Gaussian process regression

[jaykim.huijae at gmail.com]

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