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.kiro/specs/neurolite-data-detection/tasks.md

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- Write unit tests for preprocessing recommendations
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- _Requirements: 9.3_
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- [ ] 10. Create ComplexityAnalyzer for performance estimation
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- [ ] 10.1 Implement computational requirement estimation
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- [x] 10. Create ComplexityAnalyzer for performance estimation
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- [x] 10.1 Implement computational requirement estimation
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- Create CPU vs GPU suitability assessment algorithms
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- Add memory requirement prediction logic
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- Implement processing time estimation functions
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- Write unit tests for resource requirement accuracy
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- _Requirements: 10.1, 10.2_
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- [ ] 10.2 Implement model complexity assessment
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- [x] 10.2 Implement model complexity assessment
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- Code overfitting risk detection algorithms
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- Add regularization requirement identification
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- Create cross-validation strategy recommendation logic
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- Write unit tests for complexity assessment accuracy
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- _Requirements: 10.3, 10.4_
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- [ ] 11. Build central DataProfiler orchestrator
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- [-] 11. Build central DataProfiler orchestrator
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- [ ] 11.1 Implement core analysis pipeline
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- Create main analyze() method that coordinates all detectors and analyzers
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- Add error handling and graceful degradation logic

neurolite/__init__.py

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)
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from .analyzers import (
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StatisticalAnalyzer
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StatisticalAnalyzer,
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ComplexityAnalyzer
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)
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# Simple DataProfiler class for easy usage
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"DataTypeDetector",
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"QualityDetector",
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"StatisticalAnalyzer",
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"ComplexityAnalyzer",
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"NeuroLiteException",
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"UnsupportedFormatError",
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"InsufficientDataError",

neurolite/analyzers/__init__.py

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"""Analyzer modules for statistical and complexity analysis."""
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from .statistical_analyzer import StatisticalAnalyzer, DistributionAnalysis, CorrelationMatrix, RelationshipAnalysis
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from .complexity_analyzer import ComplexityAnalyzer
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__all__ = [
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'StatisticalAnalyzer',
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'DistributionAnalysis',
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'CorrelationMatrix',
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'RelationshipAnalysis'
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'RelationshipAnalysis',
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'ComplexityAnalyzer'
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]

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