Practical numeric recipes

A set of small, self-contained numeric recipes that combine core matten primitives into common data-processing patterns. Each file is a single runnable example with hard-coded data, assertions, and stable output.

These live in the 50_–56_ band, separate from the core tutorial (01_–13_), the numeric building blocks (20_–28_), and the famous-problem examples (30_–40_).

Examples

50_rowwise_scoring.rs

Row-wise weighted scoring: multiply each row of a feature matrix by a weight vector, then sum across columns to produce one score per row. Shows broadcasting between a [rows, cols] tensor and a [cols] weight vector, followed by sum_axis.

cargo run --example 50_rowwise_scoring

Source: 50_rowwise_scoring.rs

51_standardize_columns.rs

Z-score normalisation of each column (zero mean, unit variance) using only mean_axis, broadcasting, and element-wise arithmetic — no external crate needed.

cargo run --example 51_standardize_columns

Source: 51_standardize_columns.rs

52_minmax_scaling.rs

Min-max (0–1) scaling of each column using min_axis, max_axis, and broadcasting. A common feature-normalisation step before ML algorithms.

cargo run --example 52_minmax_scaling

Source: 52_minmax_scaling.rs

53_gram_matrix.rs

Gram matrix: G = X · Xᵀ, computed with matmul. Used in kernel methods and feature covariance. Shows that a single matmul call produces a symmetric [n, n] similarity matrix from an [n, d] data matrix.

cargo run --example 53_gram_matrix

Source: 53_gram_matrix.rs

54_pairwise_distance.rs

Pairwise Euclidean distances between rows using the identity ‖a−b‖² = ‖a‖² + ‖b‖² − 2aᵀb, computed with broadcasting and matmul. Demonstrates efficient distance computation without an explicit loop over pairs.

cargo run --example 54_pairwise_distance

Source: 54_pairwise_distance.rs

55_moving_average.rs

Simple moving average over a 1-D series using slice windows (slice_str). Shows a sliding-window pattern with overlapping slices and mean reduction.

cargo run --example 55_moving_average

Source: 55_moving_average.rs

56_rolling_windows_basic.rs

Rolling window sum and max over overlapping slices of a 1-D series. Extends the moving-average idea to multiple aggregations in one pass.

cargo run --example 56_rolling_windows_basic

Source: 56_rolling_windows_basic.rs

What this is not

These recipes show how to compose accepted matten APIs into common patterns. They do not imply that matten is a feature-engineering framework, a signal-processing library, or a statistics package. For preprocessing helpers with a proper API, see matten-mlprep.