Struct SupervisedTuningDatasetDistribution 

#[non_exhaustive]
pub struct SupervisedTuningDatasetDistribution {
    pub sum: i64,
    pub billable_sum: i64,
    pub min: f64,
    pub max: f64,
    pub mean: f64,
    pub median: f64,
    pub p5: f64,
    pub p95: f64,
    pub buckets: Vec<DatasetBucket>,
    /* private fields */
}

Available on crate feature gen-ai-tuning-service only.

Dataset distribution for Supervised Tuning.

Fields (Non-exhaustive)

Non-exhaustive structs could have additional fields added in future. Therefore, non-exhaustive structs cannot be constructed in external crates using the traditional Struct { .. } syntax; cannot be matched against without a wildcard ..; and struct update syntax will not work.

sum: i64

Output only. Sum of a given population of values.

billable_sum: i64

Output only. Sum of a given population of values that are billable.

min: f64

Output only. The minimum of the population values.

max: f64

Output only. The maximum of the population values.

mean: f64

Output only. The arithmetic mean of the values in the population.

median: f64

Output only. The median of the values in the population.

p5: f64

Output only. The 5th percentile of the values in the population.

p95: f64

Output only. The 95th percentile of the values in the population.

buckets: Vec<DatasetBucket>

Output only. Defines the histogram bucket.

Implementations

impl SupervisedTuningDatasetDistribution

pub fn new() -> Self

pub fn set_sum<T: Into<i64>>(self, v: T) -> Self

Sets the value of sum.

Example

let x = SupervisedTuningDatasetDistribution::new().set_sum(42);

pub fn set_billable_sum<T: Into<i64>>(self, v: T) -> Self

Sets the value of billable_sum.

Example

let x = SupervisedTuningDatasetDistribution::new().set_billable_sum(42);

pub fn set_min<T: Into<f64>>(self, v: T) -> Self

Sets the value of min.

Example

let x = SupervisedTuningDatasetDistribution::new().set_min(42.0);

pub fn set_max<T: Into<f64>>(self, v: T) -> Self

Sets the value of max.

Example

let x = SupervisedTuningDatasetDistribution::new().set_max(42.0);

pub fn set_mean<T: Into<f64>>(self, v: T) -> Self

Sets the value of mean.

Example

let x = SupervisedTuningDatasetDistribution::new().set_mean(42.0);

pub fn set_median<T: Into<f64>>(self, v: T) -> Self

Sets the value of median.

Example

let x = SupervisedTuningDatasetDistribution::new().set_median(42.0);

pub fn set_p5<T: Into<f64>>(self, v: T) -> Self

Sets the value of p5.

Example

let x = SupervisedTuningDatasetDistribution::new().set_p5(42.0);

pub fn set_p95<T: Into<f64>>(self, v: T) -> Self

Sets the value of p95.

Example

let x = SupervisedTuningDatasetDistribution::new().set_p95(42.0);

pub fn set_buckets<T, V>(self, v: T) -> Self

where T: IntoIterator<Item = V>, V: Into<DatasetBucket>,

Sets the value of buckets.

Example

use google_cloud_aiplatform_v1::model::supervised_tuning_dataset_distribution::DatasetBucket;
let x = SupervisedTuningDatasetDistribution::new()
    .set_buckets([
        DatasetBucket::default()/* use setters */,
        DatasetBucket::default()/* use (different) setters */,
    ]);

Trait Implementations

impl Clone for SupervisedTuningDatasetDistribution

fn clone(&self) -> SupervisedTuningDatasetDistribution

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for SupervisedTuningDatasetDistribution

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

Formats the value using the given formatter.

impl Default for SupervisedTuningDatasetDistribution

fn default() -> SupervisedTuningDatasetDistribution

Returns the “default value” for a type.

impl Message for SupervisedTuningDatasetDistribution

fn typename() -> &'static str

The typename of this message.

impl PartialEq for SupervisedTuningDatasetDistribution

fn eq(&self, other: &SupervisedTuningDatasetDistribution) -> bool

Tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

Tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl StructuralPartialEq for SupervisedTuningDatasetDistribution