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0.15809881687164307, -0.6031585335731506, 0.12778843939304352, -0.2751481533050537, -0.5184248685836792, 0.3922567665576935, -0.19791917502880096, 0.5361770987510681], [0.29150235652923584, -0.28401416540145874, 0.39721718430519104, -0.9424723386764526, 0.4355665445327759, -0.6626962423324585, 0.7183850407600403, 0.2681116759777069, 0.09416620433330536, -0.15624333918094635, -0.00940051581710577, -0.36639004945755005, -0.6232318878173828, 0.5930992960929871, 0.023491838946938515, 0.046709708869457245], [-0.0652083083987236, -0.7558208107948303, 0.09202488511800766, -0.8771901726722717, 0.5779791474342346, -0.4168758690357208, 0.8185129761695862, 0.2446957230567932, 0.34074464440345764, -0.06857724487781525, -0.16881665587425232, -0.395425021648407, -0.5174257755279541, 0.4399043917655945, -0.3285398483276367, 0.5491731762886047]], "biases": [-0.05683019384741783, 0.1894877403974533, 0.020973587408661842, -0.3177149295806885, -0.4189292788505554, -0.4475701153278351, -0.5198470950126648, -0.399175763130188, -0.07960782945156097, -0.32302457094192505, -0.2624129354953766, -0.5806722044944763]}}
# 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()