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Functions571 in github.com/DAMO-DI-ML/NeurIPS2022-FiLM

⨍ Functions 571◇ Types & classes 171
↓ 1 callersFunctiontime_features_from_frequency_str
Returns a list of time features that will be appropriate for the given frequency string. Parameters ---------- freq_str Frequ
utils/timefeatures.py:76
↓ 1 callersMethodtrain
(self, setting)
exp/exp_main.py:128
↓ 1 callersMethodtransformer_embedding
Input: batch*seq_len. Output: batch*seq_len*d_model.
models/pyraformer/embed.py:135
↓ 1 callersFunctiontransition
A, B transition matrices for different measures measure: the type of measure legt - Legendre (translated) legs - Legendre (scaled)
layers/S4.py:299
↓ 1 callersFunctionvariable_unroll_matrix_sequential
(A, u, s=None, variable=True)
utils/unroll.py:309
↓ 1 callersFunctionvariable_unroll_toeplitz_sequential
(A, u, s=None, variable=True, pad=False)
utils/unroll.py:323
↓ 1 callersFunctionvisual
Results visualization
utils/tools.py:79
↓ 1 callersMethodwavelet_transform
(self, x)
layers/mwt.py:675
↓ 1 callersMethodwire
(self)
models/reformer_pytorch/recorder.py:20
FunctionCORR
(pred, true)
utils/metrics.py:8
FunctionRSE
(pred, true)
utils/metrics.py:4
Method__call__
(self, val_loss, model, path)
utils/tools.py:38
Method__call__
(self, index: pd.DatetimeIndex)
utils/timefeatures.py:13
Method__call__
(self, index: pd.DatetimeIndex)
utils/timefeatures.py:23
Method__call__
(self, index: pd.DatetimeIndex)
utils/timefeatures.py:30
Method__call__
(self, index: pd.DatetimeIndex)
utils/timefeatures.py:37
Method__call__
(self, index: pd.DatetimeIndex)
utils/timefeatures.py:44
Method__call__
(self, index: pd.DatetimeIndex)
utils/timefeatures.py:51
Method__call__
(self, index: pd.DatetimeIndex)
utils/timefeatures.py:58
Method__call__
(self, index: pd.DatetimeIndex)
utils/timefeatures.py:65
Method__call__
(self, index: pd.DatetimeIndex)
utils/timefeatures.py:72
Method__call__
(self, x, y)
layers/utils.py:388
Method__getitem__
(self, index)
data_provider/data_loader.py:82
Method__getitem__
(self, index)
data_provider/data_loader.py:172
Method__getitem__
(self, index)
data_provider/data_loader.py:271
Method__getitem__
(self, index)
data_provider/data_loader.py:355
Method__getitem__
(self, index)
data_provider/data_loader_mzq.py:80
Method__getitem__
(self, index)
data_provider/data_loader_mzq.py:166
Method__getitem__
(self, index)
data_provider/data_loader_mzq.py:266
Method__getitem__
(self, index)
data_provider/data_loader_mzq.py:360
Method__init__
Slow (n^3, or n^2 if step sizes are cached) version via manual matrix mult/inv delta: optional list of step sizes to cache the transitions f
utils/op.py:148
Method__init__
(self, B, L, device="cpu")
utils/masking.py:6
Method__init__
(self, B, H, L, index, scores, device="cpu")
utils/masking.py:17
Method__init__
(self, B, L,S,device="cpu")
utils/masking.py:30
Method__init__
(self, patience=7, verbose=False, delta=0)
utils/tools.py:29
Method__init__
(self, mean, std)
utils/tools.py:68
Method__init__
(self)
utils/timefeatures.py:10
Method__init__
(self, ich=1,k=8,alpha=16,c=128,nCZ = 1,mask_flag=True, L = 0, base = 'legen
layers/mwt.py:18
Method__init__
(self, ich=1,k=8,alpha=16,c=128,nCZ = 1,mask_flag=True, L = 0, base = 'legen
layers/mwt.py:55
Method__init__
(self, ich=3,k=8,alpha=64,c=128,nCZ = 1,mask_flag=True, L = 0, base = 'legen
layers/mwt.py:93
Method__init__
(self, in_channels, out_channels, seq_len_q, seq_len_kv, modes1=16,activation='tanh')
layers/mwt.py:131
Method__init__
(self, k, alpha, c=1, nl = 1, initializer = None,
layers/mwt.py:258
Method__init__
(self, in_features:int, out_features:int, bias:bool=False, contiguous:bool=True)
layers/mwt.py:297
Method__init__
Initialize sparsemax activation Args: dim (int, optional): The dimension over which to apply the sparsemax function.
layers/mwt.py:335
Method__init__
(self, k, alpha, c=1, nl = 1, initializer = None,
layers/mwt.py:421
Method__init__
(self,alpha, **kwargs)
layers/mwt.py:452
Method__init__
(self, in_layer, out_layer, kernel_size, stride, dilation)
layers/mwt.py:474
Method__init__
(self, in_layer, out_layer)
layers/mwt.py:492
Method__init__
(self, in_layer, out_layer, kernel_size, dilation)
layers/mwt.py:514
Method__init__
(self, input_dim,output_dim,layer_n,kernel_size, depth)
layers/mwt.py:533
Method__init__
(self, k = 3, alpha = 64, L = 0, c = 1, base = 'legendre',
layers/mwt.py:590
Method__init__
(self,in_channels,out_channels,seq_len_q,seq_len_kv,modes1,c=64, k = 8,ich=512,
layers/mwt.py:752
Method__init__
(self, x, eps=0.00001)
layers/utils.py:260
Method__init__
(self, x, eps=0.00001)
layers/utils.py:298
Method__init__
(self, x, low=0.0, high=1.0)
layers/utils.py:324
Method__init__
(self, channels)
layers/Autoformer_EncDec.py:11
Method__init__
(self, kernel_size, stride)
layers/Autoformer_EncDec.py:43
Method__init__
(self, kernel_size)
layers/Autoformer_EncDec.py:64
Method__init__
(self,kernel_size)
layers/Autoformer_EncDec.py:77
Method__init__
(self)
layers/Autoformer_EncDec.py:93
Method__init__
(self, attention, d_model, d_ff=None, moving_avg=25, dropout=0.1, activation="relu")
layers/Autoformer_EncDec.py:105
Method__init__
(self, self_attention, cross_attention, d_model, c_out, d_ff=None, moving_avg=25, dropout=0.1
layers/Autoformer_EncDec.py:167
Method__init__
(self, layers, norm_layer=None, projection=None)
layers/Autoformer_EncDec.py:221
Method__init__
(self, c_in)
layers/AE_EncDec.py:7
Method__init__
(self, attention, d_model, d_ff=None, dropout=0.1, activation="relu")
layers/AE_EncDec.py:28
Method__init__
(self, self_attention, cross_attention, d_model, d_ff=None, dropout=0.1, activation="relu")
layers/AE_EncDec.py:85
Method__init__
(self, layers, norm_layer=None, projection=None)
layers/AE_EncDec.py:123
Method__init__
(self, d_input, d_output, bias=True)
layers/S4.py:153
Method__init__
L: Maximum length; this module computes an SSM kernel of length L w: (N) p: (r, N) low-rank correction to A q: (r, N)
layers/S4.py:497
Method__init__
( self, H, N=64, L=1, measure="legs", rank=1, channels
layers/S4.py:905
Method__init__
( self, dropout = 0., bucket_size = 64, n_hashes = 8,
layers/LSHAttention_reformer.py:114
Method__init__
(self, d_model, max_len=5000)
layers/Embed.py:9
Method__init__
(self, c_in, d_model)
layers/Embed.py:29
Method__init__
(self, c_in, d_model)
layers/Embed.py:44
Method__init__
(self, d_model, embed_type='fixed', freq='h')
layers/Embed.py:64
Method__init__
(self, d_model, embed_type='timeF', freq='h')
layers/Embed.py:94
Method__init__
(self, c_in, d_model, embed_type='fixed', freq='h', dropout=0.1)
layers/Embed.py:121
Method__init__
(self, c_in, d_model, embed_type='fixed', freq='h', dropout=0.1)
layers/Embed.py:133
Method__init__
(self, in_channels, out_channels, seq_len_q, seq_len_kv, modes1=0,policy=0)
layers/FourierCorrelation.py:34
Method__init__
(self, in_channels, out_channels, seq_len_q, seq_len_kv, modes1=0)
layers/FourierCorrelation.py:142
Method__init__
(self, in_channels, out_channels, seq_len, modes1=0)
layers/FourierCorrelation.py:274
Method__init__
(self, in_channels, out_channels, seq_len, modes1=0)
layers/FourierCorrelation.py:362
Method__init__
(self, mask_flag=True, factor=5, scale=None, attention_dropout=0.1, output_attention=False)
layers/SelfAttention_Family.py:49
Method__init__
(self, mask_flag=True, factor=5, scale=None, attention_dropout=0.1, output_attention=False)
layers/SelfAttention_Family.py:77
Method__init__
(self, attention, d_model, n_heads, d_keys=None, d_values=None)
layers/SelfAttention_Family.py:167
Method__init__
(self, c_in)
layers/Transformer_EncDec.py:7
Method__init__
(self, attention, d_model, d_ff=None, dropout=0.1, activation="relu")
layers/Transformer_EncDec.py:28
Method__init__
(self, self_attention, cross_attention, d_model, d_ff=None, dropout=0.1, activation="relu")
layers/Transformer_EncDec.py:82
Method__init__
(self, layers, norm_layer=None, projection=None)
layers/Transformer_EncDec.py:116
Method__init__
(self, correlation, d_model, n_heads, d_keys=None, d_values=None)
layers/AutoCorrelation.py:233
Method__init__
(self, args)
exp/exp_AE.py:26
Method__init__
(self, args)
exp/exp_basic.py:7
Method__init__
(self, args)
exp/exp_main.py:31
Method__init__
(self, root_path, flag='train', size=None, features='S', data_path='ETTh1.csv',
data_provider/data_loader.py:15
Method__init__
(self, root_path, flag='train', size=None, features='S', data_path='ETTm1.csv',
data_provider/data_loader.py:103
Method__init__
(self, root_path, flag='train', size=None, features='S', data_path='ETTh1.csv',
data_provider/data_loader.py:193
Method__init__
(self, root_path, flag='train', size=None, features='S', data_path='sin.csv',
data_provider/data_loader.py:292
Method__init__
(self, root_path, flag='train', size=None, features='S', data_path='ETTh1.csv',
data_provider/data_loader_mzq.py:17
Method__init__
(self, root_path, flag='train', size=None, features='S', data_path='ETTm1.csv',
data_provider/data_loader_mzq.py:103
Method__init__
(self, root_path, flag='train', size=None, features='S', data_path='ETTh1.csv',
data_provider/data_loader_mzq.py:190
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