DECOMPOSITION
svd
Singular Value Decomposition.
Example of usage
Factorise a labelled industrial sensor calibration matrix and verify reconstruction.
Usage / limitations
+Use when: Compress feature matrices and quantify dominant directions.
−Limitations: Matrix shape, symmetry, conditioning and numerical precision constrain validity.
Mathematical formula
A = UΣVᴴ.
Sample dataShow
{
"variable": "measurement_matrix",
"domain": "Industrial sensor calibration",
"feature_names": [
"temperature_c",
"vibration_mm_s"
],
"matrix": [
[
3.0,
1.0
],
[
1.0,
3.0
]
]
}
Code example (Python)
# DataLens.ai sample data — industrial sensor calibration
import numpy as np
A = np.array([[3.,1.],[1.,3.]])
# DataLens.ai example
# Factorise the named two- or three-sensor calibration matrix.
from scipy.linalg import svd
U,s,Vh = svd(A)
assert np.allclose((U * s) @ Vh, A)
print("Expected output verified")
# DataLens.ai example
# Factorise the named two- or three-sensor calibration matrix.
from scipy.linalg import svd
U,s,Vh = svd(A)
assert np.allclose((U * s) @ Vh, A)
print("Expected output verified")