def test_halton():
corners = np.array([[0, 2], [10, 5]])
sample = sequences.halton(dim=2, n_sample=5, bounds=corners)
out = np.array([[5., 3.], [2.5, 4.], [7.5, 2.3], [1.25, 3.3], [6.25, 4.3]])
npt.assert_almost_equal(sample, out, decimal=1)
sample = sequences.halton(dim=2, n_sample=3, bounds=corners, start_index=2)
out = np.array([[7.5, 2.3], [1.25, 3.3], [6.25, 4.3]])
npt.assert_almost_equal(sample, out, decimal=1)
def test_halton():
corners = np.array([[0, 2], [10, 5]])
sample = sequences.halton(dim=2, n_sample=5, bounds=corners)
out = np.array([[5., 3.], [2.5, 4.], [7.5, 2.3], [1.25, 3.3], [6.25, 4.3]])
npt.assert_almost_equal(sample, out, decimal=1)
sample = sequences.halton(dim=2, n_sample=3, bounds=corners, start_index=2)
out = np.array([[7.5, 2.3], [1.25, 3.3], [6.25, 4.3]])
npt.assert_almost_equal(sample, out, decimal=1)
def generate_halton_samples(self, bounds, dims, n_sample=10):
samples = sequences.halton(dim=dims, n_sample=n_sample, bounds=bounds)
return samples
def generate_halton_samples(self, bounds, dims, n_sample=10):
"""
Generate halton samples in a n-dimensional space. Defaulted to 3 dimensions
"""
samples = sequences.halton(dim=dims, n_sample=n_sample, bounds=bounds)
return samples