lib/matplotlib/mlab.py
def project(self, x, minfrac=0.):
        'project x onto the principle axes, dropping any axes where fraction of variance<minfrac'
        x = np.asarray(x)

        ndims = len(x.shape)

        if (x.shape[-1]!=self.numcols):
            raise ValueError('Expected an array with dims[-1]==%d'%self.numcols)


        Y = np.dot(self.Wt, self.center(x).T).T
        mask = self.fracs>=minfrac
        if ndims==2:
            Yreduced = Y[:,mask]
        else:
            Yreduced = Y[mask]
        return Yreduced
project x onto the principle axes, dropping any axes where fraction of variance<minfrac
project x onto the principle axes, dropping any axes where fraction of variance<minfrac
project x onto the principle axes dropping any axes where fraction of variance<minfrac
def project(self, x, minfrac=0.):
        x = np.asarray(x)
        ndims = len(x.shape)
        if (x.shape[-1]!=self.numcols):
            raise ValueError('Expected an array with dims[-1]==%d'%self.numcols)
        Y = np.dot(self.Wt, self.center(x).T).T
        mask = self.fracs>=minfrac
        if ndims==2:
            Yreduced = Y[:,mask]
        else:
            Yreduced = Y[mask]
        return Yreduced
def project ( self , x , minfrac = 0. ) : x = np . asarray ( x ) ndims = len ( x . shape ) if ( x . shape [ - 1 ] != self . numcols ) : raise ValueError ( 'Expected an array with dims[-1]==%d' % self . numcols ) Y = np . dot ( self . Wt , self . center ( x ) . T ) . T mask = self . fracs >= minfrac if ndims == 2 : Yreduced = Y [ : , mask ] else : Yreduced = Y [ mask ] return Yreduced
project x onto the principle axes, dropping any axes where fraction of variance<minfrac
project x onto the principle axes dropping any axes where fraction of variance<minfrac
'project x onto the principle axes, dropping any axes where fraction of variance<minfrac'
self
x
minfrac