Lasso

A lasso (/ˈlæsoʊ/ or /læˈsuː/), also referred to as a lariat, riata, or reata (all from Spanish la reata), is a loop of rope designed as a restraint to be thrown around a target and tightened when pulled. It is a well-known tool of the American cowboy. The word is also a verb; to lasso is to throw the loop of rope around something. Although the tool has several proper names, such terms are rarely employed by those who actually use it; nearly all cowboys simply call it a "rope," and the use of such "roping." To most cowboys, the use of other terms — especially "lasso" — identifies the user as a layman.

A lasso is made from stiff rope so that the noose stays open when the lasso is thrown. It also allows the cowboy to easily open up the noose from horseback to release the cattle because the rope is stiff enough to be pushed a little. A high quality lasso is weighted for better handling. The lariat has a small reinforced loop at one end, called a honda or hondo, through which the rope passes to form a loop. The honda can be formed by a honda knot (or another loop knot), an eye splice, a seizing, rawhide, or a metal ring. The other end is sometimes tied simply in a small, tight, overhand knot to prevent fraying. Most modern lariats are made of stiff nylon or polyester rope, usually about 5/16 or 3/8 in (8 or 9.5 mm) diameter and in lengths of 28, 30, or 35 ft (8.5, 9 or 11 m) for arena-style roping and anywhere from 45 to 70 ft (14 to 21 m) for Californio-style roping. The reata is made of braided (or less commonly, twisted) rawhide and is made in lengths from 50 ft to over 100 ft (15 m to over 30 m). Mexican maguey (agave) and cotton ropes are also used in the longer lengths.

Lasso (programming language)

Lasso is an application server and server management interface used to develop internet applications anf is a general-purpose, high-level programming language. Originally a web datasource connection tool, for Filemaker and later included in Apple Computer's FileMaker 4.0 and Claris Homepage as CDML, it has since evolved into a complex language used to develop and serve large-scale internet applications and web pages.

Lasso includes a simple template system allowing code to control generation of HTML and other content types. Lasso is object-oriented and every value is an object. It also supports procedural programming through unbound methods. The language uses traits and multiple dispatch extensively.

Lasso has a dynamic type system, where objects can be loaded and augmented at runtime, automatic memory management, a comprehensive standard library, and three compiling methodologies: dynamic (comparable to PHP-Python), just-in-time compilation (comparable to Java or .NET Framework), and pre-compiled (comparable to C). Lasso also supports Query Expressions, allowing elements within arrays and other types of sequences to be iterated, filtered, and manipulated using a natural language syntax similar to SQL. Lasso includes full Unicode character support in the standard string object, allowing it to serve and support multi-byte characters such as Japanese and Swedish, and supports transparent UTF-8 conversion when writing string data to the network or file system.

Lasso (statistics)

In statistics and statistical and machine learning, lasso (least absolute shrinkage and selection operator) (also Lasso or LASSO) is a regression analysis method that performs both variable selection and regularization in order to enhance the prediction accuracy and interpretability of the statistical model it produces. It was introduced by Robert Tibshirani in 1996 based on Leo Breiman’s Nonnegative Garrote. lasso was originally formulated for least squares models and this simple case reveals a substantial amount about the behavior of the estimator, including its relationship to ridge regression and best subset selection and the connections between lasso coefficient estimates and so-called soft thresholding. It also reveals that (like standard linear regression) the coefficient estimates need not be unique if covariates are collinear.

Though originally defined for least squares, lasso regularization is easily extended to a wide variety of statistical models including generalized linear models, generalized estimating equations, proportional hazards models, and M-estimators, in a straightforward fashion. lasso’s ability to perform subset selection relies on the form of the constraint and has a variety of interpretations including in terms of geometry, Bayesian statistics, and convex analysis.