# Enter your code here. Read input from STDIN. Print output to STDOUT

import numpy as np 
from sklearn.preprocessing import PolynomialFeatures
from sklearn.linear_model import LinearRegression


F, N = map(int, input().split())

data = [list(map(float, input().split())) for i in range(N)]

data = np.array(data)

X_train, y_train = data[:, :-1], data[:, -1]

T = int(input())

X_test = [list(map(float, input().split())) for i in range(T)]

X_test = np.array(X_test)

degree = 3

poly = PolynomialFeatures(degree)

X_train_poly = poly.fit_transform(X_train)
X_test_poly = poly.transform(X_test)

model = LinearRegression()
model.fit(X_train_poly, y_train)

predictions = model.predict(X_test_poly)

for p in predictions:
    print(round(p, 2))

import numpy as np 
from sklearn.preprocessing import PolynomialFeatures 
from sklearn.linear_model import LinearRegression

f, n = map(int, input().strip().split())
    
x_observations = [];
y_amount = []
for _ in range(n):
    l = list(map(float, input().strip().split()))
    x_observations.append(l[0:-1])
    y_amount.append(l[-1])

poly_features = PolynomialFeatures(degree=3)
X_poly = poly_features.fit_transform(np.array(x_observations))

model = LinearRegression()
model.fit(X_poly, np.array(y_amount))

#result
t = int(input().strip())
for _ in range(t):
    l = poly_features.fit_transform(np.array(list(map(float, input().strip().split()))).reshape(1, -1))
    print( round(( model.predict(l) )[0],2))