gizai:ml/classify

classification and its helpers.

import * as Classify from "gizai:ml/classify";

Types

Partial

type Partial = { model: Model; sgd: Sgd; states: Vec<SgdState>; batch: MiniBatch; rng: Random; }

A model that learns one batch of rows at a time, as scikit-learn's partial_fit: one pass of SGD over each batch, or one step of mini-batch k-means. A classifier takes its classes before the first batch.

• model: M42.Model
• sgd: M40.Sgd
• states: Vec<M40.SgdState>
• batch: M36.MiniBatch
• rng: gizlang_random.Random

Functions

labelsOf

function labelsOf(y: Vec<f64>, n: i64): Labels

The sorted distinct labels of the n rows y, and each row's class index.

Effects: fail.

fit

function fit(task: string, algorithm: string, h: Json, data: Data, seed0: u64): Model throws string

Fits algorithm for task with the hyperparameters h (scikit-learn's keyword arguments).

Effects: fail.

streams

function streams(task: string, algorithm: string): bool

Whether algorithm of task learns one batch at a time (Partial): the SGD models and mini-batch k-means. The others need all their rows at once.

Effects: fail.

partial

function partial(task: string, algorithm: string, h: Json, features: i64, targets: i64, classes: Vec<f64>, seed0: u64): Partial throws string

A model of TASK and ALGORITHM that learns batch by batch (partial_fit), with hyperparameters H.

Effects: fail.

resume

function resume(m: Model, h: Json, seed: u64): Partial throws string

A Partial that goes on from m, an SGD model (scikit-learn's warm_start: its weights, the step count again 1).

Effects: fail.

Methods

Partial.feed

function Partial.feed(self: mut Partial, x: Vec<f64>, y: Vec<f64>, n: i64): bool throws string

Learns from the n rows of x (row-major) and y (targets a row; a class value for a classifier).

Effects: fail.

Partial.result

function Partial.result(self: Partial): Model

The model learned so far.

Effects: fail.