def test_gaussian_kde_scipy_vs_cocos(points: np.ndarray,
xi: np.ndarray):
gkde_cocos = gaussian_kde(points)
gkde_scipy = ss.kde.gaussian_kde(points)
density_estimate_cocos = gkde_cocos.evaluate(xi)
density_estimate_scipy = gkde_scipy.evaluate(xi)
assert np.allclose(density_estimate_cocos,
density_estimate_scipy)
def test_gaussian_kde_scipy_vs_cocos_gpu(points: np.ndarray,
xi: np.ndarray):
gkde_cocos = gaussian_kde(cn.array(points.squeeze()),
gpu=True)
gkde_scipy = ss.kde.gaussian_kde(points)
density_estimate_cocos = gkde_cocos.evaluate(xi)
density_estimate_scipy = gkde_scipy.evaluate(xi)
assert np.allclose(density_estimate_cocos,
density_estimate_scipy)
# generate grid at which to evaluate the sample
x_grid = np.linspace(-5.0, 5.0, grid_size)
y_grid = np.linspace(-5.0, 5.0, grid_size)
xy_grid_x, xy_grid_y = np.meshgrid(x_grid, y_grid)
xy_grid = np.hstack((xy_grid_x.reshape((-1, 1)), xy_grid_y.reshape(
(-1, 1)))).T
# print(f'xy_grid.shape = {xy_grid.shape}')
# construct and evaluate scipy gaussian kde object
gaussian_kde_scipy = ss.kde.gaussian_kde(points_xy)
density_estimate_scipy = gaussian_kde_scipy.evaluate(xy_grid)
# print(f'density_estimate_scipy.shape = {density_estimate_scipy.shape}')
# construct and evaluate cocos gaussian kde object using gpu evaluation
gaussian_kde_cocos = gaussian_kde(cn.array(points_xy), gpu=True)
density_estimate_cocos = np.array(gaussian_kde_cocos.evaluate(xy_grid))
# print(f'density_estimate_cocos.shape = {density_estimate_cocos.shape}')
# evaluate cocos gaussian kde object using gpu evaluation in batches
batched_density_estimate_cocos = \
gaussian_kde_cocos.evaluate_in_batches(xy_grid, 10 * n * n)
# print(f'batched_density_estimate_cocos.shape = {batched_density_estimate_cocos.shape}')
# verify that results are numerically close
print(
f'maximum absolute difference between results gpu using Cocos and cpu using SciPy: '
f'{np.max(abs(density_estimate_cocos - density_estimate_scipy))}')
if np.allclose(density_estimate_cocos, density_estimate_scipy):
print('estimates from cocos and scipy are numerically close')