Task: | Säähavainnot |
Sender: | maweiyin24562 |
Submission time: | 2023-11-07 23:11:42 +0200 |
Language: | C++ (C++17) |
Status: | READY |
Result: | 0 |
group | verdict | score |
---|---|---|
#1 | ACCEPTED | 0 |
test | verdict | time | score | |
---|---|---|---|---|
#1 | ACCEPTED | 0.02 s | 0 | details |
#2 | ACCEPTED | 0.02 s | 0 | details |
#3 | ACCEPTED | 0.02 s | 0 | details |
#4 | ACCEPTED | 0.02 s | 0 | details |
#5 | ACCEPTED | 0.02 s | 0 | details |
#6 | ACCEPTED | 0.02 s | 0 | details |
#7 | ACCEPTED | 0.02 s | 0 | details |
#8 | ACCEPTED | 0.02 s | 0 | details |
Code
#include <iostream>#include <vector>#include <iomanip>// Simple linear regression modelclass LinearRegression {public:double slope;double intercept;LinearRegression() : slope(0.0), intercept(0.0) {}// Fit the model using provided datavoid fit(const std::vector<double>& x, const std::vector<double>& y) {double sum_x = 0.0;double sum_y = 0.0;double sum_x2 = 0.0;double sum_xy = 0.0;int n = x.size();for (int i = 0; i < n; ++i) {sum_x += x[i];sum_y += y[i];sum_x2 += x[i] * x[i];sum_xy += x[i] * y[i];}slope = (n * sum_xy - sum_x * sum_y) / (n * sum_x2 - sum_x * sum_x);intercept = (sum_y - slope * sum_x) / n;}// Predict the value for a given xdouble predict(double x) const {return slope * x + intercept;}};int main() {int n;std::cin >> n;for (int day = 0; day < n; ++day) {std::vector<double> pastTemperatures(24);for (int i = 0; i < 24; ++i) {std::cin >> pastTemperatures[i];}LinearRegression model;std::vector<double> x, y;// Prepare the data for regressionfor (int i = 0; i < 12; ++i) {x.push_back(i);y.push_back(pastTemperatures[i]);}// Fit the modelmodel.fit(x, y);// Predict the temperatures for the next 12 hoursfor (int i = 12; i < 24; ++i) {double prediction = model.predict(i);if (i == 23) {std::cout << std::fixed << std::setprecision(1) << prediction << "\n";} else {std::cout << std::fixed << std::setprecision(1) << prediction << " ";}}}return 0;}
Test details
Test 1
Verdict: ACCEPTED
input |
---|
1000 -0.4 -0.1 -0.2 -0.3 -0.4 -0.5 ... |
correct output |
---|
0.4 0.4 0.5 0.8 0.9 1.1 1.3 1.... |
user output |
---|
-0.9 -0.9 -1.0 -1.0 -1.1 -1.2 ... Truncated |
Test 2
Verdict: ACCEPTED
input |
---|
1000 2.9 2.9 2.9 2.1 2.6 2 2 2.2 2.... |
correct output |
---|
2.3 1.6 1.5 1.1 1 0.7 0.6 0.8 ... |
user output |
---|
1.8 1.8 1.7 1.6 1.5 1.5 1.4 1.... Truncated |
Test 3
Verdict: ACCEPTED
input |
---|
1000 6.6 6 6.4 6 4.6 4.6 4.2 4.3 4.... |
correct output |
---|
10 10.9 10.3 10.1 9.1 7.3 5.7 ... |
user output |
---|
2.4 2.1 1.7 1.4 1.1 0.7 0.4 0.... Truncated |
Test 4
Verdict: ACCEPTED
input |
---|
1000 19.4 20.2 19.1 18.9 18.3 17.3 ... |
correct output |
---|
18 18.2 17 17.5 17.2 16.2 12 8... |
user output |
---|
11.1 10.3 9.5 8.7 7.9 7.1 6.3 ... Truncated |
Test 5
Verdict: ACCEPTED
input |
---|
1000 -5.7 -5.8 -5.8 -5.9 -7.1 -6.9 ... |
correct output |
---|
-4.2 -4.1 -4 -3.8 -3.5 -3.2 -3... |
user output |
---|
-8.1 -8.3 -8.5 -8.7 -8.9 -9.1 ... Truncated |
Test 6
Verdict: ACCEPTED
input |
---|
1000 14.8 14.8 15.4 12.9 11.8 9.7 9... |
correct output |
---|
11.8 11 11.6 10.8 10.4 10.4 10... |
user output |
---|
5.9 5.1 4.4 3.6 2.9 2.1 1.3 0.... Truncated |
Test 7
Verdict: ACCEPTED
input |
---|
1000 0.7 1 2 1.4 0.6 -0.4 -0.9 -0.7... |
correct output |
---|
-1.3 -0.5 -0.6 -1 -3.2 -7.2 -6... |
user output |
---|
-1.3 -1.5 -1.7 -1.9 -2.1 -2.3 ... Truncated |
Test 8
Verdict: ACCEPTED
input |
---|
1000 15.1 15.3 14.9 14.4 14.4 13.7 ... |
correct output |
---|
15.6 15.9 16 15.2 14.6 14.4 13... |
user output |
---|
11.5 11.2 10.9 10.6 10.3 9.9 9... Truncated |