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matrix.py
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matrix.py
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from math import *
class matrix:
# implements basic operations of a matrix class
# ------------
#
# initialization - can be called with an initial matrix
#
def __init__(self, value=[[]]):
self.value = value
self.dimx = len(value)
self.dimy = len(value[0])
if value == [[]]:
self.dimx = 0
# -----------
#
# defines matrix equality - returns true if corresponding elements
# in two matrices are within epsilon of each other.
#
def __eq__(self, other):
epsilon = 0.01
if self.dimx != other.dimx or self.dimy != other.dimy:
return False
for i in range(self.dimx):
for j in range(self.dimy):
if abs(self.value[i][j] - other.value[i][j]) > epsilon:
return False
return True
def __ne__(self, other):
return not (self == other)
def __getitem__(self, item):
return self.value[item]
# ------------
#
# makes matrix of a certain size and sets each element to zero
#
def zero(self, dimx, dimy):
# check if valid dimensions
if dimx < 1 or dimy < 1:
raise (ValueError, "Invalid size of matrix")
else:
self.dimx = dimx
self.dimy = dimy
self.value = [[0 for col in range(dimy)] for row in range(dimx)]
# ------------
#
# makes matrix of a certain (square) size and turns matrix into identity matrix
#
def identity(self, dim):
# check if valid dimension
if dim < 1:
raise (ValueError, "Invalid size of matrix")
else:
self.dimx = dim
self.dimy = dim
self.value = [[0 for col in range(dim)] for row in range(dim)]
for i in range(dim):
self.value[i][i] = 1
def show(self):
for i in range(self.dimx):
print (self.value[i])
print (' ')
# ------------
#
# defines elmement-wise matrix addition. Both matrices must be of equal dimensions
#
def __add__(self, other):
# check if correct dimensions
if self.dimx != other.dimx or self.dimy != other.dimy:
raise (ValueError, "Matrices must be of equal dimension to add")
else:
# add if correct dimensions
res = matrix([[]])
res.zero(self.dimx, self.dimy)
for i in range(self.dimx):
for j in range(self.dimy):
res.value[i][j] = self.value[i][j] + other.value[i][j]
return res
# ------------
#
# defines elmement-wise matrix subtraction. Both matrices must be of equal dimensions
#
def __sub__(self, other):
# check if correct dimensions
if self.dimx != other.dimx or self.dimy != other.dimy:
raise (ValueError, "Matrices must be of equal dimension to subtract")
else:
# subtract if correct dimensions
res = matrix()
res.zero(self.dimx, self.dimy)
for i in range(self.dimx):
for j in range(self.dimy):
res.value[i][j] = self.value[i][j] - other.value[i][j]
return res
# ------------
#
# defines multiplication. Both matrices must be of fitting dimensions
#
def __mul__(self, other):
# check if correct dimensions
if self.dimy != other.dimx:
raise (ValueError, "Matrices must be m*n and n*p to multiply")
else:
# multiply if correct dimensions
res = matrix()
res.zero(self.dimx, other.dimy)
for i in range(self.dimx):
for j in range(other.dimy):
for k in range(self.dimy):
res.value[i][j] += self.value[i][k] * other.value[k][j]
return res
# ------------
#
# returns a matrix transpose
#
def transpose(self):
# compute transpose
res = matrix()
res.zero(self.dimy, self.dimx)
for i in range(self.dimx):
for j in range(self.dimy):
res.value[j][i] = self.value[i][j]
return res
# ------------
#
# creates a new matrix from the existing matrix elements.
#
# Example:
# l = matrix([[ 1, 2, 3, 4, 5],
# [ 6, 7, 8, 9, 10],
# [11, 12, 13, 14, 15]])
#
# l.take([0, 2], [0, 2, 3])
#
# results in:
#
# [[1, 3, 4],
# [11, 13, 14]]
#
#
# take is used to remove rows and columns from existing matrices
# list1/list2 define a sequence of rows/columns that shall be taken
# is no list2 is provided, then list2 is set to list1 (good for symmetric matrices)
#
def take(self, list1, list2=[]):
if list2 == []:
list2 = list1
if len(list1) > self.dimx or len(list2) > self.dimy:
raise (ValueError, "list invalid in take()")
res = matrix()
res.zero(len(list1), len(list2))
for i in range(len(list1)):
for j in range(len(list2)):
res.value[i][j] = self.value[list1[i]][list2[j]]
return res
# ------------
#
# creates a new matrix from the existing matrix elements.
#
# Example:
# l = matrix([[1, 2, 3],
# [4, 5, 6]])
#
# l.expand(3, 5, [0, 2], [0, 2, 3])
#
# results in:
#
# [[1, 0, 2, 3, 0],
# [0, 0, 0, 0, 0],
# [4, 0, 5, 6, 0]]
#
# expand is used to introduce new rows and columns into an existing matrix
# list1/list2 are the new indexes of row/columns in which the matrix
# elements are being mapped. Elements for rows and columns
# that are not listed in list1/list2
# will be initialized by 0.0.
#
def expand(self, dimx, dimy, list1, list2=[]):
if list2 == []:
list2 = list1
if len(list1) > self.dimx or len(list2) > self.dimy:
raise (ValueError, "list invalid in expand()")
res = matrix()
res.zero(dimx, dimy)
for i in range(len(list1)):
for j in range(len(list2)):
res.value[list1[i]][list2[j]] = self.value[i][j]
return res
def Cholesky(self, ztol=1.0e-5):
# Computes the upper triangular Cholesky factorization of
# a positive definite matrix.
# This code is based on http://adorio-research.org/wordpress/?p=4560
res = matrix()
res.zero(self.dimx, self.dimx)
for i in range(self.dimx):
S = sum([(res.value[k][i])**2 for k in range(i)])
d = self.value[i][i] - S
if abs(d) < ztol:
res.value[i][i] = 0.0
else:
if d < 0.0:
raise (ValueError, "Matrix not positive-definite")
res.value[i][i] = sqrt(d)
for j in range(i+1, self.dimx):
S = sum([res.value[k][i] * res.value[k][j] for k in range(i)])
if abs(S) < ztol:
S = 0.0
try:
res.value[i][j] = (self.value[i][j] - S)/res.value[i][i]
except Exception:
raise (ValueError, "Zero diagonal")
return res
# ------------
#
# Computes inverse of matrix given its Cholesky upper Triangular
# decomposition of matrix.
# This code is based on http://adorio-research.org/wordpress/?p=4560
def CholeskyInverse(self):
res = matrix()
res.zero(self.dimx, self.dimx)
# Backward step for inverse.
for j in reversed(range(self.dimx)):
tjj = self.value[j][j]
S = sum([self.value[j][k]*res.value[j][k]
for k in range(j+1, self.dimx)])
res.value[j][j] = 1.0 / tjj**2 - S / tjj
for i in reversed(range(j)):
res.value[i][j] = -sum([self.value[i][k] * res.value[k][j] for k in range(i + 1, self.dimx)]) \
/ self.value[i][i]
res.value[j][i] = res.value[i][j]
return res
def inverse(self):
aux = self.Cholesky()
res = aux.CholeskyInverse()
return res
# ------------
#
# prints matrix (Could be nicer!)
#
def __repr__(self):
return repr(self.value)