Comments (4)
targos
commented on August 28, 2024
What don't you understand? The current behavior is the same as the default from SKlearn (keep all components).
We can add options to configure that for sure.
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jajoe
commented on August 28, 2024
The main goal of PCA is to reduct dimensionality, so if we keep every components I'm not sure that a PCA is useful.
I'll add that tomorrow, I believe it just needs to apply that at the end of the function predict().
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targos
commented on August 28, 2024
We can talk about it tomorrow
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targos
commented on August 28, 2024
nComponents option has been added to the predict method
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Related Issues (20)
- Crashes when a feature variance is 0 HOT 5
- Noob question: How do I project my data into the PCA space? HOT 3
- Mono dimensional arrays: TypeError: First argument must be a positive number or an array HOT 8
- update readme once Matrix has a custom inspect function
- Optimize predictions when nComponents is specified
- Feature Labels HOT 2
- Scores HOT 1
- How to use `getExplainedVariance` results? HOT 3
- Reference to the feature names in getExplainedVariance HOT 1
- Add Reconstructed loadings HOT 1
- NIPALS returns nans HOT 1
- Question - Plotting Ability HOT 3
- Migrate project to typescript
- Test
- fix DOI in citation.md once published on Zenodo
- RangeError: Submatrix indices are out of range HOT 4
- Add missing typescript types and move documentation to typescript
- Mixed ES6 and CJS causes issues in some enviroments HOT 8
- Dimension of eigenvectors HOT 9
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