View RoundingError.ipynb
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View loop_rooargset.py
 def loop_iterator(iterator): object = iterator.Next() while object: yield object object = iterator.Next() def iter_collection(rooAbsCollection): iterator = rooAbsCollection.createIterator() return loop_iterator(iterator)
View newton optimization.py
 import numpy as np import tensorflow as tf # Newton's optimization method for multivariate function in tensorflow def cons(x): return tf.constant(x, dtype=tf.float32) def compute_hessian(fn, vars): mat = []
View 4Stefano.ipynb
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View safe_roofit.py
 def safe_factory(func): def wrapper(self, *args): result = func(self, *args) if not result: raise ValueError('invalid factory input "%s"' % args) return result return wrapper ROOT.RooWorkspace.factory = safe_factory(ROOT.RooWorkspace.factory)
View CartPole-v0.py
 # from http://kvfrans.com/simple-algoritms-for-solving-cartpole/ import gym from gym import wrappers import numpy as np env = gym.make('CartPole-v0') def run_episode(env, parameters): observation = env.reset()
View pisa
 # Ricerca di fisica esotica a LHC con risonanze a due corpi ## E/gamma energy calibration * multivariate regression optimized on MC to calibrate the energy of electron / converted / unconverted photons * intercalibration of calorimeter layers from 2012 + additional uncertainty * energy scale and resolution corrections validated with 13 TeV $Z\to ee$ * For $E>100-200$ GeV resolution dominated by the constant term $c=0.6%-1.5%$ * Scale uncertainty (0.4%-2%) for diphoton analysis * Preliminary photon energy resolution at 300 GeV: $\pm 80\%-100\%$
View UncorrelatedCategories.ipynb
 The data have been binned in such a way that every bin contains more than 10 events. For every bin the integral of the S+B postfit pdf has been computed ($E_i$). In the table the value of the Pearson-$\chi^2 = \sum_i (E_i - O_i)^2 / E_i$ is reported with the number of bins of $m_{\gamma\gamma}$. Note that the $\chi^2$ is taking into account only the physical pdf, and not the product of constraints. In fact it is difficult to compute the number of degrees of freedom. We have 5 NPs for the background (4 "$\alpha$" + normalization) plus all the NPs for the statistical fluctuations (100+). Since these parameters are constrained they don't count -1 in the sum of the degree of freedom (something between 0 and -1). To try to evaluate their contribution we can imagine to add the constraint pdf to the computation of the $\chi^2$. This means to add 1 "bin", to subtract 1 dof, and to add a contribution to the $\chi^2$. This can (?) be evaluated as $$-2\log (pdf(x | x_{true})) + 2\log(pdf(x_{true}|x_{true}))$$ for e