How to use get_kernels method in autotest

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    """
...

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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