FFSVM Rust: Really Fast Support Vector Machine in Rust

In One Sentence

You trained a SVM using libSVM, now you want the highest possible performance during (real-time) classification, like games or VR.

Highlights

• loads almost all libSVM types (C-SVC, ν-SVC, ε-SVR, ν-SVR) and kernels (linear, poly, RBF and sigmoid)
• produces practically same classification results as libSVM
• optimized for SIMD and can be mixed seamlessly with Rayon
• written in 100% Rust
• allocation-free during classification for dense SVMs
• 2.5x - 14x faster than libSVM for dense SVMs
• extremely low classification times for small models (e.g., 128 SV, 16 dense attributes, linear ~ 500ns)
• successfully used in Unity and VR projects (Windows & Android)

Note: Currently requires Rust nightly (March 2019 and later), because we depend on RFC 2366 (portable SIMD). Once that stabilizes we'll also go stable.

Usage

Train with libSVM (e.g., using the tool svm-train), then classify with ffsvm-rust.

From Rust:

// Replace `SAMPLE_MODEL` with a `&str` to your model.
let svm = DenseSVM::try_from(SAMPLE_MODEL)?;

let mut problem = Problem::from(&svm);
let features = problem.features();

features[0] = 0.55838;
features[1] = -0.157895;
features[2] = 0.581292;
features[3] = -0.221184;

svm.predict_value(&mut problem)?;

assert_eq!(problem.solution(), Solution::Label(42));

Status

• June 7, 2019: Gave up on 'no unsafe', but gained runtime SIMD selection.
• March 10, 2019: As soon as we can move away from nightly we'll go beta.
• Aug 5, 2018: Still in alpha, but finally on crates.io.
• May 27, 2018: We're in alpha. Successfully used internally on Windows, Mac, Android and Linux on various machines and devices. Once SIMD stabilizes and we can cross-compile to WASM we'll move to beta.
• December 16, 2017: We're in pre-alpha. It will probably not even work on your machine.

Performance

Classification time vs. libSVM for dense models.

Performance milestones during development.

All performance numbers reported for the DenseSVM. We also have support for SparseSVMs, which are slower for "mostly dense" models, and faster for "mostly sparse" models (and generally on the performance level of libSVM).

See here for details.

Tips

• For an x-fold performance increase, create a number of Problem structures, and process them with Rayon's par_iter.

Download Details:
Author: ralfbiedert
Source Code: https://github.com/ralfbiedert/ffsvm-rust
License: MIT License

#rust  #machine-learning 

What is GEEK

Buddha Community

FFSVM Rust: Really Fast Support Vector Machine in Rust

In One Sentence

You trained a SVM using libSVM, now you want the highest possible performance during (real-time) classification, like games or VR.

Highlights

• loads almost all libSVM types (C-SVC, ν-SVC, ε-SVR, ν-SVR) and kernels (linear, poly, RBF and sigmoid)
• produces practically same classification results as libSVM
• optimized for SIMD and can be mixed seamlessly with Rayon
• written in 100% Rust
• allocation-free during classification for dense SVMs
• 2.5x - 14x faster than libSVM for dense SVMs
• extremely low classification times for small models (e.g., 128 SV, 16 dense attributes, linear ~ 500ns)
• successfully used in Unity and VR projects (Windows & Android)

Note: Currently requires Rust nightly (March 2019 and later), because we depend on RFC 2366 (portable SIMD). Once that stabilizes we'll also go stable.

Usage

Train with libSVM (e.g., using the tool svm-train), then classify with ffsvm-rust.

From Rust:

// Replace `SAMPLE_MODEL` with a `&str` to your model.
let svm = DenseSVM::try_from(SAMPLE_MODEL)?;

let mut problem = Problem::from(&svm);
let features = problem.features();

features[0] = 0.55838;
features[1] = -0.157895;
features[2] = 0.581292;
features[3] = -0.221184;

svm.predict_value(&mut problem)?;

assert_eq!(problem.solution(), Solution::Label(42));

Status

• June 7, 2019: Gave up on 'no unsafe', but gained runtime SIMD selection.
• March 10, 2019: As soon as we can move away from nightly we'll go beta.
• Aug 5, 2018: Still in alpha, but finally on crates.io.
• May 27, 2018: We're in alpha. Successfully used internally on Windows, Mac, Android and Linux on various machines and devices. Once SIMD stabilizes and we can cross-compile to WASM we'll move to beta.
• December 16, 2017: We're in pre-alpha. It will probably not even work on your machine.

Performance

Classification time vs. libSVM for dense models.

Performance milestones during development.

All performance numbers reported for the DenseSVM. We also have support for SparseSVMs, which are slower for "mostly dense" models, and faster for "mostly sparse" models (and generally on the performance level of libSVM).

See here for details.

Tips

• For an x-fold performance increase, create a number of Problem structures, and process them with Rayon's par_iter.

Download Details:
Author: ralfbiedert
Source Code: https://github.com/ralfbiedert/ffsvm-rust
License: MIT License

#rust  #machine-learning 

Serde Rust: Serialization Framework for Rust

Serde

*Serde is a framework for serializing and deserializing Rust data structures efficiently and generically.*

You may be looking for:

• An overview of Serde
• Data formats supported by Serde
• Setting up #[derive(Serialize, Deserialize)]
• Examples
• API documentation
• Release notes

Serde in action

Click to show Cargo.toml. Run this code in the playground.

[dependencies]

# The core APIs, including the Serialize and Deserialize traits. Always
# required when using Serde. The "derive" feature is only required when
# using #[derive(Serialize, Deserialize)] to make Serde work with structs
# and enums defined in your crate.
serde = { version = "1.0", features = ["derive"] }

# Each data format lives in its own crate; the sample code below uses JSON
# but you may be using a different one.
serde_json = "1.0"

 

use serde::{Serialize, Deserialize};

#[derive(Serialize, Deserialize, Debug)]
struct Point {
    x: i32,
    y: i32,
}

fn main() {
    let point = Point { x: 1, y: 2 };

    // Convert the Point to a JSON string.
    let serialized = serde_json::to_string(&point).unwrap();

    // Prints serialized = {"x":1,"y":2}
    println!("serialized = {}", serialized);

    // Convert the JSON string back to a Point.
    let deserialized: Point = serde_json::from_str(&serialized).unwrap();

    // Prints deserialized = Point { x: 1, y: 2 }
    println!("deserialized = {:?}", deserialized);
}

Getting help

Serde is one of the most widely used Rust libraries so any place that Rustaceans congregate will be able to help you out. For chat, consider trying the #rust-questions or #rust-beginners channels of the unofficial community Discord (invite: https://discord.gg/rust-lang-community), the #rust-usage or #beginners channels of the official Rust Project Discord (invite: https://discord.gg/rust-lang), or the #general stream in Zulip. For asynchronous, consider the [rust] tag on StackOverflow, the /r/rust subreddit which has a pinned weekly easy questions post, or the Rust Discourse forum. It's acceptable to file a support issue in this repo but they tend not to get as many eyes as any of the above and may get closed without a response after some time.

Download Details:
Author: serde-rs
Source Code: https://github.com/serde-rs/serde
License: View license

#rust  #rustlang 

Support Vector Machine Algorithm in Machine Learning

Everything You Need to Know about Support Vector Machine Algorithms

Most beginners, when it comes to machine learning, start with regression and classification algorithms naturally. These algorithms are simple and easy to follow. However, it is essential to go beyond these two machine learning algorithms to grasp the concepts of machine learning better.

There is much more to learn in machine learning, which might not be as simple as regression and classification, but can help us solve various complex problems. Let us introduce you to one such algorithm, the Support Vector Machine Algorithm. Support Vector Machine algorithm, or SVM algorithm, is usually referred to as one such machine learning algorithm that can deliver efficiency and accuracy for both regression and classification problems.

If you dream of pursuing a career in the machine learning field, then the Support Vector Machine should be a part of your learning arsenal. At upGrad, we believe in equipping our students with the best machine learning algorithms to get started with their careers. Here’s what we think can help you begin with the SVM algorithm in machine learning.

What is a Support Vector Machine Algorithm?

SVM is a type of supervised learning algorithm that has become very popular in 2020 and will continue to be so in the future. The history of SVM dates back to 1990; it is drawn from Vapnik’s statistical learning theory. SVM can be used for both regression and classification challenges; however, it is mostly used for addressing classification challenges.

SVM is a discriminative classifier that creates hyperplanes in N-dimensional space, where n is the number of features in a dataset to help discriminate future data inputs. Sounds confusing right, don’t worry, we’ll understand it in simple layman terms.

How Does a Support Vector Machine Algorithm Work?

Before delving deep into the working of an SVM, let’s understand some of the key terminologies.

Hyperplane

Hyperplanes, which are also sometimes referred to as decision boundaries or decision planes, are the boundaries that help classify data points. The hyperplane’s side, where a new data point falls, can be segregated or attributed to different classes. The dimension of the hyperplane depends on the number of features that are attributed to a dataset. If the dataset has 2 features, then the hyperplane can be a simple line. When a dataset has 3 features, then the hyperplane is a 2-dimensional plane.

Support Vectors

Support vectors are the data points that are closest to the hyperplane and affect its position. Since these vectors affect the hyperplane positioning, they are termed as support vectors and hence the name Support Vector Machine Algorithm.

Margin

Put simply, the margin is the gap between the hyperplane and the support vectors. SVM always chooses the hyperplane that maximizes the margin. The greater the margin, the higher is the accuracy of the outcomes. There are two types of margins that are used in SVM algorithms, hard and soft.

When the training dataset is linearly separable, SVM can simply select two parallel lines that maximize the marginal distance; this is called a hard margin. When the training dataset is not fully linearly separate, then the SVM allows some margin violation. It allows some data points to stay on the wrong side of the hyperplane or between the margin and hyperplane so that the accuracy is not compromised; this is called a soft margin.

There can be many possible hyperplanes for a given dataset. The goal of VSM is to select the most maximal margin to classify new data points into different classes. When a new data point is added, the SVM determines which side of the hyperplane the data point falls. Based on the side of the hyperplane where the new data point falls, SVM then classifies it into different classes.

#artificial intelligence #machine learning #machine learning algorithm #support vector

Serde JSON: JSON Support for Serde Framework

Serde JSON

Serde is a framework for serializing and deserializing Rust data structures efficiently and generically.

[dependencies]
serde_json = "1.0"

You may be looking for:

• JSON API documentation
• Serde API documentation
• Detailed documentation about Serde
• Setting up #[derive(Serialize, Deserialize)]
• Release notes

JSON is a ubiquitous open-standard format that uses human-readable text to transmit data objects consisting of key-value pairs.

{
    "name": "John Doe",
    "age": 43,
    "address": {
        "street": "10 Downing Street",
        "city": "London"
    },
    "phones": [
        "+44 1234567",
        "+44 2345678"
    ]
}

There are three common ways that you might find yourself needing to work with JSON data in Rust.

• As text data. An unprocessed string of JSON data that you receive on an HTTP endpoint, read from a file, or prepare to send to a remote server.
• As an untyped or loosely typed representation. Maybe you want to check that some JSON data is valid before passing it on, but without knowing the structure of what it contains. Or you want to do very basic manipulations like insert a key in a particular spot.
• As a strongly typed Rust data structure. When you expect all or most of your data to conform to a particular structure and want to get real work done without JSON's loosey-goosey nature tripping you up.

Serde JSON provides efficient, flexible, safe ways of converting data between each of these representations.

Operating on untyped JSON values

Any valid JSON data can be manipulated in the following recursive enum representation. This data structure is serde_json::Value.

enum Value {
    Null,
    Bool(bool),
    Number(Number),
    String(String),
    Array(Vec<Value>),
    Object(Map<String, Value>),
}

A string of JSON data can be parsed into a serde_json::Value by the serde_json::from_str function. There is also from_slice for parsing from a byte slice &[u8] and from_reader for parsing from any io::Read like a File or a TCP stream.

use serde_json::{Result, Value};

fn untyped_example() -> Result<()> {
    // Some JSON input data as a &str. Maybe this comes from the user.
    let data = r#"
        {
            "name": "John Doe",
            "age": 43,
            "phones": [
                "+44 1234567",
                "+44 2345678"
            ]
        }"#;

    // Parse the string of data into serde_json::Value.
    let v: Value = serde_json::from_str(data)?;

    // Access parts of the data by indexing with square brackets.
    println!("Please call {} at the number {}", v["name"], v["phones"][0]);

    Ok(())
}

The result of square bracket indexing like v["name"] is a borrow of the data at that index, so the type is &Value. A JSON map can be indexed with string keys, while a JSON array can be indexed with integer keys. If the type of the data is not right for the type with which it is being indexed, or if a map does not contain the key being indexed, or if the index into a vector is out of bounds, the returned element is Value::Null.

When a Value is printed, it is printed as a JSON string. So in the code above, the output looks like Please call "John Doe" at the number "+44 1234567". The quotation marks appear because v["name"] is a &Value containing a JSON string and its JSON representation is "John Doe". Printing as a plain string without quotation marks involves converting from a JSON string to a Rust string with as_str() or avoiding the use of Value as described in the following section.

The Value representation is sufficient for very basic tasks but can be tedious to work with for anything more significant. Error handling is verbose to implement correctly, for example imagine trying to detect the presence of unrecognized fields in the input data. The compiler is powerless to help you when you make a mistake, for example imagine typoing v["name"] as v["nmae"] in one of the dozens of places it is used in your code.

Parsing JSON as strongly typed data structures

Serde provides a powerful way of mapping JSON data into Rust data structures largely automatically.

use serde::{Deserialize, Serialize};
use serde_json::Result;

#[derive(Serialize, Deserialize)]
struct Person {
    name: String,
    age: u8,
    phones: Vec<String>,
}

fn typed_example() -> Result<()> {
    // Some JSON input data as a &str. Maybe this comes from the user.
    let data = r#"
        {
            "name": "John Doe",
            "age": 43,
            "phones": [
                "+44 1234567",
                "+44 2345678"
            ]
        }"#;

    // Parse the string of data into a Person object. This is exactly the
    // same function as the one that produced serde_json::Value above, but
    // now we are asking it for a Person as output.
    let p: Person = serde_json::from_str(data)?;

    // Do things just like with any other Rust data structure.
    println!("Please call {} at the number {}", p.name, p.phones[0]);

    Ok(())
}

This is the same serde_json::from_str function as before, but this time we assign the return value to a variable of type Person so Serde will automatically interpret the input data as a Person and produce informative error messages if the layout does not conform to what a Person is expected to look like.

Any type that implements Serde's Deserialize trait can be deserialized this way. This includes built-in Rust standard library types like Vec<T> and HashMap<K, V>, as well as any structs or enums annotated with #[derive(Deserialize)].

Once we have p of type Person, our IDE and the Rust compiler can help us use it correctly like they do for any other Rust code. The IDE can autocomplete field names to prevent typos, which was impossible in the serde_json::Value representation. And the Rust compiler can check that when we write p.phones[0], then p.phones is guaranteed to be a Vec<String> so indexing into it makes sense and produces a String.

The necessary setup for using Serde's derive macros is explained on the Using derive page of the Serde site.

Constructing JSON values

Serde JSON provides a json! macro to build serde_json::Value objects with very natural JSON syntax.

use serde_json::json;

fn main() {
    // The type of `john` is `serde_json::Value`
    let john = json!({
        "name": "John Doe",
        "age": 43,
        "phones": [
            "+44 1234567",
            "+44 2345678"
        ]
    });

    println!("first phone number: {}", john["phones"][0]);

    // Convert to a string of JSON and print it out
    println!("{}", john.to_string());
}

The Value::to_string() function converts a serde_json::Value into a String of JSON text.

One neat thing about the json! macro is that variables and expressions can be interpolated directly into the JSON value as you are building it. Serde will check at compile time that the value you are interpolating is able to be represented as JSON.

let full_name = "John Doe";
let age_last_year = 42;

// The type of `john` is `serde_json::Value`
let john = json!({
    "name": full_name,
    "age": age_last_year + 1,
    "phones": [
        format!("+44 {}", random_phone())
    ]
});

This is amazingly convenient, but we have the problem we had before with Value: the IDE and Rust compiler cannot help us if we get it wrong. Serde JSON provides a better way of serializing strongly-typed data structures into JSON text.

Creating JSON by serializing data structures

A data structure can be converted to a JSON string by serde_json::to_string. There is also serde_json::to_vec which serializes to a Vec<u8> and serde_json::to_writer which serializes to any io::Write such as a File or a TCP stream.

use serde::{Deserialize, Serialize};
use serde_json::Result;

#[derive(Serialize, Deserialize)]
struct Address {
    street: String,
    city: String,
}

fn print_an_address() -> Result<()> {
    // Some data structure.
    let address = Address {
        street: "10 Downing Street".to_owned(),
        city: "London".to_owned(),
    };

    // Serialize it to a JSON string.
    let j = serde_json::to_string(&address)?;

    // Print, write to a file, or send to an HTTP server.
    println!("{}", j);

    Ok(())
}

Any type that implements Serde's Serialize trait can be serialized this way. This includes built-in Rust standard library types like Vec<T> and HashMap<K, V>, as well as any structs or enums annotated with #[derive(Serialize)].

Performance

It is fast. You should expect in the ballpark of 500 to 1000 megabytes per second deserialization and 600 to 900 megabytes per second serialization, depending on the characteristics of your data. This is competitive with the fastest C and C++ JSON libraries or even 30% faster for many use cases. Benchmarks live in the serde-rs/json-benchmark repo.

Getting help

Serde is one of the most widely used Rust libraries, so any place that Rustaceans congregate will be able to help you out. For chat, consider trying the #rust-questions or #rust-beginners channels of the unofficial community Discord (invite: https://discord.gg/rust-lang-community), the #rust-usage or #beginners channels of the official Rust Project Discord (invite: https://discord.gg/rust-lang), or the #general stream in Zulip. For asynchronous, consider the [rust] tag on StackOverflow, the /r/rust subreddit which has a pinned weekly easy questions post, or the Rust Discourse forum. It's acceptable to file a support issue in this repo, but they tend not to get as many eyes as any of the above and may get closed without a response after some time.

No-std support

As long as there is a memory allocator, it is possible to use serde_json without the rest of the Rust standard library. This is supported on Rust 1.36+. Disable the default "std" feature and enable the "alloc" feature:

[dependencies]
serde_json = { version = "1.0", default-features = false, features = ["alloc"] }

For JSON support in Serde without a memory allocator, please see the serde-json-core crate.

Link: https://crates.io/crates/serde_json

#rust  #rustlang  #encode   #json 

Support Vector Machines — Thinking like vectors!

Support vector machines work well in high dimensional space with clear margin or separation thus thinking like vectors.

Support Vector Machine(SVM) is a supervised non-linear machine learning algorithm which can be used for both classification and regression problems. SVM is used to generate multiple separating hyperplanes such that it divides segments of data space and each segment contains only one kind of data.

SVM technique is useful for data whose distribution is unknown i.e which has Non-regularity i.e data in spam classification, handwriting recognition, text categorization, speaker identification etc. I listed applications of support vector machine with it.:)

This post is about explaining support vector machines with an example, demonstration of support vector machine on a dataset and explanation of generated outputs of demonstration.

What lies behind SVM with example?

Picture exclusively created

In Support Vector Machines, we plot each data as a point in n-dimensional space(where “n” is the number of features) with the value of each feature being a value of a particular coordinate. Then, we perform classification by finding hyperplane that differentiates the classes.

Example

Consider a dataset containing Apples and Oranges. So, to classify them, we use Support Vector machine ad labelled training data on plane.

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A support vector machine(SVM) takes these data points and outputs the hyperplane (which is a two-dimension line of equation y = ax + b) that best separates the tags. The line is called the **decision boundary **i.e anything that falls to one side of it is classified as Apple and anything that falls to the other as Orange.

The hyperplane(Two-dimensional line) is best when it’s the distance to the nearest element of each data point or tag is the largest i.e specified on maximum margins.

All points on the line ax+b=0 will satisfy the equation so, we draw two parallel lines ax+b=-1 for one side and ax+b=1 for the other side such that these lines pass through a datapoint or tag in the segment which is nearest to our line, then the distance between these two lines will be our margin.

#algorithms #data-science #r #support-vector-machine #machine-learning #algorithms