/**
 * Adaptive chars-per-token (CPT) — the fit.
 *
 * The profitability gate compares `imageTokens` (exact, from pixel area) against
 * `textTokens = chars / CPT`. CPT was a hand-tuned constant per call site (4 for
 * reminders/tool_results, 2.0 for slab/history). Production telemetry showed the
 * real marginal density is ~1.5 for dense content, so the constant under-counted
 * text cost and biased the gate toward passthrough — leaving savings on the table.
 *
 * This module learns CPT from the events pxpipe already logs. Model: a request's
 * TEXT token cost decomposes into a per-bucket marginal rate times that bucket's
 * char count,
 *
 *     textTokens ≈ Σ_b α_b · chars_b        with     CPT_b = 1 / α_b
 *
 * where `textTokens` is the observed `baseline_tokens` (a free count_tokens probe
 * on the ORIGINAL uncompressed body) minus the image cost, priced off Anthropic's
 * 28×28-patch grid. Samples are built offline from events.jsonl by the caller
 * (see the CHARS_PER_TOKEN provenance note in transform.ts). Solved by ordinary
 * least squares
 * via the normal equations `α = (XᵀX)⁻¹ Xᵀy`. XᵀX is at most 6×6, so a hand-rolled
 * Gauss-Jordan inverse is microseconds and needs no dependency.
 *
 * Pure math — no `fs`, no `process`, no `Buffer`. Safe to import from the Workers
 * build.
 *
 * The fit is deliberately conservative: every guard below fails a bucket CLOSED
 * (back to the baked constant) rather than open. A wrong learned CPT would make
 * the gate image unprofitably, which costs real money; a missing one just returns
 * today's behavior.
 */
import type { BucketName } from './transform.js';
/** Column order for the design matrix. Stable + explicit so a fit is reproducible. */
export declare const CPT_BUCKETS: readonly BucketName[];
/** Minimum events before any fit is trusted. Below this, a slope is noise. */
export declare const MIN_SAMPLES = 20;
/** A bucket must actually appear this many times to get its own column;
 *  an all-but-empty column makes XᵀX singular and the slope meaningless. */
export declare const MIN_BUCKET_PRESENCE = 8;
/** Plausible CPT band. Real content runs ~1.2 (dense JSON) to ~4 (prose);
 *  anything outside this is a fit artifact, not a measurement. */
export declare const CPT_PLAUSIBLE_MIN = 0.8;
export declare const CPT_PLAUSIBLE_MAX = 6;
/** Reject the whole fit above this pivot ratio — the buckets are too collinear
 *  to separate (e.g. reminders that always grow with tool_results). */
export declare const MAX_CONDITION = 100000000;
/** One request's regressors + target. */
export interface CptSample {
    /** Pre-compression TEXT chars per bucket (the logged `bucket_chars`). */
    bucketChars: Partial<Record<BucketName, number>>;
    /** Observed text tokens: `baseline_tokens − imagePixels / 750`. */
    textTokens: number;
}
export interface CptFitResult {
    /** Learned chars-per-token, per bucket. Absent = use the baked default. */
    cpt: Partial<Record<BucketName, number>>;
    nSamples: number;
    /** Why each bucket was NOT learned. Surfaced so a null result is explainable. */
    rejected: Partial<Record<BucketName, string>>;
    /** Pivot ratio of XᵀX. Higher = less trustworthy; > MAX_CONDITION rejects. */
    conditionEstimate: number;
    /** Buckets that got a column in this fit. */
    active: BucketName[];
}
/**
 * Fit per-bucket CPT from samples. Never throws; a fit it cannot trust comes back
 * with an empty `cpt` and a populated `rejected` explaining why.
 */
export declare function fitCpt(samples: readonly CptSample[]): CptFitResult;
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