can move on. But understanding this function of the brain brings about a patient hope that given the right conditions i will understand and can be really great!
Learn more and learn often! The brain is a muscle and the more you fire the neurons through deliberate practise and learning of new things the faster it can form and strengthen new connections.
Brain detox by Dr Caroline Leaf"
The same as i discussed above!
I love these concepts so much, thankfully i have previously heard of the growth mindset and have read a few books on the subject. Although i've always had a pretty strong believe i can do anything i put my mind to (thankfully i was taught this) learning the growth mindset again as an adult really strengthens this idea that if you want something bad enough, go get it! So more than anything it strengthens my resolve to put in the work required to be great.
Same as discussed above really, be patient with myself, and give my self every opportunity to learn by knowing what my brain needs to grow. Time, repitition, food, rest.


map |
Object
|
|
ArcGIS Map |
services |
Object
|
|
Configuration for tiled services to be added to the BasemapGallery dijit |
titlePaneID |
String
|
|
The BasemapGallery dijit title pane ID |
basemapGalleryID |
String
|
|
The BasemapGallery dijit DOM node ID |
defaultServiceURL |
String
|
|
URL to an ArcGIS tiled service to be added first to the map and set it up, in case there isn't one specified in services |
showArcGISBasemaps |
Boolean
|
|
Indicates whether or not to include basemaps from ArcGIS Online |
bingMapsKey |
String
|
<optional>
|
Bing Maps Key if Bing Maps are included in services |


@INPROCEEDINGS{7037168,
author={S. Sarkar and S. Misra and C. Chakraborty and M. S. Obaidat},
booktitle={2014 IEEE Global Communications Conference},
title={Analysis of reliability and throughput under saturation condition of IEEE 802.15.6 CSMA/CA for wireless body area networks},
year={2014},
volume={},
number={},
pages={2405-2410},
keywords={Markov processes;access protocols;body area networks;carrier sense multiple access;quality of service;telecommunication network reliability;DTMC;IEEE 802.15.6 CSMA/CA saturation condition;IEEE 802.15.6 protocol standardization;WBAN;discrete-time Markov chain;medium access control layer;packet transmission;power wireless communication;quality of service;reliability analysis;throughput analysis;wireless body area network;Equations;IEEE 802.15 Standards;Markov processes;Radiation detectors;Reliability;Throughput;IEEE 802.15.6;Markov model;Reliability;Throughput;WBAN},
doi={10.1109/GLOCOM.2014.7037168},
ISSN={1930-529X},
month={Dec}
}


try:
if PARAMS["model_name"] == "SVR":
model.kernel = PARAMS["svr_kernel"]
elif PARAMS["model_name"] == "KNeighborsRegressor":
model.weights = PARAMS["knr_weights"]
except Exception as exception:
LOG.exception(exception)
exit(1)
model = model_dict[PARAMS["model_name"]]
model.normalize = bool(PARAMS["normalize"])
return model
def run(X_train, X_test, y_train, y_test, model):


"""
Utility code.
NMS adapted from Tomasz Malisiewicz's & Ross Girshick's code.
- [https://gist.github.com/quantombone/1144423]
- [https://github.com/rbgirshick/py-faster-rcnn/blob/master/lib/nms/py_cpu_nms.py]
"""
# pylint: disable=invalid-name,missing-docstring,assignment-from-no-return,logging-format-interpolation
import numpy as np
from bbox import BBox2DList
from bbox.box_modes import XYXY
[docs]def nms(bbl, scores, thresh):
"""
Perform fast non-maximum suppression on a set of bounding boxes \
given their associated confidences.
Args:
bbl (:py:class:`BBox2DList`): List of 2D bounding boxes.
scores (:py:class:`list` or :py:class:`ndarray`): Scores for each bounding box.
Raises:
ValueError: If arguments are of incorrect type or size.
"""
if bbl.shape[0] == 0:
return np.array([]).astype(np.int)
if not isinstance(scores, (list, np.ndarray)):
raise ValueError("`scores` should be a list of numpy array")
# convert to numpy array if it is a list
scores = np.asarray(scores)
if not scores.shape[0] == bbl.shape[0]:
raise ValueError("box list and scores should have the same number of elements.")
areas = bbl.w * bbl.h
order = scores.argsort()[::-1]
keep = []
while order.size > 0:
i = order[0]
keep.append(int(i))
xx1 = np.maximum(bbl.x1[i], bbl.x1[order[1:]])
yy1 = np.maximum(bbl.y1[i], bbl.y1[order[1:]])
xx2 = np.minimum(bbl.x2[i], bbl.x2[order[1:]])
yy2 = np.minimum(bbl.y2[i], bbl.y2[order[1:]])
w = np.maximum(0.0, xx2 - xx1 + 1)
h = np.maximum(0.0, yy2 - yy1 + 1)
inter = w * h
overlap = inter / (areas[i] + areas[order[1:]] - inter)
idx = np.where(overlap <= thresh)[0]
order = order[idx + 1]
return np.array(keep).astype(np.int)
[docs]def aspect_ratio(bbox, ratios):
"""
Enumerate box for each aspect ratio.
Args:
bbox (:py:class:`BBox2D`): 2D bounding box.
ratios (:py:class:`list`): list of int/float values.
"""
cx, cy = bbox.center()
w, h = bbox.w, bbox.h
size = w * h
ratios = np.asarray(ratios, dtype=np.float)
size_ratios = size / ratios
ws = np.round(np.sqrt(size_ratios))
hs = np.round(ws * ratios)
stack = np.vstack((cx - 0.5*(ws-1), cy - 0.5*(hs-1),
cx + 0.5*(ws-1), cy + 0.5*(hs-1)))
boxes = BBox2DList(stack.T, mode=XYXY)
return boxes


Founder of fashion delivery firm Clipper tables £300m takeover bid
by James WarringtonThe founder of Clipper Logistics, which provides delivery services for online retailers such as Asos and John Lewis, is said to be preparing a £300m bid to take the company private.
Read more: Asos to boost Black Friday offers after poor trading last year
Steve Parkin, who founded Clipper in 1992, has approached fellow board members about the proposed takeover, Sky News reported.
Parkin is said to have teamed up with US private equity firm Sun Capital Partners for the bid, which comes five years after Clipper’s London initial public offering (IPO).
The Leeds-based firm, which employs more than 6,000 people in the UK, specialises in handling online orders and returns for retail customers including Asda, M&S and Wilko.
Parkin, the firm’s executive chairman, holds roughly a third of its shares. With the support of allies, he is understood to count roughly 40 per cent of the company’s stock in favour of his takeover, according to the report.
Clipper’s independent non-executive directors are now said to be forming a committee to oversee a formal offer, and the company is expected to release a statement to the London Stock Exchange tomorrow morning confirming the approach.
Clipper posted a 15 per cent to £460.2m in the year to the end of April, while pre-tax profit slipped from £18m to £16.9m.
Sources told Sky News that Parkin had become disheartened by the decline in Clipper’s share price over the last year, following a strong period of growth after its IPO.
One analyst added that the founder’s desire to go private had been spurred on by his frustration at the amount of corporate governance-related commitments involved in running a public company.
Read more: John Lewis threatens to withhold 20 per cent of service charges
Clipper Logistics has been contacted for comment.
Main image credit: Getty


see_moonlight
版主
发贴: 190
技术分: 16 积分:522 |
于 2004-03-18 15:25
Bugs in GAlib Last modified 24
December 1999
This page contains a list of bugs and suggested
workarounds. Please submit to<EMAIL_ADDRESS>a bug report if the
problem you're having is not on this list.
configuration issues
version 2.4.x version 2.3.2 version 2.3.1
Compiler/Configuration Issues
--------------------------------------------------------------------------------
Random number woes
If you find that the examples don't
evolve properly, check the performance of the random number generator
using the randtest example. You can specify which random number generator
to use by modifying the gaconfig.h file. Note that that modifications to
gaconfig.h will require a rebuild of the entire GA library.
bugs in version 2.4.4
--------------------------------------------------------------------------------
On some platforms, the seed functions for the RAN1 and RAN2 random
number generator only works half of the time, on average. The other half
of the time it generates the same random seed. Thanks to Peter Ross and
George LeCompte for finding this one. In the file garandom.C, change the
sran1 and sran2 functions so that: 206 void 207 sran1(unsigned int
seed) { 208 int j; 209 long k; 210 211 idum = seed;
212 if (idum < 1) idum=1; 213 for (j=NTAB+7;j>=0;j--) {
214 k=(idum)/IQ; 215 idum=IA*(idum-k*IQ)-IR*k; 216 if (idum
< 0) idum += IM; 217 if (j < NTA iv[j] = idum; 218 }
219 iy=iv[0]; 220 }
...
280 void 281
sran2(unsigned int seed) { 282 int j; 283 long k; 284 285
idum = STA_CAST(long,seed); 286 if (idum < 1) idum=1; 287
idum2=(idum); 288 for (j=NTAB+7;j>=0;j--) { 289 k=(idum)/IQ1;
290 idum=IA1*(idum-k*IQ1)-k*IR1; 291 if (idum < 0) idum += IM1;
292 if (j < NTA iv[j] = idum; 293 }
294 iy=iv[0]; 295 }
becomes this: 206 void 207
sran1(unsigned int seed) { 208 int j; 209 long k; 210 211
idum = seed; 212 if (idum == 0) idum=1; if (idum < 0) idum =
-idum; 213 for (j=NTAB+7;j>=0;j--) { 214 k=(idum)/IQ; 215
idum=IA*(idum-k*IQ)-IR*k; 216 if (idum < 0) idum += IM; 217 if
(j < NTA iv[j] = idum; 218 } 219 iy=iv[0]; 220 }
...
280 void 281 sran2(unsigned int seed) { 282 int j; 283
long k; 284 285 idum = STA_CAST(long,seed); 286 if (idum == 0)
idum=1; if (idum < 0) idum = -idum; 287 idum2=(idum); 288
for (j=NTAB+7;j>=0;j--) { 289 k=(idum)/IQ1; 290
idum=IA1*(idum-k*IQ1)-k*IR1; 291 if (idum < 0) idum += IM1; 292
if (j < NTA iv[j] = idum; 293 } 294 iy=iv[0]; 295 }
The
RAN3 random number generator has a bug in it that was included in the
Numberical Recipes code but has since been fixed. Thanks to Peter Ross for
pointing this out. The fix is as follows in garandom.C: 353
mj=MSEED-idum;
becomes this: 353 mj=labs(MSEED-labs(idum));
The population's pointer to the genetic algorithm that contains it
is not initialized to zero. Thanks to Harald H Soleng of the Norwegian
Computing Center for finding this one. The fix is to change GAPopulation.C
so that two of the three GAPopulation constructors initialize the ga
member to nil (the copy constructor does a deep copy and thus gets the
genetic algorithm from the population it is cloning): 76 evaldata =
(GAEvalData*)0; 77 }
...
104 evaldata =
(GAEvalData*)0; 105 }
becomes this: 76 evaldata =
(GAEvalData*)0; ga = (GAGeneticAlgorithm*)0; 77 }
...
104 evaldata = (GAEvalData*)0; ga = (GAGeneticAlgorithm*)0;
105 }
Boolean parameters are not read correctly from file.
Thanks to Klaus Kirchberg for finding this one.. The fix is to modify
GAParameter.C as follows: 313 if(ival) os << "true\n"; 314
else os << "false\n";
becomes this: 313 if(ival) os
<< "1\n"; 314 else os << "0\n";
bugs in version 2.4.3
--------------------------------------------------------------------------------
The early release of the Mac project file has a bogus struct
alignment configuration for each of the examples. Be sure that the linker
is configured to PowerPC struct alignment, not 68K struct alignment. If
you use the wrong alignment, everything will compile and link just fine,
but it will crash when you run it.
bugs in version
2.4.2
--------------------------------------------------------------------------------
Example 13 does not do what it purports to do. There are a number
of places where _numbers should be replaced with _picture, and there are
some flaws in the logic. Please use the example 13 from the 2.4.3 release.
GAParameter::setvalue can corrupt memory when it frees memory
that it just allocated. The result is a bogus file name and/or application
crashing. The fix is to change GAParameter.C so that this: 95 case
STRING: 96 { 97 char* ptr=0;
becomes this: 95 case
STRING: 96 if(v != val.sval) { 97 char* ptr=0;
GAGeneticAlgorithm::TerminateUponConvergence will stop after the
first generation when doing a minimization with convergence as the
stopping criterion.
When using a GARealGenome and an allele
set with INCLUDE/EXCLUDE limits, you may find that the limits you set are
ignored. This is probably due to a bug in the
GAAlleleSet<>::allele() function. It may also be due to genetic
operators (mutate, crossover) that ignore the allele boundary/type
details.
GAAlleleSet<>::allele does not respect allele
bounds. Neither does the specialization, GAAlleleSet::allele()
Please get a new copy of GARealGenome.C and GAAllele.C from the 2.4.3
release to fix the problems. Note that you will have to define your own
GAAlleleSet<>::allele() member function if you use a type other than
float (use the GARealGenome.C code to as an example of how to do this)
There are quite a few problems with the GADemeGA object. The
problems only show themselves when you attempt to change the number of
populations and/or the replacement numbers or migration numbers. Please
get a new copy of GADemeGA.C from the 2.4.3 release to fix all the
problems.
The resizeBehaviour method for both
GA3DBinaryStringGenome and GA3DArrayGenome has two errors in it. If you
use this member function to query the resize behavior of a genome, you
will always get 0 for HEIGHT and DEPTH rather than the actual HEIGHT and
DEPTH values. The fix is to modify the if statements. In the 'if' test
statements, the variable should be which not val.
The read
member function of 1D, 2D, and 3D binary string genomes will cause a
segmentation fault in cases where the genome has length (width, height) of
zero. The fix is to change the do...while loop into a while loop. This
should be done in GA1DBinStringGenome.C, GA2DBinStringGenome.C, and
GA3DBinStringGenome.C.
Examples 5 and 14 do not work correctly
because the _evaluated flag is not properly reset in the initialize,
mutate, and crossover methods. In any custom-defined genome, you must set
_evaluated to gaFalse whenever you change the state of a genome, otherwise
the genome's evaluator will not be invoked (the cached score will be used
instead).
On some platforms, using a GARealGenome with the
GARealGaussianMutator will occasionally result in a crash when
GAUnitGaussian takes the square root of a negative number. To be sure this
never happens, change the following code in garandom.C so that this:
101 } while(rsquare >= 1.0 || rsquare == 0.0); 102 103
factor = sqrt( -2.0 * log(rsquare) / rsquare ); 104 105 cachevalue
= var1 * factor;
becomes this: 101 } while(rsquare >= 1.0
|| rsquare == 0.0); 102 double val = -2.0 * log(rsquare) /
rsquare; if(val > 0.0) factor = sqrt(val); else factor = 0.0;
104 105 cachevalue = var1 * factor;
In the DemeGA,
changing the number of populations to a larger number after the GA has
been created will result in a segmentation fault. The nPopulations member
function should be modified as follows. Insert a line of code that
modifies the population in GADemeGA::nPopulations(unsigned int n) so that
this: 264 } 265 return npop; 266 }
becomes this:
264 } pop->size(npop); 265 return npop; 266 }
The PC and Mac GAlib packages have a typo in example 5. My PERL
script for converting UNIX to PC got a little carried away. Change: 69
CompositeGenome(int element, int bond, GABin2DecPhenotype& p, 70
GAGenome::Evaluator f, void* u) : 71 GAGenome.cppompositeInitializer,
72 CompositeMutator, 73 CompositeComparator) {
to: 69
CompositeGenome(int element, int bond, GABin2DecPhenotype& p, 70
GAGenome::Evaluator f, void* u) : 71 GAGenome(CompositeInitializer,
72 CompositeMutator, 73 CompositeComparator) {
bugs in version 2.4.1 fixed in release
2.4.2
--------------------------------------------------------------------------------
The random number seed may occasionally be set to 0, resulting in
all 0s from the GARandomBit function. This bug was introduced in the 2.4.1
release as I tried to make the seed generator more robust with respect to
various implementations of 'time()' on different OSes. To fix the problem,
make the following change to random.C in the function GARandomSeed. Change
line 56 from: 56 for(unsigned int i=0; i to: 56 for(unsigned int i=0; i If you are compiling with NO_STREAMS defined,
you will have to fix error.C so that it includes stdio.h. Just move
'#include ' so that it is outside of the '#ifndef NO_STREAMS ...
#endif' directive. The filenames used in this distribution may
conflict with system files. For example, 'list.h' conflicts with
Metrowerks' 'list.h' in Codewarrior 9 and later, and 'tree.h' conflicts
with borland's 'tree.h' in version 5.x of their compiler.
bugs in version 2.4 fixed in release 2.4.1
--------------------------------------------------------------------------------
The default population evaluator forces unnecessary genome
evaluations. In population.C, remove 'gaTrue' from the call to the genome
'evaluate' member function in GAPopulation:efaultEvaluator. The GANode
and GANodeBASE classes do not have virtual destructors, so any classes
derived from them do not destruct properly. The statistics object does
not work properly with genetic algorithms that are configured to minimize.
'best' and 'worst' are reversed when minimizing and the lowest score is
not always maintained. The simple genetic algorithm does not properly
maintain the best individual when minimizing.
bugs
in version 2.3.2
--------------------------------------------------------------------------------
The remove method was never defined for the GABin2DecPhenotype
class in bin2dec.ph.C (it was declared in the header file). Ooops, sorry
about that. Upgrade to version 2.4.
A population size of 1
will cause a crash when the statistics are updated. This has been fixed in
the 2.4 release.
The remove member of the GATree class does
not properly re-set the node pointers on the subtree that is removed. To
fix this problem, make the following modifications to treetmpl.C: 69
if(node->prev != node) iter.eldest(); 70 else if(node->parent)
iter.parent(); 71 else iter.node = (GANodeBASE *)0; 72
t->insert((GANode *)GATreeBASE::remove(node), (GANode *)0, 73
GATreeBASE::ROOT); 74 75 return t; 76 }
should be
modified to 69 if(node->prev != node) iter.eldest(); 70 else
if(node->parent) iter.parent(); 71 else iter.node = (GANodeBASE
*)0; GANode *tmpnode = (GANode*)GATreeBASE::remove(node);
tmpnode->prev = tmpnode; tmpnode->next = tmpnode;
tmpnode->parent = (GANodeBASE *)0; t->insert(tmpnode,
(GANode *)0, GATreeBASE::ROOT); 74 75 return t; 76 }
The array swap and flip mutators have a bug in them that will
prevent mutation in some cases. To fix it, do the following. Change
lines 83 and 114 in array1.op.C. The first change is in
GA1DArrayFlipMutator:
83 for(n=1; n should be
83 for(n=0; n The second change is in
GA1DArraySwapMutator: 114 for(n=1; n should be
114 for(n=0; n Be careful with the random number
generator. If you use GAlib right out of the box, the GARandomBit function
caches bits rather than calling the random function multiple times. This
may or may not be a problem, depending on the underlying random function
you use. The default configuration for GAlib is to use your system's
rand() or random() function to generate random numbers. Typically, the
system random number generators are not very robust. The actual
implementation varies from system to system, but most use some sort of
linear congruential generator, and you should not expect the bits of the
numbers these RNGs generate to be uniformly random. For many applications,
the default GAlib configuration will suffice, but if you are doing
extensive statistical evaluation of results obtained using GAlib you
should consider writing your own random number generator and plugging it
in to replace rand() or random(). You can do this by modifying random.h to
use your random function. See Numerical Recipes in C for details about
the issues of using various random number generators. These issues have
been resolved in the 2.4 release of GAlib.
The scaling member
function of the GAPopulation object does not properly update the scaling
object when it is changed. If you try to change the scaling during the
course of an evolution, your program will crash (the selector returns nil
pointers after you change the selector). The 'evaluate' member of the
scaling object must be called before the selector accesses the scaling
data. In the scaling member function in population.C, add one line of
code so that
275 sm->resize(n); 276 return *sm;
becomes 275 sm->resize(n); if(evaluated)
sm->evaluate(*this); 276 return *sm;
The copy member
function of the GAPopulation object has a typo in it. Whenever you try to
copy a population, the copy member clones the worst individual in the
population rather than copying each individual in the population. To fix
the problem, change [0] to [i] as shown here. In population.C, change
0 to an i in line 153 so that
151 ind = new GAGenome * [N];
152 for(i=0; i153 ind[i] = arg.ind[0]->clone();
154 155 if(arg.sm) sm = arg.sm->clone();
becomes
151 ind = new GAGenome * [N]; 152 for(i=0; i153
ind[i] = arg.ind[i]->clone(); 154 155 if(arg.sm) sm =
arg.sm->clone();
The initialize member function of any GA class
must be called after any calls to the populationSize member function,
otherwise the scaling object does not get updated and your GA will crash
on its first selection. This means that you cannot change the population
size during the course of an evolution. To fix the problem make the
following changes: In population.C, insert a line and modify a line to
change
209 sm->resize(pops); 210 statted = sorted =
evaluated = gaFalse; 211 n = pops;
to 209
sm->resize(pops); 210 statted = sorted = gaFalse; 211 n = pops;
if(evaluated == gaTrue) evaluate(gaTrue);
and in the file
scaling.C, insert a single line to change 102 fitSum = fitAve = fitMax
= fitMin = fitVar = fitDev = 0.0; 103 return(n = popsize);
to
102 fitSum = fitAve = fitMax = fitMin = fitVar = fitDev = 0.0;
evaluated = gaFalse; 103 return(n = popsize);
bugs in version 2.3.1
--------------------------------------------------------------------------------
There was a typo in population.C. In the function GAGenome *
GAPopulation::replace(GAGenome * repl, const int which),
if(sorted) rawMax = ind[n-1]->score(); else
if(orig->score() == rawMax){ rawMax = ind[0]->score();
for(i=1; i rawMax = Min(rawMax, ind[i]->score());
} else rawMax = Max(rawMax, repl->score());
The
'Min' should be 'Max': rawMax = Max(rawMax, ind[i]->score());
Functions that return NULL would not compile with some compilers.
Functions that have this problem include member functions of the ListBASE
and List objects in listbase.h and listtmpl.h, and the TreeBASE and Tree
objects in treebase.h and treetmpl.h. ANSI C++ requires an explicit cast
of NULL. Many of the 'for' loops will not compile with some compilers.
In some cases, the loop variable is used outside the 'for' loop. Files
with this problem include allele.h, array1.op.C, binstr3.ch.C, ga.C,
list.op.C, population.C, scaling.C, selector.C, and many of the examples.
ANSI C++ specifies that a variable declared in a for loop loses scope
after the loop ends. The population object does not initialize genomes
when it is resized. SimpleGA has a bug in it that delays convergence.
A workaround is to use a SteadyStateGA with 100% replacement. This will be
only marginally slower than using a bug-free SimpleGA. Roundoff errors
in the population object could lead to negative numbers passed to the sqrt
function. The fix is to check for a sign change before the call to sqrt.
The only way a sign change can occur is if there is a roundoff error.
(There are only two calls to sqrt in population.C. The error can only
occur in the sqrt call in the function GAGenome *
GAPopulation::replace(GAGenome * repl, const int which)) The copy
constructor for the GAObjectiveVector object did not initialize the 'a'
member to NULL
临江仙
陈夕往事益归去, 古道西风依旧. 人生长河水长东, 春去遇秋来, 天际寻鹄鸿.
日月星移话沧桑, 是非功过何在? 一路风雨兼程行, 唯清风明月, 独在情怀中.
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