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public interface BoundedRangeModel
Slider
s
and ProgressBar
s.
Defines four interrelated integer properties: minimum, maximum, extent
and value. These four integers define two nested ranges like this:
minimum <= value <= value+extent <= maximumThe outer range is
minimum,maximum
and the inner
range is value,value+extent
. The inner range
must lie within the outer one, i.e. value
must be
less than or equal to maximum
and value+extent
must greater than or equal to minimum
, and maximum
must be greater than or equal to minimum
.
There are a few features of this model that one might find a little
surprising. These quirks exist for the convenience of the
Swing BoundedRangeModel clients, such as Slider
and
ScrollBar
.
value == maximum
, setExtent(10)
would change the extent (back) to zero.
For an example of specifying custom bounded range models used by sliders, see The Anatomy of a Swing-Based Program in The Java Tutorial.
DefaultBoundedRangeModel
| Modifier and Type | Method and Description |
|---|---|
void |
addChangeListener(ChangeListener x)
Adds a ChangeListener to the model's listener list.
|
int |
getExtent()
Returns the model's extent, the length of the inner range that
begins at the model's value.
|
int |
getMaximum()
Returns the model's maximum.
|
int |
getMinimum()
Returns the minimum acceptable value.
|
int |
getValue()
Returns the model's current value.
|
boolean |
getValueIsAdjusting()
Returns true if the current changes to the value property are part
of a series of changes.
|
void |
removeChangeListener(ChangeListener x)
Removes a ChangeListener from the model's listener list.
|
void |
setExtent(int newExtent)
Sets the model's extent.
|
void |
setMaximum(int newMaximum)
Sets the model's maximum to newMaximum.
|
void |
setMinimum(int newMinimum)
Sets the model's minimum to newMinimum.
|
void |
setRangeProperties(int value,
int extent,
int min,
int max,
boolean adjusting)
This method sets all of the model's data with a single method call.
|
void |
setValue(int newValue)
Sets the model's current value to
newValue if newValue
satisfies the model's constraints. |
void |
setValueIsAdjusting(boolean b)
This attribute indicates that any upcoming changes to the value
of the model should be considered a single event.
|
int getMinimum()
setMinimum(int)
void setMinimum(int newMinimum)
minimum <= value <= value+extent <= maximum
Notifies any listeners if the model changes.
newMinimum
- the model's new minimumgetMinimum()
,
addChangeListener(javax.swing.event.ChangeListener)
int getMaximum()
setMaximum(int)
,
setExtent(int)
void setMaximum(int newMaximum)
minimum <= value <= value+extent <= maximum
Notifies any listeners if the model changes.
newMaximum
- the model's new maximumgetMaximum()
,
addChangeListener(javax.swing.event.ChangeListener)
int getValue()
maximum - extent
and the lower limit is minimum
.setValue(int)
void setValue(int newValue)
newValue
if newValue
satisfies the model's constraints. Those constraints are:
minimum <= value <= value+extent <= maximumOtherwise, if
newValue
is less than minimum
it's set to minimum
, if its greater than
maximum
then it's set to maximum
, and
if it's greater than value+extent
then it's set to
value+extent
.
When a BoundedRange model is used with a scrollbar the value specifies the origin of the scrollbar knob (aka the "thumb" or "elevator"). The value usually represents the origin of the visible part of the object being scrolled.
Notifies any listeners if the model changes.
newValue
- the model's new valuegetValue()
void setValueIsAdjusting(boolean b)
Sliders and scrollbars use this property when a drag is underway.
b
- true if the upcoming changes to the value property are part of a seriesboolean getValueIsAdjusting()
setValueIsAdjusting(boolean)
int getExtent()
setExtent(int)
,
setValue(int)
void setExtent(int newExtent)
When a BoundedRange model is used with a scrollbar the extent defines the length of the scrollbar knob (aka the "thumb" or "elevator"). The extent usually represents how much of the object being scrolled is visible. When used with a slider, the extent determines how much the value can "jump", for example when the user presses PgUp or PgDn.
Notifies any listeners if the model changes.
newExtent
- the model's new extentgetExtent()
,
setValue(int)
void setRangeProperties(int value, int extent, int min, int max, boolean adjusting)
value
- an int giving the current valueextent
- an int giving the amount by which the value can "jump"min
- an int giving the minimum valuemax
- an int giving the maximum valueadjusting
- a boolean, true if a series of changes are in
progresssetValue(int)
,
setExtent(int)
,
setMinimum(int)
,
setMaximum(int)
,
setValueIsAdjusting(boolean)
void addChangeListener(ChangeListener x)
x
- the ChangeListener to addremoveChangeListener(javax.swing.event.ChangeListener)
void removeChangeListener(ChangeListener x)
x
- the ChangeListener to removeaddChangeListener(javax.swing.event.ChangeListener)
Submit a bug or feature
For further API reference and developer documentation, see Java SE Documentation. That documentation contains more detailed, developer-targeted descriptions, with conceptual overviews, definitions of terms, workarounds, and working code examples.
Copyright © 1993, 2015, Oracle and/or its affiliates. All rights reserved.


Good for showing changing data as long as the data can be simplified into 2 points without missing a key part of story


{% extends 'base.html' %} {% block content %}
It's not your fault. Must be that wrench the works. Try the home page.


Contact Us!


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Address:
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Date of Birth:
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Phone:
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Email:
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5 Conditionals
One aspect of control flow is controlling when and how parts of our code run. This is done by setting actions (or lines of code) to only run when certain conditions are met.
In R
this is handled by a group of functions under the if
statements banner.
5.1 if
statements
The basic if
statement follows the following syntax:
if(condition is met){
do this
}
There are three components to an if
statement:
- The
if
specialR
function. - The condition to be tested.
- The code to be evaluated if the condition is met.
In other words, “if this is true do this”.
A basic example:
## [1] "x is a positive number"
###################
### CHALLENGE 1 ###
###################
Use an if statement to print a suitable message reporting whether there are any people with age of >100 in the titanic dataset. Now do the same for >70.
HINT: you will need to use the na.rm argument in the any() function.
5.2 if else
statements
So far we’ve seen code that triggers if the condition is met, but what about if we want to trigger something different if the condition is not met? This is where the else
statement works in conjunction with the if
statement:
x <- 5
if(x < 0){
print("x is less than zero")
} else {
print("x is greater than or equal to zero")
}
## [1] "x is greater than or equal to zero"
What if you have more than a two category condition? Instead of “if true do this” we might have “if A do this, if B do this, if C do this”.
x <- 5
if(x < 0){
print("x is less than zero")
} else if(x == 0){
print("x equals zero")
} else if(x > 0){
print("x is greater than zero")
}
## [1] "x is greater than zero"
###################
### CHALLENGE 2 ###
###################
Randomly sample one passenger from the titanic dataset. Then construct an if else statement to print a message about whether the passenger is younger than 20, between 20 and 50, or over 50 years old.
5.3 The ifelse()
function
You may have noticed that the if
statement performs a single test.  The ifelse()
function is vectorised so it can handle vector inputs. The three inputs to this function are the test
to be applied to each element of the vector, the code to happen if the test returns TRUE (yes
), and the code to happen if the test returns FALSE (no
).
## [1] "odd" "even" "odd" "even" "odd" "even" "odd" "even" "odd"
## [10] "even"
###################
### CHALLENGE 3 ###
###################
Add a new column to the titanic dataset called "Adult" that is a binary variable indicating whether that passenger is an 18 or older.
5.4 Challenge Answers
###################
### Challenge 1 ###
###################
if(any(titanic$Age > 100,
na.rm = TRUE)){
print("There are passengers older than 100")
}
if(any(titanic$Age > 70,
na.rm = TRUE)){
print("There are passengers older than 70")
}
###################
### CHALLENGE 2 ###
###################
passenger <- titanic[sample(1:nrow(titanic), 1), ]
if(is.na(passenger$Age)){
print("The passenger has no recorded age")
} else if(passenger$Age < 20){
print("The passenger is less than 20 years old")
} else if(passenger$Age >= 20 & <= 50){
print("The passenger is between 20 and 50 years old")
} else if(passenger$Age > 50){
print("The passenger is greater than 50 years old")
}


I'm Milan, I'm 22 years old (my 22nd birthday was just a few days ago, at the 23rd of August, 2007. e.v.). I live in Serbia.
Serbia is one strange country. I live in the mid class, have a good parents, and 2 years younger sister who is completely straight, only smoked weed with me a couple of times. If you would come to Serbia asking for drugs, you would very soon
realise that there is not much to choose among them. Some of the finest stuff is very well kept in the tresors of the state pharmacies and you can get them only on the double copy prescription with whole a bunch of protocol filled before that. Nothing strange, it's like that in every country, but the only difference is that in the other more modern countries, you can find those same drugs on the street, while in Serbia you can't. The only drugs available on the street are Methamphetamine,
Amphetamine, MDMA, MDA, LSD, Heroin, Tramadol and Cocaine.
But, being the man born to be a drug-pro, I devoted my life in searching and experimenting with various drugs, and I myself became addicted to them. To be more precise, I became addicted to opiates at my 15th year of age, taking 300mg of Tramadol
for the first time in my life. Even if Tramadol is not fully an opiate, I immediately realised that I'm born to live with opiates and after that night I couldn't imagine my day without them. I simply said 'It's my duty to listen to my Will!'. It was so strong.
 Something that's deffinetly worth of existing in this society. I finished high school and went to the Microsoft specialization for the Systems Engineer. I immediately found a great job in one company. Besite that, I'm into the occult very much, and I'm an active member of Ordo Templi Orientis. In this 8 years I tried a lot of drugs. From Marihuana, Hashish, Tramadol, MDMA, MDA, Amphetamine, Methamphetamine, across Psylocibine Mushrooms, Heroin, Methadone, Morphine, Opium, DXM, Xanax, Valium, to Valoron, Artane, Ether, Chlorophorm, Procyclidine, LSD, Salvia Divinorum and Ritalin.
I somehow managed to stay balanced myself during these years, but I entered the life threatening zone in my 21st year of age (last year). I started taking heroin at my 19th, and since then I mixed it with Morphine and Tramadol sometimes. But last year in 2006, my opiate addiction got really strong. Every salary of mine went to heroin, I completely devoted my life to heroin. I lost a connection with my Brothers and Sisters from O.T.O., I stopped with my spiritual practices, I simply became a slave, as it always goes with heroin.
Now, during that time I moved from my parents from my town Vrsac to Belgrade, because that is where my company was and I had an apartment there and I lived alone and worked. I had a few pals with whom I was sharing and taking heroin and we together cared about the money for the drugs and all the rest. During these years my parents somehow thought that I'm gonna be better. When I was 16 I went to a psyshiatric hospital and was there for 2 and a half months till I completely freaked out and demanded to turn back home. It was a kind of refreshing experience for me even if they tortured me there very much, I was taking drugs more less after that hospital. But anyway, I started taking more and more as the time passed by. To come back to Belgrade, 2006.
I was visiting my parents in Vrsac every 2 weeks for a weekend and they noticed that I'm out of cash, I was always asking them for the money, and my mother who is a nurse noticed a great change in me. I was pale, with watery eyes, out of energy, and that was getting worse every time I came to visit them, or them came to Belgrade to visit me.
At one moment, I told them that I'm taking heroin and I told them everything since I started with it. My mother was expecting that. She is a great fighter and would do anything to make me feel good. Ehhh, how much I wounded her with my drug life, I cannot forgive that to myself.
After telling them that I'm addicted to heroin, they asked me if I want to go to hospital and detox. I sad no, I thought that I can do it alone with Trodon (tramadol) and Xanax. But I really within myself didn't want to quit. And I was sufring the heroin like that until I found a very good source of it which provided me with 80% pharmaceutically pure heroin.  I started with the pure heroin. After some time, I started taking it I.V. I was disciplined, always had a new syringe and a needle, always used alcohol and a water from ampoules.
[Erowid Note: 3 grams of Heroin is considered an extremely high dose, tolerant users require higher doses. See Heroin Dose Chart]
I barely could work in the company during the day, I had to take a dose at every 45 minutes. I started falling apart. I started to feel a strange electric shocks in my legs, and that meant that my nerves got an inflammation. I started to fall in the situations when I didn't have the money for my dose and I was calling everybody on the phone asking for some money, and I was calling my parents in Vrsac and I was crying while in the withdrawals.
At last I had enough of that. We decided to go to a private clinic which was the only private clinic in the whole country doing the detox with Methadone. Methadone is still very controversial theme in Serbia, but the people are slowly getting the point that it's the drug of choice. So I started the initial detox in that private clinic, my parents payed all that. I started with 30mg of Methadone twice a day, but that was barely enough to cover my withdrawals. But I managed to get used to it. And the days passed, I kinda got normal again. But, it was time to start lowering the dose until I get to 0mg.
And it started. When I came to the near end, to 5mg a day, oh boy, everything was clear to me. The withdrawals started and my life was hard again. I tried to stay clean before this, but even after 2 months of being straight, my body would still yearn for opiates and I couldn't keep myself straight.  And when I came to 1mg of Methadone a day, I simply couldn't make it. When I came to 0mg, my doctor then switched me to Xanax, Heminevrin, Lendormin for sleeping and nothing else. I managed to stay on that therapy opiate-free for a week, then the symptoms were unbeareble to me, I couldn't go to work and I, without any thinking started with heroin again. We left the private clinic and lost any contact with the doctor.
I realised that I need a Methadone maintenance. I had 2 more choices. The state hospital for addiction and methadone maintenance in Belgrade, or the similar one in Novi Sad. I decided to visit the first one in Belgrade. But it was terrible.
The women doctor who talked with me told me and my parents 'lock him up, take away his cell-phone, give him Tramadol and Valium for 7 days and that's it.'.  Is she mad I thought ??? I'm a man who has a job, no one can just lock me up and take my cell phone, and, Tramadol maybe can help the people with back pain, but woman, I need Methadone. She didn't want to hear me and said 'You cannot get Methadone, no way'. I knew it. We left the hospital, while I told the woman doctor 'You'll be giving Methadone to everybody once we enter the European Union!'.  They are giving Methadone only to the severely addicted people who have AIDS and who are at the end of their lives.
Now, I was only left with one more chance, and that was the specialised addiction clinic in Novi Sad, where they used a modern European Methadone program. I started by writting an e-mail to the head proffesor-doctor of the clinic. He answered me and we made an appointment. Oh great, I said, I made and appointment with the head of the clinic. That was promicing. And during all that time my parents were giving me the money for heroin to have as much as I need just to get through the day until we get to the clinic in Novi Sad.
At last, the day came and we arrived at the clinic. The doctor reminded me of Shawn Connery, and he looked ultra-smart. I told him my history. At first, he didn't want to give me the Methadone, but then I fell in desperation and started a play in his office. I started crying, begging, I told them that I will kill myself and that I don't know what to do and that I tried everything in the last 8 years and nothing worked and that I need Methadone! And at last he heard me! Oh boy somebody heard me! He told me to stay in the hospital for 10 days while they make a psycho-test with me and while they scrutinize me and detox me and decide what to do.
At first it was a horrible thing for me to stay at the hospital, but I agreed. I thought that I'll be tortured like in that one when I was 16 years old. But no! This was the modern hospital working like the modern European clinic.  I also made some great friendships in the hospital, we were buying weed secretly through our room windows and were smoking it in the hospital yard, and anyway, I had a great time.
After 10 days, doctor Nikola Vuckovic (the head of the clinic who took care of me) called me in his office. He told me that I was accepted for the Methadone maintenance programe! Oh BOY, I was so happy, I was feeling free at last! My parents came that day and we packed. I said hello to all the buddies in the hospital and we left. The doctor prescribed me a starting dose of 30mg of Methadone. Beside it, he gave me an antidepressant Zoloft, anxiolytic Clonopin (clonazepam) 0.5mg once a day, and in the evening one Risperidone 2mg. I was happy. Very happy. In the next few weeks, my Methadone dose got higher and higher, and I stopped at 70mg. I felt just okay with that dose.
The months passed, but I started feeling depressive and fatigued, I didn't have a motivation. Just in the first 4 hours after taking my Methadone dose, but after that I was getting very down, and my concentration was affected very much, I couldn't focus, and I had a restless leg syndrome for already 4 years. I decided to ask my doctor to prescribe me Ritalin. And it was a hell of a talk with him, he didn't even know what Ritalin is, because, there is no Ritalin in Serbia as a registered med at all! I provided him with a lot of material which I translated from English to Serbian concerning Ritalin and he also made a lot of research on Methylphenidate and after a while, he agreed to prescribe me Ritalin.
But now there was a problem how to get it. We tried everything, but it was impossible. Then, one day while I was searching the database of the meds of Serbia, I found a list of newly approved medications for the year 2007. I saw Concerta !!! YEAH !!! I immediately called the importing company who is working on providing Concerta for Serbia and they told me that I'll have to wait, that there are a bunch of legal stuff to be finished and the Concerta will be shipped to Serbia and distributed ONLY to the state pharmacy strongly secured tresors where all of the narcotics with the potential of abuse go. And I waited and waited and waited.
I was taking speed sometimes, just to get me a little high, but it was hard for me in such a depressed state of mind and with all those symptoms. In the other hand, I didn't touch heroin, Methadone was and still is doing a great job for me. After 7 months of waiting, they finally informed my doctor in Novi Sad that Concerta arrived in Serbia and that it can be distributed. Oh great I said. It was now already a year since I started Methadone maintenance, and now I'm starting with Concerta as well. We ordered one bottle of 36mg Concerta. And guess what, I was the first person who bought the first bottle of Concerta in whole Serbia, hahaha.
So now my therapy looks like this:
Methadone 60mg (I lowered a dose a little bit)
Concerta 36mg
Clonopin 0.5mg
Seroxat (paroxetine) 20mg
Risperidone 2mg in the evening
The first day of taking Concerta was interesting. I will describe the effects of this mix of mine. I woke up round 11am. Took my daily dose of Methadone, Seroxat and Clonopin. After an hour I start feeling an opiate high, but very mild, and it soothes me very much. I don't know what Seroxat and Clonopin are doing, Clonopin is just potentiating Methadone and Seroxat is giving me more euphoria, but that's not so noticable. After 3 hours after taking Methadone, Seroxat and Clonopin, I took one 36mg special-ultra-hyper constructed tablet of Concerta. Oh boy, after an hour my restless legs syndrome was gone, I felt the warmness in my body, I didn't have any more ups and downs, I was in a great mood, I could concentrate and I got motivated very much. My day started to be organized at last. Concerta, thank you to the heavens !!!
Now is the 4th day of me taking Concerta with my Methadone and I feel extra. I hope I'll get a prosperation out of this. Now, I don't need any more drugs in the world. I have my prescription drugs that keep me going and I don't have any more preassures in my mind. I'm free.
In the end, what to say. I guess the time has come for me to start building my future and my career and to make my family happy and proud of me. I stopped hanging out with every person who is on heroin or addicted to any drug, I now have nobody and have to make some new friendships. I don't say I'll become a hardcore straighter, I'll sometimes be in a mood for a joint, but nothing more beside my prescription drugs. I don't need anything else.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


ALFAGT COMMAND
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{% extends "site_base.html" %} {% load staticfiles pagination_tags %} {% load url from future %} {% block head_title %}{{ object }} {{ block.super }}{% endblock %} {% block styles %} {{ block.super }} {% endblock %} {% block body %}
On OSX your replays are located by default in:
/Users/<username>/Library/Application Support/Blizzard/StarCraft II/Accounts/<randomnumber>/<randomstring>/Replays/
On Windows your replays are located by default in:
C:\Users\<username>\Documents\Starcraft II\Accounts\<randomnumber>\<randomstring>\Replays


The username {{ user.username }} was automatically generated to conform to the Docker CLI guidelines for use as a namespace in .
Please confirm the selected username or enter a different username below:


|
| ComStatus_tv | ComLssConfigureBitTiming (uint8_t ubNetV, uint8_t ubBittimeSelV) |
| |
| ComStatus_tv | ComLssConfigureNodeId (uint8_t ubNetV, uint8_t ubNodeIdV) |
| |
| void | ComLssEventReceive (uint8_t ubNetV, uint8_t ubLssProtocolV) |
| |
| ComStatus_tv | ComLssFastscan (uint8_t ubNetV, uint32_t ulVendorIdV, uint32_t ulProdCodeV, uint32_t ulRevisionNumV, uint32_t ulSerialNumV) |
| |
| ComStatus_tv | ComLssIdentifyNonConfiguredSlave (uint8_t ubNetV) |
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| ComStatus_tv | ComLssSetTimeout (uint8_t ubNetV, uint16_t uwTimeV) |
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| ComStatus_tv | ComLssStoreConfiguration (uint8_t ubNetV) |
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| ComStatus_tv | ComLssSwitchModeGlobal (uint8_t ubNetV, uint8_t ubModeV) |
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| ComStatus_tv | ComLssSwitchModeSelective (uint8_t ubNetV, uint32_t ulVendorIdV, uint32_t ulProdCodeV, uint32_t ulRevisionNumV, uint32_t ulSerialNumV) |
| |
LSS offers the possibility to inquire and change the settings of certain parameters of the local layers on a CANopen module with LSS Slave capabilities by a CANopen module with LSS Master capabilities via the CAN Network. The following parameters can be inquired and/or changed by the use of LSS:
- Node-ID of the CANopen Slave
- Bit timing parameters of the physical layer (bit-rate)
- LSS address (Identity Object, Index 1018h)
By using LSS a LSS Slave can be configured for a CANopen network without using any devices like DIP-switches for setting the parameters.
◆ ComLssConfigureBitTiming()
| ComStatus_tv ComLssConfigureBitTiming |
( |
uint8_t |
ubNetV, |
|
|
uint8_t |
ubBittimeSelV |
|
) |
| |
- Parameters
-
| [in] | ubNetV | - CANopen Network channel |
| [in] | ubBittimeSelV | - New bit-timing value |
- Returns
- eCOM_ERR_NONE on success, negative ComErr_e value on failure
By means of the Configure Bit Timing Parameters service the LSS Master sets the new bit-timing on a LSS Slave.
◆ ComLssConfigureNodeId()
| ComStatus_tv ComLssConfigureNodeId |
( |
uint8_t |
ubNetV, |
|
|
uint8_t |
ubNodeIdV |
|
) |
| |
- Parameters
-
| [in] | ubNetV | - CANopen Network channel |
| [in] | ubNodeIdV | - New Node-ID value |
- Returns
- eCOM_ERR_NONE on success, negative ComErr_e value on failure
By means of this service the LSS Master configures the NMT-address parameter of a LSS Slave. This service allows only one LSS Slave in configuration mode. The remote result parameter confirms the success or failure of the service. In case of a failure optionally the reason is confirmed.
◆ ComLssEventReceive()
| void ComLssEventReceive |
( |
uint8_t |
ubNetV, |
|
|
uint8_t |
ubLssProtocolV |
|
) |
| |
- Parameters
-
| [in] | ubNetV | - CANopen Network channel |
| [in] | ubLssProtocolV | - LSS protocol |
The function is called by the LSS master state machine upon reception of a LSS response message issued by the LSS slave device. The parameter ubNetV defines the CANopen network. The value of the parameter ubLssProtocolV is defined by the enumeration ComLssProt_e.
◆ ComLssFastscan()
| ComStatus_tv ComLssFastscan |
( |
uint8_t |
ubNetV, |
|
|
uint32_t |
ulVendorIdV, |
|
|
uint32_t |
ulProdCodeV, |
|
|
uint32_t |
ulRevisionNumV, |
|
|
uint32_t |
ulSerialNumV |
|
) |
| |
Run Fastscan protocol.
- Parameters
-
| [in] | ubNetV | - CANopen Network channel |
| [in] | ulVendorIdV | - Vendor ID (1018h, sub 1) |
| [in] | ulProdCodeV | - Product code (1018h, sub 2) |
| [in] | ulRevisionNumV | - Revision number (1018h, sub 3) |
| [in] | ulSerialNumV | - Serial number (1018h, sub 4) |
- Returns
- eCOM_ERR_NONE on success, negative ComErr_e value on failure
The function starts the LSS Fastscan protocol on the given logical network ubNetV. If one of the parameters ulVendorIdV .. ulSerialNumV is not equal to zero the value is assumed to be known and will not be scanned. The result of the operation is notified via the ComLssEventReceive() function.
◆ ComLssIdentifyNonConfiguredSlave()
| ComStatus_tv ComLssIdentifyNonConfiguredSlave |
( |
uint8_t |
ubNetV | ) |
|
- Parameters
-
| [in] | ubNetV | - CANopen Network channel |
- Returns
- eCOM_ERR_NONE on success, negative ComErr_e value on failure
This function starts the Identify non-configured remote slave protocol. If there are non-configured remote slaves in the network, the status will be set to eCOM_LSS_STAT_SUCCESS, otherwise the status will be eCOM_LSS_STAT_TIMEOUT.
◆ ComLssSetTimeout()
| ComStatus_tv ComLssSetTimeout |
( |
uint8_t |
ubNetV, |
|
|
uint16_t |
uwTimeV |
|
) |
| |
- Parameters
-
| [in] | ubNetV | - CANopen Network channel |
| [in] | uwTimeV | - Timeout value in milliseconds |
- Returns
- eCOM_ERR_NONE on success, negative ComErr_e value on failure
This function sets a timeout value for a LSS communication. If the response to a LSS request by the master takes longer than this timeout value, the state of the communication will be changed to eCOM_LSS_STAT_TIMEOUT. The state of the LSS communication can be evaluated by calling ComLssGetStatus(). Please note that the timeout value is a multiple of the timer period (COM_TIMER_PERIOD). A value supplied to this function will be rounded towards the next lower value if necessary.
For LSS Fastscan, the configured timeout value is uwTimeV / 10 in order to increase the speed of Fastscan algorithm.
◆ ComLssStoreConfiguration()
| ComStatus_tv ComLssStoreConfiguration |
( |
uint8_t |
ubNetV | ) |
|
- Parameters
-
| [in] | ubNetV | - CANopen Network channel |
- Returns
- eCOM_ERR_NONE on success, negative ComErr_e value on failure
This function is used to store the configured parameters.
◆ ComLssSwitchModeGlobal()
| ComStatus_tv ComLssSwitchModeGlobal |
( |
uint8_t |
ubNetV, |
|
|
uint8_t |
ubModeV |
|
) |
| |
- Parameters
-
| [in] | ubNetV | - CANopen Network channel |
| [in] | ubModeV | - LSS mode |
- Returns
- eCOM_ERR_NONE on success, negative ComErr_e value on failure
- See also
- ComLssSwitchModeSelective()
The Switch Mode Services control the mode attribute of a LSS Slave. LSS provides two ways to put a LSS Slave into configuration mode, Switch Mode Global and Switch Mode Selective. Switch Mode Selective switches exactly one LSS Slave between configuration and operation mode. Switch Mode Global switches all LSS Slaves between configuration and operation mode.
Possible values for the parameter ubModeV are defined by the enumeration ComLssMode_e.
◆ ComLssSwitchModeSelective()
| ComStatus_tv ComLssSwitchModeSelective |
( |
uint8_t |
ubNetV, |
|
|
uint32_t |
ulVendorIdV, |
|
|
uint32_t |
ulProdCodeV, |
|
|
uint32_t |
ulRevisionNumV, |
|
|
uint32_t |
ulSerialNumV |
|
) |
| |
- Parameters
-
| [in] | ubNetV | - CANopen Network channel |
| [in] | ulVendorIdV | - Vendor ID (1018h, sub 1) |
| [in] | ulProdCodeV | - Product code (1018h, sub 2) |
| [in] | ulRevisionNumV | - Revision number (1018h, sub 3) |
| [in] | ulSerialNumV | - Serial number (1018h, sub 4) |
- Returns
- eCOM_ERR_NONE on success, negative ComErr_e value on failure
- See also
- ComLssSwitchModeGlobal()
The Switch Mode Services control the mode attribute of a LSS Slave. LSS provides two ways to put a LSS Slave into configuration mode, Switch Mode Global and Switch Mode Selective. Switch Mode Selective switches exactly one LSS Slave between configuration and operation mode. Switch Mode Global switches all LSS Slaves between configuration and operation mode.


Here we are again, in the third post of Machine Learning’s tutorials. Today I’m gonna tell you about Linear Regression, the most common and understandable learning algorithm. This time I will dig more deeper so that after this post, you will know what actually happened during the learning process. So no more Dogs and Cats today, but algebraic stuff. Yeah, we’re gonna work with matrices, from now on. But don’t worry, it’s not gonna be so hard today. As I told you before, we got some training data containing Features and Labels. Features are somewhat distinct which can be used to distinguish between things.
Remember this? To recall a little bit, I called ‘X’ Features, ‘y’ Labels, and ‘a’ Prediction. It may get your attention here, but it’s just a naming convention, which X is always in uppercase, and y is lowercase.
So after collecting a great deal of training data (X, y), we have them learned by the computer. Then we show the data which the computer has never seen before, which contains only X this time, and the computer will give us a prediction, called ‘a’.
I just helped you to recall about what Machine Learning is. But I think it would be better if you have a further look about Machine Learning on my first post here: What is Machine Learning?
So from the image above, the first thing coming to our minds is, how the computer can compute y from X during the learning process, and how it can compute prediction a from X?
Well, we will continue from what we were doing on the first post. The answer to the question above is: we need an learning algorithm. And in order to make an algorithm work, we need something called Activation Function.
Here’s where we left off in the first post. The reason why I mentioned it to you because you will face the term Activation Function not only in Linear Regression, or Logistic Regression in later post, but also in the more complicated algorithms which you will learn in the future. So, what is Activation Function? That’s a function which takes X as variable, and we use it to compute the prediction a.
In case of Linear Regression, the Activation Function is so simple that it’s not considered an Activation Function at all!
I’ll explain the equation above. First, the superscript and the subscript on each X, what do they mean? Imagine we have a training dataset which has 10 examples, each example has 4 features, so the superscript will indicate the ith example, and the subscript will indicate the jth feature. You will be used to this convention soon, so don’t worry if you can’t get that right now.
For the sake of simplicity, let’s consider an example, where we have 10 examples, each example only contains one feature. So the Activation Function will look like this:
Does it look similar to you? Yeah, that’s exactly a linear equation with one variable which you learned a lot at high school. If we plot it on the coordinate plane, we will obtain a straight line. That’s the idea of Linear Regression.
Imagine our training data look like this:
| X | y |
|---|---|
| 1 | 7 |
| 2 | 8 |
| 3 | 7 |
| 4 | 13 |
| 5 | 16 |
| 6 | 15 |
| 7 | 19 |
| 8 | 23 |
| 9 | 18 |
| 10 | 21 |
If we plot them on the coordinate plane, we will obtain something like this:
So, our mission now, is to find an appropriate function which can best fit those points. In case of Linear Regression with one variable, because the activation function is actually a straight line, so we will have to find a straight line which can almost go through all those points, intuitively.
But, how do we start? Well, we will start by randomizing all the parameters, which means \( \theta_0,\theta_1 \). So let’s set them both to 1. Now we can compute a by activation function: \(a=1+x\). Now if we plot X, y, and the straight line \(a=1+x\), we will have something like this:
Obviously, the straight line we obtain from \(a=1+x\) doesn’t fit our training data well. But that’s OK because we just began by randomizing the parameters, and no learning was actually performed. So here comes the next question: how can we improve the activation function so that it can fit the data better? Or I can say it differently: how can we make the computer learn to fit the data?
Obviously, we must think of some way to evaluate how well the current function is performing. One way to accomplish this task is to compute Cost Function, which takes the difference between the Label and the prediction as its variable. And among many types of Cost Function, I will introduce to you the Mean Squared Error Function, which is the most appropriate approach for Linear Regression, and yet maybe the simplest one for you to understand.
Firstly, let’s see what Mean Squared Error Function (MSE) looks like:
Now everything has just become clear. It computes the mean value of the squared errors, which are the differences between the Prediction \( h_\theta(X^{(i)}) \) and the Label \( y^{(i)} \). You can see that if the value of \( J(\theta) \) is large, it means that the difference between the Prediction and the Label is also large, which causes the straight line can not fit the training data well. In contrast, if the value of \( J(\theta) \) is close to zero, it means that the Prediction and the Label lie very closely in the coordinate plane, which we can tell that the straight line obtained from the activation function fits the training data pretty well.
Here you may have a question: why don’t we just take mean value of the difference between Prediction and Label? Why must we use the squared value instead? Well, there’s no “must” here. In this case the squared error works just fine, so it was chosen. There’s no problem if we just use the difference instead. But let’s consider this case. Imagine you have \( a^{(1)}=2 \), \( y^{(1)}=4 \) and \( a^{(2)}=5 \), \( y^{(1)}=3 \). What will happen in both cases?
No squared error:
With squared error:
As you can see, the MSE will accumulate the error without caring the sign of the error, whereas the Mean Error is likely to omit the error like the example above. Of course, in another place, using the Mean Error instead of MSE will somehow make some sense, but that’s beyond the scope of this post, so I’ll talk about it when I have chance.
So, through MSE value, we can somehow evaluate how well the activation function is performing, or how well the straight line obtained by the same function is fitting our training data. Then what will we do in the next step? We’ll come to a new concept called, Gradien Descent. Keep going, you’re half way there!
Before digging deeper into Gradient Descent. Let’s look back our MSE function:
Note that our cost function takes \( \theta \) as its variable, not \( X^{(i)} \). For the sake of simplicity, let’s say \( \theta \) contains only \( \theta_1 \). Then our cost function will look like below:
As you can see, our cost function now becomes a quadratic function with \( \theta_1 \) variable. Let’s take one more step, when we plot a quadratic function, we will obtain some figure like this:
A picture’s worth a thousands word, right? Our learning objective is to find the parameter \( \theta \) so that we can draw a straight line which can almost go through all the points in the coordinate plane. In order to accomplish that, we compute a Cost Function (in this case we use the MSE function). We want the value of the Cost Function to be as small as possible.
As we can see in the figure above, our Cost Function is now a quadratic function \( A\theta^2+B\theta+C \). Because we have \( A>0 \), so that our Cost Function is a convex function. You can think of a convex function as some function which has one or many minima. In the case of quadratic function with one variable, our Cost Function only has one minimum. Obviously, all we have to do now, is to find that minimum’s value. But how will we do that? Let’s consider the next figure:
You may remember that earlier in this post, we started by randomize the parameter \( \theta \). So with that randomized value, let’s say some value which is far from the minimum. How is it supposed to go down to the minimum? Oops, you already got it right. It just simply goes down, step by step. But mathematically, how can we force it to go down?
Look at the first arrow to the right a little bit. You may find it very familiar, there’s something that is equal to the slope of the tangent line of the Cost Function of the starting point. I’ll help you this time: that is Derivatives. To be more exact, when \( J(\theta) \) is the function of multiple variables, instead of saying Derivatives, we will use the term: Gradient. Gradient is actually a vector whose elements are Partial Derivatives. Find it hard to understand?
Derivative (one-variable function):
Gradient (multiple-variable function):
I think I’m not going any further in explaining what is behind the Gradient. You can just think of it as a way to tell the computer: which direction to move its next step from the current point. With the right direction, it will gradually make it closer and closer to the minimum, and when it’s finally there, we will have our Cost Function reach its minimum value, and as a result, we will have our final Activation Function which can best fit our training data.
So now you might understand how exactly the learning process occurs. Here comes the final step: how does the computer update the parameters to move towards the next point on the Cost Function \( J(\theta) \) graph?
With the Gradient \( \nabla J(\theta) \) obtained above, we will perform update on the parameters like below:
Note that bote \( \theta \) and \( \nabla J(\theta) \) are vectors, so I can re-write the equation above like this:
You may see the newcomer \( \alpha \). It’s called learning rate, which indicates how fast the parameters are updated at each step. Simply, if we set \( \alpha \) to be large, then it’s likely to go down faster, and reach the desired minimum faster, and vice versa, if \( \alpha \) is too small, then it will take more time until it reach the minimum. So you may ask, why don’t we just make \( \alpha \) large? Well, learning with large learning rate is always risky. Consider the figure below:
As you might see, if we set our learning rate too large, then it will behave unexpectedly, and likely never reach the minimum. So my advice is, try to set \( \alpha \) to be small at first (but not too small), then see whether it worked or not. Then you can think about increasing \( \alpha \) gradually to improve the performance.
After you know what the learning rate \( \alpha \) is. The last question (I hope) you may ask is: how do we compute the Gradient? That’s pretty easy, since our MSE function is just a quadratic function. You can compute the Partial Derivatives using the Chain Rule. It may take some time to compute, so I show you the result right below. You can confirm it yourselves afterwards.
There’s one more thing I want remind you: Weights and Bias. As I said in the first post, Weights are parameters which associate with X, and Bias is parameter which stands alone. So I show you above how to update the Weights. What about the Bias? Because Bias doesn’t associate with X, so its Partial Derivative doesn’t, either.
Until this point, you may understand why we use \( \frac{1}{2m} \) instead of \( \frac{1}{m} \) in the MSE function. Because it’s a quadratic function, using \( \frac{1}{2m} \) will make it easier for computing Partial Derivative. Everything happens for some reason, right?
Now let’s put things together. Here’s what we have:
Seems like a mess, huh? The best way to understand something is doing something with it. Now let’s solve the problem above, we will try to make the straight line to fit our training points.
At starting point, our Cost Function’s value is unacceptably large. So we have to update our parameters using Gradient Descent. Let’s do it now.
Let’s set the learning rate \( \alpha=0.03 \), here’s the new parameters after we performed the update:
With the new parameters, let’s re-compute the Cost Function:
You can see that our Cost Function’s value has dropped significantly after just one update step. Let’s plot our new Activation Function to see how well it improved:
Eh, there’s still much room for improvement. Doing similarly until the forth step, here’s what we got:
Let’s plot it onto the coordinate plane:
As you can see, we now have our straight line which can fit our training data fairly well. And if we run the update few more rounds, you may see that our Cost Function still keeps decreasing but with just slight changes. And we can barely visualize the improvement in the graph. Obviously, we just can’t ask for more in case of a straight line. And since this is the simplest algorithm that I used to explain the learning process, I hope you can now understand what is actually “behind the scenes” and visualize the learning process.
In the next post, I’ll continue with the second part on Linear Regression. I’ll show you how we can improve the performance of Linear Regression, and we will, finally, using Python’s powerful libraries to help us complete the implementation.
That’s it for today. Drop me a line if you have any question. See you in the next post!


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{% csrf_token %} First name Last name Age Email address We'll never share your email with anyone else. Male Female Other City Country Choose... China England Germany Australia Russia India United States Sri Lanka Other part of the world I have read the Consent Information and I agree to participate. Register {# #} {# Set Number#} {# #} {# #}


Dear PSC Members,
With thanks to the formidable PSC membership, I am pleased to announce that the PSC has reached an agreement with CUNY management to extend the deadline for notification of adjunct appointments for the fall semester to June 30, 2020. The agreement applies to non-teaching adjuncts and adjunct CLTs as well as to adjunct faculty.
The agreement also creates a process to enable the PSC and the University to meet before the June 30 deadline and seek to address any instances of adjuncts who have been on health insurance through their CUNY employment in the current academic year and stand to lose eligibility for health insurance for the fall. The union will do everything we can to ensure that no adjunct loses health insurance eligibility during a pandemic. That has been—and remains—one of the PSC’s primary goals.
CUNY also agrees to provide “detailed college budget information, information about proposed cuts . . . and enrollment projections.” Many campus activists have fought hard for budget transparency all spring. Now we have a commitment that detailed college budget information will be provided. Having the information will put us in a stronger position to challenge proposed budget cuts.
In addition, the agreement states that the chancellor will direct colleges to make every effort to make adjunct appointments so that health insurance for those who have been on health insurance this year will be maintained, and to make every effort to ensure that those eligible to receive three-year adjunct appointments receive them. It also stipulates that no adjunct appointments already made will be rescinded.
Finally, the agreement sets a date, June 30, by which CUNY and the PSC aim to finalize an urgent agreement on provision of health insurance for doctoral students who are enrolled at the Graduate Center without fellowship awards.
The PSC is more committed than ever to the position that there must be no cuts and no layoffs at CUNY. In the coming month, we will ask members to organize even more intensely behind the demand that CUNY become a public priority and receive increased public funding—enough to protect all jobs, expand course offerings and provide all students a full, rich, challenging education. Adjunct faculty and staff are not peripheral; they are a core academic workforce. If CUNY allows mass layoffs and cuts, New York will have no hope of an economic recovery that does anything but deepen the horrible inequalities exposed by the pandemic.
CUNY management came to the union with a proposal for an agreement because PSC members have organized and built relentless pressure this spring. We created the need for the University to hold off on devastating layoffs. But the fight is far from over. The union will continue to fight for every job. The additional month gives us precious time to build a movement to save jobs and save CUNY.
All of us in the PSC leadership, however, are aware that the extension to June 30 means a longer period of uncertainty for adjuncts awaiting notice. We don’t underestimate that hardship and anxiety—especially now. But the June 30 date is the final deadline for notification; as always, notices may be sent before that date. Union activists and staff plan to be in contact with every adjunct this summer and make sure you receive all the benefits and rights you are entitled to. And we salute the department chairs, who have worked with courage and compassion during this crisis.
Averting layoffs of potentially thousands of adjuncts scheduled to occur today and creating a process that will help the union to protect health insurance was a major step. Now we must use the coming month and the summer to support each other and amplify our demand for the fundamental economic change that will stop all cuts and all layoffs at CUNY.
In solidarity, Barbara Bowen President, PSC/CUNY


McCall: Full facts of Saracens' salary cap breach 'will come out in due course'
by By Online EditorsSaracens director of rugby Mark McCall insists not everything is black and white regarding the club’s breach of salary cap rules and insisted “all the facts of the case” will come out.
The under-fire Gallagher Premiership champions concluded a challenging few days last Saturday with a 21-12 win over Gloucester at Kingsholm.
Now the focus this weekend turns to the Heineken Champions Cup, with Saracens the holders after winning the competition in May.
Off the pitch, the Allianz Park club are appealing against a 35-point deduction and a fine of more than £5.3million for breaching salary cap rules for the last three seasons.
In an exclusive interview for Wednesday night’s Rugby Tonight programme on BT Sport, McCall said: “People are entitled to their opinions, of course they are.
(Continue reading below…)
“When they read and they hear the club have been found guilty of what they have been found guilty of, you can understand how they feel.
“Things are never as black and white as they are and I don’t think too many people know too much about all the facts of the case, but I am sure it will come out in due course.”
Saracens’ Heineken Champions Cup win in May was the third time the London club had sealed European success. Many domestic rivals have stated the titles should be taken off McCall’s team and he was asked if the breach of salary cap rules tarnishes their success over the last few seasons.
“I think in lots of peoples eyes, it will, but having been here for the last ten years and seen the hard work of our playing group and staff and the growth of our playing group from 14-year-olds through to British Lions, for us – and hopefully others will realise this – we have never shouted from the treetops about what we have won.
“It has been more about what we have built and the relationships that exist here and the relationships I have with our coaches, staff and playing group is something I cherish.
“I guess the big challenge for us now is to see how we respond to this and can those relationships, which we have worked so hard on over the last ten years, be as strong in two or three years’ time, so that will be the real test for us.”
– Press Association
WATCH: Warren Gatland’s reason for saying no to the All Blacks job


This year, Ohio’s Homecoming game will take place against Akron, who is 1-4 this season. Akron has been at the bottom of the Mid-American Conference for the past couple of years, and the game should be winnable for Ohio. Regardless of the game’s outcome, it will still be exciting. The Homecoming game is about school spirit, camaraderie and coming together to cheer on your alma mater or current team.
We’ve always said that one of the best things about sports is the way they can bring people together. Some of our own earliest and best memories involve sitting down and watching sports with our families or actually going to a game. There is nothing quite like the feeling of being in a stadium, arena or field to watch one of your favorite teams, which is something that current and former Bobcats can look forward to this weekend.
It doesn’t matter if you’re a new student from far away like Ohio field hockey midfielder Mijntje Ligtenberg, or have been out of Athens for many years like the first Ohio hockey team of 1958; Homecoming is for you.
Whether it’s programs like Ohio State and Alabama, with truckloads of five-star recruits, or Division III programs just scraping to get by, most college football teams have one thing in common: the Homecoming game.
Almost every team in college football tries to schedule a winnable game for Homecoming, as Ohio has done, and turn a simple home game into an event for students and alums alike. That is what we will see in Athens this weekend.
Whether it is athletes, members of the Marching 110, or just alums returning to Athens for a weekend, Homecoming is a special time for so many current and former Ohio students to remember why they love the school. Sports are often at the center of that.
No matter how good Ohio’s teams are, heading to a sold out Peden Stadium or a packed Convo is always going to be a good time, and Homecoming should once again serve as a great reminder of that.


{% extends 'mypf/base.html' %} {% block content %} Samuel Ngaruiya Web Developer - Python - Django Portfolio Project 1 Description for the Project Github or Project Link Project 2 Description for the Project Github or Project Link Project 3 Description for the Project Github or Project Link Project 4 Description for the Project Github or Project Link About I'm a Web Developer working on Python 3.x, Using Django 2.1 for Web Development. Previously worked as Php Developer for 1.5 years Also having knowledge of HTML, CSS, Javascript, Bootstrap, GIT. Download CV {% endblock %}
Description for the Project
I'm a Web Developer working on Python 3.x, Using Django 2.1 for Web Development. Previously worked as Php Developer for 1.5 years
Also having knowledge of HTML, CSS, Javascript, Bootstrap, GIT.


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{% extends 'base.html' %} {% block content %}
This application is a super-simple Twitter* clone built to demonstrate a CSRF attack.
To properly demonstrate CSRF, you need an authenticated user. Clicking the button below will create a new user with a random username and automatically log you in.
You can then post status updates which will appear on your profile page.
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{% endblock %}


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