[python] Matrix Multiplication in pure Python?

I'm trying to multiply two matrices together using pure Python. Input (X1 is a 3x3 and Xt is a 3x2):

X1 =  [[1.0016, 0.0, -16.0514], 
       [0.0, 10000.0, -40000.0], 
       [-16.0514, -40000.0, 160513.6437]]
Xt =  [(1.0, 1.0), 
       (0.0, 0.25), 
       (0.0, 0.0625)]

where Xt is the zip transpose of another matrix. Now here is the code:

def matrixmult (A, B):
    C = [[0 for row in range(len(A))] for col in range(len(B[0]))]
    for i in range(len(A)):
        for j in range(len(B[0])):
            for k in range(len(B)):
                C[i][j] += A[i][k]*B[k][j]
    return C

The error that python gives me is this:

IndexError: list index out of range.

Now I'm not sure if Xt is recognised as an matrix and is still a list object, but technically this should work.

When I had to do some matrix arithmetic I defined a new class to help. Within such a class you can define magic methods like __add__, or, in your use-case, __matmul__, allowing you to define x = a @ b or a @= b rather than matrixMult(a,b). __matmul__ was added in Python 3.5 per PEP 465.

I have included some code which implements this below (I excluded the prohibitively long __init__ method, which essentially creates a two-dimensional list self.mat and a tuple self.order according to what is passed to it)

class Matrix:
    def __matmul__(self, multiplier):
        if self.order[1] != multiplier.order[0]:
            raise ValueError("The multiplier was non-conformable under multiplication.")
        return [[sum(a*b for a,b in zip(srow,mcol)) for mcol in zip(*multiplier.mat)] for srow in self.mat]

    def __imatmul__(self, multiplier):
        self.mat = self @ multiplier
        return self.mat

    def __rmatmul__(self, multiplicand):
        if multiplicand.order[1] != self.order[0]:
            raise ValueError("The multiplier was non-conformable under multiplication.")
        return [[sum(a*b for a,b in zip(mrow,scol)) for scol in zip(*self.mat)] for mrow in multiplicand.mat]

Note:

• __rmatmul__ is used if b @ a is called and b does not implement __matmul__ (e.g. if I wanted to implement premultiplying by a 2D list)
• __imatmul__ is required for a @= b to work correctly;
• If a matrix is non-conformable under multiplication it means that it cannot be multiplied, usually because it has more or less rows than there are columns in the multiplicand

One liner:

def dot(m1, m2):
    return [
        [sum(x * y for x, y in zip(m1_r, m2_c)) for m2_c in zip(*m2)] for m1_r in m1
    ]

Explanation:

zip(*m2) - gets a column from the second matrix

zip(m1_r, m2_c) - creates tuple from m1 row and m2 column

sum(...) - sums multiplication row * col

Test:

m1 = [[1, 2, 3], [4, 5, 6]]
m2 = [[7, 8], [9, 10], [11, 12]]
result = dot(m1, m2)
assert result == [[58, 64], [139, 154]]

Matrix Multiplication in pure python.

def matmult(m1,m2):
r=[]
m=[]
for i in range(len(m1)):
    for j in range(len(m2[0])):
        sums=0
        for k in range(len(m2)):
            sums=sums+(m1[i][k]*m2[k][j])
        r.append(sums)
    m.append(r)
    r=[]
return m

The fault occurs here:

C[i][j]+=A[i][k]*B[k][j]

It crashes when k=2. This is because the tuple A[i] has only 2 values, and therefore you can only call it up to A[i][1] before it errors.

EDIT: Listen to Gerard's answer too, your C is wrong. It should be C=[[0 for row in range(len(A))] for col in range(len(A[0]))].

Just a tip: you could replace the first loop with a multiplication, so it would be C=[[0]*len(A) for col in range(len(A[0]))]

This is incorrect initialization. You interchanged row with col!

C = [[0 for row in range(len(A))] for col in range(len(B[0]))]

Correct initialization would be

C = [[0 for col in range(len(B[0]))] for row in range(len(A))]

Also I would suggest using better naming conventions. Will help you a lot in debugging. For example:

def matrixmult (A, B):
    rows_A = len(A)
    cols_A = len(A[0])
    rows_B = len(B)
    cols_B = len(B[0])

    if cols_A != rows_B:
      print "Cannot multiply the two matrices. Incorrect dimensions."
      return

    # Create the result matrix
    # Dimensions would be rows_A x cols_B
    C = [[0 for row in range(cols_B)] for col in range(rows_A)]
    print C

    for i in range(rows_A):
        for j in range(cols_B):
            for k in range(cols_A):
                C[i][j] += A[i][k] * B[k][j]
    return C

You can do a lot more, but you get the idea...

Here's some short and simple code for matrix/vector routines in pure Python that I wrote many years ago:

'''Basic Table, Matrix and Vector functions for Python 2.2
   Author:   Raymond Hettinger    
'''

Version = 'File MATFUNC.PY, Ver 183, Date 12-Dec-2002,14:33:42'

import operator, math, random
NPRE, NPOST = 0, 0                    # Disables pre and post condition checks

def iszero(z):  return abs(z) < .000001
def getreal(z):
    try:
        return z.real
    except AttributeError:
        return z
def getimag(z):
    try:
        return z.imag
    except AttributeError:
        return 0
def getconj(z):
    try:
        return z.conjugate()
    except AttributeError:
        return z


separator = [ '', '\t', '\n', '\n----------\n', '\n===========\n' ]

class Table(list):
    dim = 1
    concat = list.__add__      # A substitute for the overridden __add__ method
    def __getslice__( self, i, j ):
        return self.__class__( list.__getslice__(self,i,j) )
    def __init__( self, elems ):
        list.__init__( self, elems )
        if len(elems) and hasattr(elems[0], 'dim'): self.dim = elems[0].dim + 1
    def __str__( self ):
        return separator[self.dim].join( map(str, self) )
    def map( self, op, rhs=None ):
        '''Apply a unary operator to every element in the matrix or a binary operator to corresponding
        elements in two arrays.  If the dimensions are different, broadcast the smaller dimension over
        the larger (i.e. match a scalar to every element in a vector or a vector to a matrix).'''
        if rhs is None:                                                 # Unary case
            return self.dim==1 and self.__class__( map(op, self) ) or self.__class__( [elem.map(op) for elem in self] )
        elif not hasattr(rhs,'dim'):                                    # List / Scalar op
            return self.__class__( [op(e,rhs) for e in self] )
        elif self.dim == rhs.dim:                                       # Same level Vec / Vec or Matrix / Matrix
            assert NPRE or len(self) == len(rhs), 'Table operation requires len sizes to agree'
            return self.__class__( map(op, self, rhs) )
        elif self.dim < rhs.dim:                                        # Vec / Matrix
            return self.__class__( [op(self,e) for e in rhs]  )
        return self.__class__( [op(e,rhs) for e in self] )         # Matrix / Vec
    def __mul__( self, rhs ):  return self.map( operator.mul, rhs )
    def __div__( self, rhs ):  return self.map( operator.div, rhs )
    def __sub__( self, rhs ):  return self.map( operator.sub, rhs )
    def __add__( self, rhs ):  return self.map( operator.add, rhs )
    def __rmul__( self, lhs ):  return self*lhs
    def __rdiv__( self, lhs ):  return self*(1.0/lhs)
    def __rsub__( self, lhs ):  return -(self-lhs)
    def __radd__( self, lhs ):  return self+lhs
    def __abs__( self ): return self.map( abs )
    def __neg__( self ): return self.map( operator.neg )
    def conjugate( self ): return self.map( getconj )
    def real( self ): return self.map( getreal  )
    def imag( self ): return self.map( getimag )
    def flatten( self ):
        if self.dim == 1: return self
        return reduce( lambda cum, e: e.flatten().concat(cum), self, [] )
    def prod( self ):  return reduce(operator.mul, self.flatten(), 1.0)
    def sum( self ):  return reduce(operator.add, self.flatten(), 0.0)
    def exists( self, predicate ):
        for elem in self.flatten():
            if predicate(elem):
                return 1
        return 0
    def forall( self, predicate ):
        for elem in self.flatten():
            if not predicate(elem):
                return 0
        return 1
    def __eq__( self, rhs ):  return (self - rhs).forall( iszero )

class Vec(Table):
    def dot( self, otherVec ):  return reduce(operator.add, map(operator.mul, self, otherVec), 0.0)
    def norm( self ):  return math.sqrt(abs( self.dot(self.conjugate()) ))
    def normalize( self ):  return self / self.norm()
    def outer( self, otherVec ):  return Mat([otherVec*x for x in self])
    def cross( self, otherVec ):
        'Compute a Vector or Cross Product with another vector'
        assert len(self) == len(otherVec) == 3, 'Cross product only defined for 3-D vectors'
        u, v = self, otherVec
        return Vec([ u[1]*v[2]-u[2]*v[1], u[2]*v[0]-u[0]*v[2], u[0]*v[1]-u[1]*v[0] ])
    def house( self, index ):
        'Compute a Householder vector which zeroes all but the index element after a reflection'
        v = Vec( Table([0]*index).concat(self[index:]) ).normalize()
        t = v[index]
        sigma = 1.0 - t**2
        if sigma != 0.0:
            t = v[index] = t<=0 and t-1.0 or -sigma / (t + 1.0)
            v /= t
        return v, 2.0 * t**2 / (sigma + t**2)
    def polyval( self, x ):
        'Vec([6,3,4]).polyval(5) evaluates to 6*x**2 + 3*x + 4 at x=5'
        return reduce( lambda cum,c: cum*x+c, self, 0.0 )
    def ratval( self, x ):
        'Vec([10,20,30,40,50]).ratfit(5) evaluates to (10*x**2 + 20*x + 30) / (40*x**2 + 50*x + 1) at x=5.'
        degree = len(self) / 2
        num, den = self[:degree+1], self[degree+1:] + [1]
        return num.polyval(x) / den.polyval(x)

class Matrix(Table):
    __slots__ = ['size', 'rows', 'cols']
    def __init__( self, elems ):
        'Form a matrix from a list of lists or a list of Vecs'
        Table.__init__( self, hasattr(elems[0], 'dot') and elems or map(Vec,map(tuple,elems)) )
        self.size = self.rows, self.cols = len(elems), len(elems[0])
    def tr( self ):
        'Tranpose elements so that Transposed[i][j] = Original[j][i]'
        return Mat(zip(*self))
    def star( self ):
        'Return the Hermetian adjoint so that Star[i][j] = Original[j][i].conjugate()'
        return self.tr().conjugate()
    def diag( self ):
        'Return a vector composed of elements on the matrix diagonal'
        return Vec( [self[i][i] for i in range(min(self.size))] )
    def trace( self ): return self.diag().sum()
    def mmul( self, other ):
        'Matrix multiply by another matrix or a column vector '
        if other.dim==2: return Mat( map(self.mmul, other.tr()) ).tr()
        assert NPRE or self.cols == len(other)
        return Vec( map(other.dot, self) )
    def augment( self, otherMat ):
        'Make a new matrix with the two original matrices laid side by side'
        assert self.rows == otherMat.rows, 'Size mismatch: %s * %s' % (`self.size`, `otherMat.size`)
        return Mat( map(Table.concat, self, otherMat) )
    def qr( self, ROnly=0 ):
        'QR decomposition using Householder reflections: Q*R==self, Q.tr()*Q==I(n), R upper triangular'
        R = self
        m, n = R.size
        for i in range(min(m,n)):
            v, beta = R.tr()[i].house(i)
            R -= v.outer( R.tr().mmul(v)*beta )
        for i in range(1,min(n,m)): R[i][:i] = [0] * i
        R = Mat(R[:n])
        if ROnly: return R
        Q = R.tr().solve(self.tr()).tr()       # Rt Qt = At    nn  nm  = nm
        self.qr = lambda r=0, c=`self`: not r and c==`self` and (Q,R) or Matrix.qr(self,r) #Cache result
        assert NPOST or m>=n and Q.size==(m,n) and isinstance(R,UpperTri) or m<n and Q.size==(m,m) and R.size==(m,n)
        assert NPOST or Q.mmul(R)==self and Q.tr().mmul(Q)==eye(min(m,n))
        return Q, R
    def _solve( self, b ):
        '''General matrices (incuding) are solved using the QR composition.
        For inconsistent cases, returns the least squares solution'''
        Q, R = self.qr()
        return R.solve( Q.tr().mmul(b) )
    def solve( self, b ):
        'Divide matrix into a column vector or matrix and iterate to improve the solution'
        if b.dim==2: return Mat( map(self.solve, b.tr()) ).tr()
        assert NPRE or self.rows == len(b), 'Matrix row count %d must match vector length %d' % (self.rows, len(b))
        x = self._solve( b )
        diff = b - self.mmul(x)
        maxdiff = diff.dot(diff)
        for i in range(10):
            xnew = x + self._solve( diff )
            diffnew = b - self.mmul(xnew)
            maxdiffnew = diffnew.dot(diffnew)
            if maxdiffnew >= maxdiff:  break
            x, diff, maxdiff = xnew, diffnew, maxdiffnew
            #print >> sys.stderr, i+1, maxdiff
        assert NPOST or self.rows!=self.cols or self.mmul(x) == b
        return x
    def rank( self ):  return Vec([ not row.forall(iszero) for row in self.qr(ROnly=1) ]).sum()

class Square(Matrix):
    def lu( self ):
        'Factor a square matrix into lower and upper triangular form such that L.mmul(U)==A'
        n = self.rows
        L, U = eye(n), Mat(self[:])
        for i in range(n):
            for j in range(i+1,U.rows):
                assert U[i][i] != 0.0, 'LU requires non-zero elements on the diagonal'
                L[j][i] = m = 1.0 * U[j][i] / U[i][i]
                U[j] -= U[i] * m
        assert NPOST or isinstance(L,LowerTri) and isinstance(U,UpperTri) and L*U==self
        return L, U
    def __pow__( self, exp ):
        'Raise a square matrix to an integer power (i.e. A**3 is the same as A.mmul(A.mmul(A))'
        assert NPRE or exp==int(exp) and exp>0, 'Matrix powers only defined for positive integers not %s' % exp
        if exp == 1: return self
        if exp&1: return self.mmul(self ** (exp-1))
        sqrme = self ** (exp/2)
        return sqrme.mmul(sqrme)
    def det( self ):  return self.qr( ROnly=1 ).det()
    def inverse( self ):  return self.solve( eye(self.rows) )
    def hessenberg( self ):
        '''Householder reduction to Hessenberg Form (zeroes below the diagonal)
        while keeping the same eigenvalues as self.'''
        for i in range(self.cols-2):
            v, beta = self.tr()[i].house(i+1)
            self -= v.outer( self.tr().mmul(v)*beta )
            self -= self.mmul(v).outer(v*beta)
        return self
    def eigs( self ):
        'Estimate principal eigenvalues using the QR with shifts method'
        origTrace, origDet = self.trace(), self.det()
        self = self.hessenberg()
        eigvals = Vec([])
        for i in range(self.rows-1,0,-1):
            while not self[i][:i].forall(iszero):
                shift = eye(i+1) * self[i][i]
                q, r = (self - shift).qr()
                self = r.mmul(q) + shift
            eigvals.append( self[i][i] )
            self = Mat( [self[r][:i] for r in range(i)] )
        eigvals.append( self[0][0] )
        assert NPOST or iszero( (abs(origDet) - abs(eigvals.prod())) / 1000.0 )
        assert NPOST or iszero( origTrace - eigvals.sum() )
        return Vec(eigvals)

class Triangular(Square):
    def eigs( self ):  return self.diag()
    def det( self ):  return self.diag().prod()

class UpperTri(Triangular):
    def _solve( self, b ):
        'Solve an upper triangular matrix using backward substitution'
        x = Vec([])
        for i in range(self.rows-1, -1, -1):
            assert NPRE or self[i][i], 'Backsub requires non-zero elements on the diagonal'
            x.insert(0, (b[i] - x.dot(self[i][i+1:])) / self[i][i] )
        return x

class LowerTri(Triangular):
    def _solve( self, b ):
        'Solve a lower triangular matrix using forward substitution'
        x = Vec([])
        for i in range(self.rows):
            assert NPRE or self[i][i], 'Forward sub requires non-zero elements on the diagonal'
            x.append( (b[i] - x.dot(self[i][:i])) / self[i][i] )
        return x

def Mat( elems ):
    'Factory function to create a new matrix.'
    m, n = len(elems), len(elems[0])
    if m != n: return Matrix(elems)
    if n <= 1: return Square(elems)
    for i in range(1, len(elems)):
        if not iszero( max(map(abs, elems[i][:i])) ):
            break
    else: return UpperTri(elems)
    for i in range(0, len(elems)-1):
        if not iszero( max(map(abs, elems[i][i+1:])) ):
            return Square(elems)
    return LowerTri(elems)


def funToVec( tgtfun, low=-1, high=1, steps=40, EqualSpacing=0 ):
    '''Compute x,y points from evaluating a target function over an interval (low to high)
    at evenly spaces points or with Chebyshev abscissa spacing (default) '''
    if EqualSpacing:
        h = (0.0+high-low)/steps
        xvec = [low+h/2.0+h*i for i in range(steps)]
    else:
        scale, base = (0.0+high-low)/2.0, (0.0+high+low)/2.0
        xvec = [base+scale*math.cos(((2*steps-1-2*i)*math.pi)/(2*steps)) for i in range(steps)]
    yvec = map(tgtfun, xvec)
    return Mat( [xvec, yvec] )

def funfit( (xvec, yvec), basisfuns ):
    'Solves design matrix for approximating to basis functions'
    return Mat([ map(form,xvec) for form in basisfuns ]).tr().solve(Vec(yvec))

def polyfit( (xvec, yvec), degree=2 ):
    'Solves Vandermonde design matrix for approximating polynomial coefficients'
    return Mat([ [x**n for n in range(degree,-1,-1)] for x in xvec ]).solve(Vec(yvec))

def ratfit( (xvec, yvec), degree=2 ):
    'Solves design matrix for approximating rational polynomial coefficients (a*x**2 + b*x + c)/(d*x**2 + e*x + 1)'
    return Mat([[x**n for n in range(degree,-1,-1)]+[-y*x**n for n in range(degree,0,-1)] for x,y in zip(xvec,yvec)]).solve(Vec(yvec))

def genmat(m, n, func):
    if not n: n=m
    return Mat([ [func(i,j) for i in range(n)] for j in range(m) ])

def zeroes(m=1, n=None):
    'Zero matrix with side length m-by-m or m-by-n.'
    return genmat(m,n, lambda i,j: 0)

def eye(m=1, n=None):
    'Identity matrix with side length m-by-m or m-by-n'
    return genmat(m,n, lambda i,j: i==j)

def hilb(m=1, n=None):
    'Hilbert matrix with side length m-by-m or m-by-n.  Elem[i][j]=1/(i+j+1)'
    return genmat(m,n, lambda i,j: 1.0/(i+j+1.0))

def rand(m=1, n=None):
    'Random matrix with side length m-by-m or m-by-n'
    return genmat(m,n, lambda i,j: random.random())

if __name__ == '__main__':
    import cmath
    a = Table([1+2j,2,3,4])
    b = Table([5,6,7,8])
    C = Table([a,b])
    print 'a+b', a+b
    print '2+a', 2+a
    print 'a/5.0', a/5.0
    print '2*a+3*b', 2*a+3*b
    print 'a+C', a+C
    print '3+C', 3+C
    print 'C+b', C+b
    print 'C.sum()', C.sum()
    print 'C.map(math.cos)', C.map(cmath.cos)
    print 'C.conjugate()', C.conjugate()
    print 'C.real()', C.real()

    print zeroes(3)
    print eye(4)
    print hilb(3,5)

    C = Mat( [[1,2,3], [4,5,1,], [7,8,9]] )
    print C.mmul( C.tr()), '\n'
    print C ** 5, '\n'
    print C + C.tr(), '\n'

    A = C.tr().augment( Mat([[10,11,13]]).tr() ).tr()
    q, r = A.qr()
    assert q.mmul(r) == A
    assert q.tr().mmul(q)==eye(3)
    print 'q:\n', q, '\nr:\n', r, '\nQ.tr()&Q:\n', q.tr().mmul(q), '\nQ*R\n', q.mmul(r), '\n'
    b = Vec([50, 100, 220, 321])
    x = A.solve(b)
    print 'x:  ', x
    print 'b:  ', b
    print 'Ax: ', A.mmul(x)

    inv = C.inverse()
    print '\ninverse C:\n', inv, '\nC * inv(C):\n', C.mmul(inv)
    assert C.mmul(inv) == eye(3)

    points = (xvec,yvec) = funToVec(lambda x: math.sin(x)+2*math.cos(.7*x+.1), low=0, high=3, EqualSpacing=1)
    basis = [lambda x: math.sin(x), lambda x: math.exp(x), lambda x: x**2]
    print 'Func coeffs:', funfit( points, basis )
    print 'Poly coeffs:', polyfit( points, degree=5 )
    points = (xvec,yvec) = funToVec(lambda x: math.sin(x)+2*math.cos(.7*x+.1), low=0, high=3)
    print 'Rational coeffs:', ratfit( points )

    print polyfit(([1,2,3,4], [1,4,9,16]), 2)

    mtable = Vec([1,2,3]).outer(Vec([1,2]))
    print mtable, mtable.size

    A = Mat([ [2,0,3], [1,5,1], [18,0,6] ])
    print 'A:'
    print A
    print 'eigs:'
    print A.eigs()
    print 'Should be:', Vec([11.6158, 5.0000, -3.6158])
    print 'det(A)'
    print A.det()

    c = Mat( [[1,2,30],[4,5,10],[10,80,9]] )     # Failed example from Konrad Hinsen
    print 'C:\n', c
    print c.eigs()
    print 'Should be:', Vec([-8.9554, 43.2497, -19.2943])

    A = Mat([ [1,2,3,4], [4,5,6,7], [2,1,5,0], [4,2,1,0] ] )    # Kincaid and Cheney p.326
    print 'A:\n', A
    print A.eigs()
    print 'Should be:', Vec([3.5736, 0.1765, 11.1055, -3.8556])

    A = rand(3)
    q,r = A.qr()
    s,t = A.qr()
    print q is s                # Test caching
    print r is t
    A[1][1] = 1.1               # Invalidate the cache
    u,v = A.qr()
    print q is u                # Verify old result not used
    print r is v
    print u.mmul(v) == A        # Verify new result

    print 'Test qr on 3x5 matrix'
    a = rand(3,5)
    q,r = a.qr()
    print q.mmul(r) == a
    print q.tr().mmul(q) == eye(3)

m=input("row")
n=input("col")
X=[]
for i in range (m):
    m1=[]
    for j in range (n):
        m1.append(input("num"))
    X.append(m1)
Y=[]
for i in range (m):
    n1=[]
    for j in range (n):
        n1.append(input("num"))
    Y.append(n1)


# result is 3x3
result = [[0,0,0],
         [0,0,0],
         [0,0,0]]

# iterate through rows of X
for i in range(len(X)):
   # iterate through columns of Y
   for j in range(len(Y[0])):
       # iterate through rows of Y
       for k in range(len(Y)):
           result[i][j] += X[i][k] * Y[k][j]

for r in result:
   print(r)

The shape of your matrix C is wrong; it's the transpose of what you actually want it to be. (But I agree with ulmangt: the Right Thing is almost certainly to use numpy, really.)

def matrixmult (A, B):
    C = [[0 for row in range(len(A))] for col in range(len(B[0]))]
    for i in range(len(A)):
        for j in range(len(B[0])):
            for k in range(len(B)):
                C[i][j] += A[i][k]*B[k][j]
    return C

at second line you should change

C = [[0 for row in range(len(B[0]))] for col in range(len(A))]

All the below answers would return you the list.Your need to convert it to matrix

def MATMUL(X, Y):
    rows_A = len(X)
    cols_A = len(X[0])
    rows_B = len(Y)
    cols_B = len(Y[0])

    if cols_A != rows_B:
        print "Matrices are not compatible to Multiply. Check condition C1==R2"
        return

    # Create the result matrix
    # Dimensions would be rows_A x cols_B
    C = [[0 for row in range(cols_B)] for col in range(rows_A)]
    print C

    for i in range(rows_A):
        for j in range(cols_B):
            for k in range(cols_A):
                C[i][j] += A[i][k] * B[k][j]

    C = numpy.matrix(C).reshape(len(A),len(B[0]))

    return C

If you really don't want to use numpy you can do something like this:

def matmult(a,b):
    zip_b = zip(*b)
    # uncomment next line if python 3 : 
    # zip_b = list(zip_b)
    return [[sum(ele_a*ele_b for ele_a, ele_b in zip(row_a, col_b)) 
             for col_b in zip_b] for row_a in a]

x = [[1,2,3],[4,5,6],[7,8,9],[10,11,12]]
y = [[1,2],[1,2],[3,4]]

import numpy as np # I want to check my solution with numpy

mx = np.matrix(x)
my = np.matrix(y)       

Result:

>>> matmult(x,y)
[[12, 18], [27, 42], [42, 66], [57, 90]]
>>> mx * my
matrix([[12, 18],
        [27, 42],
        [42, 66],
        [57, 90]])