Documentation Index
Fetch the complete documentation index at: https://mintlify.com/Seyamalam/bun-scikit/llms.txt
Use this file to discover all available pages before exploring further.
Overview
Normalizer scales individual samples to have unit norm. This is different from other scalers which operate on features (columns). Each sample (row) is normalized independently.
Constructor
new Normalizer(options?: NormalizerOptions)
options.norm
'l1' | 'l2' | 'max'
default:"'l2'"
The norm to use to normalize each non-zero sample:
'l1': Sum of absolute values equals 1'l2': Sum of squares equals 1 (Euclidean norm)'max': Maximum absolute value equals 1
Properties
Number of features seen during fit.
Methods
fit
Fit the normalizer. This method only records the number of features for validation.
Returns: this - The fitted normalizer instance.
transform(X: Matrix): Matrix
Data matrix to normalize. Each row will be normalized independently.
Matrix - Normalized data where each row has unit norm.
fitTransform(X: Matrix): Matrix
fit(X).transform(X).
Training data matrix to fit and normalize.
Matrix - Normalized data.
Example
import { Normalizer } from 'bun-scikit';
const X = [
[1, 2, 3],
[4, 5, 6],
[7, 8, 9]
];
// L2 normalization (default)
const normL2 = new Normalizer({ norm: 'l2' });
const XL2 = normL2.fitTransform(X);
console.log('L2 normalized:', XL2);
// Each row has Euclidean norm of 1
// Row 0: [0.267, 0.534, 0.802]
// L1 normalization
const normL1 = new Normalizer({ norm: 'l1' });
const XL1 = normL1.fitTransform(X);
console.log('L1 normalized:', XL1);
// Each row sums to 1
// Row 0: [1/6, 2/6, 3/6] = [0.167, 0.333, 0.5]
// Max normalization
const normMax = new Normalizer({ norm: 'max' });
const XMax = normMax.fitTransform(X);
console.log('Max normalized:', XMax);
// Each row's max absolute value is 1
// Row 0: [1/3, 2/3, 1] = [0.333, 0.667, 1]
Example: Text Feature Vectors
import { Normalizer } from 'bun-scikit';
// Term frequency vectors for documents
const termFrequencies = [
[3, 0, 1], // Document 1: 3x term1, 0x term2, 1x term3
[2, 0, 0], // Document 2: 2x term1, 0x term2, 0x term3
[3, 0, 0], // Document 3: 3x term1, 0x term2, 0x term3
];
// Normalize to unit length for similarity comparison
const normalizer = new Normalizer({ norm: 'l2' });
const normalized = normalizer.fitTransform(termFrequencies);
console.log('Normalized TF vectors:', normalized);
// Now we can compute cosine similarity by dot product
Example: Zero-Norm Rows
import { Normalizer } from 'bun-scikit';
const X = [
[1, 2, 3],
[0, 0, 0], // Zero vector - cannot be normalized
[4, 5, 6]
];
const normalizer = new Normalizer();
const XNorm = normalizer.fitTransform(X);
console.log('Normalized:', XNorm);
// Row with all zeros remains unchanged
// [[0.267, 0.534, 0.802], [0, 0, 0], [0.456, 0.570, 0.684]]
Notes
- Normalization is performed row-wise (per sample), not column-wise (per feature)
- Rows with zero norm remain unchanged (all zeros)
- Unlike other scalers, the fit step doesn’t compute statistics - it only validates the data
- Commonly used in text classification and clustering
- Input data must be finite (no NaN or Infinity values)