764 lines
21 KiB
Python
764 lines
21 KiB
Python
"""Module contain algorithms for low-rank models.
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There is almost no typechecking of any kind here, just focus on speed
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"""
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import math
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from scipy.linalg import svd,inv
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from scipy import dot,empty,eye,newaxis,zeros,sqrt,diag,\
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apply_along_axis,mean,ones,randn,empty_like,outer,r_,c_,\
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rand,sum,cumsum,matrix, expand_dims,minimum,where,arange
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has_sym=True
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try:
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from symeig import symeig
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except:
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has_sym = False
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def pca(a, aopt,scale='scores',mode='normal',center_axis=0):
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""" Principal Component Analysis.
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Performs PCA on given matrix and returns results in a dictionary.
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:Parameters:
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a : array
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Data measurement matrix, (samples x variables)
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aopt : int
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Number of components to use, aopt<=min(samples, variables)
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:Returns:
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results : dict
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keys -- values, T -- scores, P -- loadings, E -- residuals,
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lev --leverages, ssq -- sum of squares, expvar -- cumulative
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explained variance, aopt -- number of components used
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:OtherParameters:
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mode : str
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Amount of info retained, ('fast', 'normal', 'detailed')
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center_axis : int
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Center along given axis. If neg.: no centering (-inf,..., matrix modes)
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:SeeAlso:
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- pcr : other blm
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- pls : other blm
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- lpls : other blm
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Notes
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-----
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Uses kernel speed-up if m>>n or m<<n.
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If residuals turn rank deficient, a lower number of component than given
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in input will be used. The number of components used is given in
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results-dict.
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Examples
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--------
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>>> import scipy,engines
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>>> a=scipy.asarray([[1,2,3],[2,4,5]])
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>>> dat=engines.pca(a, 2)
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>>> dat['expvarx']
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array([0.,99.8561562, 100.])
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"""
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m, n = a.shape
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assert(aopt<=min(m,n))
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if center_axis>=0:
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a = a - expand_dims(a.mean(center_axis), center_axis)
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if m>(n+100) or n>(m+100):
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u, s, v = esvd(a, amax=None) # fixme:amax option need to work with expl.var
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print s[:10]
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else:
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u, s, vt = svd(a, 0)
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v = vt.T
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e = s**2
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tol = 1e-10
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eff_rank = sum(s>s[0]*tol)
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aopt = minimum(aopt, eff_rank)
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T = u*s
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s = s[:aopt]
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T = T[:,:aopt]
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P = v[:,:aopt]
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if scale=='loads':
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T = T/s
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P = P*s
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if mode == 'fast':
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return {'T':T, 'P':P, 'aopt':aopt}
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if mode=='detailed':
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E = empty((aopt, m, n))
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ssq = []
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lev = []
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for ai in range(aopt):
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E[ai,:,:] = a - dot(T[:,:ai+1], P[:,:ai+1].T)
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ssq.append([(E[ai,:,:]**2).sum(0), (E[ai,:,:]**2).sum(1)])
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if scale=='loads':
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lev.append([((s*T)**2).sum(1), (P**2).sum(1)])
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else:
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lev.append([(T**2).sum(1), ((s*P)**2).sum(1)])
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else:
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# residuals
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E = a - dot(T, P.T)
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SEP = E**2
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ssq = [SEP.sum(0), SEP.sum(1)]
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# leverages
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if scale=='loads':
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lev = [(1./m)+(T**2).sum(1), (1./n)+((P/s)**2).sum(1)]
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else:
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lev = [(1./m)+((T/s)**2).sum(1), (1./n)+(P**2).sum(1)]
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# variances
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expvarx = r_[0, 100*e.cumsum()/e.sum()][:aopt+1]
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return {'T':T, 'P':P, 'E':E, 'expvarx':expvarx, 'levx':lev, 'ssqx':ssq, 'aopt':aopt}
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def pcr(a, b, aopt, scale='scores',mode='normal',center_axis=0):
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""" Principal Component Regression.
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Performs PCR on given matrix and returns results in a dictionary.
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:Parameters:
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a : array
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Data measurement matrix, (samples x variables)
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b : array
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Data response matrix, (samples x responses)
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aopt : int
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Number of components to use, aopt<=min(samples, variables)
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:Returns:
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results : dict
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keys -- values, T -- scores, P -- loadings, E -- residuals,
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levx -- leverages, ssqx -- sum of squares, expvarx -- cumulative
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explained variance, aopt -- number of components used
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:OtherParameters:
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mode : str
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Amount of info retained, ('fast', 'normal', 'detailed')
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center_axis : int
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Center along given axis. If neg.: no centering (-inf,..., matrix modes)
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:SeeAlso:
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- pca : other blm
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- pls : other blm
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- lpls : other blm
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Notes
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-----
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Uses kernel speed-up if m>>n or m<<n.
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If residuals turn rank deficient, a lower number of component than given
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in input will be used. The number of components used is given in results-dict.
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Examples
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--------
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>>> import scipy,engines
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>>> a=scipy.asarray([[1,2,3],[2,4,5]])
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>>> b=scipy.asarray([[1,1],[2,3]])
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>>> dat=engines.pcr(a, 2)
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>>> dat['expvarx']
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array([0.,99.8561562, 100.])
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"""
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k, l = m_shape(b)
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if center_axis>=0:
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b = b - expand_dims(b.mean(center_axis), center_axis)
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dat = pca(a, aopt=aopt, scale=scale, mode=mode, center_axis=center_axis)
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T = dat['T']
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weights = apply_along_axis(vnorm, 0, T)**2
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if scale=='loads':
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Q = dot(b.T, T*weights)
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else:
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Q = dot(b.T, T/weights)
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if mode=='fast':
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dat.update({'Q':Q})
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return dat
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if mode=='detailed':
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F = empty((aopt, k, l))
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for i in range(aopt):
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F[i,:,:] = b - dot(T[:,:i+1], Q[:,:i+1].T)
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else:
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F = b - dot(T, Q.T)
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expvary = r_[0, 100*((T**2).sum(0)*(Q**2).sum(0)/(b**2).sum()).cumsum()[:aopt]]
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#fixme: Y-var leverages
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dat.update({'Q':Q, 'F':F, 'expvary':expvary})
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return dat
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def pls(a, b, aopt=2, scale='scores', mode='normal', center_axis=-1, ab=None):
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"""Partial Least Squares Regression.
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Performs PLS on given matrix and returns results in a dictionary.
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:Parameters:
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a : array
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Data measurement matrix, (samples x variables)
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b : array
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Data response matrix, (samples x responses)
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aopt : int
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Number of components to use, aopt<=min(samples, variables)
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:Returns:
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results : dict
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keys -- values, T -- scores, P -- loadings, E -- residuals,
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levx -- leverages, ssqx -- sum of squares, expvarx -- cumulative
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explained variance of descriptors, expvary -- cumulative explained
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variance of responses, aopt -- number of components used
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:OtherParameters:
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mode : str
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Amount of info retained, ('fast', 'normal', 'detailed')
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center_axis : int
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Center along given axis. If neg.: no centering (-inf,..., matrix modes)
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:SeeAlso:
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- pca : other blm
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- pcr : other blm
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- lpls : other blm
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Notes
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-----
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Uses kernel speed-up if m>>n or m<<n.
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If residuals turn rank deficient, a lower number of component than given
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in input will be used. The number of components used is given in results-dict.
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Examples
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--------
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>>> import scipy,engines
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>>> a=scipy.asarray([[1,2,3],[2,4,5]])
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>>> b=scipy.asarray([[1,1],[2,3]])
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>>> dat=engines.pls(a, b, 2)
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>>> dat['expvarx']
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array([0.,99.8561562, 100.])
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"""
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m, n = m_shape(a)
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if ab!=None:
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mm, l = m_shape(ab)
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assert(m==mm)
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else:
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k, l = m_shape(b)
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if center_axis>=0:
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a = a - expand_dims(a.mean(center_axis), center_axis)
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b = b - expand_dims(b.mean(center_axis), center_axis)
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W = empty((n, aopt))
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P = empty((n, aopt))
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R = empty((n, aopt))
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Q = empty((l, aopt))
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T = empty((m, aopt))
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B = empty((aopt, n, l))
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if ab==None:
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ab = dot(a.T, b)
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for i in range(aopt):
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if ab.shape[1]==1: #pls 1
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w = ab.reshape(n, l)
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w = w/vnorm(w)
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elif n<l: # more yvars than xvars
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if has_sym:
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s, u = symeig(dot(ab, ab.T),range=[l,l],overwrite=True)
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else:
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u, s, vh = svd(dot(ab, ab.T))
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w = u[:,0]
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else: # standard wide xdata
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if has_sym:
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s, q = symeig(dot(ab.T, ab),range=[l,l],overwrite=True)
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else:
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q, s, vh = svd(dot(ab.T, ab))
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q = q[:,:1]
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w = dot(ab, q)
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w = w/vnorm(w)
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r = w.copy()
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if i>0:
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for j in range(0, i, 1):
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r = r - dot(P[:,j].T, w)*R[:,j][:,newaxis]
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print vnorm(r)
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t = dot(a, r)
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tt = vnorm(t)**2
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p = dot(a.T, t)/tt
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q = dot(r.T, ab).T/tt
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ab = ab - dot(p, q.T)*tt
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T[:,i] = t.ravel()
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W[:,i] = w.ravel()
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if mode=='fast' and i==aopt-1:
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if scale=='loads':
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tnorm = apply_along_axis(vnorm, 0, T)
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T = T/tnorm
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W = W*tnorm
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return {'T':T, 'W':W}
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P[:,i] = p.ravel()
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R[:,i] = r.ravel()
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Q[:,i] = q.ravel()
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B[i] = dot(R[:,:i+1], Q[:,:i+1].T)
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if mode=='detailed':
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E = empty((aopt, m, n))
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F = empty((aopt, k, l))
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for i in range(1, aopt+1, 1):
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E[i-1] = a - dot(T[:,:i], P[:,:i].T)
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F[i-1] = b - dot(T[:,:i], Q[:,:i].T)
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else:
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E = a - dot(T[:,:aopt], P[:,:aopt].T)
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F = b - dot(T[:,:aopt], Q[:,:aopt].T)
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if scale=='loads':
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tnorm = apply_along_axis(vnorm, 0, T)
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T = T/tnorm
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W = W*tnorm
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Q = Q*tnorm
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P = P*tnorm
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return {'B':B, 'Q':Q, 'P':P, 'T':T, 'W':W, 'R':R, 'E':E, 'F':F}
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def w_simpls(aat, b, aopt):
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""" Simpls for wide matrices.
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Fast pls for crossval, used in calc rmsep for wide X
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There is no P or W. T is normalised
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"""
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bb = b.copy()
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m, m = aat.shape
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U = empty((m, aopt)) # W
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T = empty((m, aopt))
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H = empty((m, aopt)) # R
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PROJ = empty((m, aopt)) # P?
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for i in range(aopt):
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q, s, vh = svd(dot(dot(b.T, aat), b), full_matrices=0)
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u = dot(b, q[:,:1]) #y-factor scores
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U[:,i] = u.ravel()
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t = dot(aat, u)
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t = t/vnorm(t)
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T[:,i] = t.ravel()
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h = dot(aat, t) #score-weights
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H[:,i] = h.ravel()
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PROJ[:,:i+1] = dot(T[:,:i+1], inv(dot(T[:,:i+1].T, H[:,:i+1])) )
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if i<aopt:
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b = b - dot(PROJ[:,:i+1], dot(H[:,:i+1].T,b) )
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C = dot(bb.T, T)
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return {'T':T, 'U':U, 'Q':C, 'H':H}
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def w_pls(aat, b, aopt):
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""" Pls for wide matrices.
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Fast pls for crossval, used in calc rmsep for wide X
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There is no P or W. T is normalised
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aat = centered kernel matrix
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b = centered y
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"""
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bb = b.copy()
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k, l = m_shape(b)
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m, m = m_shape(aat)
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U = empty((m, aopt)) # W
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T = empty((m, aopt))
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R = empty((m, aopt)) # R
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PROJ = empty((m, aopt)) # P?
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for i in range(aopt):
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if has_sym:
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s, q = symeig(dot(dot(b.T, aat), b), range=(l,l),overwrite=True)
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else:
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q, s, vh = svd(dot(dot(b.T, aat), b), full_matrices=0)
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q = q[:,:1]
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u = dot(b , q) #y-factor scores
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U[:,i] = u.ravel()
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t = dot(aat, u)
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print "Norm of t: %s" %vnorm(t)
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print "s: %s" %s
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t = t/vnorm(t)
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T[:,i] = t.ravel()
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r = dot(aat, t)#score-weights
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#r = r/vnorm(r)
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print "Norm R: %s" %vnorm(r)
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R[:,i] = r.ravel()
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PROJ[:,: i+1] = dot(T[:,:i+1], inv(dot(T[:,:i+1].T, R[:,:i+1])) )
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if i<aopt:
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b = b - dot(PROJ[:,:i+1], dot(R[:,:i+1].T, b) )
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C = dot(bb.T, T)
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return {'T':T, 'U':U, 'Q':C, 'R':R}
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def bridge(a, b, aopt, scale='scores', mode='normal', r=0):
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"""Undeflated Ridged svd(X'Y)
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"""
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m, n = m_shape(a)
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k, l = m_shape(b)
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u, s, vt = svd(b, full_matrices=0)
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g0 = dot(u*s, u.T)
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g = (1 - r)*g0 + r*eye(m)
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ag = dot(a.T, g)
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u, s, vt = svd(ag, full_matrices=0)
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W = u[:,:aopt]
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K = vt[:aopt,:].T
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T = dot(a, W)
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tnorm = apply_along_axis(vnorm, 0, T) # norm of T-columns
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if mode == 'fast':
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if scale=='loads':
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T = T/tnorm
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W = W*tnorm
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return {'T':T, 'W':W}
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U = dot(g0, K) #fixme check this
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Q = dot(b.T, dot(T, inv(dot(T.T, T)) ))
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B = zeros((aopt, n, l), dtype='f')
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for i in range(aopt):
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B[i] = dot(W[:,:i+1], Q[:,:i+1].T)
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if mode == 'detailed':
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E = empty((aopt, m, n))
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F = empty((aopt, k, l))
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for i in range(aopt):
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E[i] = a - dot(T[:,:i+1], W[:,:i+1].T)
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F[i] = b - dot(a, B[i])
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else: #normal
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F = b - dot(a, B[-1])
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E = a - dot(T, W.T)
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# leverages
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# fixme: probably need an orthogonal basis for row-space leverage
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# T (scores) are not orthogonal
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# Using a qr decomp to get an orthonormal basis for row-space
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#Tq = qr(T)[0]
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#s_lev,v_lev = leverage(aopt,Tq,W)
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# explained variance
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#var_x, exp_var_x = variances(a,T,W)
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#qnorm = apply_along_axis(norm, 0, Q)
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#var_y, exp_var_y = variances(b,U,Q/qnorm)
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if scale=='loads':
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T = T/tnorm
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W = W*tnorm
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Q = Q*tnorm
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return {'B':B, 'W':W, 'T':T, 'Q':Q, 'E':E, 'F':F, 'U':U, 'P':W}
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def nipals_lpls(X, Y, Z, a_max, alpha=.7, mean_ctr=[2, 0, 1], mode='normal', scale='scores', verbose=False):
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""" L-shaped Partial Least Sqaures Regression by the nipals algorithm.
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(X!Z)->Y
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:input:
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X : data matrix (m, n)
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Y : data matrix (m, l)
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Z : data matrix (n, o)
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:output:
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T : X-scores
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W : X-weights/Z-weights
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P : X-loadings
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Q : Y-loadings
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U : X-Y relation
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L : Z-scores
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K : Z-loads
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B : Regression coefficients X->Y
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b0: Regression coefficient intercept
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evx : X-explained variance
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evy : Y-explained variance
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evz : Z-explained variance
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:Notes:
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"""
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if mean_ctr!=None:
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xctr, yctr, zctr = mean_ctr
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X, mnX = center(X, xctr)
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Y, mnY = center(Y, xctr)
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Z, mnZ = center(Z, zctr)
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varX = pow(X, 2).sum()
|
|
varY = pow(Y, 2).sum()
|
|
varZ = pow(Z, 2).sum()
|
|
|
|
m, n = X.shape
|
|
k, l = Y.shape
|
|
u, o = Z.shape
|
|
|
|
# initialize
|
|
U = empty((k, a_max))
|
|
Q = empty((l, a_max))
|
|
T = empty((m, a_max))
|
|
W = empty((n, a_max))
|
|
P = empty((n, a_max))
|
|
K = empty((o, a_max))
|
|
L = empty((u, a_max))
|
|
B = empty((a_max, n, l))
|
|
b0 = empty((a_max, m, l))
|
|
var_x = empty((a_max,))
|
|
var_y = empty((a_max,))
|
|
var_z = empty((a_max,))
|
|
|
|
for a in range(a_max):
|
|
if verbose:
|
|
print "\n Working on comp. %s" %a
|
|
u = Y[:,:1]
|
|
diff = 1
|
|
MAX_ITER = 200
|
|
lim = 1e-16
|
|
niter = 0
|
|
while (diff>lim and niter<MAX_ITER):
|
|
niter += 1
|
|
u1 = u.copy()
|
|
w = dot(X.T, u)
|
|
w = w/sqrt(dot(w.T, w))
|
|
l = dot(Z, w)
|
|
k = dot(Z.T, l)
|
|
k = k/sqrt(dot(k.T, k))
|
|
w = alpha*k + (1-alpha)*w
|
|
w = w/sqrt(dot(w.T, w))
|
|
t = dot(X, w)
|
|
c = dot(Y.T, t)
|
|
c = c/sqrt(dot(c.T, c))
|
|
u = dot(Y, c)
|
|
diff = abs(u1 - u).max()
|
|
if verbose:
|
|
print "Converged after %s iterations" %niter
|
|
tt = dot(t.T, t)
|
|
p = dot(X.T, t)/tt
|
|
q = dot(Y.T, t)/tt
|
|
l = dot(Z, w)
|
|
|
|
U[:,a] = u.ravel()
|
|
W[:,a] = w.ravel()
|
|
P[:,a] = p.ravel()
|
|
T[:,a] = t.ravel()
|
|
Q[:,a] = q.ravel()
|
|
L[:,a] = l.ravel()
|
|
K[:,a] = k.ravel()
|
|
|
|
X = X - dot(t, p.T)
|
|
Y = Y - dot(t, q.T)
|
|
Z = (Z.T - dot(w, l.T)).T
|
|
|
|
var_x[a] = pow(X, 2).sum()
|
|
var_y[a] = pow(Y, 2).sum()
|
|
var_z[a] = pow(Z, 2).sum()
|
|
|
|
B[a] = dot(dot(W[:,:a+1], inv(dot(P[:,:a+1].T, W[:,:a+1]))), Q[:,:a+1].T)
|
|
b0[a] = mnY - dot(mnX, B[a])
|
|
|
|
# variance explained
|
|
evx = 100.0*(1 - var_x/varX)
|
|
evy = 100.0*(1 - var_y/varY)
|
|
evz = 100.0*(1 - var_z/varZ)
|
|
if scale=='loads':
|
|
tnorm = apply_along_axis(vnorm, 0, T)
|
|
T = T/tnorm
|
|
W = W*tnorm
|
|
Q = Q*tnorm
|
|
knorm = apply_along_axis(vnorm, 0, K)
|
|
L = L*knorm
|
|
K = K/knorm
|
|
|
|
return {'T':T, 'W':W, 'P':P, 'Q':Q, 'U':U, 'L':L, 'K':K, 'B':B, 'b0':b0, 'evx':evx, 'evy':evy, 'evz':evz}
|
|
|
|
|
|
|
|
def nipals_pls(X, Y, a_max, alpha=.7, ax_center=0, mode='normal', scale='scores', verbose=False):
|
|
"""Partial Least Sqaures Regression by the nipals algorithm.
|
|
|
|
(X!Z)->Y
|
|
:input:
|
|
X : data matrix (m, n)
|
|
Y : data matrix (m, l)
|
|
|
|
:output:
|
|
T : X-scores
|
|
W : X-weights/Z-weights
|
|
P : X-loadings
|
|
Q : Y-loadings
|
|
U : X-Y relation
|
|
B : Regression coefficients X->Y
|
|
b0: Regression coefficient intercept
|
|
evx : X-explained variance
|
|
evy : Y-explained variance
|
|
evz : Z-explained variance
|
|
|
|
:Notes:
|
|
|
|
"""
|
|
if ax_center>=0:
|
|
mn_x = expand_dims(X.mean(ax_center), ax_center)
|
|
mn_y = expand_dims(Y.mean(ax_center), ax_center)
|
|
X = X - mn_x
|
|
Y = Y - mn_y
|
|
|
|
varX = pow(X, 2).sum()
|
|
varY = pow(Y, 2).sum()
|
|
|
|
m, n = X.shape
|
|
k, l = Y.shape
|
|
|
|
# initialize
|
|
U = empty((k, a_max))
|
|
Q = empty((l, a_max))
|
|
T = empty((m, a_max))
|
|
W = empty((n, a_max))
|
|
P = empty((n, a_max))
|
|
B = empty((a_max, n, l))
|
|
b0 = empty((a_max, m, l))
|
|
var_x = empty((a_max,))
|
|
var_y = empty((a_max,))
|
|
|
|
t1 = X[:,:1]
|
|
for a in range(a_max):
|
|
if verbose:
|
|
print "\n Working on comp. %s" %a
|
|
u = Y[:,:1]
|
|
diff = 1
|
|
MAX_ITER = 100
|
|
lim = 1e-16
|
|
niter = 0
|
|
while (diff>lim and niter<MAX_ITER):
|
|
niter += 1
|
|
#u1 = u.copy()
|
|
w = dot(X.T, u)
|
|
w = w/sqrt(dot(w.T, w))
|
|
#l = dot(Z, w)
|
|
#k = dot(Z.T, l)
|
|
#k = k/sqrt(dot(k.T, k))
|
|
#w = alpha*k + (1-alpha)*w
|
|
#w = w/sqrt(dot(w.T, w))
|
|
t = dot(X, w)
|
|
q = dot(Y.T, t)
|
|
q = q/sqrt(dot(q.T, q))
|
|
u = dot(Y, q)
|
|
diff = vnorm(t1 - t)
|
|
t1 = t.copy()
|
|
if verbose:
|
|
print "Converged after %s iterations" %niter
|
|
#tt = dot(t.T, t)
|
|
#p = dot(X.T, t)/tt
|
|
#q = dot(Y.T, t)/tt
|
|
#l = dot(Z, w)
|
|
p = dot(X.T, t)/dot(t.T, t)
|
|
p_norm = vnorm(p)
|
|
t = t*p_norm
|
|
w = w*p_norm
|
|
p = p/p_norm
|
|
|
|
U[:,a] = u.ravel()
|
|
W[:,a] = w.ravel()
|
|
P[:,a] = p.ravel()
|
|
T[:,a] = t.ravel()
|
|
Q[:,a] = q.ravel()
|
|
|
|
X = X - dot(t, p.T)
|
|
Y = Y - dot(t, q.T)
|
|
|
|
var_x[a] = pow(X, 2).sum()
|
|
var_y[a] = pow(Y, 2).sum()
|
|
|
|
B[a] = dot(dot(W[:,:a+1], inv(dot(P[:,:a+1].T, W[:,:a+1]))), Q[:,:a+1].T)
|
|
b0[a] = mn_y - dot(mn_x, B[a])
|
|
|
|
# variance explained
|
|
evx = 100.0*(1 - var_x/varX)
|
|
evy = 100.0*(1 - var_y/varY)
|
|
|
|
if scale=='loads':
|
|
tnorm = apply_along_axis(vnorm, 0, T)
|
|
T = T/tnorm
|
|
W = W*tnorm
|
|
Q = Q*tnorm
|
|
|
|
return {'T':T, 'W':W, 'P':P, 'Q':Q, 'U':U, 'B':B, 'b0':b0, 'evx':evx, 'evy':evy}
|
|
|
|
|
|
########### Helper routines #########
|
|
|
|
def m_shape(array):
|
|
return matrix(array).shape
|
|
|
|
def esvd(data, amax=None):
|
|
"""SVD with the option of economy sized calculation
|
|
Calculate subspaces of X'X or XX' depending on the shape
|
|
of the matrix.
|
|
|
|
Good for extreme fat or thin matrices
|
|
|
|
:notes:
|
|
Numpy supports this by setting full_matrices=0
|
|
"""
|
|
m, n = data.shape
|
|
if m>=n:
|
|
kernel = dot(data.T, data)
|
|
if has_sym:
|
|
if amax==None:
|
|
amax = n
|
|
pcrange = None
|
|
else:
|
|
pcrange = [n-amax, n]
|
|
print "symm>n"
|
|
s, v = symeig(kernel, range=pcrange, overwrite=True)
|
|
s = s[::-1]
|
|
v = v[:,::-1]
|
|
else:
|
|
u, s, vt = svd(kernel)
|
|
v = vt.T
|
|
s = sqrt(s)
|
|
u = dot(data, v)/s
|
|
else:
|
|
kernel = dot(data, data.T)
|
|
if has_sym:
|
|
if amax==None:
|
|
amax = m
|
|
pcrange = None
|
|
else:
|
|
pcrange = [m-amax, m]
|
|
print "sym (m<n)"
|
|
s, u = symeig(kernel, range=pcrange, overwrite=True)
|
|
s = s[::-1]
|
|
u = u[:,::-1]
|
|
else:
|
|
u, s, vt = svd(kernel)
|
|
s = sqrt(s)
|
|
v = dot(data.T, u)/s
|
|
print s[:2]
|
|
return u, s, v
|
|
|
|
def vnorm(x):
|
|
# assume column arrays (or vectors)
|
|
return math.sqrt(dot(x.T, x))
|
|
|
|
def center(a, axis):
|
|
# 0 = col center, 1 = row center, 2 = double center
|
|
# -1 = nothing
|
|
if axis==-1:
|
|
mn = zeros((a.shape[1],))
|
|
elif axis==0:
|
|
mn = a.mean(0)
|
|
elif axis==1:
|
|
mn = a.mean(1)[:,newaxis]
|
|
elif axis==2:
|
|
mn = a.mean(0) + a.mean(1)[:,newaxis] - a.mean()
|
|
else:
|
|
raise IOError("input error: axis must be in [-1,0,1,2]")
|
|
|
|
return a - mn, mn
|
|
|
|
def scale(a, axis):
|
|
if axis==-1:
|
|
sc = zeros((a.shape[1],))
|
|
elif axis==0:
|
|
sc = a.std(0)
|
|
elif axis==1:
|
|
sc = a.std(1)[:,newaxis]
|
|
else:
|
|
raise IOError("input error: axis must be in [-1,0,1]")
|
|
|
|
return a - sc, sc
|
|
|