def test_labels_have_the_correct_number_of_lines(n_lines):
n_samples, slots, dims = 100, 4, 2
labels = combigen.generate_labels(
n_samples=n_samples,
slots=slots,
dims=dims,
n_lines=n_lines)
# Ensure the sums are exactly what we expect them to be at each combination
assert labels.sum() == n_lines*slots*n_samples
def test_label_statistics_correspond_to_correct_element_indices(idx):
line_stats = np.zeros(10)
line_stats[idx] = 1
label = combigen.generate_labels(slots=1, n_lines=1, line_stats=line_stats)
for i in range(5):
for j in range(2):
if i == idx%5 and j==idx//5:
assert label[0, 0, i, j] == 1
else:
assert label[0, 0, i, j] == 0
def test_nonuniformly_sampled_labels_return_the_correct_statistics():
slots = 100
line_stats = np.array([1,2,3,4,5])
# Create an array containing all the sums of labels with `dims` dimensions.
y_tests = np.array(
[combigen.generate_labels(
n_samples=1,
slots=slots,
dims=1,
n_lines=1,
line_stats=line_stats,).sum(axis=1)
for _ in range(100)])
# Separate out the means of sums
means = y_tests.mean(axis=0).reshape((5))
# Assert the different pair-wise sums are as expected of the stats defined
# above.
for l, m in zip(line_stats, means):
assert np.isclose(means[0]*l, m, atol=slots*0.05)
def test_generate_labels_returns_correct_shapes(shapes):
assert np.array_equal(
combigen.generate_labels(*shapes).shape,
shapes)
def test_combigen_heatmap_runs_without_errors_for_different_input_lengths(
length):
"""Lifted directly from nb0.1 c382."""
cgh.heatmap(cg.generate_labels(length))
