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Add PCA-based visualization for brain state trajectories and MNIST #75
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- 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
Reviewer's Guide by SourceryThis 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 classclassDiagram
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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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
Help me be more useful! Please click 👍 or 👎 on each comment and I'll use the feedback to improve your reviews.
Co-authored-by: sourcery-ai[bot] <58596630+sourcery-ai[bot]@users.noreply.github.com>
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: