def test_list(self):
"""
Accept Python list as input.
"""
a = [(0, 0), (0, 0)]
np.testing.assert_almost_equal([0, 0],
geometric_median(a, method='minimize'))
np.testing.assert_almost_equal([0, 0],
geometric_median(a, method='weiszfeld'))
def test_square(self):
"""
Simple square, median point obviously in the middle.
"""
a = np.array([(0, 0), (1, 0), (1, 1), (0, 1)])
np.testing.assert_almost_equal([0.5, 0.5],
geometric_median(a, method='minimize'))
np.testing.assert_almost_equal([0.5, 0.5],
geometric_median(a, method='weiszfeld'))
def test_1d(self):
"""
Geometric median should be median in 1D.
"""
a = np.array([[8, 6, 7, 5, 3, 0, 9]]).T
np.testing.assert_almost_equal(np.median(a),
geometric_median(a, method='minimize'))
np.testing.assert_almost_equal(np.median(a),
geometric_median(a, method='weiszfeld'))
def test_require2d(self):
"""
Accepting 1D input has too much error potential.
1 row of N dims or N rows of 1?
"""
a = [1, 2]
with self.assertRaises(ValueError):
np.testing.assert_almost_equal(
a, geometric_median(a, method='minimize'))
with self.assertRaises(ValueError):
np.testing.assert_almost_equal(
a, geometric_median(a, method='weiszfeld'))
def test_collinear(self):
"""
Similar to the 1D case.
"""
a = np.array([8, 6, 7, 5, 3, 0, 9])
median = np.median(a)
expected = [median, median]
a = np.array(zip(a, a))
np.testing.assert_almost_equal(expected,
geometric_median(a, method='minimize'))
np.testing.assert_almost_equal(expected,
geometric_median(a, method='weiszfeld'))
def main():
# create a bunch of point datasets for testing
n_tests = 10
n_points = 20
n_dims = 10
np.random.seed(0)
test_data = np.random.uniform(size=(n_tests, n_points, n_dims))
methods = [
'minimize',
'weiszfeld',
'auto'
]
test_results = []
for points in test_data:
results = []
for method in methods:
start_t = time()
m = geometric_median(points, method=method)
end_t = time()
t = end_t - start_t
d = total_distance([m], points)
results.append((t, d))
test_results.append(results)
test_results = np.array(test_results)
width = 0.90 / test_results.shape[1]
bar_positions = np.arange(test_results.shape[0])
colors = ['b', 'r', 'g']
# Display computation time
for method_i, method in enumerate(methods):
method_times = test_results[:,method_i,0]
plt.bar(
bar_positions + (width * method_i),
method_times,
width,
color=colors[method_i],
label=method
)
plt.xticks(bar_positions)
plt.ylabel("Computation time")
plt.xlabel("Trials")
plt.legend()
plt.show()
# Display result quality (closeness)
'''
def _test_4coplanar(self):
"""
From Wikipedia:
If 1 of the 4 points is inside the triangle formed by the other 3
points, then the geometric median is that point.
This is failing.
"""
a = [(0, 0), (1, 0), (1, 1)]
# TODO: assert that b is inside a
b = [0.90, 0.90]
a = np.r_[a, [b]]
np.testing.assert_almost_equal(b, geometric_median(a,
method='minimize'))
np.testing.assert_almost_equal(b,
geometric_median(a, method='weiszfeld'))
def main():
# create a bunch of point datasets for testing
n_tests = 10
n_points = 20
n_dims = 10
np.random.seed(0)
test_data = np.random.uniform(size=(n_tests, n_points, n_dims))
methods = ['minimize', 'weiszfeld', 'auto']
test_results = []
for points in test_data:
results = []
for method in methods:
start_t = time()
m = geometric_median(points, method=method)
end_t = time()
t = end_t - start_t
d = total_distance([m], points)
results.append((t, d))
test_results.append(results)
test_results = np.array(test_results)
width = 0.90 / test_results.shape[1]
bar_positions = np.arange(test_results.shape[0])
colors = ['b', 'r', 'g']
# Display computation time
for method_i, method in enumerate(methods):
method_times = test_results[:, method_i, 0]
plt.bar(bar_positions + (width * method_i),
method_times,
width,
color=colors[method_i],
label=method)
plt.xticks(bar_positions)
plt.ylabel("Computation time")
plt.xlabel("Trials")
plt.legend()
plt.show()
# Display result quality (closeness)
'''
def test_1point(self):
a = [(5, 5)]
np.testing.assert_almost_equal(a[0],
geometric_median(a, method='minimize'))
np.testing.assert_almost_equal(a[0],
geometric_median(a, method='weiszfeld'))