README.md

---
dataset_info:
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  - name: Y
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  - name: f
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- config_name: default
  features:
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  - name: Y
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  splits:
  - name: train
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    num_examples: 10000
  - name: test
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    num_examples: 1000
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configs:
- config_name: aligned
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  - split: test
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    path: data/train-*
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---

# Antiderivative Dataset

## Dataset Summary

The Antiderivative dataset is a collection of functions and their antiderivatives. The
dataset is generated using Gaussian random functions and their antiderivatives. 

Plot from the dataset:

![Antiderivative Dataset](plot.png)

## Dataset Structure

The dataset is structured as follows.

### Subsets

The dataset is divided into two subsets:

- `default`: the default dataset.
- `aligned`: the `X` values are aligned for all functions.

### Splits

The dataset is divided into two splits:

- `train`: the training split, consisting of 10,000 rows.
- `test`: the test split, consisting of 1,000 rows.

### Columns

Each row in the dataset consists of the following
columns: 

- `X`: the input value of the function f.
- `f`: the value of the function f at X.
- `Y`: the input value of the antiderivative Tf.
- `Tf`: the value of the antiderivative Tf at Y.

### Data

The dataset contains the following data:

| Column  | Shape   | Type    |
|---------|---------|---------|
| X       | (100,)  | float   |
| f       | (100,)  | float   |
| Y       | (100,)  | float   |
| Tf      | (100,)  | float   |