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<?php

class BaseEvent
{
    protected $eventName = '';

    public function occured()
    {
        $this->dispatcher->dispatch('event.' . $eventName . '.received');
    }

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$ grep " on only " nips*/*
nips00/0505.txt:Figure 4: A net that predicts the next character in a sequence, based on only the last character. 
nips00/0515.txt:processor that can perform a variety of instructions very quickly, but operates on only 
nips00/0534.txt:If the output neurons can take on only one of two states, then the 
nips00/0534.txt:trained on only one pass through the training data 2 . If the 
nips00/0794.txt:separated is shown in Fig. lb: In this representation unit 13 is turned on only for moves which 
nips01/0040.txt:by asking that the network function match the target function on only a finite set of 
nips01/0107.txt:two units on only 8.3% of runs. In 9 of the 10 training sets, the failure rate of the skele- 
nips01/0712.txt:Since not all synapses need to have this feature, the circuit will be placed on only a limited 
nips02/0168.txt:tree has only two immediate offspring and the splitting decision is based on only one 

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$ grep "either on" nips*/*
nips00/0422.txt:For our purposes, we will assume that a line process can be in either one of two
nips00/0760.txt:branches by the unit. If the classificaton in either one of these two branches
nips00/0760.txt:is pure enough, or equivalently either one of S and $2 is fairly close to 1,
nips01/0160.txt:in which the number of 1's is either one less or one more than half the number
nips01/0494.txt:2. two planes that partition the points so that either one quadrant contains
nips01/0748.txt:The truth table in Fig. 5 shows that the XOR is 1 (on or true) when either one of the two
nips02/0149.txt:firing, but changing the time constant of this current has almost no effect either on
nips02/0282.txt:Neither one is quite right for recognizing As because
nips03/0032.txt:for velocity storage, in what may be a more optimal configuration than either one

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from copy import deepcopy

def find_paths(rows):
    if len(rows) == 1:
        return rows

    head = rows[-1]
    previous_paths = find_paths(rows[:-1])

    paths = deepcopy(previous_paths) + [head]

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import numpy
from sklearn.ensemble import RandomForestClassifier

# lecture du fichier CSV
data = numpy.loadtxt("data.csv", delimiter=",")
# on isole la 1ère colonne "Survivant" des données dans y, le reste dans X
X, y = data[:, 1:], data[:, 0]

# instanciation du modèle
model = RandomForestClassifier()

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