def test_r_outer(self):
"""tests the r_outer function"""
result = util.r_outer([5.5, 6.5], [4.5, 7.5], operator.add)
self.assertAlmostEqual(10.0, result[0][0])
self.assertAlmostEqual(13.0, result[0][1])
self.assertAlmostEqual(11.0, result[1][0])
self.assertAlmostEqual(14.0, result[1][1])
def residual(self, max_row_variance=None):
"""computes the residual for this matrix, if max_row_variance is given,
result is normalized by the row variance"""
d_rows = util.row_means(self.values)
d_cols = util.column_means(self.values)
d_all = util.mean(d_rows)
tmp = self.values + d_all - util.r_outer(d_rows, d_cols, operator.add)
average = util.mean(np.abs(tmp))
if max_row_variance is not None:
row_var = self.row_variance()
if np.isnan(row_var) or row_var > max_row_variance:
row_var = max_row_variance
average = average / row_var
return average
def residual(self, max_row_variance=None):
"""computes the residual for this matrix, if max_row_variance is given,
result is normalized by the row variance"""
d_rows = util.row_means(self.values)
d_cols = util.column_means(self.values)
d_all = util.mean(d_rows)
tmp = self.values + d_all - util.r_outer(d_rows, d_cols, operator.add)
average = util.mean(np.abs(tmp))
if max_row_variance is not None:
row_var = self.row_variance()
if np.isnan(row_var) or row_var > max_row_variance:
row_var = max_row_variance
average = average / row_var
return average
