Add PCA-based visualization for brain state trajectories and MNIST

[leonvanbokhorst]

• Add brain state trajectory visualization using word embeddings
• Visualize emotional and cognitive state transitions in 2D space
• Calculate and display variance explained by principal components
• Use GloVe embeddings to represent semantic word meanings

Summary by Sourcery

Add new visualization features for brain state trajectories and butterfly data using PCA, and introduce a BrainLayer class for thought processing with a local LLM.

New Features:

• Introduce brain state trajectory visualization using word embeddings to visualize emotional and cognitive state transitions in 2D space.
• Implement a PCA demonstration with the MNIST dataset to explore dimensionality reduction and data reconstruction.
• Add a new module for visualizing butterfly data patterns using PCA to understand variance and principal components.
• Develop a BrainLayer class to process thoughts using a local LLM and memory comparison.

[sourcery-ai]

Reviewer's Guide by Sourcery

This PR implements a series of PCA (Principal Component Analysis) demonstrations and a brain state visualization system. The implementation includes three PCA demonstration scripts and a brain layer class that processes thoughts using embeddings. The core focus is on dimensionality reduction techniques and their applications in visualizing high-dimensional data.

Class diagram for the BrainLayer class

classDiagram
    class BrainLayer {
        +Client llm
        +Client memory
        +Collection collection
        +__init__()
        +process_thought(input_text)
    }
    note for BrainLayer "This class processes thoughts using a local LLM and memory embeddings."

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File-Level Changes

Change	Details	Files
Implementation of MNIST dataset PCA visualization	Created a comprehensive PCA demonstration using the MNIST digits dataset Implemented different compression levels visualization (10, 30, 50, 100 components) Added eigendigits visualization for the first 16 principal components Included variance explanation plots for components	pocs/pca_minst.py
Brain state trajectory visualization using word embeddings	Implemented word vector loading using GloVe embeddings Created thought streams for different emotional states Added 2D visualization of brain state trajectories Calculated and displayed variance explained by principal components	pocs/pca_brainstates.py
Butterfly pattern PCA demonstration	Created synthetic correlated data in a butterfly pattern Implemented PCA visualization with principal components Added variance explanation visualization Included detailed component analysis output	pocs/pca_butterflies.py
Brain layer implementation for thought processing	Created BrainLayer class with LLM and memory initialization Implemented three-layer thought processing system Added embedding-based memory comparison functionality Integrated with ollama and chromadb for LLM and memory storage	brain/brain_layer.py

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[sourcery-ai]

Hey @leonvanbokhorst - I've reviewed your changes - here's some feedback:

Overall Comments:

• Consider splitting this PR into two separate ones: one for the PCA demonstrations and visualizations, and another for the brain_layer.py implementation. This will help maintain better logical separation and allow for more focused review of each component.

Here's what I looked at during the review

• 🟡 General issues: 2 issues found
• 🟢 Security: all looks good
• 🟢 Testing: all looks good
• 🟢 Complexity: all looks good
• 🟢 Documentation: all looks good

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