Chokkhu

A Sovereign, Zero-Heavy-Dependency ML, Deep Learning, Computer Vision, NLP, Audio, Generative AI, GNN, Time Series, XAI, and AutoML Ecosystem Built from First Principles.

"Minimalistic Code. Sovereign Execution. Ultra-Lightweight Wheel (< 1 MB). Zero Heavy Dependencies. First Principles."

Table of Contents

1. Key Highlights & Sovereign Philosophy
2. Installation
3. Architecture & Feature Matrix
4. AutoML & End-to-End Pipeline Engine
5. Vector Retrieval & Approximate Nearest Neighbors (Milestone 1)
6. Recommendation Systems Universe (Milestone 2)
7. Causal Inference & Uplift Modeling (Milestone 3)
8. Survival Analysis & Reliability Engineering (Milestone 4)
9. Multi-Modal Vision-Language Universe (Milestone 5)
10. Scientific Machine Learning & PINNs (Milestone 6)
11. Algorithmic Fairness & Bias Mitigation (Milestone 7)
12. Edge AI, Quantization & Model Compression (Milestone 8)
13. 3D Computer Vision, Point Clouds & NeRF / 3DGS (Milestone 9)
14. Autonomous AI Agents & Execution Engines (Milestone 10)
15. Reinforcement Learning Universe (Milestone 11)
16. Advanced Graph Neural Networks Universe (Milestone 12)
17. Frontier LLMs, Linear Attention & Alignment (Milestone 13)
18. Classical ML, 2D Vision, Audio & Generative DL
19. Sovereign Explainable AI (XAI)
20. Next-Gen Generative AI, LoRA, Conformal & Stacking (Milestone 14)
21. License & Citation

Key Highlights & Sovereign Philosophy

• 100% Zero-Heavy-Dependency: Written strictly from fundamental mathematical formulations using only standard NumPy, SciPy, Pandas, Matplotlib, Seaborn, and OpenCV-headless. Zero reliance on PyTorch, TensorFlow, Scikit-Learn, Transformers, HuggingFace, SHAP, or Librosa.
• Ultra-Lightweight Distribution: An entire universe of production-ready machine learning, deep learning, computer vision, and LLMs packed into a single wheel under 1 Megabyte.
• Universal Compatibility: Cross-platform verification across Ubuntu Linux, Windows, and macOS on Python 3.9, 3.10, 3.11, 3.12, and 3.13.
• Battle-Tested Quality: 100% test pass rate with strict type checking (mypy), linting (flake8), and formatting (black).

Installation

Install Chokkhu effortlessly via pip:

pip install --upgrade chokkhu

Or install locally from source:

git clone https://github.com/tamimystic/chokkhu.git
cd chokkhu
pip install -e .

Architecture & Feature Matrix

1. AutoML & End-to-End Pipeline Engine

1.1 Automated Machine Learning in One Line (auto_train)

import chokkhu as ck
import numpy as np

# Synthetic classification dataset
X_train = np.random.randn(200, 10)
y_train = np.random.randint(0, 2, size=200)

# Run full AutoML search: profiles data, tests candidate models, performs CV ranking
result = ck.auto_train(
    X_train=X_train,
    y_train=y_train,
    task="classification",
    time_budget_secs=30,
    metric="accuracy",
    cv=5,
    random_state=42
)

print(result.summary())
y_pred = result.predict(X_train[:10])

Parameter Breakdown: ck.auto_train

1.2 Bayesian Optimization (BayesianOptimization)

from chokkhu.automl import BayesianOptimization

# Objective function to maximize
def objective(params):
    x, y = params[0], params[1]
    return -(x**2 + y**2) + 10.0

# Define parameter search bounds [(min_x, max_x), (min_y, max_y)]
bounds = [(-5.0, 5.0), (-5.0, 5.0)]

opt = BayesianOptimization(
    func=objective,
    bounds=bounds,
    n_init=5,
    n_iter=20,
    acq="ei",
    xi=0.01,
    random_state=42
)
best_params, best_value = opt.optimize()
print(f"Optimal parameters: {best_params}, Max objective: {best_value}")

Parameter Breakdown: BayesianOptimization

2. Vector Retrieval & Approximate Nearest Neighbors (ANN)

2.1 Hierarchical Navigable Small World (HNSWIndex)

import numpy as np
from chokkhu.models.retrieval import HNSWIndex

# 128-dimensional dense vector embeddings
embeddings = np.random.randn(1000, 128).astype(np.float32)
embeddings /= np.linalg.norm(embeddings, axis=1, keepdims=True)

# Build sovereign HNSW index
index = HNSWIndex(
    dim=128,
    max_elements=5000,
    M=16,
    ef_construction=100,
    ef_search=50,
    metric="cosine",
    random_state=42
)
index.add(embeddings)

# Query top-5 nearest neighbors
query = embeddings[0:1]
indices, distances = index.search(query, k=5)
print(f"Nearest indices: {indices}, Distances: {distances}")

Parameter Breakdown: HNSWIndex

2.2 Inverted File with Product Quantization (IVFPQIndex)

import numpy as np
from chokkhu.models.retrieval import IVFPQIndex

vectors = np.random.randn(2000, 64).astype(np.float32)

# Build compressed IVF-PQ index
ivfpq = IVFPQIndex(
    dim=64,
    n_clusters=16,
    n_subvectors=8,
    n_bits=8,
    n_probe=4,
    metric="l2",
    random_state=42
)
ivfpq.fit(vectors)
ivfpq.add(vectors)

query = vectors[0:1]
indices, distances = ivfpq.search(query, k=5)
print(f"IVF-PQ Top-5 matches: {indices}")

Parameter Breakdown: IVFPQIndex

3. Recommendation Systems Universe

3.1 DeepFM (Deep Factorization Machine)

import numpy as np
from chokkhu.models.recommendation import DeepFM

# Cardinalities for 3 categorical features: [100 users, 50 items, 10 categories]
cat_dims = [100, 50, 10]
model = DeepFM(
    cat_dims=cat_dims,
    num_dim=4,
    embedding_dim=16,
    dnn_hidden_dims=[64, 32],
    lr=0.001,
    dropout=0.1,
    random_state=42
)

# Training batch: categorical indices and continuous dense features
cat_inputs = np.array([[5, 12, 1], [42, 8, 3]])
num_inputs = np.random.randn(2, 4).astype(np.float32)
targets = np.array([1.0, 0.0])

# Forward pass & training step
preds = model.forward(cat_inputs, num_inputs)
loss = model.train_step(cat_inputs, num_inputs, targets)
print(f"DeepFM Predictions: {preds.squeeze()}, Loss: {loss:.4f}")

Parameter Breakdown: DeepFM

3.2 SASRec (Self-Attentive Sequential Recommendation)

import numpy as np
from chokkhu.models.recommendation import SASRec

# Sequence of interacted item IDs: shape (batch_size, max_seq_len)
item_sequences = np.array([
    [1, 5, 23, 45, 12],
    [3, 8, 19, 0, 0]    # 0 is padding
])

sasrec = SASRec(
    item_count=100,
    max_len=5,
    hidden_dim=32,
    num_heads=2,
    num_blocks=2,
    dropout_rate=0.1,
    lr=0.001,
    random_state=42
)

# Sequence embeddings & scores for candidate items
seq_embeddings = sasrec.forward(item_sequences)
print(f"Sequence representations shape: {seq_embeddings.shape}")

Parameter Breakdown: SASRec

4. Causal Inference & Uplift Modeling

4.1 Doubly Robust Learner (DoublyRobustLearner)

import numpy as np
from chokkhu.models.causal import DoublyRobustLearner

# Covariates (X), binary treatment (T), and observed outcome (y)
X = np.random.randn(500, 5)
T = np.random.binomial(1, 0.5, size=500)
# Real treatment effect = +2.5
y = 1.2 * X[:, 0] + 2.5 * T + np.random.randn(500) * 0.1

dr = DoublyRobustLearner(clip_min=0.02, clip_max=0.98, random_state=42)
dr.fit(X, T, y)

# Estimate Individual Treatment Effects (ITE) and Average Treatment Effect (ATE)
ite_preds = dr.predict_ite(X[:5])
ate_estimate = dr.estimate_ate(X, T, y)
print(f"Estimated ATE: {ate_estimate:.3f} (True: 2.500)")

Parameter Breakdown: DoublyRobustLearner

4.2 Qini Score & Cumulative Uplift (qini_score)

import numpy as np
from chokkhu.models.causal import qini_score, uplift_at_k

y_true = np.array([1, 0, 1, 1, 0, 0, 1, 0])
treatment = np.array([1, 1, 1, 0, 0, 1, 0, 0])
uplift_preds = np.array([0.8, 0.6, 0.4, 0.3, 0.2, 0.1, -0.1, -0.2])

score = qini_score(y_true=y_true, treatment=treatment, uplift_preds=uplift_preds)
top_k_uplift = uplift_at_k(y_true=y_true, treatment=treatment, uplift_preds=uplift_preds, k=0.5)
print(f"Qini Score: {score:.4f}, Uplift@50%: {top_k_uplift:.4f}")

Parameter Breakdown: qini_score

5. Survival Analysis & Reliability Engineering

5.1 Cox Proportional Hazards Regression (CoxPHRegression)

import numpy as np
from chokkhu.models.survival import CoxPHRegression, concordance_index

# Covariates, event durations, and binary censoring events (1=died, 0=censored)
X = np.random.randn(200, 4)
times = np.random.exponential(scale=10.0, size=200)
events = np.random.binomial(1, 0.7, size=200)

cox = CoxPHRegression(lr=0.01, max_iter=150, l2_reg=1e-4)
cox.fit(X, times, events)

# Predict partial hazard log-ratios and evaluate Harrell's C-index
risk_scores = cox.predict_risk(X)
c_idx = concordance_index(times, -risk_scores, events)
print(f"Concordance Index: {c_idx:.4f}")

Parameter Breakdown: CoxPHRegression

5.2 DeepSurv (Deep Cox Proportional Hazards)

import numpy as np
from chokkhu.models.survival import DeepSurv

X = np.random.randn(300, 6)
times = np.random.exponential(scale=12.0, size=300)
events = np.random.binomial(1, 0.8, size=300)

deep_surv = DeepSurv(
    input_dim=6,
    hidden_dims=[32, 16],
    lr=0.001,
    l2_reg=1e-4,
    epochs=50,
    batch_size=32,
    random_state=42
)
deep_surv.fit(X, times, events)
risks = deep_surv.predict_risk(X[:5])
print(f"DeepSurv Predicted Risk Scores: {risks.flatten()}")

Parameter Breakdown: DeepSurv

6. Multi-Modal Vision-Language Universe

6.1 Contrastive Language-Image Pretraining (CLIP)

import numpy as np
from chokkhu.models.multimodal import CLIP

# Create sovereign multi-modal CLIP model
clip = CLIP(
    vision_dim=128,
    text_dim=128,
    embed_dim=64,
    temperature=0.07,
    random_state=42
)

# Vision and text feature batches
img_features = np.random.randn(8, 128).astype(np.float32)
txt_features = np.random.randn(8, 128).astype(np.float32)

# Compute normalized embeddings & similarity matrix
img_emb, txt_emb, logits = clip.forward(img_features, txt_features)
loss = clip.compute_loss(img_emb, txt_emb)
print(f"CLIP Similarity Matrix Shape: {logits.shape}, InfoNCE Loss: {loss:.4f}")

Parameter Breakdown: CLIP

6.2 Perceiver Resampler (PerceiverResampler)

import numpy as np
from chokkhu.models.multimodal import PerceiverResampler

# Arbitrary-length visual tokens: (batch_size=2, num_tokens=196, dim=256)
visual_tokens = np.random.randn(2, 196, 256).astype(np.float32)

resampler = PerceiverResampler(
    dim=256,
    num_latents=32,
    latent_dim=512,
    num_heads=8,
    num_layers=3,
    random_state=42
)

# Resample 196 variable tokens into exactly 32 fixed LLM prefix tokens
fixed_prefix_tokens = resampler.forward(visual_tokens)
print(f"Resampled Visual Tokens Shape: {fixed_prefix_tokens.shape}") # (2, 32, 512)

Parameter Breakdown: PerceiverResampler

7. Scientific Machine Learning & PINNs

7.1 Burgers' Equation Physics-Informed Neural Network (BurgersPINN)

import numpy as np
from chokkhu.models.sciml import BurgersPINN

# Sovereign PINN for Burgers' PDE: u_t + u * u_x - nu * u_xx = 0
pinn = BurgersPINN(
    layers=[2, 32, 32, 1],
    nu=0.01 / np.pi,
    lr=0.001,
    random_state=42
)

# Spatio-temporal collocation points (x, t)
collocation_points = np.random.uniform(-1.0, 1.0, size=(100, 2))
bc_points = np.array([[-1.0, 0.2], [1.0, 0.2]]) # boundary conditions
bc_u = np.array([[0.0], [0.0]])

# Physics-informed training step (data loss + PDE residual loss)
total_loss, pde_residual = pinn.train_step(collocation_points, bc_points, bc_u)
print(f"PINN Total Loss: {total_loss:.5f}, PDE Residual: {pde_residual:.5f}")

Parameter Breakdown: BurgersPINN

7.2 Neural Ordinary Differential Equations (NeuralODE)

import numpy as np
from chokkhu.models.sciml import NeuralODE

# Model continuous dynamical state trajectory dx/dt = f_theta(x, t)
node = NeuralODE(
    in_features=2,
    hidden_dim=32,
    method="rk4",
    random_state=42
)

# Initial state at t0
x0 = np.array([[1.0, 0.0], [-0.5, 0.8]])
t_span = np.linspace(0.0, 2.0, num=10)

# Integrate trajectory forward in time via 4th-order Runge-Kutta
trajectory = node.forward(x0, t_span)
print(f"Trajectory shape: {trajectory.shape}") # (10 timestamps, 2 samples, 2 dims)

Parameter Breakdown: NeuralODE

8. Algorithmic Fairness & Bias Mitigation

8.1 Fairness Metrics & Disparate Impact Remediation

import numpy as np
from chokkhu.fairness import (
    demographic_parity_difference,
    equalized_odds_difference,
    DisparateImpactRemover,
    ReweighingTransformer
)

# Binary predictions, true labels, and protected attribute (e.g., 0 vs 1)
y_pred = np.array([1, 1, 0, 1, 0, 0, 1, 0])
y_true = np.array([1, 0, 0, 1, 1, 0, 1, 0])
sensitive_attr = np.array([1, 1, 1, 1, 0, 0, 0, 0])

dp_diff = demographic_parity_difference(y_pred, sensitive_attr)
eq_odds = equalized_odds_difference(y_true, y_pred, sensitive_attr)
print(f"Demographic Parity Diff: {dp_diff:.4f}, Equalized Odds Diff: {eq_odds:.4f}")

# Pre-processing mitigation: Repair feature distributions
features = np.random.randn(8, 4)
remover = DisparateImpactRemover(repair_level=0.8)
repaired_features = remover.fit_transform(features, sensitive_attr)

Parameter Breakdown: DisparateImpactRemover

9. Edge AI, Quantization & Model Compression

9.1 Uniform Post-Training Quantization (PostTrainingQuantizer)

import numpy as np
from chokkhu.compression import PostTrainingQuantizer

# Dense FP32 weight matrix
weights_fp32 = np.random.randn(256, 128).astype(np.float32)

ptq = PostTrainingQuantizer(
    target_bits=8,
    method="uniform_symmetric",
    per_channel=True
)

# Quantize to INT8 integers and recover dequantized approximation
q_weights, scales, zero_points = ptq.quantize(weights_fp32)
recovered_fp32 = ptq.dequantize(q_weights, scales, zero_points)

quant_error = np.mean(np.abs(weights_fp32 - recovered_fp32))
print(f"Quantized Dtype: {q_weights.dtype}, Mean Absolute Quant Error: {quant_error:.6f}")

Parameter Breakdown: PostTrainingQuantizer

10. 3D Computer Vision, Point Clouds & NeRF / 3DGS

10.1 PointNet++ Hierarchical Point Cloud Classifier (PointNet2Classifier)

import numpy as np
from chokkhu.models.vision_3d import PointNet2Classifier

# 3D Point cloud batch: (batch_size=2, num_points=512, xyz_coords=3)
point_clouds = np.random.randn(2, 512, 3).astype(np.float32)

pnet2 = PointNet2Classifier(
    num_classes=10,
    in_channels=3,
    random_state=42
)

# Forward pass using Set Abstraction (Farthest Point Sampling + Ball Query grouping)
logits = pnet2.forward(point_clouds)
print(f"PointNet++ Logits Shape: {logits.shape}") # (2, 10)

Parameter Breakdown: PointNet2Classifier

10.2 Neural Radiance Fields (NeRFMLP & volume_render)

import numpy as np
from chokkhu.models.vision_3d import NeRFMLP, volume_render, generate_camera_rays

# Generate camera rays from camera pose matrix
c2w = np.eye(4, dtype=np.float32)
rays_o, rays_d = generate_camera_rays(H=64, W=64, focal=50.0, c2w=c2w)

nerf = NeRFMLP(pos_dim=63, dir_dim=27, hidden_dim=128, num_layers=6)

# Query positional densities and directional radiance colors
sample_pts = np.random.randn(100, 63).astype(np.float32)
sample_dirs = np.random.randn(100, 27).astype(np.float32)
density, rgb = nerf.forward(sample_pts, sample_dirs)

# Differentiable volume rendering along ray z-steps
z_vals = np.linspace(2.0, 6.0, 100, dtype=np.float32)
rendered_rgb, depth_map, acc_map = volume_render(density, rgb, z_vals, rays_d[:100])
print(f"Rendered RGB shape: {rendered_rgb.shape}")

Parameter Breakdown: NeRFMLP

11. Autonomous AI Agents & Execution Engines

11.1 ReAct Reasoning & Acting Agent (ReActAgent)

from chokkhu.models.nlp.agents import Tool, ToolRegistry, ReActAgent

# 1. Register custom Python execution tools
registry = ToolRegistry()

def add_numbers(a: float, b: float) -> float:
    """Add two numbers together."""
    return float(a) + float(b)

registry.register(Tool(
    name="calculator",
    func=add_numbers,
    description="Adds two numbers a and b together."
))

# 2. Initialize sovereign ReAct agent
agent = ReActAgent(
    tools=registry,
    max_steps=5,
    verbose=True
)

# Run deterministic agent loop
result = agent.run("Calculate the sum of 15.5 and 24.5")
print(f"Agent Final Output: {result.output}")

Parameter Breakdown: ReActAgent

11.2 Tree of Thoughts Reasoning (TreeOfThoughts)

from chokkhu.models.nlp.agents import TreeOfThoughts

# Custom thought evaluator heuristic
def custom_evaluator(thought_state: str) -> float:
    return 1.0 if "valid" in thought_state else 0.2

tot = TreeOfThoughts(
    max_depth=3,
    num_branches=3,
    search_method="bfs",
    evaluator=custom_evaluator
)

best_path = tot.solve(initial_problem="Solve constraint satisfaction riddle")
print(f"Best reasoning trajectory: {best_path}")

Parameter Breakdown: TreeOfThoughts

12. Reinforcement Learning Universe

12.1 Proximal Policy Optimization with GAE (PPO)

import numpy as np
from chokkhu.models.rl import PPO

# State dim = 4 (e.g. CartPole), Action dim = 2
ppo = PPO(
    state_dim=4,
    action_dim=2,
    actor_hidden=[64, 64],
    critic_hidden=[64, 64],
    lr_actor=0.0003,
    lr_critic=0.001,
    gamma=0.99,
    gae_lambda=0.95,
    clip_epsilon=0.2,
    ppo_epochs=10,
    batch_size=32,
    entropy_coeff=0.01,
    random_state=42
)

# Collect trajectory transitions
state = np.array([0.1, -0.2, 0.05, 0.3])
action, log_prob, value = ppo.select_action(state)

# Update policy with Generalized Advantage Estimation (GAE)
states = np.random.randn(64, 4)
actions = np.random.randint(0, 2, size=64)
old_log_probs = np.random.randn(64)
rewards = np.random.randn(64)
dones = np.zeros(64, dtype=bool)
values = np.random.randn(64)

loss_dict = ppo.update(states, actions, old_log_probs, rewards, dones, values)
print(f"PPO Loss Info: {loss_dict}")

Parameter Breakdown: PPO

12.2 Soft Actor-Critic (SAC) & Multi-Armed Bandits (LinUCBBandit)

import numpy as np
from chokkhu.models.rl import SAC, LinUCBBandit

# 1. Soft Actor-Critic for continuous control
sac = SAC(
    state_dim=8,
    action_dim=2,
    actor_hidden=[64, 64],
    critic_hidden=[64, 64],
    lr=0.0003,
    gamma=0.99,
    tau=0.005,
    alpha=0.2,
    random_state=42
)
action = sac.select_action(np.random.randn(8))

# 2. Contextual Multi-Armed Bandits with LinUCB
bandit = LinUCBBandit(n_arms=5, context_dim=10, alpha=1.0, random_state=42)
context = np.random.randn(10)
chosen_arm = bandit.select_arm(context)
bandit.update(arm=chosen_arm, context=context, reward=1.0)
print(f"SAC Action: {action}, LinUCB Chosen Arm: {chosen_arm}")

Parameter Breakdown: SAC

13. Advanced Graph Neural Networks Universe

13.1 Graphormer with Laplacian Positional Encoding (Graphormer)

import numpy as np
from chokkhu.models.gnn import Graphormer, LaplacianPositionalEncoding

# Node features: (num_nodes=6, in_features=16)
x = np.random.randn(6, 16).astype(np.float32)
# Adjacency matrix
adj = np.array([
    [0, 1, 1, 0, 0, 0],
    [1, 0, 1, 0, 0, 0],
    [1, 1, 0, 1, 0, 0],
    [0, 0, 1, 0, 1, 1],
    [0, 0, 0, 1, 0, 1],
    [0, 0, 0, 1, 1, 0]
], dtype=np.float32)

# Compute graph Laplacian positional eigenvectors
pe_encoder = LaplacianPositionalEncoding(k=4, normalization="sym")
lap_pe = pe_encoder.compute(adj)

graphormer = Graphormer(
    in_features=16,
    hidden_dim=32,
    out_features=8,
    num_heads=4,
    num_layers=2,
    pe_dim=4,
    dropout=0.1,
    random_state=42
)

node_embeddings = graphormer.forward(x, adj, pe=lap_pe)
print(f"Graphormer Output Node Embeddings Shape: {node_embeddings.shape}") # (6, 8)

Parameter Breakdown: Graphormer

13.2 Equivariant GNN (EGNN) & Relational GCN (RGCNClassifier)

import numpy as np
from chokkhu.models.gnn import EGNN, RGCNClassifier

# 1. E(n) Equivariant Graph Neural Network for 3D Molecular Coordinates
h = np.random.randn(5, 16).astype(np.float32)      # Invariant scalar features
x_coords = np.random.randn(5, 3).astype(np.float32) # 3D Cartesian coordinates
edge_index = np.array([[0, 1, 2, 3], [1, 2, 3, 4]])

egnn = EGNN(
    in_node_features=16,
    hidden_dim=32,
    out_node_features=16,
    num_layers=2,
    coord_dim=3,
    random_state=42
)
h_out, x_coords_out = egnn.forward(h, x_coords, edge_index)
print(f"EGNN Equivariant 3D Coordinates Output Shape: {x_coords_out.shape}")

# 2. Relational Graph Convolutional Network (Knowledge Graphs)
edge_types = np.array([0, 1, 0, 2])
rgcn = RGCNClassifier(
    in_features=16,
    hidden_dim=32,
    out_features=4,
    num_relations=3,
    num_layers=2,
    random_state=42
)
node_logits = rgcn.forward(h, edge_index, edge_types)
print(f"R-GCN Logits Shape: {node_logits.shape}")

Parameter Breakdown: EGNN

14. Frontier LLMs, Linear Attention & Alignment

14.1 DeepSeek-V3 MoE with Multi-Head Latent Attention (DeepSeekV3)

import numpy as np
from chokkhu.models.nlp import DeepSeekV3

# Token sequence: (batch_size=2, seq_len=8)
input_tokens = np.array([
    [12, 45, 89, 230, 412, 18, 92, 5],
    [7, 102, 304, 511, 22, 67, 88, 14]
])

model = DeepSeekV3(
    vocab_size=1000,
    hidden_dim=64,
    num_layers=2,
    num_heads=4,
    q_lora_rank=16,
    kv_lora_rank=16,
    n_routed_experts=4,
    n_shared_experts=1,
    top_k=2,
    mtp_depth=1,
    random_state=42
)

# Forward pass computing next-token logits + auxiliary MoE load balancing loss
logits, aux_loss = model.forward(input_tokens)
print(f"DeepSeek-V3 Logits Shape: {logits.shape}, MoE Auxiliary Loss: {aux_loss:.4f}")

Parameter Breakdown: DeepSeekV3

14.2 Mamba Selective State Space Model (Mamba) & RWKV-v6 (RWKV6)

import numpy as np
from chokkhu.models.nlp import Mamba, RWKV6

input_ids = np.array([[15, 34, 128, 492, 10]])

# 1. Sovereign Mamba (S6 Selective State Space Model)
mamba = Mamba(
    vocab_size=1000,
    d_model=64,
    d_state=16,
    d_conv=4,
    expand=2,
    num_layers=2,
    random_state=42
)
mamba_logits = mamba.forward(input_ids)
print(f"Mamba S6 Logits Shape: {mamba_logits.shape}")

# 2. Sovereign RWKV-v6 (WKV linear attention with dynamic time-mixing)
rwkv = RWKV6(
    vocab_size=1000,
    hidden_dim=64,
    num_layers=2,
    head_size=16,
    random_state=42
)
rwkv_logits = rwkv.forward(input_ids)
print(f"RWKV-v6 Logits Shape: {rwkv_logits.shape}")

Parameter Breakdown: Mamba

14.3 Direct Preference Optimization (DPOTrainer)

import numpy as np
from chokkhu.models.nlp import DPOTrainer, MiniGPT

# Create policy model and frozen reference model
policy = MiniGPT(vocab_size=500, hidden_dim=64, num_layers=2, num_heads=2)
ref_model = MiniGPT(vocab_size=500, hidden_dim=64, num_layers=2, num_heads=2)

dpo = DPOTrainer(
    model=policy,
    ref_model=ref_model,
    beta=0.1,
    lr=1e-4
)

# Chosen vs. Rejected completion token batches
prompt_chosen = np.array([[10, 25, 42, 100], [12, 19, 88, 204]])
prompt_rejected = np.array([[10, 25, 42, 311], [12, 19, 88, 499]])

loss, implicit_reward_chosen, implicit_reward_rejected = dpo.train_step(
    prompt_chosen=prompt_chosen,
    prompt_rejected=prompt_rejected
)
print(f"DPO Loss: {loss:.4f}, Margin: {(implicit_reward_chosen - implicit_reward_rejected).mean():.4f}")

Parameter Breakdown: DPOTrainer

15. Classical ML, 2D Vision, Audio & Generative DL

15.1 Generative AI: Variational Autoencoder (VAE) & Diffusion (DDPM)

import numpy as np
from chokkhu.models import VAE, DDPM

# 1. Variational Autoencoder (VAE)
images_flat = np.random.randn(100, 784).astype(np.float32)
vae = VAE(input_dim=784, latent_dim=32, hidden_dim=128)
recon, mu, logvar = vae.forward(images_flat)
vae_loss = vae.compute_loss(images_flat, recon, mu, logvar)
print(f"VAE Loss: {vae_loss:.4f}")

# 2. Denoising Diffusion Probabilistic Model (DDPM)
ddpm = DDPM(input_dim=784, timesteps=1000, beta_start=1e-4, beta_end=0.02)
sampled_images = ddpm.sample(num_samples=4)
print(f"DDPM Generated Samples Shape: {sampled_images.shape}")

Parameter Breakdown: DDPM

15.2 Speech & Audio Signal Processing (STFT, MFCC, Conformer)

import numpy as np
from chokkhu.models import stft, melspectrogram, mfcc, Conformer

# 1-second 16kHz raw audio waveform
waveform = np.sin(2 * np.pi * 440 * np.linspace(0, 1, 16000)).astype(np.float32)

# Extract Mel-Spectrogram and MFCCs
mel_spec = melspectrogram(waveform, sample_rate=16000, n_fft=512, hop_length=160, n_mels=80)
mfcc_features = mfcc(waveform, sample_rate=16000, n_mfcc=13)
print(f"Mel-Spectrogram Shape: {mel_spec.shape}, MFCC Shape: {mfcc_features.shape}")

# Speech Conformer (Macaron-style FFN + MHA + Depthwise Conv)
conformer = Conformer(
    input_dim=80,
    num_heads=4,
    ffn_dim=128,
    num_blocks=2,
    conv_kernel_size=15
)
spec_batch = np.random.randn(2, 100, 80).astype(np.float32)
conformer_out = conformer.forward(spec_batch)
print(f"Conformer Output Shape: {conformer_out.shape}")

Parameter Breakdown: Conformer

16. Sovereign Explainable AI (XAI)

16.1 Integrated Gradients & DeepLIFT (IntegratedGradients, DeepLIFT)

import numpy as np
import chokkhu as ck
from chokkhu.explainability import IntegratedGradients, DeepLIFT

# Create a trained neural model
model = ck.NeuralNetwork(layers=[10, 32, 2], activation="relu")
sample = np.random.randn(1, 10)

# 1. Axiomatic feature attribution via Integrated Gradients
ig = IntegratedGradients(model=model, steps=50, baseline=None)
attributions = ig.attribute(sample, target_class=0)
print(f"Integrated Gradients Attributions Shape: {attributions.shape}")

# 2. DeepLIFT difference-from-reference conservation
dlift = DeepLIFT(model=model, baseline=np.zeros((1, 10)))
dlift_attributions = dlift.attribute(sample, target_class=0)
print(f"DeepLIFT Attributions: {dlift_attributions.flatten()}")

Parameter Breakdown: IntegratedGradients

17. Next-Gen Generative AI, LoRA, Conformal & Stacking (Milestone 14)

17.1 Continuous ODE Flow Matching & Rectified Flows (FlowMatching, RectifiedFlow)

import numpy as np
from chokkhu.models.generative import FlowMatching, RectifiedFlow

# Generate 2D training data
X_train = np.random.randn(200, 2) * 0.5 + 2.0

# 1. Continuous Velocity Field Flow Matching with Optimal Transport
flow = FlowMatching(input_dim=2, hidden_dim=64, num_layers=3, lr=1e-3, sigma_min=1e-4)
flow.fit(X_train, epochs=20, batch_size=32)

# Sample new points using 4th-order Runge-Kutta (RK4) integration
synthetic_samples = flow.sample(num_samples=100, steps=50, method="rk4")
print(f"Generated Flow Samples Shape: {synthetic_samples.shape}")

# 2. Rectified Flow interpolation with straight-line trajectories
rect_flow = RectifiedFlow(input_dim=2, hidden_dim=32, lr=1e-3)
rect_flow.fit(X_train, epochs=10)
rect_samples = rect_flow.sample(num_samples=50, steps=20, method="midpoint")
print(f"Rectified Flow Samples Shape: {rect_samples.shape}")

Parameter Breakdown: FlowMatching

17.2 Invertible Normalizing Flows (RealNVP, AffineCouplingLayer)

import numpy as np
from chokkhu.models.generative import RealNVP

# Continuous tabular / latent features
X = np.random.randn(100, 4)

# Multi-layer RealNVP with analytical Jacobian log-determinant
nvp = RealNVP(input_dim=4, num_layers=4, hidden_dim=32, lr=1e-3)
nvp.fit(X, epochs=15, batch_size=16)

# Exact log-likelihood computation
log_probs = nvp.log_prob(X)
print(f"Mean Log-Likelihood: {np.mean(log_probs):.4f}")

# Invertible sampling from latent base standard Gaussian
generated_data = nvp.sample(num_samples=50)
print(f"RealNVP Sample Shape: {generated_data.shape}")

Parameter Breakdown: RealNVP

17.3 Parameter-Efficient Fine-Tuning (LoRALinear, LoRAAdapter)

import numpy as np
from chokkhu.models.generative import LoRALinear, LoRAAdapter

# 1. Direct Low-Rank Linear Adaptation: W_adapted = W0 + (alpha / r) * (B @ A)
linear = LoRALinear(in_features=128, out_features=64, rank=4, alpha=8.0)

x = np.random.randn(8, 128)
y = linear.forward(x)
print(f"Adapted Linear Output Shape: {y.shape}")

# Merge adapter weights directly into frozen base weight matrix for zero-latency inference
linear.merge()
y_merged = linear.forward(x)
np.testing.assert_allclose(y, y_merged, atol=1e-5)

# 2. Attach PEFT LoRA adapters to arbitrary deep architectures
model_weights = {
    "encoder.attn.q_proj": np.random.randn(64, 64),
    "encoder.attn.v_proj": np.random.randn(64, 64),
    "classifier.dense": np.random.randn(64, 10),
}
adapter = LoRAAdapter(model_weights, target_modules=["attn.q_proj", "attn.v_proj"], rank=4, alpha=8.0)
print(f"Total Base Params: {adapter.count_parameters()['base_params']}, Trainable LoRA Params: {adapter.count_parameters()['trainable_lora_params']}")

Parameter Breakdown: LoRALinear

17.4 Distribution-Free Time Series Conformal Prediction (ConformalPredictor)

import numpy as np
from chokkhu.models.timeseries import ConformalPredictor, conformal_interval
from chokkhu.models import LinearRegression

# Generate synthetic sequential / regression dataset
X = np.linspace(0, 10, 150).reshape(-1, 1)
y = 2.5 * X.flatten() + np.sin(X.flatten()) + np.random.randn(150) * 0.5

# Base forecaster
base_model = LinearRegression()
base_model.fit(X[:80], y[:80])

# Fit conformal prediction interval with exact 95% coverage guarantee
conformal = ConformalPredictor(base_model=base_model, alpha=0.05, method="split")
conformal.calibrate(X[80:110], y[80:110])

# Predict lower and upper confidence bounds
y_pred, lower, upper = conformal.predict_interval(X[110:])
coverage = np.mean((y[110:] >= lower) & (y[110:] <= upper))
print(f"Guaranteed Coverage: >= 95%, Empirical Empirical Coverage: {coverage * 100:.1f}%")

Parameter Breakdown: ConformalPredictor

17.5 Matrix Profile Time Series Motif & Discord Mining (MatrixProfile)

import numpy as np
from chokkhu.models.timeseries import MatrixProfile, find_motifs, find_discords

# Generate continuous time series with an embedded anomaly
np.random.seed(42)
t = np.linspace(0, 50, 500)
ts = np.sin(t) + np.random.randn(500) * 0.05
ts[250:270] += 4.0  # Anomaly burst

# Fast STOMP / STAMP sliding-window distance profile computation
mp = MatrixProfile(window_size=30)
profile, profile_idx = mp.fit_transform(ts)

# Identify recurring patterns (motifs) and anomalous subsequences (discords)
motifs = find_motifs(profile, profile_idx, top_k=2)
discords = find_discords(profile, top_k=1)
print(f"Top Discord (Anomaly) Subsequence Index: {discords[0]}")

Parameter Breakdown: MatrixProfile

17.6 Multi-Fidelity Bayesian Optimization & HyperBand (BOHB)

import numpy as np
from chokkhu.automl import BOHB

# Objective function simulating hyperparameter tuning across variable budget (e.g. epochs)
def objective_fn(config, budget):
    lr = config["lr"]
    hidden = config["hidden"]
    # Simulated loss decreasing with optimal lr & budget
    loss = (np.log10(lr) + 3.0)**2 + (hidden - 64)**2 * 1e-4 + (1.0 / budget)
    return float(loss)

# Hyperparameter search space
param_distributions = {
    "lr": ("log_uniform", 1e-4, 1e-1),
    "hidden": ("int_uniform", 16, 128),
}

# Run BOHB successive halving optimization
bohb = BOHB(
    objective_fn=objective_fn,
    param_distributions=param_distributions,
    min_budget=1,
    max_budget=9,
    eta=3,
    num_iterations=3
)
best_config, best_loss = bohb.optimize()
print(f"Optimal Hyperparameters: {best_config}, Minimum Loss: {best_loss:.5f}")

Parameter Breakdown: BOHB

17.7 SuperLearner Out-of-Fold Cross-Validated Stacking (SuperLearner)

import numpy as np
from chokkhu.automl import SuperLearner
from chokkhu.models import LogisticRegression, DecisionTreeClassifier, RandomForestClassifier

# Multi-class classification dataset
X = np.random.randn(200, 10)
y = (X[:, 0] + X[:, 1] > 0).astype(int)

# Define diverse ensemble of base learners
base_learners = [
    ("logreg", LogisticRegression()),
    ("dt", DecisionTreeClassifier(max_depth=4)),
    ("rf", RandomForestClassifier(n_estimators=10, max_depth=4)),
]

# Construct SuperLearner with out-of-fold CV meta-learning
super_learner = SuperLearner(
    base_models=base_learners,
    meta_model="ridge",
    cv=5,
    task="classification",
    use_probabilities=True
)
super_learner.fit(X, y)

# Predict class probabilities and labels
y_pred_proba = super_learner.predict_proba(X[:5])
y_pred = super_learner.predict(X[:5])
print(f"Ensemble Predictions: {y_pred}")
print(f"Optimal Base Learner Weights: {super_learner.get_weights()}")

Parameter Breakdown: SuperLearner

License & Citation

Distributed under the MIT License. See LICENSE for details.

@software{chokkhu2026,
  author = {tamimystic and Inception BD Team},
  title = {Chokkhu: Sovereign Zero-Dependency Machine Learning and LLM Ecosystem},
  year = {2026},
  publisher = {GitHub},
  url = {https://github.com/tamimystic/chokkhu}
}