def test_avg_list(self):
data = [i for i in range(20)]
normdata = norms.avg_reduction(data)
def test_avg_col_matrix(self):
data = np.arange(5).reshape(5, 1)
normdata = norms.avg_reduction(data)
has_reduced = np.all(abs(normdata + 2 - np.array(data)) <= 0.0001)
self.assertTrue(has_reduced)
def test_avg_empty_col(self):
data = np.empty(5).reshape(5, 1)
normdata = norms.avg_reduction(data)
self.assertTrue(np.all(data == []))
def test_avg_2d(self):
data = np.ones((3, 4)) + np.arange(4)
normdata = norms.avg_reduction(data)
has_reduced_at_cols = np.all(abs(normdata) <= 0.0001)
self.assertTrue(has_reduced_at_cols)
def test_avg_negative_data(self):
data = np.arange(20) * -1
normdata = norms.avg_reduction(data)
negative_avg = all(abs(normdata - 9.5 - np.array(data)) <= 0.0001)
self.assertTrue(negative_avg)
def test_avg_zeros(self):
data = np.zeros(20)
normdata = norms.avg_reduction(data)
self.assertTrue(all(normdata == 0))
def test_avg_constant_data(self):
data = [1] * 20
normdata = norms.avg_reduction(data)
allnormed = all(0 <= nd <= 0.001 for nd in normdata)
self.assertTrue(allnormed)
def test_avg_is_reducing(self):
data = [i for i in range(20)]
normdata = norms.avg_reduction(data)
allnormed = all(abs(normdata + 9.5 - np.array(data)) <= 0.0001)
self.assertTrue(allnormed)