Added Serializable to all classes with a capnp write function (#3710)

* Added Serializable to all classes with a capnp write function

* Added getSchema to MovingAverage

* Fixed Serializable extensions

* getSchema returns prototype

* Safe import of capnp for moving average proto

src/nupic/algorithms/anomaly_likelihood.py

@@ -119,10 +119,11 @@
 import numbers
 import numpy
 
+from nupic.serializable import Serializable
 from nupic.utils import MovingAverage
 
 
-class AnomalyLikelihood(object):
+class AnomalyLikelihood(Serializable):
   """
   Helper class for running anomaly likelihood computation. To use it simply
   create an instance and then feed it successive anomaly scores:

src/nupic/algorithms/connections.py

@@ -22,6 +22,8 @@
 from bisect import bisect_left
 from collections import defaultdict
 
+from nupic.serializable import Serializable
+
 EPSILON = 0.00001 # constant error threshold to check equality of permanences to
                   # other floats
 
@@ -122,7 +124,7 @@ def binSearch(arr, val):
 
 
 
-class Connections(object):
+class Connections(Serializable):
   """ 
   Class to hold data representing the connectivity of a collection of cells. 
   

src/nupic/algorithms/knn_classifier.py

@@ -25,7 +25,7 @@
 
 from nupic.bindings.math import (NearestNeighbor, min_score_per_category)
 
-
+from nupic.serializable import Serializable
 
 g_debugPrefix = "KNN"
 KNNCLASSIFIER_VERSION = 1
@@ -61,7 +61,7 @@ def _labeledInput(activeInputs, cellsPerCol=32):
 
 
 
-class KNNClassifier(object):
+class KNNClassifier(Serializable):
   """
   This class implements NuPIC's k Nearest Neighbor Classifier. KNN is very
   useful as a basic classifier for many situations. This implementation contains

src/nupic/algorithms/sdr_classifier.py

@@ -30,9 +30,10 @@
 
 import numpy
 
+from nupic.serializable import Serializable
 
 
-class SDRClassifier(object):
+class SDRClassifier(Serializable):
   """
   The SDR Classifier accepts a binary input pattern from the
   level below (the "activationPattern") and information from the sensor and

src/nupic/algorithms/spatial_pooler.py

@@ -25,8 +25,7 @@
                                  GetNTAReal,
                                  Random as NupicRandom)
 from nupic.math import topology
-
-
+from nupic.serializable import Serializable
 
 realDType = GetNTAReal()
 uintType = "uint32"
@@ -89,7 +88,7 @@ class BinaryCorticalColumns(_SparseMatrixCorticalColumnAdapter,
 
 
 
-class SpatialPooler(object):
+class SpatialPooler(Serializable):
   """
   This class implements the spatial pooler. It is in charge of handling the
   relationships between the columns of a region and the inputs bits. The

src/nupic/algorithms/temporal_memory.py

@@ -29,14 +29,15 @@
 from operator import mul
 
 from nupic.algorithms.connections import Connections, binSearch
+from nupic.serializable import Serializable
 from nupic.support.group_by import groupby2
 
 EPSILON = 0.00001 # constant error threshold to check equality of permanences to
                   # other floats
 
 
 
-class TemporalMemory(object):
+class TemporalMemory(Serializable):
   """
   Class implementing the Temporal Memory algorithm. 
 

src/nupic/encoders/base.py

@@ -26,6 +26,7 @@
 import numpy
 
 from nupic.encoders.utils import bitsToString
+from nupic.serializable import Serializable
 
 defaultDtype = numpy.uint8
 
@@ -64,7 +65,7 @@ def _isSequence(obj):
 
 
 
-class Encoder(object):
+class Encoder(Serializable):
   """
   An encoder converts a value to a sparse distributed representation.
 

src/nupic/frameworks/opf/model.py

@@ -27,10 +27,10 @@
 from abc import ABCMeta, abstractmethod
 
 import nupic.frameworks.opf.opf_utils as opf_utils
+from nupic.serializable import Serializable
 
 
-
-class Model(object):
+class Model(Serializable):
   """ This is the base class that all OPF Model implementations should
   subclass.
 

src/nupic/regions/anomaly_likelihood_region.py

@@ -24,8 +24,10 @@
 from nupic.algorithms.anomaly_likelihood import AnomalyLikelihood
 from nupic.bindings.regions.PyRegion import PyRegion
 
+from nupic.serializable import Serializable
 
-class AnomalyLikelihoodRegion(PyRegion):
+
+class AnomalyLikelihoodRegion(PyRegion, Serializable):
   """Region for computing the anomaly likelihoods."""
 
 

src/nupic/regions/anomaly_region.py

@@ -26,9 +26,10 @@
 from nupic.algorithms import anomaly
 from nupic.bindings.regions.PyRegion import PyRegion
 
+from nupic.serializable import Serializable
 
 
-class AnomalyRegion(PyRegion):
+class AnomalyRegion(PyRegion, Serializable):
   """Region for computing the anomaly score."""
 
 

src/nupic/utils.py

@@ -26,9 +26,16 @@
 
 import numbers
 
+from nupic.serializable import Serializable
+try:
+  import capnp
+except ImportError:
+  capnp = None
+if capnp:
+  from nupic.movingaverage_capnp import MovingAverageProto
 
 
-class MovingAverage(object):
+class MovingAverage(Serializable):
   """Helper class for computing moving average and sliding window"""
 
 
@@ -127,3 +134,9 @@ def write(self, proto):
     proto.windowSize = self.windowSize
     proto.slidingWindow = self.slidingWindow
     proto.total = self.total
+
+
+  @classmethod
+  def getSchema(cls):
+    return MovingAverageProto
+