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# HABEO
## Vision
We are looking forward to a world where every single individual can have access to the new crypto systems.
Our goal is to simplify the way digital objects are acquired, swapped and transferred while lowering the fees
incurred in such transactions.
We want people to feel safe and comfortable sending and receiving digital objects as part of everyday transactions.
With HABEO individuals will be able to quickly and easily use it in exchange for goods and services from small to
large in a practical and secure way.
Although today this may seem utopic, HABEO is here to spearhead the effort to accomplish this goal.
## Name
HABEO - this is a word from the Latin language that has multiple meanings: have, accept, hold, keep, take, possess,
do, consider, cultivate, foster, control and direct.
All of these meanings align with our vision for the token so we absolutely believe this name is a perfect fit for
it.
## Key features summary
* **Fair early distribution**: Our early distribution eliminates the need for a third party. Anyone interested in
acquiring the token during the early distribution period has to simply send a pre-established amount of BNB to the
contract and the contract will provide the sender with the corresponding number of tokens. The early distribution
period has clear dates and clear limits. Only one operation is allowed per wallet.
* **Layered taxation**: We believe that not all operations should be taxed the same. Some transactions need to have
very low fees while others should be contributing a larger share. We’ve implemented what we think is a very fair tax
structure.
* **Throttled swaps at exchange**: To make the token more stable, we’ve enabled throttling in the amount of swap
operations per wallet per day. The number of transfers between wallets is not limited. But swap operations need to
happen at least 8 hrs. apart.
* **Limit on the size of transactions**: Transactions between wallets have a size limit. It’s a high limit but we
think it will help with market stability. After the token becomes widely adopted, this limit will become obsolete
and will hardly ever be reached.
* **Dividends through reflections**: Everytime a transaction is taxed, 45% of the tax is redistributed among each
and every one of the token owners in a proportional way.
* **Auto LP**: Right after the early distribution period ends, all the accumulated BNB becomes liquidity for HABEO.
After the initial liquidity, the protocol will continuously add to the pool in a balanced and predictive way by
liquifying 45% of the collected taxes.
* **Auto burn**: At the time of the closing of the early distribution period, HABEO will burn 50% of the supply.
HABEO will continue burning tokens (10% of the taxes) until the supply end goal is reached. Then, burning will
automatically stop, forever.
* **Burn wallet special feature**: This burn wallet is like no other. It will eat up reflections while burning is
still active but, when the limit is reached, it will stop receiving reflections. This will increase the dividends
that all participating accounts receive from then on.
* **Ownership renounced**: We built it for everyone, so no one has ownership over it.
* **LP Locked forever**: HABEO is here to stay. Liquidity tokens are sent to the burn wallet.
* **Donations for the dev team**: We’ve enabled a mechanism to receive donations for the team. If you feel like
thanking the team with some coins, you can do that very easily.
## Key numbers
* **Total supply of HABEO at deployment**: 1,000,000,000,000,000 HABEO (1 quadrillion)
* **Max days early distribution is available**: 30 days
* **Max number of BNB to collect at early distribution**: 3,086 BNB (at least 2,500)
* **Burn at launch**: 500,000,000,000,000 HABEO (500 trillion)
* **Total supply of HABEO after last burn**: 25,000,000,000,000 HABEO (25 trillion)
* **Min and Max taxation**: 2% - 60%
* **Portion of tax used for reflection**: 45%
* **Portion of tax used for auto LP**: 45%
* **Portion of tax used for burn**: 10%
## Features explained
### Fair early distribution
First and foremost, only one request for HABEO is allowed per wallet.
Our early distribution period starts the day we deploy HABEO and will be open for 30 days OR until the BNB goal is
reached. Whatever happens first.
We think that approximately 2500 BNB is a great number to start the liquidity pool with so we set up the swap value
structure in a way that will result in a pool between 2,500 and 3,086 BNB.
If by day 30 the target is not reached, the early distribution period will be automatically closed.
Once the early distribution is done, no one will be able to acquire HABEO at a fixed number of BNB through that
direct channel.
When we reach this stage, all the BNB collected will automatically be sent to the PancakeSwap HABEO/BNB pair
together with the remaining to form the initial liquidity pool.
The number of remaining tokens will depend on the results of the early distribution following this formula:
Total Supply - Initial Burn - Tokens Distributed = Tokens at initial LP paired with the resulting BNB
There are 7 different options to obtain HABEO at the early distribution process that result in a variable number of
tokens that can be acquired on each operation.
The options are as follows:
* 25 BNB will distribute 1,806,250,000,000 HABEO = 0.000000000013841 BNB x HABEO (27.75% fewer compared to 5 BNB)
* 10 BNB will distribute 850,000,000,000 HABEO = 0.000000000011765 BNB x HABEO (15% fewer compared to 5 BNB)
* 5 BNB will distribute 500,000,000,000 HABEO = 0.000000000010000 BNB x HABEO (Sweet spot)
* 1 BNB will distribute 90,000,000,000 HABEO = 0.000000000011110 BNB x HABEO (10% fewer compared to 5 BNB)
* 0.5 BNB will distribute 40,500,000,000 HABEO = 0.000000000012340 BNB x HABEO (19% fewer compared to 5 BNB)
* 0.2 BNB will distribute 16,200,000,000 HABEO = 0.000000000012340 BNB x HABEO (Same proportion to 0.5 BNB)
* 0.1 BNB will distribute 8,100,000,000 HABEO = 0.000000000012340 BNB x HABEO (Same proportion to 0.2 BNB)
We’ve decided that for the early distribution, we will not use any intermediaries. As HABEO automatically adds the
tokens to the LP, we do not want any charges for acquiring tokens. Not for us, not for anyone wanting to join the
project.
Simply send the amount of BNB that you want to join with to the contract address and the contract will send the
right amount of HABEO to your wallet.
The reason for the different valuations at the different layers is to encourage everyone to acquire enough but not
too many tokens. To accomplish this, we made it less of a value to become an instant whale, while also allowing
smaller wallets to get a decent deal.
We think that 500,000,000,000 tokens is an ideal number of tokens for people to acquire so we set that amount as the
ideal number of tokens to acquire during the early distribution in relation to the BNB provided. We cannot prevent
anyone from acquiring more tokens using different wallets, but we can make getting a very big wallet a little less
interesting.
### Layered taxation
This key feature makes HABEO the most fair token in the ecosystem.
We want to encourage the use of HABEO for day to day operations, we want to encourage the community to acquire
HABEO, we want to encourage the community to swap some HABEO for other tokens but, we want to discourage our holders
from releasing large amounts of HABEO all at once.
Everyone is still free to swap all their holdings at once, but the tax will be high.
This is the layered taxation table:
* Acquisition from exchange: 2% tax
* Transfer between accounts: 2% tax
* Swap at exchange:
* Up to 1% of the wallet’s balance: 2% tax
* Up to 2% of the wallet’s balance: 4% tax
* Up to 3% of the wallet’s balance: 6% tax
* Up to 4% of the wallet’s balance: 8% tax
* Up to 5% of the wallet’s balance: 10% tax
* Up to 10% of the wallet’s balance: 20% tax
* Up to 20% of the wallet’s balance: 30% tax
* Up to 40% of the wallet’s balance: 40% tax
* Up to 75% of the wallet’s balance: 50% tax
* Up to 100% of the wallet’s balance: 60% tax
Although all holders can swap all their holdings at a lower tax rate by executing multiple transactions, this will
take time (because of the throttled swap operations) so it won’t impact the token valuation against other tokens as
much in a short period of time.
### Throttled swaps at exchange
In combination with the layered taxation, we have implemented a limit in the number of swaps at the exchange that
wallets can execute in a certain period of time.
We don’t want HABEO to be a pump and dump style of token nor for the whales to easily impact its swapping value.
HABEO is limited to execute swap transactions at exchanges with 8 hrs cool down periods. This gives everyone the
ability to acquire or swap HABEO three times per day at the most.
Once a transaction is submitted by a wallet, 8 hrs must pass for the next transaction attempt to be successful.
We think this is a very good mechanism to minimize the chances of pump and dump operations.
### Limit on the size of transactions
While transactions to and from the exchanges do not have size limits, transactions between different wallets have a
limit of 10,000,000,000 HABEO per transaction.
As transactions between wallets do not have a cool down period, anyone that needs it can transfer more HABEO using
multiple transactions.
This limit might seem constraining at the time because in the beginning, some wallets hold a high number of tokens.
However, as the popularity of HABEO and the number of wallets increase, this limit will start looking less and less
of a constraint to the point that it will look anecdotal.
We feel that this number is a good balance that will help with the growth and will definitely become a distant
memory once we reach full throttle.
### Dividends through reflections
As introduced by previous tokens, HABEO includes reflections as a way to reward holders.
Part of the tax from every transaction (45% of it, to be precise) is distributed among the holders of the token.
This means, EVERY WALLET receives a portion of the distribution.
The size of the slice each wallet receives is proportional to the number of HABEO the wallet holds at the time of
the operation.
Wallets with more HABEO receive more rewards.
Basically, this is a sort of dividend that’s allocated immediately to the wallets participating in the project.
### Auto LP
As we’ve mentioned before, the end of the early distribution process triggers the first event of auto LP in the
protocol.
This event however, is not the only auto LP event that will happen with HABEO.
Part of the tax from every transaction (45% of it, to be precise) is collected towards periodically adding liquidity
to the LP.
HABEO accumulates the fees over a period of time until they reach 100,000,000 HABEO.
Once this number is reached, the protocol swaps half of the tokens from the fees for BNB through PancakeSwap.
Once the BNB has been received, HABEO uses these BNBs and the remaining half of the tokens and sends them to
PancakeSwap to add to the LP.
This is the right way to add liquidity without affecting the swapping value.
### Auto burn
As you may have noticed, we have accounted for only 90% of the tax fees so far.
The remaining 10% is sent to the burn wallet.
When closing the early distribution process, the burn wallet receives 500,000,000,000,000 HABEO effectively halving
the amount of HABEO in circulation forever.
As we want HABEO to be truly deflationary, we set the protocol to continue the burn at small increments.
This way, the amount in circulation keeps getting lower and the scarcity adds pressure for HABEO’s swapping value to
consistently rise.
As the burn effectively happens by sending tokens to the burn wallet, this wallet will be the largest holder of
HABEO for a while. This means that the burn wallet will be receiving reflections at a higher rate than any other
wallet and thus accelerating the deflationary aspect of HABEO.
However, as we do not want HABEO to burn all of the supplies, we have set a limit to the amount of HABEO that will
be burnt to a total of 975,000,000,000,000 HABEO.
At the end of the burning process, there will only be 25,000,000,000,000 HABEO in circulation.
This is exactly 2.5% of the initial supply.
### Burn wallet special feature
The fact that the burn wallet is the largest wallet out there, helps with burning HABEO faster but, on the flip
side, lowers the amount of dividends that each wallet receives when transactions are taxed.
That’s why we’ve built a great feature that will reward long time holders.
When HABEO is done with the burning, the burn wallet will be stripped from the privilege of earning reflections
which means every other wallet will dramatically increase their holding share and thus will earn reflections at a
much faster rate.
This is the fairest way to reward holders for their continued support.
### Ownership renounced
There are usually certain maintenance needs that contracts have (for example, adding/removing exchanges) that
justify the developers/creators of the contract to retain ownership.
When developers retain the ownership of the contract, they create a situation in which holders need to trust them
and their loyalty. This situation hinders the ability to fully trust and fully commit to the project.
We have built functionality into the contract that gives us the ability to easily include exchanges when they
onboard HABEO to their listings.
This way we do not have the need to retain ownership of the contract so the ownership is being renounced at the time
of deployment.
### LP Locked forever
To add to the trust issues, oftentimes the LP of projects is either not locked or locked for short periods of time.
This is another reason why some projects never take off.
Having access to the LP tokens enables those that have provided the LP to take it away leaving the project with no
resources to enable swaps. This is commonly known as “rug pulling”.
With HABEO, we want to eliminate all of the possible scenarios that create trust issues.
That’s why we lock the LP tokens forever.
Meaning NO ONE, EVER can take the initial liquidity away.
### Donations for the dev team
Because we’ve created a very good project that we hope will grow into a magnificent community, we wanted to create a
feature that would allow for donations to the dev team.
Simply send BNB to the contract address anytime AFTER the early distribution period is done and the contract will
forward them to our wallet.
You can also send any token to our developer wallet directly if you would rather do that.
## Roadmap
We have decided to create HABEO in an attempt to enable as many people as possible to interact with crypto.
We think that only by moving in this direction, crypto will truly become universal and will solve some of the
world’s economic inequities.
However, we have noticed that creating the best token is only part of the solution as there are at least three big
bumps on the road that we also want to contribute to eradicate.
The first one, is knowledge. Crypto is still an enigma for the vast majority of the world.
Second, availability is not the same for different parts of the world. We can see daily how certain tools are
available in some places and not others.
Last but not least, ease of use. We think that this one goes hand in hand with knowledge but let's face it,
obtaining crypto usually involves many steps and some of them involve some level of technical knowledge.
As we are giving HABEO away to the community, we are enabling all participants to contribute and become part of the
solution.
There are some steps in our vision that are going to be easily achieved and some others that will take a large
collaborative effort and a decent amount of time to become a reality.
We think that in an ideal world, we could have a blockchain that is inclusive enough to allow everyone to
participate with very little effort.
Although this might sound like wishful thinking, having a strong community seeking that goal will definitely make it
more achievable.
We can't say that this is a true roadmap in the way a traditional roadmap works but, these are the steps that we
hope we can achieve as a community.
If we ever reach the point in which we can make our final vision a reality, it will be after we strengthen our
community.
A good way to strengthen our community is to enable everyone to contribute with whatever skills they can bring to
the table.
We have thought of several milestones (both short-term and mid-term) that we think can help create the environment
of collaborative effort that will give us the most chances of succeeding at accomplishing the end goal.
### Short term goals
#### Create a supportive educational community
As explained above, two of the main blockers for the widespread use of tokens can be resolved with education:
* **Difficulty of use**: Although tokens and all cryptos are becoming more accessible, there are many hoops to jump
through in order to be able to acquire the desired tokens.
* **Lack of education**: A large portion of the public using tokens has no clue what they are doing and once they
had a bad experience, they are scared from using tokens ever again.
Although we know that making the token available in multiple exchanges can be beneficial, we think that the first
step we need to take in order to tackle these issues is to create a community that focuses on training and education
of its members.
We want to provide training and tools that will enable anyone interested in using HABEO in particular and tokens in
general, to understand and have confidence using crypto.
No matter what level of understanding they have at the time of joining, they will be able to achieve their goals by
leaning on a supportive community and learn as much as they are willing to in the process.
As part of this effort, our website will be hosting training in the form of documents and video.
Because we want our community to be inclusive, all documents will be translated to as many languages as possible
(with the help of our community). We want everyone in the world to have a fair chance at learning and using HABEO
with confidence.
Our videos will be recorded in a few languages and the transcripts will be translated to the same languages as the
documents.
Our team has kickstarted the process of education in English, Spanish and Portuguese while also giving Italian and
French a try.
With the help of the community, this can be an ever improving goal that has already started.
_ETA_: Already in progress.
#### Availability in multiple exchanges
Initially, we will focus our efforts on making the token available to current users while strengthening the
community.
We started by listing HABEO in PancakeSwap. After the token becomes live, we can start onboarding other exchanges.
Although being available in multiple exchanges will help with the reach to other potential users, we think the best
way to manage this is organically so there's no need of investing a large amount of time and resources to accomplish
it.
HABEO is ready from the get go to onboard other exchanges. Our vision is: "If we build it, they will come".
_ETA_: This will be a continuous moving target. We anticipate that we will have several exchanges reach out by the
end of the year 2021 but we won't sweat if this is not the case.
### Mid term goals
#### Creating bridges with other blockchains
The logical next step is to enable users from other blockchains to have the ability to use HABEO and its community.
In order to accomplish this, we would need to create bridges to other blockchains.
It is probably a good idea to first work with the ETH network because of the similitude with the BSC Network.
If other networks are worthy, our community can always focus resources in building the new bridges.
_ETA_: Seems like a good checkpoint to reach before thinking about a bridge would be once HABEO reaches critical
mass (100,000 users approximately) and our community becomes self-sufficient with regards to administering our
forums. We anticipate this being the scenario by Q1 2022.
#### HABEO Wallet
There are several wallet products out there that have the ability to work with BSC tokens.
If our community decides to build "the one wallet to rule them all", we'll have to make sure it covers the most
important aspects that other wallets don't cover.
Since our goals are simplicity, education and availability, the wallet would have to follows those principles.
It would have to be: multi chain, multi protocol, multi language and with integrated conversion tools.
_ETA_: This work could be started at any point in time. We should not focus efforts if it is not needed. I would
think that a good target date to start would be Q2 2022.
#### HABEO Exchange
The exchange should come after the wallet. Even though a new wallet would integrate with some of the existing
exchanges, our community can build a new exchange that falls in line with the culture of HABEO’s community.
It should follow our principles for simplicity, education and availability.
_ETA_: We think that this should not be part of the efforts until at least the end of 2022.
### Long term goals
#### HABEO blockchain
Arriving to this place would mean that what was initially an utopia, is now a very real posibility.
As we explained early on this document, we think that what will give HABEO the most chances of helping people around
the world and solving their inability to participate in the crypto world is to be running on its own blockchain.
HABEO's blockchain should be a Proof Of Stake blockchain that uses the most efficient algorithms to validate
transactions cheaply, quickly and securely.
We must make sure that we have the best and most efficient blockchain for our community while also making sure that
we are not affecting the environment in a negative way.
The method in which this goal can be achieved should be discussed over time but, as a first idea, it seems that we
could base our development on a ETH 2.0 fork.
_ETA_: As every other goal, this one could be moved up or down based on the strength of our community. Initially, it
seems that Q4 2023 could be a good target.


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Copyright 2014 Google Inc. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.


Share and enjoy!
`Prev `__
S
`Next `__
--------------
**Share and enjoy!**: imp.
1. Commonly found at the end of software release announcements and
`*README file* <../R/README-file.html>`__\ s, this phrase indicates
allegiance to the hacker ethic of free information sharing (see `*hacker
ethic* <../H/hacker-ethic.html>`__, sense 1).
2. The motto of the complaints division of Sirius Cybernetics
Corporation (the ultimate gaggle of incompetent
`*suit* `__\ s) in Douglas Adams's *Hitch Hiker's Guide to
the Galaxy*. The irony of using this as a cultural recognition signal
appeals to hackers.
--------------
+------------------------------+----------------------------+------------------------------+
| `Prev `__ | `Up <../S.html>`__ | `Next `__ |
+------------------------------+----------------------------+------------------------------+
| sharchive | `Home <../index.html>`__ | shareware |
+------------------------------+----------------------------+------------------------------+


Tournaments
Softball IQ
Clinics
Camps
Individual Instruction
Facility Rental
This is where text goes that will talk about how awesome something or someone is.
This is where text goes that will talk about other awesome things
Details about awesome things happening with Softball Chicks.
Details about awesome things happening with clinicsSoftball Clinics
Lots of great catching, fielding, pitching & hitting clinics.
We offer indvidual and small group training sessions
What's your softball IQ test it and learn more.
Indoor and field rentals


https://github.com/touch1295/PROJECT_2_Macro_economic_Trends_Stock_Dashboard
https://www.kaggle.com/devarshraval/us-macroeconomic-data-19962020-source-fred?select=macrodata.csv
https://www.alphavantage.co/documentation/


Stability: 3 - Stable
You can access this module with:
var zlib = require('zlib');
This provides bindings to Gzip/Gunzip, Deflate/Inflate, and DeflateRaw/InflateRaw classes. Each class takes the same options, and is a readable/writable Stream.
Compressing or decompressing a file can be done by piping an fs.ReadStream into a zlib stream, then into an fs.WriteStream.
var gzip = zlib.createGzip();
var fs = require('fs');
var inp = fs.createReadStream('input.txt');
var out = fs.createWriteStream('input.txt.gz');
inp.pipe(gzip).pipe(out);
Compressing or decompressing data in one step can be done by using the convenience methods.
var input = '.................................';
zlib.deflate(input, function(err, buffer) {
if (!err) {
console.log(buffer.toString('base64'));
}
});
var buffer = new Buffer('eJzT0yMAAGTvBe8=', 'base64');
zlib.unzip(buffer, function(err, buffer) {
if (!err) {
console.log(buffer.toString());
}
});
To use this module in an HTTP client or server, use the accept-encoding on requests, and the content-encoding header on responses.
Note: these examples are drastically simplified to show the basic concept. Zlib encoding can be expensive, and the results ought to be cached. See Memory Usage Tuning below for more information on the speed/memory/compression tradeoffs involved in zlib usage.
// client request example
var zlib = require('zlib');
var http = require('http');
var fs = require('fs');
var request = http.get({ host: 'izs.me',
path: '/',
port: 80,
headers: { 'accept-encoding': 'gzip,deflate' } });
request.on('response', function(response) {
var output = fs.createWriteStream('izs.me_index.html');
switch (response.headers['content-encoding']) {
// or, just use zlib.createUnzip() to handle both cases
case 'gzip':
response.pipe(zlib.createGunzip()).pipe(output);
break;
case 'deflate':
response.pipe(zlib.createInflate()).pipe(output);
break;
default:
response.pipe(output);
break;
}
});
// server example
// Running a gzip operation on every request is quite expensive.
// It would be much more efficient to cache the compressed buffer.
var zlib = require('zlib');
var http = require('http');
var fs = require('fs');
http.createServer(function(request, response) {
var raw = fs.createReadStream('index.html');
var acceptEncoding = request.headers['accept-encoding'];
if (!acceptEncoding) {
acceptEncoding = '';
}
// Note: this is not a conformant accept-encoding parser.
// See http://www.w3.org/Protocols/rfc2616/rfc2616-sec14.html#sec14.3
if (acceptEncoding.match(/\bdeflate\b/)) {
response.writeHead(200, { 'content-encoding': 'deflate' });
raw.pipe(zlib.createDeflate()).pipe(response);
} else if (acceptEncoding.match(/\bgzip\b/)) {
response.writeHead(200, { 'content-encoding': 'gzip' });
raw.pipe(zlib.createGzip()).pipe(response);
} else {
response.writeHead(200, {});
raw.pipe(response);
}
}).listen(1337);
Returns a new Gzip object with an options.
Returns a new Gunzip object with an options.
Returns a new Deflate object with an options.
Returns a new Inflate object with an options.
Returns a new DeflateRaw object with an options.
Returns a new InflateRaw object with an options.
Returns a new Unzip object with an options.
Not exported by the zlib
module. It is documented here because it is the base
class of the compressor/decompressor classes.
kind
defaults to zlib.Z_FULL_FLUSH
.
Flush pending data. Don't call this frivolously, premature flushes negatively impact the effectiveness of the compression algorithm.
Dynamically update the compression level and compression strategy. Only applicable to deflate algorithm.
Reset the compressor/decompressor to factory defaults. Only applicable to the inflate and deflate algorithms.
Compress data using gzip.
Decompress a gzip stream.
Compress data using deflate.
Decompress a deflate stream.
Compress data using deflate, and do not append a zlib header.
Decompress a raw deflate stream.
Decompress either a Gzip- or Deflate-compressed stream by auto-detecting the header.
All of these take a string or buffer as the first argument, an optional second
argument to supply options to the zlib classes and will call the supplied
callback with callback(error, result)
.
Every method has a *Sync
counterpart, which accept the same arguments, but
without a callback.
Compress a string with Deflate.
Compress a string with DeflateRaw.
Compress a string with Gzip.
Decompress a raw Buffer with Gunzip.
Decompress a raw Buffer with Inflate.
Decompress a raw Buffer with InflateRaw.
Decompress a raw Buffer with Unzip.
Each class takes an options object. All options are optional.
Note that some options are only relevant when compressing, and are ignored by the decompression classes.
zlib.Z_NO_FLUSH
)See the description of deflateInit2
and inflateInit2
at
http://zlib.net/manual.html#Advanced for more information on these.
From zlib/zconf.h
, modified to node's usage:
The memory requirements for deflate are (in bytes):
(1 << (windowBits+2)) + (1 << (memLevel+9))
that is: 128K for windowBits=15 + 128K for memLevel = 8 (default values) plus a few kilobytes for small objects.
For example, if you want to reduce the default memory requirements from 256K to 128K, set the options to:
{ windowBits: 14, memLevel: 7 }
Of course this will generally degrade compression (there's no free lunch).
The memory requirements for inflate are (in bytes)
1 << windowBits
that is, 32K for windowBits=15 (default value) plus a few kilobytes for small objects.
This is in addition to a single internal output slab buffer of size
chunkSize
, which defaults to 16K.
The speed of zlib compression is affected most dramatically by the
level
setting. A higher level will result in better compression, but
will take longer to complete. A lower level will result in less
compression, but will be much faster.
In general, greater memory usage options will mean that node has to make
fewer calls to zlib, since it'll be able to process more data in a
single write
operation. So, this is another factor that affects the
speed, at the cost of memory usage.
All of the constants defined in zlib.h are also defined on
require('zlib')
.
In the normal course of operations, you will not need to ever set any of
these.  This section is taken almost directly from the zlib
documentation. See
http://zlib.net/manual.html#Constants for more details.
Allowed flush values.
zlib.Z_NO_FLUSH
zlib.Z_PARTIAL_FLUSH
zlib.Z_SYNC_FLUSH
zlib.Z_FULL_FLUSH
zlib.Z_FINISH
zlib.Z_BLOCK
zlib.Z_TREES
Return codes for the compression/decompression functions. Negative values are errors, positive values are used for special but normal events.
zlib.Z_OK
zlib.Z_STREAM_END
zlib.Z_NEED_DICT
zlib.Z_ERRNO
zlib.Z_STREAM_ERROR
zlib.Z_DATA_ERROR
zlib.Z_MEM_ERROR
zlib.Z_BUF_ERROR
zlib.Z_VERSION_ERROR
Compression levels.
zlib.Z_NO_COMPRESSION
zlib.Z_BEST_SPEED
zlib.Z_BEST_COMPRESSION
zlib.Z_DEFAULT_COMPRESSION
Compression strategy.
zlib.Z_FILTERED
zlib.Z_HUFFMAN_ONLY
zlib.Z_RLE
zlib.Z_FIXED
zlib.Z_DEFAULT_STRATEGY
Possible values of the data_type field.
zlib.Z_BINARY
zlib.Z_TEXT
zlib.Z_ASCII
zlib.Z_UNKNOWN
The deflate compression method (the only one supported in this version).
zlib.Z_DEFLATED
For initializing zalloc, zfree, opaque.
zlib.Z_NULL


ftso - First Stochastic
ssto - Second Stochastic
tsto - Third Stochastic
stochastictype=Fast stochasticdays=12 stochasticmovavgdays=3 Function=Stochastic
stochastictype=Slow stochasticdays=12 stochasticmovavgdays=3 Function=Stochastic
{{chart | json}}


--- layout: post title: Wall Art for the Growth Nerd date: 2014-07-29 00:06:28.000000000 -05:00 type: post published: true status: publish categories: [feed] tags: - patents - stuff - technology meta: _edit_last: '8005175' geo_public: '0' _publicize_pending: '1' author: login: dvollrath email<EMAIL_ADDRESS>display_name: dvollrath first_name: '' last_name: '' ---
Prints of classic patent diagrams (and by classic, I mean Lego and Edison's telegraph) available at Etsy. Good deal at only 7 bucks a pop. Perfect for your favorite "technology is the key to long-run growth" economists. (h/t Gizmodo).


"The Struggle you're in today is developing the Strenght you need tomorrow"
Welcome
Stay tuned as there will be a lot more interesting things to be browsed around , till then ,thank you for checking it out!
"The Struggle you're in today is developing the Strenght you need tomorrow"
Welcome
Stay tuned as there will be a lot more interesting things to be browsed around , till then ,thank you for checking it out!


foodyBOT
10 May 2021
K
21:30
Karan
/start
f
21:30
foodyBOT
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21:31
Karan
thin
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can I know you flavor ? Hawaiian , veggie , capsicum , Corn ,BBQS
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veggie
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Thanks for your response. now we get your details. Type Ok to continue.
21:31
Choose an item
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Enter your first name?
K
21:31
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karan
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Enter your last name?
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choudhary
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21:32
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9050122367
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Your order is placed Thanks karan choudhary we will be contact you at the time of delivery
9050122367
at 9am with city palwal (code 111202)
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bye
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Sorry, I didn't get that. Can you rephrase?


--- layout: post title: Episode 055 | Nathan Carroll - Family and Fitness link: http://traffic.libsyn.com/developeronfire/DeveloperOnFire-55-NathanCarroll.mp3 length: 41943040 speaker: Dave Rael duration: '44:06' subtitle: 'Developer On Fire: Nathan Carroll' guest_name: Nathan Carroll image: /assets/images/NathanCarroll.jpg guest_blog: https://boost.training/ guest_twitter: DNCarroll disqus_url: http://developeronfire.com/Podcast/Episodes/nathan-carroll-family-and-fitness episode_number: 55 book_recommendations: - { url: "http://www.amazon.com/dp/0811218511/?tag=devonfir-20", title: "The Way of Chuang Tzu", author: "Thomas Merton"} - { url: "http://www.amazon.com/dp/0553208845/?tag=devonfir-20", title: "Siddhartha", author: "Hermann Hesse"} - { url: "http://www.amazon.com/dp/0062113720/?tag=devonfir-20", title: "Lost Horizon: A Novel", author: "James Hilton"} - { url: "http://www.amazon.com/dp/0060616881/?tag=devonfir-20", title: "Awakening the Mind, Lightening the Heart : Core Teachings of Tibetan Buddhism", author: "Lama Dalai"} tags: - { url: "http://www.amazon.com/dp/0811218511/?tag=devonfir-20", title: "The Way of Chuang Tzu", author: "Thomas Merton"} - { url: "http://www.amazon.com/dp/0553208845/?tag=devonfir-20", title: "Siddhartha", author: "Hermann Hesse"} - { url: "http://www.amazon.com/dp/0062113720/?tag=devonfir-20", title: "Lost Horizon: A Novel", author: "James Hilton"} - { url: "http://www.amazon.com/dp/0060616881/?tag=devonfir-20", title: "Awakening the Mind, Lightening the Heart : Core Teachings of Tibetan Buddhism", author: "Lama Dalai"} redirect_from: - "/55/" - "/055/" - "/0055/" - "/nathan-carroll-family-and-fitness/" - "/Nathan-Carroll/" - "/NathanCarroll/" - "/Podcast/Episodes/55/" - "/Podcast/Episodes/055/" - "/Podcast/Episodes/0055/" - "/Podcast/Episodes/nathan-carroll-family-and-fitness/" - "/Podcast/Episodes/Nathan-Carroll/" - "/Podcast/Episodes/NathanCarroll/" guid: http://developeronfire.com:80/Podcast/Episodes/nathan-carroll-family-and-fitness ---
Nathan Carroll has been developing software in .NET since 1.0. He is the author of the open source frameworks BattleAxe, and ULFBERHT.js. He is a workout fanatic and has built several fitness web sites over the past 8 that have evolved into his current web site Boost.Training. He also enjoys reading, cooking, and hiking with family.
1. Ask questions
2. Be honest in your work
3. Be tenacious


Update existing business.
emailLogo: Image information for Company Logo (What is the Company Logo? See here: http://userguide.getfivestars.com/wp-content/uploads/2014/10/logo-uploader-option.png ) We will resize your image for you. However for best results we recommend sizing the image. The max width is 260px. The following formats are supported: PNG, JPEG & GIF.
feedbackBanner: Image information for Feedback Page Banner (What is the Feedback Page Banner? See here: http://userguide.getfivestars.com/wp-content/uploads/2014/12/feedback-page-banner-example.jpg ) We will resize your image for you. However for best results we recommend sizing the image currectly. The minimum width is 760px. The banner can be as tall or as narrow as you want. A good size is 250px in height. The following formats are supported: PNG, JPEG & GIF
emailImage: Image information for replacing the Google Map with a Logo or Image (What is the Google Maps image replacement? See here: http://userguide.getfivestars.com/wp-content/uploads/2014/05/replace-google-maps-with-image.jpg ) We will resize your image for you. However for best results we recommend sizing the image currectly. The ideal size is 160px by 160px. The following formats are supported: PNG, JPEG & GIF
| Name | Type | Required | Example | Description |
|---|---|---|---|---|
| businessId | integer | yes | 376 | Business id. |
| businessName | string | no | Peach Trees Intl. | Business name. |
| businessType | string | no | LocalBusiness | Schema.org business type. |
| city | string | no | Haiku | Business city. |
| clientId | string(40) | yes | 04f78...9f9f3 | Client id is a unique user identifier provided by GetFiveStars. |
| country | string | no | United States | Business country. |
| customField | string | no | 1234 | Custom ID (whitelabeled accounts only). |
| emailImage | string | no | http://domain.com/image-url | Email Picture. |
| emailLogo | string | no | http://domain.com/image-url | Company Logo. |
| feedbackBanner | string | no | http://domain.com/image-url | Feedback Page Banner. |
| hash | string(64) | yes | bd13a40...303f3c5 | Hash is a digital request signature. Learn how to generate hash here. |
| organisationType | string | no | company | Organisation type: company, corporation, non profit, school, office, practice, agency, church, restaurant, event, firm, store, dealership |
| phone | string | no |<PHONE_NUMBER> | Mobile phone number. |
| state | string | no | HI | Business state. |
| streetAddress | string | no | 4150 Hana Hwy | Business street address. |
| websiteUrl | string | no | https://www.peachtreeintl.abc-company.com | Business website url. |
| zip | string | no | 96708 | Business zip code. |
{ "businessId":"376", "businessName":"Peach Trees Intl.", "businessType":"LocalBusiness", "city":"Haiku", "clientId":"80338307b78b8d84c8b4d40e63e235cc312fcebe", "country":"United States", "customField":"1234", "hash":"3b288032e87d12c08c95dc8a96128f19ec1ec207af383328413d18f9e0aaf1fd", "organisationType":"company", "phone":"808-333-3334", "state":"HI", "streetAddress":"4150 Hana Hwy", "websiteUrl":"https:\/\/www.peachtreeintl.abc-company.com", "zip":"96708" }
{ "errorCode":0, "errorMessage":"Success" }
| errorCode | errorMessage |
|---|---|
| -1 | Unknown error |
| 1 | Invalid hash sign |
| 2 | Invalid clientId |
| 3 | Server error - please contact support |
| 5 | This user cannot manage selected business |
| 14 | Invalid business name |
| 25 | Business does not exist |


The Transfer-Client is a Java application for transfering data via Sedex. It offers a
graphical user interface to the end-user for controlling the sending process.
In detail, the Transfer-Client provides the following tasks:
- Task 1: Waiting for incoming result files.
- Task 2: Moving the result files to the stage folder.
- Task 3: Creating sedex envelope files.
- Task 4: Moving the result files together with the generated envelope files to the outbox sedex folder.
- Task 5: Waiting for receipts.
Copyright 2018-2020 Statistisches Amt des Kantons Zürich
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.


NNTPCchan is a federated imageboard that spans across several networks including tor, i2p and anonet. There are no central servers so that as long as 1 server is left the forum will continue to function.
How is this different from *chan?
NNTPChan is by design invulnerable to global censorship. All moderation is local to each server.
How does moderation work then?
Moderation is done with our special tripcodes (ed25519-sha512 signed posts). Anyone can give moderation suggestions, but that doesn't mean anyone will accept them. Those who trust the actions of a moderator can have their frontend whitelist the moderation actions and have them performed without oversight by another moderator.
How can I remove content from nntpchan completely?
‾\(._.)/‾ I don't know. You probably can't unless every server agrees to remove the content, even then, nothing prevents someone from reposting it. You can't delete what you post on the internet.
do you allow child porn?
no.
Do you allow XYZ content?
If it violates USA Law or causes problems with my host, no. Otherwise, probably.
Someone posted something I don't like but it's not illegal
That is not my problem.


James W England
Democrat
Retired
I received a PhD in mathematics in 1964and taught mathematics at the University of Virginia and Swarthmore College..I served as Dean of the Faculty at Occidental College and as Provost at Swarthmore Collegae and at Temple University
80023
As a university provost I was the Chief academic Officer and had 23 Deans of the Colleges that made up the University report to me. I allocated the operating funds of the Colleges and directed the Academic programs of these Colleges. As such I needed to be able to resolve conflicts over collegiate budgets and be certain that each college had the funds needed to carry out their mission.
I was elected to the Wallingford/Swarthmore School Board as both a Democrat and Republician-in Pa one can regester in both primaries for school board which I did and I won both primaries. I regu;ar;y donate small sums of money to Democratic parts canidates(less than $1,000)
Red Cross as a DAT captian, volunteer tax preparer with AARP in Broomfield and Vita in Adams County for the past 12 years. I have worked as a poll worker for the past four or five elections.
I voted for the creation of this Commission and believe strongly in its mission.
My background as a College and University administer required me to bring about consesus on a wide variety of academic and financial issues. As a School Board member I helped the community work through difficult issues such as closing one high school. You could contact Phil Weiser for background information on me as he was a student at Swarthmore College while I was Provost.
As a mathematician I worked on many of the important issues aplied and pure mathemdatics. I have taught the full range of courses from Calculus through Graduate courses in mathematical analysis.
Male


Test purpose
- Create a resizable <canvas> element
- Create a Phrase entity to display in the canvas
- Create hook functions for the Phrase that will be invoked as the <canvas> dimensions are changed
- Check that appropriate hooks are fired, and the Phrase entity updated, in response to changes in the canvas element's dimensions - five states ('banner', 'landscape', 'rectangle', 'portrait', 'skyscraper') should be viewable as the canvas corner is dragged horizontally right (and up) and then diagonally down/left
Touch test: canvas should be resizable
Annotated code


Predicting the outcome of a US presidential election using Bayesian optimal experimental design¶
In this tutorial, we explore the use of optimal experimental design techniques to create an optimal polling strategy to predict the outcome of a US presidential election. In a previous tutorial, we explored the use of Bayesian optimal experimental design to learn the working memory capacity of a single person. Here, we apply the same concepts to study a whole country.
To begin, we need a Bayesian model of the winner of the election w
, as well as the outcome y
of any poll we may plan to conduct. The experimental design is the number of people \(n_i\) to poll in each state. To set up our exploratory model, we are going to make a number of simplifying assumptions. We will use historical election data 1976-2012 to construct a plausible prior and the 2016 election as our test set: we imagine that we are conducting polling just before the 2016 election.
Choosing a prior¶
In our model, we include a 51 dimensional latent variabe alpha
. For each of the 50 states plus DC we define
and we assume all other voters vote Republican. Right before the election, the value of \(\alpha\) is unknown and we wish to estimate it by conducting a poll with \(n_i\) people in state \(i\) for \(i=1, ..., 51\) . The winner \(w\) of the election is decided by the Electoral College system. The number of electoral college votes gained by the Democrats in state \(i\) is
(this is a rough approximation of the true system). All other electoral college votes go to the Republicans. Here \(k_i\) is the number of electoral college votes alloted to state \(i\), which are listed in the following data frame.
[3]:
# Data path
BASE_URL = "https://d2hg8soec8ck9v.cloudfront.net/datasets/us_elections/"
[4]:
import pandas as pd
import torch
from urllib.request import urlopen
electoral_college_votes = pd.read_pickle(urlopen(BASE_URL + "electoral_college_votes.pickle"))
print(electoral_college_votes.head())
ec_votes_tensor = torch.tensor(electoral_college_votes.values, dtype=torch.float).squeeze()
Electoral college votes
State
AL 9
AK 3
AZ 11
AR 6
CA 55
The winner \(w\) of the election is
In code, this is expressed as follows
[5]:
def election_winner(alpha):
dem_win_state = (alpha > 0.).float()
dem_electoral_college_votes = ec_votes_tensor * dem_win_state
w = (dem_electoral_college_votes.sum(-1) / ec_votes_tensor.sum(-1) > .5).float()
return w
We are interested in polling strategies that will help us predict \(w\), rather than predicting the more complex state-by-state results \(\alpha\).
To set up a fully Bayesian model, we need a prior for \(\alpha\). We will base the prior on the outcome of some historical presidential elections. Specifically, we’ll use the following dataset of state-by-state election results for the presidential elections 1976-2012 inclusive. Note that votes for parties other than Democrats and Republicans have been ignored.
[7]:
frame = pd.read_pickle(urlopen(BASE_URL + "us_presidential_election_data_historical.pickle"))
print(frame[[1976, 1980, 1984]].head())
1976 1980 1984
Democrat Republican Democrat Republican Democrat Republican
State
AL<PHONE_NUMBER>70<PHONE_NUMBER>92<PHONE_NUMBER>49
AK 44058 71555 41842 86112 62007 138377
AZ<PHONE_NUMBER>42<PHONE_NUMBER>88<PHONE_NUMBER>16
AR<PHONE_NUMBER>53<PHONE_NUMBER>64<PHONE_NUMBER>74
CA 3742284 3882244 3083661 4524858 3922519 5467009
Based on this data alone, we will base our prior mean for \(\alpha\) solely on the 2012 election. Our model will be based on logistic regression, so we will transform the probability of voting Democrat using the logit function. Specifically, we’ll choose a prior mean as follows:
[8]:
results_2012 = torch.tensor(frame[2012].values, dtype=torch.float)
prior_mean = torch.log(results_2012[..., 0] / results_2012[..., 1])
Our prior distribution for \(\alpha\) will be a multivariate Normal with mean prior_mean
. The only thing left to decide upon is the covariance matrix. Since alpha
values are logit-transformed, the covariance will be defined in logit space as well.
Aside: The prior covariance is important in a number of ways. If we allow too much variance, the prior will be uncertain about the outcome in every state, and require polling everywhere. If we allow too little variance, we may be caught off-guard by an unexpected electoral outcome. If we assume states are independent, then we will not be able to pool information across states; but assume too much correlation and we could too faithfully base predictions about one state from poll results in another.
We select the prior covariance by taking the empirical covariance from the elections 1976 - 2012 and adding a small value 0.01
to the diagonal.
[9]:
idx = 2 * torch.arange(10)
as_tensor = torch.tensor(frame.values, dtype=torch.float)
logits = torch.log(as_tensor[..., idx] / as_tensor[..., idx + 1]).transpose(0, 1)
mean = logits.mean(0)
sample_covariance = (1/(logits.shape[0] - 1)) * (
(logits.unsqueeze(-1) - mean) * (logits.unsqueeze(-2) - mean)
).sum(0)
prior_covariance = sample_covariance + 0.01 * torch.eye(sample_covariance.shape[0])
Setting up the model¶
We are now in a position to define our model. At a high-level the model works as follows:
\(\alpha\) is multivariate Normal
\(w\) is a deterministic function of \(\alpha\)
\(y_i\) is Binomial(\(n_i\), sigmoid(\(\alpha_i\))) so we are assuming that people respond to the poll in exactly the same way that they will vote on election day
In Pyro, this model looks as follows
[10]:
import pyro
import pyro.distributions as dist
def model(polling_allocation):
# This allows us to run many copies of the model in parallel
with pyro.plate_stack("plate_stack", polling_allocation.shape[:-1]):
# Begin by sampling alpha
alpha = pyro.sample("alpha", dist.MultivariateNormal(
prior_mean, covariance_matrix=prior_covariance))
# Sample y conditional on alpha
poll_results = pyro.sample("y", dist.Binomial(
polling_allocation, logits=alpha).to_event(1))
# Now compute w according to the (approximate) electoral college formula
dem_win = election_winner(alpha)
pyro.sample("w", dist.Delta(dem_win))
return poll_results, dem_win, alpha
Understanding the prior¶
Before we go any further, we’re going to study the model to check it matches with our intuition about US presidential elections.
First of all, let’s look at an upper and lower confidence limit for the proportion of voters who will vote Democrat in each state.
[11]:
std = prior_covariance.diag().sqrt()
ci = pd.DataFrame({"State": frame.index,
"Lower confidence limit": torch.sigmoid(prior_mean - 1.96 * std),
"Upper confidence limit": torch.sigmoid(prior_mean + 1.96 * std)}
).set_index("State")
print(ci.head())
Lower confidence limit Upper confidence limit
State
AL 0.272258 0.517586
AK 0.330472 0.529117
AZ 0.321011 0.593634
AR 0.214348 0.576079
CA 0.458618 0.756616
The prior on \(\alpha\) implicitly defines our prior on w
. We can investigate this prior by simulating many times from the prior.
[12]:
_, dem_wins, alpha_samples = model(torch.ones(100000, 51))
prior_w_prob = dem_wins.float().mean()
print("Prior probability of Dem win", prior_w_prob.item())
Prior probability of Dem win 0.6799200177192688
Since our prior is based on 2012 and the Democrats won in 2012, it makes sense that we would favour a Democrat win in 2016 (this is before we have seen any polling data or incorporated any other information).
We can also investigate which states, a priori, are most marginal.
[13]:
dem_prob = (alpha_samples > 0.).float().mean(0)
marginal = torch.argsort((dem_prob - .5).abs()).numpy()
prior_prob_dem = pd.DataFrame({"State": frame.index[marginal],
"Democrat win probability": dem_prob.numpy()[marginal]}
).set_index('State')
print(prior_prob_dem.head())
Democrat win probability
State
FL 0.52501
NC 0.42730
NH 0.61536
OH 0.61997
VA 0.63738
This is a sanity check, and seems to accord with our intuitions. Florida is frequently an important swing state and is top of our list of marginal states under the prior. We can also see states such as Pennsylvania and Wisconsin near the top of the list – we know that these were instrumental in the 2016 election.
Finally, we take a closer look at our prior covariance. Specifically, we examine states that we expect to be more or less correlated. Let’s begin by looking at states in New England
[14]:
import numpy as np
def correlation(cov):
return cov / np.sqrt(np.expand_dims(np.diag(cov.values), 0) * np.expand_dims(np.diag(cov.values), 1))
new_england_states = ['ME', 'VT', 'NH', 'MA', 'RI', 'CT']
cov_as_frame = pd.DataFrame(prior_covariance.numpy(), columns=frame.index).set_index(frame.index)
ne_cov = cov_as_frame.loc[new_england_states, new_england_states]
ne_corr = correlation(ne_cov)
print(ne_corr)
State ME VT NH MA RI CT
State
ME 1.000000 0.817323 0.857351 0.800276 0.822024 0.825383
VT 0.817323 1.000000 0.834723 0.716342 0.754026 0.844140
NH 0.857351 0.834723 1.000000 0.871370 0.803803 0.873496
MA 0.800276 0.716342 0.871370 1.000000 0.813665 0.835148
RI 0.822024 0.754026 0.803803 0.813665 1.000000 0.849644
CT 0.825383 0.844140 0.873496 0.835148 0.849644 1.000000
Clearly, these states tend to vote similarly. We can also examine some states of the South which we also expect to be similar.
[15]:
southern_states = ['LA', 'MS', 'AL', 'GA', 'SC']
southern_cov = cov_as_frame.loc[southern_states, southern_states]
southern_corr = correlation(southern_cov)
print(southern_corr)
State LA MS AL GA SC
State
LA 1.000000 0.554020 0.651511 0.523784 0.517672
MS 0.554020 1.000000 0.699459 0.784371 0.769198
AL 0.651511 0.699459 1.000000 0.829908 0.723015
GA 0.523784 0.784371 0.829908 1.000000 0.852818
SC 0.517672 0.769199 0.723015 0.852818 1.000000
These correlation matrices show that, as expected, logical groupings of states tend to have similar voting trends. We now look at the correlations between the groups (e.g. between Maine and Louisiana).
[16]:
cross_cov = cov_as_frame.loc[new_england_states + southern_states, new_england_states + southern_states]
cross_corr = correlation(cross_cov)
print(cross_corr.loc[new_england_states, southern_states])
State LA MS AL GA SC
State
ME 0.329438 0.309352 -0.000534 0.122375 0.333679
VT -0.036079 0.009653 -0.366604 -0.202065 0.034438
NH 0.234105 0.146826 -0.105781 0.008411 0.233084
MA 0.338411 0.122257 -0.059107 -0.025730 0.182290
RI 0.314088 0.188819 -0.066307 -0.022142 0.186955
CT 0.139021 0.074646 -0.205797 -0.107684 0.125023
Now, we see weaker correlation between New England states and Southern states than the correlation within those grouping. Again, this is as expected.
Measuring the expected information gain of a polling strategy¶
The prior we have set up appears to accord, at least approximately, with intuition. However, we now want to add a second source of information from polling. We aim to use our prior to select a polling strategy that will be most informative about our target \(w\). A polling strategy, in this simplified set-up, is the number of people to poll in each state. (We ignore any other covariates such as regional variation inside states, demographics, etc.) We might imagine that polling 1000 people in Florida (the most marginal state), will be much more effective than polling 1000 people in DC (the least marginal state). That’s because the outcome in DC is already quite predictable, just based on our prior, whereas the outcome in Florida is really up for grabs.
In fact, the information that our model will gain about \(w\) based on conducting a poll with design \(d\) and getting outcome \(y\) can be described mathematically as follows:
Since the outcome of the poll is at present unknown, we consider the expected information gain [1]
Variational estimators of EIG¶
In the working memory tutorial, we used the ‘marginal’ estimator to find the EIG. This involved estimating the marginal density \(p(y|d)\). In this experiment, that would be relatively difficult: \(y\) is 51-dimensional with some rather tricky constraints that make modelling its density difficult. Furthermore, the marginal estimator requires us to know \(p(y|w)\) analytically, which we do not.
Fortunately, other variational estimators of EIG exist: see [2] for more details. One such variational estimator is the ‘posterior’ estimator, based on the following representation
Here, \(H(p(w))\) is the prior entropy on \(w\) (we can compute this quite easily). The important term involves the variational approximation \(q(w|y)\). This \(q\) can be used to perform amortized variational inference. Specifically, it takes as input \(y\) and outputs a distribution over \(w\). The bound is maximised when \(q(w|y) = p(w|y)\) [2]. Since \(w\) is a binary random variable, we can think of \(q\) as a classifier that tries to decide, based on the poll outcome, who the eventual winner of the election will be. In this notebook, \(q\) will be a neural classifier. Training a neural classifier is a fair bit easier than learning the marginal density of \(y\), so we adopt this method to estimate the EIG in this tutorial.
[17]:
from torch import nn
class OutcomePredictor(nn.Module):
def __init__(self):
super().__init__()
self.h1 = nn.Linear(51, 64)
self.h2 = nn.Linear(64, 64)
self.h3 = nn.Linear(64, 1)
def compute_dem_probability(self, y):
z = nn.functional.relu(self.h1(y))
z = nn.functional.relu(self.h2(z))
return self.h3(z)
def forward(self, y_dict, design, observation_labels, target_labels):
pyro.module("posterior_guide", self)
y = y_dict["y"]
dem_prob = self.compute_dem_probability(y).squeeze()
pyro.sample("w", dist.Bernoulli(logits=dem_prob))
We’ll now use this to compute the EIG for several possible polling strategies. First, we need to compute the \(H(p(w))\) term in the above formula.
[18]:
prior_entropy = dist.Bernoulli(prior_w_prob).entropy()
Let’s consider four simple polling strategies. 1. Poll 1000 people in Florida only 2. Poll 1000 people in DC only 3. Poll 1000 people spread evenly over the US 4. Using a polling allocation that focuses on swing states
[19]:
from collections import OrderedDict
poll_in_florida = torch.zeros(51)
poll_in_florida[9] = 1000
poll_in_dc = torch.zeros(51)
poll_in_dc[8] = 1000
uniform_poll = (1000 // 51) * torch.ones(51)
# The swing score measures how close the state is to 50/50
swing_score = 1. / (.5 - torch.tensor(prior_prob_dem.sort_values("State").values).squeeze()).abs()
swing_poll = 1000 * swing_score / swing_score.sum()
swing_poll = swing_poll.round()
poll_strategies = OrderedDict([("Florida", poll_in_florida),
("DC", poll_in_dc),
("Uniform", uniform_poll),
("Swing", swing_poll)])
We’ll now compute the EIG for each option. Since this requires training the network (four times) it may take several minutes.
[20]:
from pyro.contrib.oed.eig import posterior_eig
from pyro.optim import Adam
eigs = {}
best_strategy, best_eig = None, 0
for strategy, allocation in poll_strategies.items():
print(strategy, end=" ")
guide = OutcomePredictor()
pyro.clear_param_store()
# To reduce noise when comparing designs, we will use the precomputed value of H(p(w))
# By passing eig=False, we tell Pyro not to estimate the prior entropy on each run
# The return value of `posterior_eig` is then -E_p(w,y)[log q(w|y)]
ape = posterior_eig(model, allocation, "y", "w", 10, 12500, guide,
Adam({"lr": 0.001}), eig=False, final_num_samples=10000)
eigs[strategy] = prior_entropy - ape
print(eigs[strategy].item())
if eigs[strategy] > best_eig:
best_strategy, best_eig = strategy, eigs[strategy]
Florida 0.3088182508945465
DC 0.000973820686340332
Uniform 0.30316317081451416
Swing 0.3254041075706482
Running the experiment¶
We have now scored our four candidate designs and can choose the best one to use to actually gather new data. In this notebook, we will simulate the new data using the results from the 2016 election. Specifically, we will assume that the outcome of the poll comes from our model, where we condition the value of alpha
to correspond to the actual results in 2016.
First, we retrain \(q\) with the chosen polling strategy.
[21]:
best_allocation = poll_strategies[best_strategy]
pyro.clear_param_store()
guide = OutcomePredictor()
posterior_eig(model, best_allocation, "y", "w", 10, 12500, guide,
Adam({"lr": 0.001}), eig=False)
[21]:
tensor(0.3653, grad_fn=<DivBackward0>)
The value of \(\alpha\) implied by the 2016 results is computed in the same way we computed the prior.
[23]:
test_data = pd.read_pickle(urlopen(BASE_URL + "us_presidential_election_data_test.pickle"))
results_2016 = torch.tensor(test_data.values, dtype=torch.float)
true_alpha = torch.log(results_2016[..., 0] / results_2016[..., 1])
[24]:
conditioned_model = pyro.condition(model, data={"alpha": true_alpha})
y, _, _ = conditioned_model(best_allocation)
Let’s view the outcome of our poll.
[25]:
outcome = pd.DataFrame({"State": frame.index,
"Number of people polled": best_allocation,
"Number who said they would vote Democrat": y}
).set_index("State")
print(outcome.sort_values(["Number of people polled", "State"], ascending=[False, True]).head())
Number of people polled Number who said they would vote Democrat
State
FL 191.0 89.0
NE 66.0 30.0
NJ 41.0 22.0
OH 40.0 19.0
VT 35.0 22.0
Analysing the data¶
Having collected our data, we can now perform inference under our model to obtain the posterior probability of a Democrat win. There are many ways to perform the inference: for instance we could use variational inference with Pyro’s SVI
or MCMC such as Pyro’s NUTS
. Using these methods, we would compute the posterior over alpha
, and use this to obtain the posterior over w
.
However, a quick way to analyze the data is to use the neural network we have already trained. At convergence, we expect the network to give a good approximation to the true posterior, i.e. \(q(w|y) \approx p(w|y)\).
[26]:
q_w = torch.sigmoid(guide.compute_dem_probability(y).squeeze().detach())
[27]:
print("Prior probability of Democrat win", prior_w_prob.item())
print("Posterior probability of Democrat win", q_w.item())
Prior probability of Democrat win 0.6799200177192688
Posterior probability of Democrat win 0.40527692437171936
This completes the whole process of experimental design, obtaining data, and data analysis. By using our analysis model and prior to design the polling strategy, we were able to choose the design that led to the greatest expected gain in information.
Conclusions¶
In this tutorial, we showed how optimal experimental design principles can be applied to electoral polling. If we have the option of first designing the polling strategy using our prior, this can help lead to better predictions once we have collected our data.
Our model could be enhanced in a number of ways: a useful guide to the kinds of models used in political science can be found in [3]. Our optimal design strategy will only ever be as good as our prior, so there is certainly a lot of use in investing time in choosing a good prior.
It might also be possible to search over the possible polling strategies in a more sophisticated way. For instance, simulated annealing [4] is one technique that can be used for optimization in high-dimensional discrete spaces.
Our polling strategies were chosen with one objective in mind: predict the final outcome \(w\). If, on the other hand, we wanted to make more fine-grained predictions, we could use the same procedure but treat \(\alpha\) as our target instead of \(w\).
Finally, it’s worth noting that there is a connection to Bayesian model selection: specifically, in a different model we might have \(w\) a binary random variable to choose between different sub-models. Although conceptually different, our election set-up and the model selection set-up share many mathematical features which means that a similar experimental design approach could be used in both cases.
References¶
[1] Chaloner, K. and Verdinelli, I., 1995. Bayesian experimental design: A review. Statistical Science, pp.273-304.
[2] Foster, A., Jankowiak, M., Bingham, E., Horsfall, P., Teh, Y.W., Rainforth, T. and Goodman, N., 2019. Variational Bayesian Optimal Experimental Design. Advances in Neural Information Processing Systems 2019 (to appear).
[3] Gelman, A., Carlin, J.B., Stern, H.S., Dunson, D.B., Vehtari, A. and Rubin, D.B., 2013. Bayesian data analysis. Chapman and Hall/CRC.
[4] Kirkpatrick, S., Gelatt, C.D. and Vecchi, M.P., 1983. Optimization by simulated annealing. Science, 220(4598), pp.671-680.


Real native apps that run on Android, iPhone & tablets
Your organization’s custom app downloads right from Google Play and the iTunes Store ready for use. No clunky web browsers or mobile sites with pinch and zoom. An easy-to-use, clean look your users will love. And you control everything - right from your website admin panel.
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Every time a user launches the application, the app automatically checks for new content updates. Forget costly development and long review periods. You make changes in the admin control panel and your users see them instantly the next time they open the app.
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to the same ID as the page.

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Two dudes talking about data science while they drink fine craft brews from around the world.
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This project demonstrates how easy you can develop automated web functional (acceptance) tests with iTest2.
Tip: This project serves a reference too. For example, you want to know how to verify a HTML link present in a web page, pressing Ctrl+Shift+N, typing 'Assert', and selecting 'Assert (href) Link' will take you there.


light
Area
Degree
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Closeness
Clustering
Color
Degree
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Compare metrics?
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Minimum passes
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close
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|
1-Wire API for .NET Version 4.00
|
Inherits com::dalsemi::onewire::application::monitor::AbstractDeviceMonitor.
Public Member Functions | |
| DeviceMonitor (DSPortAdapter adapter) | |
| Create a simple monitor that does not search branches. | |
| void | setAdapter (DSPortAdapter adapter) |
| Sets this monitor to search a new DSPortAdapter. | |
| OWPath | getDevicePath (Long address) |
| Returns the OWPath of the device with the given address. | |
| void | setDoAlarmSearch (boolean findAlarmingParts) |
| Sets this monitor to search for alarming parts. | |
| boolean | getDoAlarmSearch () |
| See if Gets this monitor to search for alarming parts. | |
| void | search (Vector arrivals, Vector departures) throws OneWireException, OneWireIOException |
| Performs a search of the 1-Wire network without searching branches. |
Class DeviceMonitor represents the monitor that searches the 1-Wire net for new arrivals. This monitor performs a simple search, meaning that no branches are explicitly traversed. If a branch is activated/deactivated between search cycles, this monitor will see the arrival/departure of new devices without reference to the branch which they lie on.
| DeviceMonitor | ( | DSPortAdapter | adapter | ) |
Create a simple monitor that does not search branches.
| the | DSPortAdapter this monitor should search |
| OWPath getDevicePath | ( | Long | address | ) | [virtual] |
Returns the OWPath of the device with the given address.
| address | a Long object representing the address of the device |
Implements AbstractDeviceMonitor.
| boolean getDoAlarmSearch | ( | ) |
See if Gets this monitor to search for alarming parts.
| the | DSPortAdapter this monitor should search |
| void search | ( | Vector | arrivals, |
| Vector | departures | ||
| ) | throws OneWireException, OneWireIOException [virtual] |
Performs a search of the 1-Wire network without searching branches.
| arrivals | A vector of Long objects, represent new arrival addresses. |
| departures | A vector of Long objects, represent departed addresses. |
Implements AbstractDeviceMonitor.
| void setAdapter | ( | DSPortAdapter | adapter | ) | [virtual] |
Sets this monitor to search a new DSPortAdapter.
| the | DSPortAdapter this monitor should search |
Implements AbstractDeviceMonitor.
| void setDoAlarmSearch | ( | boolean | findAlarmingParts | ) |
Sets this monitor to search for alarming parts.
| the | DSPortAdapter this monitor should search |


This application was prepared by Illinois-Indiana Sea Grant under award NA12NOS4190105 from NOAA’s Office of Ocean and Coastal Resource Management, U.S. Department of Commerce. The statements, findings, conclusions, and recommendations are those of the author(s) and do not necessarily reflect the views of NOAA’s Office of Ocean and Coastal Resource Management or the U.S. Department of Commerce.


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Struct Source
Represents a parsing source
Inherited Members
Namespace: Stringier.Patterns
Assembly: Stringier.Patterns.dll
Syntax
public struct Source
Remarks
This is a text buffer with an included implicit cursor. This cursor (Position) is not exposed to the downstream programmer, but is available for direct manipulation by patterns and parsers within this assembly.
Constructors
| Improve this Doc View SourceSource(Result)
Construct a new Source, using the Result
Declaration
public Source(Result Result)
Parameters
| Type | Name | Description |
|---|---|---|
| Result | Result | A Result to use as the source |
Source(Stream)
Construct a new Source from the specified Stream
Declaration
public Source(Stream Stream)
Parameters
| Type | Name | Description |
|---|---|---|
| System.IO.Stream | Stream | A System.IO.Stream to use as a source |
Source(ReadOnlySpan<Char>)
Construct a new Source from the specified Span
Declaration
public Source(ReadOnlySpan<char> Span)
Parameters
| Type | Name | Description |
|---|---|---|
| System.ReadOnlySpan<System.Char> | Span | A System.ReadOnlySpan<T> to use as a source |
Source(Span<Char>)
Construct a new Source from the specified Span
Declaration
public Source(Span<char> Span)
Parameters
| Type | Name | Description |
|---|---|---|
| System.Span<System.Char> | Span | A System.Span<T> to use as a source |
Source(String)
Construct a new Source from the specified String
Declaration
public Source(string String)
Parameters
| Type | Name | Description |
|---|---|---|
| System.String | String | A System.String to use as a source |
Properties
| Improve this Doc View SourceEOF
Whether currently at the end of the source
Declaration
public bool EOF { get; }
Property Value
| Type | Description |
|---|---|
| System.Boolean |
Length
The remaining length of the source
Declaration
public int Length { get; }
Property Value
| Type | Description |
|---|---|
| System.Int32 |
Position
The position within the source buffer
Declaration
public int Position { get; }
Property Value
| Type | Description |
|---|---|
| System.Int32 |
Remarks
This is mutable for internal manipulation, such as advancing on a match or resetting the index after a failed consume.
Methods
| Improve this Doc View SourceEquals(Source)
Declaration
public bool Equals(Source other)
Parameters
| Type | Name | Description |
|---|---|---|
| Source | other | The Source to compare to |
Returns
| Type | Description |
|---|---|
| System.Boolean |
|
Remarks
Source equality doesn't care at all where the cursor is currently. Conceptually, the cursor is a separate object, and is only present inside of this structure for performance considerations and type requirements.
Equals(Object)
Determines whether the specified object is equal to the current object.
Declaration
public override bool Equals(object obj)
Parameters
| Type | Name | Description |
|---|---|---|
| System.Object | obj | The object to compare with the current object. |
Returns
| Type | Description |
|---|---|
| System.Boolean | Always |
Overrides
GetHashCode()
Returns the hash code for this instance.
Declaration
public override int GetHashCode()
Returns
| Type | Description |
|---|---|
| System.Int32 | A 32-bit signed integer hash code. |
Overrides
Restore(SourceState)
Attempt to restore the state of this Source from the specified State
.
Declaration
public void Restore(SourceState State)
Parameters
| Type | Name | Description |
|---|---|---|
| SourceState | State | A SourceState object to restore from. |
Store()
Store the current state of this Source into a SourceState object.
Declaration
public SourceState Store()
Returns
| Type | Description |
|---|---|
| SourceState | The SourceState representing the current state. |
ToString()
Returns a string that represents the current object.
Declaration
public override string ToString()
Returns
| Type | Description |
|---|---|
| System.String | A string that represents the current object. |
Overrides
Operators
| Improve this Doc View SourceEquality(Source, Source)
Declaration
public static bool operator ==(Source left, Source right)
Parameters
| Type | Name | Description |
|---|---|---|
| Source | left | |
| Source | right |
Returns
| Type | Description |
|---|---|
| System.Boolean |
Inequality(Source, Source)
Declaration
public static bool operator !=(Source left, Source right)
Parameters
| Type | Name | Description |
|---|---|---|
| Source | left | |
| Source | right |
Returns
| Type | Description |
|---|---|
| System.Boolean |


I recieved some Adderall from a friend who had a surplus of the drug, and was willing to sell some to me. This would be my first dosage of any kind of upper. My experience with psychoactives is limited to caffeine pills (if you would consider that a drug), inhalents, and I every now and than, some Vicodin.
The one thing I noticed throughout the entire high, was that I never 'lost control' of thought. I could easily stay focused on one task, and if needed, I could switch over to another. There wasn't any time I ever passed out, and I can clearly remember all events that took place.
I took the first 20mg pill at 2:00am, Thursday morning.  Once I had begun to work on a French essay, I started to feel the first effects. There was a numb, tingling sensation that started from my head, and worked its way to my chest. After about 15 minutes, this feeling spread throughout the rest of my body. My essay, which I had been dreading to do (the entire paper was to be written in French) was a breeze. Normally, such an assignment would take roughly half an hour to complete, but after taking the Adderall, I completed the assignment in nearly 15 minutes. Once I had finished my homework, I decided to listen to some music.
I have read that listening to music while on Amphetamines is like hearing a good song for the first time. So I sat on the floor next to my computer and closed my eyes. At that point, my entire outlook on everything was extremely positive. Absolutely nothing could have dampered my spirits. I set my WinAmp playlist with mostly techno songs that would play for the next hour and a half. The music seemed to pulse throughout my entire body. Everything felt wonderful, and I cannot express how much joy I experienced while listening to the music. The entire time I felt almost beyond euphoric.
There was an extremely blissful feeling as I began to drift aimlessly back into thought. I didn't open my eyes once during that time, but I have never felt so physically and spiritually aware of my surroundings in my life. By the time the songs finished, I thought that the high had gone away, because my extremely light-weight, and distinct euphoric feeling had seemingly left. Without getting up, I groped around and found another pill.
It was 4:30 when I took my second 20mgs. After popping the pill, I attempted to get up, but the powerful ecstatic feeling rushed back to me as soon as I tried to move my body upwards. I tried once again to get up, this time more slowly. Apparently my first high was still at work, and I debated whether or not taking the second pill was a good idea, but that train of thought quickly dissolved into happier thoughts.
At about 5 o'clock I began to draw. This act seemed to only make me happier, as I drew whatever I was thinking about, or if I was listening to a song, I tried to draw the beat that I heard. It may sound weird when reading this, but it felt like the most perfectly normal feeling imaginable. For about half an hour, there was a period where there wasn't any conscious thought at all. It was quite an enjoyable experience. I don't know if this was the second pill joining with the first, or if I was just so focused on drawing that nothing else seemed important. Either way, it was an extremely delightful feeling. By the time I finished drawing, I had used around a sketchpad of paper, and it was 6:00am in the morning.
I spent the next hour laying in my bed, and listening to music once again. At approximatly 7:00am, I pulled myself out of bed, and the same rush that occured earlier hit me. I was still in a very dreamy mood, as if I was unstoppable, nothing could put me down. I left my house at 7:25am, and arrive at school carrying one last pill, at 7:35am. I spent the next 25 minutes hanging out with my friends, who were for the most part aware of my current status. At 8:00am, I took the third and last 20mgs, and headed to my first class of the day.
In my first period class we were grouped up to create models for a movie. During the hour and a half class, I rarely left my chair, trying to move as little as possible. It felt as if all my senses were heightened. Everytime I moved my leg, I could feel my muscles working to lift it. I took in every aspect of the classroom with a new regard for the sounds I was hearing, what I was smelling, and what I was seeing. I still retained the happy, almost invincible feeling that I had earlier. It felt as if I had been in the class for hardly 30 minutes when the bell sounded to switch classes.
It was 9:42 when my second block class began. We started on a simple 2-page worksheet, which I shot through. I was finished at least 5 minutes ahead of everyone else. Once we turned those papers in, we began watching a movie in which we had to take notes on. The notes were required to have us write at least 15 observations about the movie. Normally, I do the bare minimum in my classes, but by the time the movie had finished, I had completed 2 pages of notes, front and back. I was soaking up information like a sponge, yet was still VERY talkative. The teacher was suspicious of my work, but was unable to detect that I was on any kind of drug.
Once that class was over, I headed over to lunch. It was there that I began to feel my first bad come-down affects. My stomach began to have sharp pains, and my mouth was exceedingly dry. Although I could not stomach down any food, I was able to drink 2 bottles of water, and chew through 4 pieces of gum before the half hour lunch was over. At the end of the lunch, one of my friends notced that my eyes were extemely dilated.
During my third period class, I was hardly aware of the lesson. I had a splitting headache, and felt like throwing up at any moment (if anyone has ever over-dosed on caffeine pills, you know the feeling). Unlike my other two classes, this one seemed incredibly long. My happy/unstoppable feeling had vanished as soon as my throw-up feeling materialized.
Instead of attending my fourth block class, I headed to the locker room, where I felt I could lie down uninterrupted. I spent nearly an hour and 15 minutes in a dazed stupor. I felt as if I were hardly conscious.
I've decided that in my next undertaking, I will eat before I take any pills, and perhaps try taking them all at once. If there were any way to combat the come-down affect efficiently, I would most certainly label this 'The Most Flawless Drug' ever created.
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.


findorbit4Err Gets 4x4 closed orbit with BPM errors INPUTS 1. RING Ring structure 2. dP Energy offset 3. indbpm Indices of BPMs (Extended BPM any point to monitor) OUTPUTS 1. orbit 4x4 orbit in meters NOTES 1. Alignment errors (T1,T2) are considered here and the BPM reading is modified accordingly. See also findorbit4 ApplyBPMErr bpm_matrices
0001 function orbit = findorbit4Err(RING, dP, indbpm, varargin) 0002 %findorbit4Err Gets 4x4 closed orbit with BPM errors 0003 % 0004 % INPUTS 0005 % 1. RING Ring structure 0006 % 2. dP Energy offset 0007 % 3. indbpm Indices of BPMs (Extended BPM any point to monitor) 0008 % 0009 % OUTPUTS 0010 % 1. orbit 4x4 orbit in meters 0011 % 0012 % NOTES 0013 % 1. Alignment errors (T1,T2) are considered here and the BPM reading is 0014 % modified accordingly. 0015 % 0016 % See also findorbit4 ApplyBPMErr bpm_matrices 0017 0018 % Get 4x4 closed orbit 0019 orbit = findorbit4(RING, dP,indbpm, varargin{:}); 0020 0021 % Get transformation matrices for applying error on BPM readings 0022 [rel,tel,trand] = bpm_matrices(RING(indbpm)); 0023 0024 % Process BPM reading according to alignment errors 0025 bpmreading = bpm_process(orbit([1,3],:),rel,tel,trand); 0026 0027 orbit(1,:)=bpmreading(1,:); 0028 orbit(3,:)=bpmreading(2,:); 0029 0030 0031 return


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UQTk: Uncertainty Quantification Toolkit 3.1.3
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Header for suite of functions to help map one kind of a PC variable to another. More...
Go to the source code of this file.
Functions | |
| double | PCtoPC (double x, const std::string pcIn, double in1, double in2, const std::string pcOut, double out1, double out2) |
| Implements a map y=f(x), where f is a function mapping one PC domain (pcIn with parameters in1,in2) to another (pcOut with parameters out1,out2) More... | |
| void | PCtoPC (Array2D< double > &xx, const std::string pcIn, double in1, double in2, Array2D< double > &yy, const std::string pcOut, double out1, double out2) |
| Implements PCtoPC() map entrywise from array xx to yy. More... | |
| double | rtbis_mod (double func(double, const std::string, double, double, const std::string, double, double), const double x1, const double x2, const double xacc, double x, const std::string pcIn, double in1, double in2, const std::string pcOut, double out1, double out2) |
| Bisection method for root-finding, modified to invert PCtoPC maps. More... | |
| void | linint (Array2D< double > &xydata, const double x, double &y, int col) |
| Auxiliary linear interpolation function. More... | |
| void | linint (Array2D< double > &xydata, const double x, double &y) |
| Auxiliary linear interpolation function. More... | |
Header for suite of functions to help map one kind of a PC variable to another.
Perhaps use more robust tools, like dcdflib.
Need more testing of these tools.
| void linint | ( | Array2D< double > & | xydata, |
| const double | x, | ||
| double & | y | ||
| ) |
Auxiliary linear interpolation function.
| void linint | ( | Array2D< double > & | xydata, |
| const double | x, | ||
| double & | y, | ||
| int | col | ||
| ) |
Auxiliary linear interpolation function.
| void PCtoPC | ( | Array2D< double > & | xx, |
| const std::string | pcIn, | ||
| double | in1, | ||
| double | in2, | ||
| Array2D< double > & | yy, | ||
| const std::string | pcOut, | ||
| double | out1, | ||
| double | out2 | ||
| ) |
Implements PCtoPC() map entrywise from array xx to yy.
| double PCtoPC | ( | double | x, |
| const std::string | pcIn, | ||
| double | in1, | ||
| double | in2, | ||
| const std::string | pcOut, | ||
| double | out1, | ||
| double | out2 | ||
| ) |
Implements a map y=f(x), where f is a function mapping one PC domain (pcIn with parameters in1,in2) to another (pcOut with parameters out1,out2)
| [in] | x | : Input scalar x |
| [in] | pcIn | : PC type for input x (options are LU, HG, LG, JB, SW, TG, RB, pdf) |
| [in] | in1 | : Parameter #1 for input PC (if relevant, i.e. for LG, JB, SW, TG, RB) |
| [in] | in2 | : Parameter #2 for input PC (if relevant, i.e. for JB, SW, RB) |
| [in] | pcOut | : PC type for output y (options are LU, HG, LG, JB, SW, TG, RB, pdf) |
| [in] | out1 | : Parameter #1 for output PC (if relevant, i.e. for LG, JB, SW, TG, RB) |
| [in] | out2 | : Parameter #2 for output PC (if relevant, i.e. for JB, SW, RB) |
| double rtbis_mod | ( | double | funcdouble, const std::string, double, double, const std::string, double, double, |
| const double | x1, | ||
| const double | x2, | ||
| const double | xacc, | ||
| double | x, | ||
| const std::string | pcIn, | ||
| double | in1, | ||
| double | in2, | ||
| const std::string | pcOut, | ||
| double | out1, | ||
| double | out2 | ||
| ) |
Bisection method for root-finding, modified to invert PCtoPC maps.


amazon-chime-sdk
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com.amazonaws.services.chime.sdk.meetings.session
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MeetingSessionStatusCode
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NetworkBecamePoor
NetworkBecamePoor
NetworkBecamePoor
The network has become poor and is no longer good enough for VoIP.