32 lines
1.1 KiB
Python
32 lines
1.1 KiB
Python
import datetime
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import numpy as np
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from model import CompactPredictor
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import torch
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def main():
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model = CompactPredictor(10).to('cpu', dtype=torch.float32)
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model.load_state_dict(torch.load('./pred/checkpoints/long_term.pt'))
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model.eval()
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# inference
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initial = 997029
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last = initial
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start_time = '2025-03-17 00:13:17'
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for i in range(1, 120):
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hour = i / 0.5
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sec = hour * 3600
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time_d = np.log2(sec)
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data = [time_d, np.log2(initial+1), # time_delta, current_views
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6.111542, 8.404707, 10.071566, 11.55888, 12.457823,# grows_feat
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0.009225, 0.001318, 28.001814# time_feat
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]
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np_arr = np.array([data])
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tensor = torch.from_numpy(np_arr).to('cpu', dtype=torch.float32)
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output = model(tensor)
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num = output.detach().numpy()[0][0]
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views_pred = int(np.exp2(num)) + initial
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current_time = datetime.datetime.strptime(start_time, '%Y-%m-%d %H:%M:%S') + datetime.timedelta(hours=hour)
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print(current_time.strftime('%m-%d %H:%M:%S'), views_pred, views_pred - last)
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last = views_pred
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if __name__ == '__main__':
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main() |