def test_recipr0():
X = np.array([[2,1],[-4,0]])
Y = recipr0(X)
yield assert_array_almost_equal, Y, np.array([[0.5,1],[-0.25,0]])
# check that lists have arrived
XL = [0, 1, -1]
yield assert_array_almost_equal, recipr0(XL), [0, 1, -1]
# scalars
yield assert_equal, recipr0(-1), -1
yield assert_equal, recipr0(0), 0
yield assert_equal, recipr0(2), 0.5
def _extract_sd(self, results):
delay = self.IRF.delay
self.T1 = np.zeros(self.gamma0.shape)
nrow = self.gamma0.shape[0]
for i in range(nrow):
self.T1[i] = self.gamma1[i] * pos_recipr(np.sqrt(results.cov_beta(matrix=self.deltamatrix[i])))
a1 = 1 + 1. * pos_recipr(self.T0sq)
gdot = np.array(([(self.r * (a1 - 2.) *
recipr0(self.gamma0 * a1**2)),
recipr0(self.gamma0 * a1)] *
recipr0(delay.dforward(self.deltahat))))
Cov = results.cov_beta
E = self.effectmatrix
D = self.deltamatrix
nrow = self.effectmatrix.shape[0]
cov = np.zeros((nrow,)*2 + self.T0sq.shape[1:])
for i in range(nrow):
for j in range(i + 1):
cov[i,j] = (gdot[0,i] * gdot[0,j] * Cov(matrix=E[i],
other=E[j]) +
gdot[0,i] * gdot[1,j] * Cov(matrix=E[i],
other=D[j]) +
gdot[1,i] * gdot[0,j] * Cov(matrix=D[i],
other=E[j]) +
gdot[1,i] * gdot[1,j] * Cov(matrix=D[i],
other=D[j]))
cov[j,i] = cov[i,j]
nout = self.weights.shape[0]
self._sd = np.zeros(self._effect.shape)
for r in range(nout):
var = 0
for i in range(nrow):
var += cov[i,i] * np.power(self.weights[r,i], 2)
for j in range(i):
var += 2 * cov[i,j] * self.weights[r,i] * self.weights[r,j]
self._sd[r] = np.sqrt(var)
def _extract_effect(self, results):
delay = self.IRF.delay
self.gamma0 = np.dot(self.effectmatrix, results.beta)
self.gamma1 = np.dot(self.deltamatrix, results.beta)
nrow = self.gamma0.shape[0]
self.T0sq = np.zeros(self.gamma0.shape)
for i in range(nrow):
self.T0sq[i] = (self.gamma0[i]**2 *
pos_recipr(results.cov_beta(matrix=self.effectmatrix[i])))
self.r = self.gamma1 * recipr0(self.gamma0)
self.rC = self.r * self.T0sq / (1. + self.T0sq)
self.deltahat = delay.inverse(self.rC)
self._effect = np.dot(self.weights, self.deltahat)
