matplotlab 0.1.2

Extended plotting and ML utilities library

Matplotlab

Extended plotting and machine learning utilities library for educational purposes.

A comprehensive Python library providing:

• Reinforcement Learning (RL) - Monte Carlo, TD Learning, Policy/Value Iteration, Dynamic Programming
• Artificial Neural Networks (ANN) - Deep Learning implementations with PyTorch and TensorFlow
• Visualization Tools - Enhanced plotting capabilities for ML workflows

Installation

# Install from PyPI
pip install matplotlab

# Or install from source
git clone https://github.com/Sohail-Creates/matplotlab.git
cd matplotlab
pip install -e .

Quick Start

Reinforcement Learning

from matplotlab import rl

# Create environment and find optimal policy
env = rl.create_frozenlake_env()
policy, V, iterations = rl.policy_iteration(env, gamma=0.99)
print(f"Converged in {iterations} iterations")

# Visualize results
rl.plot_value_heatmap(V)
rl.plot_grid_policy(policy)

# NEW: See complete lab workflow (Lab 1-6 + OEL)
rl.flowlab3()  # Shows complete Lab 3 code from import to visualization
rl.flowlab5()  # Shows complete Lab 5 (Policy Iteration) workflow

Artificial Neural Networks

from matplotlab import ann
import torch.nn as nn

# Create simple MLP model
model = ann.create_mlp_model(input_size=10, hidden_sizes=[16, 8], output_size=1)

# Or create CNN
cnn_model = ann.create_fashion_cnn()

# Training is straightforward
for epoch in range(50):
    y_pred = model(X_train)
    loss = loss_fn(y_pred, y_train)
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()

Features

✅ Reinforcement Learning Module (43 functions)

• Environments: FrozenLake, Custom GridWorld
• Algorithms: Monte Carlo, TD Learning, Policy Iteration, Value Iteration
• MDP Utilities: State transitions, reward functions, probability computations
• Visualization: Heatmaps, policy arrows, convergence plots
• Lab Workflows (NEW): Complete code references for Labs 1-6 and OEL
  • flowlab1() through flowlab6() - Show full lab code workflows
  • flowoel() - Complete OEL implementation reference
  • Perfect for when you forget the sequence of steps!

✅ Artificial Neural Networks Module (76 functions)

• Tensor Operations: PyTorch basics, autograd
• Perceptron: sklearn implementation
• ADALINE: Manual and PyTorch versions
• MLP: Multi-layer perceptron for classification and regression
• CNN: Convolutional neural networks (simple nn.Sequential style)
• Filters: Custom CNN filters with TensorFlow
• Transfer Learning: Pre-trained model fine-tuning

Requirements

• Python >= 3.7
• NumPy >= 1.21.0
• Matplotlib >= 3.4.0
• PyTorch >= 1.10.0 (for ANN module)
• TensorFlow >= 2.8.0 (for CNN filters)
• Scikit-learn >= 1.0.0 (for perceptron)
• Gymnasium >= 0.28.0 (for RL module)

Key Design Philosophy

Simple, Beginner-Friendly Code:

• Uses nn.Sequential() for neural networks (no complex classes)
• Clear variable names: X_train, y_train, model, loss_fn
• Simple for loops and if-else statements
• No lambda functions or advanced Python features
• Easy to understand and modify

Documentation

• 111 total functions (35 RL + 76 ANN)
• Complete docstrings for every function
• Usage examples included
• See documentation files for details

License

MIT License - Free for educational use

Links

• PyPI: https://pypi.org/project/matplotlab/
• GitHub: https://github.com/Sohail-Creates/matplotlab/

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For educational purposes | ML/RL implementations made simple