def test_matrix_centred_on_mean_has_dat_0():
d = ClassroomHeights(10, 10)
rows = []
n_rows = np.shape(d.m)[0]
for i in range(n_rows):
row = d.m[i, :]
mean = to_test.row_mean_of(row)
rows.append(row - mean)
m = np.asmatrix(np.stack(rows))
assert math.isclose(np.linalg.det(m), 0., abs_tol=1e-3)
import distributions.covariance as cov
import graphics.files as f
import graphics.heatmap as g
from data.ClassroomHeights import ClassroomHeights
if __name__ == "__main__":
d = ClassroomHeights(10, 100)
(height, width) = np.shape(d.m)
# add some dependency
rows = []
for i in range(height):
row = d.m[i, :]
if r.uniform(0, 1) < 0.05:
ignore_1 = row[:, 0:-1]
mean = cov.row_mean_of(ignore_1)
centred = ignore_1 - mean
new_row = np.append([0.], centred)
rows.append(np.asmatrix(new_row))
else:
rows.append(row - cov.row_mean_of(row))
c = cov.squared_with_bessel(np.asmatrix(np.stack(rows)).T)
p = np.linalg.inv(c)
(ws, vs) = np.linalg.eig(c)
print('Number of eigenvalues = '.format(len(ws)))
for w in ws:
print('eigenvalue = {}'.format(w))
g.add_heatmap(d.m, "Data")
def test_mean_centred_matrix_needs_conditioning():
x = to_test.row_mean_of(m)
with pytest.raises(Exception) as e:
np.linalg.inv(x)
pytest.fail(e)
def test_mean_of_matrix():
mean = to_test.row_mean_of(m)
assert np.array_equal(mean.T, np.asmatrix([3.5, 1.5]))
def test_mean_of_row_vector():
assert to_test.row_mean_of(v) == 2.5
def test_mean_of_col_vector():
mean = to_test.row_mean_of(v.T)
assert np.array_equal(mean, v.T), "actual = {}".format(mean)