模型概览

FSRS 模型根据评分和经过时间计算新的记忆状态，通过遗忘曲线估算回忆概率，并根据记忆状态与目标保留率计算基础间隔。

本节的 FSRS-3 至 FSRS-6 术语与公式依据 awesome-fsrs Wiki 校对，主要参考其中的 The Algorithm。TypeScript API 与框架职责边界以 ts-fsrs 本身为准。

模型边界

模型只负责记忆状态更新、遗忘曲线计算和基础间隔计算。scheduler 负责组织复习流程，chrono 负责日期与经过时间，middleware 负责学习步骤、模糊处理、最大间隔限制和队列状态策略。

使用当前 ts-fsrs Model 契约完成以下任务：[描述任务]。

阅读 https://open-spaced-repetition.github.io/ts-fsrs/zh-CN/guide/model/index.md 以及所选模型版本的页面。
可按需对照 FSRS-6（https://open-spaced-repetition.github.io/ts-fsrs/zh-CN/guide/model/fsrs-6.md）、FSRS-5（https://open-spaced-repetition.github.io/ts-fsrs/zh-CN/guide/model/fsrs-5.md）、FSRS-4.5（https://open-spaced-repetition.github.io/ts-fsrs/zh-CN/guide/model/fsrs-4dot5.md）、FSRS-4（https://open-spaced-repetition.github.io/ts-fsrs/zh-CN/guide/model/fsrs-4.md）和 FSRS-3（https://open-spaced-repetition.github.io/ts-fsrs/zh-CN/guide/model/fsrs-3.md）的公式与说明。 FSRS-7: https://open-spaced-repetition.github.io/ts-fsrs/zh-CN/guide/model/fsrs-7.md.
实现模型前先定义配置 schema 和记忆状态 schema。
将模型限制为 step、nextInterval、forgettingCurve 和 forward 行为。
向 nextInterval 显式传入目标保留率。
由 scheduler 组织复习流程，由 chrono 处理时间，由 middleware 应用学习步骤、模糊处理、最大间隔限制和队列状态策略。
仅在配置已经完成 migrate、clip 和 check 后使用 bypass。
使用当前公共 API，并为每个实现的公式添加聚焦测试。

公共符号

符号	含义
$D$	难度，范围为 1（最简单）至 10（最困难）。
$S$	FSRS-3 至 FSRS-6 的稳定性，单位为天；当 $t = S$ 时回忆概率为 90%。
$R$	经过 $t$ 天后能够回忆的估计概率。
$G$	评分：1 为 Again、2 为 Hard、3 为 Good、4 为 Easy。
$t$	距离上次复习经过的天数。
$r$	`nextInterval` 使用的目标保留率。
$I(r, S)$	根据稳定性 $S$ 和目标保留率 $r$ 计算的基础间隔，单位为天，尚未应用 middleware 策略。
$S_{\min}$ 、 $S_{\max}$	模型允许的稳定性下限和上限。
$D_{\min}$ 、 $D_{\max}$	模型允许的难度下限和上限。
$w_i$	训练得到的模型权重。

FSRS-7 根据其实现快照单独说明：它增加快稳定性，使用混合曲线并返回小数天间隔。上表中稳定性的 90% 定义和 I(r, S) 记法适用于 FSRS-3 至 FSRS-6。

使用版本化模型

DefaultScheduler 默认使用 FSRS-7，可通过 version 选择任一支持的 FSRS 版本。也可以直接创建并调用各版本模型；下面以 FSRS-6 为例：

import { type Rating = 0 | 1 | 2 | 3 | 4
const Rating: Readonly<{
    readonly Manual: 0;
    readonly Again: 1;
    readonly Hard: 2;
    readonly Good: 3;
    readonly Easy: 4;
}>
Rating } from 'ts-fsrs'
import { const FSRS6_DEFAULT_WEIGHTS: number[]FSRS6_DEFAULT_WEIGHTS, const FSRS6Model: Model<{
    readonly name: "fsrs-6";
    readonly config: SRSSchema<{
        input: {
            readonly weights: number[];
            readonly enableShortTerm: boolean;
            readonly numRelearningSteps: number;
        };
        output: {
            readonly weights: number[];
            readonly enableShortTerm: boolean;
            readonly numRelearningSteps: number;
        };
    }>;
    readonly memoryState: SRSSchema<{
        input: {
            stability: number;
            difficulty: number;
        };
        output: {
            stability: number;
            difficulty: number;
        };
    }>;
    readonly algorithm: FSRS6Algorithm;
}>
FSRS6Model } from 'ts-fsrs/models/fsrs-6'

const const model: ModelCore<{
    readonly config: {
        readonly weights: number[];
        readonly enableShortTerm: boolean;
        readonly numRelearningSteps: number;
    };
    readonly memoryState: {
        stability: number;
        difficulty: number;
    };
    readonly algorithm: FSRS6Algorithm;
}>
model = const FSRS6Model: Model<{
    readonly name: "fsrs-6";
    readonly config: SRSSchema<{
        input: {
            readonly weights: number[];
            readonly enableShortTerm: boolean;
            readonly numRelearningSteps: number;
        };
        output: {
            readonly weights: number[];
            readonly enableShortTerm: boolean;
            readonly numRelearningSteps: number;
        };
    }>;
    readonly memoryState: SRSSchema<{
        input: {
            stability: number;
            difficulty: number;
        };
        output: {
            stability: number;
            difficulty: number;
        };
    }>;
    readonly algorithm: FSRS6Algorithm;
}>
FSRS6Model.Model<{ readonly name: "fsrs-6"; readonly config: SRSSchema<{ input: { readonly weights: number[]; readonly enableShortTerm: boolean; readonly numRelearningSteps: number; }; output: { readonly weights: number[]; readonly enableShortTerm: boolean; readonly numRelearningSteps: number; }; }>; readonly memoryState: SRSSchema<{ input: { stability: number; difficulty: number; }; output: { stability: number; difficulty: number; }; }>; readonly algorithm: FSRS6Algorithm; }>.create: (ctx: {
    readonly config: {
        readonly weights: number[];
        readonly enableShortTerm: boolean;
        readonly numRelearningSteps: number;
    } | {
        readonly weights: number[];
        readonly enableShortTerm: boolean;
        readonly numRelearningSteps: number;
    };
    readonly migrate?: boolean;
    readonly clip?: boolean;
    readonly check?: boolean;
    readonly bypass?: boolean;
}) => ModelCore<{
    readonly config: {
        readonly weights: number[];
        readonly enableShortTerm: boolean;
        readonly numRelearningSteps: number;
    };
    readonly memoryState: {
        stability: number;
        difficulty: number;
    };
    readonly algorithm: FSRS6Algorithm;
}>
create({
  config: {
    weights: const FSRS6_DEFAULT_WEIGHTS: number[]FSRS6_DEFAULT_WEIGHTS,
    enableShortTerm: true,
    numRelearningSteps: 1, // Constrains weights; middleware runs the steps.
  },
})

const memoryState = const model: ModelCore<{
    readonly config: {
        readonly weights: number[];
        readonly enableShortTerm: boolean;
        readonly numRelearningSteps: number;
    };
    readonly memoryState: {
        stability: number;
        difficulty: number;
    };
    readonly algorithm: FSRS6Algorithm;
}>
model.ModelCore<{ readonly config: { readonly weights: number[]; readonly enableShortTerm: boolean; readonly numRelearningSteps: number; }; readonly memoryState: { stability: number; difficulty: number; }; readonly algorithm: FSRS6Algorithm; }>.step: (input: ModelStepInput<{
    stability: number;
    difficulty: number;
}>) => {
    stability: number;
    difficulty: number;
}
step({
  ModelStepInput<{ stability: number; difficulty: number; }>.memoryState: Readonly<{
    stability: number;
    difficulty: number;
}> | null
memoryState: null,
  ModelStepInput<{ stability: number; difficulty: number; }>.rating: 1 | 2 | 3 | 4rating: const Rating: Readonly<{
    readonly Manual: 0;
    readonly Again: 1;
    readonly Hard: 2;
    readonly Good: 3;
    readonly Easy: 4;
}>
Rating.Good,
  ModelStepInput<{ stability: number; difficulty: number; }>.elapsedDays: numberelapsedDays: 0,
})
const desiredRetention = 0.9
const interval = const model: ModelCore<{
    readonly config: {
        readonly weights: number[];
        readonly enableShortTerm: boolean;
        readonly numRelearningSteps: number;
    };
    readonly memoryState: {
        stability: number;
        difficulty: number;
    };
    readonly algorithm: FSRS6Algorithm;
}>
model.ModelCore<{ readonly config: { readonly weights: number[]; readonly enableShortTerm: boolean; readonly numRelearningSteps: number; }; readonly memoryState: { stability: number; difficulty: number; }; readonly algorithm: FSRS6Algorithm; }>.nextInterval: (memoryState: Readonly<{
    stability: number;
    difficulty: number;
}>, desiredRetention: number) => number
nextInterval(memoryState, desiredRetention)
const retrievabilityAtInterval = const model: ModelCore<{
    readonly config: {
        readonly weights: number[];
        readonly enableShortTerm: boolean;
        readonly numRelearningSteps: number;
    };
    readonly memoryState: {
        stability: number;
        difficulty: number;
    };
    readonly algorithm: FSRS6Algorithm;
}>
model.ModelCore<{ readonly config: { readonly weights: number[]; readonly enableShortTerm: boolean; readonly numRelearningSteps: number; }; readonly memoryState: { stability: number; difficulty: number; }; readonly algorithm: FSRS6Algorithm; }>.forgettingCurve: (memoryState: Readonly<{
    stability: number;
    difficulty: number;
}>, elapsedDays: number) => number
forgettingCurve(memoryState, interval)

console.log(
  JSON.stringify(
    {
      memoryState,
      desiredRetention,
      interval,
      retrievabilityAtInterval,
    },
    null,
    2
  )
)

示例先根据 90% 的目标保留率计算复习间隔，再通过 forgettingCurve 估算到达该间隔时的回忆概率。由于 FSRS-6 的 nextInterval 返回整数天，结果可能与 90% 略有差异。

在 FSRS-6 配置中，enableShortTerm 控制模型的同日稳定性更新。numRelearningSteps 只参与权重裁剪与检查；重新学习步骤仍由 middleware 调度。

定义模型

defineModel 会保留具体的配置、记忆状态和算法类型，并把 schema、默认值与模型核心工厂组织为一个定义。它本身不会解析配置：

下面通过实现一个 SM-2 变体，演示如何定义模型。

import {
  function clamp(value: number, min: number, max: number): numberclamp,
  function defineModel<const Name extends string | symbol, const ConfigSchema extends AnySchema, const MemoryStateSchema extends AnyObjectSchema, const Algorithm>(definition: {
    readonly name: Name;
    readonly schema: ModelSchema<{
        readonly config: ConfigSchema;
        readonly memoryState: MemoryStateSchema;
    }>;
    readonly defaultValue: {
        readonly memoryState: (ctx: {
            readonly config: SchemaOutput<ConfigSchema>;
        }) => SchemaInput<MemoryStateSchema>;
    };
    readonly create: ModelCreate<{
        readonly config: ConfigSchema;
        readonly memoryState: MemoryStateSchema;
    }, Algorithm>;
}): Model<{
    readonly name: Name;
    readonly config: ConfigSchema;
    readonly memoryState: MemoryStateSchema;
    readonly algorithm: Algorithm;
}>
defineModel,
  type interface ModelForwardInput<MemoryState extends object>ModelForwardInput,
  type interface ModelStepInput<MemoryState extends object>ModelStepInput,
  type Rating = 0 | 1 | 2 | 3 | 4
const Rating: Readonly<{
    readonly Manual: 0;
    readonly Again: 1;
    readonly Hard: 2;
    readonly Good: 3;
    readonly Easy: 4;
}>
Rating,
} from 'ts-fsrs'
import { z } from 'zod'

const configSchema = z.object({
  weights: z.tuple([
    z.number().min(0.01).max(100),
    z.number().min(0.01).max(100),
    z.number().min(1.3).max(10),
    z.number().nonnegative(),
    z.number().min(5),
    z.number(),
  ]),
})
const memoryStateSchema = z.object({
  interval: z.number().min(0).max(36500),
  easeFactor: z.number().min(1.3).max(10),
  repetitions: z.number().int().nonnegative(),
})
type type MemoryState = {
    interval: number;
    easeFactor: number;
    repetitions: number;
}
MemoryState = z.output<typeof memoryStateSchema>

const const SM2Model: Model<{
    readonly name: "sm2";
    readonly config: z.ZodObject<{
        weights: z.ZodTuple<[z.ZodNumber, z.ZodNumber, z.ZodNumber, z.ZodNumber, z.ZodNumber, z.ZodNumber], null>;
    }, z.core.$strip>;
    readonly memoryState: z.ZodObject<{
        interval: z.ZodNumber;
        easeFactor: z.ZodNumber;
        repetitions: z.ZodNumber;
    }, z.core.$strip>;
    readonly algorithm: null;
}>
SM2Model = defineModel<"sm2", z.ZodObject<{
    weights: z.ZodTuple<[z.ZodNumber, z.ZodNumber, z.ZodNumber, z.ZodNumber, z.ZodNumber, z.ZodNumber], null>;
}, z.core.$strip>, z.ZodObject<{
    interval: z.ZodNumber;
    easeFactor: z.ZodNumber;
    repetitions: z.ZodNumber;
}, z.core.$strip>, null>(definition: {
    readonly name: "sm2";
    readonly schema: ModelSchema<{
        readonly config: z.ZodObject<{
            weights: z.ZodTuple<[z.ZodNumber, z.ZodNumber, z.ZodNumber, z.ZodNumber, z.ZodNumber, z.ZodNumber], null>;
        }, z.core.$strip>;
        readonly memoryState: z.ZodObject<{
            ...;
        }, z.core.$strip>;
    }>;
    readonly defaultValue: {
        ...;
    };
    readonly create: ModelCreate<...>;
}): Model<...>
defineModel({
  name: 'sm2',
  schema: ModelSchema<{
    readonly config: z.ZodObject<{
        weights: z.ZodTuple<[z.ZodNumber, z.ZodNumber, z.ZodNumber, z.ZodNumber, z.ZodNumber, z.ZodNumber], null>;
    }, z.core.$strip>;
    readonly memoryState: z.ZodObject<{
        interval: z.ZodNumber;
        easeFactor: z.ZodNumber;
        repetitions: z.ZodNumber;
    }, z.core.$strip>;
}>
schema: { ModelSchema<{ readonly config: ZodObject<{ weights: ZodTuple<[ZodNumber, ZodNumber, ZodNumber, ZodNumber, ZodNumber, ZodNumber], null>; }, $strip>; readonly memoryState: ZodObject<...>; }>.config: z.ZodObject<{
    weights: z.ZodTuple<[z.ZodNumber, z.ZodNumber, z.ZodNumber, z.ZodNumber, z.ZodNumber, z.ZodNumber], null>;
}, z.core.$strip>
config: configSchema, ModelSchema<{ readonly config: ZodObject<{ weights: ZodTuple<[ZodNumber, ZodNumber, ZodNumber, ZodNumber, ZodNumber, ZodNumber], null>; }, $strip>; readonly memoryState: ZodObject<...>; }>.memoryState: z.ZodObject<{
    interval: z.ZodNumber;
    easeFactor: z.ZodNumber;
    repetitions: z.ZodNumber;
}, z.core.$strip>
memoryState: memoryStateSchema },
  defaultValue: {
    memoryState: ({ config }) => ({
      interval: 0,
      easeFactor: config.weights[2],
      repetitions: 0,
    }),
  },
  create: ModelCreate<{
    readonly config: z.ZodObject<{
        weights: z.ZodTuple<[z.ZodNumber, z.ZodNumber, z.ZodNumber, z.ZodNumber, z.ZodNumber, z.ZodNumber], null>;
    }, z.core.$strip>;
    readonly memoryState: z.ZodObject<{
        interval: z.ZodNumber;
        easeFactor: z.ZodNumber;
        repetitions: z.ZodNumber;
    }, z.core.$strip>;
}, null>
create({ config }) {
    const parsed = configSchema.ZodType<any, any, $ZodObjectInternals<{ weights: ZodTuple<[ZodNumber, ZodNumber, ZodNumber, ZodNumber, ZodNumber, ZodNumber], null>; }, $strip>>.parse(data: unknown, params?: z.core.ParseContext<z.core.$ZodIssue>): {
    weights: [number, number, number, number, number, number];
}
parse(config)
    const w = parsed.weights
    const const step: ({ memoryState, rating }: ModelStepInput<MemoryState>) => {
    interval: number;
    easeFactor: number;
    repetitions: number;
}
step = ({ memoryState, rating }: interface ModelStepInput<MemoryState extends object>ModelStepInput<type MemoryState = {
    interval: number;
    easeFactor: number;
    repetitions: number;
}
MemoryState>) => {
      const previous = memoryState ?? {
        interval: 0,
        easeFactor: parsed.weights[2],
        repetitions: 0,
      }
      // Again / Hard / Good / Easy -> SM-2 quality 2 / 3 / 4 / 5.
      const quality = rating + 1
      const repetitions = rating > const Rating: Readonly<{
    readonly Manual: 0;
    readonly Again: 1;
    readonly Hard: 2;
    readonly Good: 3;
    readonly Easy: 4;
}>
Rating.Again ? previous.repetitions + 1 : 0
      const interval = function clamp(value: number, min: number, max: number): numberclamp(
        repetitions <= 1
          ? w[0]
          : repetitions === 2
            ? w[1]
            : previous.interval * previous.easeFactor,
        0.01,
        36500
      )
      const easeFactor = function clamp(value: number, min: number, max: number): numberclamp(
        previous.easeFactor - w[3] * (quality - w[4]) ** 2 + w[5],
        1.3,
        10
      )
      return { interval, easeFactor, repetitions }
    }
    const const forward: ({ history, initialState, }: ModelForwardInput<MemoryState>) => {
    interval: number;
    easeFactor: number;
    repetitions: number;
}[]
forward = ({
      history: readonly ModelReview[]history,
      initialState,
    }: interface ModelForwardInput<MemoryState extends object>ModelForwardInput<type MemoryState = {
    interval: number;
    easeFactor: number;
    repetitions: number;
}
MemoryState>) => {
      const const states: {
    interval: number;
    easeFactor: number;
    repetitions: number;
}[]
states: type MemoryState = {
    interval: number;
    easeFactor: number;
    repetitions: number;
}
MemoryState[] = []
      let memoryState = initialState ?? null
      for (const const review: ModelReviewreview of history: readonly ModelReview[]history) {
        memoryState = const step: ({ memoryState, rating }: ModelStepInput<MemoryState>) => {
    interval: number;
    easeFactor: number;
    repetitions: number;
}
step({
          ModelStepInput<{ interval: number; easeFactor: number; repetitions: number; }>.memoryState: Readonly<{
    interval: number;
    easeFactor: number;
    repetitions: number;
}> | null
memoryState,
          ModelStepInput<{ interval: number; easeFactor: number; repetitions: number; }>.rating: 1 | 2 | 3 | 4rating: const review: ModelReviewreview.ModelReview.rating: 1 | 2 | 3 | 4rating,
          ModelStepInput<{ interval: number; easeFactor: number; repetitions: number; }>.elapsedDays: numberelapsedDays: const review: ModelReviewreview.ModelReview.deltaT: numberdeltaT,
        })
        const states: {
    interval: number;
    easeFactor: number;
    repetitions: number;
}[]
states.push(memoryState)
      }
      return const states: {
    interval: number;
    easeFactor: number;
    repetitions: number;
}[]
states
    }
    return {
      ModelCore<{ readonly config: { weights: [number, number, number, number, number, number]; }; readonly memoryState: { interval: number; easeFactor: number; repetitions: number; }; readonly algorithm: null; }>.config: {
    weights: [number, number, number, number, number, number];
}
config: parsed,
      ModelCore<{ readonly config: { weights: [number, number, number, number, number, number]; }; readonly memoryState: { interval: number; easeFactor: number; repetitions: number; }; readonly algorithm: null; }>.bounds: ModelBoundsShape<{
    interval: number;
    easeFactor: number;
    repetitions: number;
}>
bounds: {
        iMin: 0.01,
        iMax: 36500,
        eMin: 1.3,
        eMax: 10,
        rMin: 0,
        rMax: Infinity,
      },
      ModelCore<{ readonly config: { weights: [number, number, number, number, number, number]; }; readonly memoryState: { interval: number; easeFactor: number; repetitions: number; }; readonly algorithm: null; }>.algorithm: nullalgorithm: null,
      ModelCore<{ readonly config: { weights: [number, number, number, number, number, number]; }; readonly memoryState: { interval: number; easeFactor: number; repetitions: number; }; readonly algorithm: null; }>.step: (input: ModelStepInput<{
    interval: number;
    easeFactor: number;
    repetitions: number;
}>) => {
    interval: number;
    easeFactor: number;
    repetitions: number;
}
step,
      // Invert the benchmark curve to satisfy the Model interval contract.
      ModelCore<{ readonly config: { weights: [number, number, number, number, number, number]; }; readonly memoryState: { interval: number; easeFactor: number; repetitions: number; }; readonly algorithm: null; }>.nextInterval: (memoryState: Readonly<{
    interval: number;
    easeFactor: number;
    repetitions: number;
}>, desiredRetention: number) => number
nextInterval: (state: Readonly<type MemoryState = {
    interval: number;
    easeFactor: number;
    repetitions: number;
}
MemoryState>, desiredRetention: number) =>
        Math.max(
          1,
          Math.round(
            (state.interval * Math.log(desiredRetention)) / Math.log(0.9)
          )
        ),
      ModelCore<{ readonly config: { weights: [number, number, number, number, number, number]; }; readonly memoryState: { interval: number; easeFactor: number; repetitions: number; }; readonly algorithm: null; }>.forgettingCurve: (memoryState: Readonly<{
    interval: number;
    easeFactor: number;
    repetitions: number;
}>, elapsedDays: number) => number
forgettingCurve: (state: Readonly<type MemoryState = {
    interval: number;
    easeFactor: number;
    repetitions: number;
}
MemoryState>, elapsedDays: number) =>
        0.9 ** (elapsedDays / state.interval),
      ModelCore<{ readonly config: { weights: [number, number, number, number, number, number]; }; readonly memoryState: { interval: number; easeFactor: number; repetitions: number; }; readonly algorithm: null; }>.forward: (input: ModelForwardInput<{
    interval: number;
    easeFactor: number;
    repetitions: number;
}>) => readonly {
    interval: number;
    easeFactor: number;
    repetitions: number;
}[]
forward,
    }
  },
})

const const model: ModelCore<{
    readonly config: {
        weights: [number, number, number, number, number, number];
    };
    readonly memoryState: {
        interval: number;
        easeFactor: number;
        repetitions: number;
    };
    readonly algorithm: null;
}>
model = const SM2Model: Model<{
    readonly name: "sm2";
    readonly config: z.ZodObject<{
        weights: z.ZodTuple<[z.ZodNumber, z.ZodNumber, z.ZodNumber, z.ZodNumber, z.ZodNumber, z.ZodNumber], null>;
    }, z.core.$strip>;
    readonly memoryState: z.ZodObject<{
        interval: z.ZodNumber;
        easeFactor: z.ZodNumber;
        repetitions: z.ZodNumber;
    }, z.core.$strip>;
    readonly algorithm: null;
}>
SM2Model.Model<{ readonly name: "sm2"; readonly config: ZodObject<{ weights: ZodTuple<[ZodNumber, ZodNumber, ZodNumber, ZodNumber, ZodNumber, ZodNumber], null>; }, $strip>; readonly memoryState: ZodObject<...>; readonly algorithm: null; }>.create: (ctx: {
    readonly config: {
        weights: [number, number, number, number, number, number];
    } | {
        weights: [number, number, number, number, number, number];
    };
    readonly migrate?: boolean;
    readonly clip?: boolean;
    readonly check?: boolean;
    readonly bypass?: boolean;
}) => ModelCore<{
    readonly config: {
        weights: [number, number, number, number, number, number];
    };
    readonly memoryState: {
        interval: number;
        easeFactor: number;
        repetitions: number;
    };
    readonly algorithm: null;
}>
create({
  config: { weights: [1, 6, 2.5, 0.02, 7, 0.18] },
})
const const states: readonly {
    interval: number;
    easeFactor: number;
    repetitions: number;
}[]
states = const model: ModelCore<{
    readonly config: {
        weights: [number, number, number, number, number, number];
    };
    readonly memoryState: {
        interval: number;
        easeFactor: number;
        repetitions: number;
    };
    readonly algorithm: null;
}>
model.ModelCore<{ readonly config: { weights: [number, number, number, number, number, number]; }; readonly memoryState: { interval: number; easeFactor: number; repetitions: number; }; readonly algorithm: null; }>.forward: (input: ModelForwardInput<{
    interval: number;
    easeFactor: number;
    repetitions: number;
}>) => readonly {
    interval: number;
    easeFactor: number;
    repetitions: number;
}[]
forward({
  ModelForwardInput<{ interval: number; easeFactor: number; repetitions: number; }>.history: readonly ModelReview[]history: [
    { ModelReview.rating: 1 | 2 | 3 | 4rating: const Rating: Readonly<{
    readonly Manual: 0;
    readonly Again: 1;
    readonly Hard: 2;
    readonly Good: 3;
    readonly Easy: 4;
}>
Rating.Good, ModelReview.deltaT: numberdeltaT: 0 },
    { ModelReview.rating: 1 | 2 | 3 | 4rating: const Rating: Readonly<{
    readonly Manual: 0;
    readonly Again: 1;
    readonly Hard: 2;
    readonly Good: 3;
    readonly Easy: 4;
}>
Rating.Good, ModelReview.deltaT: numberdeltaT: 1 },
    { ModelReview.rating: 1 | 2 | 3 | 4rating: const Rating: Readonly<{
    readonly Manual: 0;
    readonly Again: 1;
    readonly Hard: 2;
    readonly Good: 3;
    readonly Easy: 4;
}>
Rating.Good, ModelReview.deltaT: numberdeltaT: 6 },
    { ModelReview.rating: 1 | 2 | 3 | 4rating: const Rating: Readonly<{
    readonly Manual: 0;
    readonly Again: 1;
    readonly Hard: 2;
    readonly Good: 3;
    readonly Easy: 4;
}>
Rating.Again, ModelReview.deltaT: numberdeltaT: 15 },
    { ModelReview.rating: 1 | 2 | 3 | 4rating: const Rating: Readonly<{
    readonly Manual: 0;
    readonly Again: 1;
    readonly Hard: 2;
    readonly Good: 3;
    readonly Easy: 4;
}>
Rating.Good, ModelReview.deltaT: numberdeltaT: 1 },
    { ModelReview.rating: 1 | 2 | 3 | 4rating: const Rating: Readonly<{
    readonly Manual: 0;
    readonly Again: 1;
    readonly Hard: 2;
    readonly Good: 3;
    readonly Easy: 4;
}>
Rating.Good, ModelReview.deltaT: numberdeltaT: 1 },
  ],
})
console.log(JSON.stringify(const states: readonly {
    interval: number;
    easeFactor: number;
    repetitions: number;
}[]
states, null, 2)) // Intervals: 1, 6, 15, 1, 1, 6 days.

Schema 描述公共边界，并在 scheduler 组合 schema 时使用。直接调用 create 时，具体实现必须先完成所需的解析和规范化，再返回 config、bounds、algorithm、step、nextInterval、forgettingCurve 和 forward。该核心应保持确定性，且不包含调度策略。

bypass 仅用于已处理的配置

只有调用方已经明确完成该模型的 migrate、clip、schema 校验和范围检查后，才能使用 FSRS6Model.create({ config, bypass: true })。它会跳过这些 create 阶段的处理以及冻结。不要对来自用户、存储或网络的原始配置使用 bypass。

模型版本

FSRS-7 — 双分量实现参考，34 个权重与数值求间隔。
FSRS-6 — 支持可训练衰减与同日更新。
FSRS-5 — 使用固定幂函数遗忘曲线，并支持同日更新。
FSRS-4.5 — 使用固定幂函数曲线，不包含同日公式。
FSRS-4 — 本项目中首个使用幂函数曲线的版本。
FSRS-3 — 使用较早的指数遗忘曲线。

#模型概览

#公共符号

#使用版本化模型

#定义模型

#模型版本

模型概览

公共符号

使用版本化模型

定义模型

模型版本