Reorganize the source under src/dunshire.

[dunshire.git] / matrices.py

diff --git a/matrices.py b/matrices.py
deleted file mode 100644 (file)
index a33259e..0000000
--- a/matrices.py
+++ /dev/null
@@ -1,84 +0,0 @@
-"""
-Utility functions for working with CVXOPT matrices (instances of the
-``cvxopt.base.matrix`` class).
-"""
-
-from math import sqrt
-from cvxopt import matrix
-
-def append_col(left, right):
-    """
-    Append the matrix ``right`` to the right side of the matrix ``left``.
-
-    EXAMPLES:
-
-        >>> A = matrix([1,2,3,4], (2,2))
-        >>> B = matrix([5,6,7,8,9,10], (2,3))
-        >>> print(append_col(A,B))
-        [  1   3   5   7   9]
-        [  2   4   6   8  10]
-        <BLANKLINE>
-
-    """
-    return matrix([left.trans(), right.trans()]).trans()
-
-def append_row(top, bottom):
-    """
-    Append the matrix ``bottom`` to the bottom of the matrix ``top``.
-
-    EXAMPLES:
-
-        >>> A = matrix([1,2,3,4], (2,2))
-        >>> B = matrix([5,6,7,8,9,10], (3,2))
-        >>> print(append_row(A,B))
-        [  1   3]
-        [  2   4]
-        [  5   8]
-        [  6   9]
-        [  7  10]
-        <BLANKLINE>
-
-    """
-    return matrix([top, bottom])
-
-def identity(domain_dim):
-    """
-    Return a ``domain_dim``-by-``domain_dim`` dense integer identity
-    matrix.
-
-    EXAMPLES:
-
-       >>> print(identity(3))
-       [ 1  0  0]
-       [ 0  1  0]
-       [ 0  0  1]
-       <BLANKLINE>
-
-    """
-    if domain_dim <= 0:
-        raise ValueError('domain dimension must be positive')
-
-    entries = [int(i == j)
-               for i in range(domain_dim)
-               for j in range(domain_dim)]
-    return matrix(entries, (domain_dim, domain_dim))
-
-
-def norm(matrix_or_vector):
-    """
-    Return the Frobenius norm of ``matrix_or_vector``, which is the same
-    thing as its Euclidean norm when it's a vector (when one of its
-    dimensions is unity).
-
-    EXAMPLES:
-
-        >>> v = matrix([1,1])
-        >>> print('{:.5f}'.format(norm(v)))
-        1.41421
-
-        >>> A = matrix([1,1,1,1], (2,2))
-        >>> norm(A)
-        2.0
-
-    """
-    return sqrt(sum([x**2 for x in matrix_or_vector]))