Balance Type Symmetry Asymmetry Radial Classifier

Keep independently observed balance types with overlapping readings; not image recognition. Independently annotated feature columns in order: bilateral visual balance independently witnessed, asymmetric visual balance independently witnessed, radial organization independently witnessed. Compatible classes are not established identities; unknown evidence retained. No automatic semantic parsing, diagnosis, authenticity or safety determination.

Description

Bilateral balance, asymmetric balance and radial organization can support overlapping visual readings.

Bilateral balance, asymmetric balance and radial organization can support overlapping visual readings.^[1]

This tool keeps compatible and witnessed classes separate using three independently annotated features.

When to use Balance Type Symmetry Asymmetry Radial Classifier

Use this tool to retain compatible and witnessed classes separate using three independently annotated features.

How to use Balance Type Symmetry Asymmetry Radial Classifier and how it works

Independently assess each listed feature or criterion in the fixed order; record unknown evidence with a zero known flag and a valid binary observation placeholder.
Enter observedFeatureFlags, featureKnownFlags and run the tool.
Read result.compatibleClasses, result.witnessedClasses, result.unknownFeatureIndices using the meanings and sign conventions below.
Review missing and unknown evidence separately; resolve it through independent observation rather than treating compatible classes or placeholders as established facts.

Input field	Meaning	Value type	Requirement
observedFeatureFlags	Binary 0-or-1 feature observations in the fixed column order below.	array of number	Required; branch conditions below
featureKnownFlags	Binary 0-or-1 known flags; unknown placeholders do not establish or refute a class.	array of number	Required; branch conditions below

Enter observed-feature and evidence-known binary arrays in the exact feature order below. Require the fixed feature count and integer 0-or-1 flags, including valid placeholders for unknown observations. Each rule uses 1 for a required present feature, 0 for a required absent feature and -1 for an unconstrained feature. A class is compatible when every known constrained feature matches; it is witnessed only when every constrained feature is known and matches. Unknown placeholders do not decide compatibility. Return compatible and witnessed class names in fixed rule order plus zero-based unknown indices. Classes may overlap, and an empty witnessed list is a successful result rather than an input error.

compatible(rule)=all(rule_j==-1 or known_j==0 or observed_j==rule_j); witnessed(rule)=all(rule_j==-1 or (known_j==1 and observed_j==rule_j))

Use the stated ordering, units and branch conditions.

Zero-based column	Meaning
0	bilateral visual balance independently witnessed
1	asymmetric visual balance independently witnessed
2	radial organization independently witnessed

Class token	Fixed rule in column order
bilateral-balance	[1, -1, -1]
asymmetric-balance	[-1, 1, -1]
radial-organization	[-1, -1, 1]

Output field	Meaning	Value type
result	Object containing the fields below.	object
result.compatibleClasses	Fixed rule names not contradicted by known constrained feature evidence.	array of string
result.witnessedClasses	Fixed rule names whose constrained features are all known and satisfied.	array of string
result.unknownFeatureIndices	Zero-based indices whose evidence-known flag is zero.	array

json

{
  "input": {
    "observedFeatureFlags": [
      1,
      1,
      1
    ],
    "featureKnownFlags": [
      1,
      1,
      1
    ]
  },
  "output": {
    "result": {
      "compatibleClasses": [
        "bilateral-balance",
        "asymmetric-balance",
        "radial-organization"
      ],
      "witnessedClasses": [
        "bilateral-balance",
        "asymmetric-balance",
        "radial-organization"
      ],
      "unknownFeatureIndices": []
    }
  }
}

Limitations and assumptions

The evidence annotations are supplied by the caller. Unknown observations remain unknown, and compatible readings can overlap. The tool does not analyze images or texts, collect observations, prove a theory, certify a design or determine artistic quality.

Supply explicit values and dense aligned arrays. Unexpected fields, wrong types and nonfinite scalar values are rejected; fixed-token, alignment and other branch conditions are explained above. A successful false flag, empty match list or unknown-evidence list differs from an input error. Extremely large inputs or nonrepresentable results may fail.

Alternative or Complementary methods

Use Visual Rhythm Motif Repetition Interval Counter for a related operation: Visual repetition can be summarized through intervals between independently identified instances of one motif.

Use Rule of Thirds Composition Grid Checker for a related operation: A rule-of-thirds grid places lines at one third and two thirds of normalized image dimensions.

References

Balance Type Symmetry Asymmetry Radial Classifier — tool.io

Similar or alternative tools

Visual Rhythm Motif Repetition Interval Counter
Visual repetition can be summarized through intervals between independently identified instances of one motif.
Rule of Thirds Composition Grid Checker
A rule-of-thirds grid places lines at one third and two thirds of normalized image dimensions.