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[0.3066559135913849, -0.9984031319618225, 1.420783519744873, -1.2911618947982788, 0.6313174962997437, -0.9135478734970093, 1.297324299812317, 0.3818303048610687, 0.08125432580709457, -0.44287508726119995, -0.06337403506040573, -0.5498045682907104, -0.7316040396690369, 1.6306573152542114, -0.1832856833934784, 0.1727650910615921], [0.07617273926734924, -1.4292161464691162, 1.2176958322525024, -1.2145943641662598, 0.7056134343147278, -0.6711724400520325, 1.4353125095367432, 0.44333258271217346, 0.26524490118026733, -0.35978376865386963, -0.19692401587963104, -0.5370153188705444, -0.639231264591217, 1.577088713645935, -0.40973982214927673, 0.6197758913040161]], "biases": [0.00698229530826211, 0.1871551275253296, 0.00918138213455677, -0.23116371035575867, -0.4316602945327759, -0.5104711055755615, -0.6728451251983643, -0.5090742707252502, -0.2276889979839325, -0.40025243163108826, -0.29755014181137085, -0.7260891795158386
# 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()