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src/g/p/GPy-0.4.9/GPy/examples/dimensionality_reduction.py   GPy(Download)
    """
    from GPy.likelihoods.gaussian import Gaussian
    import GPy
 
    num_inputs = 13
def gplvm_oil_100(optimize=True, verbose=1, plot=True):
    import GPy
    data = GPy.util.datasets.oil_100()
    Y = data['X']
    # create simple GP model
def sparse_gplvm_oil(optimize=True, verbose=0, plot=True, N=100, Q=6, num_inducing=15, max_iters=50):
    import GPy
    _np.random.seed(0)
    data = GPy.util.datasets.oil()
    Y = data['X'][:N]
def swiss_roll(optimize=True, verbose=1, plot=True, N=1000, num_inducing=15, Q=4, sigma=.2):
    import GPy
    from GPy.util.datasets import swiss_roll_generated
    from GPy.models import BayesianGPLVM
 
def bgplvm_oil(optimize=True, verbose=1, plot=True, N=200, Q=7, num_inducing=40, max_iters=1000, **k):
    import GPy
    from GPy.likelihoods import Gaussian
    from matplotlib import pyplot as plt
 

src/g/p/GPy-HEAD/GPy/examples/dimensionality_reduction.py   GPy(Download)
    """
    from GPy.likelihoods.gaussian import Gaussian
    import GPy
 
    num_inputs = 13
def gplvm_oil_100(optimize=True, verbose=1, plot=True):
    import GPy
    data = GPy.util.datasets.oil_100()
    Y = data['X']
    # create simple GP model
def sparse_gplvm_oil(optimize=True, verbose=0, plot=True, N=100, Q=6, num_inducing=15, max_iters=50):
    import GPy
    _np.random.seed(0)
    data = GPy.util.datasets.oil()
    Y = data['X'][:N]
def swiss_roll(optimize=True, verbose=1, plot=True, N=1000, num_inducing=15, Q=4, sigma=.2):
    import GPy
    from GPy.util.datasets import swiss_roll_generated
    from GPy.models import BayesianGPLVM
 
def bgplvm_oil(optimize=True, verbose=1, plot=True, N=200, Q=7, num_inducing=40, max_iters=1000, **k):
    import GPy
    from GPy.likelihoods import Gaussian
    from matplotlib import pyplot as plt
 

src/g/p/GPy-0.4.9/GPy/testing/examples_tests.py   GPy(Download)
import unittest
import numpy as np
import GPy
import inspect
import pkgutil

src/g/p/GPy-0.4.9/GPy/examples/tutorials.py   GPy(Download)
pb.ion()
import numpy as np
import GPy
 
def tuto_GP_regression(optimize=True, plot=True):

src/g/p/GPy-0.4.9/GPy/examples/stochastic.py   GPy(Download)
import pylab as pb
import numpy as np
import GPy
 
def toy_1d(optimize=True, plot=True):

src/g/p/GPy-0.4.9/GPy/examples/regression.py   GPy(Download)
import pylab as pb
import numpy as np
import GPy
 
def olympic_marathon_men(optimize=True, plot=True):

src/g/p/GPy-0.4.9/GPy/examples/non_gaussian.py   GPy(Download)
import GPy
import numpy as np
import matplotlib.pyplot as plt
from GPy.util import datasets
 

src/g/p/GPy-0.4.9/GPy/examples/classification.py   GPy(Download)
"""
import pylab as pb
import GPy
 
default_seed = 10000

src/g/p/GPy-HEAD/GPy/testing/examples_tests.py   GPy(Download)
import unittest
import numpy as np
import GPy
import inspect
import pkgutil

src/g/p/GPy-HEAD/GPy/examples/tutorials.py   GPy(Download)
pb.ion()
import numpy as np
import GPy
 
def tuto_GP_regression(optimize=True, plot=True):

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