Struct Dataset

pub struct Dataset { /* private fields */ }

A collection of input vectors matched with their expected output.

You can construct a Dataset manually like so:

// Note that the inputs and target outputs are both vectors, even though the latter has just
// one element
let data = vec![
    (vec![0.0, 0.0], vec![0.0]),
    (vec![0.0, 1.0], vec![1.0]),
    (vec![1.0, 0.0], vec![1.0]),
    (vec![1.0, 1.0], vec![0.0]),
];

let dataset = scholar::Dataset::from(data);

Implementations

impl Dataset

pub fn from_csv( file_path: impl AsRef<Path>, includes_headers: bool, num_inputs: usize, ) -> Result<Self, ParseCsvError>

Parses a Dataset from a CSV file.

Arguments

• file_path - The path to the CSV file
• includes_headers - Whether the CSV has a header row or not
• num_inputs - The number of columns in the CSV that are designated as inputs (to a Machine Learning model)

Examples

// Parses the first four columns of 'iris.csv' as inputs, and the remaining columns as
// target outputs
let dataset = scholar::Dataset::from_csv("iris.csv", false, 4);

pub fn split(self, train_portion: f64) -> (Self, Self)

Splits the dataset into two, with the size of each determined by the given train_portion. This is useful for separating it into training and testing segments.

Examples

let dataset = scholar::Dataset::from_csv("iris.csv", false, 4)?;

// Randomly allocates 75% of the original dataset to `training_data`, and the rest
// to `testing_data`
let (training_data, testing_data) = dataset.split(0.75);

Panics

This method panics if the given train_portion isn’t between 0 and 1.

pub fn rows(&self) -> usize

Returns the number of rows in the dataset.

Examples

// Data for the XOR problem
let data = vec![
    (vec![0.0, 0.0], vec![0.0]),
    (vec![0.0, 1.0], vec![1.0]),
    (vec![1.0, 0.0], vec![1.0]),
    (vec![1.0, 1.0], vec![0.0]),
];

let dataset = scholar::Dataset::from(data);
assert_eq!(dataset.rows(), 4);

Trait Implementations

impl Debug for Dataset

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl From<Vec<(Vec<f64>, Vec<f64>)>> for Dataset

fn from(data: Vec<(Vec<f64>, Vec<f64>)>) -> Self

Converts to this type from the input type.

impl<'a> IntoIterator for &'a Dataset

type Item = &'a (Vec<f64>, Vec<f64>)

The type of the elements being iterated over.

type IntoIter = DatasetIterator<'a>

Which kind of iterator are we turning this into?

fn into_iter(self) -> Self::IntoIter

Creates an iterator from a value.