wassname/scikit-image
1
0
Fork 0
mirror of https://github.com/wassname/scikit-image.git synced 2026-06-29 17:05:09 +08:00
Code Issues Packages Projects Releases Wiki Activity

updating tests

Browse Source
This commit is contained in:
Kevin Keraudren
2016-05-08 08:30:47 +01:00
parent 9fa6548f7d
commit 307a4e9936
1 changed files with 14 additions and 13 deletions
Download Patch File Download Diff File Expand all files Collapse all files
skimage/measure/tests/test_fit.py
+14 -13
View File
@@ -32,7 +32,8 @@ def test_line_model_estimate():
model_est.estimate(data)
# test whether estimated parameters almost equal original parameters
x = np.random.rand(100, 2)
random_state = np.random.RandomState(1234)
x = random_state.rand(100, 2)
assert_almost_equal(model0.predict(x), model_est.predict(x), 1)
@@ -75,8 +76,8 @@ def test_line_modelND_estimate():
10 * np.arange(-100,100)[...,np.newaxis] * model0.params[1])
# add gaussian noise to data
np.random.seed(1234)
data = data0 + np.random.normal(size=data0.shape)
random_state = np.random.RandomState(1234)
data = data0 + random_state.normal(size=data0.shape)
# estimate parameters of noisy data
model_est = LineModelND()
@@ -130,8 +131,8 @@ def test_circle_model_estimate():
data0 = model0.predict_xy(t)
# add gaussian noise to data
np.random.seed(1234)
data = data0 + np.random.normal(size=data0.shape)
random_state = np.random.RandomState(1234)
data = data0 + random_state.normal(size=data0.shape)
# estimate parameters of noisy data
model_est = CircleModel()
@@ -172,8 +173,8 @@ def test_ellipse_model_estimate():
data0 = model0.predict_xy(t)
# add gaussian noise to data
np.random.seed(1234)
data = data0 + np.random.normal(size=data0.shape)
random_state = np.random.RandomState(1234)
data = data0 + random_state.normal(size=data0.shape)
# estimate parameters of noisy data
model_est = EllipseModel()
@@ -206,7 +207,8 @@ def test_ransac_shape():
data0[outliers[2], :] = (-100, -10)
# estimate parameters of corrupted data
model_est, inliers = ransac(data0, CircleModel, 3, 5, random_state=np.random.RandomState(1))
model_est, inliers = ransac(data0, CircleModel, 3, 5,
random_state=1)
# test whether estimated parameters equal original parameters
assert_equal(model0.params, model_est.params)
@@ -230,7 +232,8 @@ def test_ransac_geometric():
dst[outliers[2]] = (50, 50)
# estimate parameters of corrupted data
model_est, inliers = ransac((src, dst), AffineTransform, 2, 20, random_state=random_state)
model_est, inliers = ransac((src, dst), AffineTransform, 2, 20,
random_state=random_state)
# test whether estimated parameters equal original parameters
assert_almost_equal(model0.params, model_est.params)
@@ -238,22 +241,20 @@ def test_ransac_geometric():
def test_ransac_is_data_valid():
np.random.seed(1)
is_data_valid = lambda data: data.shape[0] > 2
model, inliers = ransac(np.empty((10, 2)), LineModelND, 2, np.inf,
is_data_valid=is_data_valid)
is_data_valid=is_data_valid, random_state=1)
assert_equal(model, None)
assert_equal(inliers, None)
def test_ransac_is_model_valid():
np.random.seed(1)
def is_model_valid(model, data):
return False
model, inliers = ransac(np.empty((10, 2)), LineModelND, 2, np.inf,
is_model_valid=is_model_valid)
is_model_valid=is_model_valid, random_state=1)
assert_equal(model, None)
assert_equal(inliers, None)