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import GPy | |
import GPflow | |
import numpy as np | |
rng = np.random.RandomState(0) | |
X = rng.randn(1000,2) | |
Y = rng.randn(1000,1) | |
Xtest = rng.randn(10,2) | |
# GPy |
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import GPy | |
import GPflow | |
import numpy as np | |
rng = np.random.RandomState(0) | |
X = rng.randn(100,2) | |
Y = rng.randn(100,1) | |
Xtest = rng.randn(10,2) | |
# GPy |
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import numpy as np | |
import GPy | |
from sklearn.svm import SVR | |
import sys | |
import memory_profiler as mprof | |
model = sys.argv[1] | |
feats = np.random.random(size=(100,10)) | |
labels = np.random.random(size=(100,1)) |
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Python 2.7.10 |Anaconda 2.4.0 (64-bit)| (default, Oct 19 2015, 18:04:42) | |
Type "copyright", "credits" or "license" for more information. | |
IPython 4.0.0 -- An enhanced Interactive Python. | |
? -> Introduction and overview of IPython's features. | |
%quickref -> Quick reference. | |
help -> Python's own help system. | |
object? -> Details about 'object', use 'object??' for extra details. | |
In [1]: import numpy as np |
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danielb@elgon:~/Research/Datasets/QE$ head -1 eamt11/fr-en/feats_17.tsv | |
13.0 15.0 4.38461538462 -31.6027 -34.8847 0.8 310.461538462 1185.38461538 0.153846153846 15632.0 0.166666666667 16934.6666667 0.181818181818 18474.1818182 1.0 0.0769230769231 0.0666666666667 | |
danielb@elgon:~/Research/Datasets/QE$ head -1 wptp-12_daniel/qe_17 | |
19.0 17.0 4.4210525 -43.948 -39.2668 1.0 144.0 0.04820285 0.0 0.7894737 0.0 0.5 0.0 0.23529412 0.8947368 2.0 2.0 |
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import numpy as np | |
import GPy as gp | |
X = np.random.uniform(-3.,3.,(2000,17)) | |
Y = np.sin(X) + np.random.randn(2000,17)*0.05 | |
kernel = gp.kern.rbf(D=17, variance=1., lengthscale=1.) | |
m = gp.models.GP_regression(X,Y,kernel,normalize_X=True,normalize_Y=True) | |
m.constrain_positive('') | |
m.optimize() |