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
