svd_lls, a Python code which uses the Singular Value Decomposition (SVD) to construct and plot the best affine and linear relationships in the sense of least square, between two vectors of data.
The linear relationship will have the form
( y - mean(y) ) = slope * ( x - mean(x) )
The affine relationship will have the form
y = slope * x + intercept
The information on this web page is distributed under the MIT license.
svd_lls is available in a MATLAB version and an Octave version and a Python version.
svd_circle, a Python code which analyzes a linear map of the unit circle caused by an arbitrary 2x2 matrix A, using the singular value decomposition (SVD).
svd_powers, a Python code which applies singular value decomposition (SVD) analysis to powers A[i,j] = x[i]^j.
svd_snowfall, a Python code which reads a file containing historical snowfall data and analyzes the data with the singular value decomposition (SVD).
svd_sphere, a Python code which analyzes a linear map of the unit sphere caused by an arbitrary 3x3 matrix A, using the singular value decomposition (SVD).
svd_test, a Python code which demonstrates the singular value decomposition (SVD) for a simple example.
svd_truncated_test, a Python code which demonstrates the computation of the reduced or truncated Singular Value Decomposition (SVD) that is useful for cases when one dimension of the matrix is much smaller than the other.
