Miscellaneous

Miscellaneous functions do not fit under any of the other categories, but still provide useful mechanisms for creating and processing information.

The examples for each function use the following notation:

• Square brackets ([]) indicate arrays.
• Curly braces ({}) indicate groups.
• Arrows (==>) separate inputs and outputs. Inputs are shown on the left side of the arrow. Outputs are shown on the right side of the arrow.

Combination Converter

The Combination Converter function takes an array argument, compares each array element to the column header value, and assigns True if there is a header that matches.

If the combination of columns with True assigned exists in the combination table configured by the user, the function returns the converted value.

Usage Information

Category	Details
Number of Arguments	1
Mandatory Argument Names and Datatypes	arg: A string array or integer array. Scalar Support: True Array Support: True Group of Scalars Support: False Group of Arrays Support: True
Optional Argument Names and Datatypes	N/A
Modifiers	ignore null (default: True): If True, null values are skipped. If False, null values are compared. Right-click menu that brings up the conversion table.
Output Datatype(s)	The datatype of the Value column of the conversion table.

Examples

Sample Combination Table:
|--------------------------------------------------|
| apple | orange | pecan | salad | Converted Value |
|--------------------------------------------------|
| TRUE  |  TRUE  | FALSE | FALSE |      0.2        |
| TRUE  |  FALSE | FALSE | FALSE |      0.1        |
| FALSE |  TRUE  | TRUE  | FALSE |      0.6        |
| TRUE  |  FALSE | FALSE | TRUE  |      0.9        |
| TRUE  |  TRUE  | TRUE  | TRUE  |      0.4        |
|--------------------------------------------------|

combinationConverter(['apple', 'orange']) ==> 0.2
combinationConverter(['apple','orange','pecan']) ==> null

combinationConverter(['apple','orange',null], ignore_null=True) ==> 0.2
combinationConverter(['apple','orange',null], ignore_null=False) ==> null

combinationConverter({['apple', 'orange'],['salad','apple']}) ==> {0.2,0.9}
combinationConverter({['apple', 'orange'],['salad','apple','pecan']}) ==> {0.2,null}

First Non-Null

The First Non-Null function returns the first non-null value received, evaluated in the order that arguments are supplied.

Usage Information

Category	Details
Number of Arguments	2+
Mandatory Argument Names and Datatypes	arg: Any datatype. All input arguments must share the same datatype. Scalar Support: True Array Support: True Group of Scalars Support: True Group of Arrays Support: False
Optional Argument Names and Datatypes	N/A
Modifiers	N/A
Output Datatype(s)	The output datatype matches the input datatype.

Examples

firstNonNull(null,null,"input3",null,"input5") ==> "input3"

firstNonNull([null,null,3],null,[1,2,3]) ==> [null,null,3]

firstNonNull({1,null,null},{10,20,null},8) ==> {1,20,8}

Get System Confidence

The Get System Confidence function returns the system-computed confidence for the given solution. (This is the same value as the resulting COG_confidence output field.) If risk computation is not enabled for the template, the functions returns a null value.

Usage Information

Category	Details
Number of Arguments	0
Mandatory Argument Names and Datatypes	N/A
Optional Argument Names and Datatypes	N/A
Modifiers	N/A
Output Datatype(s)	Float

Examples

getSystemConfidence() ==> 0.65
getSystemConfidence() ==> null // when no risk table is configured

Get URL Part

The Get URL Part function returns the part of the specified URL that corresponds to the provided part string.

Usage Information

Category	Details
Number of Arguments	2
Mandatory Argument Names and Datatypes	url: A URL containing the part to return. Scalar Support: True Array Support: True Group of Scalars Support: True Group of Arrays Support: True
Optional Argument Names and Datatypes	N/A
Modifiers	part: A string indicating the part of the URL to return. The available options are: scheme user host port path query fragment
Output Datatype(s)	String

Examples

getUrlPart("https://www.google.com",part="scheme") ==> "https"
getUrlPart("https://www.google.com",part="query") ==> null

getUrlPart(["https://www.google.com"],part="scheme") ==> "https"
getUrlPart(["https://www.google.com","https://www.amazon.com","https://cogynt.io"],part="scheme") ==> ["https","https","https"]

getUrlPart({"https://www.google.com","https://www.amazon.com","https://cogynt.io"},part="scheme") ==> {"https","https","https"}

getUrlPart({[],[],[]},part="scheme") ==> {[],[],[]}

JSON Builder

The JSON Builder function builds a JSON object from its arguments. The fields of the JSON object, and therefore the function's arguments, are defined by a schema. Use it to create nested JSON structures and to reduce the number of unnecessary fields exposed in the outcome schema of a pattern.

Usage Information

Category	Details
Number of Arguments	One for each field defined in the schema. The function has no arguments until a schema is defined.
Mandatory Argument Names and Datatypes	One argument for each schema field, named for the field's Name (or its Path, if no name is set) and of the field's Data Type. Scalar Support: True Array Support: True Group of Scalars Support: True Group of Arrays Support: True
Optional Argument Names and Datatypes	N/A
Modifiers	schema: The list of fields that make up the JSON object. Right-click the modifier and select Add Schema (or Update Schema once a schema is defined) to bring up the JSON Builder Schema window.
Output Datatype(s)	JSON

Defining the Schema

The schema determines which fields the JSON object contains, and each schema field adds one argument to the function.

To define the schema of a JSON Builder:

1. In the diagramming area, right-click the schema modifier of the function and select Add Schema. The JSON Builder Schema window opens.
2. Click ADD FIELD to add a field to the schema. For each field:
   1. From the Data Type dropdown menu, select the datatype of the field.
   2. In the Paths field, enter the path of the field in the JSON object. Use | as a delimiter to define nested fields. Each path must be unique.
   3. In the Name field, enter the name of the field as it should be displayed in Cogynt applications and on the function's argument.
3. Click the arrow beside ADD FIELD to instead add fields from an existing Kafka topic or schema, as when defining the fields of an event type.
4. Click Save. An argument for each schema field is added to the function.

note

• Hover over a field and click X to remove it from the schema.
• The function's arguments are listed in alphabetical order of their field names, or of their paths for fields that have no name.

To change the schema, right-click the schema modifier and select Update Schema. Arguments for fields that remain in the schema keep their connections, provided their paths are unchanged. Changing a field's path replaces its argument, and removing a field from the schema removes its argument along with any connections to it.

To remove a field from the schema directly from the diagram, right-click its argument and select Remove Field.

Examples

Sample Schema:
Path: id               Name: ID              Data Type: Integer
Path: customer|name    Name: Customer Name   Data Type: String
Path: customer|city    Name: Customer City   Data Type: String

Arguments, in alphabetical order of the field names: Customer City, Customer Name, ID

jsonBuilder("London", "Ada", 1001) ==> {"id": 1001, "customer": {"name": "Ada", "city": "London"}}

Lexicon Match

The Lexicon Match function returns an array of matching lexicon entries (words and phrases in the selected lexicon node) if the input expression matches the lexicon. Otherwise, it returns a null value.

Usage Information

Category	Details
Number of Arguments	1
Mandatory Argument Names and Datatypes	arg: A string. Scalar Support: True Array Support: True Group of Scalars Support: True Group of Arrays Support: True
Optional Argument Names and Datatypes	N/A
Modifiers	lexicon node: The specific lexicon node to search.
Output Datatype(s)	String array

Examples

lexicon = {
  "node1": ['Abc','Def','Ghi'],
  "node2": ['123','456','789']
}

lexMatch("Abc 123 Ghi", lexicon['node1']) ==> ['Abc','Ghi']
lexMatch({"Abc 123 Ghi","123 789 Abc"}, lexicon['node2']) ==> {["123"],["123", "789"]}

Make URL

The Make URL function generates a URL based on the input parameters.

All arguments are required, but all except the host argument can be null. The scheme value defaults to http if not provided. The host value can be an actual host name or a domain name (for example, cogility.com). See the Java URI class documentation for more details.

Usage Information

Category	Details
Number of Arguments	1
Mandatory Argument Names and Datatypes	host: A string indicating the host. Scalar Support: True Array Support: False Group of Scalars Support: True Group of Arrays Support: False
Optional Argument Names and Datatypes	scheme (Default: http): A string indicating the scheme to use. userinfo (Default: null): A string indicating the user info to use. port (Default: null): A string indicating the port to use. path (Default: null): A string indicating the path to use. query (Default: null): A string indicating the query to use. fragment (Default: null): A string indicating the fragment to use.
Modifiers	N/A
Output Datatype(s)	String array

Examples

makeUrl() ==> "http://"

Random UUID

The Random UUID function returns a unique UUID.

Usage Information

Category	Details
Number of Arguments	0
Mandatory Argument Names and Datatypes	N/A
Optional Argument Names and Datatypes	N/A
Modifiers	N/A
Output Datatype(s)	UUID

Examples

randomUuid() ==> '5ee968d0-6301-4826-9a81-a7062a050215'

Value Converter

The Value Converter function takes the argument and converts it into a value based on a user-defined conversion table.

Usage Information

Category	Details
Number of Arguments	1
Mandatory Argument Names and Datatypes	arg: A string, integer, string array, or integer array. Scalar Support: True Array Support: True Group of Scalars Support: True Group of Arrays Support: True
Optional Argument Names and Datatypes	N/A
Modifiers	Right-click menu that brings up the conversion table.
Output Datatype(s)	The datatype of the Value column of the conversion table.

Examples

Sample Conversion Table:
{
  'abc': 0.2,
  '123': 0.5,
  'doremi': 0.1
}

valueConverter('doremi') ==> 0.1
valueConverter('abc 123') ==> null
valueConverter(null) ==> null

valueConverter(['abc', '123']) ==> [0.2, 0.5]
valueConverter([null, '123']) ==> [null, 0.5]

valueConverter({'abc', '123'}) ==> {0.2, 0.5}
valueConverter({null, '123'}) ==> {null, 0.5}

valueConverter({['abc',null],['doremi','123']}) ==> {[0.2,null],[0.1,0.5]}
valueConverter({null,['abc','123']}) ==> {null,[0.2,0.5]}

Weighted Risk

The Weighted Risk function performs the same computation of risk as when using the risk table in the pattern view (i.e., the complement of the product of complements of weights).

Usage Information

Category	Details
Number of Arguments	1+
Mandatory Argument Names and Datatypes	arg: A float or float array. Scalar Support: True Array Support: True Group of Scalars Support: True Group of Arrays Support: False
Optional Argument Names and Datatypes	N/A
Modifiers	ignore null (default: True): If True, the computations set null values to 0, and a valid computation occurs. If False, the resulting value will be null instead.
Output Datatype(s)	Float

note

If the first input argument is an array, the number of arguments should only be 1.

The function's output will always have a positive value between 0 and 1.

Examples

weightedRisk(0.2,0.5,0.1) ==> 0.64
weightedRisk(0.2,0.5,null, ignore_null=False) ==> null
weightedRisk(0.2,0.5,null, ignore_null=True) ==> 0.6

weightedRisk([0.2, 0.5, 0.1]) ==> 0.64
weightedRisk([0.2, 0.5, null], ignore_null=False) ==> null
weightedRisk([0.2, 0.5, null], ignore_null=True) ==> 0.6

weightedRisk({0,2,0.5,0.1},{0.5,0.2,0.5},{0.1,0.1,0.2}) ==> {0.64,0.64,0.64}

weightedRisk({[0,2,0.5,0.1],[0,2,0.5,0.1],[0,2,0.5,0.1]}) ==> {0.64,0.64,0.64}

When

The When function evaluates a defined expression based on the truth or falsity of the submitted argument. When the argument is nonzero or True, when returns the if true option. Otherwise, it returns the if false option.

Usage Information

Category	Details
Number of Arguments	2
Mandatory Argument Names and Datatypes	boolean: The expression to evaluate. if true or if false: The outputs to return if the input expression is true or false, respectively. Scalar Support: True Array Support: True Group of Scalars Support: True Group of Arrays Support: True
Optional Argument Names and Datatypes	if true: The output to return if the input expression is true. It can be any datatype, but it must share the same datatype as the input of if false. if false: The output to return if the input expression is false. It can be any datatype, but it must share the same datatype as the input of if true.
Modifiers	N/A
Output Datatype(s)	Shares the same datatype as the input value for if true and/or if false.

note

At least one of the two optional arguments must be non-null.

Examples

when(True,if_true="ABC",if_false="123") ==> "ABC"
when(True,if_true="ABC") ==> "ABC"
when(False,if_true="ABC") ==> null
when(True,if_false="123") ==> null

when([True,False,True],1,2) ==> [1,2,1]
when([True,False,True],if_true=["A","B","C"],if_false=["D","E","F"]) ==> ["A","E","C"]

when({True,False,True},{"A","B","C"},{"D","E","F"}) ==> {"A","E","C"}

when({[True,False,True],[False,True],[False,False,True]},if_true=[1,2,3],if_false=["A","B","C"]) ==> TBD