feat: 添加RUL-Mamba模型及相关组件
新增锂电池剩余使用寿命预测模型RUL-Mamba，包含以下主要组件： 1. 添加Mamba模块作为核心时序建模组件 2. 实现特征注意力网络(FAN)和门控残差网络(GRN) 3. 新增数据预处理和归一化层 4. 添加模型训练和评估脚本 5. 补充README文档说明使用方法 6. 添加可视化辅助工具Helper_Plot.py 7. 实现多种时间序列处理层(Embedding、AutoCorrelation等) 8. 添加配置文件requirements.txt 9. 补充测试数据集TJU battery data
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import torch
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import torch.nn as nn
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import numpy as np
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import math
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class PositionalEmbedding(nn.Module):
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def __init__(self, d_model, n_position=1024):
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super(PositionalEmbedding, self).__init__()
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# Not a parameter
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self.register_buffer('pos_table', self._get_sinusoid_encoding_table(n_position, d_model))
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def _get_sinusoid_encoding_table(self, n_position, d_model):
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''' Sinusoid position encoding table '''
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def get_position_angle_vec(position):
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return [position / np.power(10000, 2 * (hid_j // 2) / d_model) for hid_j in range(d_model)]
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sinusoid_table = np.array([get_position_angle_vec(pos_i) for pos_i in range(n_position)])
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sinusoid_table[:, 0::2] = np.sin(sinusoid_table[:, 0::2])
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sinusoid_table[:, 1::2] = np.cos(sinusoid_table[:, 1::2])
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return torch.FloatTensor(sinusoid_table).unsqueeze(0)
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def forward(self, x):
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return self.pos_table[:, :x.size(1)].clone().detach()
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class TokenEmbedding(nn.Module):
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def __init__(self, c_in, d_model):
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super(TokenEmbedding, self).__init__()
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padding = 1 if torch.__version__ >= '1.5.0' else 2
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self.tokenConv = nn.Conv1d(in_channels=c_in, out_channels=d_model,
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kernel_size=3, padding=padding, padding_mode='circular', bias=False)
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for m in self.modules():
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if isinstance(m, nn.Conv1d):
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nn.init.kaiming_normal_(m.weight, mode='fan_in', nonlinearity='leaky_relu')
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def forward(self, x):
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x = self.tokenConv(x.permute(0, 2, 1)).transpose(1, 2)
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return x
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class FixedEmbedding(nn.Module):
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def __init__(self, c_in, d_model):
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super(FixedEmbedding, self).__init__()
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w = torch.zeros(c_in, d_model).float()
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w.require_grad = False
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position = torch.arange(0, c_in).float().unsqueeze(1)
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div_term = (torch.arange(0, d_model, 2).float() * -(math.log(10000.0) / d_model)).exp()
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w[:, 0::2] = torch.sin(position * div_term)
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w[:, 1::2] = torch.cos(position * div_term)
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self.emb = nn.Embedding(c_in, d_model)
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self.emb.weight = nn.Parameter(w, requires_grad=False)
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def forward(self, x):
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return self.emb(x).detach()
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class TemporalEmbedding(nn.Module):
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def __init__(self, d_model, embed_type='fixed', freq='h'):
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super(TemporalEmbedding, self).__init__()
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minute_size = 4
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hour_size = 24
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weekday_size = 7
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day_size = 32
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month_size = 13
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Embed = FixedEmbedding if embed_type == 'fixed' else nn.Embedding
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if freq == 't':
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self.minute_embed = Embed(minute_size, d_model)
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self.hour_embed = Embed(hour_size, d_model)
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self.weekday_embed = Embed(weekday_size, d_model)
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self.day_embed = Embed(day_size, d_model)
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self.month_embed = Embed(month_size, d_model)
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def forward(self, x):
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x = x.long()
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minute_x = self.minute_embed(x[:, :, 4]) if hasattr(self, 'minute_embed') else 0.
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hour_x = self.hour_embed(x[:, :, 3])
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weekday_x = self.weekday_embed(x[:, :, 2])
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day_x = self.day_embed(x[:, :, 1])
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month_x = self.month_embed(x[:, :, 0])
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return hour_x + weekday_x + day_x + month_x + minute_x
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class TimeFeatureEmbedding(nn.Module):
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def __init__(self, d_model, embed_type='timeF', freq='h'):
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super(TimeFeatureEmbedding, self).__init__()
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freq_map = {'h': 4, 't': 5, 's': 6, 'm': 1, 'a': 1, 'w': 2, 'd': 3, 'b': 3}
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d_inp = freq_map[freq]
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self.embed = nn.Linear(d_inp, d_model, bias=False)
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def forward(self, x):
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return self.embed(x)
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class DataEmbedding(nn.Module):
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def __init__(self, c_in, d_model, embed_type='fixed', freq='h', dropout=0.1):
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super(DataEmbedding, self).__init__()
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self.value_embedding = TokenEmbedding(c_in=c_in, d_model=d_model)
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self.position_embedding = PositionalEmbedding(d_model=d_model)
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self.temporal_embedding = TemporalEmbedding(d_model=d_model, embed_type=embed_type,
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freq=freq) if embed_type != 'timeF' else TimeFeatureEmbedding(
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d_model=d_model, embed_type=embed_type, freq=freq)
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self.dropout = nn.Dropout(p=dropout)
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def forward(self, x, x_mark):
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x = self.value_embedding(x) + self.temporal_embedding(x_mark) + self.position_embedding(x)
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return self.dropout(x)
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class DataEmbedding_wo_temp(nn.Module):
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def __init__(self, c_in, d_model, dropout=0.1):
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super(DataEmbedding_wo_temp, self).__init__()
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self.value_embedding = TokenEmbedding(c_in=c_in, d_model=d_model)
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self.position_embedding = PositionalEmbedding(d_model=d_model)
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self.dropout = nn.Dropout(p=dropout)
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def forward(self, x, x_mark=None):
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x = self.value_embedding(x) + self.position_embedding(x)
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return self.dropout(x)
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def PositionalEncoding(q_len, d_model, normalize=True):
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pe = torch.zeros(q_len, d_model)
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position = torch.arange(0, q_len).unsqueeze(1)
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div_term = torch.exp(torch.arange(0, d_model, 2) * -(math.log(10000.0) / d_model))
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pe[:, 0::2] = torch.sin(position * div_term)
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pe[:, 1::2] = torch.cos(position * div_term)
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if normalize:
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pe = pe - pe.mean()
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pe = pe / (pe.std() * 10)
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return pe
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SinCosPosEncoding = PositionalEncoding
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def Coord2dPosEncoding(q_len, d_model, exponential=False, normalize=True, eps=1e-3, verbose=False):
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x = .5 if exponential else 1
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i = 0
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for i in range(100):
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cpe = 2 * (torch.linspace(0, 1, q_len).reshape(-1, 1) ** x) * (torch.linspace(0, 1, d_model).reshape(1, -1) ** x) - 1
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pv(f'{i:4.0f} {x:5.3f} {cpe.mean():+6.3f}', verbose)
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if abs(cpe.mean()) <= eps: break
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elif cpe.mean() > eps: x += .001
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else: x -= .001
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i += 1
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if normalize:
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cpe = cpe - cpe.mean()
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cpe = cpe / (cpe.std() * 10)
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return cpe
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def Coord1dPosEncoding(q_len, exponential=False, normalize=True):
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cpe = (2 * (torch.linspace(0, 1, q_len).reshape(-1, 1)**(.5 if exponential else 1)) - 1)
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if normalize:
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cpe = cpe - cpe.mean()
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cpe = cpe / (cpe.std() * 10)
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return cpe
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def positional_encoding(pe, learn_pe, q_len, d_model):
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# Positional encoding
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if pe == None:
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W_pos = torch.empty((q_len, d_model)) # pe = None and learn_pe = False can be used to measure impact of pe
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nn.init.uniform_(W_pos, -0.02, 0.02)
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learn_pe = False
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elif pe == 'zero':
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W_pos = torch.empty((q_len, 1))
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nn.init.uniform_(W_pos, -0.02, 0.02)
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elif pe == 'zeros':
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W_pos = torch.empty((q_len, d_model))
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nn.init.uniform_(W_pos, -0.02, 0.02)
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elif pe == 'normal' or pe == 'gauss':
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W_pos = torch.zeros((q_len, 1))
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torch.nn.init.normal_(W_pos, mean=0.0, std=0.1)
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elif pe == 'uniform':
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W_pos = torch.zeros((q_len, 1))
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nn.init.uniform_(W_pos, a=0.0, b=0.1)
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elif pe == 'lin1d': W_pos = Coord1dPosEncoding(q_len, exponential=False, normalize=True)
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elif pe == 'exp1d': W_pos = Coord1dPosEncoding(q_len, exponential=True, normalize=True)
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elif pe == 'lin2d': W_pos = Coord2dPosEncoding(q_len, d_model, exponential=False, normalize=True)
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elif pe == 'exp2d': W_pos = Coord2dPosEncoding(q_len, d_model, exponential=True, normalize=True)
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elif pe == 'sincos': W_pos = PositionalEncoding(q_len, d_model, normalize=True)
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else: raise ValueError(f"{pe} is not a valid pe (positional encoder. Available types: 'gauss'=='normal', \
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'zeros', 'zero', uniform', 'lin1d', 'exp1d', 'lin2d', 'exp2d', 'sincos', None.)")
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return nn.Parameter(W_pos, requires_grad=learn_pe)
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