def sigmoid_activation(s, a, b):
return sigmoid(a * s + b)
def test_sigmoid(value, expected):
result = sigmoid(value)
assert_array_almost_equal(result, expected, decimal=1)
XX = np.array([*bspl(tt)])
XX = ((XX - XX.min()) / XX.ptp()) * 2 - 1.0
XX[:, 0] = XX[:, 0]
XX[:, 1] = XX[:, 1]
XX[:, 2] = XX[:, 2]
NN = N * 4
fig = plt.figure(figsize=(15, 15))
ax = fig.add_subplot(111, projection="3d")
ax.grid(False)
ax.axis(False)
w = 1.0
b = 3.0
D = sigmoid(w * np.sqrt(XX[:, 0]**2) + b)
dds = []
for i in range(NN - 1):
x, y, z = XX[i:i + 2, 0], XX[i:i + 2, 1], XX[i:i + 2, 2]
d = (sigmoid(w * np.sqrt(x[0]**2) + b) - D.min()) / D.ptp()
dds.append(d)
ax.plot(x, y, z, color=plt.cm.YlOrRd(int(255 * d)), lw=1)
plt.tight_layout()
plt.show()
D = sigmoid(1.0 * np.sqrt(XX[:, 0]**2) + 3.0)
dds = []
for i in range(NN - 1):
x, y, z = XX[i:i + 2, 0], XX[i:i + 2, 1], XX[i:i + 2, 2]
d = (sigmoid(1.0 * np.sqrt(x[0]**2) + 3.0) - D.min()) / D.ptp()