models.py
def decision_function(self, x):
    '''
    Computes a matrix of real-valued scores reflecting the strength 
    of belief that an instance is in a particular class.
    
    Params:
      x - input features
    
    Returns:
      a real-valued matrix of dimension (# instances) x (# classes)
    '''
    zdv = self.lm.decision_function(x)
    return self.pca.inverse_transform(zdv)

    Computes a matrix of real-valued scores reflecting the strength 
    of belief that an instance is in a particular class.
    
    Params:
      x - input features
    
    Returns:
      a real-valued matrix of dimension (# instances) x (# classes)
    
Computes a matrix of real-valued scores reflecting the strength
of belief that an instance is in a particular class.

input features

a real-valued matrix of dimension (# instances) x (# classes)
Computes a matrix of real - valued scores reflecting the strength of belief that an instance is in a particular class . input features a real - valued matrix of dimension ( # instances ) x ( # classes )
def decision_function(self, x):
    zdv = self.lm.decision_function(x)
    return self.pca.inverse_transform(zdv)
def decision_function ( self , x ) : zdv = self . lm . decision_function ( x ) return self . pca . inverse_transform ( zdv )
Computes a matrix of real-valued scores reflecting the strength
of belief that an instance is in a particular class.
Computes a matrix of real - valued scores reflecting the strength of belief that an instance is in a particular class .
'''
    Computes a matrix of real-valued scores reflecting the strength 
    of belief that an instance is in a particular class.
    
    Params:
      x - input features
    
    Returns:
      a real-valued matrix of dimension (# instances) x (# classes)
    '''
self
x