Feature: Normalize

[JakobEliasWagner]

Feature: Normalize

Description

This pull request introduces a new feature for data preprocessing: Z-Normalization. Z-Normalization, also known as standardization, is a technique used to scale data so that it has a mean of 0 and a standard deviation of 1. This process is essential in many machine learning algorithms as it ensures that each feature contributes equally to the analysis, improving the performance and stability of the models.

Which issue does this PR tackle?

• Addresses the need for a standardized data preprocessing step in our machine learning pipeline. The lack of a z-normalization feature has been a significant gap in our data preprocessing capabilities, leading to suboptimal performance in models sensitive to the scale of data.

How does it solve the problem?

• Implements Z-normalization.
• Offers alias Standardization.
• Offering compatibility with multi-dimensional tensors.

How are the changes tested?

• Added unit tests:
  • 4 tests against trivial examples.
  • 1 test with common 8 students set.
  • 1 test testing for singularity.
  • 1 test assuring the alias is set correctly
  • 2 tests with higher dimensional data and multiple scaling dimensions

Checklist for Contributors

• [x] Scope: This PR tackles exactly one problem.
• [x] Conventions: The branch follows the feature/title-slug convention.
• [x] Conventions: The PR title follows the Bugfix: Title convention.
• [x] Coding style: The code passes all pre-commit hooks.
• [x] Documentation: All changes are well-documented.
• [x] Tests: New features are tested and all tests pass successfully.
• [x] Changelog: Updated CHANGELOG.md for new features or breaking changes.
• [x] Review: A suitable reviewer has been assigned.

Checklist for Reviewers:

• [x] The PR solves the issue it claims to solve and only this one.
• [x] Changes are tested sufficiently and all tests pass.
• [x] Documentation is complete and well-written.
• [x] Changelog has been updated, if necessary.

[JakobEliasWagner]

The reason as to why this test fails is unrelated to eps. Removed the comment in 99d6b1a.

[JakobEliasWagner]

The previous plot is related to the range of the scaled tensor. The tests are performed for a range in the order of one -> 1e-7 is appropriate for testing.

[samuelburbulla]

I improved the docs a little and set the random seed, because tests failed from time to time.