Documentation and Manhattandist: Difference between pages

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PLS_Toolbox documentation is contained in various documents including:
===Purpose===
Calculates Manhattan Distance between Samples (rows) of a Dataset Object (DSO) or a matrix.
* PLS_Toolbox Manual - containing the chemometrics tutorial and how-to use PLS_Toolbox functions and GUIs. This document is only available with copies of PLS_Toolbox or Solo.
:: '''Matlab command:'''  pls_toolboxhelp
* PLS_Toolbox Reference Manual - a static version of our on-line Wiki documentation on all functions (these documents). The live, continuously updated version of these documents is available on-line from our website: [http://wiki.eigenvector.com/ Eigenvector Research Software Documentation]
::'''Matlab command:'''  helppls
* DataSet Object Manual - a technical description of the DataSet Object (see also the PLS_Toolbox Manual). This document is also available on-line from our website: [http://software.eigenvector.com/DataSet/DataObject500.pdf DataSet Object Manual]
:: '''Matlab command:'''  pls_toolboxhelp dso


The PDF documents require the Adobe Acrobat PDF Reader which can be [http://www.adobe.com/products/acrobat/readermain.html downloaded FREE from the Internet].
===Synopsis===
 
(Sub topic of [[Getting_Started_Documentation_and_Help|Getting Started Documentation and Help]])
:distances = manhattandist(x)
:distances = manhattandist(x,basis)
:distances = manhattandist(x,options)
:distances = manhattandist(x,basis,options)
 
===Description===
 
Calculates the Manhattan Distance, sum of the absolute value differences, from each row to every other row in the supplied matrix or, optionally, all rows of (x) to all rows in a second matrix (basis).
 
====Inputs====
 
* '''x''' = A DSO or a matrix.
 
====Optional Inputs====
 
* '''basis''' = A second DSO/matrix to compare the first DSO/matrix against when calculating Manhattan distance.
* '''options''' = Discussed below.
 
====Outputs====
 
* '''distances''' = A m-by-m matrix containing the comprehensive calculated Manhattan distances between samples.
 
====Options====
 
options = A structure array with the following fields:
 
* '''waitbar''': [{'auto'}| 'on' | 'off' ], display waitbar. 'Auto' setting will automatically display a waitbar if computation takes longer than 3 seconds.
* '''diag''': {default: 0} Defines the values to be used when comparing a sample to itself. Technically this distance is zero however in some instances, using an alternate value (e.g.: inf) is useful for flagging these self-calculated distances.
 
===See Also===

Revision as of 13:16, 15 August 2017

Purpose

Calculates Manhattan Distance between Samples (rows) of a Dataset Object (DSO) or a matrix.

Synopsis

distances = manhattandist(x)
distances = manhattandist(x,basis)
distances = manhattandist(x,options)
distances = manhattandist(x,basis,options)

Description

Calculates the Manhattan Distance, sum of the absolute value differences, from each row to every other row in the supplied matrix or, optionally, all rows of (x) to all rows in a second matrix (basis).

Inputs

  • x = A DSO or a matrix.

Optional Inputs

  • basis = A second DSO/matrix to compare the first DSO/matrix against when calculating Manhattan distance.
  • options = Discussed below.

Outputs

  • distances = A m-by-m matrix containing the comprehensive calculated Manhattan distances between samples.

Options

options = A structure array with the following fields:

  • waitbar: [{'auto'}| 'on' | 'off' ], display waitbar. 'Auto' setting will automatically display a waitbar if computation takes longer than 3 seconds.
  • diag: {default: 0} Defines the values to be used when comparing a sample to itself. Technically this distance is zero however in some instances, using an alternate value (e.g.: inf) is useful for flagging these self-calculated distances.

See Also