Best Python code snippet using autotest_python
simulate.py
Source:simulate.py
...168 df.to_pickle(f'results/predicted/{hostname}_{kernel}.pkl.gz')169 print(f"saved {filename}")170 return data171def simulate_host(hostname, kernels=None):172 kernels_args = get_kernels(hostname=hostname)173 if kernels is not None:174 kernels_args = [(kn, ka) for kn, ka in kernels_args if kn in kernels]175 for kernel, args in get_kernels(hostname=hostname):176 if kernels is not None and kernel not in kernels:177 continue178def main():179 hosts = get_hostnames()180 if len(sys.argv) > 1:181 if any([h in sys.argv for h in get_hostnames()]):182 hosts = [h for h in get_hostnames() if h in sys.argv]183 if '--serial' in sys.argv:184 for hostname in hosts:185 kernels = get_kernels(hostname=hostname)186 if any([kn in sys.argv for kn, ka in get_kernels(hostname=hostname)]):187 kernels = [(kn, ka) for kn, ka in get_kernels(hostname=hostname) if kn in sys.argv]188 for kernel, args in kernels:189 simulate(hostname, kernel, args)190 else:191 with mp.Pool() as p:192 for hostname in hosts:193 kernels = get_kernels(hostname=hostname)194 if any([kn in sys.argv for kn, ka in get_kernels(hostname=hostname)]):195 kernels = [(kn, ka) for kn, ka in get_kernels(hostname=hostname) if kn in sys.argv]196 if '--serial' in sys.argv:197 for kernel, args in kernels:198 simulate(hostname, kernel, args)199 else:200 p.starmap_async(simulate, [(hostname, kernel, args) for kernel, args in kernels])201 p.close()202 p.join()203if __name__ == "__main__":...
word_sets.py
Source:word_sets.py
...15 else:16 return 0.171819def get_kernels(phi):20 """21 :param phi: topics-words matrix, shape T x W, stochastic over W22 :return: list of kernels for each topic2324 kernel of the topic t is the set of the words w such that phi[t, w] > 1 / W25 """26 T, W = phi.shape27 return [28 set(np.where(phi[t, :] * W > 1)[0])29 for t in range(T)30 ]313233def get_top_words(phi, top_size):34 """35 :param phi: topics-words matrix, shape T x W, stochastic over W36 :param top_size: the size of the top37 :return: list of top words for each topic3839 top words is the set of words w of the largest top_size phi[t, w]40 of the topic41 """42 return [43 set(values)44 for values in np.argpartition(phi, -top_size, axis=1)[:, -top_size:]45 ]464748def calc_kernels_sizes(phi):49 """50 :param phi: topics-words matrix, shape T x W, stochastic over W51 :return: kernel sizes of the topics5253 kernel of the topic t is the set of the words w such that phi[t, w] > 1 / W54 """55 return [len(kernel) for kernel in get_kernels(phi)]565758def calc_avg_pairwise_jaccards(sets):59 """60 :param sets: list of sets61 :return: average jaccard distance between these sets62 """63 size = len(sets)64 res = 0.65 for i in range(size):66 for j in range(size):67 if i != j:68 res += calc_jaccard_distance(sets[i], sets[j])69 return res / size / (size - 1)707172def calc_avg_pairwise_kernels_jaccards(phi):73 """74 :param phi: topics-words matrix, shape T x W, stochastic over W75 :return: average pairwise jaccard distance of the topics' kernels7677 kernel of the topic t is the set of the words w such that phi[t,w] > 1 / W78 """79 return calc_avg_pairwise_jaccards(get_kernels(phi))808182def calc_avg_top_words_jaccards(phi, top_size):83 """84 :param phi: topics-words matrix, shape T x W, stochastic over W85 :param top_size: the size of the top86 :return: average pairwise jaccard distance of the topics' tops87 """
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RBF.py
Source:RBF.py
...6 def __init__(self):7 layer.__init__(self)8 def init_kernels(self, numOfConnections):9 self.kernels = uniform.init_kernels(numOfConnections)10 def get_kernels(self):11 return self.kernels12 def get_params(self):13 return [self.get_kernels()]14 def connect(self, *layers):15 assert len(layers) == 116 self.intputShape = layers[0].get_outputShape()17 self.set_inputTensor( layers[0].get_outputTensor() )18 self.init_kernels( self.get_inputShape()[1] ) #assume inputShape is 1D19 outputTensor = self.get_RBFTensor()20 self.set_outputTensor( outputTensor )21 def get_inputShape(self):22 return self.intputShape23 def get_outputShape(self):24 return (1, 1)25 def verify_shape(self):26 pass27 def get_RBFTensor(self):28 '''29 Subclass should modify this to return a tensor indicating the function it uses as the base function30 '''31 raise NotImplementedError()32class fixedRBFLayer(RBFLayer):33 '''34 Uses user defined kernels and during the training, the kernels are not changed35 However, the computation may report an mis-match error if user defined kernels with wrong shape36 '''37 def __init__(self, kernels):38 RBFLayer.__init__(self)39 self.kernels = kernels40 def init_kernels(self, numOfConnections):41 '''42 Ignore the input43 '''44 pass45 def get_params(self):46 return None47class GassinRBFLayer(RBFLayer):48 def __init__(self):49 RBFLayer.__init__(self)50 def get_RBFTensor(self):51 return ( T.pow( self.get_kernels() - self.get_inputTensor(), 2 ) ).sum()52class fixedGassinRBFLayer(fixedRBFLayer):53 def __init__(self, kernels):54 fixedRBFLayer.__init__(self, kernels)55 def get_RBFTensor(self):...
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