使用LU分解求逆矩阵

Question

I have written the following Matrix class in cython for the matrix inversion and some other linear algebra operations. I tried to use the LU decomposition , in order to compute the inverse of a matrix. The speed of code is good. I tried to implement this code in cython . I have checked every line of my code and compared with the given code couple of times but I still return wrong answer.

matrix.pyx

from libcpp.vector cimport vector
import cython
cimport cython  
import numpy as np
cimport numpy as np
import ctypes                                             
from libc.math cimport log, exp, pow, fabs                                              
from libc.stdint cimport *
from libcpp.string cimport string
from libc.stdio cimport *
from libcpp cimport bool
cdef extern from "<iterator>" namespace "std" nogil:
    cdef cppclass iterator[Category, T, Distance, Pointer, Reference]:
        pass
    cdef cppclass output_iterator_tag:
        pass
    cdef cppclass input_iterator_tag:
        pass
    cdef cppclass forward_iterator_tag(input_iterator_tag):
        pass

cdef extern from "<algorithm>" namespace "std" nogil:       
   void fill [ForwardIterator, T](ForwardIterator, ForwardIterator, T& )

cdef class Matrix:    
     def __cinit__(self, size_t rows=0, size_t columns=0, bint Identity=False, bint ones=False):
         self._rows=rows
         self._columns=columns
         self.matrix=new vector[double]()
         self.matrix.resize(rows*columns)

         if Identity:
            self._IdentityMatrix()

         if ones:
            self._fillWithOnes()

     def __dealloc__(self):
         del self.matrix

     property rows:
        def __get__(self):
            return self._rows
        def __set__(self, size_t x):
            self._rows = x    
     property columns:
        def __get__(self):
            return self._columns
        def __set__(self, size_t y):
            self._columns = y    

     cpdef double getVal(self, size_t r, size_t c):
           return self.matrix[0][r*self._columns+c]

     cpdef void setVal(self, size_t r, size_t c, double v): 
           self.matrix[0][r*self._columns+c] = v

     @cython.boundscheck(False)
     @cython.wraparound(False)
     cdef void _fillWithOnes(self):
          fill(self.matrix.begin(),self.matrix.end(),1.)

     cdef void _IdentityMatrix(self):
          cdef size_t i 
          if (self._rows!=self._columns):
             raise Exception('In order to generate identity matrix, the number of rows and columns must be equal')
          else:
             for i from 0 <= i <self._columns:
                 self.setVal(i,i,1.0)

     @cython.boundscheck(False)
     @cython.wraparound(False)
     cpdef Matrix Inv(self):               
           cdef Matrix A_inverse = Matrix(self._rows,self._columns)
           cdef MatrixList LU = ludcmp(self)
           cdef Matrix A    = LU.get(0)
           cdef Matrix indx = LU.get(1)
           cdef Matrix d    = LU.get(2)
           cdef double det  = d.getVal(0,0)
           cdef int i, j
           cdef np.ndarray[np.float64_t, ndim=2] L   = np.zeros((self._rows,self._columns),dtype=np.float64)
           cdef np.ndarray[np.float64_t, ndim=2] U   = np.zeros((self._rows,self._columns),dtype=np.float64)
           cdef Matrix col = Matrix(self._rows,1)
           for i from 0 <= i < self._rows: 
               for j from 0 <= j < self._columns:  
                   if (j>i):
                       U[i,j]=A.getVal(i,j)
                       L[i,j]=0
                   elif (j<i):
                       U[i,j]=0
                       L[i,j]=A.getVal(i,j)
                   else:
                      U[i,j]=A.getVal(i,j)
                      L[i,j]=1
           print "product of a lower triangular matrix L and an upper triangular matrix U:", np.dot(L, U)
           for i from 0 <= i < self._rows: 
               det*= A.getVal(i,i)
               for j from 0 <= j < self._columns:
                   if (i==j):
                      col.setVal(j,0,1)
               col=lubksb(A, indx, col)     
               for j from 0 <= j < self._columns:
                   A_inverse.setVal(j,i,col.getVal(j,0))
           print "determinant of matrix %.4f"%(det)
           return A_inverse

cdef class MatrixList:
     def __cinit__(self):
         self.inner = []

     cdef void append(self, Matrix a):
          self.inner.append(a)

     cdef Matrix get(self, int i):
          return <Matrix> self.inner[i]

     def __len__(self):
         return len(self.inner)


@cython.boundscheck(False)
@cython.wraparound(False)    
cdef Matrix lubksb(Matrix a, Matrix indx, Matrix b):
     cdef int n = a.rows
     cdef int i, ip, j
     cdef int ii = 0
     cdef double su
     for i from 0 <= i < n: 
         ip = <int>indx.getVal(i,0)
         su = b.getVal(ip,0)
         b.setVal(ip,0, b.getVal(i,0))
         if (ii):
             for j from ii <= j < (i-1): 
                 su -= a.getVal(i,j) * b.getVal(j,0)
         elif (su):
            ii = i       
         b.setVal(i, 0, su)
     for i from n > i >= 0: 
         su = b.getVal(i,0)
         for j from (i+1) <= j < n:
             su -= a.getVal(i,j) * b.getVal(j,0)
         b.setVal(i, 0, su/a.getVal(i,i))
     return b

@cython.boundscheck(False)
@cython.wraparound(False)    
cdef MatrixList ludcmp(Matrix a):
     #Given a matrix a_{nxn}, this routine replaces it by the LU decomposition of a row-wise permutation of itself.
     cdef MatrixList LU = MatrixList()
     cdef int n = a.rows
     cdef int i, j, k, imax
     cdef double big, dum, su, temp
     cdef Matrix vv   = Matrix(n,1)
     cdef Matrix indx = Matrix(n,1) #an output vector that records the row permutation effected by the partial pivoting
     cdef Matrix d    = Matrix(1,1, ones= True)  #an output as +1 or -1 depending on whether the number of row interchanges was even or odd, respectively
     cdef double TINY = 1.1e-16
     for i from 0 <= i < n: 
         big = 0.0
         for j from 0 <= j < n:
             temp=fabs(a.getVal(i,j))
             if (temp > big):
                big=temp
         if (big ==0.0):
             raise Exception("ERROR! ludcmp: Singular matrix\n")
         vv.setVal(i,0,1.0/big)

     for j from 0 <= j < n:
         for i from 0 <= i < j: 
             su = a.getVal(i,j)
             for k from 0 <= k < i:
                 su -= a.getVal(i,k)*a.getVal(k,j)
             a.setVal(i,j,su)

         big=0.0
         for i from j<= i< n:
             su = a.getVal(i,j)
             for k from 0 <= k < j:
                 su -= a.getVal(i,k)*a.getVal(k,j)
             a.setVal(i, j, su)
             dum=vv.getVal(i,0)*fabs(su )
             if (dum >= big):
                big=dum
                imax=i

         if (j != imax):
            for k from 0 <= k < n:
                dum = a.getVal(imax,k)
                a.setVal(imax, k, a.getVal(j,k))
                a.setVal(j,k, dum)
            d.setVal(0, 0, -d.getVal(0,0))
            vv.setVal(imax, 0, vv.getVal(j, 0))
         indx.setVal(j, 0, imax)
         if (a.getVal(j,j) == 0.0):
             a.setVal(j,j, TINY)
         if (j != (n-1)):
            dum=1.0/a.getVal(j,j)
            for i from (j+1)<= i <n:
                a.setVal(i,j, a.getVal(i,j)*dum)
     LU.append(<Matrix>a)
     LU.append(<Matrix>indx)
     LU.append(<Matrix>d)
     return LU

matrix.pxd

from libcpp.vector cimport vector
cdef class MatrixList:
     cdef list inner
     cdef void append(self, Matrix a)
     cdef Matrix get(self, int i)

cdef class Matrix:
     cdef vector[double] *matrix   
     cdef size_t _rows
     cdef size_t _columns
     cdef bint Identity
     cdef bint ones

     cpdef double getVal(self, size_t r, size_t c)
     cpdef void setVal(self, size_t r, size_t c, double v)
     cpdef Matrix transpose(self)
     cdef void _IdentityMatrix(self)
     cdef void _fillWithOnes(self)
     cpdef Matrix Inv(self)         
cdef Matrix lubksb(Matrix a, Matrix indx, Matrix b)    
cdef MatrixList ludcmp(Matrix a)

Any help to find the bug will be appreciated.

example:

import numpy
from matrix import Matrix
from numpy.linalg import inv
import timeit
import numpy as np
r=numpy.random.random((100, 100))
d=Matrix(r.shape[0],r.shape[1])
for i in range(d.rows):
     for j in range(d.columns):
         d.setVal(i,j,r[i,j])        


start = timeit.default_timer()
x=d.Inv()
stop = timeit.default_timer()
print "LU decomposition:", stop - start 

Answer 1

I figured out that there are very small mistakes I made in the lubksb function and by fixing them I am getting the right answer. Here is the fixed code:

@cython.boundscheck(False)
@cython.wraparound(False)    
cdef Matrix lubksb(Matrix a, Matrix indx, Matrix b):
     cdef int n = a.rows
     cdef int i, ip, j
     cdef int ii = 0
     cdef double su
     for i from 0 <= i < n: 
         ip = <int>indx.getVal(i,0)
         su = b.getVal(ip,0)
         b.setVal(ip,0, b.getVal(i,0))
         if (ii>=0):
             for j from ii <= j <= (i-1): 
                 su -= a.getVal(i,j) * b.getVal(j,0)
         elif (su):
            ii = i       
         b.setVal(i, 0, su)
     for i from n >= i >= 0: 
         su = b.getVal(i,0)
         for j from (i+1) <= j < n:
             su -= a.getVal(i,j) * b.getVal(j,0)
         if (a.getVal(i,i)==0.0):
             a.setVal(i,i, 1.1e-16)
         b.setVal(i, 0, su/a.getVal(i,i))
     return b