def _closest(self, fpoint, centroids):
closest_index = None
closest_distance = None
for i, point in enumerate(centroids):
dist = euclidian_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):
return True if sum([
euclidian_distance(centroids_old[i], centroids[i])
for i in range(self.K)
]) == 0 else False
def _dist_from_centers(self):
return np.array([
min([euclidian_distance(x, c) for c in self.centroids])
for x in self.X
])