(self, inputs, **kwargs)
return q
student t-distribution, as same as used in t-SNE algorithm.
                 q_ij = 1/(1+dist(x_i, u_j)^2), then normalize it.
        Arguments:
            inputs: the variable containing data, shape=(n_samples, n_features)
        Return:
            q: student's t-distribution, or soft labels for each sample. shape=(n_samples, n_clusters)
student t-distribution, as same as used in t-SNE algorithm.
                 q_ij = 1/(1+dist(x_i, u_j)^2), then normalize it.
        Arguments:
            inputs: the variable containing data, shape=(n_samples, n_features)
        Return:
            q: student's t-distribution, or soft labels for each sample. shape=(n_samples, n_clusters)
student t - distribution as same as used in t - SNE algorithm . q_ij = 1 / ( 1 + dist ( x_i u_j ) ^2 ) then normalize it . Arguments : inputs : the variable containing data shape = ( n_samples n_features ) Return : q : student s t - distribution or soft labels for each sample . shape = ( n_samples n_clusters )
def call(self, inputs, **kwargs):
        """ student t-distribution, as same as used in t-SNE algorithm.
                 q_ij = 1/(1+dist(x_i, u_j)^2), then normalize it.
        Arguments:
            inputs: the variable containing data, shape=(n_samples, n_features)
        Return:
            q: student's t-distribution, or soft labels for each sample. shape=(n_samples, n_clusters)
        """
        q = 1.0 / (1.0 + (K.sum(K.square(K.expand_dims(inputs, axis=1) - self.clusters), axis=2) / self.alpha))
        q **= (self.alpha + 1.0) / 2.0
        q = K.transpose(K.transpose(q) / K.sum(q, axis=1))
        return q
def call ( self , inputs , * * kwargs ) : q = 1.0 / ( 1.0 + ( K . sum ( K . square ( K . expand_dims ( inputs , axis = 1 ) - self . clusters ) , axis = 2 ) / self . alpha ) ) q **= ( self . alpha + 1.0 ) / 2.0 q = K . transpose ( K . transpose ( q ) / K . sum ( q , axis = 1 ) ) return q