What is Universal Dollar (U8D) | What is Universal Dollar token | What is U8D token

Stablecoins have been hitting headlines recently and for good reason. Stablecoin utility is at an all-time high and their versatility is being captured in ways we’d have never imagined just six months ago. Even  JPMorgan Chase executives are weighing in on stablecoin regulation.

Stablecoins are tokens built on top of blockchains, with values pegged to conventional currencies such as the US dollar. This allows users to exit volatile assets into stable units with minimal volatility, meaning they:

  • Can be used as a convenient medium to settle balances between different exchanges, wallets, and even banks (in the case of USDC).
  • or be used as a way for traders to park the profit or value against other volatile cryptos like BTC or ETH.

It’s understandable that stablecoins have been playing an essential, if not, critical role in the crypto ecosystem today. With Tether and USDC being the most commonly adopted stablecoins.

There are a variety of different stablecoins on the market. Where each stablecoin caters to different users and has different pegging mechanisms. With parameters such as collateralization, centralization, governance, supplementation, composability, and team background, etc. For example, USDC is a centralized and collateralized stablecoin, as it’s issued by Circle with every USDC pegged to one USD that Circle has stored in the custodian. DAI, on the other hand, is a decentralized but collateralized stablecoin with its supply based on the ETH and other assets staked in the MakerDAO vaults. It’s worth noting that normally you should always be able to redeem your stablecoins to USD or other assets on at least a 1:1 basis.

Algorithmic stablecoins, however, are a new breed in the market. Putting the “stablecoin” title aside, they are first and foremost tokens with native supply and price stabilization algorithms, with the goal to keep the token price on track towards a certain set price, normally denominated in US dollars. The price of the algorithmic stablecoins is also often highly reflexive at the beginning based on the market demand and supply, hoping it would eventually plateau close to the $1 range. One of the core concepts in algorithms and mechanics here is called “rebase”. Rebase is normally triggered at a predefined condition or interval so as to correct the imbalances between supply and demand, by issuing more tokens when the price goes higher. On the other hand, various mechanics would be introduced to reduce the supply when the price drops.

The origin of this genre goes back as far as in 2013 with a project called Bitshares. The DeFi boom in 2020 also pushed several new implementations of algorithmic stablecoins such as AMPL, BASED, YAM, and later ESD, DSD (a fork of ESD), Basis Cash (a fork of Basis), Mithril Cash (a fork of Basis Cash) to the market’s attention. ESD, in particular, gained a lot of traction, peaking at over $560 million USD in market cap leading this category.

The Value Proposition

With more building blocks merging in the DeFi economy, quality collateral is a fundamental requirement.

When a highly volatile asset like ETH is used as collateral, the system would likely require over-collateralization or even increase liquidation risks for margin calls for borrowers or traders. While a normal stablecoin reduces the risks above, it doesn’t generate additional yield from the assets locked up.

Here is where the algorithmic stablecoins may come into play. In an ideal world, algorithmic stablecoins could provide fluid collateralization so as to generate rewards while minimizing volatility.

ESD vs DSD vs BAC vs MIC

There are several algorithmic stablecoin projects on the market. They often need to go through several rebase or expansion (price or market cap going up) / contraction (price or market cap going down) cycles so as to track the price towards a stable zone, and hopefully a stable price eventually.

Image for post

It’s also worth noting that unbonding ESD from the DAO requires a “staging” period, in which ESD tokens are temporarily “staged” for 15 epochs (5 days), neither tradable by their owner nor accruing inflationary rewards. ESD’s staging model thus functions similarly to Basis Cash Shares, as both bonding ESD to the DAO and purchasing Basis Cash Shares presuppose risk (liquidity risk for ESD; price risk for BAS) with the potential for future inflationary rewards.

Introducing Universal Dollar (U8D)

Existing algorithmic stablecoin projects have gained incredible traction and see high trading volumes and liquidity. They’ve been regarded as exciting instruments for speculation, arbitrage, and trading. But at the end of the day, they do not fulfil the purpose of being stable units of value. We need an algorithmic stablecoin that can get out of initial bootstrap volatility as fast as possible and provide a smooth expansion and contraction phase towards a stable zone. Based on the ESD success we are creating a new algorithmic superfluid stablecoin, Universal Dollar (U8D), with better capital flow by adding several unique components.

Similar to ESD (and by proxy, DSD), U8D has elastic supply and moves between expansion and contraction periods.

Expansion: When TWAP > 1 USDC, the protocol prints additional U8Ds which distribute between DAO and LP participants to incentivize them to sell and bring the price to the peg.

Contraction: When TWAP < 1 USDC, the protocol issues debt which can be converted to coupons by burning U8Ds with a premium determined by supply/debt ratio. Coupons can be redeemed back to U8D 1:1 when TWAP returns to the peg. By burning U8D for coupons, participants are helping the price return to the peg.

Now let’s see what makes U8D different:

Streaming

In pre-existing projects, there has always been a risk of a sudden dump from DAO participants which are unlocking their tokens from the liquidity pool and DAO bonding, which brings significant price volatility and speculation in the run-up to the tokens actually unlocking from the DAO. Instead of just waiting in anxiety for your tokens to be unlocked from staging(after bonding), U8D gets rid of ESD’s lockups periods and allows users to instantly stream it to the wallet in small portions (a portion per block, similar to how founder shares in utility token projects are vested in tranches). Users can Unbond tokens from the DAO to Stage, and then are able to start a Stream from Stage to their wallets, so tokens will be unlocked and applied for withdrawal by a portion. The Base Stream Time is 3 days for DAO and 1.5 days for LP rewards.

We also get rid of Fluid/Frozen status. Therefore, users can always deposit/provide USDC to earn rewards. On the other hand, Once you send tokens to stage, the only way to withdraw it is streaming.

Fast Streaming

There is also a possibility to have fast streaming which can reduce stream time by a factor of 2, but this also applies a penalty to the network participant (25% penalty in U8D currently in stage).

If the TWAP is above 1$, this penalty will be instantly distributed 70/30 to DAO and LP bonders (which gives bonders more incentives to bond).

If the TWAP is below $1, this penalty will be burned (thus the price will increase).

When you accelerate the streaming of your LP tokens (only possible when there are no more claimable rewards) then the penalty portion of the LP will be removed from the Uniswap pool, USDC will be used to buy tokens from the market and then all tokens are distributed to DAO and LP, or burned, as it was described above.

Overall streaming helps the system to prevent huge price fluctuations and smoothing curves, bringing the system to better stability.

Fair Distribution

U8D will launch without a pre-mine, nor an investor or team allocation. In contrast to other implementations of this idea that had announced their launch after several days of purchasing and bonding tokens into the DAO privately, while advancing epochs without the public, we will take the fair launch a step further:

  1. There are first 10,000 U8D minted and sent to deployer address.
  2. Then deployer adds 10,000 USDC and 10,000 U8D to seed the Uniswap pool.
  3. Then received UNI-V2 LP tokens are sent to the 0x000…0 address.

Parameters

Image for post

Important Time

Launch: Jan 23th 12 AM UTC+0

Contracts

  • 0x2137fFbbB279218E1a61d4483DeD9C9a017e9257 DAO (U8DS)
  • 0x888888877A18532b78d259577d00057054C50Dd8 U8D
  • 0x8cb7c5422672F5432363C628358A5e7eA6938DC2 Oracle
  • 0x78c54b20CC4C2db6E7A9758aE16579D866BA6FFD UniswapV2 USDC:U8D Pair
  • 0x111eB123d0CEeEa59A3736ae1767F9E756bE8160 LP Incentivization Pool

Looking for more information…

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Would you like to earn U8D right now! ☞ CLICK HERE

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Thank for visiting and reading this article! I’m highly appreciate your actions! Please share if you liked it!

#blockchain #bitcoin #crypto #universal dollar #u8d

What is GEEK

Buddha Community

What is Universal Dollar (U8D) | What is Universal Dollar token | What is U8D token

What is Universal Dollar (U8D) | What is Universal Dollar token | What is U8D token

Stablecoins have been hitting headlines recently and for good reason. Stablecoin utility is at an all-time high and their versatility is being captured in ways we’d have never imagined just six months ago. Even  JPMorgan Chase executives are weighing in on stablecoin regulation.

Stablecoins are tokens built on top of blockchains, with values pegged to conventional currencies such as the US dollar. This allows users to exit volatile assets into stable units with minimal volatility, meaning they:

  • Can be used as a convenient medium to settle balances between different exchanges, wallets, and even banks (in the case of USDC).
  • or be used as a way for traders to park the profit or value against other volatile cryptos like BTC or ETH.

It’s understandable that stablecoins have been playing an essential, if not, critical role in the crypto ecosystem today. With Tether and USDC being the most commonly adopted stablecoins.

There are a variety of different stablecoins on the market. Where each stablecoin caters to different users and has different pegging mechanisms. With parameters such as collateralization, centralization, governance, supplementation, composability, and team background, etc. For example, USDC is a centralized and collateralized stablecoin, as it’s issued by Circle with every USDC pegged to one USD that Circle has stored in the custodian. DAI, on the other hand, is a decentralized but collateralized stablecoin with its supply based on the ETH and other assets staked in the MakerDAO vaults. It’s worth noting that normally you should always be able to redeem your stablecoins to USD or other assets on at least a 1:1 basis.

Algorithmic stablecoins, however, are a new breed in the market. Putting the “stablecoin” title aside, they are first and foremost tokens with native supply and price stabilization algorithms, with the goal to keep the token price on track towards a certain set price, normally denominated in US dollars. The price of the algorithmic stablecoins is also often highly reflexive at the beginning based on the market demand and supply, hoping it would eventually plateau close to the $1 range. One of the core concepts in algorithms and mechanics here is called “rebase”. Rebase is normally triggered at a predefined condition or interval so as to correct the imbalances between supply and demand, by issuing more tokens when the price goes higher. On the other hand, various mechanics would be introduced to reduce the supply when the price drops.

The origin of this genre goes back as far as in 2013 with a project called Bitshares. The DeFi boom in 2020 also pushed several new implementations of algorithmic stablecoins such as AMPL, BASED, YAM, and later ESD, DSD (a fork of ESD), Basis Cash (a fork of Basis), Mithril Cash (a fork of Basis Cash) to the market’s attention. ESD, in particular, gained a lot of traction, peaking at over $560 million USD in market cap leading this category.

The Value Proposition

With more building blocks merging in the DeFi economy, quality collateral is a fundamental requirement.

When a highly volatile asset like ETH is used as collateral, the system would likely require over-collateralization or even increase liquidation risks for margin calls for borrowers or traders. While a normal stablecoin reduces the risks above, it doesn’t generate additional yield from the assets locked up.

Here is where the algorithmic stablecoins may come into play. In an ideal world, algorithmic stablecoins could provide fluid collateralization so as to generate rewards while minimizing volatility.

ESD vs DSD vs BAC vs MIC

There are several algorithmic stablecoin projects on the market. They often need to go through several rebase or expansion (price or market cap going up) / contraction (price or market cap going down) cycles so as to track the price towards a stable zone, and hopefully a stable price eventually.

Image for post

It’s also worth noting that unbonding ESD from the DAO requires a “staging” period, in which ESD tokens are temporarily “staged” for 15 epochs (5 days), neither tradable by their owner nor accruing inflationary rewards. ESD’s staging model thus functions similarly to Basis Cash Shares, as both bonding ESD to the DAO and purchasing Basis Cash Shares presuppose risk (liquidity risk for ESD; price risk for BAS) with the potential for future inflationary rewards.

Introducing Universal Dollar (U8D)

Existing algorithmic stablecoin projects have gained incredible traction and see high trading volumes and liquidity. They’ve been regarded as exciting instruments for speculation, arbitrage, and trading. But at the end of the day, they do not fulfil the purpose of being stable units of value. We need an algorithmic stablecoin that can get out of initial bootstrap volatility as fast as possible and provide a smooth expansion and contraction phase towards a stable zone. Based on the ESD success we are creating a new algorithmic superfluid stablecoin, Universal Dollar (U8D), with better capital flow by adding several unique components.

Similar to ESD (and by proxy, DSD), U8D has elastic supply and moves between expansion and contraction periods.

Expansion: When TWAP > 1 USDC, the protocol prints additional U8Ds which distribute between DAO and LP participants to incentivize them to sell and bring the price to the peg.

Contraction: When TWAP < 1 USDC, the protocol issues debt which can be converted to coupons by burning U8Ds with a premium determined by supply/debt ratio. Coupons can be redeemed back to U8D 1:1 when TWAP returns to the peg. By burning U8D for coupons, participants are helping the price return to the peg.

Now let’s see what makes U8D different:

Streaming

In pre-existing projects, there has always been a risk of a sudden dump from DAO participants which are unlocking their tokens from the liquidity pool and DAO bonding, which brings significant price volatility and speculation in the run-up to the tokens actually unlocking from the DAO. Instead of just waiting in anxiety for your tokens to be unlocked from staging(after bonding), U8D gets rid of ESD’s lockups periods and allows users to instantly stream it to the wallet in small portions (a portion per block, similar to how founder shares in utility token projects are vested in tranches). Users can Unbond tokens from the DAO to Stage, and then are able to start a Stream from Stage to their wallets, so tokens will be unlocked and applied for withdrawal by a portion. The Base Stream Time is 3 days for DAO and 1.5 days for LP rewards.

We also get rid of Fluid/Frozen status. Therefore, users can always deposit/provide USDC to earn rewards. On the other hand, Once you send tokens to stage, the only way to withdraw it is streaming.

Fast Streaming

There is also a possibility to have fast streaming which can reduce stream time by a factor of 2, but this also applies a penalty to the network participant (25% penalty in U8D currently in stage).

If the TWAP is above 1$, this penalty will be instantly distributed 70/30 to DAO and LP bonders (which gives bonders more incentives to bond).

If the TWAP is below $1, this penalty will be burned (thus the price will increase).

When you accelerate the streaming of your LP tokens (only possible when there are no more claimable rewards) then the penalty portion of the LP will be removed from the Uniswap pool, USDC will be used to buy tokens from the market and then all tokens are distributed to DAO and LP, or burned, as it was described above.

Overall streaming helps the system to prevent huge price fluctuations and smoothing curves, bringing the system to better stability.

Fair Distribution

U8D will launch without a pre-mine, nor an investor or team allocation. In contrast to other implementations of this idea that had announced their launch after several days of purchasing and bonding tokens into the DAO privately, while advancing epochs without the public, we will take the fair launch a step further:

  1. There are first 10,000 U8D minted and sent to deployer address.
  2. Then deployer adds 10,000 USDC and 10,000 U8D to seed the Uniswap pool.
  3. Then received UNI-V2 LP tokens are sent to the 0x000…0 address.

Parameters

Image for post

Important Time

Launch: Jan 23th 12 AM UTC+0

Contracts

  • 0x2137fFbbB279218E1a61d4483DeD9C9a017e9257 DAO (U8DS)
  • 0x888888877A18532b78d259577d00057054C50Dd8 U8D
  • 0x8cb7c5422672F5432363C628358A5e7eA6938DC2 Oracle
  • 0x78c54b20CC4C2db6E7A9758aE16579D866BA6FFD UniswapV2 USDC:U8D Pair
  • 0x111eB123d0CEeEa59A3736ae1767F9E756bE8160 LP Incentivization Pool

Looking for more information…

WebsiteExplorerExplorer 2Source CodeSocial ChannelMessage BoardMessage Board 2Coinmarketcap

Would you like to earn U8D right now! ☞ CLICK HERE

Top exchanges for token-coin trading. Follow instructions and make unlimited money

BinanceBittrexPoloniexBitfinexHuobi

Thank for visiting and reading this article! I’m highly appreciate your actions! Please share if you liked it!

#blockchain #bitcoin #crypto #universal dollar #u8d

Royce  Reinger

Royce Reinger

1658068560

WordsCounted: A Ruby Natural Language Processor

WordsCounted

We are all in the gutter, but some of us are looking at the stars.

-- Oscar Wilde

WordsCounted is a Ruby NLP (natural language processor). WordsCounted lets you implement powerful tokensation strategies with a very flexible tokeniser class.

Features

  • Out of the box, get the following data from any string or readable file, or URL:
    • Token count and unique token count
    • Token densities, frequencies, and lengths
    • Char count and average chars per token
    • The longest tokens and their lengths
    • The most frequent tokens and their frequencies.
  • A flexible way to exclude tokens from the tokeniser. You can pass a string, regexp, symbol, lambda, or an array of any combination of those types for powerful tokenisation strategies.
  • Pass your own regexp rules to the tokeniser if you prefer. The default regexp filters special characters but keeps hyphens and apostrophes. It also plays nicely with diacritics (UTF and unicode characters): Bayrūt is treated as ["Bayrūt"] and not ["Bayr", "ū", "t"], for example.
  • Opens and reads files. Pass in a file path or a url instead of a string.

Installation

Add this line to your application's Gemfile:

gem 'words_counted'

And then execute:

$ bundle

Or install it yourself as:

$ gem install words_counted

Usage

Pass in a string or a file path, and an optional filter and/or regexp.

counter = WordsCounted.count(
  "We are all in the gutter, but some of us are looking at the stars."
)

# Using a file
counter = WordsCounted.from_file("path/or/url/to/my/file.txt")

.count and .from_file are convenience methods that take an input, tokenise it, and return an instance of WordsCounted::Counter initialized with the tokens. The WordsCounted::Tokeniser and WordsCounted::Counter classes can be used alone, however.

API

WordsCounted

WordsCounted.count(input, options = {})

Tokenises input and initializes a WordsCounted::Counter object with the resulting tokens.

counter = WordsCounted.count("Hello Beirut!")

Accepts two options: exclude and regexp. See Excluding tokens from the analyser and Passing in a custom regexp respectively.

WordsCounted.from_file(path, options = {})

Reads and tokenises a file, and initializes a WordsCounted::Counter object with the resulting tokens.

counter = WordsCounted.from_file("hello_beirut.txt")

Accepts the same options as .count.

Tokeniser

The tokeniser allows you to tokenise text in a variety of ways. You can pass in your own rules for tokenisation, and apply a powerful filter with any combination of rules as long as they can boil down into a lambda.

Out of the box the tokeniser includes only alpha chars. Hyphenated tokens and tokens with apostrophes are considered a single token.

#tokenise([pattern: TOKEN_REGEXP, exclude: nil])

tokeniser = WordsCounted::Tokeniser.new("Hello Beirut!").tokenise

# With `exclude`
tokeniser = WordsCounted::Tokeniser.new("Hello Beirut!").tokenise(exclude: "hello")

# With `pattern`
tokeniser = WordsCounted::Tokeniser.new("I <3 Beirut!").tokenise(pattern: /[a-z]/i)

See Excluding tokens from the analyser and Passing in a custom regexp for more information.

Counter

The WordsCounted::Counter class allows you to collect various statistics from an array of tokens.

#token_count

Returns the token count of a given string.

counter.token_count #=> 15

#token_frequency

Returns a sorted (unstable) two-dimensional array where each element is a token and its frequency. The array is sorted by frequency in descending order.

counter.token_frequency

[
  ["the", 2],
  ["are", 2],
  ["we",  1],
  # ...
  ["all", 1]
]

#most_frequent_tokens

Returns a hash where each key-value pair is a token and its frequency.

counter.most_frequent_tokens

{ "are" => 2, "the" => 2 }

#token_lengths

Returns a sorted (unstable) two-dimentional array where each element contains a token and its length. The array is sorted by length in descending order.

counter.token_lengths

[
  ["looking", 7],
  ["gutter",  6],
  ["stars",   5],
  # ...
  ["in",      2]
]

#longest_tokens

Returns a hash where each key-value pair is a token and its length.

counter.longest_tokens

{ "looking" => 7 }

#token_density([ precision: 2 ])

Returns a sorted (unstable) two-dimentional array where each element contains a token and its density as a float, rounded to a precision of two. The array is sorted by density in descending order. It accepts a precision argument, which must be a float.

counter.token_density

[
  ["are",     0.13],
  ["the",     0.13],
  ["but",     0.07 ],
  # ...
  ["we",      0.07 ]
]

#char_count

Returns the char count of tokens.

counter.char_count #=> 76

#average_chars_per_token([ precision: 2 ])

Returns the average char count per token rounded to two decimal places. Accepts a precision argument which defaults to two. Precision must be a float.

counter.average_chars_per_token #=> 4

#uniq_token_count

Returns the number of unique tokens.

counter.uniq_token_count #=> 13

Excluding tokens from the tokeniser

You can exclude anything you want from the input by passing the exclude option. The exclude option accepts a variety of filters and is extremely flexible.

  1. A space-delimited string. The filter will normalise the string.
  2. A regular expression.
  3. A lambda.
  4. A symbol that names a predicate method. For example :odd?.
  5. An array of any combination of the above.
tokeniser =
  WordsCounted::Tokeniser.new(
    "Magnificent! That was magnificent, Trevor."
  )

# Using a string
tokeniser.tokenise(exclude: "was magnificent")
# => ["that", "trevor"]

# Using a regular expression
tokeniser.tokenise(exclude: /trevor/)
# => ["magnificent", "that", "was", "magnificent"]

# Using a lambda
tokeniser.tokenise(exclude: ->(t) { t.length < 4 })
# => ["magnificent", "that", "magnificent", "trevor"]

# Using symbol
tokeniser = WordsCounted::Tokeniser.new("Hello! محمد")
tokeniser.tokenise(exclude: :ascii_only?)
# => ["محمد"]

# Using an array
tokeniser = WordsCounted::Tokeniser.new(
  "Hello! اسماءنا هي محمد، كارولينا، سامي، وداني"
)
tokeniser.tokenise(
  exclude: [:ascii_only?, /محمد/, ->(t) { t.length > 6}, "و"]
)
# => ["هي", "سامي", "وداني"]

Passing in a custom regexp

The default regexp accounts for letters, hyphenated tokens, and apostrophes. This means twenty-one is treated as one token. So is Mohamad's.

/[\p{Alpha}\-']+/

You can pass your own criteria as a Ruby regular expression to split your string as desired.

For example, if you wanted to include numbers, you can override the regular expression:

counter = WordsCounted.count("Numbers 1, 2, and 3", pattern: /[\p{Alnum}\-']+/)
counter.tokens
#=> ["numbers", "1", "2", "and", "3"]

Opening and reading files

Use the from_file method to open files. from_file accepts the same options as .count. The file path can be a URL.

counter = WordsCounted.from_file("url/or/path/to/file.text")

Gotchas

A hyphen used in leu of an em or en dash will form part of the token. This affects the tokeniser algorithm.

counter = WordsCounted.count("How do you do?-you are well, I see.")
counter.token_frequency

[
  ["do",   2],
  ["how",  1],
  ["you",  1],
  ["-you", 1], # WTF, mate!
  ["are",  1],
  # ...
]

In this example -you and you are separate tokens. Also, the tokeniser does not include numbers by default. Remember that you can pass your own regular expression if the default behaviour does not fit your needs.

A note on case sensitivity

The program will normalise (downcase) all incoming strings for consistency and filters.

Roadmap

Ability to open URLs

def self.from_url
  # open url and send string here after removing html
end

Are you using WordsCounted to do something interesting? Please tell me about it.

Gem Version 

RubyDoc documentation.

Demo

Visit this website for one example of what you can do with WordsCounted.


Contributors

See contributors.

Contributing

  1. Fork it
  2. Create your feature branch (git checkout -b my-new-feature)
  3. Commit your changes (git commit -am 'Add some feature')
  4. Push to the branch (git push origin my-new-feature)
  5. Create new Pull Request

Author: Abitdodgy
Source Code: https://github.com/abitdodgy/words_counted 
License: MIT license

#ruby #nlp 

Words Counted: A Ruby Natural Language Processor.

WordsCounted

We are all in the gutter, but some of us are looking at the stars.

-- Oscar Wilde

WordsCounted is a Ruby NLP (natural language processor). WordsCounted lets you implement powerful tokensation strategies with a very flexible tokeniser class.

Are you using WordsCounted to do something interesting? Please tell me about it.

 

Demo

Visit this website for one example of what you can do with WordsCounted.

Features

  • Out of the box, get the following data from any string or readable file, or URL:
    • Token count and unique token count
    • Token densities, frequencies, and lengths
    • Char count and average chars per token
    • The longest tokens and their lengths
    • The most frequent tokens and their frequencies.
  • A flexible way to exclude tokens from the tokeniser. You can pass a string, regexp, symbol, lambda, or an array of any combination of those types for powerful tokenisation strategies.
  • Pass your own regexp rules to the tokeniser if you prefer. The default regexp filters special characters but keeps hyphens and apostrophes. It also plays nicely with diacritics (UTF and unicode characters): Bayrūt is treated as ["Bayrūt"] and not ["Bayr", "ū", "t"], for example.
  • Opens and reads files. Pass in a file path or a url instead of a string.

Installation

Add this line to your application's Gemfile:

gem 'words_counted'

And then execute:

$ bundle

Or install it yourself as:

$ gem install words_counted

Usage

Pass in a string or a file path, and an optional filter and/or regexp.

counter = WordsCounted.count(
  "We are all in the gutter, but some of us are looking at the stars."
)

# Using a file
counter = WordsCounted.from_file("path/or/url/to/my/file.txt")

.count and .from_file are convenience methods that take an input, tokenise it, and return an instance of WordsCounted::Counter initialized with the tokens. The WordsCounted::Tokeniser and WordsCounted::Counter classes can be used alone, however.

API

WordsCounted

WordsCounted.count(input, options = {})

Tokenises input and initializes a WordsCounted::Counter object with the resulting tokens.

counter = WordsCounted.count("Hello Beirut!")

Accepts two options: exclude and regexp. See Excluding tokens from the analyser and Passing in a custom regexp respectively.

WordsCounted.from_file(path, options = {})

Reads and tokenises a file, and initializes a WordsCounted::Counter object with the resulting tokens.

counter = WordsCounted.from_file("hello_beirut.txt")

Accepts the same options as .count.

Tokeniser

The tokeniser allows you to tokenise text in a variety of ways. You can pass in your own rules for tokenisation, and apply a powerful filter with any combination of rules as long as they can boil down into a lambda.

Out of the box the tokeniser includes only alpha chars. Hyphenated tokens and tokens with apostrophes are considered a single token.

#tokenise([pattern: TOKEN_REGEXP, exclude: nil])

tokeniser = WordsCounted::Tokeniser.new("Hello Beirut!").tokenise

# With `exclude`
tokeniser = WordsCounted::Tokeniser.new("Hello Beirut!").tokenise(exclude: "hello")

# With `pattern`
tokeniser = WordsCounted::Tokeniser.new("I <3 Beirut!").tokenise(pattern: /[a-z]/i)

See Excluding tokens from the analyser and Passing in a custom regexp for more information.

Counter

The WordsCounted::Counter class allows you to collect various statistics from an array of tokens.

#token_count

Returns the token count of a given string.

counter.token_count #=> 15

#token_frequency

Returns a sorted (unstable) two-dimensional array where each element is a token and its frequency. The array is sorted by frequency in descending order.

counter.token_frequency

[
  ["the", 2],
  ["are", 2],
  ["we",  1],
  # ...
  ["all", 1]
]

#most_frequent_tokens

Returns a hash where each key-value pair is a token and its frequency.

counter.most_frequent_tokens

{ "are" => 2, "the" => 2 }

#token_lengths

Returns a sorted (unstable) two-dimentional array where each element contains a token and its length. The array is sorted by length in descending order.

counter.token_lengths

[
  ["looking", 7],
  ["gutter",  6],
  ["stars",   5],
  # ...
  ["in",      2]
]

#longest_tokens

Returns a hash where each key-value pair is a token and its length.

counter.longest_tokens

{ "looking" => 7 }

#token_density([ precision: 2 ])

Returns a sorted (unstable) two-dimentional array where each element contains a token and its density as a float, rounded to a precision of two. The array is sorted by density in descending order. It accepts a precision argument, which must be a float.

counter.token_density

[
  ["are",     0.13],
  ["the",     0.13],
  ["but",     0.07 ],
  # ...
  ["we",      0.07 ]
]

#char_count

Returns the char count of tokens.

counter.char_count #=> 76

#average_chars_per_token([ precision: 2 ])

Returns the average char count per token rounded to two decimal places. Accepts a precision argument which defaults to two. Precision must be a float.

counter.average_chars_per_token #=> 4

#uniq_token_count

Returns the number of unique tokens.

counter.uniq_token_count #=> 13

Excluding tokens from the tokeniser

You can exclude anything you want from the input by passing the exclude option. The exclude option accepts a variety of filters and is extremely flexible.

  1. A space-delimited string. The filter will normalise the string.
  2. A regular expression.
  3. A lambda.
  4. A symbol that names a predicate method. For example :odd?.
  5. An array of any combination of the above.
tokeniser =
  WordsCounted::Tokeniser.new(
    "Magnificent! That was magnificent, Trevor."
  )

# Using a string
tokeniser.tokenise(exclude: "was magnificent")
# => ["that", "trevor"]

# Using a regular expression
tokeniser.tokenise(exclude: /trevor/)
# => ["magnificent", "that", "was", "magnificent"]

# Using a lambda
tokeniser.tokenise(exclude: ->(t) { t.length < 4 })
# => ["magnificent", "that", "magnificent", "trevor"]

# Using symbol
tokeniser = WordsCounted::Tokeniser.new("Hello! محمد")
tokeniser.tokenise(exclude: :ascii_only?)
# => ["محمد"]

# Using an array
tokeniser = WordsCounted::Tokeniser.new(
  "Hello! اسماءنا هي محمد، كارولينا، سامي، وداني"
)
tokeniser.tokenise(
  exclude: [:ascii_only?, /محمد/, ->(t) { t.length > 6}, "و"]
)
# => ["هي", "سامي", "وداني"]

Passing in a custom regexp

The default regexp accounts for letters, hyphenated tokens, and apostrophes. This means twenty-one is treated as one token. So is Mohamad's.

/[\p{Alpha}\-']+/

You can pass your own criteria as a Ruby regular expression to split your string as desired.

For example, if you wanted to include numbers, you can override the regular expression:

counter = WordsCounted.count("Numbers 1, 2, and 3", pattern: /[\p{Alnum}\-']+/)
counter.tokens
#=> ["numbers", "1", "2", "and", "3"]

Opening and reading files

Use the from_file method to open files. from_file accepts the same options as .count. The file path can be a URL.

counter = WordsCounted.from_file("url/or/path/to/file.text")

Gotchas

A hyphen used in leu of an em or en dash will form part of the token. This affects the tokeniser algorithm.

counter = WordsCounted.count("How do you do?-you are well, I see.")
counter.token_frequency

[
  ["do",   2],
  ["how",  1],
  ["you",  1],
  ["-you", 1], # WTF, mate!
  ["are",  1],
  # ...
]

In this example -you and you are separate tokens. Also, the tokeniser does not include numbers by default. Remember that you can pass your own regular expression if the default behaviour does not fit your needs.

A note on case sensitivity

The program will normalise (downcase) all incoming strings for consistency and filters.

Roadmap

Ability to open URLs

def self.from_url
  # open url and send string here after removing html
end

Contributors

See contributors.

Contributing

  1. Fork it
  2. Create your feature branch (git checkout -b my-new-feature)
  3. Commit your changes (git commit -am 'Add some feature')
  4. Push to the branch (git push origin my-new-feature)
  5. Create new Pull Request

Author: abitdodgy
Source code: https://github.com/abitdodgy/words_counted
License: MIT license

#ruby  #ruby-on-rails 

aaron silva

aaron silva

1622197808

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