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))

# 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())
train_data= []
for i in range(N):
    rows=list(map(float,input().split()))
    train_data.append(rows)
T = int(input())
test_data=[list(map(float,input().split())) for _ in range(T)]

X_train=[]
y_train=[]
for row in train_data:
    X_train.append(row[:F]) 
    y_train.append(row[F]) 

X_train = np.array(X_train)  # Convert to numpy array for model input
y_train = np.array(y_train)
X_test=np.array(test_data)
#print(X_test)

poly=PolynomialFeatures(degree=3)
X_train_poly=poly.fit_transform(X_train)
X_test_poly=poly.transform(X_test)

model=LinearRegression()
model.fit(X_train_poly,y_train)
y_pred=model.predict(X_test_poly)

for pred in y_pred:
    print(pred)