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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] /= (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)
maximum = 0
minimum = 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:
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()