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If you’re mathematically inclined,
then you could use the [pigeonhole principle](https://en.wikipedia.org/wiki/Pigeonhole_principle)
to describe hash collisions more formally:
> Given *m* items and *n* containers,
> if *m* > *n*,
> then there’s at least one container
> with more than one item.
In this context,
items are a potentially infinite number of values
that you feed into the hash function,
while containers are their hash values
assigned from a finite pool.
Build a Hash Table in Python With TDD
Bartosz Zaczyński
• Finding and Understanding the Data
• Cleaning the Data and Feature Engineering
• Tuning and Evaluating
• Using the Model and Presenting Results
The Machine Learning Process
Codecademy
Eigengrau
ˈaɪɡənɡɹaʊ
*Noun*
• The dark gray color seen by the eyes in perfect darkness as a result of signals from the optic nerves.
**Example Sentences**
“Before my eyes adjusted to the dark, all I could see was eigengrau.”
“Henry awoke in the eigengrau of total darkness, so he quickly turned on his lamp.”
“The darkness of the sub-basement seemed eigengrau to my eyes.”
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**Word Origin**
German, mid-20th century
---
**Why this word?**
“Eigengrau” is a German loanword used to describe the specific color of total darkness as perceived by human eyes.
Rather than black, the color that human optic nerves discern in pure darkness is a dark gray, which is why eigengrau is sometimes called “brain gray.”
Eigengrau is close to what might be called “charcoal gray,” but the word’s German roots specify that this is a gray created inside the viewer’s brain.
In German, “eigengrau” translates to “own gray,” or “intrinsic gray,” suggesting the shade might shift, depending on the viewer’s perception.
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