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fix(card): improve SM-2 algorithm An An SM-2 algorithm for LEARNING/RElearning cards now uses correct steps ( RELEARNING_STEPS for relearning cards)

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Ultraworked with [Sisyphus](https://github.com/code-yeongyu/oh-my-opencode)
Co-authored-by: Sisyphus <clio-agent@sisyphuslabs.ai>
This commit is contained in:
goddonebianu
2026-03-13 15:36:29 +08:00
parent cbb9326f84
commit 4243cdc68b
1 changed files with 19 additions and 25 deletions
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36
src/modules/card/card-service.ts
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@@ -126,7 +126,7 @@ function scheduleNewCard(ease: ReviewEase, currentFactor: number): {
};
}
function scheduleLearningCard(ease: ReviewEase, currentFactor: number, left: number): {
function scheduleLearningCard(ease: ReviewEase, currentFactor: number, left: number, isRelearning: boolean): {
type: CardType;
queue: CardQueue;
ivl: number;
@@ -134,12 +134,13 @@ function scheduleLearningCard(ease: ReviewEase, currentFactor: number, left: num
newFactor: number;
newLeft: number;
} {
const steps = SM2_CONFIG.LEARNING_STEPS;
const steps = isRelearning ? SM2_CONFIG.RELEARNING_STEPS : SM2_CONFIG.LEARNING_STEPS;
const totalSteps = steps.length;
const cardType = isRelearning ? CardType.RELEARNING : CardType.LEARNING;
if (ease === 1) {
return {
type: CardType.LEARNING,
type: cardType,
queue: CardQueue.LEARNING,
ivl: 0,
due: Math.floor(Date.now() / 1000) + steps[0] * 60,
@@ -152,9 +153,11 @@ function scheduleLearningCard(ease: ReviewEase, currentFactor: number, left: num
if (ease === 2) {
if (stepIndex === 0 && steps.length >= 2) {
const avgStep = (steps[0] + steps[1]) / 2;
const step0 = steps[0] ?? 1;
const step1 = steps[1] ?? step0;
const avgStep = (step0 + step1) / 2;
return {
type: CardType.LEARNING,
type: cardType,
queue: CardQueue.LEARNING,
ivl: 0,
due: Math.floor(Date.now() / 1000) + avgStep * 60,
@@ -162,35 +165,26 @@ function scheduleLearningCard(ease: ReviewEase, currentFactor: number, left: num
newLeft: left,
};
}
if (stepIndex < steps.length - 1) {
const currentStepDelay = steps[stepIndex] ?? steps[0] ?? 1;
return {
type: CardType.LEARNING,
type: cardType,
queue: CardQueue.LEARNING,
ivl: 0,
due: Math.floor(Date.now() / 1000) + steps[stepIndex] * 60,
due: Math.floor(Date.now() / 1000) + currentStepDelay * 60,
newFactor: currentFactor,
newLeft: left,
};
}
const ivl = SM2_CONFIG.GRADUATING_INTERVAL_GOOD;
return {
type: CardType.REVIEW,
queue: CardQueue.REVIEW,
ivl,
due: calculateDueDate(ivl),
newFactor: SM2_CONFIG.DEFAULT_FACTOR,
newLeft: 0,
};
}
if (ease === 3) {
if (stepIndex < steps.length - 1) {
const nextStep = stepIndex + 1;
const nextStepDelay = steps[nextStep] ?? steps[0];
return {
type: CardType.LEARNING,
type: cardType,
queue: CardQueue.LEARNING,
ivl: 0,
due: Math.floor(Date.now() / 1000) + steps[nextStep] * 60,
due: Math.floor(Date.now() / 1000) + nextStepDelay * 60,
newFactor: currentFactor,
newLeft: nextStep * 1000 + (totalSteps - nextStep),
};
@@ -361,7 +355,7 @@ export async function serviceAnswerCard(
nextReviewDate: calculateNextReviewTime(result.ivl),
};
} else if (card.type === CardType.LEARNING || card.type === CardType.RELEARNING) {
const result = scheduleLearningCard(ease, card.factor, card.left);
const result = scheduleLearningCard(ease, card.factor, card.left, card.type === CardType.RELEARNING);
updateData = {
type: result.type,
queue: result.queue,