def _closest(self, fpoint, centroids):
closest_index = None
closest_distance = None
for i, point in enumerate(centroids):
dist = euclidean_distance(self.X[fpoint], point)
if closest_index is None or dist < closest_distance:
closest_index = i
closest_distance = dist
return closest_index
def _closest(self, fpoint, centroids):
closest_index = None
closest_distance = None
for i, point in enumerate(centroids):
dist = euclidean_distance(self.X[fpoint], point)
if closest_index is None or dist < closest_distance:
closest_index = i
closest_distance = dist
return closest_index
def _is_converged(self, centroids_old, centroids):
"""Check if the distance between old and new centroids is zero."""
distance = 0
for i in range(self.K):
distance += euclidean_distance(centroids_old[i], centroids[i])
return distance == 0
def _dist_from_centers(self):
"""Calculate distance from centers."""
return np.array([
min([euclidean_distance(x, c) for c in self.centroids])
for x in self.X
])
def test_euclidean_distance():
assert euclidean_distance([1, 2, 3], [3, 2, 1]) == math.sqrt(8)
assert euclidean_distance([1, 2, 1], [3, 2, 1]) != math.sqrt(8)
with pytest.raises(ValueError):
euclidean_distance([1, 2], [3, 2, 1]) != math.sqrt(8)
def _is_converged(self, centroids_old, centroids):
return True if sum([euclidean_distance(centroids_old[i], centroids[i]) for i in range(self.K)]) == 0 else False
def _dist_from_centers(self):
return np.array([min([euclidean_distance(x, c) for c in self.centroids]) for x in self.X])
def _is_converged(self, centroids_old, centroids):
"""Check if the distance between old and new centroids is zero."""
distance = 0
for i in range(self.K):
distance += euclidean_distance(centroids_old[i], centroids[i])
return distance == 0
def _dist_from_centers(self):
"""Calculate distance from centers."""
return np.array([min([euclidean_distance(x, c) for c in self.centroids]) for x in self.X])
def _is_converged(self, centroids_old, centroids):
return True if sum([
euclidean_distance(centroids_old[i], centroids[i])
for i in range(self.K)
]) == 0 else False
def _dist_from_centers(self):
return np.array([
min([euclidean_distance(x, c) for c in self.centroids])
for x in self.X
])
