Concurrency and async / await
Details about the async def
syntax for path operation functions and some background about asynchronous code, concurrency, and parallelism.
In a hurry?¶
TL;DR:
If you are using third party libraries that tell you to call them with await
, like:
results = await some_library()
Then, declare your path operation functions with async def
like:
@app.get('/')
async def read_results():
results = await some_library()
return results
Note
You can only use await
inside of functions created with async def
.
If you are using a third party library that communicates with something (a database, an API, the file system, etc) and doesn't have support for using await
, (this is currently the case for most database libraries), then declare your path operation functions as normally, with just def
, like:
@app.get('/')
def results():
results = some_library()
return results
If your application (somehow) doesn't have to communicate with anything else and wait for it to respond, use async def
.
If you just don't know, use normal def
.
Note: you can mix def
and async def
in your path operation functions as much as you need and define each one using the best option for you. FastAPI will do the right thing with them.
Anyway, in any of the cases above, FastAPI will still work asynchronously and be extremely fast.
But by following the steps above, it will be able to do some performance optimizations.
Technical Details¶
Modern versions of Python have support for "asynchronous code" using something called "coroutines", with async
and await
syntax.
Let's see that phrase by parts in the sections below, below:
- Asynchronous Code
async
andawait
- Coroutines
Asynchronous Code¶
Asynchronous code just means that the language has a way to tell the computer / program that at some point in the code, he will have to wait for something else to finish somewhere else. Let's say that something else is called "slow-file".
So, during that time, the computer can go and do some other work, while "slow-file" finishes.
Then the computer / program will come back every time it has a chance because it's waiting again, or whenever he finished all the work he had at that point. And it will see if any of the tasks he was waiting for has already finished doing whatever it had to do.
And then it takes the first task to finish (let's say, our "slow-file") and continues whatever it had to do with it.
That "wait for something else" normally refers to I/O operations that are relatively "slow" (compared to the speed of the processor and the RAM memory), like waiting for:
- the data from the client to be sent through the network
- the data sent by your program to be received by the client through the network
- the contents of a file in the disk to be read by the system and given to your program
- the contents your program gave to the system to be written to disk
- a remote API operation
- a database operation to finish
- a database query to return the results
- etc.
As the execution time is consumed mostly by waiting for I/O operations, so they call them "I/O bound".
It's called "asynchronous" because the computer / program doesn't have to be "synchronized" with the slow task, waiting for the exact moment that the task finishes, while doing nothing, to be able to take the task result and continue the work.
Instead of that, by being an "asynchronous" system, once finished, the task can wait in line a little bit (some microseconds) for the computer / program to finish whatever it went to do, and then come back to take the results and continue working with them.
For "synchronous" (contrary to "asynchronous") they commonly also use the term "sequential", because the computer / program follows all the steps in sequence before switching to a different task, even if those steps involve waiting.
Concurrency and Burgers¶
This idea of asynchronous code described above is also sometimes called "concurrency". It is different from "parallelism".
Concurrency and parallelism both relate to "different things happening more or less at the same time".
But the details between concurrency and parallelism are quite different.
To see the difference, imagine the following story about burgers:
Concurrent Burgers¶
You go with your crush to get fast food, you stand in line while the cashier takes the orders from the people in front of you.

You pay.
The cashier says something to the guy in the kitchen so he knows he has to prepare your burgers (even though he is currently preparing the ones for the previous clients).
The cashier gives you the number of your turn.

As you are seating on the table with your crush, while you wait for the burgers, you can spend that time admiring how awesome, cute and smart your crush is.
While waiting and talking to your crush, from time to time, you check the number displayed on the counter to see if it's your turn already.
Then at some point, it finally is your turn.

Imagine you are the computer / program in that story.
While you are at the line, you are just idle, waiting for your turn, not doing anything very "productive". But the line is fast because the cashier is only taking the orders, so that's fine.
Then, when it's your turn, you do actual "productive" work, you process the menu, decide what you want, get your crush's choice, pay, check that you give the correct bill or card, check that you are charged correctly, check that the order has the correct items, etc.
But then, even though you still don't have your burgers, your work with the cashier is "on pause", because you have to wait for your burgers to be ready.
But as you go away from the counter and seat on the table with a number for your turn, you can switch your attention to your crush, and "work" on that. Then you are again doing something very "productive", as is flirting with your crush.
Then the cashier says "I'm finished with doing the burgers" by putting your number on the counter display, but you don't jump like crazy immediately when the displayed number changes to your turn number. You know no one will steal your burgers because you have the number of your turn, and they have theirs.
So you wait for your crush to finish the story (finish the current work / task being processed), smile gently and say that you are going for the burgers.
Then you go to the counter, to the initial task that is now finished, pick the burgers, say thanks and take them to the table. That finishes that step / task of interaction with the counter. That in turn, creates a new task, of "eating burgers", but the previous one of "getting burgers" is finished.
Parallel Burgers¶
You go with your crush to get parallel fast food.

Everyone before you is waiting for their burgers to be ready before leaving the counter because each of the 8 cashiers goes himself and prepares the burger right away before getting the next order.

You pay.
The cashier goes to the kitchen.

This is "synchronous" work, you are "synchronized" with the cashier/cook. You have to wait and be there at the exact moment that the cashier/cook finishes the burgers and gives them to you, or otherwise, someone else might take them.

There was not much talk or flirting as most of the time was spent waiting in front of the counter.
In this scenario of the parallel burgers, you are a computer / program with two processors (you and your crush), both waiting and dedicating their attention to be "waiting on the counter" for a long time.
The fast food store has 8 processors (cashiers/cooks). While the concurrent burgers store might have had only 2 (one cashier and one cook).
But still, the final experience is not the best.
This would be the parallel equivalent story for burgers.
For a more "real life" example of this, imagine a bank.
Up to recently, most of the banks had multiple cashiers and a big line.
All of the cashiers doing all the work with one client after the other.
And you have to wait in the line for a long time or you lose your turn.
You probably wouldn't want to take your crush with you to do errands at the bank.
Burger Conclusion¶
In this scenario of "fast food burgers with your crush", as there is a lot of waiting, it makes a lot more sense to have a concurrent system.
This is the case for most of the web applications.
Many, many users, but your server is waiting for their not-so-good connection to send their requests.
And then waiting again for the responses to come back.
This "waiting" is measured in microseconds, but still, summing it all, it's a lot of waiting in the end.
That's why it makes a lot of sense to use asynchronous code for web APIs.
Most of the existing popular Python frameworks (including Flask and Django) were created before the new asynchronous features in Python existed. So, the ways they can be deployed support parallel execution and an older form of asynchronous execution that is not as powerful as the new capabilities.
Even though the main specification for asynchronous web Python (ASGI) was developed at Django, to add support for WebSockets.
That kind of asynchronicity is what made NodeJS popular (even though NodeJS is not parallel) and that's the strength of Go as a programing language.
And that's the same level of performance you get with FastAPI.
And as you can have parallelism and asynchronicity at the same time, you get higher performance than most of the tested NodeJS frameworks and on par with Go, which is a compiled language closer to C (all thanks to Starlette).
Is concurrency better than parallelism?¶
Nope! That's not the moral of the story.
Concurrency is different than parallelism. And it is better on specific scenarios that involve a lot of waiting. Because of that, it generally is a lot better than parallelism for web application development. But not for everything.
So, to balance that out, imagine the following short story:
You have to clean a big, dirty house.
Yep, that's the whole story.
There's no waiting anywhere, just a lot of work to be done, on multiple places of the house.
You could have turns as in the burgers example, first the living room, then the kitchen, but as you are not waiting for anything, just cleaning and cleaning, the turns wouldn't affect anything.
It would take the same amount of time to finish with or without turns (concurrency) and you would have done the same amount of work.
But in this case, if you could bring the 8 ex-cashier/cooks/now-cleaners, and each one of them (plus you) could take a zone of the house to clean it, you could do all the work in parallel, with the extra help, and finish much sooner.
In this scenario, each one of the cleaners (including you) would be a processor, doing their part of the job.
And as most of the execution time is taken by actual work (instead of waiting), and the work in a computer is done by a CPU, they call these problems "CPU bound".
Common examples of CPU bound operations are things that require complex math processing.
For example:
- Audio or image processing
- Computer vision: an image is composed of millions of pixels, each pixel has 3 values / colors, processing that normally requires computing something on those pixels, all at the same time)
- Machine Learning: it normally requires lots of "matrix" and "vector" multiplications. Think of a huge spreadsheet with numbers and multiplying all of them together at the same time.
- Deep Learning: this is a sub-field of Machine Learning, so, the same applies. It's just that there is not a single spreadsheet of numbers to multiply, but a huge set of them, and in many cases, you use a special processor to build and / or use those models.
Concurrency + Parallelism: Web + Machine Learning¶
With FastAPI you can take the advantage of concurrency that is very common for web development (the same main attractive of NodeJS).
But you can also exploit the benefits of parallelism and multiprocessing (having multiple processes running in parallel) for CPU bound workloads like those in Machine Learning systems.
That, plus the simple fact that Python is the main language for Data Science, Machine Learning and especially Deep Learning, make FastAPI a very good match for Data Science / Machine Learning web APIs and applications (among many others).
To see how to achieve this parallelism in production see the section about Deployment.
async
and await
¶
Modern versions of python have a very intuitive way to define asynchronous code. This makes it look just like normal "sequential" code and do the "awaiting" for you at the right moments.
When there is an operation that will require waiting before giving the results and has support for these new Python features, you can code it like:
burgers = await get_burgers(2)
The key here is the await
. It tells Python that it has to wait for get_burgers(2)
to finish doing its thing before storing the results in burgers
. With that, Python will know that it can go and do something else in the meanwhile (like receiving another request).
For await
to work, it has to be inside a function that supports this asynchronicity. To do that, you just declare it with async def
:
async def get_burgers(number: int):
# Do some asynchronous stuff to create the burgers
return burgers
...instead of def
:
# This is not asynchronous
def get_sequential_burgers(number: int):
# Do some sequential stuff to create the burgers
return burgers
With async def
, Python knows that, inside that function, it has to be aware of await
expressions, and that it can "pause" the execution of that function and go do something else before coming back.
When you want to call an async def
function, you have to "await" it. So, this won't work:
# This won't work, because get_burgers was defined with: async def
burgers = get_burgers(2)
So, if you are using a library that tells you that you can call it with await
, you need to create the path operation functions that uses it with async def
, like in:
@app.get('/burgers')
async def read_burgers():
burgers = await get_burgers(2)
return burgers
More technical details¶
You might have noticed that await
can only be used inside of functions defined with async def
.
But at the same time, functions defined with async def
have to be "awaited". So, functions with async def
can only be called inside of functions defined with async def
too.
So, about the egg and the chicken, how do you call the first async
function?
If you are working with FastAPI you don't have to worry about that, because that "first" function will be your path operation function, and FastAPI will know how to do the right thing.
But if you want to use async
/ await
without FastAPI, check the official Python docs.
Other forms of asynchronous code¶
This style of using async
and await
is relatively new in the language.
But it makes working with asynchronous code a lot easier.
This same syntax (or almost identical) was also included recently in modern versions of JavaScript (in Browser and NodeJS).
But before that, handling asynchronous code was quite more complex and difficult.
In previous versions of Python, you could have used threads or Gevent. But the code is way more complex to understand, debug, and think about.
In previous versions of NodeJS / Browser JavaScript, you would have used "callbacks". Which lead to callback hell.
Coroutines¶
Coroutine is just the very fancy term for the thing returned by an async def
function. Python knows that it is something like a function that it can start and that it will end at some point, but that it might be paused internally too, whenever there is an await
inside of it.
But all this functionality of using asynchronous code with async
and await
is many times summarized as using "coroutines". It is comparable to the main key feature of Go, the "Goroutines".
Conclusion¶
Let's see the same phrase from above:
Modern versions of Python have support for "asynchronous code" using something called "coroutines", with
async
andawait
syntax.
That should make more sense now.
All that is what powers FastAPI (through Starlette) and what makes it have such an impressive performance.
Very Technical Details¶
Warning
You can probably skip this.
These are very technical details of how FastAPI works underneath.
If you have quite some technical knowledge (co-routines, threads, blocking, etc) and are curious about how FastAPI handles async def
vs normal def
, go ahead.
Path operation functions¶
When you declare a path operation function with normal def
instead of async def
, it is run in an external threadpool that is then awaited, instead of being called directly (as it would block the server).
If you are coming from another async framework that does not work in the way described above and you are used to define trivial compute-only path operation functions with plain def
for a tiny performance gain (about 100 nanoseconds), please note that in FastAPI the effect would be quite opposite. In these cases, it's better to use async def
unless your path operation functions use code that performs blocking IO.
Still, in both situations, chances are that FastAPI will still be faster than (or at least comparable to) your previous framework.
Dependencies¶
The same applies for dependencies. If a dependency is a standard def
function instead of async def
, it is run in the external threadpool.
Sub-dependencies¶
You can have multiple dependencies and sub-dependencies requiring each other (as parameters of the function definitions), some of them might be created with async def
and some with normal def
. It would still work, and the ones created with normal def
would be called on an external thread instead of being "awaited".
Other utility functions¶
Any other utility function that you call directly can be created with normal def
or async def
and FastAPI won't affect the way you call it.
This is in contrast to the functions that FastAPI calls for you: path operation functions and dependencies.
If your utility function is a normal function with def
, it will be called directly (as you write it in your code), not in a threadpool, if the function is created with async def
then you should await for that function when you call it in your code.
Again, these are very technical details that would probably be useful if you came searching for them.
Otherwise, you should be good with the guidelines from the section above: In a hurry?.


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About us
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Services
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Contact Us
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(512)632-5630


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|
ADC
9.2
Analog to Digital Conversor library for the Teensy LC microprocessor
|
#include <ADC.h>
Public Member Functions | |
| ADC () | |
| void | resetError () |
ADC settings | |
| void | setReference (ADC_REFERENCE type) |
| Set the voltage reference for all ADCs, default is vcc. More... | |
| void | setResolution (uint8_t bits) |
| Change the resolution for all ADCs. More... | |
| void | setConversionSpeed (ADC_CONVERSION_SPEED speed) |
| Sets the conversion speed for all ADCs. More... | |
| void | setSamplingSpeed (ADC_SAMPLING_SPEED speed) |
| Sets the sampling speed for all ADCs. More... | |
| void | setAveraging (uint8_t num) |
| Set the number of averages for all ADCs. More... | |
| void | enableInterrupts (void(*isr)(void), uint8_t priority=255) |
| Enable interrupts for all ADCs. More... | |
| void | disableInterrupts () |
| Disable interrupts for all ADCs. | |
| void | enableDMA () |
| Enable DMA request for all ADCs. More... | |
| void | disableDMA () |
| Disable DMA request for all ADCs. | |
| void | enableCompare (int16_t compValue, bool greaterThan) |
| Enable the compare function to a single value. More... | |
| void | enableCompareRange (int16_t lowerLimit, int16_t upperLimit, bool insideRange, bool inclusive) |
| Enable the compare function to a range. More... | |
| void | disableCompare () |
| Disable the compare function. More... | |
Blocking conversion methods | |
| int | analogRead (uint8_t pin, int8_t adc_num=-1) |
| Returns the analog value of the pin. More... | |
| int | analogRead (ADC_INTERNAL_SOURCE pin, int8_t adc_num=-1) |
| int | analogReadDifferential (uint8_t pinP, uint8_t pinN, int8_t adc_num=-1) |
| Reads the differential analog value of two pins (pinP - pinN). More... | |
Non-blocking conversion methods | |
| bool | startSingleRead (uint8_t pin, int8_t adc_num=-1) |
| Starts an analog measurement on the pin and enables interrupts. More... | |
| bool | startSingleDifferential (uint8_t pinP, uint8_t pinN, int8_t adc_num=-1) |
| int | readSingle (int8_t adc_num=-1) |
| Reads the analog value of a single conversion. More... | |
Continuous conversion methods | |
| bool | startContinuous (uint8_t pin, int8_t adc_num=-1) |
| Starts continuous conversion on the pin. More... | |
| bool | startContinuousDifferential (uint8_t pinP, uint8_t pinN, int8_t adc_num=-1) |
| Starts continuous conversion between the pins (pinP-pinN). More... | |
| int | analogReadContinuous (int8_t adc_num=-1) |
| Reads the analog value of a continuous conversion. More... | |
| void | stopContinuous (int8_t adc_num=-1) |
| Stops continuous conversion. More... | |
Public Attributes | |
| ADC_Module *const | adc0 = &adc0_obj |
| Object to control the ADC0. | |
| ADC_Module *const | adc [ADC_NUM_ADCS] = {adc0} |
| Array with the ADC Modules. | |
Static Public Attributes | |
| static const uint8_t | channel2sc1aADC0 [ADC_MAX_PIN+1] |
| Translate pin number to SC1A nomenclature. More... | |
| static const uint8_t | sc1a2channelADC0 [ADC_MAX_PIN+1] |
| Translate pin number to SC1A nomenclature for differential pins. More... | |
| static const ADC_Module::ADC_NLIST | diff_table_ADC0 [ADC_DIFF_PAIRS] |
| Translate differential pin number to SC1A nomenclature. More... | |
|
inline |
| int ADC::analogRead | ( | uint8_t | pin, |
| int8_t | adc_num = -1 |
||
| ) |
Returns the analog value of the pin.
It waits until the value is read and then returns the result. If a comparison has been set up and fails, it will return ADC_ERROR_VALUE. This function is interrupt safe, so it will restore the adc to the state it was before being called If more than one ADC exists, it will select the module with less workload, you can force a selection using adc_num. If you select ADC1 in a board with just one ADC, it will return ADC_ERROR_VALUE.
| pin | can be any of the analog pins |
| adc_num | ADC_X ADC module |
| int ADC::analogReadContinuous | ( | int8_t | adc_num = -1 | ) |
Reads the analog value of a continuous conversion.
Set the continuous conversion with with analogStartContinuous(pin) or startContinuousDifferential(pinP, pinN). If single-ended and 16 bits it's necessary to typecast it to an unsigned type (like uint16_t), otherwise values larger than 3.3/2 V are interpreted as negative!
| adc_num | ADC_X ADC module |
Set the continuous conversion with with analogStartContinuous(pin) or startContinuousDifferential(pinP, pinN).
| int ADC::analogReadDifferential | ( | uint8_t | pinP, |
| uint8_t | pinN, | ||
| int8_t | adc_num = -1 |
||
| ) |
Reads the differential analog value of two pins (pinP - pinN).
It waits until the value is read and then returns the result. This function is interrupt safe, so it will restore the adc to the state it was before being called If more than one ADC exists, it will select the module with less workload, you can force a selection using adc_num
| pinP | must be A10 or A12. |
| pinN | must be A11 (if pinP=A10) or A13 (if pinP=A12). |
| adc_num | ADC_X ADC module |
|
inline |
|
inline |
Enable the compare function to a single value.
A conversion will be completed only when the ADC value is >= compValue (greaterThan=1) or < compValue (greaterThan=0) Call it after changing the resolution Use with interrupts or poll conversion completion with isComplete()
| compValue | value to compare |
| greaterThan | true or false |
|
inline |
Enable the compare function to a range.
A conversion will be completed only when the ADC value is inside (insideRange=1) or outside (=0) the range given by (lowerLimit, upperLimit),including (inclusive=1) the limits or not (inclusive=0). See Table 31-78, p. 617 of the freescale manual. Call it after changing the resolution Use with interrupts or poll conversion completion with isComplete()
| lowerLimit | lower value to compare |
| upperLimit | upper value to compare |
| insideRange | true or false |
| inclusive | true or false |
|
inline |
|
inline |
Enable interrupts for all ADCs.
An IRQ_ADCx Interrupt will be raised when the conversion is completed (including hardware averages and if the comparison (if any) is true).
| isr | function (returns void and accepts no arguments) that will be executed after an interrupt. |
| priority | Interrupt priority, highest is 0, lowest is 255. |
| int ADC::readSingle | ( | int8_t | adc_num = -1 | ) |
|
inline |
|
inline |
|
inline |
Sets the conversion speed for all ADCs.
| speed | can be any from the ADC_CONVERSION_SPEED enum. |
|
inline |
Set the voltage reference for all ADCs, default is vcc.
| type | Any of ADC_REFERENCE |
|
inline |
Change the resolution for all ADCs.
| bits | is the number of bits of resolution. For single-ended measurements: 8, 10, 12 or 16 bits. For differential measurements: 9, 11, 13 or 16 bits. If you want something in between (11 bits single-ended for example) select the immediate higher and shift the result one to the right. Whenever you change the resolution, change also the comparison values (if you use them). |
|
inline |
Sets the sampling speed for all ADCs.
Increase the sampling speed for low impedance sources, decrease it for higher impedance ones.
| speed | can be any of the ADC_SAMPLING_SPEED. |
| bool ADC::startContinuous | ( | uint8_t | pin, |
| int8_t | adc_num = -1 |
||
| ) |
Starts continuous conversion on the pin.
It returns as soon as the ADC is set, use analogReadContinuous() to read the value.
| pin | can be any of the analog pins |
| adc_num | ADC_X ADC module |
| bool ADC::startContinuousDifferential | ( | uint8_t | pinP, |
| uint8_t | pinN, | ||
| int8_t | adc_num = -1 |
||
| ) |
Starts continuous conversion between the pins (pinP-pinN).
It returns as soon as the ADC is set, use analogReadContinuous() to read the value.
| pinP | must be A10 or A12. |
| pinN | must be A11 (if pinP=A10) or A13 (if pinP=A12). |
| adc_num | ADC_X ADC module |
| bool ADC::startSingleDifferential | ( | uint8_t | pinP, |
| uint8_t | pinN, | ||
| int8_t | adc_num = -1 |
||
| ) |
Start a differential conversion between two pins (pinP - pinN) and enables interrupts. It returns immediately, get value with readSingle(). If this function interrupts a measurement, it stores the settings in adc_config
| pinP | must be A10 or A12. |
| pinN | must be A11 (if pinP=A10) or A13 (if pinP=A12). |
| adc_num | ADC_X ADC module |
| bool ADC::startSingleRead | ( | uint8_t | pin, |
| int8_t | adc_num = -1 |
||
| ) |
Starts an analog measurement on the pin and enables interrupts.
It returns immediately, get value with readSingle(). If this function interrupts a measurement, it stores the settings in adc_config
| pin | can be any of the analog pins |
| adc_num | ADC_X ADC module |
| void ADC::stopContinuous | ( | int8_t | adc_num = -1 | ) |
|
static |
Translate pin number to SC1A nomenclature.
|
static |
|
static |


~inc:menu.inc~
RF Data Setup 2
crystal_26m_en [48]:
0: 40MHz
1: 26MHz
2: 24MHz
gpio0_3_func [49]:
sdio_configure [50]:
0: Auto by pin strapping
1: SDIO dataoutput is at negative edges (SDIO V1.1)
2: SDIO dataoutput is at positive edges (SDIO V2.0)
bt_configure [51]:
0: None,no bluetooth
1: GPIO0 -> WLAN_ACTIVE/ANT_SEL_WIFI, MTMS -> BT_ACTIVE, MTCK -> BT_PRIORITY, U0RXD -> ANT_SEL_BT
2: None, have bluetooth
3: GPIO0 -> WLAN_ACTIVE/ANT_SEL_WIFI, MTMS -> BT_PRIORITY, MTCK -> BT_ACTIVE, U0RXD -> ANT_SEL_BT
bt_protocol [52]:
0: WiFi-BT are not enabled. Antenna is for WiFi
1: WiFi-BT are not enabled. Antenna is for BT
2: WiFi-BT 2-wire are enabled, (only use BT_ACTIVE), independent ant
3: WiFi-BT 3-wire are enabled, (when BT_ACTIVE = 0, BT_PRIORITY must be 0), independent ant
4: WiFi-BT 2-wire are enabled, (only use BT_ACTIVE), share ant
5: WiFi-BT 3-wire are enabled, (when BT_ACTIVE = 0, BT_PRIORITY must be 0), share ant
dual_ant_configure [53]:
0: None
1: dual_ant (antenna diversity for WiFi-only): GPIO0 + U0RXD
2: T/R switch for External PA/LNA: GPIO0 is high and U0RXD is low during Tx
3: T/R switch for External PA/LNA: GPIO0 is low and U0RXD is high during Tx
gpio1_2_func [54]:
share_xtal [55]:
0: Off
1: Forcely On
2: Automatically On according to XPD_DCDC
3: Automatically On according to GPIO2
spur_freq_cfg_2 [64]:
spur_freq_cfg_div_2 [65]:
spur_freq_en_h_2 [66]:
spur_freq_cfg_msb [68]:
spur_freq_cfg_2_msb [69]:
spur_freq_cfg_3_low [70]:
spur_freq_cfg_3_high [71]:
spur_freq_cfg_4_low [72]:
spur_freq_cfg_4_high [73]:
<< Prev *
* Next >>
~inc:footer.inc~


The type of the objet that the create call generates. Can generally be inferred from the create attribute.
The default timeout for acquisitions, or undefined/Infinity for no timeout. If defined, must be >= 0.
Called to create a new pool item. Can be asynchronous or asynchronous. If you need creation timeouts, you'll need to implement them yourself.
Called to dispose of an item. If asynchronous, the results of disposal will be awaited before space in the pool is released.
Specifies how long a pool item can remain idle before it is automatically disposed of. Note that if disposal would drop the pool below its minimum size, disposal will typically not occur. If defined, must be >= 0, if undefined or Infinity, no idle timeout will be triggered.
The maximum pool size, or Infinite for no maximum. Must be >= 0.
The minimum pool size. If this value is greater than zero, then the pool will always guarantee that the pool has at least this many items pre-created. This value must be 0 <= min <= max.
A callback that will be called just before an item is successfully acquired from the pool. Take care that this function does not throw.
A callback that will be called if there is an error during creation, verification, or disposal of a queue item.
A callback that will be called just after an item has successfully been released to the pool. Take care that this function does not throw.
A callback that will be called if an acquire attempt times out. Take care that this method does not throw.
Which Promise class to use. Defaults to ES6 Promise
.
Called to verify that an item is still valid for use. Note that this function will be called with a InspectableItem -- e.g., a wrapped item with attributes you could use to determine item validity.
If this function returns a Promise, that Promise will be awaited for its results.
Generated using TypeDoc
Pool options


Welcome!
Instructions:
create a grammatically sound sentence by choosing a word in each column. A word tile will turn green when the chosen word is correct. Red when it is wrong.
Let's begin!


Create custom holes runtime
Using menu, you can create custom holes at a specific dimensions and positions.
Produced by: SmarTeam3D
Powered by: BabylonJS
Length:
LengthA:
LengthB:
Thickness:
Side:
Form:
Radius:
X:
Y/Z:
Width:
Height:
X:
Y/Z:
Width:
Height:
Corner:
X:
Y/Z:
Height:
Radius:
X:
Y/Z:
Create custom holes runtime
Using menu, you can create custom holes at a specific dimensions and positions.
Produced by: SmarTeam3D
Powered by: BabylonJS


Use this area to configure settings for the sbt launcher. You can select from the following options:
Bundled: use this default option if you want the bundled
sbt launcher
.
Custom: use this option to specify an alternative sbt launcher.


Several clever definitions of Alloy language constructs allow you to write models which look like a lot like Java/C++ OO code. The key to getting this to work is the interpretation of signature declarations (a "sig" statements) and the "." operator (relational composition). Signatures are technically sets, but they can be thought of as objects in the OO paradigm.
Consider the following excerpt. A signature sig S, extending E, has a field F of type T.
we write:
sig S extends E {
F:T
}
fact {
all s:S | s.F in X
}
The fragment can be interpreted in an Object Oriented sense.
The OO interpretation can be helpful for understanding a model at a glance, but can be dangerous if you don't have any deeper understanding. As you become more familiar with Alloy, you will find yourself thinking in set theoretic terms.


We use machine learning to make the most accurate health predictions.
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{{ form.height.label }} {{ form.height(class="form-control", placeholder="in inches") }}
{{ form.weight.label }} {{ form.weight(class="form-control", placeholder="in pounds") }}
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{{ form[field].label(class="control-label") }} {{ form[field](class="form-control") }}
{% endfor %}
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THE NINTH GATE
Reviewed by Harvey Karten Artisan Entertainment Director: Roman Polanski Writer: John Brownjohn, Enrique Urbizu, Roman Polanski. Novel by Arturo Perez-Reverte, "El Club Dumas" Cast: Johnny Depp, Frank Langella, Lena Olin, Emmanuelle Seigner, Barbara Jefford, Jack Taylor, Jose Lopez Rodero, James Russo, Tony Amoni
The public fascination with movies on occult themes is bottomless. Given the general routines of our lives--our daily trips to work, shopping for this and that, suburban barbecues on weekends while city people go to the zoo--we seek out drama and conflict more exotic than that of ritual battles between political candidates who debate such trivial matters as whether a building on a deserted street should be seized and destroyed to make room for a wider road. In short, we look for battles that have higher stakes than squabbles within cities or a discordant national dialogue or even rows between nations. We want nothing less than a clash between good and evil, in which selling your soul to the devil does not mean taking bribes to award a contract to x or y construction company but a literal conjuring up of the Lucifer to achieve power beyond ordinary human scope.
Few directors can comply with audience demands to be pulled out of their mundane worlds better than the sixty-six year old Roman Polanski. We can understand Polanski's interest in the obsessions of the principal characters of his absorbing new movie "The Ninth Gate" when we grasp his difficult youth. Polanski had a terrifying childhood. At the age of eight, his parents were shipped off from Krakow, Poland, to a Nazi concentration camp where his mother died. Roman escaped from the Jewish quarters (ghetto) of Krakow before its destruction, was hidden by Catholic families as he wandered the Polish countryside, and in more than one instance became the victim of a sadistic game by Germans: They used him for target practice, watching Polanski dodge bullets by soldiers who used him for target practice in much the way that Ralph Fiennes' character exercised his power in Steven Spielberg's blockbuster, "Schindler's List." His early childhood, then, was given over to more fear than perhaps the most abused kids in the U.S. today experience. This anxiety translates to his films, and though Polanski is capable of breaking loose from his characteristic style with an exquisitely literary and cinematic "Tess," he is best known today for his adaptation of Ira Levin's modern thriller, "Rosemary's Baby"--in which an innocent woman does not suspect the bargain that her husband has made with satanic forces.
Something of "Rosemary's Baby" comes forth in "The Ninth Gate," though not until the penultimate scene. Most of the lengthy--but not overlong--drama accommodates a supernatural tone, but happily, no ridiculous aliens burst from the abdomens of human beings and no stereotypical bloodsuckers seduce their victims into eternal, if profane, life. Polanski relies on a noir inflection, shooting many of his scenes in dimly lit interiors and displaying his central character as a chain smoker with scholarly, owlish glasses who becomes involved with two fetching women that could have come from a Hammett novel.
The talented and diversified Johnny Depp performs in the role of Dean Corso, a man with an enormous knowledge of rare books who is also a mercenary willing to sell his talents to the highest bidder. Essentially a book detective, he takes on a commission from the fabulously rich collector Boris Balkan, Ph.D. (Frank Langella), whose vast personal library includes the exceedingly rare "The Nine Gates of the Kingdom of Shadows" published during the 1600s and allegedly co-authored by Lucifer himself. Balkan offers a huge sum to Corso, who must travel to Portugal and France to find the other two copies of this rare book, presumably to determine whether Balkan's copy is authentic or a forgery. In the course of his investigation he interviews the sinuous Liana Telfer (Lena Olin), recently widowed from the previous owner of the book, and is followed everywhere by a mysterious, green-eyed blonde whose name is not revealed (Emmanuelle Seigner) but whose identity becomes clearer in the concluding scenes.
Frank Langella, not unaccustomed to villainous roles during his career on stage and screen, looms physically large in this production. Wearing an unusual rug on his head, an expensive pin-striped suit on his body, and a heavy pair of eyeglasses on the bridge of his nose, Langella is convincing as a man whose millions do not provide what he is really after--more power than that enjoyed by any human being. Since the engravings in the books he seeks would complete a set of illustrations he already owns and allow him to conjure the devil--much as Rabbi Loew animates an avenging monster in Paul Wegener's 1920 film "The Golem"--Balkan will stop at nothing to gain possession of the volumes.
Whether you think the film comes apart near the finish as many critics have offered depends really on your age. A youthful audience may turn on from the unleashing of psychic energy as black-robed men listen to a sinister supplication in Latin, while a more enlightened crowd will appreciate the human aspects of the drama, especially the remarkably adept performance by Depp. "The Ninth Gate" is far and away superior to the blatantly exploitative "End of Days," avoiding most of the special effects beloved of Peter Hyams while displaying a humorous scene involving Jose Lopez Rodero who is f/x'd as a pair of twins, Pablo and Pedro Ceniza. Though the final minute of the film will have most of the audience scratching their heads and wondering "Is that all?" despite its generous 132 minutes, "The Ninth Gate" is for the most part an appropriately restrained, deftly acted and smartly stylized with a dynamite score by Wojciech Kilar.
Rated R. Running Time: 132 minutes. (C) 2000 Harvey Karten<EMAIL_ADDRESS>The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


Need 100 Food Packs
{{panelOpenState ? '2 contributors' : 'Angoda, Colombo'}}
Urgently need 100 food packs to distribute among the flood victims. Please contact if you like to contribute<PHONE_NUMBER>
Drug Awareness Session
{{panelOpenState ? '1 contributors' : 'Peradeniya, Kandy'}}
Would appreciate if anyone can host a drug awareness event fot the youth around Kandy
A Wheelchair needed
{{panelOpenState ? '0 contributors' : 'Kodaikal, Mannar'}}
We are looking for a wheelchair new/used to support a 60 year old patient who suffers from arthrites


ABOUT ME
I am inressted in making things perfect as much as it can be.
Clean Code
Written in Kotlin/Java according to Android Developer Best Practice Documentations.
Great Features
From the most complicated one to the simplest one, the only thing that matters is time.
Responsive Design
According to Android's Materail Design documentation that every space should be used efficiently.
Well Documented
As my Github open-sources repositories are, for having a better future of the project's code.
SOME FACTS ABOUT ME
Projects Completed
Android App Made
Courses Taken
Got Awards
MY PORTFOLIO
Latest projects are with the most gained professional development skills
- All
- Farza Al-Yemen
- Tatbela
- Namaa
- Other Android Apps
MY CERTIFICATES
Thanks for everyone who has been a part in my self-learning journey
Certificate Of Appreciation
from Faculty of Tourism & Hotels Head DepartmentNanodegree Certificate
from UdacityCertificate of Completion
from UdacityCertificate of Completion
from UdemyCertificate of Completion
from UdemyMy Awards
CONTACT ME
Anytime you will be welcomed
Dasser Basyouni
You can contact me for profissional work, help or anything you want.
<EMAIL_ADDRESS>+20<PHONE_NUMBER>


This dataset represents commuter flows (work travel) between origin
and destination zones (see cents
).
The data is from the UK and is available as open data:
http://wicid.ukdataservice.ac.uk/.
data(flow)
A data frame with 49 rows and 15 columns
The variables are as follows:
Area.of.residence. id of origin zone
Area.of.workplace id of destination zone
All. Travel to work flows by all modes
[,4:15]. Flows for different modes
id. unique id of flow
Although these variable names are unique to UK data, the data
structure is generalisable and typical of flow data from any source.
The key variables are the origin and destination ids, which link to
the cents
georeferenced spatial objects.
not_run({ # This is how the dataset was constructed - see # https://github.com/npct/pct - if download to ~/repos flow <- readRDS("~/repos/pct/pct-data/national/flow.Rds") data(cents) o <- flow$Area.of.residence %in% cents$geo_code[-1] d <- flow$Area.of.workplace %in% cents$geo_code[-1] flow <- flow[o & d, ] # subset flows with o and d in study area library(devtools) flow$id <- paste(flow$Area.of.residence, flow$Area.of.workplace) use_data(flow, overwrite = TRUE) # Convert flows to SpatialLinesDataFrame flowlines <- od2line(flow = flow, zones = cents) # use_data(flowlines, overwrite = TRUE) # Convert flows to routes routes_fast <- line2route(l = flowlines, plan = "fastest") routes_slow <- line2route(l = flowlines, plan = "quietest") use_data(routes_fast) use_data(routes_slow) })


#include <menus.h>
Inheritance diagram for TMenuBox::
Public Methods | |
| TMenuBox (const TRect &bounds, TMenu *aMenu, TMenuView *aParentMenu) | |
| virtual void | draw () |
| virtual TRect | getItemRect (TMenuItem *item) |
Static Public Methods | |
| TStreamable* | build () |
Static Public Attributes | |
| const char* | frameChars |
| const char* const | name |
Protected Methods | |
| TMenuBox (StreamableInit) |
Definition at line 569 of file menus.h.
|
|
Creates a TMenuBox object by calling TMenuView::TMenuView(bounds). The `bounds' parameter is then adjusted to accommodate the width and length of the items in `aMenu'.
The menu data member is set to `aMenu', which provides the menu selections. Allows an explicit `aParentMenu' which is set to parentMenu. |
|
|
Each streamable class needs a "builder" to allocate the correct memory for its objects together with the initialized virtual table pointers. This is achieved by calling this constructor with an argument of type StreamableInit. |
|
|
Called to create an object in certain stream-reading situations. Reimplemented from TMenuView. |
|
|
Draws the framed menu box and associated menu items in the default colors. Reimplemented from TView. |
|
|
Returns the rectangle occupied by the given menu item. It can be used to determine if a mouse click has occurred on a given menu selection. Reimplemented from TMenuView. |
|
|
Undocumented. |
|
|
Undocumented. Reimplemented from TMenuView. Reimplemented in TMenuPopup. |


minions_js
This is a boid simulation.
Minions will try to gather around their leader - the mouse pointer.
Left click to scare them away you monster
Minions will try to form packs in order to feel safe.
The background is a visualisation of the quadtree spatial partitioning algorithm used to bucket the minions for efficient collision detection.
Source code


Delphix Profiler Security Scan Results
Timestamp:<PHONE_NUMBER>3954
Run
JobName
Results
1
<IP_ADDRESS>:1521:orcl ... Schema: DELPHIXDB ... Profile Set: Financial
Report
2
<IP_ADDRESS>:1521:orcl ... Schema: DELPHIXDB ... Profile Set: HIPAA
Report
Powered by Delphix Masking APIs v5.2


The RR/RRRR format (see section on RR/RRRR format within TIMESTAMP_FORMAT scalar function page) works slightly differently than YY/YYYY. The RR and RRRR format elements can be used to alter how a specification for a year is to be interpreted by adjusting the value to produce a 2-digit value or a 4-digit value depending on the leftmost two digits of the current year according to the following rules:
| First two digits of the current year (ZZ) | Last two digits of the current year (XX) | Two-digit year in string-expression (YY) | Resulting year |
| 0-99 | 0-50 | 0-49 | ZZYY |
| 0-99 | 51-99 | 0-49 | (ZZ+1)YY |
| 0-99 | 0-50 | 50-99 | (ZZ-1)YY |
| 0-99 | 51-99 | 50-99 | ZZYY |
Examples:
For the current year being ZZXX and a string-expression year of YY:


Democratic debate: Joe Biden pushes for Obamacare reform over Medicare for all
ATLANTA -- Former Vice President Joe Biden is continuing to push for modifications to Obama-era health care reforms over the holistic "Medicare for all" proposals from two of his Democratic presidential rivals.
In Wednesday night's Democratic debate, Biden argued that voters are hesitant to make the transformative, government-backed changes pushed by candidates including Bernie Sanders and Elizabeth Warren.
Biden says he doesn't want to force anyone to give up private health insurance.
Warren says that she wants to "bring as many people in and get as much help to the American people as fast as we can."


All registered nurses choose from one of four specialisms as part of their nursing degree (adult nursing, children's nursing, mental health nursing or learning disability nursing). They can specialise in other areas, like community care, after they have qualified.
Select all that apply.


I graduated from Auburn University in May 2018 with a Bachelors of Engineering in Software Engineering. My education has provided me with a broad background in computing languages, web development and application development. My weapon of choice is Python; however, I also have a good knowlege of Java, C++, and JavaScript. You can see my open source contributions on my Github.
Working under Dr. Dozier, my goal was to turn correspondence into a list of traits that could be used in our general regression neural network. I was able to use Python and various libraries to create a feature vector that had more than 200 traits. This python script was used at scale to classify every email in the ENRON email data set. This experience was incredibly valuable in learning the intricacies of production code.
Created a C# Plugin for AutoDesk Navisworks and a Python Flask Backend. The C# Plugin makes API calls to our RESTful API and interfaces with AutoDesk Navisworks to retrieve safety information about a selected part of a model. The Python Flask Backend uses SQLAlchemy to add and retrieve Safety Information to our relational database.
A game that is like Connect 4 with a rotating game board.
GPA: 3.2
When I'm not coding, I enjoy most of my time being outdoors. I have yet to brave a Denver winter, but I look forward to skiing and slipping on ice when the time comes. I also love using my National Parks Pass to explore the many parks that Colorado has to offer.


Here be a sample post with a custom background image. To utilize this “feature” just add the following YAML to a post’s front matter.
Syntax Highlighting Post
Syntax highlighting is a feature that displays source code, in different colors and fonts according to the category of terms. This feature facilitates writing in a structured language such as a programming language or a markup language as both structures and syntax errors are visually distinct. Highlighting does not affect the meaning of the text itself; it is intended only for human readers.1
Sample Link Post
This theme supports link posts, made famous by John Gruber. To use, just add link: http://url-you-want-linked
to the post’s YAML front matter and you’re done.
A Post with a Video
Post with Large Feature Image and Text
This is a sample post with a large feature image up top and tons of text. Odio ad blue bottle vinyl, 90’s narwhal commodo bitters pour-over nostrud. Ugh est hashtag in, fingerstache adipisicing laboris esse Pinterest shabby chic Portland. Shoreditch bicycle rights anim, flexitarian laboris put a bird on it vinyl cupidatat narwhal. Hashtag artisan skateboard, flannel Bushwick nesciunt salvia aute fixie do plaid post-ironic dolor McSweeney’s. Cliche pour-over chambray nulla four loko skateboard sapiente hashtag.


This module verifies whether an uploaded file matches the selected format. Instead of relying on the file extension or browser-provided MIME type, at least for the formats below, the file itself is inspected to determine its format through signature matching. If a signature is not available, the file extension is checked instead.
Signature checking is currently available for the following:
none
Options are provided in this module's settings that allow excluding Office (2007) Open XML documents (.docx) and RTF documents from being accepted as Word documents.
When a file is uploaded, the detected file format is automatically verified against the selected format. If the format does not match, the upload will not be completed and a notice is displayed.
Version 3.0.1
- Fixed missing setting export/import hooks
Version 3.0.0
- Added file format identification using PHP Fileinfo
Version 2.0.1
- Added file extension verification of MS Office 2007+ formats
Version 2.0.0
- Added settings import/export
- Added module installation and activation as part of standard OpenConf installation
- Added support for custom Author/Chair name
- Added alternative file extensions for jpg and mpg
- Renamed module to File Format Check
Version 1.0.5
- Fixed HTML file type verification
Version 1.0.4
- Dropped setting MOD_FILETYPE_allow_officeopenxml as .docx/.xlsx/.pptx added as MIME types
- Changed paperid to mediumint(6)
- Changed author-file-validate to handle both submission and upload (dropped author-upload-validate)
Version 1.0.3
- Added token check for settings.inc
Version 1.0.2
- Added support for file type validation of files uploaded during initial submission
Version 1.0.1
- Fixed extraneous print in author-upload-validate.inc
Version 1.0.0
- Initial Release


Get both for only $30
Hearing squeaking noises? Let us check your brakes.
Have your car running right. Replace your old and worn out filters, spark plugs,and wires.
Don't let your worn suspension parts wear out your tires. Replace your shocks/struts for a more comfortable ride and for better braking
Replace your conventional oil every 3,000 miles/3 months, or 5,000 miles/5 months for synthetic oil.
Car Won't start. Let us diagnose your vehicle's problem. We specialize in non-starting issues.
We can test your worn out charging and starting components, and replace them if they pose a problem
We specialize in diagnosing your car's problems. Let us tell you what is wrong with your car.
Light not working? We can fix electrical problems that can range from non-working cigarette lighters to burnt wiring harnesses.


Bowers & Wilkins
An homage to Dieter Rams
Get our freshest content delivered to your inbox
For the latest stories, news, events, promotions and exclusive offers, sign up today. We know you get a
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At Vase, one of our main priorities is the privacy of our visitors. This Privacy Policy document contains types of information that is collected and recorded by Vase and how we use it.
If you have additional questions or require more information about our Privacy Policy, do not hesitate to contact us.
Vase follows a standard procedure of using log files. These files log visitors when they use app. The information collected by log files include internet protocol (IP) addresses, browser type, Internet Service Provider (ISP), date and time stamp, referring/exit pages, and possibly the number of clicks. These are not linked to any information that is personally identifiable. The purpose of the information is for analyzing trends, administering the app, tracking users' movement on the app, and gathering demographic information.
You may consult this list to find the Privacy Policy for each of the advertising partners of Vase.
Third-party ad servers or ad networks uses technologies like cookies, JavaScript, or Beacons that are used in their respective advertisements and links that appear on Vase. They automatically receive your IP address when this occurs. These technologies are used to measure the effectiveness of their advertising campaigns and/or to personalize the advertising content that you see on this app or other apps or websites.
Note that Vase has no access to or control over these cookies that are used by third-party advertisers.
Vase's Privacy Policy does not apply to other advertisers or websites. Thus, we are advising you to consult the respective Privacy Policies of these third-party ad servers for more detailed information. It may include their practices and instructions about how to opt-out of certain options.
Another part of our priority is adding protection for children while using the internet. We encourage parents and guardians to observe, participate in, and/or monitor and guide their online activity.
Vase does not knowingly collect any Personal Identifiable Information from children under the age of 13. If you think that your child provided this kind of information on our App, we strongly encourage you to contact us immediately and we will do our best efforts to promptly remove such information from our records.
This Privacy Policy applies only to our online activities and is valid for visitors to our App with regards to the information that they shared and/or collect in Vase. This policy is not applicable to any information collected offline or via channels other than this app. Our Privacy Policy was created with the help of the App Privacy Policy Generator from App-Privacy-Policy.com
By using our app, you hereby consent to our Privacy Policy and agree to its Terms and Conditions.


Knoppmyth R5.5 and Mythbuntu 8.04.1 and fail
Neither one of these distributions would come up clean, but at least Mythbuntu was giving me enough information to look for things online. Knoppmyth I was really at a loss!
FINALLY I figured out that lirc was modprobe'ing and using the device from the USB VFD display and IR receiver instead of the IR Blaster on my PVR-150. In addition in Mythbuntu, networking was coming up before mysql, and this also screwed things up.
sudo rmmod lirc_imon
sudo /etc/init.d/lirc restart
sudo /etc/init.d/mysql restart
sudo /etc/ini.d/mythtv-backend restart
Will do wonders, assuming you've otherwise setup lirc correctly.
So I need to make things permanent and figure out how to stop the tuner cards from swapping devices on boots, as well as stop crashing every now and then. I may go back to Knoppmyth just because I'm more familiar with where it keeps files.
About this entry
You"re currently reading "Knoppmyth R5.5 and Mythbuntu 8.04.1 and fail," an entry on a crick in the net
- Published:
- 07.29.08 / 7pm
- Category:
- MythTV


|
gbmath
v0.051
c++ math library
|
Triangle by three points. More...
#include <triangle.h>
Public Member Functions | |
| triangle () | |
| triangle (const triangle &t) | |
| triangle (const vec3 _p1, const vec3 _p2, const vec3 _p3) | |
| void | set (const vec3 _p1, const vec3 _p2, const vec3 _p3) |
| vec3 | middle_point () const |
| Compute and return the middle point of the triangle. More... | |
| plane_s | get_plane () const |
| Compute and return the plane to the points of the triangle. More... | |
| operator std::string () const | |
| void | operator= (const std::string &str) throw (std::invalid_argument) |
Public Attributes | |
| vec3 | p1 |
| the first point (vertex) triangular More... | |
| vec3 | p2 |
| the second point (vertex) triangular More... | |
| vec3 | p3 |
| the third point (vertex) triangular More... | |
Friends | |
| std::ostream & | operator<< (std::ostream &stream, const triangle &t) |
Triangle by three points.
Definition at line 18 of file triangle.h.
|
inline |
Definition at line 26 of file triangle.h.
|
inline |
Definition at line 31 of file triangle.h.
Definition at line 36 of file triangle.h.
|
inline |
Compute and return the plane to the points of the triangle.
Definition at line 59 of file triangle.h.
|
inline |
Compute and return the middle point of the triangle.
Definition at line 49 of file triangle.h.
|
inline |
Definition at line 86 of file triangle.h.
|
inline |
Definition at line 93 of file triangle.h.
Definition at line 43 of file triangle.h.
|
friend |
Definition at line 80 of file triangle.h.
| vec3 gbmath::triangle::p1 |
the first point (vertex) triangular
Definition at line 21 of file triangle.h.
| vec3 gbmath::triangle::p2 |
the second point (vertex) triangular
Definition at line 22 of file triangle.h.
| vec3 gbmath::triangle::p3 |
the third point (vertex) triangular
Definition at line 23 of file triangle.h.


HR Info
Sale History
Manage Employee
Return MainPage
Logout
TO BE GREATER!
Saling Trend
Market Percentage
/


| | References |
| 1 | Martin, A., Gross-Camp, N., Kebede, B., & McGuire, S. (2014). Measuring effectiveness, efficiency and equity in an experimental Payments for Ecosystem Services trial. Global Environmental Change, 28, 216–226. doi:10.1016/j.gloenvcha.2014.07.003 |
| 2 | Performance and prospects of payments for ecosystem services programs: Evidence from China |
| 3 | Kalame, F. B., Aidoo, R., Nkem, J., Ajayie, O. C., Kanninen, M., Luukkanen, O., & Idinoba, M. (2011). Modified taungya system in Ghana: a win–win practice for forestry and adaptation to climate change? Environmental Science & Policy, 14(5), 519–530. doi:10.1016/j.envsci.2011.03.011 |
| 4 | Hobbs, J. J., Grainger, J., & El-Bastawisi, I. Y. (1998). Inception of the Bedouin Support Programme in the St. Katherine Natural Protectorate, Sinai, Egypt. Mountain Research and Development, 18(3), 235. doi:10.2307/3674035 |


Capítulo 7 Bayesian Statistics
7.1 Math for understanding the Bayesian approach
\[P(A_k|B)=\frac{P(A_k\cap B)}{P(B)}\]
\[P(B|A_k)=\frac{P(B\cap A_k)}{P(A_k)} \therefore P(A_k|B)=\frac{}{}\]
7.1.1 Ejercicio
Morgan asks Smith to pick a number between 20 and 50, Smith tells Morgan a characteristic of the number he picked (it is a multiple of 5). With this information, determine the probability of Smith’s pick being also a multiple of 3 using ven diagrams and conditional probability.
Población total de 1000, una enfermedad se presenta en la población con prevalencia del 10%. Para poder
7.2 Conceptos sueltos que debo cubrir
- Tamaño Muestral Equivalente.
- Ajuste de Laplace.
- Laplace sugiere sumar un 1 (\(\frac{n_1+1}{n_1+n_0+2}\))
\[\frac{n_1+\alpha}{n_1+n_0+(\alpha + \beta)}\]
- Redes bayesianas, escribir sobre ésto.
\[p(p|D) \sim \beta (\alpha + n_1, \beta +n_0)\]
\[p(p) \sim \beta(\alpha, \beta)\]
- Regresión Bayesiana.
7.3 Entropy and Information Gain
7.3.1 Key definitios
Formula for Entropy
\[H(x) = -\sum_{i=1}^{n}P(x_i)*log_2P(x_i)\]
Information Gain tells us how important a given attribute of the feature vectors is for predicting that characteristic. Mathematically it is expressed as follows:
\[Gain(x,y) = H(x)-\sum_{v\in Values(A_i)}P(A_i=v)H(S_v)\]
7.3.2 Examples
<IP_ADDRESS> Example 1
#Creating matrix
test<-matrix(c(346,74,124,26,470,100), nrow = 3, ncol = 2, byrow = TRUE)
colnames(test) <- c("count", "%")
rownames(test) <- c("Yes", "No", "Overall")
test
## count %
## Yes 346 74
## No 124 26
## Overall 470 100
Following the formula for entropy shown in the previous section we can obtain the entropy value for this dataset:
\[H(dataset) = -[\{P(yes)*log_2P(yes)+P(no)*log_2P(no)\}] = [(0.74*log_2(0.74))+(0.26*log_2(0.26))]\]
This operation gives the following result:
#Entropy for the node
H <- -((0.74*log(0.74, base = 2))+(0.26*log(0.26, base = 2)))
H
## [1] 0.8267464
We can, therefore, conclude that the entropy in this node is 0.827
<IP_ADDRESS> Example 2. Usig Entropy and Information Gain to classify
We will use a very simple dataset:
test2 <- matrix(c(1,1,1,"I",1,1,0,"I",0,0,1,"II",1,0,0,"II"), ncol = 4, nrow = 4, byrow = TRUE)
colnames(test2) <- c("X", "Y", "Z", "Class")
test2 <- as.data.frame(test2)
test2
## X Y Z Class
## 1 1 1 1 I
## 2 1 1 0 I
## 3 0 0 1 II
## 4 1 0 0 II
The challenge is presented to use only one attribute to predict which Class
each value belongs to. The approximation is done following these steps:
- Calculate entropy values for each attribute.
- Calculate information gain.
- Select the attribute with higher information gain result.
X1 <- table(test2$X == 1)
X0 <- table(test2$X == 0)
data.frame(X1, X0)
## Var1 Freq Var1.1 Freq.1
## 1 FALSE 1 FALSE 3
## 2 TRUE 3 TRUE 1
Step 1
- Entropy for attribute
X
\[H(X_1) = -\left[ \frac{2}{3}*log_2\frac{2}{3} + \frac{1}{3}*log_2\frac{1}{3} \right] = 0.9183 \]
#X1 <- -((2/3*log(2/3, base = 2))+(1/3*log(1/3, base = 2)))
#X0 <- -((1/1*log(1/1, base = 2)))
#Hx <-
- Information Gain for attribute
X
\[I = 1-P(Y=1)*H(Y=1)-P(X=0)*H(X=0)=1-\left( \frac{3}{4}*0.9183\right)-\left(\frac{1}{4}*0\right)=1-0.688725-0\] \[= \boldsymbol{0.311275}\]
Step 2
- Entropy for attribute
Y
\[H(X_1) = -\left[ \frac{2}{2}*log_2\frac{2}{2} \right] = 0 \]
- Information Gain for attribute
Y
\[I = 1-P(Y=1)*H(Y=1)-P(Y=0)*H(Y=0)=1- 0-0= \boldsymbol{1}\]
Step 3
- Entropy for attribute
Z
\[H(Z_1) = -\left[ \frac{1}{2}*log_2\frac{1}{2}\right] = 0.5 \]
- Information Gain for attribute
Z
\[I = 1-P(Z=1)*H(Z=1)-P(Z=0)*H(Z=0)=1- 0.5-0.5= \boldsymbol{0}\]
According to the results these are the corresponding information gain results for each attribute:
X
: 0.311275Y
: 1Z
: 0
We can then safely state that using attribute Y
to predict the target variable Class
is the best option based on information gain.
7.4 Bayesian Linear Regression
Before exploring the Bayesian approach to regressions, it is important to refresh our memory and review the traditional approach to regressions.
7.4.1 Linear Regression
For this we will use the example of the cereals data set (Data and Library, n.d.).
library(readr)
cereals <- read_delim("D:/Dropbox/MsC UABC/2o Semestre/Clases/Estadistica/estadistica-syllabus/datasets/cereals.csv",
"\t", escape_double = FALSE, trim_ws = TRUE)
str(cereals)
## Classes 'tbl_df', 'tbl' and 'data.frame': 77 obs. of 16 variables:
## $ name : chr "100%_Bran" "100%_Natural_Bran" "All-Bran" "All-Bran_with_Extra_Fiber" ...
## $ mfr : chr "N" "Q" "K" "K" ...
## $ type : chr "C" "C" "C" "C" ...
## $ calories: int 70 120 70 50 110 110 110 130 90 90 ...
## $ protein : int 4 3 4 4 2 2 2 3 2 3 ...
## $ fat : int 1 5 1 0 2 2 0 2 1 0 ...
## $ sodium : int 130 15 260 140 200 180 125 210 200 210 ...
## $ fiber : num 10 2 9 14 1 1.5 1 2 4 5 ...
## $ carbo : num 5 8 7 8 14 10.5 11 18 15 13 ...
## $ sugars : int 6 8 5 0 8 10 14 8 6 5 ...
## $ potass : int 280 135 320 330 -1 70 30 100 125 190 ...
## $ vitamins: int 25 0 25 25 25 25 25 25 25 25 ...
## $ shelf : int 3 3 3 3 3 1 2 3 1 3 ...
## $ weight : num 1 1 1 1 1 1 1 1.33 1 1 ...
## $ cups : num 0.33 1 0.33 0.5 0.75 0.75 1 0.75 0.67 0.67 ...
## $ rating : num 68.4 34 59.4 93.7 34.4 ...
## - attr(*, "spec")=List of 2
## ..$ cols :List of 16
## .. ..$ name : list()
## .. .. ..- attr(*, "class")= chr "collector_character" "collector"
## .. ..$ mfr : list()
## .. .. ..- attr(*, "class")= chr "collector_character" "collector"
## .. ..$ type : list()
## .. .. ..- attr(*, "class")= chr "collector_character" "collector"
## .. ..$ calories: list()
## .. .. ..- attr(*, "class")= chr "collector_integer" "collector"
## .. ..$ protein : list()
## .. .. ..- attr(*, "class")= chr "collector_integer" "collector"
## .. ..$ fat : list()
## .. .. ..- attr(*, "class")= chr "collector_integer" "collector"
## .. ..$ sodium : list()
## .. .. ..- attr(*, "class")= chr "collector_integer" "collector"
## .. ..$ fiber : list()
## .. .. ..- attr(*, "class")= chr "collector_double" "collector"
## .. ..$ carbo : list()
## .. .. ..- attr(*, "class")= chr "collector_double" "collector"
## .. ..$ sugars : list()
## .. .. ..- attr(*, "class")= chr "collector_integer" "collector"
## .. ..$ potass : list()
## .. .. ..- attr(*, "class")= chr "collector_integer" "collector"
## .. ..$ vitamins: list()
## .. .. ..- attr(*, "class")= chr "collector_integer" "collector"
## .. ..$ shelf : list()
## .. .. ..- attr(*, "class")= chr "collector_integer" "collector"
## .. ..$ weight : list()
## .. .. ..- attr(*, "class")= chr "collector_double" "collector"
## .. ..$ cups : list()
## .. .. ..- attr(*, "class")= chr "collector_double" "collector"
## .. ..$ rating : list()
## .. .. ..- attr(*, "class")= chr "collector_double" "collector"
## ..$ default: list()
## .. ..- attr(*, "class")= chr "collector_guess" "collector"
## ..- attr(*, "class")= chr "col_spec"
<IP_ADDRESS> Data Preparation
As we can see, this dataset needs som data preparation before a linear regression model can be generated.
#Converting to numeric data those variables that need to be read as such>
cereals$calories <- as.numeric(cereals$calories)
cereals$protein <- as.numeric(cereals$protein)
cereals$fat <- as.numeric(cereals$fat)
cereals$sodium <- as.numeric(cereals$sodium)
cereals$fiber <- as.numeric(cereals$fiber)
cereals$carbo <- as.numeric(cereals$carbo)
cereals$sugars <- as.numeric(cereals$sugars)
cereals$potass <- as.numeric(cereals$potass)
cereals$vitamins <- as.character(cereals$vitamins)
cereals$weight <- as.numeric(cereals$weight)
cereals$cups <- as.numeric(cereals$cups)
cereals$rating <- as.numeric(cereals$rating)
str(cereals)
## Classes 'tbl_df', 'tbl' and 'data.frame': 77 obs. of 16 variables:
## $ name : chr "100%_Bran" "100%_Natural_Bran" "All-Bran" "All-Bran_with_Extra_Fiber" ...
## $ mfr : chr "N" "Q" "K" "K" ...
## $ type : chr "C" "C" "C" "C" ...
## $ calories: num 70 120 70 50 110 110 110 130 90 90 ...
## $ protein : num 4 3 4 4 2 2 2 3 2 3 ...
## $ fat : num 1 5 1 0 2 2 0 2 1 0 ...
## $ sodium : num 130 15 260 140 200 180 125 210 200 210 ...
## $ fiber : num 10 2 9 14 1 1.5 1 2 4 5 ...
## $ carbo : num 5 8 7 8 14 10.5 11 18 15 13 ...
## $ sugars : num 6 8 5 0 8 10 14 8 6 5 ...
## $ potass : num 280 135 320 330 -1 70 30 100 125 190 ...
## $ vitamins: chr "25" "0" "25" "25" ...
## $ shelf : int 3 3 3 3 3 1 2 3 1 3 ...
## $ weight : num 1 1 1 1 1 1 1 1.33 1 1 ...
## $ cups : num 0.33 1 0.33 0.5 0.75 0.75 1 0.75 0.67 0.67 ...
## $ rating : num 68.4 34 59.4 93.7 34.4 ...
## - attr(*, "spec")=List of 2
## ..$ cols :List of 16
## .. ..$ name : list()
## .. .. ..- attr(*, "class")= chr "collector_character" "collector"
## .. ..$ mfr : list()
## .. .. ..- attr(*, "class")= chr "collector_character" "collector"
## .. ..$ type : list()
## .. .. ..- attr(*, "class")= chr "collector_character" "collector"
## .. ..$ calories: list()
## .. .. ..- attr(*, "class")= chr "collector_integer" "collector"
## .. ..$ protein : list()
## .. .. ..- attr(*, "class")= chr "collector_integer" "collector"
## .. ..$ fat : list()
## .. .. ..- attr(*, "class")= chr "collector_integer" "collector"
## .. ..$ sodium : list()
## .. .. ..- attr(*, "class")= chr "collector_integer" "collector"
## .. ..$ fiber : list()
## .. .. ..- attr(*, "class")= chr "collector_double" "collector"
## .. ..$ carbo : list()
## .. .. ..- attr(*, "class")= chr "collector_double" "collector"
## .. ..$ sugars : list()
## .. .. ..- attr(*, "class")= chr "collector_integer" "collector"
## .. ..$ potass : list()
## .. .. ..- attr(*, "class")= chr "collector_integer" "collector"
## .. ..$ vitamins: list()
## .. .. ..- attr(*, "class")= chr "collector_integer" "collector"
## .. ..$ shelf : list()
## .. .. ..- attr(*, "class")= chr "collector_integer" "collector"
## .. ..$ weight : list()
## .. .. ..- attr(*, "class")= chr "collector_double" "collector"
## .. ..$ cups : list()
## .. .. ..- attr(*, "class")= chr "collector_double" "collector"
## .. ..$ rating : list()
## .. .. ..- attr(*, "class")= chr "collector_double" "collector"
## ..$ default: list()
## .. ..- attr(*, "class")= chr "collector_guess" "collector"
## ..- attr(*, "class")= chr "col_spec"
<IP_ADDRESS> Data Quality Report for Categorical Variables
setwd("D:/Dropbox/MsC UABC/2o Semestre/Clases/Estadistica/estadistica-syllabus")
source('QOfCategoricalF.R')
DQRcat <- QOfCategoricalF(cereals[,c(1,2,3,12,13)])
DQRcat
## Count Miss Card Mode ModeFrec ModePerc Mode2
## name 77 0 77 100%_Bran 1 1.3% 100%_Natural_Bran
## mfr 77 0 7 K 23 29.87% G
## type 77 0 2 C 74 96.1% H
## vitamins 77 0 3 25 63 81.82% 0
## shelf 77 0 3 3 36 46.75% 2
## Mode2Frec Mode2Perc
## name 1 1.3%
## mfr 22 28.57%
## type 3 3.9%
## vitamins 8 10.39%
## shelf 21 27.27%
<IP_ADDRESS> Data Quality Report for Continuous Variables
source('QOfContinuousF.R')
DQRcont <- QOfContinuousF(cereals[,c(4:11, 14:16)])
DQRcont
## Count Miss Card Min Qrt1 Median Qrt3 Max Mean
## calories 77 0 11 50.00000 100.00 110.00 110.00 160.00000 106.88
## protein 77 0 6 1.00000 2.00 3.00 3.00 6.00000 2.55
## fat 77 0 5 0.00000 0.00 1.00 2.00 5.00000 1.01
## sodium 77 0 27 0.00000 130.00 180.00 210.00 320.00000 159.68
## fiber 77 0 13 0.00000 1.00 2.00 3.00 14.00000 2.15
## carbo 77 0 22 -1.00000 12.00 14.00 17.00 23.00000 14.60
## sugars 77 0 17 -1.00000 3.00 7.00 11.00 15.00000 6.92
## potass 77 0 36 -1.00000 40.00 90.00 120.00 330.00000 96.08
## weight 77 0 7 0.50000 1.00 1.00 1.00 1.50000 1.03
## cups 77 0 12 0.25000 0.67 0.75 1.00 1.50000 0.82
## rating 77 0 77 18.04285 33.17 40.40 50.83 93.70491 42.67
## Sdev
## calories 19.48
## protein 1.09
## fat 1.01
## sodium 83.83
## fiber 2.38
## carbo 4.28
## sugars 4.44
## potass 71.29
## weight 0.15
## cups 0.23
## rating 14.05
7.4.2 Logistic Regression
<IP_ADDRESS> Splitting train and test dataset
#Spliting inot trainning and test datasets
library(caTools)
set.seed(123)
split = sample.split(cereals$rating, SplitRatio = 2/3)
training_set = subset(cereals, split == TRUE)
test_set = subset(cereals, split == FALSE)
# Feature Scaling
#training_set[, c(4:11,14:16)] = scale(training_set[, c(4:11,14:16)])
# test_set[, 2:3] = scale(test_set[, 2:3])
<IP_ADDRESS> Fitting SLR to training set
regressor <- lm(formula = rating ~ sugars,
data = cereals)
summary(regressor)
##
## Call:
## lm(formula = rating ~ sugars, data = cereals)
##
## Residuals:
## Min 1Q Median 3Q Max
## -17.853 -5.677 -1.439 5.160 34.421
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 59.2844 1.9485 30.43 < 2e-16 ***
## sugars -2.4008 0.2373 -10.12 1.15e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.196 on 75 degrees of freedom
## Multiple R-squared: 0.5771, Adjusted R-squared: 0.5715
## F-statistic: 102.3 on 1 and 75 DF, p-value: 1.153e-15
<IP_ADDRESS> Visualizing SLR on the copmlete cereals
dataset
library(ggplot2)
ggplot() +
geom_point(aes(x = cereals$sugars, y = cereals$rating),
colour = "red") +
geom_line(aes(x = cereals$sugars, y = predict(regressor, newdata = cereals)),
colour = "blue") +
ggtitle("Sugar vs Prediction of Rating variables") +
theme(plot.title = element_text(hjust = 0.5)) +
xlab("Amount of Sugars") +
ylab("Raiting")
7.5 Examples of Bayesian models
Taken from Rasmus Bååth´s blog.
7.5.1 Bayesian A testing for Swedish Fish Incorporated
Swedish Fish Incorporated is the largest Swedish company delivering fish by mail order. They are now trying to get into the lucrative Danish market by selling one year Salmon subscriptions. The marketing department have done a pilot study and tried the following marketing method:
A: Sending a mail with a colorful brochure that invites people to sign up for a one year salmon subscription.
The marketing department sent out 16 mails of type A. Six Danes that received a mail signed up for one year of salmon and marketing now wants to know, how good is method A?
<IP_ADDRESS> Question I: Build a Bayesian model that answers the question: What would the rate of sign-up be if method A was used on a larger number of people?
Hint 1: The answer is not a single number but a distribution over probable rates of sign-up.
Hint 2: As part of you generative model you’ll want to use the binomial distribution, which you can sample from in R using the rbinom(n, size, prob). The binomial distribution simulates the following process n times: The number of “successes” when performing size trials, where the probability of “success” is prob.
Hint 3: A commonly used prior for the unknown probability of success in a binomial distribution is a uniform distribution from 0 to 1. You can draw from this distribution by running runif(1, min = 0, max = 1)
Hint 4: Here is a code scaffold that you can build upon.
- First of all we need to declare the amount of draws our model will make and the prior distribution.
# Number of random draws from the prior
set.seed(123)
n_draw <- 100000
prior <- runif(n_draw,0,1) # Here you sample n_draws draws from the prior
hist(prior,
col = "pink",
main = "Hisogram of Prior Distribution for Strategy A") # It's always good to eyeball the prior to make sure it looks ok.
- Once this is finished we declare a generative model and simulate data with the parameters from the priors and the model.
# Here you define the generative model
generative_model <- function(rate) {
subscribers <- rbinom(1, size = 16, prob = rate)
subscribers
}
# Here you simulate data using the parameters from the prior and the
# generative model
subscribers <- rep(NA, n_draw)
for(i in 1:n_draw) {
subscribers[i] <- generative_model(prior[i])
}
- Next we work on filtering the draws that mached the data and generate a histogram of it, this will be the visualization of the Posterior Distribution.
# Here you filter off all draws that do not match the data.
posteriorA <- prior[subscribers == 6]
hist(posteriorA,
col = "coral2",
xlim = c(0,1)) # Eyeball the posterior
length(posteriorA) # See that we got enought draws left after the filtering.
## [1] 5897
# There are no rules here, but you probably want to aim
# for >1000 draws.
# Now you can summarize the posterior, where a common summary is to take the mean
# or the median posterior, and perhaps a 95% quantile interval.
summary(posteriorA)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.06941 0.30652 0.38475 0.38865 0.46124 0.83222
As we can see from the previous calculations, if strategy A was used in a larger population we could expect a sign up rate from 20% to 60%, this result shows a considerable amount of uncertanty.
<IP_ADDRESS> Question II: What’s the probability that method A is better than telemarketing?
So marketing just told us that the rate of sign-up would be 20% if salmon subscribers were snared by a telemarketing campaign instead (to us it’s very unclear where marketing got this very precise number from). So given the model and the data that we developed in the last question, what’s the probability that method A has a higher rate of sign-up than telemarketing?
Hint 1: If you have a vector of samples representing a probability distribution, which you should have from the last question, calculating the amount of probability above a certain value is done by simply counting the number of samples above that value and dividing by the total number of samples.
Hint 2: The answer to this question is a one-liner.
sum(posteriorA > 0.2)/length(posteriorA)
## [1] 0.9659149
As we can see, it is more likely that strategy A will produce higher sign up rattings than telemarketing.
<IP_ADDRESS> Question III: If method A was used on 100 people what would be the number of sign-ups?
Hint 1: The answer is again not a single number but a distribution over probable numbers of sign-ups.
Hint 2: As before, the binomial distribution is a good candidate for how many people that sign up out of the 100 possible.
Hint 3: Make sure you don’t “throw away” uncertainty, for example by using a summary of the posterior distribution calculated in the first question. Use the full original posterior sample!
Hint 4: The general patter when calculating “derivatives” of posterior samples is to go through the values one-by-one, and perform a transformation (say, plugging in the value in a binomial distribution), and collect the new values in a vector.
# But since rbinom is vectorized we can simply write it like this:
signupsA <- rbinom(n = length(posteriorA), size = 100, prob = posteriorA)
hist(signupsA, xlim = c(0, 100),
col = "pink",
main = "Histograms of Signups Projected in 100
Potential Clients With Strategy A")
The probable number of signups if strategy A were to be used is between 20 and 60.
7.5.2 Calculating probabilies for a new marketing idea.
The CEO of Swedish Fish Incorporated came with the “greatest idea” for marketing: along with the colorful brochure he wants to send a frozen salmon to the potential costumers. He has learned on or two things from marketing and had his idea already tested with 16 danes, out of these 10 sigend up for the service. What’s the probability of this strategy to generate signups in a larger population?
- First we need to generate the analysis for the new strategy:
- A prior distribution to start from, we’ll go with a uniform distribution:
n_draw <- 100000
prior <- runif(n_draw,0,1) # Here you sample n_draws draws from the prior
hist(prior,
col = "dodgerblue",
main = "Hisogram of Prior Distribution for Strategy B") # It's always good to eyeball the prior to make sure it looks ok.
- The regenerative model and all the calculations will be done the same as strategy A, but instead of selecting those who resulted in 6, we will select those draws who resulted in 10.
## $breaks
## [1] 0.20 0.25 0.30 0.35 0.40 0.45 0.50 0.55 0.60 0.65 0.70 0.75 0.80 0.85
## [15] 0.90 0.95
##
## $counts
## [1] 1 25 67 143 299 483<PHONE_NUMBER> 882 681 407 185 48
## [15] 3
##
## $density
## [1] 0.003406575 0.085164367 0.228240504 0.487140181 1.018565832
## [6] 1.645375575 2.476579799 3.134048714 3.406574689 3.004598876
## [11] 2.319877363 1.386475898 0.630216317 0.163515585 0.010219724
##
## $mids
## [1] 0.225 0.275 0.325 0.375 0.425 0.475 0.525 0.575 0.625 0.675 0.725
## [12] 0.775 0.825 0.875 0.925
##
## $xname
## [1] "posteriorB"
##
## $equidist
## [1] TRUE
##
## attr(,"class")
## [1] "histogram"
## [1] 5871
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.2237 0.5323 0.6127 0.6091 0.6913 0.9103
- It is time to compare these two aproximations:
par(mfrow = c(2,1))
hist(signupsA, xlim = c(0, 100),
col = "pink",
main = "Histograms of Signups Projected in 100
Potential Clients With Strategy A")
hist(signupsB, xlim = c(0, 100),
col = "dodgerblue4",
main = "Histograms of Signups Projected in 100
Potential Clients With Strategy B")
- It appears that Strategy B is more likely to yield higher signups. In order to check if it is so we will generate a table of rate differences:
Where the variable rate_diff
is calculated as follows, since we are interested in variable B:
\[rate\_diff = \text{rateB}-\text{rateA}\]
The next table is a representation of the first values obtained:
## PostA PostB rate_diff
## 1 0.4145463 0.4686832 0.05413691
## 2 0.2659726 0.6099820 0.34400938
## 3 0.4348927 0.6927303 0.25783757
## 4 0.3694889 0.6500233 0.28053442
## 5 0.4394315 0.4623911 0.02295960
## 6 0.3117022 0.7195024 0.40780021
A histogram of the values recorded in rate_diff
is usefull to visualize the probability of strategy B yelding more signups than strategy A.
It becomes obvious that the greater part of the probability is beyond 0, meaning that strategy B is more likely to bring mor signups to the company. If the CEO wanted to know how much likely the probability of strategy B is to be sucessful (more signups) than strategy A, this could be computated as well with the followin lines of code:
sum((rate$rate_diff > 0)/length(rate$rate_diff))
## [1] 0.9143246
Strategy B is 91.4% more likely to bring more signups than strategy A.


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Global Unichip, Alchip see clear order visibility through 1Q20
by Cage Chao, Taipei; Willis Ke, DIGITIMESBuoyed by strong demand for NRE and ASIC design services, Global Unichip and Alchip have seen clear order visibility through the first half of 2020 and are expected to smoothly fulfill orders with strong capacity support from TSMC, according to industry sources.
The sources said the lingering US-China trade war has inspired first-tier brand vendors worldwide to develop chip solutions in house and prompted China's semiconductor industry to more aggressively boost self-sufficiency. This has allowed Global Unichip and Alchip to enjoy rapid influx of orders for NRE (non-recurring engineering) and ASIC design services from clients in China and other countries in the second half of 2019.
Robust shipments have sent Global Unichip's October revenues hitting the year's high of NT$909 million (US$29.81 million), with the revenues for November and December expected to each soar past the NT$1 billion mark.
Alichip's revenues shot up 65.53% on year to NT$417 million in October and are expected to challenge a new high in December when its shipments to China's CPU clients peak.
Both firms are members of the TSMC Grand Alliance, and the foundry house has promised to grant them priority capacity support in the first quarter of 2020 enabling them to better satisfy clients' demand, industry sources said.
Their profits are also expected to improve significantly in 2020, as the average selling price (ASP) for ASICs built using advanced nodes is almost double that general ASICs, the sources indicated.