def test_model_learn(config):
"""
Compares the model output with exact GP
"""
method = config['method']
sparsify_factor = config['sparse_factor']
np.random.seed(12000)
names = []
num_input_samples = 20
gaussian_sigma = .2
X, Y, kernel = DataSource.normal_generate_samples(num_input_samples, gaussian_sigma)
train_n = int(0.5 * num_input_samples)
Xtrain = X[:train_n, :]
Ytrain = Y[:train_n, :]
Xtest = X[train_n:, :]
Ytest = Y[train_n:, :]
kernel1 = ModelLearn.get_kernels(Xtrain.shape[1], 1, True)
kernel2 = ModelLearn.get_kernels(Xtrain.shape[1], 1, True)
gaussian_sigma = 1.0
#number of inducing points
num_inducing = int(Xtrain.shape[0] * sparsify_factor)
num_samples = 10000
cond_ll = UnivariateGaussian(np.array(gaussian_sigma))
n1, _ = ModelLearn.run_model(Xtest, Xtrain, Ytest, Ytrain, cond_ll, kernel1, method,
'test_' + ModelLearn.get_ID(), 'test', num_inducing,
num_samples, sparsify_factor, ['mog', 'll', 'hyp'], IdentityTransformation, True,
logging.DEBUG, True)
n2, _ =ModelLearn.run_model(Xtest, Xtrain, Ytest, Ytrain, cond_ll, kernel2, 'gp',
'test_' + ModelLearn.get_ID(), 'test', num_inducing,
num_samples, sparsify_factor, ['mog', 'll', 'hyp'], IdentityTransformation)
PlotOutput.plot_output('test', ModelLearn.get_output_path(), [n1, n2], None, False)
def plot():
PlotOutput.plot_output_all('boston', ModelLearn.get_output_path(),
lambda x: x['method'] == 'full', False)
def plot():
PlotOutput.plot_output_all('boston', ModelLearn.get_output_path(),
lambda x: x['method'] == 'full', False)
