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ec1b8bb 52796fe ec1b8bb 52796fe | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 | import torch
import torch.nn as nn
class BaseTimeSeriesModel(nn.Module):
def __init__(self):
super(BaseTimeSeriesModel, self).__init__()
def reset_weights(self):
for layer in self.children():
if hasattr(layer, 'reset_parameters'):
layer.reset_parameters()
class RNNModel(BaseTimeSeriesModel):
def __init__(self, input_size, hidden_size=64, num_layers=2, dropout=0.2, output_size=1):
super(RNNModel, self).__init__()
self.rnn = nn.RNN(input_size, hidden_size, num_layers,
batch_first=True, dropout=dropout)
self.fc = nn.Linear(hidden_size, output_size)
def forward(self, x):
out, _ = self.rnn(x)
return self.fc(out[:, -1, :])
class LSTMModel(BaseTimeSeriesModel):
def __init__(self, input_size, hidden_size=64, num_layers=2, dropout=0.2, output_size=1):
super(LSTMModel, self).__init__()
self.lstm = nn.LSTM(input_size, hidden_size, num_layers,
batch_first=True, dropout=dropout)
self.fc = nn.Linear(hidden_size, output_size)
def forward(self, x):
out, _ = self.lstm(x)
return self.fc(out[:, -1, :])
class GRUModel(BaseTimeSeriesModel):
def __init__(self, input_size, hidden_size=64, num_layers=2, dropout=0.2, output_size=1):
super(GRUModel, self).__init__()
self.gru = nn.GRU(input_size, hidden_size, num_layers,
batch_first=True, dropout=dropout)
self.fc = nn.Linear(hidden_size, output_size)
def forward(self, x):
out, _ = self.gru(x)
return self.fc(out[:, -1, :])
class CNNModel(BaseTimeSeriesModel):
def __init__(self, input_size, output_size=1, kernel_size=3, num_filters=32):
super(CNNModel, self).__init__()
self.conv1 = nn.Conv1d(in_channels=input_size, out_channels=num_filters, kernel_size=kernel_size)
self.relu = nn.ReLU()
self.pool = nn.AdaptiveMaxPool1d(1)
self.flatten = nn.Flatten()
self.fc = nn.Linear(num_filters, output_size)
def forward(self, x):
x = x.permute(0, 2, 1)
x = self.relu(self.conv1(x))
x = self.pool(x)
x = self.flatten(x)
return self.fc(x)
class PositionalEncoding(nn.Module):
def __init__(self, d_model, max_len=500):
super(PositionalEncoding, self).__init__()
pe = torch.zeros(max_len, d_model)
position = torch.arange(0, max_len).unsqueeze(1)
div = torch.exp(torch.arange(0, d_model, 2) * (-torch.log(torch.tensor(10000.0)) / d_model))
pe[:, 0::2] = torch.sin(position * div)
pe[:, 1::2] = torch.cos(position * div)
pe = pe.unsqueeze(0)
self.register_buffer('pe', pe)
def forward(self, x):
return x + self.pe[:, :x.size(1)]
class TransformerModel(BaseTimeSeriesModel):
def __init__(self, input_size, d_model=64, nhead=4, num_layers=2, output_size=1, dropout=0.1):
super(TransformerModel, self).__init__()
self.input_proj = nn.Linear(input_size, d_model)
self.pos_encoder = PositionalEncoding(d_model)
encoder_layers = nn.TransformerEncoderLayer(d_model=d_model, nhead=nhead, dropout=dropout)
self.transformer_encoder = nn.TransformerEncoder(encoder_layers, num_layers)
self.decoder = nn.Linear(d_model, output_size)
def forward(self, x):
x = self.input_proj(x)
x = self.pos_encoder(x)
x = self.transformer_encoder(x)
return self.decoder(x[:, -1, :])
class MLPModel(BaseTimeSeriesModel):
def __init__(self, input_size, hidden_sizes=[64, 64], output_size=1, dropout=0.2):
super(MLPModel, self).__init__()
layers = []
prev_size = input_size
for h in hidden_sizes:
layers.append(nn.Linear(prev_size, h))
layers.append(nn.ReLU())
layers.append(nn.Dropout(dropout))
prev_size = h
layers.append(nn.Linear(prev_size, output_size))
self.net = nn.Sequential(*layers)
def forward(self, x):
x = x.reshape(x.size(0), -1)
return self.net(x)
class BiLSTMModel(nn.Module):
def __init__(self, input_size, hidden_size, output_size, num_layers=2, dropout=0.3):
super(BiLSTMModel, self).__init__()
self.hidden_size = hidden_size
self.num_layers = num_layers
self.bilstm = nn.LSTM(input_size, hidden_size, num_layers,
batch_first=True, dropout=dropout, bidirectional=True)
self.fc = nn.Sequential(
nn.Linear(hidden_size * 2, 128),
nn.ReLU(),
nn.Dropout(dropout),
nn.Linear(128, output_size)
)
def forward(self, x):
h0 = torch.zeros(self.num_layers * 2, x.size(0), self.hidden_size).to(x.device)
c0 = torch.zeros(self.num_layers * 2, x.size(0), self.hidden_size).to(x.device)
out, _ = self.bilstm(x, (h0, c0))
out = out[:, -1, :]
return self.fc(out)
class HybridModel(BaseTimeSeriesModel):
def __init__(self, input_size, hidden_size=64, output_size=1, num_layers=1, dropout=0.2):
super(HybridModel, self).__init__()
self.conv1 = nn.Conv1d(in_channels=input_size, out_channels=32, kernel_size=3)
self.relu = nn.ReLU()
self.pool = nn.AdaptiveMaxPool1d(10)
self.bilstm = nn.LSTM(input_size=32, hidden_size=hidden_size,
num_layers=num_layers, batch_first=True, bidirectional=True)
self.fc = nn.Sequential(
nn.Linear(hidden_size * 2, 64),
nn.ReLU(),
nn.Dropout(dropout),
nn.Linear(64, output_size)
)
def forward(self, x):
x = x.permute(0, 2, 1)
x = self.relu(self.conv1(x))
x = self.pool(x)
x = x.permute(0, 2, 1)
out, _ = self.bilstm(x)
out = out[:, -1, :]
return self.fc(out)
class CNN_GRU(BaseTimeSeriesModel):
def __init__(self, input_size=1, hidden_size=64, num_layers=1, dropout=0.2, output_size=1):
super(CNN_GRU, self).__init__()
self.conv1 = nn.Conv1d(in_channels=input_size, out_channels=32, kernel_size=3, padding=1)
self.relu = nn.ReLU()
self.gru = nn.GRU(input_size=32, hidden_size=hidden_size, num_layers=num_layers, batch_first=True, dropout=dropout if num_layers > 1 else 0.0)
self.fc = nn.Linear(hidden_size, output_size)
def forward(self, x):
x = x.transpose(1, 2) # [B, T, C] → [B, C, T]
x = self.relu(self.conv1(x)) # [B, 32, T]
x = x.transpose(1, 2) # [B, T, 32]
out, _ = self.gru(x)
return self.fc(out[:, -1, :]) |