import java.util.Scanner; import java.util.Arrays; public class Solution { public static void main(String[] args) { Scanner scan = new Scanner(System.in); int m = scan.nextInt(); int n = scan.nextInt(); double [][] X = new double[n][m + 1]; double [][] Y = new double[n][1]; for (int row = 0; row < n; row++) { X[row][0] = 1; for (int col = 1; col <= m; col++) { X[row][col] = scan.nextDouble(); } Y[row][0] = scan.nextDouble(); } /* Calculating B */ double [][] xtx = multiply(transpose(X),X); double [][] xtxInv = invert(xtx); double [][] xty = multiply(transpose(X), Y); double [][] B = multiply(xtxInv, xty); int sizeB = B.length; /* Calculating values for "q" feature sets */ int q = scan.nextInt(); for (int i = 0; i < q; i++) { double result = B[0][0]; for (int row = 1; row < sizeB; row++) { result += scan.nextDouble() * B[row][0]; } System.out.println(result); } scan.close(); } /* Multipling two matrices */ public static double[][] multiply(double [][] A, double [][] B) { int aRows = A.length; int aCols = A[0].length; int bRows = B.length; int bCols = B[0].length; double [][] C = new double[aRows][bCols]; int cRows = C.length; int cCols = C[0].length; for (int row = 0; row < cRows; row++) { for (int col = 0; col < cCols; col++) { for (int k = 0; k < aCols; k++) { C[row][col] += A[row][k] * B[k][col]; } } } return C; } public static double[][] transpose(double [][] matrix) { int originalRows = matrix.length; int originalCols = matrix[0].length; int rows = originalCols; int cols = originalRows; double [][] result = new double[rows][cols]; for (int row = 0; row < originalRows; row++) { for (int col = 0; col < originalCols; col++) { result[col][row] = matrix[row][col]; } } return result; } /* Matrix inversion */ public static double[][] invert(double a[][]) { int n = a.length; double x[][] = new double[n][n]; double b[][] = new double[n][n]; int index[] = new int[n]; for (int i=0; i<n; ++i) b[i][i] = 1; // Transform the matrix into an upper triangle gaussian(a, index); // Update the matrix b[i][j] for (int i=0; i<n-1; ++i) for (int j=i+1; j<n; ++j) for (int k=0; k<n; ++k) b[index[j]][k] -= a[index[j]][i]*b[index[i]][k]; // Backward substitutions for (int i=0; i<n; ++i) { x[n-1][i] = b[index[n-1]][i]/a[index[n-1]][n-1]; for (int j=n-2; j>=0; --j) { x[j][i] = b[index[j]][i]; for (int k=j+1; k<n; ++k) { x[j][i] -= a[index[j]][k]*x[k][i]; } x[j][i] /= a[index[j]][j]; } } return x; } // Method to carry out the partial-pivoting Gaussian elimination public static void gaussian(double a[][], int index[]) { int n = index.length; double c[] = new double[n]; // Initializing the index for (int i=0; i<n; ++i) index[i] = i; // Finding rescaling factors for (int i=0; i<n; ++i) { double c1 = 0; for (int j=0; j<n; ++j) { double c0 = Math.abs(a[i][j]); if (c0 > c1) c1 = c0; } c[i] = c1; } // Searching the pivoting element from each column int k = 0; for (int j=0; j<n-1; ++j) { double pi1 = 0; for (int i=j; i<n; ++i) { double pi0 = Math.abs(a[index[i]][j]); pi0 /= c[index[i]]; if (pi0 > pi1) { pi1 = pi0; k = i; } } // Interchanging rows according to the pivoting order int itmp = index[j]; index[j] = index[k]; index[k] = itmp; for (int i=j+1; i<n; ++i) { double pj = a[index[i]][j]/a[index[j]][j]; a[index[i]][j] = pj; for (int l=j+1; l<n; ++l) a[index[i]][l] -= pj*a[index[j]][l]; } } } }