from pybrain.auxiliary import GaussianProcess from pybrain.datasets import SupervisedDataSet from scipy import mgrid, sin, cos, array, ravel from pylab import show, figure ds = SupervisedDataSet(1, 1) gp = GaussianProcess(indim=1, start=-3, stop=3, step=0.05) figure() x = mgrid[-3:3:0.2] y = 0.1*x**2 + x + 1 z = sin(x) + 0.5*cos(y) ds.addSample(-2.5, -1) ds.addSample(-1.0, 3) gp.mean = 0 # new feature "autonoise" adds uncertainty to data depending on # it's distance to other points in the dataset. not tested much yet. # gp.autonoise = True gp.trainOnDataset(ds) gp.plotCurves(showSamples=True) # you can also test the gp on single points, but this deletes the # original testing grid. it can be restored with a call to _buildGrid() print(gp.testOnArray(array([[0.4]]))) # --- example on how to use the GP in 2 dimensions
from pybrain.auxiliary import GaussianProcess from pybrain.datasets import SupervisedDataSet from scipy import mgrid, sin, cos, array, ravel from pylab import show, figure ds = SupervisedDataSet(1, 1) gp = GaussianProcess(indim=1, start=-3, stop=3, step=0.05) figure() x = mgrid[-3:3:0.2] y = 0.1*x**2 + x + 1 z = sin(x) + 0.5*cos(y) ds.addSample(-2.5, -1) ds.addSample(-1.0, 3) gp.mean = 0 # new feature "autonoise" adds uncertainty to data depending on # it's distance to other points in the dataset. not tested much yet. # gp.autonoise = True gp.trainOnDataset(ds) gp.plotCurves(showSamples=True) # you can also test the gp on single points, but this deletes the # original testing grid. it can be restored with a call to _buildGrid() print(gp.testOnArray(array([[0.4]]))) # --- example on how to use the GP in 2 dimensions
