CSES - Datatähti 2024 alku - Results
Submission details
Task:Säähavainnot
Sender:qanpi
Submission time:2023-11-12 19:51:54 +0200
Language:Python3 (CPython3)
Status:READY
Result:64
Feedback
groupverdictscore
#1ACCEPTED63.88
Test results
testverdicttimescore
#1ACCEPTED0.28 s8.13details
#2ACCEPTED0.28 s8.75details
#3ACCEPTED0.28 s8.38details
#4ACCEPTED0.28 s7.75details
#5ACCEPTED0.28 s8.13details
#6ACCEPTED0.28 s8details
#7ACCEPTED0.28 s7details
#8ACCEPTED0.28 s7.75details

Code

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0.01682172901928425, -0.08230225741863251, 0.12020991742610931, -0.02073150873184204, -0.1339094489812851, -0.44634488224983215, 0.03520064800977707, 0.08989029377698898, 0.30624040961265564, -0.14821244776248932, -0.17564822733402252], [-0.12047623097896576, 0.12598438560962677, -0.021448582410812378, -0.20328515768051147, 0.21214979887008667, -0.25423702597618103, 0.11112727969884872, 0.2641475200653076, -0.2833070158958435, -0.20980936288833618, 0.38493606448173523, -0.05376392602920532, -0.06050250679254532, -0.008969414979219437, -0.14420829713344574, 0.1099403128027916, -0.07141557335853577, 0.10981780290603638, -0.5049321055412292, -0.204447403550148, 0.08266355097293854, 0.08218034356832504, -0.1343565732240677, -0.25840917229652405], [-0.1365094929933548, 0.12224053591489792, -0.14588171243667603, -0.1430470496416092, 0.1320897936820984, -0.30143091082572937, 0.1448480486869812, 0.3622437119483948, -0.31529754400253296, -0.20993906259536743, 0.11740659177303314, 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-0.31877484917640686, -0.057346317917108536, 0.008461590856313705, -0.3716873526573181, 0.2525428235530853, 0.007810561917722225, -0.3968866765499115, 0.1034017950296402, -0.2378971129655838, -0.013789568096399307, -0.5270647406578064, 0.2768254578113556, 0.12586893141269684, 0.07956596463918686, -0.04033513739705086, 0.015186364762485027]], "biases": [0.026380842551589012, 0.04842095077037811, 0.11628054082393646, 0.13093259930610657, 0.13757164776325226, 0.05468164011836052, 0.1958463042974472, 0.13597829639911652, 0.16145382821559906, 0.2557888925075531, 0.13032940030097961, 0.14008896052837372]}}

# def normalize(arr, mx, mn):
#     for i in range(len(arr)): 
#         for j in range(len(arr[i])):
#             arr[i][j] = arr[i][j]*100 / (mx - mn)
    
#     return arr

def relu(x):
    return max(0.0, x)

def custom_predict(input):
    for key, layer in layers.items(): 
        # print(layer)
        config = layer["config"]
        weights = layer["weights"]
        biases = layer["biases"]

        # print(config)
        neurons = config["units"]
        output = [0]*neurons

        for n in range(neurons):
            activation = 0

            assert len(input) == len(weights[n])

            for (i, w) in zip(input, weights[n]): 
                activation += i * w
            activation += biases[n]
            
            # print(activation)
            if(config["activation"] == 'relu'):
                output[n] = relu(activation)
            else: 
                output[n] = activation
        
        input = output 

    return input

# import sys 
# import numpy as np
# file = open("data.txt", "r")
# sys.stdin = file
# total_correct = 0
# total_incorrect = 0

from statistics import variance, mean

# training_vars = []
# correct_vars = []
# incorrect_vars = []

data = []
days = int(input())

for i in range(days):
    temps = [float(x) for x in input().split()]
    data.append(temps)

TRAINING_MEAN = 4.9959406249999985
TRAINING_VAR = 6.964563233695652

for d in data: 
    avg = sum(d[:24]) / 24
    var = variance(d[:24])

    if abs(TRAINING_MEAN - avg) < 17.5 and abs(TRAINING_VAR - var) < 50: 
        ans = custom_predict(d[:24])
        print(" ".join([format(x, ".1f") for x in ans]))

        # truth = d[24:]
        # diff = np.abs(np.array(ans) - np.array(truth)) 
    
        # correct = np.count_nonzero(diff < 0.75)
        # incorrect = np.count_nonzero(diff >= 2.05)
    else:
        print("? " * 12)
#         incorrect = 0
#         correct = 0


#     var = variance(d[:24])
#     training_vars.append(var)

#     if (incorrect > 4):
#         incorrect_vars.append(var)
#     else:
#         correct_vars.append(var)
 
#     total_correct += correct
#     total_incorrect += incorrect


# score = 25 * (total_correct - total_incorrect) / days

# print(mean(correct_vars))
# print(mean(incorrect_vars))
# print(mean(training_vars))
# print(total_correct, total_incorrect, score)

# file.close()

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
0.2 0.2 0.1 0.1 0.0 -0.1 -0.1 ...
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
2.6 2.5 2.4 2.2 2.1 2.0 2.0 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
10.1 10.1 9.6 8.9 8.1 7.1 6.3 ...
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
17.5 17.7 17.2 16.8 16.3 15.7 ...
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
-4.4 -4.3 -4.2 -4.1 -4.2 -4.2 ...
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
12.8 12.9 12.4 11.9 11.3 10.6 ...
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
-1.6 -1.6 -1.7 -1.9 -2.2 -2.5 ...
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
15.1 15.3 14.8 14.3 14.0 13.6 ...
Truncated