def eval_sections_independent( values, nsec=10 ):
et = ml_tools.eval_tools()
a,p,v = values
s = np.sqrt(v)
inds = np.argsort( s )
chunks = chunk_it( inds, nsec )
fraction = []
err = []
means = []
for i,c in enumerate( chunks ):
ca = a[ c ]
cp = p[ c ]
cs = s[ c ]
fraction.append( float(i+1)/nsec )
means.append( np.mean(cs) )
err.append( np.sqrt( et.mean_square_error(ca,cp) ) )
return np.array( fraction ), np.array( means ), np.array( err )
def eval_sections_overall( values, nsec=10 ):
et = ml_tools.eval_tools()
a,p,v = values
s = np.sqrt(v)
inds = np.argsort( s )
chunks = chunk_it( inds, nsec )
fraction = []
err = []
means = []
for i in range( len(chunks) ):
sub_chunks = chunks[:i+1]
selection = np.concatenate( sub_chunks )
sa = a[ selection ]
sp = p[ selection ]
ss = s[ selection ]
fraction.append( float(i+1)/nsec )
means.append( np.mean(ss) )
err.append( np.sqrt( et.mean_square_error(sa,sp) ) )
return np.array( fraction ), np.array( means ), np.array( err )
