32 lines
1.2 KiB
Python
32 lines
1.2 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(16).to('cpu', dtype=torch.float32)
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model.load_state_dict(torch.load('./pred/checkpoints/model_20250315_0530.pt'))
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model.eval()
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# inference
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initial = 999269
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last = initial
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start_time = '2025-03-15 01:03:21'
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for i in range(1, 48):
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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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2.801318, 3.455128, 3.903391, 3.995577, 4.641488, 5.75131, 6.723868, 6.105322, 8.141023, 9.576701, 10.665067, # grows_feat
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0.043993, 0.72057, 28.000902 # 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'), views_pred, views_pred - last)
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last = views_pred
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if __name__ == '__main__':
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main() |