common/processing.py
def pca_reconst(self, data, eigenvector):
		"""
		Returns reconstructed signal based on Eigen Vectors and Compression Factor 

		:param data: 		original data in high space dimension 
		:param eigenvector: eigenvectors to project data from high dimensional to latens space
		:param k: 			latent space
		:return: 			reconstructed signal  
		""" 
		return np.linalg.multi_dot([eigenvector[:, :self.cf], eigenvector[:, :self.cf].T, data])

		Returns reconstructed signal based on Eigen Vectors and Compression Factor 

		:param data: 		original data in high space dimension 
		:param eigenvector: eigenvectors to project data from high dimensional to latens space
		:param k: 			latent space
		:return: 			reconstructed signal  
		
Returns reconstructed signal based on Eigen Vectors and Compression Factor
Returns reconstructed signal based on Eigen Vectors and Compression Factor
def pca_reconst(self, data, eigenvector):
		return np.linalg.multi_dot([eigenvector[:, :self.cf], eigenvector[:, :self.cf].T, data])
def pca_reconst ( self , data , eigenvector ) : return np . linalg . multi_dot ( [ eigenvector [ : , : self . cf ] , eigenvector [ : , : self . cf ] . T , data ] )
Returns reconstructed signal based on Eigen Vectors and Compression Factor
Returns reconstructed signal based on Eigen Vectors and Compression Factor
"""
		Returns reconstructed signal based on Eigen Vectors and Compression Factor 

		:param data: 		original data in high space dimension 
		:param eigenvector: eigenvectors to project data from high dimensional to latens space
		:param k: 			latent space
		:return: 			reconstructed signal  
		"""
None
self
data
original data in high space dimension
original data in high space dimension
eigenvector
eigenvectors to project data from high dimensional to latens space
eigenvectors to project data from high dimensional to latens space
k
None