topsis-ranker

# TOPSIS Ranker ## Overview The TOPSIS Ranker skill implements the Technique for Order of Preference by Similarity to Ideal Solution methodology for multi-criteria decision analysis. It ranks alternatives based on their geometric distance from ideal and anti-ideal solutions, providing intuitive and mathematically sound rankings. ## Capabilities - Decision matrix normalization (vector, linear, max-min) - Weighted normalized matrix calculation - Ideal and anti-ideal solution identification - Euclidean distance calculation - Relative closeness coefficient computation - Alternative ranking generation - Sensitivity analysis on weights - Visualization of results ## Used By Processes - Multi-Criteria Decision Analysis (MCDA) - Tech Stack Evaluation - Geographic Market Analysis ## Usage ### Decision Matrix Construction ```python # Define decision matrix (alternatives x criteria) decision_matrix = { "alternatives": ["Option A", "Option B", "Option C", "Option D"], "criteria": ["Cost", "Quality", "Time", "Risk"], "values": [ [100000, 85, 12, 3], # Option A [150000, 92, 8, 2], # Option B [80000, 78, 15, 4], # Option C [120000, 88, 10, 2] # Option D ], "weights": [0.3, 0.35, 0.2, 0.15], "criteria_type": ["cost", "benefit", "cost", "cost"] # minimize/maximize } ``` ### Normalization Methods 1. **Vector Normalization**: r_ij = x_ij / sqrt(sum(x_ij^2)) 2. **Linear Normalization**: r_ij = x_ij / max(x_j) for benef...