def test_default_summary_fmt_errorcheck(): with pytest.raises(ValueError): set_default_summary_fmt("this_format_definitely_does_not_exist")
# This notebook shows how to use the Bayesian GPLVM model. This is an unsupervised learning method usually used for dimensionality reduction. For an in-depth overview of GPLVMs,see **[1, 2]**. # %% import gpflow import numpy as np import matplotlib.pyplot as plt import tensorflow as tf import gpflow from gpflow.utilities import ops, print_summary from gpflow.config import set_default_float, default_float, set_default_summary_fmt from gpflow.ci_utils import ci_niter set_default_float(np.float64) set_default_summary_fmt("notebook") # %matplotlib inline # %% [markdown] # ## Data # We are using the "three phase oil flow" dataset used initially for demonstrating the Generative Topographic mapping from **[3]**. # %% data = np.load('./data/three_phase_oil_flow.npz') # %% [markdown] # Following the GPflow notation we assume this dataset has a shape of `[num_data, output_dim]` # %% Y = tf.convert_to_tensor(data['Y'], dtype=default_float())
def test_default_summary_fmt_setting(): set_default_summary_fmt("html") assert default_summary_fmt() == "html" set_default_summary_fmt(None) assert default_summary_fmt() is None