from sklearn import linear_model
or
import numpy as np

def transpose(mat):
    return [list(row) for row in zip(*mat)]

def inverse(mat):
    size = len(mat)
    identity = [[1 if i == j else 0 for j in range(size)] for i in range(size)]
    augmented = [row[:] + identity_row for row, identity_row in zip(mat, identity)]
    for i in range(size):
        pivot = augmented[i][i]
        for j in range(size * 2):
            augmented[i][j] /= pivot
        for k in range(size):
            if k != i:
                factor = augmented[k][i]
                for j in range(size * 2):
                    augmented[k][j] -= factor * augmented[i][j]
    return [row[size:] for row in augmented]

def matrix(X, Y):
    return [[sum(x * y for x, y in zip(X_row, Y_col)) for Y_col in zip(*Y)] for X_row in X]

def linear_reg(X, Y):
    tx = transpose(X)
    xty = matrix(tx, Y)
    xtx = matrix(tx, X)
    inv_xtx = inverse(xtx)
    return [item for sublist in matrix(inv_xtx, xty) for item in sublist]

m, n = map(int, input().split(" "))
XY = [list(map(float, input().split(" "))) for _ in range(n)]
X = [[1] + x[:m] for x in XY]
Y = [[x[m]] for x in XY]
q = int(input())
qX = [list(map(float, input().split(" "))) for _ in range(q)]
NX = [[1] + x[:m] for x in qX]

b = linear_reg(X, Y)

for nx in NX:
    y = sum(bi * fi for bi, fi in zip(b, nx))
    print(f"{y:.2f}")

from sklearn import linear_model

X = []
y = []
shape = list(map(int, input().split()))
new_X = []

for _ in range(shape[1]):
    parts = list(map(float, input().split()))
    X.append(parts[:shape[0]])
    y.append(parts[-1])
    
for _ in range(int(input())):
    new_X.append(list(map(float, input().split())))
    
lm = linear_model.LinearRegression()
lm.fit(X, y)
y_hat = lm.predict(new_X)
for y in y_hat:
    print(f'{y:.2f}')

#python
import numpy as np
m, n=map(int, input().split())
Y, X, res=[], [], []
for _ in range(n):
    temp=list(map(float, input().split()))
    Y.append(temp[-1])
    temp.pop()
    X.append([1.0] + temp)
Xtranspose=np.transpose(X)
coeffset=np.dot(np.dot(np.linalg.inv(np.dot(Xtranspose, X)), Xtranspose), Y)
q=int(input())
for x in range(q):
    s=coeffset[0]
    temp=list(map(float, input().split()))
    for i in range(m):
        s+=temp[i]*coeffset[i+1]
    res.append(s)
print(*res, sep='\n')

from sklearn import linear_model
from numpy import multiply as mult
m,n = map(int, input().split())
lst_features, lst_y = [], []

for _ in range(n):
    *features, y = map(float, input().split())
    lst_features.append(features)
    lst_y.append(y)

lm = linear_model.LinearRegression()
lm.fit(lst_features, lst_y)
a = lm.intercept_
b = lm.coef_

for _ in range(int(input())):
    feature_set = list(map(float, input().split()))
    mult_sum = sum(mult(b, feature_set))
    print(round(a + mult_sum, 2))

    static void Main(String[] args) {
        var xs = System.Console.ReadLine().Trim().Split(' ');
        int m = int.Parse(xs[0]) + 1;
        int n = int.Parse(xs[1]);
        var ws = new double[n,m]; /* row,col */
        for(int i = 0; i < n; i++)
        {
            var zs = System.Console.ReadLine().Trim().Split(' ');
            for (int k=0;k<m;k++)
            {
                ws[i,k] = double.Parse(zs[k]);
            }
        }
        var bs = GetBs(ws, n, m);
        var q = int.Parse(System.Console.ReadLine().Trim());
        for(int i = 0; i < q; i++)
        {
            var zs = System.Console.ReadLine().Trim().Split(' ');
            var qs = new double[1,m];
            qs[0,0] = 1;
            for (int k=0;k<(m-1);k++)
            {
                qs[0,k+1] = double.Parse(zs[k]);
            }
            var qsbs = Product(qs, bs);
            System.Console.WriteLine(qsbs[0,0].ToString("f2"));
        }
    }

    static double[,] GetBs(double[,] ws, int row, int col)
    {
        var xs = new double[row, col];
        var ys = new double[row, 1];
        int coly = col - 1;
        for(int i = 0; i < row; i++)
        {
            xs[i, 0] = 1;
            ys[i, 0] = ws[i, coly];
            for(int j = 0; j < coly; j++)
            {
                xs[i, j + 1] = ws[i, j];            
            }
        }
        var txs = Transpose(xs);
        var txsxs = Product(txs, xs);
        var txsxsInv = Inverse(txsxs);
        var txsxsInvtxs = Product(txsxsInv, txs);
        var txsxsInvtxsys = Product(txsxsInvtxs, ys);
        return txsxsInvtxsys;
    }
	/* optimization cache */
    static Dictionary<string, double> DeterminantDic = new Dictionary<string, double>();
    static double Determinant(double[,] xs)
    {
        var pkey = MatrixKey(xs);
        double mx;
        if (DeterminantDic.TryGetValue(pkey, out mx))
        {
            return mx;
        }
        double det = 0;
        int r = xs.GetUpperBound(0) + 1;
        if (r == 2)
        {  
            det = xs[0,0] * xs[1,1] - xs[0,1] * xs[1,0];
        }
        else
        {
            int c = xs.GetUpperBound(1) + 1;
            for(int i=0;i<c;i++)
            {
                det += ((double)(-1.0 * (i % 2 == 0 ? -1.0 : 1.0)) * xs[0, i] * Determinant(SubMatrix(xs, 0, i)));
            }
        }
        DeterminantDic.Add(pkey, det);
        return det;
    }
    static double[,] SubMatrix(double[,] xs, int row, int col)
    {
        int r = xs.GetUpperBound(0);
        int c = xs.GetUpperBound(1);
        var ds = new double[r, c];
        int i2 = 0;
        for(int i = 0; i <= r; i++)
        {
            if (i != row)
            {
                int j2 = 0;
                for(int j = 0; j <= c; j++)
                {
                    if (j != col)
                    {
                        ds[i2, j2] = xs[i,j];
                        j2++;
                    }
                }
                i2++;
            }
        }
        return ds;
    }
    static double[,] Inverse(double[,] xs)
    {
        var k = 1.0/Determinant(xs);
        
        int r = xs.GetUpperBound(0) + 1;
        int c = xs.GetUpperBound(1) + 1;
        var ds = new double[r, c];
        for (int i=0; i < r; i++)
        {
            for(int j=0; j < c; j++)
            {
                ds[i, j] =
                    (-1.0 * (((j + i) % 2 == 0) ? -1.0 : 1.0)) * 
                    Determinant(SubMatrix(xs, i, j)) *
                    k;
            }
        }
        
        return ds;
    }
    static double[,] Product(double[,] xs, double[,] ys)
    {
        int row = xs.GetUpperBound(0) + 1;
        int colx = xs.GetUpperBound(1) + 1;
        int col = ys.GetUpperBound(1) + 1;
        var ds = new double[row, col];
        for(int i = 0; i < row; i++)
        {
            for(int j = 0; j < col; j++)
            {
                double r = 0;
                for(int k = 0; k < colx; k++)
                {
                    r += xs[i,k] * ys[k,j];
                }
                ds[i,j] = r;            
            }
        }
        return ds;
    }
    static double[,] Transpose(double[,] xs)
    {
        int row = xs.GetUpperBound(0) + 1;
        int col = xs.GetUpperBound(1) + 1;
        var ds = new double[col, row];
        for(int i = 0; i < row; i++)
        {
            for(int j = 0; j < col; j++)
            {
                ds[j, i] = xs[i, j];
            }
        }
        return ds;
    }
    static string MatrixKey(double[,] xs)
    {
        string k = string.Empty;
        int row = xs.GetUpperBound(0) + 1;
        int col = xs.GetUpperBound(1) + 1;
        for(int i = 0; i < row; i++)
        {
            for(int j = 0; j < col; j++)
            {
                k = string.Concat(k, k.Length == 0 ? string.Empty : "#", xs[i, j].ToString("f5"));
            }
        }
        return k;
    }