def test_fit_point2_vline(self):
reference_points = [[0, -2], [0, 4]]
target_distances = [2, 4]
expected = [0, 0]
point, t = fit_point(reference_points, target_distances)
self.assertTrue(
math.isclose(distance(point, expected), 0, abs_tol=0.0001), point)
self.assertLess(t, SMALL_MAX_T)
def test_fit_point1_d0(self):
reference_point = [5, 3]
target_distance = 0
point, t = fit_point([reference_point], [target_distance])
self.assertTrue(
math.isclose(distance(point, reference_point), 0, abs_tol=.0001),
point)
self.assertLess(t, SMALL_MAX_T)
def test_fit_point3(self):
reference_points = [[math.sqrt(8), math.sqrt(8)], [0, -4], [-4, 0]]
target_distances = [4, 4, 4]
expected = [0, 0]
point, t = fit_point(reference_points,
target_distances,
visualize=True)
self.assertTrue(
math.isclose(distance(point, expected), 0, abs_tol=0.0001), point)
self.assertLess(t, SMALL_MAX_T)
def test_fit_point2_hline(self):
reference_points = [[-1, 0], [5, 0]]
target_distances = [2, 4]
expected = [1, 0]
point, t = fit_point(reference_points,
target_distances,
visualize=True)
self.assertTrue(
math.isclose(distance(point, expected), 0, abs_tol=0.0001), point)
self.assertLess(t, SMALL_MAX_T)
def test_fit_point(self):
reference_points_1 = [[0, -2, 3], [1, 0, -6], [2, 4, 9], [3, 6, -12]]
#reference_points_1 = [[0, -2, 3], [1, 0, -6], [0, 4, 9], [3, 6, -12]]
reference_points_2 = [[3, -2, 3], [2, 0, -6], [1, 4, 9], [0, 6, -12]]
target_distances_1 = [5, 4, 3, 2]
target_distances_2 = [1, 2, 3, 4]
#target_distances_2 = [1, 1, 4, 3]
point_11, t_11 = fit_point(reference_points_1,
target_distances_1,
visualize=True)
self.assertTrue(all([not math.isnan(p) for p in point_11]), point_11)
self.assertLess(t_11, BIG_MAX_T)
point_12, t_12 = fit_point(reference_points_1,
target_distances_2,
visualize=True)
self.assertTrue(all([not math.isnan(p) for p in point_12]), point_12)
self.assertLess(t_12, BIG_MAX_T)
point_21, t_21 = fit_point(reference_points_2,
target_distances_1,
visualize=True)
self.assertTrue(all([not math.isnan(p) for p in point_21]), point_21)
self.assertLess(t_21, BIG_MAX_T)
point_22, t_22 = fit_point(reference_points_2,
target_distances_2,
visualize=True)
self.assertTrue(all([not math.isnan(p) for p in point_22]), point_22)
self.assertLess(t_22, BIG_MAX_T)
self.assertGreater(distance(point_11, point_12), 1)
self.assertGreater(distance(point_11, point_21), 1)
self.assertGreater(distance(point_11, point_22), 1)
self.assertGreater(distance(point_12, point_21), 1)
self.assertGreater(distance(point_12, point_22), .75)
self.assertGreater(distance(point_21, point_22), 1)
def _fit_top_k(self, predictions):
## Fitting across top-k points.
colour_probabilities = {}
for output, probability in predictions.items():
self.mapped_output(output)
colour = self.output_colour(output)
if colour not in colour_probabilities:
colour_probabilities[colour] = 0
colour_probabilities[colour] += probability
colour, probability = max(colour_probabilities.items(),
key=lambda item: item[1])
total = sum(colour_probabilities.values())
fit = geometry.fit_proportion((colour, [255, 255, 255]),
(total - probability, probability))
return "rgb(%d, %d, %d)" % tuple([round(i) for i in fit])
maximum_distance = None
for pair in itertools.combinations(
[colour for colour in colour_probabilities.keys()], 2):
distance = geometry.distance(pair[0], pair[1])
if maximum_distance is None or distance > maximum_distance:
maximum_distance = distance
inverted = [
item for item in mlbase.regmax(
{c: 1.0 - p
for c, p in colour_probabilities.items()}).items()
]
fit, _ = geometry.fit_point(
[item[0] for item in inverted],
[item[1] * maximum_distance for item in inverted])
return "rgb(%d, %d, %d)" % tuple([round(i) for i in fit])
}
#for word, probability in predictions[key].items():
# colour = self.colour_embeddings[word]
#
# if colour not in interpolation_points:
# interpolation_points[colour] = (word, probability)
# else:
# if interpolation_points[colour][1] < probability:
# interpolation_points[colour] = (word, probability)
#
if len(interpolation_points) == 1:
colours[key] = "rgb(%d, %d, %d)" % next(iter(interpolation_points.keys()))
else:
maximum_distance = None
for pair in itertools.combinations([colour for colour in interpolation_points.keys()], 2):
distance = geometry.distance(pair[0], pair[1])
if maximum_distance is None or distance > maximum_distance:
maximum_distance = distance
lowest_probability = min([p for w, p in interpolation_points.values()])
highest_probability = max([p for w, p in interpolation_points.values()])
maximum_domain = highest_probability + lowest_probability
prediction_distances = [(w, maximum_distance + (-p * maximum_distance / maximum_domain)) for w, p in interpolation_points.values()]
fit, _ = geometry.fit_point([colour_embeddings[item[0]] for item in prediction_distances], [item[1] for item in prediction_distances], epsilon=0.1, visualize=False)
print("rgb(%d, %d, %d)" % tuple([round(i) for i in fit]))