A platform for building language / NLP-based applications using NuPIC and Cortical.io's REST. The current version is v0.2.
###NOTE this repo contains experimental code. A few disclaimers:
- the contents may
- change without warning or explanation
- change quickly, or not at all
- not function properly
- be buggy and sloppy; don't judge :)
- work with external partners will not be included here
- we might decide at some point to not do our NLP research in the open anymore and instead delete the whole repository
The motivation here is we would like to move quickly in research while maintaining transparency.
Requirements:
You must have a valid REST API key from Cortical.io.
To install, run:
python setup.py install
Then, set up the following environment variables with your REST API credentials:
export CORTICAL_API_KEY=api_key
from fluent.model import Model
from fluent.term import Term
model = Model()
term1 = Term().createFromString("coyote")
term2 = Term().createFromString("eats")
term3 = Term().createFromString("mouse")
# Train
for _ in range(3):
model.feedTerm(term1)
model.feedTerm(term2)
model.feedTerm(term3)
model.resetSequence()
# Test
term4 = Term().createFromString("wolf")
model.feedTerm(term4)
prediction = model.feedTerm(term2)
print prediction.closestString()
# => "mouse"
The read tool can read a text document word-by-word, predicting each next word as it goes. You can find it at tools/read.py.
Here is an example (after some training):
=> ./tools/read.py data/childrens_stories.txt --checkpoint=cache/model
Sequence # | Term # | Current Term | Predicted Term
----------------------------------------------------------------------
1 | 1 | The | woods
1 | 2 | Ugly | duckling
1 | 3 | Duckling |
3 | 1 | A | duck
3 | 2 | duck | the
3 | 3 | made | her
3 | 4 | her | nest
3 | 5 | nest | under
3 | 6 | under | some
3 | 7 | some | leaves
3 | 8 | leaves | she
4 | 1 | She | sat
4 | 2 | sat | unpopular
4 | 3 | on | the
4 | 4 | the | eggs
4 | 5 | eggs |
4 | 6 | to | keep
4 | 7 | keep | them
4 | 8 | them | warm
...
To run the Fox demo:
=> ./tools/read.py data/associations/foxeat.txt -r
Sequence # | Term # | Current Term | Predicted Term
----------------------------------------------------------------------
1 | 1 | frog |
1 | 2 | eat |
1 | 3 | flies |
2 | 1 | cow |
2 | 2 | eat | flies
2 | 3 | grain |
3 | 1 | elephant |
3 | 2 | eat | grain
3 | 3 | leaves |
4 | 1 | goat |
4 | 2 | eat | grain
4 | 3 | grass |
5 | 1 | wolf |
5 | 2 | eat | grass
5 | 3 | rabbit |
6 | 1 | cat |
6 | 2 | likes |
6 | 3 | ball |
7 | 1 | elephant | eat
7 | 2 | likes |
7 | 3 | water |
8 | 1 | sheep |
8 | 2 | eat | grass
8 | 3 | grass |
9 | 1 | cat | likes
9 | 2 | eat |
9 | 3 | salmon |
10 | 1 | wolf | eat
10 | 2 | eat | rabbit
10 | 3 | mice |
11 | 1 | lion |
11 | 2 | eat | grass
11 | 3 | cow | eat
12 | 1 | dog |
12 | 2 | likes | water
12 | 3 | sleep |
13 | 1 | coyote |
13 | 2 | eat | grass
13 | 3 | mice |
14 | 1 | coyote | eat
14 | 2 | eat | mice
14 | 3 | rodent |
15 | 1 | coyote | eat
15 | 2 | eat | rodent
15 | 3 | rabbit |
16 | 1 | wolf | eat
16 | 2 | eat | mice
16 | 3 | squirrel |
17 | 1 | cow | eat
17 | 2 | eat | grain
...
If you want to use a different CEPT retina, you'll have to make changes in the following places:
- Specify which retina you want to use in
pycept.Cept(...)influent/cept.py. - Update
numberOfColsinfluent/model.py.