def test_infer_dim_3():
n, p = 100, 5
rng = np.random.RandomState(0)
X = rng.randn(n, p) * 0.1
X[:10] += np.array([3, 4, 5, 1, 2])
X[10:20] += np.array([6, 0, 7, 2, -1])
X[30:40] += 2 * np.array([-1, 1, -1, 1, -1])
X = da.from_array(X, chunks=(n, p))
pca = dd.PCA(n_components=p, svd_solver="full")
pca.fit(X)
spect = pca.explained_variance_
assert _infer_dimension_(spect, n, p) > 2
def test_infer_dim_2():
# TODO: explain what this is testing
# Or at least use explicit variable names...
n, p = 1000, 5
rng = np.random.RandomState(0)
X = rng.randn(n, p) * 0.1
X[:10] += np.array([3, 4, 5, 1, 2])
X[10:20] += np.array([6, 0, 7, 2, -1])
dX = da.from_array(X, chunks=(n, p))
pca = dd.PCA(n_components=p, svd_solver="full")
pca.fit(dX)
spect = pca.explained_variance_
assert _infer_dimension_(spect, n, p) > 1