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What is the Vertical attribute for? I am wondering before I put 100$ of VC in this stat. (I won't pay a dime btw.

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PMML 4.3 - Transformation Dictionary and Derived Fields

DivX for Mac is a digital video software suite with all the tools you need to experience DivX videos on the Mac. DivX for Mac 6.5 includes the DivX Codec, DivX Player and the DivX Web Player. Note: This attribute has been deprecated. Id: The id attribute assigns a unique name to a tag. This allows style sheets or scripts to reference the tag. See id Example: class: The class attribute assigns a class name to a tag. The class name does not need to be unique. Attribute values are shown in Character status window (Alt+T). Attribute stones can be obtained while hunting in different locations. Attribute crystals can be obtained while hunting mobs and gathering elemental energy on Gracia continent. Weapon attributes increase attack for the corresponding element.

At various places the mining models use simple functions in order to map user data to values that are easier to use in the specific model. For example, neural networks internally work with numbers, usually in the range from 0 to 1. Numeric input data are mapped to the range [0..1], and categorical fields are mapped to series of 0/1 indicators.

PMML defines various kinds of simple data transformations:

• Normalization: map values to numbers, the input can be continuous or discrete.
• Discretization: map continuous values to discrete values.
• Value mapping: map discrete values to discrete values.
• Text Indexing: derive a frequency-based value for a given term.
• Functions: derive a value by applying a function to one or more parameters
• Aggregation: summarize or collect groups of values, e.g., compute average.
• Lag: use a previous value of the given input field.

• In this chapter we will discuss about DTD Attributes. Attribute gives more information about an element or more precisely it defines a property of an element. An XML attribute is always in the form of a name-value pair. An element can have any number of unique attributes. Attribute declaration is.
• Invalid XML - The following XML document would be invalid. This is because the managerid attribute of the second element contains a value that isn't the same as a value of another element that contains an attribute with a type of ID.
• Nov 20, 2016  2 Responses to “D&D Did You Know’s: Comeliness in AD&D 2E” krackothunder Says: November 21, 2016 at 1:06 AM Reply. I did not, as a matter of fact. I’ve only ever known Appearence as a seventh stat (given that Beauty’s Caress is just so OP for Cha-based casters).

The corresponding XML elements appear as content of a surrounding markup DerivedField, which provides a common element for the various mappings. They can also appear at several places in the definition of specific models such as neural network or Naïve Bayes models. Transformed fields have a name such that statistics and the model can refer to these fields.

The transformations in PMML do not cover the full set of preprocessing functions which may be needed to collect and prepare the data for mining. There are too many variations of preprocessing expressions. Instead, the PMML transformations represent expressions that are created automatically by a mining system. A typical example is the normalization of input values in neural networks. Similarly, a discretization might be constructed by a mining system that computes quantile ranges in order to transform skewed data.

If a DerivedField is contained in TransformationDictionary or LocalTransformations, then the name attribute is required. For DerivedFields which are contained inline in models, name is optional.

The TransformationDictionary allows for transformations to be defined once and used by any model element in the PMML document. For information on the use and naming of DerivedFields in the TransformationDictionary, see Scope of Fields.

The transformation expression in the content of DerivedField defines how the values of the new field are computed.

The attribute optype is needed in order to eliminate cases where the resulting type is not known. If there is a value mapping in a DerivedField, it is not known how to interpret the output. A value mapping might look like this:

But it is not known whether 0.1 has to be interpreted as a number or as a string (=categorical value). Hence optype is required in order to make parsing and interpretation of models simpler.

A DerivedField may have a list of Value elements that define the ordering of the values for an ordinal field. The attribute property must not be used for Value elements within a DerivedField. That is, the list cannot specify values that are interpreted as missing or invalid.

Constant

Constant values can be used in expressions which have multiple arguments. The actual value of a constant is given by the content of the element. For example, <Constant>1.05</Constant> represents the number 1.05. The dataType of Constant can be optionally specified. If the ParameterField definition includes a dataType, the Constant will inherit the dataType specified in the ParameterField. If the dataType is not specified in the ParameterField definition or Constant element, it will be inferred by the content of the element. A Constant that consists solely of numbers will be treated as an integer, if a decimal point is present among the numbers the Constant will be treated as a float. The presence of any non-numeric characters will result in the Constant being treated as a string. Conflicting dataType specifications will be resolved as specified in the Functions document.

FieldRef

Field references are simply pass-throughs to fields previously defined in the DataDictionary, a DerivedField, or a result field. For example, they are used in clustering models in order to define center coordinates for fields that don't need further normalization.

A missing input will produce a missing result. The optional attribute mapMissingTo may be used to map a missing result to the value specified by the attribute. If the attribute is not present, the result remains missing.

Normalization

The elements for normalization provide a basic framework for mapping input values to specific value ranges, usually the numeric range [0 .. 1]. Normalization is used, e.g., in neural networks and clustering models.

NormContinuous: defines how to normalize an input field by piecewise linear interpolation. The mapMissingTo attribute defines the value the output is to take if the input is missing. If the mapMissingTo attribute is not specified, then missing input values produce a missing result.

The sequence of LinearNorm elements defines a sequence of points for a stepwise linear interpolation function. The sequence must contain at least two elements. Within NormContinous the elements LinearNorm must be strictly sorted by ascending value of orig. Given two points (a1, b1) and (a2, b2) such that there is no other point (a3, b3) with a1<a3<a2, then the normalized value is

b1+ ( x-a1)/(a2-a1)*(b2-b1) for a1 ≤ x ≤ a2.

Missing input values are mapped to missing output.
If the input value is not within the range [a1..an] then it is treated>a according to outliers (compare with outliers in MiningSchema):

asIs: Extrapolates the normalization from the nearest interval.

asMissingValues: Maps to a missing value.

asExtremeValues: Maps to the next value from the nearest interval so that the function is continuous.

The graph above shows the default behavior with an extrapolation for values less than a1 or greater than a3.

The graph below depicts a mapping where outliers are mapped using asExtremeValues:

NormContinuous can be used to implement simple normalization functions such as the z-score transformation' (X - m ) / s, where m is the mean value and s is the standard deviation.

In this example we assume that outliers are treated 'asIs'.

Normalize discrete values

Many mining models encode string values into numeric values in order to perform mathematical computations. For example, regression and neural network models often split categorical and ordinal fields into multiple dummy fields. This kind of normalization is supported in PMML by the element NormDiscrete.

Discretization

Decision table for Discretize

('*' stands for any combination)

input value	matching interval	defaultValue	mapMissingTo	=>	result
val	Interval_i	*	*	=>	binValue_i
val	none	someVal	*	=>	someVal
val	none	not specified	*	=>	missing
missing	*	*	someVal	=>	someVal
missing	*	*	not specified	=>	missing

Example:

A definition such as:

takes the field Profit as input and maps values less than 0 to negative and other values to positive. A missing value for Profit is mapped to a missing value.

In SQL this definition corresponds to a CASE expression

CASE When 'Profit' < 0 Then 'negative' When 'Profit' >= 0 Then 'positive' End

Discretize can also be used to create a missing value indicator for a continuous variable. In this case, the DiscretizeBin element is superfluous and can be dropped.

Example:

Any discrete value can be mapped to any possibly different discrete value by listing the pairs of values. This list is implemented by a table, so it can be given inline by a sequence of XML markups or by a reference to an external table. The same technique is used for a Taxonomy because the tables can become quite large.

The types InlineTable and TableLocator are defined in the Taxonomy schema.

The mapMissingTo attribute defines the value the output column is to take if any of the input columns are missing.

Different string values can be mapped to one value but it is an error if the table entries used for matching are not unique. The value mapping may be partial. I.e., if an input value does not match a value in the mapping table, then the result can be a missing value. See the decision table below for the possible combinations.

Decision table for MapValues

('*' stands for any combination)

input value	matching value	defaultValue	mapMissingTo	=>	result
val	in row i	*	*	=>	outputColumn in row i
val	none	someVal	*	=>	someVal
val	none	not specified	*	=>	missing
missing	*	*	someVal	=>	someVal
missing	*	*	not specified	=>	missing

Example:

A definition such as

maps abbreviation from the field gender to their corresponding full words. That is, m is mapped to male and f is mapped to female.

In SQL this definition corresponds to a CASE expression

CASE 'gender' When 'm' Then 'male' When 'f' Then 'female' End

Example:

Here is an example for the multi-dimensional case, mapping from state and band to salary:

state	band 1	band 2
MN	10,000	20,000
IL	12,000	23,000
NY	20,000	30,000

Respective PMML would look like this:

Example:

The MapValues element can be used to create missing value indicators for categorical variables. In this case, only one FieldColumnPair needs to be specified and the column attribute can be omitted.

Extracting term frequencies from text

To leverage textual input in a PMML model, we can use a TextIndex expression to extract frequency information from the text input field, for a given term. The TextIndex element fully configures how the text input should be indexed, including case sensitivity, normalization and other settings. It has a single EXPRESSION element nested within containing the term value to look for, which will usually be a simple Constant.

The TextIndex element fully configures how the text in textField should be processed and translated into a frequency metric for a particular term of interest. The actual frequency metric to be returned is defined through the localTermWeights attribute. The options are described in more detail by T. G. Kolda and D. P. O'Leary, A Semi-Discrete Matrix Decomposition of Latent Semantic Indexing in Information Retrieval, ACM Transactions on Information Systems, Volume 16, 1998, pages 322-346.

• termFrequency: use the number of times the term occurs in the document (x = freq_i).
• binary: use 1 if the term occurs in the document or 0 if it doesn't (x = χ(freq_i)).
• logarithmic: take the logarithm (base 10) of 1 + the number of times the term occurs in the document. (x = log(1 + freq_i))
• augmentedNormalizedTermFrequency: this formula adds to the binary frequency a 'normalized' component expressing the frequency of a term relative to the highest frequency of terms observed in that document (x = 0.5 * (χ(freq_i) + (freq_i / max_k(freq_k))) )

The isCaseSensitive attribute defines whether or not the case used in the text input should exactly match the case usage in the terms for which the frequency should be returned. Through maxLevenshteinDistance, small spelling mistakes can be accommodated, accepting a certain number of character additions, ommissions or replacements. See also this article. To capture compound words more easily, the wordSeparatorCharacterRE attribute can be used to pass a regular expression containing possible word separator characters. For example, if '[s-]' is passed, the strings 'user-friendly' and 'user friendly' would both match the term 'user friendly'. More complex normalization operations can be addressed through the TextIndexNormalization element. Note that the default value for attribute wordSeparatorCharacterRE is considered to be the spacecharacter. The wordSeparatorCharacterRE attribute applies to the given text unless attribute tokenize is set to 'false'.

When attribute tokenize is set to 'true', which is its default value, wordSeparatorCharacterRE should beapplied to the text input field as well as the given term. This process will result in one or more tokens for which the leading and trailing punctuations should then be removed. The punctuation-freetokenized term can then be described by one word or a sequence of words, depending on the number or resulting tokens.

To calculate a term's frequency, count number of 'hits' in the text for the particular term according to the value of countHits. A 'hit' is defined as an occurrence of the term in the input text, meeting the case requirements defined through isCaseSensitive within the maxLevenshteinDistance.For example, if the given term is defined as 'brown fox' and the input text is 'The quick browny foxy jumps over the lazy dog. The brown fox runs away and to be with another brown foxy.'. If attribute maxLevenshteinDistance is set to 1, the text 'browny foxy' will not be considered a 'hit' since its Levenshtein distance is 2 (the sum of the Levenshteindistances for words 'browny' and 'foxy' after the input text has been tokenized). The text'brown fox' that is retrieved next is a 'hit' since the Levenshtein distance is 0. The text'brown foxy.' is also a 'hit' since the Levenshtein distance is 1. Note that the punctuation has been removed before computing the Levenshtein distance.

The number of 'hits' in the text can be counted in two different ways. These are:

• allHits: count all hits
• bestHits: count all hits with the lowest Levenshtein distance

For example, if the input text is defined as 'I have a doog. My dog is white. The doog is friendly' and the attribute maxLevenshteinDistance is set to 1,the number of hits for term 'dog' will be 3 if attribute countHits is set to 'allHits' and 1 if it is set to 'bestHits'.

An example

In the above example, the DerivedFieldsunFrequency will contain the number of hits for the term 'sun' in the input field myTextField, regardless of case and with at most one spelling mistake. For example, if the value of myTextField is 'The Sun was setting while the captain's son reached the bounty island, minutes after their ship had sunk to the bottom of the ocean', sunFrequency will be 3 as 'Sun', 'son' and 'sunk' all match the term 'sun' with a Levenshtein distance of 0 or 1. If the maximum Levenshtein distance were to be 0, only 'Sun' would have matched.

Predictive models using text as an input are likely to be looking for more than a single term. Therefore, it is often convenient to define the TextIndex element just once inside a DefineFunction elementand then invoke it with Apply elements as shown in the following example.

Normalizing text input

While the Levenshtein distance is useful to cover small spelling mistakes, it's not necessarily suitable to capture different forms of the same term, such as cases of nouns, conjugations of verbs or even synonyms. One or more TextIndexNormalization elements can be nested within a TextIndex to normalize input text into a more term-friendly form.

A TextIndexNormalization element offers more advanced ways of normalizing text input into a more controlled vocabulary that corresponds to the terms being used in invocations of this indexing function. The normalization operation is defined through a translation table, specified through a TableLocator or InlineTable element.

If an input in the inField column is encountered in the text, it is replaced by the value in the outField column. If there is a regexField column and its value for that row is true, the string in the inField column should be treated as a PCRE regular expression. For regular expression rows, attributes maxLevenshteinDistance and isCaseSentive are ignored.

By default, the translation table is applied once, applying each row once from top to bottom. If the recursive flag is set to true, the normalization table is reapplied until none of its rows causes a change to the input text. If multiple TextIndexNormalization elements are defined, they are applied in the order in which they appear. That is, if more than one TextIndexNormalizationelement is defined, the output of applying a TextIndexNormalization element will serve as the input to the next TextIndexNormalization element. This enables, for example, to have a first normalization step to take care of morphological translations to get each word into some base form (stemming), a second normalization step to combine synonyms by applying some sort of taxonomy and perhaps a third step to look for particular sequences of normalized tokens.

By default, the TextIndexNormalization element inherits the values for isCaseSensitive, maxLevenshteinDistance, wordSeparatorCharacterRE and tokenizefrom the TextIndex element, but they can be overridden per TextIndexNormalization element.

For each TextIndexNormalizationelement, wordSeparatorCharacterRE does not apply to the inField and outField columns forregular expression rows. However, it applies to both inField and outField for non-regular expression rows.

An example with normalization

For example, when processing the text fragment 'Testing the app for a few days convinced me the interfaces are excellent!', applying the first normalization block yields 'Testing the app for a few days convinced me the interface be excellent!'. Applying the second block then yields 'Testing the app for a few days convinced me the ui_good!', which will produce a frequency value of 1 for the 'isGoodUI' field.

Aggregations

Association rules and sequences refer to sets of items. These sets can be defined by an aggregation over sets of input records. The records are grouped together by one of the fields and the values in this grouping field partition the sets of records for an aggregation. This corresponds to the conventional aggregation in SQL with a GROUP BY clause. Input records with missing value in the groupField are simply ignored. This behavior is similar to the aggregate functions in the presence of NULL values in SQL.

A definition such as:

builds sets of item values; for each transaction, i.e. for each value in the field transaction there is one set of items.

Lag

A 'lag' is here defined as the value of the given input field a fixed number of records prior to the current one, assuming there are that many; and optionally assuming that they are part of the same block of records (aka 'case history'), as defined by the values of one or more input fields (the 'block specifiers'). If the desired value is not present, for a given record, the lag will be set to missing.

Warning!

Lags are only meaningful if the data are already sorted in the desired order (normally chronologically within case histories). If should not be assumed that a consumer will do the requisite sorting.

• field in a Lag element is the name of the input field to be 'lagged'. In a BlockIndicator element, it is the name of a field used to define the block of records to be searched. A block thus defined will consist of one or more consecutive records in which the indicator fields have the same values.
• n indicates that the value to be used is to be taken from nth record prior to the current one, if there are that many. If one or more BlockIndicator elements are defined, the record must be part of the same block as the current one. If either condition does not hold, then the value of the DerivedField will be set to missing. n must be a positive integer. If not specified, it will be set to 1 (indicating the record just before the current one).

Simple Lag Example

In this example, the value returned will be that of Receipts in the record immediately prior to the current one, except that on the first record, it will be missing.

Slightly More Complex Lag Example

Here, the value returned will be that of Receipts on the record just before the previous one, except that on the first two records, it will be missing.

Example Lag Within a Block

In this example, the value returned will be that of AmtPaid on the next previous record, if it has the same value of CustomerID as the current one, otherwise it will be missing.

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Attributes are a way to modify one or more declarations. The general forms are:

Linkage Attribute

D provides an easy way to call C functions and operating system API functions, as compatibility with both is essential. The LinkageType is case sensitive, and is meant to be extensible by the implementation (they are not keywords). C and D must be supplied, the others are what makes sense for the implementation. C++ offers limited compatibility with C++. Objective-C offers limited compatibility with Objective-C, see the Interfacing to Objective-C documentation for more information. System is the same as Windows on Windows platforms, and C on other platforms. Implementation Note: for Win32 platforms, Windows should exist.

C function calling conventions are specified by:

Note that extern(C)

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can be provided for all types of declarations, including struct or class, even though there is no corresponding match on the C side. In that case, the attribute is ignored. This behavior applies for nested functions and nested variables as well. However, for static member methods and static nested functions, adding extern(C) will change the calling convention, but not the mangling.

D conventions are:

Windows API conventions are:

The Windows convention is distinct from the C convention only on Win32 platforms, where it is equivalent to the stdcall convention.

Note that a lone extern declaration is used as a storage class.

C++ Namespaces

The linkage form extern (C++, IdentifierList) creates C++ declarations that reside in C++ namespaces. The IdentifierList specifies the namespaces.

refers to the C++ declaration:

and can be referred to with or without qualification:

Namespaces create a new named scope that is imported into its enclosing scope.

Multiple identifiers in the IdentifierList create nested namespaces:

refers to the C++ declaration:

align Attribute

Specifies the alignment of:

1. variables
2. struct fields
3. union fields
4. class fields
5. struct, union, and class types

align by itself sets it to the default, which matches the default member alignment of the companion C compiler.

AssignExpression specifies the alignment which matches the behavior of the companion C compiler when non-default alignments are used. It must be a positive power of 2.

A value of 1 means that no alignment is done; fields are packed together.

The alignment for the fields of an aggregate does not affect the alignment of the aggregate itself - that is affected by the alignment setting outside of the aggregate.

Setting the alignment of a field aligns it to that power of 2, regardless of the size of the field.

Do not align references or pointers that were allocated using NewExpression on boundaries that are not a multiple of size_t. The garbage collector assumes that pointers and references to GC allocated objects will be on size_t byte boundaries.

The AlignAttribute is reset to the default when entering a function scope or a non-anonymous struct, union, class, and restored when exiting that scope. It is not inherited from a base class.

deprecated Attribute

It is often necessary to deprecate a feature in a library, yet retain it for backwards compatibility. Such declarations can be marked as deprecated, which means that the compiler can be instructed to produce an error if any code refers to deprecated declarations:

Optionally a string literal or manifest constant can be used to provide additional information in the deprecation message.

Calling CTFE-able functions or using manifest constants is also possible.

Implementation Note: The compiler should have a switch specifying if deprecated should be ignored, cause a warning, or cause an error during compilation.

Visibility Attribute

Visibility is an attribute that is one of private, package, protected, public, or export. They may be referred to as protection attributes in documents predating DIP22.

Symbols with private visibility can only be accessed from within the same module. Private member functions are implicitly final and cannot be overridden.

package extends private so that package members can be accessed from code in other modules that are in the same package. If no identifier is provided, this applies to the innermost package only, or defaults to private if a module is not nested in a package.

package may have an optional parameter in the form of a dot-separated identifier list which is resolved as the qualified package name. The package must be either the module's parent package or one of its anscestors. If this optional parameter is present, the symbol will be visible in the specified package and all of its descendants.

protected only applies inside classes (and templates as they can be mixed in) and means that a symbol can only be seen by members of the same module, or by a derived class. If accessing a protected instance member through a derived class member function, that member can only be accessed for the object instance which can be implicitly cast to the same type as ‘this’. protected module members are illegal.

public means that any code within the executable can see the member. It is the default visibility attribute.

export means that any code outside the executable can access the member. export is analogous to exporting definitions from a DLL.

Visibility participates in symbol name lookup.

const Attribute

The const attribute changes the type of the declared symbol from T to const(T), where T is the type specified (or inferred) for the introduced symbol in the absence of const.

immutable Attribute

The immutable attribute modifies the type from T to immutable(T), the same way as const does.

inout Attribute

The inout attribute modifies the type from T to inout(T), the same way as const does.

shared Attribute

The shared attribute modifies the type from T to shared(T), the same way as const does.

__gshared Attribute

By default, non-immutable global declarations reside in thread local storage. When a global variable is marked with the __gshared attribute, its value is shared across all threads.

__gshared may also be applied to member variables and local variables. In these cases, __gshared is equivalent to static, except that the variable is shared by all threads rather than being thread local.

Unlike the shared attribute, __gshared provides no safe-guards against data races or other multi-threaded synchronization issues. It is the responsibility of the programmer to ensure that access to variables marked __gshared is synchronized correctly.

__gshared is disallowed in safe mode.

@disable Attribute

A reference to a declaration marked with the @disable attribute causes a compile time error. This can be used to explicitly disallow certain operations or overloads at compile time rather than relying on generating a runtime error.

Disabling struct no-arg constructor disallows default construction of the struct.

Disabling struct postblit makes the struct not copyable.

@safe, @trusted, and @system Attribute

See Function Safety.

@nogc Attribute

See No-GC Functions.

@property Attribute

See Property Functions.

nothrow Attribute

See Nothrow Functions.

pure Attribute

See Pure Functions.

ref Attribute

See Ref Functions.

return Attribute

See Return Ref Parameters.

override Attribute

The override attribute applies to virtual functions. It means that the function must override a function with the same name and parameters in a base class. The override attribute is useful for catching errors when a base class's member function gets its parameters changed, and all derived classes need to have their overriding functions updated.

static Attribute

The static attribute applies to functions and data. It means that the declaration does not apply to a particular instance of an object, but to the type of the object. In other words, it means there is no this reference. static is ignored when applied to other declarations.

Static functions are never virtual.

Static data has one instance per thread, not one per object.

Static does not have the additional C meaning of being local to a file. Use the private attribute in D to achieve that. For example:

auto Attribute

The auto attribute is used when there are no other attributes and type inference is desired.

For functions, the auto attribute means return type inference. See Auto Functions.

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scope Attribute

The scope attribute is used for local variables and for class declarations. For class declarations, the scope attribute creates a scope class. For local declarations, scope implements the RAII (Resource Acquisition Is Initialization) protocol. This means that the destructor for an object is automatically called when the reference to it goes out of scope. The destructor is called even if the scope is exited via a thrown exception, thus scope is used to guarantee cleanup.

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If there is more than one scope variable going out of scope at the same point, then the destructors are called in the reverse order that the variables were constructed.

scope cannot be applied to globals, statics, data members, ref or out parameters. Arrays of scopes are not allowed, and scope function return values are not allowed. Assignment to a scope, other than initialization, is not allowed. Rationale: These restrictions may get relaxed in the future if a compelling reason to appears.

abstract Attribute

An abstract member function must be overridden by a derived class. Only virtual member functions may be declared abstract; non-virtual member functions and free-standing functions cannot be declared abstract.

Classes become abstract if any of its virtual member functions are declared abstract or if they are defined within an abstract attribute. Note that an abstract class may also contain non-virtual member functions.

Classes defined within an abstract attribute or with abstract member functions cannot be instantiated directly. They can only be instantiated as a base class of another, non-abstract, class.

Member functions declared as abstract can still have function bodies. This is so that even though they must be overridden, they can still provide ‘base class functionality’, e.g. through super.foo() in a derived class. Note that the class is still abstract and cannot be instantiated directly.

User-Defined Attributes

User-Defined Attributes (UDA) are compile-time expressions that can be attached to a declaration. These attributes can then be queried, extracted, and manipulated at compile time. There is no runtime component to them.

A user-defined attribute looks like:

If there are multiple UDAs in scope for a declaration, they are concatenated:

UDAs can be extracted into an expression tuple using __traits:

If there are no user-defined attributes for the symbol, an empty tuple is returned. The expression tuple can be turned into a manipulatable tuple:

Of course the tuple types can be used to declare things:

The attribute of the type name is not the same as the attribute of the variable:

Of course, the real value of UDAs is to be able to create user-defined types with specific values. Having attribute values of basic types does not scale. The attribute tuples can be manipulated like any other tuple, and can be passed as the argument list to a template.

Whether the attributes are values or types is up to the user, and whether later attributes accumulate or override earlier ones is also up to how the user interprets them.

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UDAs cannot be attached to template parameters.

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