def test_single_input_CV(): if not alamopy.has_alamo(): return False # Specify number of points 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 = np.zeros((ndata, 1)) # specify simulator as examples.sixcamel sim = sixcamel for i in range(ndata): z[i, 0] = sim(x[i][0], x[i][1]) # z[i,1] = 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) # NON GENERIC alamo_settings = { 'almname': "cam6", 'monomialpower': (1, 2, 3, 4, 5, 6), 'multi2power': (1, 2), 'simulator': almsim, 'maxiter': 20, 'cvfun': True } res = alamopy.doalamo(x, z, lmo=3)
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 = 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 _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, savepyfcn = 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 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