def build_model(self):
super().build_model()
if self._vdata_in is not None and self._vdata_out is not None:
self.alamopy_results = alamopy.alamo(self._rdata_in, self._rdata_out, xval=self._vdata_in,
zval=self._vdata_out, **self.config)
else:
self.alamopy_results = alamopy.alamo(self._rdata_in, self._rdata_out, **self.config)
self._res = self.alamopy_results
self._model = self.alamopy_results['model']
self.handle_results(self._res)
self.pkl_info['Run settings'] = self.config.value()
self.pkl_info['Results'] = self._results
self.pkl_info['Expression'] = self._model
def test_basic():
if has_alamo_flag:
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 DV():
"""
ALAMO regression of saturated vapor density
"""
global DVfun
DataImport.DV(molecule)
Values = DataImport.DVValues
xval, zval = [], []
for x in Values:
manVal = DataManipulation.DV(x)
xval.append(manVal[0])
zval.append(manVal[1])
res = alamopy.alamo(
xval,
zval,
zlabels=["%sDV" % molecule],
almname="%sDV" % molecule,
monomialpower=(1, 2, 3, 4, 5, 6),
savetrace=True,
)
# print(res['model'], res['ssr'], res['R2'])
if res is None:
raise Exception("Model does not exist for Saturated Vapor Density")
DVfun = importlib.import_module("%sDV" % molecule, "f")
def DL():
"ALAMO regression of Saturated Liquid Density"
global DLfun
DataImport.DL(molecule)
Values = DataImport.DLValues
xval, zval = [], []
for x in Values:
manVal = DataManipulation.DL(x)
xval.append(manVal[0])
zval.append(manVal[1])
res = alamopy.alamo(
xval,
zval,
zlabels=["%sDL" % molecule],
almname="%sDL" % molecule,
monomialpower=(-1, 1, 2, 3),
expfcns=1,
savetrace=True,
savescratch=True,
)
if res is None:
raise Exception("Model does not exist for Saturated Liquid Density")
DLfun = importlib.import_module("%sDL" % molecule, "f")
def PV():
"ALAMO regression of vapor pressure"
global PVfun
DataImport.PV(molecule)
Values = DataImport.Values
xval, zval = [], []
for x in Values:
manVal = DataManipulation.PV(x)
xval.append([manVal[0], manVal[1]])
zval.append(manVal[2])
res = alamopy.alamo(
xval,
zval,
zlabels=["%sPV" % molecule],
almname="%sPV" % molecule,
monomialpower=(1, 2, 3, 4, 5, 6),
)
# print(res['model'], res['ssr'], res['R2'])
if res is None:
raise Exception("Model does not exist for Vapor Pressure")
PVfun = importlib.import_module("%sPV" % molecule, "f")
