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Refactor: Simplify PCA implementation and improve visualization #76
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- Simplified PCA implementation by initializing and fitting PCA for each number of components within the loop. - Removed redundant transformations and reconstruction steps, enhancing code clarity. - Updated comments for better understanding of the PCA process and its visualization. - Improved the display of reconstructed images and compression ratios.
Reviewer's Guide by SourceryThe PR introduces two main changes: a simplification of the PCA implementation in the MNIST example and the addition of a new transformer-based model for learning arithmetic operations. The PCA changes focus on code clarity and efficiency by removing redundant steps, while the new transformer implementation provides a comprehensive example of using attention mechanisms for mathematical operations. Sequence diagram for PCA compression processsequenceDiagram
participant User
participant PCA
User->>PCA: Initialize PCA with n_components
User->>PCA: Fit and transform data
PCA-->>User: Transformed data
User->>PCA: Inverse transform data
PCA-->>User: Reconstructed image
Note right of User: Repeats for each n_components in list
Class diagram for the AdditionTransformer and PositionalEncodingclassDiagram
class PositionalEncoding {
+PositionalEncoding(int d_model, int max_len=1000)
+forward(Tensor x) Tensor
pe: Tensor
}
class AdditionTransformer {
+AdditionTransformer(int vocab_size=12, int d_model=128, int nhead=8, int num_layers=4, int max_len=20, float dropout=0.1)
+forward(Tensor src) Tensor
embedding: nn.Embedding
pos_encoder: PositionalEncoding
dropout: nn.Dropout
transformer_encoder: nn.TransformerEncoder
decoder: nn.Sequential
}
PositionalEncoding --> AdditionTransformer
File-Level Changes
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Hey @leonvanbokhorst - I've reviewed your changes - here's some feedback:
Overall Comments:
- Please update the PR title to be more descriptive of the changes (e.g., 'Simplify PCA implementation and add transformer-based addition learning model')
Here's what I looked at during the review
- 🟡 General issues: 1 issue 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
Simplify the PCA implementation by initializing and fitting PCA for each number of components within the loop, removing redundant steps. Update comments for clarity. Add a new transformer-based addition learning model as an educational example, demonstrating the application of attention mechanisms to arithmetic operations.
Enhancements:
Documentation: