def test_gamma():
A = np.ones((2, INT_OVERFLOW))
A[0][0] = 5
A.attach_grad()
with mx.autograd.record():
B = npx.gamma(A)
assert B.shape == (2, INT_OVERFLOW)
assert B[0][0] == 24
B.backward()
assert A.grad.shape == (2, INT_OVERFLOW)
assert_almost_equal(A.grad[0][0], np.array([36.1428]), \
rtol=1e-3, atol=1e-5)
import d2l_dx
from mxnet import autograd, gluon, np, npx
from mxnet.gluon import nn
npx.set_np()
maxdegree = 20
n_train, n_test = 100, 100
true_w = np.zeros(maxdegree)
true_w[0:4] = np.array([5.0, 1.2, -3.4, 5.6])
features = np.random.normal(size=(n_train + n_test, 1))
features = np.random.shuffle(features)
power = np.arange(maxdegree).reshape(1, -1)
poly_features = np.power(features, power)
poly_features = poly_features / (npx.gamma(np.arange(maxdegree) + 1).reshape(
1, -1))
labels = np.dot(poly_features, true_w)
labels += np.random.normal(scale=0.1, size=labels.shape)
print(poly_features.shape, true_w.shape, labels.shape)
print("----------------------")
print(poly_features[0], true_w)
print("----------------------")
print(features[0])
print("----------------------")
print(poly_features[0])
print("----------------------")
print(labels[0])