Get the full singular value matrix of self

        Returns
        -------
        Matrix : Matrix
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def full_s(self):
        
        x = np.zeros((self.shape),dtype=np.float32)

        x[:self.s.shape[0],:self.s.shape[0]] = self.s.as_2d
        s = Matrix(x=x, row_names=self.row_names,
                          col_names=self.col_names, isdiagonal=False,
                          autoalign=False)
        return s
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def get_matrix(self):
        """"""
        if self.parent:
            return self.get_local_matrix() * (self._prev_parent_matrix or self.parent.get_matrix())
        else:
            return self.get_local_matrix()
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def _get_shift_matrix(self):
        """"""
        return np.array([[1., 0., self.x_shift, 0.],
                         [0., 1., self.y_shift, 0.],
                         [0., 0.,           1., 0.],
                         [0., 0.,           0., 1.]], dtype=np.float32)
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def U(self):
        
        if self._U is None:
            sinv = N.diag(1/self.singular_values)
            self._U = dot(self.arr,self.V.T,sinv)
        return self._U
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def get_matrix(self):
        
        matrix = Matrix()
        cairo.cairo_pattern_get_matrix(self._pointer, matrix._pointer)
        self._check_status()
        return matrix
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function() {
            	var matrix_rows = [];
            	var n = this.elements.length;
            	for (var i = 0; i < n; i++) {
            	    matrix_rows.push($V(this.elements[i]).inspect());
            	}
            	return matrix_rows.join('\n');
                }
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def asML(self):
        
        return newlinalg.SparseMatrix(self.numRows, self.numCols, self.colPtrs, self.rowIndices,
                                      self.values, self.isTransposed)
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def get_matrix(self, indices):
        """"""
        new = numpy.empty(self.samples1.shape)
        for idx in range(len(indices)):
            if indices[idx]:
                new[idx] = self.samples1[idx]
            else:
                new[idx] = self.samples2[idx]

        if self.poly:
            new = self.poly(*new)
        return new
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def trans_from_matrix(matrix):
    
    t = np.zeros((4, 4))
    for i in range(4):
        for j in range(4):
            t[i, j] = matrix.GetElement(i, j)
    return t
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def get_adjacent_matrix(self):
        
        edges = self.edges
        num_edges = len(edges) + 1
        adj = np.zeros([num_edges, num_edges])

        for k in range(num_edges - 1):
            adj[edges[k].L, edges[k].R] = 1
            adj[edges[k].R, edges[k].L] = 1

        return adj
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def get_matrix(self):
        """"""
        matrix = Matrix()
        cairo.cairo_get_matrix(self._pointer, matrix._pointer)
        self._check_status()
        return matrix
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def _ar_matrix(self):
        
        Y = np.array(self.data[self.max_lag:self.data.shape[0]])
        X = self.data[(self.max_lag-1):-1]

        if self.ar != 0:
            for i in range(1, self.ar):
                X = np.vstack((X,self.data[(self.max_lag-i-1):-i-1]))

        return X
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public Matrix getD () {
      Matrix X = MatrixFactory.createMatrix(n,n);
      for (int i = 0; i < n; i++) {
         X.set(i,i,d[i]);
         if (e[i] > 0) {
				X.set(i,i+1,e[i]);
         } else if (e[i] < 0) {
				X.set(i,i-1,e[i]);
         }
      }
      return X;
   }
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def _q_matrix(self):
        
        return np.array([
            [self.q[0], -self.q[1], -self.q[2], -self.q[3]],
            [self.q[1],  self.q[0], -self.q[3],  self.q[2]],
            [self.q[2],  self.q[3],  self.q[0], -self.q[1]],
            [self.q[3], -self.q[2],  self.q[1],  self.q[0]]])
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def metric_tensor(self) -> np.ndarray:
        
        return dot(self._matrix, self._matrix.T)
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def get_pij_matrix(t, diag, A, A_inv):
    
    return A.dot(np.diag(np.exp(diag * t))).dot(A_inv)
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def volume( self ):
        
        return np.dot( self.matrix[0], np.cross( self.matrix[1], self.matrix[2] ) )