mirror of https://github.com/Mai-with-u/MaiBot.git
127 lines
5.0 KiB
Python
127 lines
5.0 KiB
Python
import numpy as np
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from datetime import datetime, timedelta
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from rich.traceback import install
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install(extra_lines=3)
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class MemoryBuildScheduler:
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def __init__(self, n_hours1, std_hours1, weight1, n_hours2, std_hours2, weight2, total_samples=50):
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"""
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初始化记忆构建调度器
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参数:
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n_hours1 (float): 第一个分布的均值（距离现在的小时数）
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std_hours1 (float): 第一个分布的标准差（小时）
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weight1 (float): 第一个分布的权重
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n_hours2 (float): 第二个分布的均值（距离现在的小时数）
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std_hours2 (float): 第二个分布的标准差（小时）
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weight2 (float): 第二个分布的权重
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total_samples (int): 要生成的总时间点数量
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"""
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# 验证参数
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if total_samples <= 0:
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raise ValueError("total_samples 必须大于0")
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if weight1 < 0 or weight2 < 0:
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raise ValueError("权重必须为非负数")
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if std_hours1 < 0 or std_hours2 < 0:
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raise ValueError("标准差必须为非负数")
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# 归一化权重
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total_weight = weight1 + weight2
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if total_weight == 0:
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raise ValueError("权重总和不能为0")
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self.weight1 = weight1 / total_weight
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self.weight2 = weight2 / total_weight
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self.n_hours1 = n_hours1
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self.std_hours1 = std_hours1
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self.n_hours2 = n_hours2
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self.std_hours2 = std_hours2
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self.total_samples = total_samples
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self.base_time = datetime.now()
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def generate_time_samples(self):
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"""生成混合分布的时间采样点"""
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# 根据权重计算每个分布的样本数
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samples1 = max(1, int(self.total_samples * self.weight1))
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samples2 = max(1, self.total_samples - samples1) # 确保 samples2 至少为1
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# 生成两个正态分布的小时偏移
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hours_offset1 = np.random.normal(loc=self.n_hours1, scale=self.std_hours1, size=samples1)
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hours_offset2 = np.random.normal(loc=self.n_hours2, scale=self.std_hours2, size=samples2)
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# 合并两个分布的偏移
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hours_offset = np.concatenate([hours_offset1, hours_offset2])
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# 将偏移转换为实际时间戳（使用绝对值确保时间点在过去）
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timestamps = [self.base_time - timedelta(hours=abs(offset)) for offset in hours_offset]
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# 按时间排序（从最早到最近）
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return sorted(timestamps)
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def get_timestamp_array(self):
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"""返回时间戳数组"""
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timestamps = self.generate_time_samples()
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return [int(t.timestamp()) for t in timestamps]
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# def print_time_samples(timestamps, show_distribution=True):
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# """打印时间样本和分布信息"""
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# print(f"\n生成的{len(timestamps)}个时间点分布：")
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# print("序号".ljust(5), "时间戳".ljust(25), "距现在（小时）")
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# print("-" * 50)
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# now = datetime.now()
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# time_diffs = []
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# for i, timestamp in enumerate(timestamps, 1):
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# hours_diff = (now - timestamp).total_seconds() / 3600
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# time_diffs.append(hours_diff)
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# print(f"{str(i).ljust(5)} {timestamp.strftime('%Y-%m-%d %H:%M:%S').ljust(25)} {hours_diff:.2f}")
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# # 打印统计信息
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# print("\n统计信息：")
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# print(f"平均时间偏移：{np.mean(time_diffs):.2f}小时")
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# print(f"标准差：{np.std(time_diffs):.2f}小时")
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# print(f"最早时间：{min(timestamps).strftime('%Y-%m-%d %H:%M:%S')} ({max(time_diffs):.2f}小时前)")
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# print(f"最近时间：{max(timestamps).strftime('%Y-%m-%d %H:%M:%S')} ({min(time_diffs):.2f}小时前)")
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# if show_distribution:
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# # 计算时间分布的直方图
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# hist, bins = np.histogram(time_diffs, bins=40)
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# print("\n时间分布（每个*代表一个时间点）：")
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# for i in range(len(hist)):
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# if hist[i] > 0:
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# print(f"{bins[i]:6.1f}-{bins[i + 1]:6.1f}小时: {'*' * int(hist[i])}")
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# # 使用示例
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# if __name__ == "__main__":
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# # 创建一个双峰分布的记忆调度器
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# scheduler = MemoryBuildScheduler(
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# n_hours1=12, # 第一个分布均值（12小时前）
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# std_hours1=8, # 第一个分布标准差
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# weight1=0.7, # 第一个分布权重 70%
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# n_hours2=36, # 第二个分布均值（36小时前）
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# std_hours2=24, # 第二个分布标准差
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# weight2=0.3, # 第二个分布权重 30%
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# total_samples=50, # 总共生成50个时间点
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# )
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# # 生成时间分布
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# timestamps = scheduler.generate_time_samples()
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# # 打印结果，包含分布可视化
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# print_time_samples(timestamps, show_distribution=True)
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# # 打印时间戳数组
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# timestamp_array = scheduler.get_timestamp_array()
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# print("\n时间戳数组（Unix时间戳）：")
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# print("[", end="")
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# for i, ts in enumerate(timestamp_array):
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# if i > 0:
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# print(", ", end="")
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# print(ts, end="")
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# print("]")
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