def testWriteRead(self):
c1 = CLAClassifier([1], 0.1, 0.1, 0)
# Create a vector of input bit indices
input1 = [1, 5, 9]
result = c1.compute(recordNum=0,
patternNZ=input1,
classification={'bucketIdx': 4, 'actValue': 34.7},
learn=True, infer=True)
proto1 = ClaClassifier_capnp.ClaClassifierProto.new_message()
c1.write(proto1)
# Write the proto to a temp file and read it back into a new proto
with tempfile.TemporaryFile() as f:
proto1.write(f)
f.seek(0)
proto2 = ClaClassifier_capnp.ClaClassifierProto.read(f)
# Load the deserialized proto
c2 = CLAClassifier.read(proto2)
self.assertEqual(c1.steps, c2.steps)
self.assertAlmostEqual(c1.alpha, c2.alpha)
self.assertAlmostEqual(c1.actValueAlpha, c2.actValueAlpha)
self.assertEqual(c1._learnIteration, c2._learnIteration)
self.assertEqual(c1._recordNumMinusLearnIteration, c2._recordNumMinusLearnIteration)
self.assertEqual(c1._patternNZHistory, c2._patternNZHistory)
self.assertEqual(c1._activeBitHistory.keys(), c2._activeBitHistory.keys())
for bit, nSteps in c1._activeBitHistory.keys():
c1BitHistory = c1._activeBitHistory[(bit, nSteps)]
c2BitHistory = c2._activeBitHistory[(bit, nSteps)]
self.assertEqual(c1BitHistory._id, c2BitHistory._id)
self.assertEqual(c1BitHistory._stats, c2BitHistory._stats)
self.assertEqual(c1BitHistory._lastTotalUpdate, c2BitHistory._lastTotalUpdate)
self.assertEqual(c1BitHistory._learnIteration, c2BitHistory._learnIteration)
self.assertEqual(c1._maxBucketIdx, c2._maxBucketIdx)
self.assertEqual(len(c1._actualValues), len(c2._actualValues))
for i in xrange(len(c1._actualValues)):
self.assertAlmostEqual(c1._actualValues[i], c2._actualValues[i], 5)
self.assertEqual(c1._version, c2._version)
self.assertEqual(c1.verbosity, c2.verbosity)
result1 = c1.compute(recordNum=1,
patternNZ=input1,
classification={'bucketIdx': 4, 'actValue': 34.7},
learn=True, infer=True)
result2 = c2.compute(recordNum=1,
patternNZ=input1,
classification={'bucketIdx': 4, 'actValue': 34.7},
learn=True, infer=True)
self.assertEqual(result1.keys(), result2.keys())
for key in result1.keys():
for i in xrange(len(c1._actualValues)):
self.assertAlmostEqual(result1[key][i], result2[key][i], 5)
def testWriteRead(self):
c1 = CLAClassifier([1], 0.1, 0.1, 0)
# Create a vector of input bit indices
input1 = [1, 5, 9]
result = c1.compute(recordNum=0,
patternNZ=input1,
classification={'bucketIdx': 4, 'actValue': 34.7},
learn=True, infer=True)
proto1 = ClaClassifier_capnp.ClaClassifierProto.new_message()
c1.write(proto1)
# Write the proto to a temp file and read it back into a new proto
with tempfile.TemporaryFile() as f:
proto1.write(f)
f.seek(0)
proto2 = ClaClassifier_capnp.ClaClassifierProto.read(f)
# Load the deserialized proto
c2 = CLAClassifier.read(proto2)
self.assertEqual(c1.steps, c2.steps)
self.assertAlmostEqual(c1.alpha, c2.alpha)
self.assertAlmostEqual(c1.actValueAlpha, c2.actValueAlpha)
self.assertEqual(c1._learnIteration, c2._learnIteration)
self.assertEqual(c1._recordNumMinusLearnIteration, c2._recordNumMinusLearnIteration)
self.assertEqual(c1._patternNZHistory, c2._patternNZHistory)
self.assertEqual(c1._activeBitHistory.keys(), c2._activeBitHistory.keys())
for bit, nSteps in c1._activeBitHistory.keys():
c1BitHistory = c1._activeBitHistory[(bit, nSteps)]
c2BitHistory = c2._activeBitHistory[(bit, nSteps)]
self.assertEqual(c1BitHistory._id, c2BitHistory._id)
self.assertEqual(c1BitHistory._stats, c2BitHistory._stats)
self.assertEqual(c1BitHistory._lastTotalUpdate, c2BitHistory._lastTotalUpdate)
self.assertEqual(c1BitHistory._learnIteration, c2BitHistory._learnIteration)
self.assertEqual(c1._maxBucketIdx, c2._maxBucketIdx)
self.assertEqual(len(c1._actualValues), len(c2._actualValues))
for i in xrange(len(c1._actualValues)):
self.assertAlmostEqual(c1._actualValues[i], c2._actualValues[i], 5)
self.assertEqual(c1._version, c2._version)
self.assertEqual(c1.verbosity, c2.verbosity)
result1 = c1.compute(recordNum=1,
patternNZ=input1,
classification={'bucketIdx': 4, 'actValue': 34.7},
learn=True, infer=True)
result2 = c2.compute(recordNum=1,
patternNZ=input1,
classification={'bucketIdx': 4, 'actValue': 34.7},
learn=True, infer=True)
self.assertEqual(result1.keys(), result2.keys())
for key in result1.keys():
for i in xrange(len(c1._actualValues)):
self.assertAlmostEqual(result1[key][i], result2[key][i], 5)
def read(proto):
"""
proto: CLAClassifierRegionProto capnproto object
"""
impl = proto.classifierImp
if impl == 'py':
return CLAClassifier.read(proto.claClassifier)
elif impl == 'cpp':
return FastCLAClassifier.read(proto.claClassifier)
elif impl == 'diff':
raise NotImplementedError("CLAClassifierDiff.read not implemented")
else:
raise ValueError('Invalid classifier implementation (%r). Value must be '
'"py" or "cpp".' % impl)
def read(proto):
"""
proto: CLAClassifierRegionProto capnproto object
"""
impl = proto.classifierImp
if impl == 'py':
return CLAClassifier.read(proto.claClassifier)
elif impl == 'cpp':
return FastCLAClassifier.read(proto.claClassifier)
elif impl == 'diff':
raise NotImplementedError("CLAClassifierDiff.read not implemented")
else:
raise ValueError(
'Invalid classifier implementation (%r). Value must be '
'"py" or "cpp".' % impl)
