Removed dir ib_tests

ib_tests/test_regression.py

@@ -1,37 +0,0 @@
-"""
-Test the regression we get with the new transformations.
-
-Author:
-    Ilias Bilionis
-
-Date:
-    3/8/2015
-
-"""
-
-
-import sys
-import os
-# Make sure we load the GP that is here
-sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
-import GPy
-import matplotlib.pyplot as plt
-import numpy as np
-import triangle
-
-
-if __name__ == '__main__':
-    m = GPy.examples.regression.olympic_marathon_men(optimize=True)
-    plt.show(block=True)
-    print m
-    mcmc = GPy.inference.mcmc.samplers.Metropolis_Hastings(m)
-    mcmc.sample(Ntotal=100000, Nburn=10000, Nthin=100, tune_interval=1000, tune_throughout=True)
-    samples = np.array(mcmc.chains[-1])
-    fig = triangle.corner(samples)
-    m.plot()
-    fig = plt.figure()
-    for i in xrange(samples.shape[1]):
-        ax = fig.add_subplot(samples.shape[1], 1, i + 1)
-        ax.plot(samples[:, i], linewidth=1.5)
-    plt.show(block=True)
-

ib_tests/test_transformation_of_pdf.py

@@ -1,67 +0,0 @@
-"""
-Test the transformation of a PDF.
-
-Author:
-    Ilias Bilionis
-
-Date:
-    8/4/2015
-
-"""
-
-
-import sys
-import os
-# Make sure we load the GP that is here
-sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
-print 'trying'
-import GPy
-print 'done'
-import matplotlib.pyplot as plt
-import numpy as np
-import scipy.stats as st
-import scipy.integrate as integrate
-
-
-class TestModel(GPy.core.Model):
-    def __init__(self):
-        GPy.core.Model.__init__(self, 'test_model')
-        theta = GPy.core.Param('theta', 1.)
-        self.link_parameter(theta)
-
-    def log_likelihood(self):
-        return 0.
-
-
-if __name__ == '__main__':
-    m = TestModel()
-    prior = GPy.priors.LogGaussian(0., .9)
-    m.theta.set_prior(prior)
-
-    # The following should return the PDF in terms of the transformed quantities
-    p_phi = lambda(phi): np.exp(-m._objective_grads(phi)[0])
-
-    # Let's look at the transformation phi = log(exp(theta - 1))
-    trans = GPy.constraints.Exponent()
-    m.theta.constrain(trans)
-    # Plot the transformed probability density
-    phi = np.linspace(-8, 8, 100)
-    fig, ax = plt.subplots()
-    # Let's draw some samples of theta and transform them so that we see
-    # which one is right
-    theta_s = prior.rvs(10000)
-    # Transform it to the new variables
-    phi_s = trans.finv(theta_s)
-    # And draw their histogram
-    ax.hist(phi_s, normed=True, bins=100, alpha=0.25, label='Empirical')
-    # This is to be compared to the PDF of the model expressed in terms of these new
-    # variables
-    ax.plot(phi, [p_phi(p) for p in phi], label='Transformed PDF', linewidth=2)
-    ax.set_xlim(-3, 10)
-    ax.set_xlabel(r'transformed $\theta$', fontsize=16)
-    ax.set_ylabel('PDF', fontsize=16)
-    plt.legend(loc='best')
-    # Now let's test the gradients
-    m.checkgrad(verbose=True)
-    # And show the plot
-    plt.show(block=True)

ib_tests/test_transformation_plot.py

@@ -1,24 +0,0 @@
-"""
-Tests whether or not the tansformation plot works as expected.
-It does not work on the normal build.
-
-Author:
-    Ilias Bilionis
-
-Date:
-    3/8/2015
-"""
-
-
-import sys
-import os
-# Make sure we load the GP that is here
-sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
-import GPy
-import matplotlib.pyplot as plt
-
-
-if __name__ == '__main__':
-    f = GPy.constraints.Logexp()
-    f.plot()
-    plt.show(block=True)