kpt
37 lines
1.2 KiB
Python
37 lines
1.2 KiB
Python
'''
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用于测试预测逻辑
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@Author: QST520
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'''
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import numpy as np
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import datetime
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import matplotlib.pyplot as plt
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def datetime_to_number(date: str): # 格式化日期转换为 integer
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date_number = datetime.datetime.strptime(date, "%Y-%m-%d")
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base_number = datetime.datetime.strptime("2024-1-1", "%Y-%m-%d")
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return date_number.__sub__(base_number).days
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if __name__ == '__main__': # 预测 demo
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xs = [
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'2024-6-4', '2024-6-5', '2024-6-6', '2024-6-7', '2024-6-8', '2024-6-9',
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'2024-6-10', '2024-6-11', '2024-6-12', '2024-6-13'
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]
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ys = [15, 14, 16, 15, 16, 13, 12, 11, 9, 8]
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xs = np.array(list(map(datetime_to_number, xs)))
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ys = np.array(ys)
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fit = np.polyfit(xs, ys, 1)
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fn = np.poly1d(fit)
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print('2024-6-14 PREDICTION: ' +
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str(int(fn(datetime_to_number('2024-6-14')))))
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print('2024-6-15 PREDICTION: ' +
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str(int(fn(datetime_to_number('2024-6-15')))))
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print('2024-6-16 PREDICTION: ' +
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str(int(fn(datetime_to_number('2024-6-16')))))
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print('2024-6-17 PREDICTION: ' +
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str(int(fn(datetime_to_number('2024-6-17')))))
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print('2024-6-18 PREDICTION: ' +
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str(int(fn(datetime_to_number('2024-6-18')))))
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