Mathematics

Package gorgonia is a library that helps facilitate machine learning in Go. Write and evaluate mathematical equations involving multidimensional arrays easily. Do differentiation with them just as easily. Autodiff showcases automatic differentiation Basic example of representing mathematical equations as graphs. In this example, we want to represent the following equation Gorgonia provides an API that is fairly idiomatic - most of the functions in in the API return (T, error). This is useful for many cases, such as an interactive shell for deep learning. However, it must also be acknowledged that this makes composing functions together a bit cumbersome. To that end, Gorgonia provides two alternative methods. First, the `Lift` based functions; Second the `Must` function Linear Regression Example The formula for a straight line is We want to find an `m` and a `c` that fits the equation well. We'll do it in both float32 and float64 to showcase the extensibility of Gorgonia This example showcases the reasons for the more confusing functions. This example showcases dealing with errors. This is part 2 of the raison d'être of the more complicated functions - dealing with errors SymbolicDiff showcases symbolic differentiation

Package levenshtein implements distance and similarity metrics for strings, based on the Levenshtein measure. The Levenshtein `Distance` between two strings is the minimum total cost of edits that would convert the first string into the second. The allowed edit operations are insertions, deletions, and substitutions, all at character (one UTF-8 code point) level. Each operation has a default cost of 1, but each can be assigned its own cost equal to or greater than 0. A `Distance` of 0 means the two strings are identical, and the higher the value the more different the strings. Since in practice we are interested in finding if the two strings are "close enough", it often does not make sense to continue the calculation once the result is mathematically guaranteed to exceed a desired threshold. Providing this value to the `Distance` function allows it to take a shortcut and return a lower bound instead of an exact cost when the threshold is exceeded. The `Similarity` function calculates the distance, then converts it into a normalized metric within the range 0..1, with 1 meaning the strings are identical, and 0 that they have nothing in common. A minimum similarity threshold can be provided to speed up the calculation of the metric for strings that are far too dissimilar for the purpose at hand. All values under this threshold are rounded down to 0. The `Match` function provides a similarity metric, with the same range and meaning as `Similarity`, but with a bonus for string pairs that share a common prefix and have a similarity above a "bonus threshold". It uses the same method as proposed by Winkler for the Jaro distance, and the reasoning behind it is that these string pairs are very likely spelling variations or errors, and they are more closely linked than the edit distance alone would suggest. The underlying `Calculate` function is also exported, to allow the building of other derivative metrics, if needed.

Package pbc provides structures for building pairing-based cryptosystems. It is a wrapper around the Pairing-Based Cryptography (PBC) Library authored by Ben Lynn (https://crypto.stanford.edu/pbc/). This wrapper provides access to all PBC functions. It supports generation of various types of elliptic curves and pairings, element initialization, I/O, and arithmetic. These features can be used to quickly build pairing-based or conventional cryptosystems. The PBC library is designed to be extremely fast. Internally, it uses GMP for arbitrary-precision arithmetic. It also includes a wide variety of optimizations that make pairing-based cryptography highly efficient. To improve performance, PBC does not perform type checking to ensure that operations actually make sense. The Go wrapper provides the ability to add compatibility checks to most operations, or to use unchecked elements to maximize performance. Since this library provides low-level access to pairing primitives, it is very easy to accidentally construct insecure systems. This library is intended to be used by cryptographers or to implement well-analyzed cryptosystems. Cryptographic pairings are defined over three mathematical groups: G1, G2, and GT, where each group is typically of the same order r. Additionally, a bilinear map e maps a pair of elements — one from G1 and another from G2 — to an element in GT. This map e has the following additional property: If G1 == G2, then a pairing is said to be symmetric. Otherwise, it is asymmetric. Pairings can be used to construct a variety of efficient cryptosystems. The PBC library currently supports 5 different types of pairings, each with configurable parameters. These types are designated alphabetically, roughly in chronological order of introduction. Type A, D, E, F, and G pairings are implemented in the library. Each type has different time and space requirements. For more information about the types, see the documentation for the corresponding generator calls, or the PBC manual page at https://crypto.stanford.edu/pbc/manual/ch05s01.html. This package must be compiled using cgo. It also requires the installation of GMP and PBC. During the build process, this package will attempt to include <gmp.h> and <pbc/pbc.h>, and then dynamically link to GMP and PBC. Most systems include a package for GMP. To install GMP in Debian / Ubuntu: For an RPM installation with YUM: For installation with Fink (http://www.finkproject.org/) on Mac OS X: For more information or to compile from source, visit https://gmplib.org/ To install the PBC library, download the appropriate files for your system from https://crypto.stanford.edu/pbc/download.html. PBC has three dependencies: the gcc compiler, flex (http://flex.sourceforge.net/), and bison (https://www.gnu.org/software/bison/). See the respective sites for installation instructions. Most distributions include packages for these libraries. For example, in Debian / Ubuntu: The PBC source can be compiled and installed using the usual GNU Build System: After installing, you may need to rebuild the search path for libraries: It is possible to install the package on Windows through the use of MinGW and MSYS. MSYS is required for installing PBC, while GMP can be installed through a package. Based on your MinGW installation, you may need to add "-I/usr/local/include" to CPPFLAGS and "-L/usr/local/lib" to LDFLAGS when building PBC. Likewise, you may need to add these options to CGO_CPPFLAGS and CGO_LDFLAGS when installing this package. This package is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. For additional details, see the COPYING and COPYING.LESSER files. This example generates a pairing and some random group elements, then applies the pairing operation. This example computes and verifies a Boneh-Lynn-Shacham signature in a simulated conversation between Alice and Bob.

Package iotanalytics provides the API client, operations, and parameter types for AWS IoT Analytics. IoT Analytics allows you to collect large amounts of device data, process messages, and store them. You can then query the data and run sophisticated analytics on it. IoT Analytics enables advanced data exploration through integration with Jupyter Notebooks and data visualization through integration with Amazon QuickSight. Traditional analytics and business intelligence tools are designed to process structured data. IoT data often comes from devices that record noisy processes (such as temperature, motion, or sound). As a result the data from these devices can have significant gaps, corrupted messages, and false readings that must be cleaned up before analysis can occur. Also, IoT data is often only meaningful in the context of other data from external sources. IoT Analytics automates the steps required to analyze data from IoT devices. IoT Analytics filters, transforms, and enriches IoT data before storing it in a time-series data store for analysis. You can set up the service to collect only the data you need from your devices, apply mathematical transforms to process the data, and enrich the data with device-specific metadata such as device type and location before storing it. Then, you can analyze your data by running queries using the built-in SQL query engine, or perform more complex analytics and machine learning inference. IoT Analytics includes pre-built models for common IoT use cases so you can answer questions like which devices are about to fail or which customers are at risk of abandoning their wearable devices.

Package math32 provides basic constants and mathematical functions for float32 types. At its core, it's mostly just a wrapper in form of float32(math.XXX). This applies to the following functions: Everything else is a float32 implementation. Implementation schedule is sporadic an uncertain. But eventually all functions will be replaced

Package math provides helper functions for mathematical operations over all integer Go types. Almost all files in this package are automatically generated. To regenerate this package This package relies on github.com/davecheney/godoc2md.

Package srp Secure Remote Password protocol The principal interface provided by this package is the SRP type. The end aim of the caller is to to have an SRP server and SRP client arrive at the same key. See the documentation for the SRP structure and its methods for the nitty gritty of use. BUG(jpg): This does not use the same padding and hashing scheme as in RFC 5054, and therefore is not interoperable with those clients and servers. Perhaps someday we'll add an RFC 5054 mode that does that, but today is not that day. It would be nice if this package could be used without having some understanding of the SRP protocol, but too much of the language and naming depends on at least some familiarity. Here is a summary. The Secure Remote Password protocol involves a server and a client proving to each other that they know (or can derive) their long term secrets. The client's long term secret is known as "x" and the corresponding server secret, the verifier, is known as "v". The verifier is mathematically related to x and is computed by the client on first enrollment and transmitted to the server. Typically the server will store the verifier and the client will derive x from a user secret such as a password. Because the verifier can used like a password hash with respect to cracking, the derivation of x should be designed to resist password cracking if the verifier is compromised. The client and the server must both use the same Diffie-Hellman group to perform their computations. The server and the client each send an ephemeral public key to each other. (The client sends A; the server sends B.) With their private knowledge of their own ephemeral secrets (a or b) and their private knowledge of x (for the client) and v (for the server) along with public knowledge they are able to prove to each other that they know their respective secrets and can generate a session key, K, which may be used for further encryption during the session. Quoting from http://srp.stanford.edu/design.html (with some modification for KDF and and checks) This package does not address the actual communication between client and server. But through the SRP type it not only performs the calculations needed, it also performs safety and sanity checks on its input, and it hides everything from the caller except what the caller absolutely needs to provide. The key derivation function, KDF() 1. Both client and server: Checking whether methods have returned without error. This is particularly true of SRP.Key() and SetOthersPublic() 2. Client: Using an appropriate key derivation function for deriving x from the user's password (and nudging user toward a good password) 3. Server: Storing the v securely (sent by the client on first enrollment). A captured v can be used to impersonate the server. The verifier, v, can also be used like a password hash in a password cracking attempt 4. Both: Proving to each other that both have the same key. The package includes methods that can assist with that. ExampleServerClientKey is an example.

Package vecf64 provides common functions and methods for slices of float64. In the days of yore, scientists who computed with computers would use arrays to represent vectors, each value representing magnitude and/or direction. Then came the C++ Standard Templates Library, which sought to provide this data type in the standard library. Now, everyone conflates a term "vector" with dynamic arrays. In the C++ book, Bjarne Stroustrup has this to say: Go has a better name for representing dynamically allocated arrays of any type - "slice". However, "slice" is both a noun and verb and many libraries that I use already use "slice"-as-a-verb as a name, so I had to settle for the second best name: "vector". It should be noted that while the names used in this package were definitely mathematically inspired, they bear only little resemblance the actual mathematical operations performed. The names of the operations assume you're working with slices of float64s. Hence `Add` performs elementwise addition between two []float64. Operations between []float64 and float64 are also supported, however they are differently named. Here are the equivalents: You may note that for the []float64 - float64 binary operations, the scalar (float64) is always the first operand. In operations that are not commutative, an additional function is provided, suffixed with "R" (for reverse) This package does not provide range checking. If indices are out of range, the functions will panic. This package should play well with BCE. TODO: provide SIMD vectorization for Incr and []float32-float64 functions. Pull requests accepted

Package ecc provides mathematical operations over elliptic curves.

Package decimal provides a high-performance, arbitrary precision, floating-point decimal library. This package provides floating-point decimal numbers, useful for financial programming or calculations where a larger, more accurate representation of a number is required. In addition to basic arithmetic operations (addition, subtraction, multiplication, and division) this package offers various mathematical functions, including the exponential function, various logarithms, and the ability to compute continued fractions. While lean, this package is full of features. It implements interfaces like “fmt.Formatter” and intuitively utilizes verbs and flags as described in the “fmt” package. (Also included: “fmt.Scanner”, “fmt.Stringer”, “encoding.TextUnmarshaler”, and “encoding.TextMarshaler”.) It allows users to specific explicit contexts for arithmetic operations, but doesn't require it. It provides access to NaN payloads and is more lenient when parsing a decimal from a string than the GDA specification requires. API interfaces have been changed slightly to work more seamlessly with existing Go programs. For example, many “Quantize” implementations require a decimal as both the receiver and argument which isn't very user friendly. Instead, this library accepts a simple “int” which can be derived from an existing decimal if required. It contains two modes of operation designed to make transitioning to various GDA "quirks" (like always rounding lossless operations) easier. There are three primary goals of this library: By adhering to the General Decimal Arithmetic specification, this package has a well-defined structure for its arithmetic operations. Decimal libraries are inherently slow; this library works diligently to minimize memory allocations and utilize efficient algorithms. Performance regularly benchmarks as fast or faster than many other popular decimal libraries. Libraries should be intuitive and work out of the box without having to configure too many settings; however, precise settings should still be available. The following type is supported: The zero value for a Big corresponds with 0, meaning all the following are valid: Method naming is the same as math/big's, meaning: In general, its conventions mirror math/big's. It is suggested to read the math/big package comments to gain an understanding of this package's conventions. Arguments to Binary and Unary methods are allowed to alias, so the following is valid: Unless otherwise specified, the only argument that will be modified is the result (“z”). This means the following is valid and race-free: But this is not:

Package srp Secure Remote Password protocol The principle interface provided by this package is the SRP type. The end aim of the caller is to to have an SRP server and SRP client arrive at the same Key. See the documentation for the SRP structure and its methods for the nitty gritty of use. BUG(jpg): This does not use the same padding and hashing scheme as in RFC 5054, and therefore is not interoperable with those clients and servers. Perhaps someday we'll add an RFC 5054 mode that does that, but today is not that day. It would be nice if this package could be used without having some understanding of the SRP protocol, but too much of the language and naming is depends on at least some familiarity. Here is a summary. The Secure Remote Password protocol involves a server and a client proving to each other that they know (or can derive) their long term secrets. The client long term secret is known as "x" and the corresponding server secret, the verifier, is known as "v". The verifier is mathematically related to x and is computed by the client on first enrollment and transmistted to the server. Typically the server will store the verifier and the client will derive x from a user secret such as a password. Because the verifier can used like a password hash with respect to cracking, the derivation of x should be designed to resist password cracking if the verifier compromised. The client and the server must both use the same Diffie-Hellman group to perform their computations. The server and the client each send an ephemeral public key to each other (The client sends A; the server sends B) With their private knowledge of their own ephemeral secrets (a or b) and their private knowledge of x (for the client) and v (for the server) along with public knowledge they are able to prove to each other that they know their respective secrets and can generate a session key, K, which may be used for further encryption during the session. Quoting from http://srp.stanford.edu/design.html (with some modification for KDF) This package does not address the actual communication between client and server. But through the SRP type it not only performs the calculations needed, it also performs safety and sanity checks on its input, and it hides everything from the caller except what the caller absolutely needs to provide. The key derivation function, KDF() 1. Both client and server: Checking whether methods have returned without error. This is particularly true of SRP.Key() and SetOthersPublic() 2. Client: Using an appropriate key derivation function for deriving x from the user's password (and nudging user toward a good password) 3. Server: Storing the v (send by the client on first enrollment) securely. A captured v can be used to masquerade as the server and be used like a password hash in a password cracking attempt 4. Both: Proving to each other that both have the same key. The package includes methods that can assist with that. ExampleServerClientKey is an example

SubOver is a tool for discovering subdomain takeovers

Package xirho implements an iterated function system fractal art renderer. An iterated function system is a collection of functions from points to points. Starting with a randomly selected point, we choose a function at random, apply that function to the point, and plot its new location, then repeat ad infinitum. With some additional steps, the result images can be stunning. The mathematical terminology used in xirho's documentation and API is as follows. A point is an element of R³ × [0, 1], i.e. a 3D point plus a color coordinate. A function, sometimes function type, is a procedure which maps points to points, possibly using additional fixed parameters to control the exact mapping. (Other IFS implementations typically refer to functions in this sense as variations.) A node is a particular instance of a function and its fixed parameters. An iterated function system, or just system, is a non-empty list of nodes, a Markov chain giving the probability of the algorithm transitioning from each node in the list to each other node in the list, an additional node applied to each output point to serve as a possibly nonlinear camera, and a mapping of color coordinates to colors. The Markov chain of a system may also be called the weights graph, or just the graph. Xirho does not include a designer to produce systems to render. Existing parameters can be loaded through the encoding and encoding/flame subpackages, or programmed by hand. To use xirho to render a system, create a Render containing the System and a Hist to plot points, then call its Render method with a non-trivial context. (The context closing is the only way that Render returns.) Alternatively, the RenderAsync method provides an API to manage rendering concurrently, e.g. to support a UI. For fine-grained control of the rendering process, the System.Iter method can be used directly.

Package pbc provides structures for building pairing-based cryptosystems. It is a wrapper around the Pairing-Based Cryptography (PBC) Library authored by Ben Lynn (https://crypto.stanford.edu/pbc/). This wrapper provides access to all PBC functions. It supports generation of various types of elliptic curves and pairings, element initialization, I/O, and arithmetic. These features can be used to quickly build pairing-based or conventional cryptosystems. The PBC library is designed to be extremely fast. Internally, it uses GMP for arbitrary-precision arithmetic. It also includes a wide variety of optimizations that make pairing-based cryptography highly efficient. To improve performance, PBC does not perform type checking to ensure that operations actually make sense. The Go wrapper provides the ability to add compatibility checks to most operations, or to use unchecked elements to maximize performance. Since this library provides low-level access to pairing primitives, it is very easy to accidentally construct insecure systems. This library is intended to be used by cryptographers or to implement well-analyzed cryptosystems. Cryptographic pairings are defined over three mathematical groups: G1, G2, and GT, where each group is typically of the same order r. Additionally, a bilinear map e maps a pair of elements — one from G1 and another from G2 — to an element in GT. This map e has the following additional property: If G1 == G2, then a pairing is said to be symmetric. Otherwise, it is asymmetric. Pairings can be used to construct a variety of efficient cryptosystems. The PBC library currently supports 5 different types of pairings, each with configurable parameters. These types are designated alphabetically, roughly in chronological order of introduction. Type A, D, E, F, and G pairings are implemented in the library. Each type has different time and space requirements. For more information about the types, see the documentation for the corresponding generator calls, or the PBC manual page at https://crypto.stanford.edu/pbc/manual/ch05s01.html. This package must be compiled using cgo. It also requires the installation of GMP and PBC. During the build process, this package will attempt to include <gmp.h> and <pbc/pbc.h>, and then dynamically link to GMP and PBC. Most systems include a package for GMP. To install GMP in Debian / Ubuntu: For an RPM installation with YUM: For installation with Fink (http://www.finkproject.org/) on Mac OS X: For more information or to compile from source, visit https://gmplib.org/ To install the PBC library, download the appropriate files for your system from https://crypto.stanford.edu/pbc/download.html. PBC has three dependencies: the gcc compiler, flex (http://flex.sourceforge.net/), and bison (https://www.gnu.org/software/bison/). See the respective sites for installation instructions. Most distributions include packages for these libraries. For example, in Debian / Ubuntu: The PBC source can be compiled and installed using the usual GNU Build System: After installing, you may need to rebuild the search path for libraries: It is possible to install the package on Windows through the use of MinGW and MSYS. MSYS is required for installing PBC, while GMP can be installed through a package. Based on your MinGW installation, you may need to add "-I/usr/local/include" to CPPFLAGS and "-L/usr/local/lib" to LDFLAGS when building PBC. Likewise, you may need to add these options to CGO_CPPFLAGS and CGO_LDFLAGS when installing this package. This package is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. For additional details, see the COPYING and COPYING.LESSER files. This example generates a pairing and some random group elements, then applies the pairing operation. This example computes and verifies a Boneh-Lynn-Shacham signature in a simulated conversation between Alice and Bob.

Package pbc provides structures for building pairing-based cryptosystems. It is a wrapper around the Pairing-Based Cryptography (PBC) Library authored by Ben Lynn (https://crypto.stanford.edu/pbc/). This wrapper provides access to all PBC functions. It supports generation of various types of elliptic curves and pairings, element initialization, I/O, and arithmetic. These features can be used to quickly build pairing-based or conventional cryptosystems. The PBC library is designed to be extremely fast. Internally, it uses GMP for arbitrary-precision arithmetic. It also includes a wide variety of optimizations that make pairing-based cryptography highly efficient. To improve performance, PBC does not perform type checking to ensure that operations actually make sense. The Go wrapper provides the ability to add compatibility checks to most operations, or to use unchecked elements to maximize performance. Since this library provides low-level access to pairing primitives, it is very easy to accidentally construct insecure systems. This library is intended to be used by cryptographers or to implement well-analyzed cryptosystems. Cryptographic pairings are defined over three mathematical groups: G1, G2, and GT, where each group is typically of the same order r. Additionally, a bilinear map e maps a pair of elements — one from G1 and another from G2 — to an element in GT. This map e has the following additional property: If G1 == G2, then a pairing is said to be symmetric. Otherwise, it is asymmetric. Pairings can be used to construct a variety of efficient cryptosystems. The PBC library currently supports 5 different types of pairings, each with configurable parameters. These types are designated alphabetically, roughly in chronological order of introduction. Type A, D, E, F, and G pairings are implemented in the library. Each type has different time and space requirements. For more information about the types, see the documentation for the corresponding generator calls, or the PBC manual page at https://crypto.stanford.edu/pbc/manual/ch05s01.html. This package must be compiled using cgo. It also requires the installation of GMP and PBC. During the build process, this package will attempt to include <gmp.h> and <pbc/pbc.h>, and then dynamically link to GMP and PBC. Most systems include a package for GMP. To install GMP in Debian / Ubuntu: For an RPM installation with YUM: For installation with Fink (http://www.finkproject.org/) on Mac OS X: For more information or to compile from source, visit https://gmplib.org/ To install the PBC library, download the appropriate files for your system from https://crypto.stanford.edu/pbc/download.html. PBC has three dependencies: the gcc compiler, flex (http://flex.sourceforge.net/), and bison (https://www.gnu.org/software/bison/). See the respective sites for installation instructions. Most distributions include packages for these libraries. For example, in Debian / Ubuntu: The PBC source can be compiled and installed using the usual GNU Build System: After installing, you may need to rebuild the search path for libraries: It is possible to install the package on Windows through the use of MinGW and MSYS. MSYS is required for installing PBC, while GMP can be installed through a package. Based on your MinGW installation, you may need to add "-I/usr/local/include" to CPPFLAGS and "-L/usr/local/lib" to LDFLAGS when building PBC. Likewise, you may need to add these options to CGO_CPPFLAGS and CGO_LDFLAGS when installing this package. This package is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. For additional details, see the COPYING and COPYING.LESSER files. This example generates a pairing and some random group elements, then applies the pairing operation. This example computes and verifies a Boneh-Lynn-Shacham signature in a simulated conversation between Alice and Bob.

Package vecf32 provides common functions and methods for slices of float32. In the days of yore, scientists who computed with computers would use arrays to represent vectors, each value representing magnitude and/or direction. Then came the C++ Standard Templates Library, which sought to provide this data type in the standard library. Now, everyone conflates a term "vector" with dynamic arrays. In the C++ book, Bjarne Stroustrup has this to say: Go has a better name for representing dynamically allocated arrays of any type - "slice". However, "slice" is both a noun and verb and many libraries that I use already use "slice"-as-a-verb as a name, so I had to settle for the second best name: "vector". It should be noted that while the names used in this package were definitely mathematically inspired, they bear only little resemblance the actual mathematical operations performed. The names of the operations assume you're working with slices of float32s. Hence `Add` performs elementwise addition between two []float32. Operations between []float32 and float32 are also supported, however they are differently named. Here are the equivalents: You may note that for the []float64 - float64 binary operations, the scalar (float64) is always the first operand. In operations that are not commutative, an additional function is provided, suffixed with "R" (for reverse) This package does not provide range checking. If indices are out of range, the functions will panic. This package should play well with BCE. TODO(anyone): provide SIMD vectorization for Incr and []float32-float64 functions Pull requests accepted

Package math provides helper functions for mathematical operations over all integer Go types. Almost all files in this package are automatically generated. To regenerate this package This package relies on github.com/davecheney/godoc2md.

SubOver is a tool for discovering subdomain takeovers

Package math provides basic constants and mathematical functions.

Package calc implements the mathematical formulas for modelling road cycling power outlined by Martin et al's 'Validation of a Mathematical Model for Road Cycling Power' (https://www.ncbi.nlm.nih.gov/pubmed/28121252) and other miscellaenous cycling power related formulas.