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-0.07607559859752655, 0.2264176607131958, -0.28487536311149597, -0.09532354772090912, -0.5725015997886658, 0.33569034934043884, 1.6310118436813354, 0.014051337726414204, 0.2715412676334381, -0.832463800907135, -0.14146481454372406, -0.7267753481864929, 0.47120073437690735], [-0.4844234883785248, 0.23333986103534698, 0.3711671233177185, -0.23304779827594757, 0.29491645097732544, -0.3546754717826843, 0.7761468291282654, 1.79520583152771, -0.49582844972610474, -0.11666426807641983, -0.11421363055706024, -0.09849900752305984, -1.1098111867904663, 0.021674271672964096]], "biases": [0.4380376636981964, 0.3052440881729126, 0.2598321735858917, 0.22661542892456055, 0.11338268220424652, 0.13968326151371002, 0.16384154558181763, 0.03674895316362381, -0.13025738298892975, -0.25334617495536804, -0.3833767771720886, -0.34676069021224976]}}
# 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
data = []
days = int(input())
for i in range(days):
temps = [float(x) for x in input().split()]
data.append(temps)
for d in data:
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)
# total_correct += correct
# total_incorrect += incorrect
# score = 25 * (total_correct - total_incorrect) / days
# print(total_correct, total_incorrect, score)
# file.close()