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
def mean(self):
"""returns the mean value"""
return util.mean(self.values)
def mean(self):
"""returns the mean value"""
return util.mean(self.values)
def test_mean_with_nans(self):
"""tests the mean() function"""
array = np.array([2.0, 3.0, np.nan, 1.0])
result = util.mean(array)
self.assertAlmostEqual(2.0, result)
def test_mean_with_nans(self):
"""tests the mean() function"""
array = np.array([2.0, 3.0, np.nan, 1.0])
result = util.mean(array)
self.assertAlmostEqual(2.0, result)
