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# 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) < 16 and abs(TRAINING_VAR - var) < 30:
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()