Take care to point out Python libraries available on Hacker Rank: https://www.hackerrank.com/environment
To solve this, simply note that “the polynomial has order less than 4” according to the problem constraints, and implement cubic regression via scikit-learn’s API.
In particular, sklearn does this by a polynomial feature transform and then a linear regression. You can also use a SVM with polynomial kernel - though we’ve never tried and don’t know if this will be fast enough to satisfy the HR computation time limits.
from sklearn.preprocessing import PolynomialFeatures
from sklearn.pipeline import Pipeline
from sklearn.linear_model import LinearRegression
import numpy as np
def map_float(x):
return [float(i) for i in x]
f, n = map(int, input().split())
model = Pipeline([('poly', PolynomialFeatures(degree=3)),
('linear', LinearRegression(fit_intercept=False))])
train = [map_float(input().split()) for i in range(n)]
X = [row[:-1] for row in train]
y = [row[-1:] for row in train]
model.fit(X, y)
n = int(input())
X_pred = [map_float(input().split()) for i in range(n)]
for i in model.predict(X_pred):
print(i[0])