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")