modified_adsorption.py
def results(self):
        """
        Return the class determined by the maximum in each row of the Yh matrix. Doesnt
        take into account the dummy label
        """
        result_complete = []
        self._mad_class_index = np.squeeze(np.asarray(self._Yh[:, :self._Yh.shape[1] - 1].todense().argmax(axis=1)))
        self._label_results = np.array([self._labels[r] for r in self._mad_class_index])
        print self._label_results
        for i in range(len(self._label_results)):
            result_complete.append((self._nodes_list[i], self._label_results[i],
                                    self._golden_labels.get(self._nodes_list[i], "NO GOLDEN LABEL")))
        print result_complete
        self._result_complete = result_complete
        return self._label_results, self._result_complete

        Return the class determined by the maximum in each row of the Yh matrix. Doesnt
        take into account the dummy label
        
Return the class determined by the maximum in each row of the Yh matrix. Doesnt
take into account the dummy label
Return the class determined by the maximum in each row of the Yh matrix . Doesnt take into account the dummy label
def results(self):
        result_complete = []
        self._mad_class_index = np.squeeze(np.asarray(self._Yh[:, :self._Yh.shape[1] - 1].todense().argmax(axis=1)))
        self._label_results = np.array([self._labels[r] for r in self._mad_class_index])
        print self._label_results
        for i in range(len(self._label_results)):
            result_complete.append((self._nodes_list[i], self._label_results[i],
                                    self._golden_labels.get(self._nodes_list[i], "NO GOLDEN LABEL")))
        print result_complete
        self._result_complete = result_complete
        return self._label_results, self._result_complete
def results ( self ) : result_complete = [ ] self . _mad_class_index = np . squeeze ( np . asarray ( self . _Yh [ : , : self . _Yh . shape [ 1 ] - 1 ] . todense ( ) . argmax ( axis = 1 ) ) ) self . _label_results = np . array ( [ self . _labels [ r ] for r in self . _mad_class_index ] ) print self . _label_results for i in range ( len ( self . _label_results ) ) : result_complete . append ( ( self . _nodes_list [ i ] , self . _label_results [ i ] , self . _golden_labels . get ( self . _nodes_list [ i ] , "NO GOLDEN LABEL" ) ) ) print result_complete self . _result_complete = result_complete return self . _label_results , self . _result_complete
Return the class determined by the maximum in each row of the Yh matrix.
Return the class determined by the maximum in each row of the Yh matrix .
"""
        Return the class determined by the maximum in each row of the Yh matrix. Doesnt
        take into account the dummy label
        """
self