I'd like to detect anomalies using the algorithm API listed in the algorithm API of the Nupic 1.0.1 documentation

tokizaki · November 1, 2017, 8:43am

Although I succeeded in detecting anomalies at run.py in the tutorial, I do not know how to detect anomalies in programs listed in the 1.0.1 Quick Start algorithm API.
The imported nupic module is completely different from run.py and algorithm API program, so it can not be copied and pasted, and it seems that algorithm API does not refer to inferenceType of model_params.
The program of the algorithm API is modified not to read from the yaml file but to read the parameters from model_params, so the prediction works.
How do I become able to detect anomalies with 1.0.1 Quick Start program?

rhyolight · November 1, 2017, 1:15pm

Here is the complete running script from the docs you can start with:

github.com

numenta/nupic/blob/master/docs/examples/algo/complete-algo-example.py

import csv
import datetime
import numpy
import os
import yaml

from nupic.algorithms.sdr_classifier_factory import SDRClassifierFactory
from nupic.algorithms.spatial_pooler import SpatialPooler
from nupic.algorithms.temporal_memory import TemporalMemory
from nupic.encoders.date import DateEncoder
from nupic.encoders.random_distributed_scalar import \
  RandomDistributedScalarEncoder

_NUM_RECORDS = 3000
_EXAMPLE_DIR = os.path.dirname(os.path.abspath(__file__))
_INPUT_FILE_PATH = os.path.join(_EXAMPLE_DIR, os.pardir, "data", "gymdata.csv")
_PARAMS_PATH = os.path.join(_EXAMPLE_DIR, os.pardir, "params", "model.yaml")

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tokizaki · November 1, 2017, 5:45pm

Yes. I have not seen inferenceType though I tried it.
How can we detect anomalies with this program?

whereandtear · April 10, 2019, 3:29pm

Crossposting to my reply in this relevant thread.

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I'd like to detect anomalies using the algorithm API listed in the algorithm API of the Nupic 1.0.1 documentation

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