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Minor fixes to MVLogNormal discretization #1507

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Oct 31, 2024
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Minor fixes to MVLogNormal discretization #1507

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47 changes: 31 additions & 16 deletions HARK/distribution.py
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Original file line number Diff line number Diff line change
Expand Up @@ -963,11 +963,11 @@ def _approx_equiprobable(
)

if np.array_equal(self.Sigma, np.diag(np.diag(self.Sigma))):
ind_atoms = np.empty((self.M, N))
ind_atoms = np.empty((self.M, N + 2 * tail_N))

for i in range(self.M):
if self.Sigma[i, i] == 0.0:
x_atoms = np.repeat(np.exp(self.mu[i]), N)
x_atoms = np.repeat(np.exp(self.mu[i]), N + 2 * tail_N)
ind_atoms[i] = x_atoms
else:
x_atoms = (
Expand All @@ -983,7 +983,22 @@ def _approx_equiprobable(
atoms = np.stack(
[ar.flatten() for ar in list(np.meshgrid(*atoms_list))], axis=1
).T
pmv = np.repeat(1 / (N**self.M), N**self.M)

interiors = np.empty([self.M, (N + 2 * tail_N) ** (self.M)])

inners = np.zeros(N + 2 * tail_N)

if tail_N > 0:
inners[:tail_N] = [(tail_N - i) for i in range(tail_N)]
inners[-tail_N:] = [(i + 1) for i in range(tail_N)]

for i in range(self.M):
inners_i = [inners for _ in range((N + 2 * tail_N) ** i)]

interiors[i] = np.repeat(
[*inners_i], (N + 2 * tail_N) ** (self.M - (i + 1))
)

else:
if tail_bound is not None:
if type(tail_bound) is float:
Expand Down Expand Up @@ -1024,10 +1039,10 @@ def eval(params, z):
excl = []

for j in range(len(z)):
if z[j, 0] != z[j, 1]:
inds.append(j)
else:
if z[j, 0] == z[j, 1]:
excl.append(j)
elif params[j] != 0.0:
inds.append(j)

dim = len(inds)

Expand Down Expand Up @@ -1111,21 +1126,21 @@ def eval(params, z):

atoms[i] = xi_atoms

max_locs = np.argmax(np.abs(interiors), axis=0)
max_locs = np.argmax(np.abs(interiors), axis=0)

max_inds = np.stack([max_locs, np.arange(len(max_locs))], axis=1)
max_inds = np.stack([max_locs, np.arange(len(max_locs))], axis=1)

prob_locs = interiors[max_inds[:, 0], max_inds[:, 1]]
prob_locs = interiors[max_inds[:, 0], max_inds[:, 1]]

def prob_assign(x):
if x == 0:
return 1 / (N**self.M)
else:
return 0.0
def prob_assign(x):
if x == 0:
return 1 / (N**self.M)
else:
return 0.0

prob_vec = np.vectorize(prob_assign)
prob_vec = np.vectorize(prob_assign)

pmv = prob_vec(prob_locs)
pmv = prob_vec(prob_locs)

limit = {
"dist": self,
Expand Down
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