add: a short-term feature in pred model
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@ -37,6 +37,7 @@ class VideoPlayDataset(Dataset):
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]
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]
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else:
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else:
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self.feature_windows = [
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self.feature_windows = [
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( 5 * MINUTE, 0 * MINUTE),
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( 15 * MINUTE, 0 * MINUTE),
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( 15 * MINUTE, 0 * MINUTE),
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( 40 * MINUTE, 0 * MINUTE),
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( 40 * MINUTE, 0 * MINUTE),
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( 1 * HOUR, 0 * HOUR),
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( 1 * HOUR, 0 * HOUR),
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@ -8,16 +8,16 @@ def main():
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model.load_state_dict(torch.load('./pred/checkpoints/model_20250315_0530.pt'))
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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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model.eval()
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# inference
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# inference
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initial = 999269
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initial = 99906
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last = initial
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last = initial
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start_time = '2025-03-15 01:03:21'
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start_time = '2025-03-16 14:48:42'
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for i in range(1, 48):
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for i in range(1, 48):
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hour = i / 0.5
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hour = i / 4
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sec = hour * 3600
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sec = hour * 3600
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time_d = np.log2(sec)
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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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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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2.456146, 3.562719, 4.106399, 1.0, 1.0, 5.634413, 6.619818, 1.0, 8.608774, 10.19127, 11.412958, # grows_feat
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0.043993, 0.72057, 28.000902 # time_feat
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0.617153, 0.945308, 22.091431 # time_feat
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]
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]
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np_arr = np.array([data])
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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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tensor = torch.from_numpy(np_arr).to('cpu', dtype=torch.float32)
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