Information theory library: core measures (entropy, MI, KL divergence), advanced methods (channel capacity, geometry, decomposition), and sequence analysis.
| Directory |
Purpose |
core/ |
Discrete and continuous entropy, MI, KL divergence, estimation |
advanced/ |
Channel capacity, information geometry, PID, hypothesis testing |
analysis/ |
Sequence information profiles, complexity, dataset comparison |
| File |
Key Functions |
syntactic.py |
shannon_entropy(), mutual_information(), kl_divergence(), jensen_shannon_divergence(), transfer_entropy(), renyi_entropy() |
continuous.py |
differential_entropy(), mutual_information_continuous(), copula_entropy(), information_flow_network() |
estimation.py |
entropy_estimator() (plugin, Miller-Madow, Chao-Shen, jackknife), entropy_bootstrap_confidence() |
| File |
Key Functions |
channel.py |
channel_capacity(), rate_distortion(), information_bottleneck() |
decomposition.py |
partial_information_decomposition(), co_information(), o_information() |
hypothesis.py |
mi_permutation_test(), independence_test(), entropy_confidence_interval() |
semantic.py |
semantic_similarity(), information_content(), semantic_entropy() |
geometry.py |
Re-exports from fisher_rao.py and information_projection.py |
| File |
Key Functions |
analysis.py |
information_profile(), information_signature(), analyze_sequence_information() |
advanced_analysis.py |
fisher_information(), variation_of_information() |
from metainformant.information.metrics.core.syntactic import shannon_entropy, mutual_information
from metainformant.information.metrics.advanced.channel import channel_capacity
h = shannon_entropy([0.5, 0.25, 0.25])
mi = mutual_information(x_data, y_data)
cap = channel_capacity(transition_matrix)