====== std.ml_full ======

Erweiterte Machine-Learning-Algorithmen und Modelle: Deep Learning Layers, Convolutional Networks, Rekurrente Netze (LSTM), Gradient Boosting und Ensemble-Methoden. Aufbauend auf ''std.ml'' mit schwergewichtigen Implementierungen für Produktionseinsatz.

→ [[lyx_-_programmiersprache:units:ml|std.ml]] · [[lyx_-_programmiersprache:units|Standard Library]] · [[lyx_-_programmiersprache:units:graphdb|std.graphdb]]

<box tip>
> **Die Doppel von ''ExpF64'', ''LogF64'' und ''SqrtF64'' sind entfernt** ([[https://github.com/SEOLizer/LyX-Compiler/issues/1572|#1572]] behoben). Die Unit importiert jetzt ''std.math''; wer sie mitimportiert, bekommt dieselben Werte wie überall sonst — mit 1.1.2E nachgemessen:
>
> <code>
LogF64(100) = 4.605170
ExpF64(1)   = 2.718281
</code>
>
> Vorher lieferte ''LogF64'' hier **immer 0** und ''ExpF64'' eine grobe Näherung, ohne jede Meldung — und zwar im ganzen Programm, weil die Namen im flachen Namensraum kollidierten.
</box>

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===== Typen =====

==== DecisionTreeNode (struct) ====

^ Feld ^ Typ ^
| ''featureIdx'' | ''int64'' |
| ''threshold'' | ''f64'' |
| ''left'' | ''int64'' |
| ''right'' | ''int64'' |
| ''classLabel'' | ''int64'' |
| ''nSamples'' | ''int64'' |
| ''impurity'' | ''f64'' |
| ''featureImportance'' | ''f64'' |

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===== Funktionen =====

^ Signatur ^ Sichtbarkeit ^ Beschreibung ^
| ''SquareF64(x: f64): f64'' | pub | — |
| ''SqrtF64(x: f64): f64'' | pub | SSE2-optimized square root (uses sqrtsd when available) Currently falls back to Newton's method |
| ''SqrtF64SSE(x: f64): f64'' | pub | — |
| ''ExpF64(x: f64): f64'' | pub | Exponential function - simple approximation exp(x) ≈ 1 + x for small x, use lookup for common values |
| ''SigmoidF64(z: f64): f64'' | pub | Sigmoid function: σ(z) = 1 / (1 + e&#94;(-z)) Use piecewise approximation |
| ''LogF64(x: f64): f64'' | pub | Log (natural logarithm) - inverse of exp |
| ''LinearRegressionInit(): void'' | pub | Initialize model - with small non-zero weights |
| ''LinearRegressionWeight(): f64'' | pub | Get weight |
| ''LinearRegressionBias(): f64'' | pub | Get bias |
| ''LinearRegressionLoss(): f64'' | pub | Get last loss |
| ''LinearRegressionEpochs(): int64'' | pub | Get epochs |
| ''LinearRegressionPredict(x: f64): f64'' | pub | Predict single value (linear) |
| ''LogisticRegressionInit(): void'' | pub | Initialize logistic regression model |
| ''LogisticRegressionWeight(): f64'' | pub | Get weight |
| ''LogisticRegressionBias(): f64'' | pub | Get bias |
| ''LogisticRegressionLoss(): f64'' | pub | Get last loss (binary cross-entropy) |
| ''LogisticRegressionAccuracy(): f64'' | pub | Get accuracy |
| ''LogisticRegressionEpochs(): int64'' | pub | Get epochs |
| ''LogisticRegressionPredictProb(x: f64): f64'' | pub | Predict probability (sigmoid output) Returns value between 0 and 1 |
| ''LogisticRegressionPredict(x: f64): int64'' | pub | Predict class (binary: 0 or 1) Returns 0 if prob < 0.5, else 1 |
| ''LinearRegressionFitDataFrame(df: int64, xCol: int64, yCol: int64): void'' | pub | df: DataFrame pointer xCol: column name for feature (int64 pointer) yCol: column name for target (int64 pointer) |
| ''LinearRegressionDataPointCount(): int64'' | pub | Get number of data points from last DataFrame fit |
| ''LogisticRegressionFitDataFrame(df: int64, xCol: int64, yCol: int64, learningRate: f64, epochs: int64): void'' | pub | DataFrame-based Logistic Regression (arbitrary data points) Train Logistic Regression from DataFrame |
| ''LinearRegressionFitArrays(xData: int64, yData: int64, n: int64): void'' | pub | xData: pointer to f64 array (x values) yData: pointer to f64 array (y values) n: number of data points |
| ''LogisticRegressionFitArrays(xData: int64, yData: int64, n: int64, learningRate: f64, epochs: int64): void'' | pub | Array-based Logistic Regression (arbitrary data points) Train Logistic Regression from f64 arrays |
| ''KNNInit(k: int64): void'' | pub | Initialize k-NN model k: number of neighbors (usually odd: 1, 3, 5, 7...) |
| ''KNNGetK(): int64'' | pub | Get k value |
| ''KNNGetNSamples(): int64'' | pub | Get number of training samples |
| ''KNNFit(features: int64, labels: int64, n: int64, nFeatures: int64): void'' | pub | labels: pointer to labels array (n labels) n: number of training samples nFeatures: number of features per sample |
| ''KNNDistance(a: int64, b: int64, n: int64): f64'' | pub | Euclidean distance between two feature vectors |
| ''KNNDistanceSSE(a: int64, b: int64, n: int64): f64'' | pub | SSE-optimized Euclidean distance (future: uses AVX) |
| ''KNNManhattanDistance(a: int64, b: int64, n: int64): f64'' | pub | Manhattan distance (L1) |
| ''KNNFindNeighbors(query: int64, k: int64): int64'' | pub | Find k nearest neighbors (indices array) |
| ''KNNMajorityVote(neighborIndices: int64, k: int64): int64'' | pub | Majority vote |
| ''KNNWeightedVote(neighborIndices: int64, query: int64, k: int64): int64'' | pub | Weighted majority vote |
| ''KNNPredict(query: int64): int64'' | pub | Predict class |
| ''KNNPredictProba(query: int64, nClasses: int64): int64'' | pub | Predict probabilities |
| ''KNNCrossValidation(features: int64, labels: int64, n: int64, nFeatures: int64, k: int64, folds: int64): f64'' | pub | Cross-validation |
| ''KNNScore(features: int64, labels: int64, n: int64): f64'' | pub | Score on training set |
| ''KMeansInit(k: int64): void'' | pub | Cluster centroid vector Note: stored as contiguous f64 array at km_centroids Initialize k-Means |
| ''KMeansGetK(): int64'' | pub | Get number of clusters |
| ''KMeansGetIter(): int64'' | pub | Get current iteration |
| ''KMeansGetInertia(): f64'' | pub | Get inertia (sum of squared distances) |
| ''KMeansInitCentroids(features: int64, n: int64, nFeatures: int64, k: int64): void'' | pub | Initialize centroids using k-means++ algorithm |
| ''KMeansFit(features: int64, n: int64, nFeatures: int64, k: int64): void'' | pub | n: number of samples nFeatures: number of features k: number of clusters |
| ''KMeansPredictCluster(point: int64): int64'' | pub | Get cluster assignment for a point |
| ''KMeansGetAssignments(): int64'' | pub | Get all cluster assignments |
| ''KMeansGetCentroids(): int64'' | pub | Get cluster centroids |
| ''KMeansGetClusterSize(clusterIdx: int64): int64'' | pub | Get cluster size |
| ''KMeansGetCentroid(idx: int64, featureIdx: int64): f64'' | pub | Get centroids as array (for visualization) |
| ''KMeansSilhouette(features: int64, n: int64, nFeatures: int64): f64'' | pub | Calculate silhouette score (quality metric) |
| ''KMeansElbow(features: int64, n: int64, nFeatures: int64, maxK: int64): int64'' | pub | Elbow method: calculate inertia for different k values |
| ''KMeansMiniBatchInit(k: int64, batchSize: int64): void'' | pub | Initialize mini-batch k-means |
| ''KMeansMiniBatchFit(features: int64, n: int64, nFeatures: int64, k: int64): void'' | pub | Fit mini-batch k-means (faster approximation) |
| ''NaiveBayesInit(nClasses: int64, vocabSize: int64): void'' | pub | Initialize Naive Bayes |
| ''NaiveBayesFit(documents: int64, docLengths: int64, labels: int64, n: int64): void'' | pub | docLengths: lengths of each document labels: class label for each document n: number of documents |
| ''NBLog(x: f64): f64'' | pub | Log function approximation |
| ''NaiveBayesPredict(docWords: int64, docLen: int64): int64'' | pub | Predict class for a document |
| ''NaiveBayesPredictProba(docWords: int64, docLen: int64): int64'' | pub | Predict probabilities for all classes |
| ''NaiveBayesGetWordCount(classIdx: int64, wordIdx: int64): f64'' | pub | Get word count for class |
| ''NaiveBayesGetClassCount(classIdx: int64): int64'' | pub | Get class count |
| ''NaiveBayesScore(documents: int64, docLengths: int64, labels: int64, n: int64): f64'' | pub | Score on training data |
| ''NaiveBayesBernoulliInit(nClasses: int64, vocabSize: int64): void'' | pub | Initialize Bernoulli Naive Bayes |
| ''NaiveBayesBernoulliFit(documents: int64, docLengths: int64, labels: int64, n: int64): void'' | pub | Bernoulli NB training |
| ''NaiveBayesBernoulliPredict(docWords: int64, docLen: int64): int64'' | pub | Bernoulli NB prediction (with complement) |
| ''DecisionTreeInit(maxDepth: int64, criterion: int64): void'' | pub | Initialize Decision Tree |
| ''DTGiniImpurity(labels: int64, n: int64): f64'' | pub | Calculate Gini impurity |
| ''DTEntropyImpurity(labels: int64, n: int64): f64'' | pub | Calculate Entropy impurity |
| ''DTMSEImpurity(values: int64, n: int64): f64'' | pub | Calculate MSE impurity (for regression) |
| ''DecisionTreePredict(featureVector: int64): int64'' | pub | Predict using Decision Tree |
| ''DecisionTreePredictProba(featureVector: int64, nClasses: int64): int64'' | pub | Predict probabilities |
| ''DecisionTreeScore(features: int64, labels: int64, n: int64): f64'' | pub | Score Decision Tree |
| ''DecisionTreeGetDepth(): int64'' | pub | Get tree depth |
| ''DecisionTreeGetNLeafs(): int64'' | pub | Get number of leaf nodes (approximation) |

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//Signaturen automatisch aus ''std/ml_full.lyx'' erzeugt (Stand 2026-08-02, lyxc 1.0.21A). Beschreibungen stammen aus den Quellkommentaren und sind teilweise unvollständig.//