def sum_1(val, rowIndices, n, p): S = np.zeros(n, dtype = val.dtype) for i in range(n): left = rowIndices[i] right = rowIndices[i+1] S[i] += _sum(val[left:right]) return S
def is_all_finite(arr):
r"""Check if the array values are all finite.
Args:
arr (array_like): sequence of values.
Returns:
bool: ``True`` if values are all finite.
"""
return isfinite(_sum(asarray(arr)))
def mean_1(val, rowIndices, n, p): A = zeros(n, dtype = val.dtype) for i in range(n): left = rowIndices[i] right = rowIndices[i+1] A[i] += _sum(val[left:right]) if A[i] > 0: A[i] /= p return A
def cost(rv):
"""compute cost from a 1-d array of model parameters,
where: cost = | sum( infeasibility ) | """
# converting rv to scenario
points = scenario()
points.load(rv, pts)
# calculate infeasibility
Rv = graphical_distance(model, points, ytol=cutoff, ipop=ipop, \
imax=imax, **kwds)
v = infeasibility(Rv, cutoff)
# converting v to E
return _sum(v) #XXX: abs ?
def cost(rv):
"""compute cost from a 1-d array of model parameters,
where: cost = | sum( infeasibility ) | """
# converting rv to scenario
points = scenario()
points.load(rv, pts)
# calculate infeasibility
Rv = graphical_distance(model, points, ytol=cutoff, ipop=ipop, \
imax=imax, **kwds)
v = infeasibility(Rv, cutoff)
# converting v to E
return _sum(v) #XXX: abs ?
def smallestDivisor(self, nums: List[int], threshold: int) -> int:
lower, upper = 1, max(nums)
nums_array = array(nums)
while lower < upper:
mid = (lower + upper) >> 1
res = _sum(ceil(nums_array / mid))
if res > threshold:
lower = mid + 1
else:
upper = mid
return lower
def lu_slogdet(LU):
r"""Natural logarithm of a LU decomposition.
Args:
LU (tuple): LU decomposition.
Returns:
tuple: sign and log-determinant.
"""
LU = (asarray(LU[0], float), asarray(LU[1], float))
adet = _sum(log(_abs(LU[0].diagonal())))
s = prod(sign(LU[0].diagonal()))
nrows_exchange = LU[1].size - _sum(
LU[1] == arange(LU[1].size, dtype="int32"))
odd = nrows_exchange % 2 == 1
if odd:
s *= -1.0
return (s, adet)
def _calculate_g_raw_at_t(self, time):
"""
Calculates the Gauss Coefficients in raw ordering given the parameters calculated by inverval() and _bspline().
Parameters
----------
time:
Returns
-------
Gauss Coefficients for a particular time in raw ordering.
"""
gt = self.gt
b = self.bspline(time)
i = self._interval(time)
bo = self.bspl_order
g_raw = _sum(b[i:i+bo]*gt[:, i:i+bo], axis=1)
return g_raw
def _calculate_SAgh_raw_at_t(self, time):
"""
Calculates the second time derivatives of Gauss Coefficients in raw ordering given the parameters calculated by inverval() and _bspline().
Parameters
----------
time:
date in years to calculate SAg_raw
Returns
-------
SAg_raw:
Second time derivatives of Gauss Coefficients for a particular time in raw ordering.
"""
gt = self.gt
SAb = self.bspline.d2(time)
i = self._interval(time)
bo = self.bspl_order
SAg_raw = _sum(SAb[i:i+bo]*gt[:, i:i+bo], axis=1)
return SAg_raw
def trace2(A, B):
r"""Trace of :math:`\mathrm A \mathrm B`.
Args:
A (array_like): Left-hand side.
B (array_like): Right-hand side.
Returns:
float: Trace of :math:`\mathrm A \mathrm B`.
"""
A = asarray(A, float)
B = asarray(B, float)
layout_error = "Wrong matrix layout."
if not (len(A.shape) == 2 and len(B.shape) == 2):
raise ValueError(layout_error)
if not (A.shape[1] == B.shape[0] and A.shape[0] == B.shape[1]):
raise ValueError(layout_error)
return _sum(A.T * B)
addlocker_write_running(outputHEAD+betanum2betastr(0)+'.h5')
nextsave_quit = False
while True:
if beta >= _max(sim_tdmrg['save_list']):
output = outputHEAD+'{0}.h5'.format(betanum2betastr(beta))
print("It shouldn't start with BETA MAX")
break
if _max(sim_tdmrg['dw_one_serie'])>sim_tdmrg['stop_dw_limit']:
nextsave_quit = True
print("PRECISION MAX REACH")
start = clock.time()
apply_eleven_layer(dmps,dgateIT,sim_tdmrg)
sim_tdmrg['dw_total'] += _sum(sim_tdmrg['dw_one_serie'])
print("discarded weight in one step :",sim_tdmrg['dw_one_serie'])
print("discarded weight accumulate :",sim_tdmrg['dw_total'])
try:
elapsed = (clock.time() - start)
except:
elapsed = float('nan')
sim_tdmrg['right_direction'] = (not sim_tdmrg['right_direction'])
sim_tdmrg['odd_bonds'] = (not sim_tdmrg['odd_bonds'])
beta = round(beta+2*dbeta,4)
if beta>=_max(sim_tdmrg['save_list']):
print("BETA MAX REACH")
addlocker_write_running(outputHEAD+'00.0000.h5')
nextsave_quit = False
while True:
if time >= _max(sim_tmpo['save_list']):
output = outputHEAD+'{0}.h5'.format(timenum2timestr(time))
print("It shouldn't start with TIME MAX")
break
if _max(sim_tmpo['dw_one_serie'])>sim_tmpo['stop_dw_limit']:
nextsave_quit = True
print("PRECISION MAX REACH")
start = clock.time()
apply_eleven_layer(dmps,dgateRT,sim_tmpo)
sim_tmpo['dw_total'] += _sum(sim_tmpo['dw_one_serie'])
print("discarded weight in one step :",sim_tmpo['dw_one_serie'])
print("discarded weight accumulate :",sim_tmpo['dw_total'])
try:
elapsed = (clock.time() - start)
except:
elapsed = float('nan')
sim_tmpo['right_direction'] = (not sim_tmpo['right_direction'])
sim_tmpo['odd_bonds'] = (not sim_tmpo['odd_bonds'])
time = round(time+dtime,4)
if time>=_max(sim_tmpo['save_list']):
print("TIME MAX REACH")
