This video clip is demonstrating the Castor dimension reduction first
system this time using the animals dataset instead of the
state's data set. So the computational pipeline for this
tool is we start off with the set of weights
again all initialized to one and our high dimensional data
and we create a global projection from that high dimensional
space. After we've created the projection, then we
start running K means on the projection on the two
dimensional data in order to generate clusters. And so
now we don't have the same cluster overdraw that we
saw in Gemini, there's a little bit of overdraw
here, but it's definitely not as significant because while
we're trying to create nice, round, convex hulls. And
so again, we can start mapping some semantic meaning
to some of these clusters. So here we've got
a bunch of aquatic dwelling animals, a beaver and
an otter and a dolphin. Here are some larger
herbivorous animals, a pig and a buffalo and a
rhinoceros, but also a walrus and a humpback whale.
Similar cluster here, larger herbivorous animals, draft
and deer and horse and moose some other animals that
are herbivorous squirrel and spider monkey and skunk. We've
got a bunch of pets here a Chihuahua, Persian
cat and a dalmatian. And we've got a lot of
predators up here in this cluster, a leopard,
a bobcat and a tiger. Well, these don't
exactly match up to what we want because rats and
raccoons and weasels aren't necessarily predators and grizzly bears and
polar bears definitely are not pets. And so this
initial projection is a good start, but we want
to train the system to learn something else about our
data. And so maybe the first interaction that I
want to do to replicate the use case that's shown
in chapter six is to grab the grizzly bear and
pull the grizzly bear up into the predators cluster when
I do that. Well, the blue again indicates
which nodes recently changed cluster assignments and the lines indicate
which cluster they have moved from. And so now
we've got a decent predators cluster because the rat,
the raccoon and the weasel all got pulled away from
our predators. The polar bear also did get pulled
away from our pets cluster, which is a good
thing for sure. It got added to our aquatic
animals cluster. And that makes sense because polar bears
spend a decent amount of time in the water.
It's swim score on the 0 to 100 scale is
a 39.17 and its water score is 45.9. And
so it definitely fits in an aquatic cluster, maybe
not quite as well as a beaver and an otter
do, but it's still an aquatic animal. But
we're interested in the ferocity component of the polar bear.
And so the next interaction that I might perform
is to grab the polar bear and move it up
into our predators cluster as well. So as I've
done that the system is trying to come up with
a nice low energy projection state. Our polar bear
and grizzly bear are still up in our predators cluster.
Our predators cluster still maintains its old membership
assignments. Nothing else got added in or removed.
Now are humpback whale are blue whale and our walrus
have all gotten added to our aquatic animals cluster,
which makes a good bit of sense. We've added
the mouse and the hamster to our pets cluster,
which makes a decent amount of sense. There's still
a raccoon in this cluster; there's still a mole
in this cluster that we could get rid of with
some further interactions. We've also started to condense down
this into a larger single cluster for large, herbivorous
animals. And so now joining the pig in the
buffalo and the elephant, for example, are a
cow and an ox and a moose and a sheep.
And, so we could continue this set of interactions
to reclassify and learn new information about our data.