菜单
GNN advanced statistical analysis and result aggregation. Use when performing statistical analysis on simulation results, cross-simulation aggregation, computing information-theoretic metrics, or creating analytical visualizations of pipeline outputs.
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基于 SOC 职业分类
Generalized Notation Notation (GNN) processing pipeline for Active Inference generative models. Use when working with GNN files, running the 25-step pipeline, parsing model specifications, generating simulations, or producing visualizations and reports from GNN model definitions.
GNN command-line interface dispatch and health checks. Use when invoking the `gnn` CLI, validating subcommand availability, or checking command-line access to pipeline modules.
GNN LLM-enhanced analysis and model interpretation. Use when generating natural language descriptions of GNN models, getting AI-assisted model explanations, or performing LLM-powered analysis of Active Inference specifications.
GNN Language Server Protocol integration. Use when starting editor diagnostics, checking hover behavior, or validating LSP support for GNN files.
GNN Model Context Protocol processing and tool registration. Use when registering GNN operations as MCP tools, building MCP server configurations, or integrating GNN capabilities with LLM tool-use workflows.
GNN pipeline orchestration and configuration management. Use when configuring pipeline execution, setting step inclusion/exclusion, managing pipeline state, or customizing the 25-step execution flow.
| name | gnn-statistical-analysis |
| description | GNN advanced statistical analysis and result aggregation. Use when performing statistical analysis on simulation results, cross-simulation aggregation, computing information-theoretic metrics, or creating analytical visualizations of pipeline outputs. |
Performs advanced statistical analysis on pipeline outputs including simulation results, cross-framework comparison, Active Inference metrics, and comprehensive statistical reporting.
# Run analysis
python src/16_analysis.py --target-dir input/gnn_files --output-dir output --verbose
# As part of pipeline
python src/main.py --only-steps 16 --verbose
from analysis import (
process_analysis, perform_statistical_analysis,
calculate_variable_statistics, calculate_connection_statistics,
calculate_complexity_metrics, calculate_maintainability_index,
analyze_framework_outputs, generate_framework_comparison_report,
analyze_simulation_traces, analyze_free_energy,
compute_shannon_entropy, compute_kl_divergence,
compute_variational_free_energy, compute_expected_free_energy,
generate_analysis_summary
)
# Process analysis step (used by pipeline)
process_analysis(target_dir, output_dir, verbose=True)
# Statistical analysis
stats = perform_statistical_analysis(parsed_data)
# Complexity metrics
metrics = calculate_complexity_metrics(model_data)
# Framework comparison
report = generate_framework_comparison_report(results)
# Active Inference metrics
entropy = compute_shannon_entropy(distribution)
kl_div = compute_kl_divergence(p, q)
vfe = compute_variational_free_energy(beliefs, observations)
efe = compute_expected_free_energy(policy, beliefs)
process_analysis — main pipeline processing functionperform_statistical_analysis — core statistical analysiscalculate_complexity_metrics, calculate_maintainability_index, calculate_technical_debtanalyze_framework_outputs, generate_framework_comparison_reportcompute_shannon_entropy, compute_kl_divergence, compute_variational_free_energy, compute_expected_free_energyanalyze_simulation_traces, analyze_free_energy, analyze_policy_convergencegenerate_analysis_summary — summary report generationoutput/16_analysis_output/This module registers tools with the GNN MCP server (see mcp.py):
compute_complexity_metricsget_analysis_resultslist_analysis_toolsprocess_analysis