ahp-weighting

# AHP Weighting Use the AHP (Analytic Hierarchy Process) to determine scoring-dimension weights, outputting a weight vector. ## HARD-GATE <HARD-GATE> - The number of input dimensions must be in the range [2, 9] (AHP applicability range) - The elements of the output weight vector must sum to 1.0 (±0.001 tolerance allowed) - The consistency ratio CR must be computed and reported; if CR > 0.1 a warning must be flagged </HARD-GATE> ## Pipeline 1. **Precondition check**: verify the dimension list is non-empty and its count is in the range [2, 9] 2. **Dimension list confirmation**: output the dimension list for the caller to confirm; if a comparison matrix is already provided, skip to step 4 3. **Pairwise comparison matrix construction**: for each pair of dimensions (i, j) assign a Saaty scale value (1-9); the matrix must satisfy a[j][i] = 1/a[i][j] 4. **Eigenvector computation**: normalize each column then take row means to obtain the priority vector (weights) 5. **Consistency ratio check**: compute the largest eigenvalue λ_max → consistency index CI = (λ_max - n)/(n-1) → CR = CI/RI (look up the Saaty RI table); CR < 0.1 is acceptable 6. **Output**: return the AHPWeights object; if CR > 0.1 attach revision suggestions ## Output Format ```json { "dimensions": ["importance", "feasibility", "novelty", "impact"], "comparison_matrix": [[1, 3, 2, 2], [0.33, 1, 0.5, 0.5], [0.5, 2, 1, 1], [0.5, 2, 1, 1]], "weights": { "importance": 0.40, "feasibility": 0.15, "novelty": 0.23, ...