common/processing.py
def fit(self, dataset):
		"""
		fit the pca to find eigen values and eigen vectors of training set. 

		:param dataset: trainset in shape M (number of features) x N (number of samples)
		:retrun Ex: 	Eigen-Values of PCA 
		:return Vx: 	Eigen-Vectors of PCA 
		:return k:		Optimal Sub-space domain 
		"""
		energy_percentage = 0.97
		Cx = np.dot(dataset, dataset.T)/self.dim 
		Ex, Vx = np.linalg.eigh(Cx)
		Ex = Ex[::-1]
		Vx = Vx[:, ::-1]
		lcumsum = np.cumsum(Ex/np.sum(Ex))
		k = np.argmax(lcumsum > energy_percentage)
		return Ex,Vx,k

		fit the pca to find eigen values and eigen vectors of training set. 

		:param dataset: trainset in shape M (number of features) x N (number of samples)
		:retrun Ex: 	Eigen-Values of PCA 
		:return Vx: 	Eigen-Vectors of PCA 
		:return k:		Optimal Sub-space domain 
		
fit the pca to find eigen values and eigen vectors of training set.
fit the pca to find eigen values and eigen vectors of training set .
def fit(self, dataset):
		energy_percentage = 0.97
		Cx = np.dot(dataset, dataset.T)/self.dim 
		Ex, Vx = np.linalg.eigh(Cx)
		Ex = Ex[::-1]
		Vx = Vx[:, ::-1]
		lcumsum = np.cumsum(Ex/np.sum(Ex))
		k = np.argmax(lcumsum > energy_percentage)
		return Ex,Vx,k
def fit ( self , dataset ) : energy_percentage = 0.97 Cx = np . dot ( dataset , dataset . T ) / self . dim Ex , Vx = np . linalg . eigh ( Cx ) Ex = Ex [ : : - 1 ] Vx = Vx [ : , : : - 1 ] lcumsum = np . cumsum ( Ex / np . sum ( Ex ) ) k = np . argmax ( lcumsum > energy_percentage ) return Ex , Vx , k
fit the pca to find eigen values and eigen vectors of training set.
fit the pca to find eigen values and eigen vectors of training set .
"""
		fit the pca to find eigen values and eigen vectors of training set. 

		:param dataset: trainset in shape M (number of features) x N (number of samples)
		:retrun Ex: 	Eigen-Values of PCA 
		:return Vx: 	Eigen-Vectors of PCA 
		:return k:		Optimal Sub-space domain 
		"""
Eigen-Vectors of PCA
Eigen - Vectors of PCA
Vx
Optimal Sub-space domain
Optimal Sub - space domain
k
self
dataset
trainset in shape M (number of features) x N (number of samples)
trainset in shape M ( number of features ) x N ( number of samples )
retrun
Eigen-Values of PCA
Eigen - Values of PCA