舆情预测函数定义

utils/predict_demo1.py

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+import numpy as np
+import datetime
+import matplotlib.pyplot as plt
+
+
+def datetime_to_number(date: str):  # 格式化日期转换为 integer
+    date_number = datetime.datetime.strptime(date, "%Y-%m-%d")
+    base_number = datetime.datetime.strptime("2024-1-1", "%Y-%m-%d")
+    return (date_number - base_number).days
+
+
+def predict_future_values(data):
+    # 提取并排序日期
+    sorted_dates = sorted(data.keys(), key=lambda date: datetime.datetime.strptime(date, "%Y-%m-%d"))
+    sorted_data = {k: data[k] for k in sorted_dates}
+
+    # 将日期转换为整数并提取相应的值
+    xs = np.array([datetime_to_number(date) for date in sorted_data.keys()])
+    ys = np.array([data[date] for date in sorted_data.keys()])
+
+    # 拟合线性回归模型
+    fit = np.polyfit(xs, ys, 1)
+    fn = np.poly1d(fit)
+
+    # 获取最新日期，并生成未来三天的日期
+    latest_date = sorted_dates[-1]
+    latest_date_obj = datetime.datetime.strptime(latest_date, "%Y-%m-%d")
+    future_dates = [(latest_date_obj + datetime.timedelta(days=i)).strftime("%Y-%m-%d") for i in range(1, 6)]
+
+    # 预测未来日期的值
+    predictions = {}
+    for date in future_dates:
+        date_num = datetime_to_number(date)
+        if int(fn(date_num))<=0:
+            predictions[date] = 0
+        else:
+            predictions[date] = int(fn(date_num))
+
+    return predictions
+
+
+if __name__ == '__main__':
+    data = {'2024-06-15': 1, '2024-06-18': 1, '2024-06-22': 1, '2024-06-23': 1, '2024-07-01': 3, '2024-07-02': 4, '2024-07-03': 4, '2024-07-04': 14}
+    predictions = predict_future_values(data)
+    print(predictions)
+    # for date, value in predictions.items():
+    #     print(f'{date} PREDICTION: {value}')