/**
 * Advanced Dataset Characterization Analyzer
 * Core engine for sophisticated dataset analysis beyond basic statistics
 *
 * Risk-averse implementation strategy:
 * - Incremental analysis with fallbacks
 * - Comprehensive error handling
 * - Progressive enhancement of existing capabilities
 * - Backward compatibility maintained
 */
import type { Section1Result } from '../../overview/types';
import type { Section2Result } from '../../quality/types';
import type { Section3Result } from '../../eda/types';
import type { DatasetComplexityProfile, DatasetCharacterizationConfig, CharacterizationProgress } from './types';
/**
 * Main analyzer class for advanced dataset characterization
 */
export declare class DatasetCharacterizationAnalyzer {
    private config;
    private warnings;
    private startTime;
    private progress;
    constructor(config?: Partial<DatasetCharacterizationConfig>);
    /**
     * Main analysis method - performs comprehensive dataset characterization
     */
    analyze(section1Result: Section1Result, section2Result: Section2Result, section3Result: Section3Result, progressCallback?: (progress: CharacterizationProgress) => void): Promise<DatasetComplexityProfile>;
    /**
     * Performs incremental analysis with comprehensive error handling
     */
    private performIncrementalAnalysis;
    /**
     * Analyzes intrinsic dimensionality using multiple methods with fallbacks
     */
    private analyzeIntrinsicDimensionality;
    /**
     * PCA-based dimensionality analysis with eigenvalue decomposition
     */
    private pcaBasedDimensionalityAnalysis;
    /**
     * Correlation-based dimensionality analysis as fallback
     */
    private correlationBasedDimensionalityAnalysis;
    /**
     * Simple fallback for dimensionality analysis
     */
    private simpleDimensionalityFallback;
    /**
     * Analyze feature interactions (placeholder for now - will implement incrementally)
     */
    private analyzeFeatureInteractions;
    /**
     * Analyze non-linearity (placeholder for now)
     */
    private analyzeNonLinearity;
    /**
     * Analyze separability for classification tasks (placeholder)
     */
    private analyzeSeparability;
    /**
     * Analyze noise characteristics (placeholder)
     */
    private analyzeNoise;
    /**
     * Analyze sparsity patterns (placeholder)
     */
    private analyzeSparsity;
    /**
     * Analyze temporal complexity (placeholder)
     */
    private analyzeTemporalComplexity;
    /**
     * Initialize configuration with defaults
     */
    private initializeConfig;
    /**
     * Initialize progress tracking
     */
    private initializeProgress;
    /**
     * Reset analysis state for new analysis
     */
    private resetAnalysisState;
    /**
     * Update progress and notify callback
     */
    private updateProgress;
    /**
     * Estimate remaining time based on progress
     */
    private estimateTimeRemaining;
    /**
     * Calculate total analysis steps
     */
    private calculateTotalSteps;
    /**
     * Check if specific analysis should be performed
     */
    private shouldPerformAnalysis;
    /**
     * Validate inputs before analysis
     */
    private validateInputs;
    /**
     * Extract data context from previous section results
     */
    private extractDataContext;
    /**
     * Extract numeric features from Section 3 results
     */
    private extractNumericFeatures;
    /**
     * Extract categorical features from Section 3 results
     */
    private extractCategoricalFeatures;
    /**
     * Extract temporal features from Section 3 results
     */
    private extractTemporalFeatures;
    /**
     * Add warning to collection
     */
    private addWarning;
    /**
     * Handle analysis errors with proper categorization
     */
    private handleAnalysisError;
    /**
     * Generate analysis metadata
     */
    private generateAnalysisMetadata;
    /**
     * Calculate overall complexity score (placeholder)
     */
    private calculateOverallComplexityScore;
    /**
     * Determine confidence level (placeholder)
     */
    private determineConfidenceLevel;
    private extractCorrelationMatrix;
    private computeEigenvalues;
    private applyCriteriaForDimensionality;
    private analyzeFeatureImportanceFromEigenVectors;
    private findHighlyCorrelatedGroups;
}
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