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import torch
from torch import nn
import numpy as np
import math
from .common import _cal_freq_list
"""
Direct encoding
"""
class Direct(nn.Module):
def __init__(self):
super(Direct, self).__init__()
# adding this class variable is important to determine
# the dimension of the follow-up neural network
self.embedding_dim = 2
def forward(self, coords):
# place lon lat coordinates in a -pi, pi range
coords = torch.deg2rad(coords) - torch.pi
return coords
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