CSES - Datatähti 2024 alku - Results
Submission details
Task:Säähavainnot
Sender:qanpi
Submission time:2023-11-12 20:27:28 +0200
Language:Python3 (CPython3)
Status:READY
Result:0
Feedback
groupverdictscore
#10
Test results
testverdicttimescore
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Code

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-1.2147554159164429, -1.4278466701507568, 0.6052550673484802, -0.35134610533714294, 0.5242153406143188, 0.4491434097290039, 0.5767356157302856, 1.0707166194915771, 0.34085094928741455, -0.3178752362728119, 1.0178940296173096, 0.7347964644432068], [-1.2304673194885254, 0.8915618062019348, 0.5709460973739624, 1.072951316833496, -1.0635080337524414, 0.7950056195259094, 1.573177456855774, 0.5706725716590881, 0.2439052313566208, -1.9572638273239136, 0.1920507848262787, 0.02988816797733307, -1.5376942157745361, -1.329377293586731, 0.715063214302063, 0.16885997354984283, 0.206073597073555, 0.27920934557914734, 0.18557697534561157, 0.8523187637329102, 0.5518726706504822, -0.3579968810081482, 0.9430497884750366, 0.2060416042804718], [-1.2916921377182007, 0.7434307336807251, 0.8656293749809265, 0.6042015552520752, -0.8892676830291748, 0.6013134717941284, 1.8434096574783325, -0.23347219824790955, 0.08854400366544724, -1.9159187078475952, -0.13251829147338867, -0.12651467323303223, -1.6942123174667358, -1.4436453580856323, 0.12089548259973526, 0.2157483547925949, -0.10407280176877975, 0.8586121797561646, -0.24194516241550446, 0.883091390132904, 1.0191878080368042, 0.03890696167945862, 0.593977689743042, 0.635360062122345], [-1.1873043775558472, 1.067132830619812, 0.9793083667755127, 0.1566007435321808, -0.919447660446167, 0.27629953622817993, 2.1459648609161377, -0.25091153383255005, 0.08561711013317108, -1.8014302253723145, -0.1743265986442566, 0.4105626344680786, -1.9463810920715332, -1.2386174201965332, 0.7609845399856567, 0.431960791349411, -0.005147584713995457, 1.0678354501724243, 0.056417398154735565, 0.09594099223613739, 0.8734935522079468, -0.24859632551670074, 0.0666753426194191, -0.04725276306271553], [-1.3734147548675537, 1.0086727142333984, 0.71490877866745, 0.27258527278900146, -0.8053406476974487, 0.1023617833852768, 2.3745312690734863, -0.6853972673416138, -0.3395940661430359, -1.9882084131240845, -0.6343898177146912, 0.3810119926929474, -1.8487979173660278, -0.9710056781768799, 0.8510057330131531, 0.3737245500087738, -0.44289320707321167, 0.4626912474632263, 0.26151397824287415, 0.16307955980300903, 1.5040981769561768, 0.00455916253849864, 0.3493972420692444, 0.44941264390945435], [-1.4637080430984497, 1.0611878633499146, 1.0555686950683594, 0.3142684996128082, -0.7139841318130493, 0.18036533892154694, 2.4807050228118896, -0.33138877153396606, 0.0875711664557457, -1.700677514076233, -0.47118622064590454, 0.32000356912612915, -1.995522379875183, -0.9646382927894592, 0.7572317719459534, 0.11611860245466232, -0.4766184091567993, 0.8854584693908691, -0.08819019049406052, 0.1384536623954773, 1.2698644399642944, -0.291640967130661, -0.3237047493457794, 0.12360519170761108]], "biases": [-0.4964740574359894, -0.36336997151374817, -0.3027651906013489, 0.12282179296016693, 0.18807312846183777, 0.03139925003051758, 0.16811123490333557, 0.14027225971221924, 0.16433481872081757, 0.14124421775341034, 0.05739234760403633, 0.1031545102596283]}}

from decimal import * 

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

def relu(x):
    return max(Decimal(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 = Decimal(0)

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

            for (i, w) in zip(input, weights[n]): 
                activation += Decimal(i) * Decimal(w)
            activation += Decimal(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


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

from statistics import variance, mean

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

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

TRAINING_MEAN = Decimal(4.9959406249999985)
TRAINING_VAR = Decimal(6.964563233695652)

maximum = Decimal(0)
minimum = Decimal(0)
for row in data: 
    for item in row[:24]:
        maximum = max(item, maximum)
        minimum = min(item, minimum)

data = normalize(data, maximum, minimum)

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

    if abs(TRAINING_MEAN - avg) < 17: 
    # if True:
        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:

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
(empty)

Test 2

Verdict:

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
(empty)

Test 3

Verdict:

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
(empty)

Test 4

Verdict:

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
(empty)

Test 5

Verdict:

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
(empty)

Test 6

Verdict:

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
(empty)

Test 7

Verdict:

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
(empty)

Test 8

Verdict:

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
(empty)