def main():
# Specify number of poitns to be used in the training set
# Validation data can be provided optionally
ndata = 10
x = np.random.uniform([-2, -1], [2, 1], (ndata, 2))
z = [0] * ndata
# specify simulator as examples.sixcamel
sim = examples.sixcamel
for i in range(ndata):
z[i] = sim(x[i][0], x[i][1])
# Use alamopy's python function wrapper to avoid using ALAMO's I/O format
almsim = alamopy.wrapwriter(sim)
# Call alamo through the alamopy wrapper
res = alamopy.doalamo(x,
z,
almname='cam6',
monomialpower=(1, 2, 3, 4, 5, 6),
multi2power=(1, 2),
simulator=almsim,
expandoutput=True,
maxiter=20,
cvfun=True)
# print res
print('Model: {}'.format(res['model']))
def main():
# Specify number of poitns to be used in the training set
# Validation data can be provided optionally
ndata = 50
nval = 500
lb = [-15, -15]
ub = [15, 15]
x = np.random.uniform(lb, ub, (ndata, 2))
xval = np.random.uniform(lb, ub, (nval, 2))
z = [0] * ndata
zval = [0] * nval
# specify simulator as examples.sixcamel
sim = examples.ackley
for i in range(ndata):
z[i] = sim(x[i][0], x[i][1])
for i in range(nval):
zval[i] = sim(xval[i][0], xval[i][1])
# Use alamopy's python function wrapper to avoid using ALAMO's I/O format
almsim = alamopy.wrapwriter(sim)
# Call alamo through the alamopy wrapper
res = alamopy.doalamo(x,
z,
xval=xval,
zval=zval,
almname='ackley',
monomialpower=(1, 2, 3, 4, 5, 6),
expfcns=1,
multi2power=(1, 2),
expandoutput=True)
# Calculate confidence intervals
conf_inv = alamopy.almconfidence(res)
print('Model: {}'.format(res['model']))
print('Confidence Intervals : {}'.format(conf_inv['conf_inv']))
def test_basic():
has_alamo_flag = alamopy.multos.has_alamo()
x = [[0.17361977, -0.44326123], [-0.30192964, 0.68955226],
[-1.98112458, -0.75686176], [0.68299634, 0.65170551],
[-1.45317364, 0.15018666], [1.56528782, -0.58159576],
[-1.25868712, -0.78324622], [-1.12121003, 0.95724757],
[1.2467326, -0.65611797], [1.26489899, -0.45185251]]
z = [
-0.58978634828943055, -0.85834512885363479, 4.0241154669754113,
0.91057814668811488, 1.9147616212616931, 0.29103827202206878,
2.4290896722960778, 0.99199475534877579, 0.59688699266830847,
1.167850366995701
]
xival = [[5, 5], [2, 2]]
zival = [5, 2]
if not has_alamo_flag:
alamo(x, z, savepyfcn=False) #, xval=xival, zval=zival, mock=True)
# alamo(x, z, xlabels=["T", "V"], zlabels= ["P"], savepyfcn=False)
#doalamo(x, z, xval=xival, zval=zival, lmo=3, savepyfcn=False)
else:
ndata = 10
x = np.random.uniform([-2, -1], [2, 1], (ndata, 2))
z = [0] * ndata
# specify simulator as examples.sixcamel
sim = examples.sixcamel
for i in range(ndata):
z[i] = sim(x[i][0], x[i][1])
# Use alamopy's python function wrapper to avoid using ALAMO's I/O format
almsim = wrapwriter(sim)
# Call alamo through the alamopy wrapper
res = alamo(x,
z,
almname='cam6',
monomialpower=(1, 2, 3, 4, 5, 6),
multi2power=(1, 2),
simulator=almsim,
expandoutput=True,
maxiter=20) #,cvfun=True)
conf_inv = almconfidence(res)
print('Model: {}'.format(res['model']))
print('Confidence Intervals : {}'.format(conf_inv['conf_inv']))
almplot(res, show=False)
def buildSimWrapper(data, debug):
""" Builds an executable simulator to sample for data
Args:
data: shared alamo data options
debug: Additional options may be specified and will be applied
to the .alm
"""
if not isinstance(data['stropts']['simulator'], type('string')):
try:
data['stropts']['simulator'] = data['stropts']['simulator'].__name__
except Exception:
raise almerror.AlamoInputError('Simulator must be provided as a string'
'and obey ALAMOs simulator conventions'
'OR must be a python function whose name'
'can be obtained via .__name__')
data['stropts']['simulator'] = alamopy.wrapwriter(data['stropts']['simulator'])
debug['simwrap'] = True