114 lines
2.6 KiB
Python
114 lines
2.6 KiB
Python
from scipy import apply_along_axis,newaxis,zeros,\
|
|
median,round_,nonzero,dot,argmax,any,sqrt,ndarray,\
|
|
trace,zeros_like,sign,sort,real,argsort,rand,array,\
|
|
matrix
|
|
from scipy.linalg import norm,svd,inv,eig
|
|
from scipy.stats import median,mean
|
|
|
|
def normalise(a,axis=0,return_scales=False):
|
|
s = apply_along_axis(norm,axis,a)
|
|
if axis==0:
|
|
s = s[newaxis]
|
|
else:
|
|
s = s[:,newaxis]
|
|
|
|
a_s = a/s
|
|
if return_scales:
|
|
return a_s,s
|
|
return a_s
|
|
|
|
def sub2ind(shape,i,j):
|
|
"""Indices from subscripts. Only support for 2d"""
|
|
row,col = shape
|
|
ind = []
|
|
for k in xrange(len(i)):
|
|
for m in xrange(len(j)):
|
|
ind.append(i[k]*col + j[m])
|
|
return ind
|
|
|
|
|
|
def sorted_eig(a, b=None,sort_by='sm'):
|
|
"""
|
|
Just eig with real part of output sorted:
|
|
This is for convenience only, not general!
|
|
|
|
sort_by='sm': return the eigenvectors by eigenvalues
|
|
of smallest magnitude first. (default)
|
|
'lm': returns largest eigenvalues first
|
|
|
|
output: just as eig with 2 outputs
|
|
-- s,v (eigvals,eigenvectors)
|
|
(This is reversed output compared to matlab)
|
|
|
|
"""
|
|
s,v = eig(a,b)
|
|
s = real(s) # dont expect any imaginary part
|
|
v = real(v)
|
|
ind = argsort(s)
|
|
if sort_by=='lm':
|
|
ind = ind[::-1]
|
|
v = v.take(ind,1)
|
|
s = s.take(ind)
|
|
|
|
return s,v
|
|
|
|
def str2num(string_number):
|
|
"""Convert input (string number) into number, if float(string_number) fails, a nan is inserted.
|
|
"""
|
|
missings = ['','nan','NaN','NA']
|
|
try:
|
|
num = float(string_number)
|
|
except:
|
|
if string_number in missings:
|
|
num = nan
|
|
else:
|
|
print "Found strange entry: %s" %string_number
|
|
raise
|
|
return num
|
|
|
|
def randperm(n):
|
|
r=rand(n)
|
|
dict={}
|
|
for i in range(n):
|
|
dict[r[i]]=i
|
|
r=sort(r)
|
|
out=zeros(n)
|
|
for i in range(n):
|
|
out[i]=dict[r[i]]
|
|
return array(out,dtype='i')
|
|
|
|
def mat_center(X,axis=0,ret_mn=False):
|
|
"""Mean center matrix along axis.
|
|
|
|
X -- matrix, data
|
|
axis -- dim,
|
|
ret_mn -- bool, return mean
|
|
|
|
output:
|
|
Xc, [mnX]
|
|
|
|
NB: axis = 1 is column-centering, axis=0=row-centering
|
|
default is row centering (axis=0)
|
|
"""
|
|
|
|
try:
|
|
rows,cols = X.shape
|
|
except ValueError:
|
|
print "The X data needs to be two-dimensional"
|
|
|
|
if axis==0:
|
|
mnX = mean(X,axis)[newaxis]
|
|
Xs = X - mnX
|
|
|
|
elif axis==1:
|
|
mnX = mean(X,axis)[newaxis]
|
|
Xs = (X.T - mnX).T
|
|
if ret_mn:
|
|
return Xs,mnX
|
|
else:
|
|
return Xs
|
|
|
|
def m_shape(array):
|
|
"""Returns the array shape on the form of a numpy.matrix."""
|
|
return matrix(array).shape
|