Hi, I am trying to use Gaussian process regression for Near Infrared spectra. I have reference data(spectra), concentrations of reference data and sample data, and I am trying to predict concentrations of sample data. Here is my code.
from sklearn.gaussian_process import GaussianProcess gp = GaussianProcess() gp.fit(reference, concentration) concentration_pred = gp.predict(sample) The results always gave me the same concentration even though I used different sample data. When I used some parts of reference data as sample data, it predicted concentration well. But whenever I use different data than reference data, it always gave me the same concentration. Can I get some help with this problem? What am I doing wrong? I would appreciate any help. Thanks, Jay -- https://mail.python.org/mailman/listinfo/python-list
