Examples index

All matten examples live in examples/. They are grouped by purpose.

Core examples (numeric Tensor)

These examples demonstrate the default matten API. No extra features required.

File	What it shows
`00_quickstart.rs`	First look: create, add, reshape
`01_create_tensor.rs`	All construction APIs
`02_shape_and_size.rs`	Shape inspection
`03_reshape_flatten.rs`	Reshape and flatten
`04_elementwise_ops.rs`	Element-wise arithmetic
`05_scalar_ops.rs`	Scalar multiplication and division
`06_broadcasting.rs`	NumPy-style broadcasting
`07_transpose_swap_axes.rs`	Axis permutation
`08_slicing_builder.rs`	Slice builder API (canonical)
`09_slice_str.rs`	String slice API (convenience)
`10_json_roundtrip.rs`	JSON serialization round-trip
`11_csv_numeric_loading.rs`	Numeric CSV loading
`12_boundary_error_handling.rs`	Handling errors at data boundaries
`13_resource_limits.rs`	`MattenLimits`, `try_zeros`/`try_ones`/`try_full`
`14_concatenate_stack.rs`	Shape composition: `concatenate` and `stack` (RFC-039)
`15_norm_trace_outer.rs`	Linalg core-lite: `norm`, `trace`, `outer` (RFC-041)
`16_variance_std.rs`	Statistics core-lite: `var`/`std`, `var_axis`/`std_axis` (RFC-040)

Math examples

File	What it shows
`20_dot_product.rs`	Vector dot product
`21_matrix_vector_product.rs`	Matrix × vector
`22_matrix_multiplication.rs`	Matrix × matrix
`23_sum_mean.rs`	Whole-tensor and axis reductions
`24_min_max.rs`	Min and max with NaN policy
`25_normalize_vector.rs`	L2 normalisation
`26_cosine_similarity.rs`	Cosine similarity
`27_axis_reductions.rs`	Axis reductions and NaN propagation
`28_column_statistics.rs`	Per-column statistics workflow

Applied problems (famous small math)

Recognizable small math / numerical-computing problems, used to show what a Tensor can represent. These live in a fresh 30+ band so the core suite above stays stable. Write-ups: Beginner applied math, Matrix iteration, Numerical methods, and ML-like.

File	What it shows
`30_magic_square_checker.rs`	Row/column/diagonal sums via `get`
`31_fibonacci_matrix_power.rs`	Fibonacci via repeated `matmul`
`32_graph_path_counting.rs`	Walk counting via adjacency-matrix powers
`33_markov_chain_weather.rs`	Distribution over time via vector × matrix `matmul`
`34_tiny_pagerank.rs`	PageRank power iteration via matrix × vector `matmul`
`35_linear_regression_gradient_descent.rs`	Batch gradient descent via `matmul` + `transpose`
`36_heat_equation_1d.rs`	Explicit finite-difference stencil as `matmul` iteration
`37_kmeans_small.rs`	Lloyd’s k-means on a `[points, features]` data matrix
`38_nearest_neighbor_classification.rs`	1-NN classification over a labeled data matrix
`39_finite_difference_derivative.rs`	Central-difference derivative on a `linspace` grid
`40_trapezoidal_integration.rs`	Trapezoidal rule via `linspace` + elementwise + `sum`

Vector distance and cosine similarity are already covered above — see 54_pairwise_distance.rs, 25_normalize_vector.rs, and 26_cosine_similarity.rs rather than a duplicate in this band.

Practical numeric recipes (`50_`–`56_`)

Common data-processing patterns that combine multiple primitives. See Practical numeric recipes for the full write-up.

File	What it shows
`50_rowwise_scoring.rs`	Row-wise weighted scoring
`51_standardize_columns.rs`	Column standardisation (z-score)
`52_minmax_scaling.rs`	Min-max feature scaling
`53_gram_matrix.rs`	Gram matrix (X × Xᵀ)
`54_pairwise_distance.rs`	Pairwise Euclidean distances
`55_moving_average.rs`	Simple moving average
`56_rolling_windows_basic.rs`	Rolling window sum and max

Dynamic examples (`--features dynamic`)

These require the dynamic feature for heterogeneous data ingestion. JSON and CSV are equal on-ramps here: from_json_dynamic and from_csv_dynamic differ only in the input format — both land messy data in a dynamic tensor that the same inspect → clean → convert workflow turns into a numeric Tensor.

File	Features	What it shows
`dynamic_00_quickstart.rs`	`dynamic,json,csv`	Dynamic lifecycle overview
`dynamic_01_mixed_elements.rs`	`dynamic`	Mixed `Element` types
`dynamic_02_missing_values.rs`	`dynamic,csv`	Missing value detection
`dynamic_03_fill_none.rs`	`dynamic`	Filling missing values
`dynamic_04_numeric_coercion.rs`	`dynamic`	Element-level coercion
`dynamic_05_dirty_csv_cleanup.rs`	`dynamic,csv`	Real-world CSV cleanup
`dynamic_06_numeric_policy.rs`	`dynamic`	`NumericPolicy` API
`dynamic_07_on_ramp_summary.rs`	`dynamic`	Complete on-ramp workflow
`dynamic_08_json_ingestion.rs`	`dynamic,json`	JSON ingestion (mixed/missing → clean `f64`)

Companion crate examples

These live in each companion crate’s own examples/ directory, not in core matten. See Companion crate examples for the write-up.

Crate	Example	What it shows
`matten-ndarray`	`from_arrayd`, `to_arrayd`	`ArrayD` ↔ `Tensor` interop (copies, shape-preserving)
`matten-mlprep`	`mlprep_standardize_columns`, `mlprep_minmax_scale`, `mlprep_add_bias_column`, `mlprep_train_test_split`	Small deterministic preprocessing
`matten-data`	`csv_to_tensor`	CSV → clean → numeric `Tensor` (production-ready candidate)

Running examples

# Numeric core (no features needed):
cargo run --example 00_quickstart
cargo run --example 27_axis_reductions

# Dynamic:
cargo run --example dynamic_06_numeric_policy --features dynamic
cargo run --example dynamic_07_on_ramp_summary --features dynamic,csv
cargo run --example dynamic_08_json_ingestion --features dynamic,json

Scope rule

Every example demonstrates accepted APIs only. Examples are not a back door for adding new mathematical operations, dataframe behavior, or ML scope.

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