The Observation Theory EncyclopediaFrom TSKAboutBy kindBy chapterBy Lean fileLedgerProvenance

dot product concept

DefinitionThe sum of the coordinatewise products of two vectors. Equation 0.1. Also cosine similarity, cosine reader.
Example(1, 2) · (3, 4) = 3 + 8 = 11.
BookData Mining as Observation, draft 0.2, commit f3914f0; entry id dot-product, kind concept.
Statusno ledger row names this entry. Corrections: none recorded.
Defining equation

Book equation 0.1.

Assumptions and scope
  • The sum of the coordinatewise products of two vectors, symmetric and bilinear. The cosine, the dot product over the two lengths, lies between minus one and one by Cauchy–Schwarz and is unchanged when either vector is scaled by a positive factor, while the dot product scales with the vector.
  • A cosine reader has declared length a nuisance and a dot-product reader has not, which is the reader difference of chapter 3, and cosine 0.995 between keys and their reconstruction said nothing about the softmax that read them.
Prior artnone recorded
Evidencelean/DataMiningAsObservation/DotProduct.lean
Reviewednot yet reviewed; generated 2026-09-10 from records at the commits on the provenance page.
xyθprojection of y on x
The sum of the products of matching coordinates.

Equation

Book equation 0.1.

\[x\cdot y=\sum_{i=1}^{d}x_i y_i,\qquad \|x\|=\sqrt{x\cdot x},\qquad \cos\theta=\frac{x\cdot y}{\|x\|\,\|y\|}.\]

Book equation 11.1.

\[\cos\big(k,\hat k\big)=0.995\qquad\text{while}\qquad \mathrm{PPL}:\ 12.24\ \to\ 10643.\]

Conditions

Conditions are curated in entries.toml rather than read from a record.

Ledger

none

First stated

Chapter 0 section 0.1 and chapter 3 section 3.2 of Data Mining as Observation, with the program’s cosine-versus-consumer case in turboquant-pro/docs/KV_KEYS_FINDING.md:1-49.

Measurements

none

Failures and corrections

none

Invariance envelope

none declared

Machine checked

lean/DataMiningAsObservation/DotProduct.lean, theorems self_nonneg, dot_comm, dot_sq_le, cosine_mem, dot_smul, cosine_smul, at observation-data-mining f3914f0; what the check covers is stated in the book’s appendix C.

Used in

Data Mining as Observation primer L, chapters 0, 1, 2, 3, 8, 11, 12.

Related

projection; Euclidean distance; quotient; nuisance.

See also

Ledger rows that cite the entry’s records without naming it: NEG-2.

Sources-table rows that share a record with the entry without naming it: chapter 1 section 1.4, chapter 2 section 2.5, chapter 3 section 3.2, chapter 8 section 8.2, chapter 11 section 11.1, chapter 11 section 11.2.

Status

Generated 2026-09-10 by encyclopedia/generate.py; book at observation-data-mining f3914f0; the commit of every record is listed in the encyclopedia’s provenance.

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