mlai-textbooks 0.1.0

Textbook ML/AI algorithms (Bishop PRML + Norvig AIMA) backed by established libraries as subroutines

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