def testSetFieldStats(self):
"""Test setting the min and max using setFieldStats"""
def _dumpParams(enc):
return (enc.n, enc.w, enc.minval, enc.maxval, enc.resolution,
enc._learningEnabled, enc.recordNum, enc.radius,
enc.rangeInternal, enc.padding, enc.nInternal)
sfs = AdaptiveScalarEncoder(name='scalar',
n=14,
w=5,
minval=1,
maxval=10,
periodic=False,
forced=True)
reg = AdaptiveScalarEncoder(name='scalar',
n=14,
w=5,
minval=1,
maxval=100,
periodic=False,
forced=True)
self.assertNotEqual(
_dumpParams(sfs), _dumpParams(reg),
("Params should not be equal, since the two encoders "
"were instantiated with different values."))
# set the min and the max using sFS to 1,100 respectively.
sfs.setFieldStats("this", {"this": {"min": 1, "max": 100}})
#Now the parameters for both should be the same
self.assertEqual(_dumpParams(sfs), _dumpParams(reg),
("Params should now be equal, but they are not. sFS "
"should be equivalent to initialization."))
def testNonPeriodicEncoderMinMaxNotSpec(self):
"""Non-periodic encoder, min and max not specified"""
l = AdaptiveScalarEncoder(name="scalar", n=14, w=5, minval=None,
maxval=None, periodic=False, forced=True)
def _verify(v, encoded, expV=None):
if expV is None:
expV = v
self.assertTrue(numpy.array_equal(
l.encode(v),
numpy.array(encoded, dtype=defaultDtype)))
self.assertLessEqual(
abs(l.getBucketInfo(l.getBucketIndices(v))[0].value - expV),
l.resolution/2)
def _verifyNot(v, encoded):
self.assertFalse(numpy.array_equal(
l.encode(v), numpy.array(encoded, dtype=defaultDtype)))
_verify(1, [1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0])
_verify(2, [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1])
_verify(10, [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1])
_verify(3, [0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0])
_verify(-9, [1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0])
_verify(-8, [1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0])
_verify(-7, [0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0])
_verify(-6, [0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0])
_verify(-5, [0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0])
_verify(0, [0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0])
_verify(8, [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0])
_verify(8, [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0])
_verify(10, [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1])
_verify(11, [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1])
_verify(12, [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1])
_verify(13, [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1])
_verify(14, [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1])
_verify(15, [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1])
#"""Test switching learning off"""
l = AdaptiveScalarEncoder(name="scalar", n=14, w=5, minval=1, maxval=10,
periodic=False, forced=True)
_verify(1, [1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0])
_verify(10, [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1])
_verify(20, [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1])
_verify(10, [0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0])
l.setLearning(False)
_verify(30, [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1], expV=20)
_verify(20, [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1])
_verify(-10, [1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0], expV=1)
_verify(-1, [1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0], expV=1)
l.setLearning(True)
_verify(30, [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1])
_verifyNot(20, [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1])
_verify(-10, [1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0])
_verifyNot(-1, [1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0])
def testMissingValues(self):
"""missing values"""
# forced: it's strongly recommended to use w>=21, in the example we force
# skip the check for readib.
mv = AdaptiveScalarEncoder(name="mv", n=14, w=3, minval=1, maxval=8,
periodic=False, forced=True)
empty = mv.encode(SENTINEL_VALUE_FOR_MISSING_DATA)
self.assertEqual(empty.sum(), 0)
def setUp(self):
# forced: it's strongly recommended to use w>=21, in the example we force
# skip the check for readibility
self._l = AdaptiveScalarEncoder(name="scalar",
n=14,
w=5,
minval=1,
maxval=10,
periodic=False,
forced=True)
def __new__(self, w, minval=None, maxval=None, periodic=False, n=0, radius=0,
resolution=0, name=None, verbosity=0, clipInput=False,
space="absolute", forced=False):
self._encoder = None
if space == "absolute":
ret = AdaptiveScalarEncoder(w, minval, maxval, periodic, n, radius,
resolution, name, verbosity, clipInput,
forced=forced)
else:
ret = DeltaEncoder(w, minval, maxval, periodic, n, radius, resolution,
name, verbosity, clipInput, forced=forced)
return ret
def __init__(self, w, minval=None, maxval=None, periodic=False, n=0, radius=0,
resolution=0, name=None, verbosity=0, clipInput=True, forced=False):
"""[ScalarEncoder class method override]"""
self._learningEnabled = True
self._stateLock = False
self.width = 0
self.encoders = None
self.description = []
self.name = name
if periodic:
#Delta scalar encoders take non-periodic inputs only
raise Exception('Delta encoder does not encode periodic inputs')
assert n!=0 #An adaptive encoder can only be intialized using n
self._adaptiveScalarEnc = AdaptiveScalarEncoder(w=w, n=n, minval=minval,
maxval=maxval, clipInput=True, name=name, verbosity=verbosity, forced=forced)
self.width+=self._adaptiveScalarEnc.getWidth()
self.n = self._adaptiveScalarEnc.n
self._prevAbsolute = None #how many inputs have been sent to the encoder?
self._prevDelta = None