mlai-textbooks
pip install mlai-textbooks → import ml_ai_library
Textbook ML/AI algorithms from Bishop PRML and Norvig AIMA — every algorithm delegates to an established library as its numerical engine. No hand-rolled subroutines.
| Module | Algorithms | Backed by |
|---|---|---|
bishop.linear_models |
Bayesian LR, Logistic IRLS, RVM | sklearn, numpy |
bishop.sampling |
Rejection, Importance, MH, Gibbs, Ensemble MCMC | emcee, scipy |
bishop.sequential |
Kalman Filter, RTS Smoother, Particle Filter | scipy.linalg, numpy |
bishop.mixture_models |
GMM, Bayesian GMM, K-Means | sklearn |
bishop.dimensionality |
PCA, Kernel PCA, Factor Analysis, t-SNE | sklearn |
bishop.kernel_methods |
GP Regression, SVM, Kernel PCA | sklearn |
bishop.neural_networks |
MLP, CNN, RNN, VAE, GAN | PyTorch |
norvig.search |
BFS, DFS, IDDFS, UCS, A*, Greedy, Beam | networkx |
norvig.csp |
Backtracking, AC-3 | python-constraint2 |
norvig.logic |
PropKB (TELL/ASK), Unify, FOL-BC | sympy |
norvig.adversarial |
Minimax, Alpha-Beta, MCTS | mcts |
norvig.mdp |
Value Iteration, Policy Iteration | numpy |
norvig.nlp |
N-Gram LM, CYK Parser, Viterbi POS | nltk |
norvig.game_theory |
Nash Equilibria, Maximin, Dominant Strategy | nashpy |
norvig.planning |
STRIPS, HTN Planning | (pure Python) |
norvig.rl |
Q-Learning, SARSA, REINFORCE | gymnasium, PyTorch |
llm_agents.* |
ReAct, Planning, Logic, RL-Policy, Multi-Agent | litellm |
Quick Start
# A* search using networkx internally
from ml_ai_library.norvig.search import GraphProblem, astar_search
import networkx as nx
G = nx.DiGraph()
G.add_edge('A', 'B', weight=1); G.add_edge('B', 'C', weight=2)
node = astar_search(GraphProblem(G, 'A', 'C'))
print(node.solution()) # ['B', 'C']
# Nash equilibria via nashpy
from ml_ai_library.norvig.game_theory import NormalFormGame, nash_equilibria
import numpy as np
game = NormalFormGame(np.array([[3,0],[5,1]]), np.array([[3,5],[0,1]]))
print(nash_equilibria(game))
# Ensemble MCMC via emcee
from ml_ai_library.bishop.sampling import emcee_sample
samples = emcee_sample(lambda x: -0.5 * x @ x, ndim=2, n_steps=500)
# Kalman filter via scipy.linalg
from ml_ai_library.bishop.sequential import KalmanFilter
import numpy as np
kf = KalmanFilter(F=np.eye(2), H=np.eye(2),
Q=0.01*np.eye(2), R=0.1*np.eye(2),
x0=np.zeros(2), P0=np.eye(2))
means, covs = kf.filter(observations)
# LLM ReAct agent
from ml_ai_library.llm_agents.react_agent import ReActAgent
agent = ReActAgent(model="gpt-4o-mini")
print(agent.run("What is 2+2?"))
License
MIT
1 maintainer
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