multiple_futures_prediction/my_utils.py [44:53]:
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  x_mean = pred[:,:,0] 
  y_mean = pred[:,:,1]
  x_sigma = pred[:,:,2]
  y_sigma = pred[:,:,3] 
  rho = pred[:,:,4]
  ohr = torch.pow(1-torch.pow(rho,2),-0.5) # type: ignore
  x = data[:,:, 0]; y = data[:,:, 1]
  results = torch.pow(ohr, 2)*(torch.pow(x_sigma, 2)*torch.pow(x-x_mean, 2) + torch.pow(y_sigma, 2)*torch.pow(y-y_mean, 2) 
            -2*rho*torch.pow(x_sigma, 1)*torch.pow(y_sigma, 1)*(x-x_mean)*(y-y_mean)) - torch.log(x_sigma*y_sigma*ohr)
  results = results*mask[:,:,0] # nSteps by nBatch
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multiple_futures_prediction/my_utils.py [58:67]:
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  x_mean = pred[:,:,0]
  y_mean = pred[:,:,1]
  x_sigma = pred[:,:,2]
  y_sigma = pred[:,:,3]
  rho = pred[:,:,4]
  ohr = torch.pow(1-torch.pow(rho,2),-0.5) # type: ignore
  x = data[:,:, 0]; y = data[:,:, 1]
  results = torch.pow(ohr, 2)*(torch.pow(x_sigma, 2)*torch.pow(x-x_mean, 2) + torch.pow(y_sigma, 2)*torch.pow(y-y_mean, 2) 
            -2*rho*torch.pow(x_sigma, 1)*torch.pow(y_sigma, 1)*(x-x_mean)*(y-y_mean)) - torch.log(x_sigma*y_sigma*ohr)
  results = results*mask[:,:,0] # nSteps by nBatch
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