python/methylnet/models.py
def encode(self, x):
		"""Encode input into latent representation.

		Parameters
		----------
		x : type
			Input methylation data.

		Returns
		-------
		torch.tensor
			Learned mean vector of embeddings.
		torch.tensor
			Learned variance of learned mean embeddings.
		"""
		x = self.encoder(x)
		#print(x.size())
		#x = x.view(x.size(0), -1)
		mean = self.z_mean(x)
		var = self.z_var(x)
		#print('mean',mean.size())
		return mean, var
Encode input into latent representation.

		Parameters
		----------
		x : type
			Input methylation data.

		Returns
		-------
		torch.tensor
			Learned mean vector of embeddings.
		torch.tensor
			Learned variance of learned mean embeddings.
		
Encode input into latent representation.
Parameters

x : type
Input methylation data.

Returns

torch.tensor
Learned mean vector of embeddings.
torch.tensor
Learned variance of learned mean embeddings.
Encode input into latent representation . Parameters x : type Input methylation data . Returns torch . tensor Learned mean vector of embeddings . torch . tensor Learned variance of learned mean embeddings .
def encode(self, x):
		x = self.encoder(x)
		mean = self.z_mean(x)
		var = self.z_var(x)
		return mean, var
def encode ( self , x ) : x = self . encoder ( x ) mean = self . z_mean ( x ) var = self . z_var ( x ) return mean , var
Encode input into latent representation.
Encode input into latent representation .
"""Encode input into latent representation.

		Parameters
		----------
		x : type
			Input methylation data.

		Returns
		-------
		torch.tensor
			Learned mean vector of embeddings.
		torch.tensor
			Learned variance of learned mean embeddings.
		""" #print(x.size()) #x = x.view(x.size(0), -1) #print('mean',mean.size())
self
x