Nextcloud MCP Server

"""Custom PCA implementation for dimensionality reduction. Implements Principal Component Analysis without scikit-learn dependency. Used for reducing high-dimensional embeddings (768-dim) to 2D for visualization. """ import logging import numpy as np logger = logging.getLogger(__name__) class PCA: """Principal Component Analysis for dimensionality reduction. Simple implementation that finds principal components via eigendecomposition of the covariance matrix. Suitable for small-to-medium datasets. Attributes: n_components: Number of principal components to keep mean_: Mean of training data (set during fit) components_: Principal components (eigenvectors) explained_variance_: Variance explained by each component explained_variance_ratio_: Fraction of total variance explained """ def __init__(self, n_components: int = 2): """Initialize PCA. Args: n_components: Number of components to keep (default: 2) """ if n_components < 1: raise ValueError(f"n_components must be >= 1, got {n_components}") self.n_components = n_components self.mean_: np.ndarray | None = None self.components_: np.ndarray | None = None self.explained_variance_: np.ndarray | None = None self.explained_variance_ratio_: np.ndarray | None = None def fit(self, X: np.ndarray) -> "PCA": """Fit PCA model to data. Args: X: Training data of shape (n_samples, n_features) Returns: self (for method chaining) Raises: ValueError: If X has fewer features than n_components """ X = np.asarray(X) if X.ndim != 2: raise ValueError(f"X must be 2D array, got shape {X.shape}") n_samples, n_features = X.shape if n_features < self.n_components: raise ValueError( f"n_components={self.n_components} > n_features={n_features}" ) # Center data self.mean_ = np.mean(X, axis=0) X_centered = X - self.mean_ # Compute covariance matrix # Use (X^T X) / (n-1) for numerical stability with high-dim data cov = np.cov(X_centered.T) # Eigendecomposition eigenvalues, eigenvectors = np.linalg.eigh(cov) # Sort by eigenvalue (descending) idx = np.argsort(eigenvalues)[::-1] eigenvalues = eigenvalues[idx] eigenvectors = eigenvectors[:, idx] # Keep top n_components self.components_ = eigenvectors[:, : self.n_components].T self.explained_variance_ = eigenvalues[: self.n_components] # Calculate explained variance ratio total_variance = np.sum(eigenvalues) if total_variance > 0: self.explained_variance_ratio_ = self.explained_variance_ / total_variance else: self.explained_variance_ratio_ = np.zeros(self.n_components) logger.debug( f"PCA fit: {n_samples} samples, {n_features} features → " f"{self.n_components} components, " f"explained variance: {self.explained_variance_ratio_}" ) return self def transform(self, X: np.ndarray) -> np.ndarray: """Transform data to principal component space. Args: X: Data to transform of shape (n_samples, n_features) Returns: Transformed data of shape (n_samples, n_components) Raises: ValueError: If PCA not fitted yet """ if self.mean_ is None or self.components_ is None: raise ValueError("PCA not fitted yet. Call fit() first.") X = np.asarray(X) if X.ndim != 2: raise ValueError(f"X must be 2D array, got shape {X.shape}") # Center using training mean X_centered = X - self.mean_ # Project onto principal components X_transformed = np.dot(X_centered, self.components_.T) return X_transformed def fit_transform(self, X: np.ndarray) -> np.ndarray: """Fit PCA model and transform data in one step. Args: X: Training data of shape (n_samples, n_features) Returns: Transformed data of shape (n_samples, n_components) """ self.fit(X) return self.transform(X)