CriticWeightResult class

Result of CRITIC (Criteria Importance Through Intercriteria Correlation) objective weighting calculation.

Contains both final normalized criteria weights (summing to 1.0) and all intermediate calculation steps:

  • criteriaIds: Ordered list of criteria IDs matching the input order.
  • normalizedMatrix: Min-max normalized matrix (r_ij) keyed by alternative ID -> criteria ID.
  • standardDeviations: Sample standard deviation (sigma_j, divisor m - 1) per criteria ID.
  • correlationMatrix: Pearson correlation matrix (R = r_jk) keyed by criteria ID -> criteria ID.
  • informationContent: Amount of information (C_j = sigma_j * sum_k(1 - r_jk)) per criteria ID.
  • weights: Final objective weights (w_j = C_j / sum_j(C_j)) per criteria ID (summing to 1.0).

Constructors

CriticWeightResult({required List<String> criteriaIds, required Map<String, Map<String, double>> normalizedMatrix, required Map<String, double> standardDeviations, required Map<String, Map<String, double>> correlationMatrix, required Map<String, double> informationContent, required Map<String, double> weights})
const

Properties

correlationMatrix → Map<String, Map<String, double>>
Pearson correlation matrix (R) between normalized criteria columns. Key: Criteria ID (j) -> Criteria ID (k) -> correlation coefficient r_jk.
final
criteriaIds → List<String>
Ordered list of criteria IDs matching the input order.
final
hashCode → int
The hash code for this object.
no setterinherited
informationContent → Map<String, double>
Information content / contrast intensity and conflict measure (C_j = sigma_j * sum_k(1 - r_jk)) keyed by criteria ID.
final
normalizedMatrix → Map<String, Map<String, double>>
Min-max normalized matrix (r_ij). Benefit: (x - min) / (max - min); Cost: (max - x) / (max - min). Key: Alternative ID -> Criteria ID -> normalized value.
final
runtimeType → Type
A representation of the runtime type of the object.
no setterinherited
standardDeviations → Map<String, double>
Sample standard deviation (sigma_j, divisor m - 1) of each normalized column, keyed by criteria ID.
final
weights → Map<String, double>
Final normalized weights (w_j = C_j / sum_j(C_j)) keyed by criteria ID, summing to 1.0.
final

Methods

applyToCriteria(List<WeightedDecisionCriteria> criteria) → List<WeightedDecisionCriteria>
Applies the calculated weights (weightPercent = w_j * 100) to criteria, preserving each criterion's benefit/cost type and other attributes.
applyToMatrix(List<WeightedDecisionMatrix> rawMatrix) → List<WeightedDecisionMatrix>
Applies the calculated weights (weightPercent = w_j * 100) to all criteria inside rawMatrix, returning a new matrix ready for SawCalculator.
applyToTopsisRawMatrix(TopsisRawMatrix rawMatrix) → TopsisRawMatrix
Applies the calculated weights (weightPercent = w_j * 100) to rawMatrix, returning a new TopsisRawMatrix ready for TopsisCalculator.
noSuchMethod(Invocation invocation) → dynamic
Invoked when a nonexistent method or property is accessed.
inherited
toJson() → Map<String, dynamic>
Converts this result and all intermediate values into a JSON-compatible map.
toString() → String
A string representation of this object.
inherited

Operators

operator ==(Object other) → bool
The equality operator.
inherited