Hi there! Are you looking for the official Deno documentation? Try docs.deno.com for all your Deno learning needs.

GoogleCloudMlV1__ParameterSpec

import type { GoogleCloudMlV1__ParameterSpec } from "https://googleapis.deno.dev/v1/ml:v1.ts";

Represents a single hyperparameter to optimize.

interface GoogleCloudMlV1__ParameterSpec {
categoricalValues?: string[];
discreteValues?: number[];
maxValue?: number;
minValue?: number;
parameterName?: string;
scaleType?:
| "NONE"
| "UNIT_LINEAR_SCALE"
| "UNIT_LOG_SCALE"
| "UNIT_REVERSE_LOG_SCALE";
type?:
| "PARAMETER_TYPE_UNSPECIFIED"
| "DOUBLE"
| "INTEGER"
| "CATEGORICAL"
| "DISCRETE";
}

§Properties

§
categoricalValues?: string[]
[src]

Required if type is CATEGORICAL. The list of possible categories.

§
discreteValues?: number[]
[src]

Required if type is DISCRETE. A list of feasible points. The list should be in strictly increasing order. For instance, this parameter might have possible settings of 1.5, 2.5, and 4.0. This list should not contain more than 1,000 values.

§
maxValue?: number
[src]

Required if type is DOUBLE or INTEGER. This field should be unset if type is CATEGORICAL. This value should be integers if type is INTEGER.

§
minValue?: number
[src]

Required if type is DOUBLE or INTEGER. This field should be unset if type is CATEGORICAL. This value should be integers if type is INTEGER.

§
parameterName?: string
[src]

Required. The parameter name must be unique amongst all ParameterConfigs in a HyperparameterSpec message. E.g., "learning_rate".

§
scaleType?: "NONE" | "UNIT_LINEAR_SCALE" | "UNIT_LOG_SCALE" | "UNIT_REVERSE_LOG_SCALE"
[src]

Optional. How the parameter should be scaled to the hypercube. Leave unset for categorical parameters. Some kind of scaling is strongly recommended for real or integral parameters (e.g., UNIT_LINEAR_SCALE).

§
type?: "PARAMETER_TYPE_UNSPECIFIED" | "DOUBLE" | "INTEGER" | "CATEGORICAL" | "DISCRETE"
[src]

Required. The type of the parameter.