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impetuous

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提交 ff7933c0 编写于 作者: rictjo's avatar rictjo
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test,refurb++

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Showing with 154 addition and 119 deletion
setup.py
浏览文件 @ ff7933c0
...@@ -5,7 +5,7 @@ with open("README.md", "r") as fh: ...@@ -5,7 +5,7 @@ with open("README.md", "r") as fh:
setuptools.setup( setuptools.setup(
name = "impetuous-gfa", name = "impetuous-gfa",
version = "0.73.0", version = "0.73.1",
author = "Richard Tjörnhammar", author = "Richard Tjörnhammar",
author_email = "richard.tjornhammar@gmail.com", author_email = "richard.tjornhammar@gmail.com",
description = "Impetuous Quantification, a Statistical Learning library for Humans : Alignments, Clustering, Enrichments and Group Analysis", description = "Impetuous Quantification, a Statistical Learning library for Humans : Alignments, Clustering, Enrichments and Group Analysis",
......
src/impetuous/reducer.py
浏览文件 @ ff7933c0
...@@ -21,6 +21,16 @@ import pandas as pd ...@@ -21,6 +21,16 @@ import pandas as pd
import numpy as np import numpy as np
from scipy.stats import rankdata from scipy.stats import rankdata
from impetuous.quantification import qvalues, permuter from impetuous.quantification import qvalues, permuter
try :
from numba import jit
bUseNumba = True
except ImportError :
#print ( "ImportError:"," NUMBA. WILL NOT USE IT")
bUseNumba = False
except OSError:
#print ( "OSError:"," NUMBA. WILL NOT USE IT")
bUseNumba = False
# #
# REGULAR CONTRAST # REGULAR CONTRAST
contrast = lambda A,B : ( A-B )/( A+B ) contrast = lambda A,B : ( A-B )/( A+B )
...@@ -62,12 +72,12 @@ def get_procentile ( vals, procentile = 50 ): ...@@ -62,12 +72,12 @@ def get_procentile ( vals, procentile = 50 ):
pi0 = lambda pvs : 1. pi0 = lambda pvs : 1.
def padded_rolling_window ( tv, tau ) : # SIMPLY RETURN THE PADDED INDICES def padded_rolling_window ( ran, tau ) :
if tau==1 : if tau==1 :
return ( tv ) return ( [ (i,None) for i in range(len(ran)) ] )
if len(tv)<tau : if len(tv)<tau :
return ( [ np.mean(v_) for v_ in tv ] ) return ( [ (0,N) for v_ in ran ] )
centered = lambda x:(x[0],x[1]) ; N=len(tv); centered = lambda x:(x[0],x[1]) ; N=len(ran);
w = int( np.floor(np.abs(tau)*0.5) ) ; w = int( np.floor(np.abs(tau)*0.5) ) ;
jid = lambda i,w,N:[int((i-w)>0)*((i-w)%N),int(i+w<N)*((i+w)%N)+int(i+w>=N)*(N-1)] jid = lambda i,w,N:[int((i-w)>0)*((i-w)%N),int(i+w<N)*((i+w)%N)+int(i+w>=N)*(N-1)]
idx = [ centered( jid(i,w,N) ) for i in range(N) ] idx = [ centered( jid(i,w,N) ) for i in range(N) ]
...@@ -104,77 +114,13 @@ def svd_reduced_mean ( x,axis=0,keep=[0] ) : ...@@ -104,77 +114,13 @@ def svd_reduced_mean ( x,axis=0,keep=[0] ) :
else : else :
return ( xred ) return ( xred )
return ( x ) return ( x )
#
import math # Numerical Analysis by Burden and Faires
# YOU SHOULD READ Numerical Analysis by Burden and Faires # GOLUBS SVD PAPER
def rich_rot ( a , b , direction = 0 ) : #
if a==0 and b==0 : #
c = 0
s = 0
r = 0
else :
r = math.sqrt( a*a + b*b )
if direction == 0 :
if a == 0 :
s = r / b
c = 0
else :
s = b / r
c = ( r - s*b ) / a
else :
if a == 0 :
s = 0
c = r / b
else :
s = - a / r
c = ( r - s*b ) / a
return ( c , s , r )
def diagonalize_2b2( A , tol=1E-13 , maxiter = 10 ) :
M = A[:2,:2].copy()
M0 = A[:2,:2].copy()
k = 0
ERR = 1
G_ = None
H_ = None
for k in range( maxiter ) :
# LEFT
c,s,r = rich_rot( M0[0,0],M0[1,0])
G0 = np.array( [[c,s],[-s,c]] )
M = np.dot( G0 , M0 )
# RIGHT
M = M.T
c,s,r = rich_rot( M[0,0],M[1,0])
H0 = np.array( [[c,s],[-s,c]] )
M = np.dot( H0 , M )
# BUILD
M0 = M.T
ERR = np.sqrt( M0[1,0]**2+M0[0,1]**2 )
if G_ is None :
G_ = G0
else :
G_ = np.dot(G0,G_)
if H_ is None :
H_ = H0
else :
H_ = np.dot(H0,H_)
if ERR < tol :
break
return ( G_ , M0 , H_ )
def skew_zero ( shape ):
return ( np.zeros(np.prod(shape)).reshape(shape) )
def skew_eye ( shape ) :
Z = skew_zero(shape)
n = np.min(shape)
for i in range(n):
Z[i][i] = 1
return ( Z )
def kth_householder ( A , k ): def kth_householder ( A , k ):
# THE K:TH HOUSHOLDER ITERATION # THE K:TH HOUSHOLDER ITERATION
# NOMECLATURE CORRESPONDS TO GOLUBS PAPER
A = np .array( A ) A = np .array( A )
n_ , m_ = np .shape(A) n_ , m_ = np .shape(A)
if n_ < 2 : if n_ < 2 :
...@@ -204,7 +150,7 @@ def kth_householder ( A , k ): ...@@ -204,7 +150,7 @@ def kth_householder ( A , k ):
return ( Pk,Ak,Qk ) return ( Pk,Ak,Qk )
def Householder_transformation ( A ): def Householder_transformation ( A ):
A = np.array( A ) A = np.array( A )
n = np.min( np.shape( A ) ) n = np.min( np.shape( A ) )
if n < 2 : if n < 2 :
return ( A ) return ( A )
...@@ -218,7 +164,7 @@ def Householder_transformation ( A ): ...@@ -218,7 +164,7 @@ def Householder_transformation ( A ):
return ( A1 , P ) return ( A1 , P )
def Householder_reduction ( A ): def Householder_reduction ( A ):
A = np.array( A ) A = np.array( A )
n = np.min( np.shape( A ) ) n = np.min( np.shape( A ) )
if n < 2 : if n < 2 :
return ( A ) return ( A )
...@@ -233,61 +179,125 @@ def Householder_reduction ( A ): ...@@ -233,61 +179,125 @@ def Householder_reduction ( A ):
VT = Q0.T VT = Q0.T
return ( U , S , VT ) return ( U , S , VT )
def rich_rot ( a , b , direction = 0 ) :
if a==0 and b==0 :
c = 0
s = 0
r = 0
else :
r = np.sqrt( a*a + b*b )
if direction == 0 :
if a == 0 :
s = r / b
c = 0
else :
s = b / r
c = ( r - s*b ) / a
else :
if a == 0 :
s = 0
c = r / b
else :
s = - a / r
c = ( r - s*b ) / a
return ( c , s , r )
def skew_zero ( shape ) :
return ( np.zeros(np.prod(shape)).reshape(shape) )
def skew_eye ( shape ) :
Z = np.zeros( shape[0]*shape[1] ).reshape(shape[0],shape[1])
n = shape[0] if (shape[0]<=shape[1]) else shape[1]
for i in range(n):
Z[i][i] = 1
return ( Z )
def diagonalize_2b2( A , tol=1E-13 , maxiter = 100 ) :
M = A[:2,:2].copy()
M0 = A[:2,:2].copy()
k = 0
ERR = 1
G_ = None
H_ = None
for k in range( maxiter ) :
# LEFT
c,s,r = rich_rot( M0[0,0],M0[1,0])
G0 = np.array( [[c,s],[-s,c]] )
M = np.dot( G0 , M0 )
# RIGHT
M = M.T
c,s,r = rich_rot( M[0,0],M[1,0])
H0 = np.array( [[c,s],[-s,c]] )
M = np.dot( H0 , M )
# BUILD
M0 = M.T
ERR = np.sqrt( M0[1,0]**2+M0[0,1]**2 )
if G_ is None :
G_ = G0
else :
G_ = np.dot(G0,G_)
if H_ is None :
H_ = H0
else :
H_ = np.dot(H0,H_)
if ERR < tol :
break
return ( G_ , M0 , H_ )
def diagonalize_tridiagonal ( tridiagonal , def diagonalize_tridiagonal ( tridiagonal ,
maxiter = 1000 , maxiter = 1000 ,
tol = 1E-16 ): tol = 1E-16 ) :
quench = lambda A_,tol_ : np.array([[ a if a**2>tol_**2 else 0 for a in al] for al in A_]) S = tridiagonal.copy()
dot_assign = lambda A_, B_ : B_ if A_ is None else np.dot( B_ , A_ )
S = tridiagonal.copy()
n_ , m_ = np.shape( S ) n_ , m_ = np.shape( S )
tol22 = tol*0.1
maxi22 = int( np.ceil( maxiter*0.1 ))
sI = skew_eye ( [ n_ , n_ ] ) sI = skew_eye ( [ n_ , n_ ] )
tI = skew_eye ( [ m_ , m_ ] ) tI = skew_eye ( [ m_ , m_ ] )
zI = skew_eye ( np.shape(S) ) zI = skew_eye ( np.shape(S) )
GI = None GI = sI.copy()
HI = None HI = tI.copy()
# #
sI_ = sI.copy() sI_ = sI.copy()
tI_ = tI.copy() tI_ = tI.copy()
nm_ = np.min(np.shape(S)) - 1 shape = np.shape(S)
# nm_ = shape[0] if (shape[0]<=shape[1]) else shape[1] - 1
for k in range ( maxiter ) : for k in range ( maxiter ) :
for i in range ( nm_ ): for i in range ( nm_ ) :
sI_ = sI.copy() sI_ = sI .copy()
tI_ = tI.copy() tI_ = tI .copy()
if True : A = S[ i:i+2 , i:i+2 ].copy()
A = S[ i:i+2 , i:i+2 ].copy() G , Z , H = diagonalize_2b2 ( A , tol=tol22 , maxiter=maxi22 )
if True : sI_[ i:i+2 , i:i+2 ] = G
G , Z , H = diagonalize_2b2 ( A , tol=tol , maxiter=maxiter ) GI = np.dot( sI_ , GI )
sI_[ i:i+2 , i:i+2 ] = G tI_[ i:i+2 , i:i+2 ] = H
GI = dot_assign(GI,sI_) HI = np.dot( tI_ , HI )
S = np.dot( np.dot( sI_ , S ) , tI_.T )
tI_[ i:i+2 , i:i+2 ] = H for ir in range( 2,nm_+1-i ):
HI = dot_assign(HI,tI_) ii = i
jj = i+ir
S = np.dot( np.dot( sI_ , S ) , tI_.T ) idx = [ (ii,ii),(ii,jj),(jj,ii),(jj,jj) ]
for ir in range( 2,nm_+1-i ): jdx = [ (0,0),(0,1),(1,0),(1,1) ]
ii = i A = np.array( [ S[i] for i in idx] ).reshape(2,2)
jj = i+ir G , Z , H = diagonalize_2b2 ( A , tol=tol22 , maxiter=maxi22 )
idx = [ (ii,ii),(ii,jj),(jj,ii),(jj,jj) ] sI_ = sI .copy()
jdx = [ (0,0),(0,1),(1,0),(1,1) ] tI_ = tI .copy()
A = np.array( [ S[i] for i in idx] ).reshape(2,2) # ROW ORDERED H = H.T
G , Z , H = diagonalize_2b2 ( A , tol=tol , maxiter=maxiter ) for i_,j_ in zip(idx,jdx) :
sI_ = sI.copy() sI_[i_] = G[j_]
tI_ = tI.copy() tI_[i_] = H[j_]
H = H.T tI_= tI_.T
for i_,j_ in zip(idx,jdx) : GI = np.dot( sI_ , GI )
sI_[i_] = G[j_] HI = np.dot( tI_ , HI )
tI_[i_] = H[j_] S = np.dot( np.dot( sI_ , S ) , tI_.T )
tI_= tI_.T ERR = np.sum( S**2*(1-skew_eye([n_,m_]) ) )
GI = dot_assign(GI,sI_) if ERR < tol :
HI = dot_assign(HI,tI_)
S = np.dot( np.dot( sI_ , S ) , tI_.T )
ERR = sum( np.diag(S[:nm_],-1)**2 ) + sum( np.diag(S[:nm_] ,1)**2 )
if ERR < 2.0*tol**2 :
break; break;
# RETURNS THE MATRICES NEEDED TO CREATE THE INPUT DATA
# WHERE R[1] IS THE SINGULAR VALUE VECTOR
# DATA = np.dot( np.dot( R[0],R[1]),R[2] )
return ( GI.T , S , HI ) return ( GI.T , S , HI )
def AugumentedReducedDecomposition ( A , maxiter=1000 , tol=1E-16 ): def AugumentedReducedDecomposition ( A , maxiter=1000 , tol=1E-16 ):
...@@ -347,7 +357,6 @@ def hyper_params ( df_ , label = 'generic' , sep = ',' , power=1., centered=-1 ) ...@@ -347,7 +357,6 @@ def hyper_params ( df_ , label = 'generic' , sep = ',' , power=1., centered=-1 )
# #
return ( np.mean(lvs) , np.std(lvs) ) return ( np.mean(lvs) , np.std(lvs) )
def hyper_rdf ( df_ , label = 'generic' , sep = ',' , power=1. , def hyper_rdf ( df_ , label = 'generic' , sep = ',' , power=1. ,
diagnostic_output = False , MEAN=None , STD=None , centered=-1 ) : diagnostic_output = False , MEAN=None , STD=None , centered=-1 ) :
# #
...@@ -451,3 +460,29 @@ if __name__ == '__main__' : ...@@ -451,3 +460,29 @@ if __name__ == '__main__' :
print ( padded_rolling_average( np.array( svd_reduced_mean( Xdf ).values ) ,tau=4 ) ) print ( padded_rolling_average( np.array( svd_reduced_mean( Xdf ).values ) ,tau=4 ) )
print ( padded_rolling_average( v0,tau=4 ) ) print ( padded_rolling_average( v0,tau=4 ) )
print ( v0 ) print ( v0 )
if True :
import time
A_ = np.array([ [ 22 , 10 , 2 , 3 , 7 ] ,
[ 14 , 7 , 10 , 0 , 8 ] ,
[ -1 , 13 , -1 , -11 , 3 ] ,
[ -3 , -2 , 13 , -2 , 4 ] ,
[ 9 , 8 , 1 , -2 , 4 ] ,
[ 9 , 1 , -7 , 5 , -1 ] ,
[ 2 , -6 , 6 , 5 , 1 ] ,
[ 4 , 5 , 0 , -2 , 2 ] ] )
t0 = time.time()
U,S,VT = ASVD( A_ )
dt = time.time() - t0
print ( dt );
print ( np.dot(np.dot(U,S),VT) )
df = lambda data:pd.DataFrame(data)
O,Z,WT = np.linalg.svd(A_)
print ( df( O).apply(np.abs) - df( U).apply(np.abs) )
print ( df( Z).apply(np.abs) - df(np.diag(S)).apply(np.abs) )
print ( df(VT).apply(np.abs) - df(WT).apply(np.abs) )
print ( np.sqrt(1248),20,np.sqrt(384),0,0 )
print ( np.diag(S) )
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