Compute inverse for each matrix in a list of matrices.
    This is faster than calling numpy.linalg.inv for each matrix.
979770
def array_inverse(A):
    
    A = np.ascontiguousarray(A, dtype=float)
    b = np.identity(A.shape[2], dtype=A.dtype)

    n_eq = A.shape[1]
    n_rhs = A.shape[2]
    pivots = np.zeros(n_eq, np.intc)
    identity  = np.eye(n_eq)
    def lapack_inverse(a):
        b = np.copy(identity)
        pivots = np.zeros(n_eq, np.intc)
        results = np.linalg.lapack_lite.dgesv(n_eq, n_rhs, a, n_eq, pivots, b, n_eq, 0)
        if results['info'] > 0:
            raise np.linalg.LinAlgError('Singular matrix')
        return b

    return np.array([lapack_inverse(a) for a in A])
825080
public static boolean isInverse(DMatrixRMaj a , DMatrixRMaj b , double tol ) {
        if( a.numRows != b.numRows || a.numCols != b.numCols ) {
            return false;
        }

        int numRows = a.numRows;
        int numCols = a.numCols;

        for( int i = 0; i < numRows; i++ ) {
            for( int j = 0; j < numCols; j++ ) {
                double total = 0;
                for( int k = 0; k < numCols; k++ ) {
                    total += a.get(i,k)*b.get(k,j);
                }

                if( i == j ) {
                    if( !(Math.abs(total-1) <= tol) )
                        return false;
                } else if( !(Math.abs(total) <= tol) )
                    return false;
            }
        }

        return true;
    }
1076755
def inverse(self):
        
        inverses = []
        for segment in self:
            if len(segment) < 2:
                inverses.append([])
            else:
                inverses.append(segment.inverse())
        return inverses
457736
def one_hot(cls, ij, sz):
        
        if isinstance(sz, int):
            sz = (sz, sz)
        if isinstance(ij, int):
            ij = (ij, ij)
        m = np.zeros(sz)
        m[ij[0], ij[1]] = 1.0
        return Matrix(m)
638105
public void inverse(double[] a) {
        int n = a.length;

        if (!Math.isPower2(n)) {
            throw new IllegalArgumentException("The data vector size is not a power of 2.");
        }

        if (n < ncof) {
            throw new IllegalArgumentException("The data vector size is less than wavelet coefficient size.");
        }

        int start = n >> (int) Math.floor(Math.log2(n/(ncof-1)));
        for (int nn = start; nn <= n; nn <<= 1) {
            backward(a, nn);
        }
    }
443817
public static INDArray invert(INDArray arr, boolean inPlace) {
        if(arr.rank() == 2 && arr.length() == 1){
            //[1,1] edge case. Matrix inversion: [x] * [1/x] = [1]
            if(inPlace){
                return arr.rdivi(1.0);
            } else {
                return arr.rdiv(1.0);
            }
        }
        if (!arr.isSquare()) {
            throw new IllegalArgumentException("invalid array: must be square matrix");
        }

        //FIX ME: Please
       /* int[] IPIV = new int[arr.length() + 1];
        int LWORK = arr.length() * arr.length();
        INDArray WORK = Nd4j.create(new double[LWORK]);
        INDArray inverse = inPlace ? arr : arr.dup();
        Nd4j.getBlasWrapper().lapack().getrf(arr);
        Nd4j.getBlasWrapper().lapack().getri(arr.size(0),inverse,arr.size(0),IPIV,WORK,LWORK,0);*/

        RealMatrix rm = CheckUtil.convertToApacheMatrix(arr);
        RealMatrix rmInverse = new LUDecomposition(rm).getSolver().getInverse();


        INDArray inverse = CheckUtil.convertFromApacheMatrix(rmInverse, arr.dataType());
        if (inPlace)
            arr.assign(inverse);
        return inverse;

    }
678982
def inverse(self):
        
        return OperatorComp(self.right.inverse, self.left.inverse,
                            self.__tmp)
1153508
private static WlBorderCoef<WlCoef_F32> computeBorderCoefficients( BorderIndex1D border ,
																	   WlCoef_F32 forward ,
																	   WlCoef_F32 inverse ) {
		int N = Math.max(forward.getScalingLength(),forward.getWaveletLength());
		N += N%2;
		N *= 2;
		border.setLength(N);

		// Because the wavelet transform is a linear invertible system the inverse coefficients
		// can be found by creating a matrix and inverting the matrix.  Boundary conditions are then
		// extracted from this inverted matrix.
		DMatrixRMaj A = new DMatrixRMaj(N,N);
		for( int i = 0; i < N; i += 2 ) {
			
			for( int j = 0; j < forward.scaling.length; j++ ) {
				int index = border.getIndex(j+i+forward.offsetScaling);
				A.add(i,index,forward.scaling[j]);
			}

			for( int j = 0; j < forward.wavelet.length; j++ ) {
				int index = border.getIndex(j+i+forward.offsetWavelet);
				A.add(i+1,index,forward.wavelet[j]);
			}
		}

		LinearSolverDense<DMatrixRMaj> solver = LinearSolverFactory_DDRM.linear(N);
		if( !solver.setA(A) || solver.quality() < 1e-5) {
			throw new IllegalArgumentException("Can't invert matrix");
		}

		DMatrixRMaj A_inv = new DMatrixRMaj(N,N);
		solver.invert(A_inv);

		int numBorder = UtilWavelet.borderForwardLower(inverse)/2;

		WlBorderCoefFixed<WlCoef_F32> ret = new WlBorderCoefFixed<>(numBorder, numBorder + 1);
		ret.setInnerCoef(inverse);

		// add the lower coefficients first
		for( int i = 0; i < ret.getLowerLength(); i++) {
			computeLowerCoef(inverse, A_inv, ret, i*2);
		}

		// add upper coefficients
		for( int i = 0; i < ret.getUpperLength(); i++) {
			computeUpperCoef(inverse, N, A_inv, ret, i*2);
		}

		return ret;
	}
1169676
public DenseMatrix inverse() {
        int m = lu.nrows();
        int n = lu.ncols();

        if (m != n) {
            throw new IllegalArgumentException(String.format("Matrix is not square: %d x %d", m, n));
        }

        DenseMatrix inv = Matrix.zeros(n, n);
        for (int i = 0; i < n; i++) {
            inv.set(i, piv[i], 1.0);
        }

        solve(inv);
        return inv;
    }
234716
def iterate(matrix, expansion, inflation):
    
    # Expansion
    matrix = expand(matrix, expansion)

    # Inflation
    matrix = inflate(matrix, inflation)

    return matrix
1223035
def inv_matrix(self) -> np.ndarray:
        
        if self._inv_matrix is None:
            self._inv_matrix = inv(self._matrix)
            self._inv_matrix.setflags(write=False)
        return self._inv_matrix
1035019
private static int[] elmhes(DenseMatrix A) {
        int n = A.nrows();
        int[] perm = new int[n];

        for (int m = 1; m < n - 1; m++) {
            double x = 0.0;
            int i = m;
            for (int j = m; j < n; j++) {
                if (Math.abs(A.get(j, m - 1)) > Math.abs(x)) {
                    x = A.get(j, m - 1);
                    i = j;
                }
            }
            perm[m] = i;
            if (i != m) {
                for (int j = m - 1; j < n; j++) {
                    double swap = A.get(i, j);
                    A.set(i, j, A.get(m, j));
                    A.set(m, j, swap);
                }
                for (int j = 0; j < n; j++) {
                    double swap = A.get(j, i);
                    A.set(j, i, A.get(j, m));
                    A.set(j, m, swap);
                }
            }
            if (x != 0.0) {
                for (i = m + 1; i < n; i++) {
                    double y = A.get(i, m - 1);
                    if (y != 0.0) {
                        y /= x;
                        A.set(i, m - 1, y);
                        for (int j = m; j < n; j++) {
                            A.sub(i, j, y * A.get(m, j));
                        }
                        for (int j = 0; j < n; j++) {
                            A.add(j, m, y * A.get(j, i));
                        }
                    }
                }
            }
        }

        return perm;
    }
1000303
def get_matrices():
    
    with open('hwrt/misc/is_one_symbol_classifier.pickle', 'rb') as f:
        a = pickle.load(f)

    arrays = []
    for el1 in a.input_storage:
        for el2 in el1.__dict__['storage']:
            if isinstance(el2, cuda.CudaNdarray):
                arrays.append({'storage': numpy.asarray(el2),
                               'name': el1.name})
            else:
                logging.warning("was type %s. Do nothing." % type(el2))
                logging.debug(el1.name)
    return arrays
144997
public void setToInverseL( double a[] ) {

        // the more direct method which takes full advantage of the sparsity of the data structures proved to
        // be difficult to get right due to the conjugates and reordering.
        // See comparable real number code for an example.
        for (int col = 0; col < n; col++) {
            Arrays.fill(vv,0);
            vv[col*2] = 1;
            TriangularSolver_ZDRM.solveL_diagReal(t, vv, n);
            TriangularSolver_ZDRM.solveConjTranL_diagReal(t, vv, n);
            for( int i = 0; i < n; i++ ) {
                a[(i*numCols+col)*2  ] = vv[i*2];
                a[(i*numCols+col)*2+1] = vv[i*2+1];
            }
        }
        // NOTE: If you want to make inverse faster take advantage of the sparsity
    }
1279686
def inverse(self):
        """"""
        inv = np.empty(self.size, np.int)
        inv[self.sorter] = self.sorted_group_rank_per_key
        return inv
972266
@Override
    public DenseMatrix inverse() {
        int m = lu.nrows();
        int n = lu.ncols();

        if (m != n) {
            throw new IllegalArgumentException(String.format("Matrix is not square: %d x %d", m, n));
        }

        int nb = LAPACK.getInstance().ilaenv(1, "DGETRI", "", n, -1, -1, -1);
        if (nb < 0) {
            logger.warn("LAPACK ILAENV error code: {}", nb);
        }

        if (nb < 1) nb = 1;

        int lwork = lu.ncols() * nb;
        double[] work = new double[lwork];
        intW info = new intW(0);
        LAPACK.getInstance().dgetri(lu.ncols(), lu.data(), lu.ld(), piv, work, lwork, info);

        if (info.val != 0) {
            logger.error("LAPACK DGETRI error code: {}", info.val);
            throw new IllegalArgumentException("LAPACK DGETRI error code: " + info.val);
        }

        return lu;
    }
513784
def to_nmatrix
      each_vector.select do |vector|
        vector.numeric? && !vector.include_values?(*Daru::MISSING_VALUES)
      end.map(&:to_a).transpose.to_nm
    end