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replace math.exp with np.exp #2939

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Merged
junpenglao merged 1 commit into from
Apr 21, 2018
Merged

replace math.exp with np.exp #2939

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4 changes: 1 addition & 3 deletions pymc3/step_methods/hmc/hmc.py
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Original file line number Diff line number Diff line change
@@ -1,5 +1,3 @@
import math

import numpy as np

from ..arraystep import Competence
Expand Down Expand Up @@ -111,7 +109,7 @@ def _hamiltonian_step(self, start, p0, step_size):
'Divergence encountered, large integration error.',
None, state)

accept_stat = min(1, math.exp(energy_change))
accept_stat = min(1, np.exp(energy_change))

if div_info is not None or np.random.rand() >= accept_stat:
end = start
Expand Down
16 changes: 7 additions & 9 deletions pymc3/step_methods/step_sizes.py
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Original file line number Diff line number Diff line change
@@ -1,5 +1,3 @@
import math

import numpy as np
from scipy import stats

Expand All @@ -8,22 +6,22 @@

class DualAverageAdaptation(object):
def __init__(self, initial_step, target, gamma, k, t0):
self._log_step = math.log(initial_step)
self._log_step = np.log(initial_step)
self._log_bar = self._log_step
self._target = target
self._hbar = 0.
self._k = k
self._t0 = t0
self._count = 1
self._mu = math.log(10 * initial_step)
self._mu = np.log(10 * initial_step)
self._gamma = gamma
self._tuned_stats = []

def current(self, tune):
if tune:
return math.exp(self._log_step)
return np.exp(self._log_step)
else:
return math.exp(self._log_bar)
return np.exp(self._log_bar)

def update(self, accept_stat, tune):
if not tune:
Expand All @@ -34,15 +32,15 @@ def update(self, accept_stat, tune):
w = 1. / (count + t0)
self._hbar = ((1 - w) * self._hbar + w * (self._target - accept_stat))

self._log_step = self._mu - self._hbar * math.sqrt(count) / self._gamma
self._log_step = self._mu - self._hbar * np.sqrt(count) / self._gamma
mk = count ** -k
self._log_bar = mk * self._log_step + (1 - mk) * self._log_bar
self._count += 1

def stats(self):
return {
'step_size': math.exp(self._log_step),
'step_size_bar': math.exp(self._log_bar),
'step_size': np.exp(self._log_step),
'step_size_bar': np.exp(self._log_bar),
}

def warnings(self):
Expand Down