def run_cras(row_totals: numpy.matrix,
column_totals: numpy.matrix,
conditions: dict,
maximum_iterations: int=10000,
tol: float=0.00001) -> numpy.matrix:
"""
:param row_totals:
:param column_totals:
:param conditions: a condition is a group of cells and a total, dict(tuple(tuples): float),
a cell is (row_index, col_index)
:param maximum_iterations: big number to stop this running forever
:param tol: how close is close enough?
:return:
"""
# TODO remove this hack [max(thing.shape)]
a = numpy.matrix(numpy.ones(shape=(max(row_totals.shape), max(column_totals.shape))))
e = numpy.matrix(numpy.ones(shape=(len(row_totals), 1)))
for _ in range(maximum_iterations):
r_hat = row_scaling(a, row_totals, e)
a = matrix_multiply(r_hat, a)
s_hat = column_scaling(a, column_totals, e)
a = matrix_multiply(a, s_hat)
a = apply_conditions(a, conditions)
if is_matrix_close_to_i(r_hat, tol) and is_matrix_close_to_i(s_hat, tol):
return a
return a
def run_ras(row_totals: numpy.matrix,
column_totals: numpy.matrix,
maximum_iterations: int=10000,
a: numpy.matrix=None,
tol: float=0.00001) -> numpy.matrix:
"""
creates a matrix who's respective rows sum to the row_totals and who's respective columns sum to the column_totals
within the tolerance.
:param row_totals:
:param column_totals:
:param maximum_iterations: big number to stop this running forever
:param tol: how close is close enough?
:return:
"""
if not a:
a = numpy.matrix(numpy.ones(shape=(row_totals.shape[0], column_totals.shape[1])))
e = numpy.matrix(numpy.ones(shape=row_totals.shape))
for _ in range(maximum_iterations):
r_hat = row_scaling(a, row_totals, e)
a = matrix_multiply(r_hat, a)
s_hat = column_scaling(a, column_totals, e)
a = matrix_multiply(a, s_hat)
if is_matrix_close_to_i(r_hat, tol) and is_matrix_close_to_i(s_hat, tol):
return a
return a
def test_negative_i(self):
is_it = is_matrix_close_to_i(numpy.identity(4) * -1, 0.00001)
self.assertFalse(is_it)
def test_near_identity(self):
is_it = is_matrix_close_to_i(numpy.identity(3) + 0.001 * numpy.identity(3), 0.01)
self.assertTrue(is_it)
def test_ones(self):
is_it = is_matrix_close_to_i(numpy.ones((3, 3)), 0.1)
self.assertFalse(is_it)
def test_type(self):
is_it = is_matrix_close_to_i(numpy.matrix([[]]), 0)
self.assertTrue(type(is_it), bool)
