def test_hermemulx(self):
assert_equal(herme.hermemulx([0]), [0])
assert_equal(herme.hermemulx([1]), [0,1])
for i in range(1, 5):
ser = [0]*i + [1]
tgt = [0]*(i - 1) + [i, 0, 1]
assert_equal(herme.hermemulx(ser), tgt)
def test_hermemulx(self):
assert_equal(herme.hermemulx([0]), [0])
assert_equal(herme.hermemulx([1]), [0, 1])
for i in range(1, 5):
ser = [0] * i + [1]
tgt = [0] * (i - 1) + [i, 0, 1]
assert_equal(herme.hermemulx(ser), tgt)
def modelMoments(self, theta, line=0, order=-1):
"""
Calculate the moments predicted by the model.
Note, THACO actually calculates the zeroth moment
"""
noise, center, scale, herm = self.unpackTheta(theta)
# Since the Probablist's Hermite functions are orthogonal given the unit normal,
# to integrate the mean and variance just get the weights multiplied by x.
hermx = hermemulx(herm[line])
hermxx = hermemulx(hermx)
normFactor = np.sqrt(2 * np.pi) * scale[line]
m0 = normFactor * herm[line][0]
m1 = (center[line] -
self.linesLam[line]) * m0 + normFactor * hermx[0] * scale[line]
m2 = normFactor * hermxx[0] * scale[line]**2
return np.array([m0, m1 * 1e3, m2 * 1e6])
