# Copyright (C) 2007 Darren Lee Weber
#
# This program is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 2 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program; if not, write to the Free Software
# Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA
# 02111-1307, USA.
__version__ = "$Revision: 1.9 $" # $Date: 2007/06/14 00:24:57 $
class Matrix:
"""
Create and manipulate a matrix object
Matrix(data, dim)
data = list of lists (currently only 2D)
dim=(row,col) tuple of int
For example,
#data = [[0.0] * c for i in xrange(r)]
data = [[0.0,0.1],[1.0,1.1],[2.0,2.1]]
rowN =len(data)
colN =len(data[0])
m = Matrix(data)
m = Matrix(data,dim =(rowN, colN))
d1 = [[0.0, 0.1], [1.0, 1.1], [2.0, 2.1]] # 3x2 matrix
d2 = [[0.0, 0.1, 0.2], [1.0, 1.1, 1.2]] # 2x3 matrix
m1 = Matrix(d1)
m2 = Matrix(d2)
#m3 = m1 + m2 # dimension error
m3 = m1 + m2.transpose()
m3 = m1 - m2.transpose()
m3 = m1 * m2 # 3x3
m3 = m2 * m1 # 2x2
m1[2,:]
m1[:,2]
"""
def __init__(self, data=None, dim=None):
"""
create a matrix instance.
m = Matrix([data [, dim]])
<datais a 2D matrix comprised of a nested list of floats
<dimis a tuple of int values for the row and column size
(r,c)
eg:
data = [[0.0,0.1],[1.0,1.1],[2.0,2.1]]
dim = (3,2) # or (len(data),len( data[0]))
"""
if data != None:
# check data for the cell types (ensure float)?
self.data = data
r = len(data)
c = len(data[0])
# Are all the rows the same length?
rowLenCheck = sum([len(data[i]) != c for i in range(r)])
if rowLenCheck 0:
raise ValueError
else:
self.dim = (r,c)
if dim != None:
if (dim[0] == r) and (dim[1] == c):
self.dim = (r,c)
else:
# over-ride the dim input, do not reshape data!
# print a warning?
self.dim = (r,c)
else:
if dim != None:
if len(dim) == 2:
self.dim = tuple(dim)
r = dim[0]
c = dim[1]
else:
# maybe a new exception type?
arg = ("len(dim) != 2: ", dim)
raise ValueError, arg
# BEGIN ALT ----------------------------------------
# Does this give unique memory for each element?
# self.data = [[0.0] * c for i in xrange(r)]
# It seems that the initialization does not generate
# unique memory elements because all list elements
# refer to the same number object (0.0), but
# modification of any element creates a unique value,
# without changing any other values, eg:
##>>x = [[0.0] * 3 for i in xrange(2)]
##>>id(x)
# 3079625068L
# >>id(x[0][0])
# 136477300
# >>id(x[0][1])
# 136477300
# >>id(x[1][1])
# 136477300
# >>x[0][0] = 1.0
# >>x
# [[1.0, 0.0, 0.0], [0.0, 0.0, 0.0]]
# >>>
# END ALT ----------------------------------------
# create a zero row vector, with unique memory for
each element
self.data = [[x * 0.0 for x in range(c)]]
for i in range(1,r):
self.data.appen d([x * 0.0 for x in range(c)])
else:
self.data = []
self.dim = (0,0)
#print self.__doc__
def __getitem__(sel f, i):
"""
matrix[r,c] returns values from matrix.data, eg:
data = [[0.0,0.1],[1.0,1.1],[2.0,2.1]]
m = Matrix(data)
m[2,:]
>[2.0, 2.1000000000000 001]
r = i[0]
c = i[1]
#print "index: (%s, %s)" % (r,c)
#print "value: ", self.data[r][c]
return self.data[r][c]
def reshape(self, newdim=None):
'reshape a matrix object: matrix.reshape( newdim)'
print "something to implement later"
pass
def transpose(self) :
'transpose a matrix: m2 = m1.transpose()'
m = Matrix(dim=(sel f.dim[1],self.dim[0]))
for r in range(self.dim[0]):
for c in range(self.dim[1]):
m.data[c][r] = self.data[r][c]
return m
def __add__(self, q):
'''
matrix addition:
m3 = matrix1 + matrix2
m3 = matrix1 + float
m3 = matrix1 + int
'''
if isinstance(q, Matrix):
if self.dim != q.dim:
arg = ("p.dim != q.dim", self.dim, q.dim)
raise IndexError, arg
else:
# do the addition
m = Matrix(dim=self .dim)
for r in range(self.dim[0]): # rows of p and q
m.data[r] = map(lambda x, y: x + y, self.data[r],
q.data[r])
return m
elif isinstance(q, float) or isinstance(q, int):
# add a scalar value
m = Matrix(dim=self .dim)
for r in range(self.dim[0]): # rows
m.data[r] = map(lambda x: x + q, self.data[r])
return m
else:
arg = ("q is not a matrix, float or int", q)
raise TypeError, arg
def __sub__(self, q):
'''
matrix subtraction:
m3 = matrix1 - matrix2
m3 = matrix1 - float
m3 = matrix1 - int
'''
if isinstance(q, Matrix):
if self.dim != q.dim:
arg = ("p.dim != q.dim", self.dim, q.dim)
raise IndexError, arg
else:
# do the subtraction
m = Matrix(dim=self .dim)
for r in range(self.dim[0]): # rows of p and q
m.data[r] = map(lambda x, y: x - y, self.data[r],
q.data[r])
return m
elif isinstance(q, float) or isinstance(q, int):
# subtract a scalar value
m = Matrix(dim=self .dim)
for r in range(self.dim[0]): # rows
m.data[r] = map(lambda x: x - q, self.data[r])
return m
else:
arg = ("q is not a matrix, float or int", q)
raise TypeError, arg
def __mul__(self, q):
"""
multiply two matrices:
m = p * q # p.dim[1] == q.dim[0]
multiply a matrix with a scalar:
m = p * q # where q is a float or int value
"""
if isinstance(q, Matrix):
if self.dim[1] != q.dim[0]:
arg = ("p.dim[1] != q.dim[0]", self.dim[1], q.dim[0])
raise IndexError, arg
else:
# do the multiplication
m = Matrix(dim=(sel f.dim[0], q.dim[1]))
for r in range(self.dim[0]): # rows of p
for c in range(q.dim[1]): # cols of q
# get the dot product of p(r,:) with q(:,c)
pRowVec = self.data[r]
qColVec = [q.data[a][c] for a in
xrange(q.dim[0])]
m.data[r][c] = sum(map(lambda x, y: x * y,
pRowVec, qColVec))
return m
elif isinstance(q, float) or isinstance(q, int):
# subtract a scalar value
m = Matrix(dim=self .dim)
for r in range(self.dim[0]): # rows
m.data[r] = map(lambda x: x * q, self.data[r])
return m
else:
arg = ("q is not a matrix, float or int", q)
raise TypeError, arg
def __div__(self, q):
"""
Divide a matrix with a scalar, eg:
m = p / q # where q is a float or int value
This operator will not return a matrix inverse
"""
if isinstance(q, Matrix):
# let's not do matrix divide in python, leave the inverse
# to a c/c++ library
arg = ("q is a matrix: will not calculate inverse", q)
raise TypeError, arg
elif isinstance(q, float) or isinstance(q, int):
# divide a scalar value
m = Matrix(dim=self .dim)
for r in range(self.dim[0]): # rows
m.data[r] = map(lambda x: x / q, self.data[r])
return m
else:
arg = ("q is not a matrix, float or int", q)
raise TypeError, arg
def __len__(self):
return self.dim[0] * self.dim[1]
def __str__(self):
# print the matrix data
s = ""
for r in range(self.dim[0]):
for c in range(self.dim[1]):
s += "%f " % (self.data[r][c])
s += "\n"
return s
def printFormat(sel f, format):
"""
print the matrix data nicely formatted, eg:
matrix.printFor mat("%8.4f")
"""
for r in range(self.dim[0]):
for c in range(self.dim[1]):
print format % (self.data[r][c]),
def __repr__(self):
# return something that will recreate the object
return "Matrix(%s, %s)" % (self.data, self.dim)
#
--------------------------------------------------------------------------------
# Explore the functionality - should be unit testing
testing = 0
if testing:
d1 = [[0.0, 0.1], [1.0, 1.1], [2.0, 2.1]] # 3x2 matrix
d2 = [[0.0, 0.1, 0.2], [1.0, 1.1, 1.2]] # 2x3 matrix
m1 = Matrix(d1)
m2 = Matrix(d2)
#m3 = m1 + m2 # "dimension" error
m3 = m1 + m2.transpose()
m3 = m1 - m2.transpose()
m3 = m1 * m2 # 3x3
m3 = m2 * m1 # 2x2
m3 += 10.0
m3 -= 10.0
m3 += 10
m3 -= 10
m3 /= 10.0
m3 *= 10.0
m3 /= 10
m3 *= 10