random.uniform

as donto believed on

From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-08.)

random.uniform has 34 facts recorded in Dontopedia across 9 references, with 7 live disagreements.

34 facts·16 predicates·9 sources·7 in dispute

Mostly:rdf:type(8), has argument(4), generates(3)

Full Name`fullName`

random.uniform^[6]all time · 4c756ad1 Aa7d 45d8 84ba Dc5835cb7cf0

Inbound mentions (14)

Other subjects in dontopedia point AT this entity as a value. These are inverse relationships — e.g. "X motherOf this subject" — and answer questions the forward facts can't. Grouped by predicate.

`calls`Calls(2)

Execute Query Functionex:execute-query-function
Simulate Latencyex:simulate-latency

`callsFunction`Calls Function(2)

Random Uniform Callex:random-uniform-call
Response Time Assignmentex:response-time-assignment

`uses`Uses(2)

Time Sleepex:time-sleep
Vectors Creationex:vectors-creation

`call`Call(1)

Optimized Search Queryex:optimized-search-query

`calledFunction`Called Function(1)

Random Uniform Callex:random-uniform-call

`enables`Enables(1)

Import Statementsex:import-statements

`generatedBy`Generated by(1)

Angleex:angle

`generationMethod`Generation Method(1)

Dataset Creationex:dataset-creation

`inverseOwnerOf`Inverse Owner of(1)

Randomex:random

`providesFunction`Provides Function(1)

Random Moduleex:random-module

`usesSameMethod`Uses Same Method(1)

Example Vector Generationex:example-vector-generation

Other facts (31)

The long tail: predicates that appear too rarely to warrant their own section. Filter or scroll to find a specific one. Each row links to its source.

31 facts

Predicate	Value	Ref
Rdf:type	Random Function	[1]
Rdf:type	Function Call	[2]
Rdf:type	Random Function	[3]
Rdf:type	Function Call	[4]
Rdf:type	Python Function	[5]
Rdf:type	Function	[6]
Rdf:type	Python Function	[8]
Rdf:type	Random Function	[9]
Has Argument	0	[2]
Has Argument	500	[2]
Has Argument	0	[5]
Has Argument	1	[5]
Generates	Random Response Time	[3]
Generates	Continuous Values	[7]
Generates	Random Duration	[8]
Returns Range	0-to-1	[1]
Returns Range	0.01-to-0.1	[9]
Returns Type	Float	[2]
Returns Type	Float	[6]
Parameter	0	[3]
Parameter	500	[3]
Returns	Float Value	[4]
Returns	Sleep Duration	[8]
Used by	Simulate Complexity Function	[1]
Function Owner	Random	[2]
Causes	Random Response Time	[3]
Has Lower Bound	0	[4]
Is Used by	Simulate Latency	[6]
Parameter Type	Float	[6]
Module	random	[8]
Arguments	[0.01, 0.1]	[8]

Timeline axis is valid_time — when each source says the fact was true in the world, not when Dontopedia learned about it. Retracted rows are kept for provenance; coloured stripes indicate the context kind.

34 undated

—

typebeam/f4969f28-cf8a-4b78-a807-f2aad0a4773a

ex:RandomFunction

—

usedBybeam/f4969f28-cf8a-4b78-a807-f2aad0a4773a

ex:simulate-complexity-function

—

returnsRangebeam/f4969f28-cf8a-4b78-a807-f2aad0a4773a

0-to-1

—

typebeam/1bcbed5d-3802-432d-8909-860dd7d89bb4

ex:FunctionCall

—

functionOwnerbeam/1bcbed5d-3802-432d-8909-860dd7d89bb4

ex:random

—

hasArgumentbeam/1bcbed5d-3802-432d-8909-860dd7d89bb4

—

hasArgumentbeam/1bcbed5d-3802-432d-8909-860dd7d89bb4

500

—

returnsTypebeam/1bcbed5d-3802-432d-8909-860dd7d89bb4

ex:float

—

typebeam/4464e9c5-5d50-4535-bfc8-e9d0f474f1ca

ex:RandomFunction

—

labelbeam/4464e9c5-5d50-4535-bfc8-e9d0f474f1ca

random.uniform

—

generatesbeam/4464e9c5-5d50-4535-bfc8-e9d0f474f1ca

ex:random-response-time

—

parameterbeam/4464e9c5-5d50-4535-bfc8-e9d0f474f1ca

—

parameterbeam/4464e9c5-5d50-4535-bfc8-e9d0f474f1ca

500

—

causesbeam/4464e9c5-5d50-4535-bfc8-e9d0f474f1ca

ex:random-response-time

—

typebeam/82230382-8bc4-4da4-8f74-b604a44e2862

ex:FunctionCall

—

labelbeam/82230382-8bc4-4da4-8f74-b604a44e2862

random.uniform

—

hasLowerBoundbeam/82230382-8bc4-4da4-8f74-b604a44e2862

—

returnsbeam/82230382-8bc4-4da4-8f74-b604a44e2862

ex:float-value

—

typeblah/omega/774

ex:PythonFunction

—

hasArgumentblah/omega/774

—

hasArgumentblah/omega/774

—

typebeam/4c756ad1-aa7d-45d8-84ba-dc5835cb7cf0

ex:Function

—

fullNamebeam/4c756ad1-aa7d-45d8-84ba-dc5835cb7cf0

random.uniform

—

isUsedBybeam/4c756ad1-aa7d-45d8-84ba-dc5835cb7cf0

ex:simulate-latency

—

parameterTypebeam/4c756ad1-aa7d-45d8-84ba-dc5835cb7cf0

ex:float

—

returnsTypebeam/4c756ad1-aa7d-45d8-84ba-dc5835cb7cf0

ex:float

—

generatesbeam/c12a5314-5117-4beb-a829-e08beb503951

ex:continuous-values

—

typebeam/cd7d311b-5b1f-40b1-81c4-e92c33828061

ex:PythonFunction

—

modulebeam/cd7d311b-5b1f-40b1-81c4-e92c33828061

random

—

argumentsbeam/cd7d311b-5b1f-40b1-81c4-e92c33828061

[0.01, 0.1]

—

returnsbeam/cd7d311b-5b1f-40b1-81c4-e92c33828061

ex:sleep-duration

—

generatesbeam/cd7d311b-5b1f-40b1-81c4-e92c33828061

ex:random-duration

—

typebeam/094d5784-9736-417a-b216-d7a8d4224478

ex:RandomFunction

—

returnsRangebeam/094d5784-9736-417a-b216-d7a8d4224478

0.01-to-0.1

References (9)

9 references

[1]F4969f28 Cf8a 4b78 A807 F2aad0a4773a3 facts
ctx:claims/beam/f4969f28-cf8a-4b78-a807-f2aad0a4773a
- full textbeam-chunk
  text/plain1 KBdoc:beam/f4969f28-cf8a-4b78-a807-f2aad0a4773a
  Show excerpt
  | Compliance Issues | 3 | 6 | | **Total** | **15** | **24** | ### Conclusion By adjusting your timeline to account for more detailed analysis of…
[2]1bcbed5d 3802 432d 8909 860dd7d89bb45 facts
ctx:claims/beam/1bcbed5d-3802-432d-8909-860dd7d89bb4
- full textbeam-chunk
  text/plain1 KBdoc:beam/1bcbed5d-3802-432d-8909-860dd7d89bb4
  Show excerpt
  ### Next Steps 1. **Refine the Logic**: Refine the logic based on your specific use case and requirements. 2. **Integrate with the API**: Integrate these checks into your Flask API endpoint to perform the compliance audit. 3. **Test Thorou…
[3]4464e9c5 5d50 4535 Bfc8 E9d0f474f1ca6 facts
ctx:claims/beam/4464e9c5-5d50-4535-bfc8-e9d0f474f1ca
- full textbeam-chunk
  text/plain1 KBdoc:beam/4464e9c5-5d50-4535-bfc8-e9d0f474f1ca
  Show excerpt
  2. **Test Thoroughly**: Test the system with various data inputs to ensure it correctly identifies compliance issues. 3. **Document**: Document the system and the audit logic for future reference and maintenance. By following this framewor…
[4]82230382 8bc4 4da4 8f74 B604a44e28624 facts
ctx:claims/beam/82230382-8bc4-4da4-8f74-b604a44e2862
- full textbeam-chunk
  text/plain1 KBdoc:beam/82230382-8bc4-4da4-8f74-b604a44e2862
  Show excerpt
  16. **Security Features**: Availability of security features such as encryption, access control, etc. ### Improved Evaluation Script Here's an improved version of your evaluation script that includes more comprehensive metrics and a struct…
[5]7743 facts
ctx:discord/blah/omega/774
- full textomega-774
  text/plain2 KBdoc:agent/omega-774/ab2c9545-1237-4db2-9368-88d2aa8fff45
  Show excerpt
  [2025-12-13 14:58] omega [bot]: Your Python retry code for 429 and 502 errors looks solid and follows best practices with exponential backoff. To make it even more robust and production-ready, consider these quick refinements: - **Add jitt…
[6]4c756ad1 Aa7d 45d8 84ba Dc5835cb7cf05 facts
ctx:claims/beam/4c756ad1-aa7d-45d8-84ba-dc5835cb7cf0
[7]C12a5314 5117 4beb A829 E08beb5039511 fact
ctx:claims/beam/c12a5314-5117-4beb-a829-e08beb503951
- full textbeam-chunk
  text/plain1 KBdoc:beam/c12a5314-5117-4beb-a829-e08beb503951
  Show excerpt
  dense_scores = np.random.rand(num_queries, num_documents) # Test queries test_queries = np.random.rand(num_queries, num_documents) predictions = [] for i in range(num_queries): query = test_queries[i] sparse_scores_i = sparse_scor…
[8]Cd7d311b 5b1f 40b1 81c4 E92c338280615 facts
ctx:claims/beam/cd7d311b-5b1f-40b1-81c4-e92c33828061
- full textbeam-chunk
  text/plain1 KBdoc:beam/cd7d311b-5b1f-40b1-81c4-e92c33828061
  Show excerpt
  Here, `-w 4` specifies 4 worker processes, and `-t 3` sets a 3-second timeout. ### Step 3: Hybrid Query Logic Implement the hybrid query logic to handle both sparse and dense queries efficiently. Here's an example: ```python from flask i…
[9]094d5784 9736 417a B216 D7a8d42244782 facts
ctx:claims/beam/094d5784-9736-417a-b216-d7a8d4224478
- full textbeam-chunk
  text/plain1 KBdoc:beam/094d5784-9736-417a-b216-d7a8d4224478
  Show excerpt
``` Here, `-w 4` specifies 4 worker processes, and `-t 2.5` sets a 2.5-second timeout. ### Step 4: Implement Hybrid Ranking Logic Here's a complete example implementation: ```python from flask import Flask, request, jsonify from flask_l…

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random.uniform

Full NamefullName

Inbound mentions (14)

callsCalls(2)

callsFunctionCalls Function(2)

usesUses(2)

callCall(1)

calledFunctionCalled Function(1)

enablesEnables(1)

generatedByGenerated by(1)

generationMethodGeneration Method(1)

inverseOwnerOfInverse Owner of(1)

providesFunctionProvides Function(1)

usesSameMethodUses Same Method(1)

Other facts (31)

Timeline

References (9)

See also

Keep researching

Full Name`fullName`

`calls`Calls(2)

`callsFunction`Calls Function(2)

`uses`Uses(2)

`call`Call(1)

`calledFunction`Called Function(1)

`enables`Enables(1)

`generatedBy`Generated by(1)

`generationMethod`Generation Method(1)

`inverseOwnerOf`Inverse Owner of(1)

`providesFunction`Provides Function(1)

`usesSameMethod`Uses Same Method(1)