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---
dataset_info:
- config_name: aligned
features:
- name: Tf
sequence: float64
- name: X
sequence: float64
- name: Y
sequence: float64
- name: f
sequence: float64
splits:
- name: train
num_bytes: 32160000
num_examples: 10000
- name: test
num_bytes: 3216000
num_examples: 1000
download_size: 33511553
dataset_size: 35376000
- config_name: default
features:
- name: Tf
sequence: float64
- name: X
sequence: float64
- name: Y
sequence: float64
- name: f
sequence: float64
splits:
- name: train
num_bytes: 32160000
num_examples: 10000
- name: test
num_bytes: 3216000
num_examples: 1000
download_size: 44590226
dataset_size: 35376000
configs:
- config_name: aligned
data_files:
- split: train
path: aligned/train-*
- split: test
path: aligned/test-*
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
# Derivative Dataset
## Dataset Summary
The Derivative dataset is a collection of functions and their derivatives. The dataset
is generated using Gaussian random functions and their derivatives.
Plot from the dataset:
## 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 derivative Tf.
- `Tf`: the value of the derivative 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 |
|