MCRLLM: Multivariate Curve Resolution by Log-Likelihood Maximization
Project description
MCRLLM: Multivariate Curve Resolution by Log-Likelihood Maximization.
Method first presented in:
Lavoie F.B., Braidy N. and Gosselin R. (2016) Including Noise Characteristics in MCR to improve Mapping and Component Extraction from Spectral Images, Chemometrics and Intelligent Laboratory Systems, 153, 40-50.
Input variables
X(nxk): 2D spectral matrix : n spectra acquired over k energy levels
Note: 3D spectral image can be unfold to 2D matrix prior to analysis.
Input and output arguments
MCRLLM requires 3 inputs : X dat, number of components to compute (nb) and use of phi exponent.
Refer to paper above for use of phi. To use it: 'phi', if not: 'standard'
decomposition = mcr.mcrllm(X,nb,'phi')
Results
Show S and C for each iteration (all) for only final results (final).
allS = decomposition.allS
S_final = decomposition.S
allC = decomposition.allC
C_final = decomposition.C
Sini = decomposition.Sini
Example:
import mcrllm as mcr
decomposition = mcr.mcrllm(X,7,'phi')
#Iterate each component 20 times
decomposition.iterate(20)
S_final = decomposition.S
C_final = decomposition.C
plt.figure()
plt.plot(S_final.T)
plt.title('S',fontsize=16)
plt.figure()
plt.plot(C_final)
plt.title('C',fontsize=16)
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