def testNonPeriodicBottomUp(self):
"""Test Non-periodic encoder bottom-up"""
l = ScalarEncoder(name="scalar", n=14, w=5, minval=1, maxval=10,
periodic=False, forced=True)
self.assertTrue(numpy.array_equal(
l.encode(1),
numpy.array([1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],
dtype=defaultDtype)))
self.assertTrue(numpy.array_equal(
l.encode(2),
numpy.array([0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],
dtype=defaultDtype)))
self.assertTrue(numpy.array_equal(
l.encode(10),
numpy.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1],
dtype=defaultDtype)))
# Test that we get the same encoder when we construct it using resolution
# instead of n
d = l.__dict__
l = ScalarEncoder(name="scalar", resolution=1, w=5, minval=1, maxval=10,
periodic=False, forced=True)
self.assertEqual(l.__dict__, d)
# Test that we get the same encoder when we construct it using radius
# instead of n
l = ScalarEncoder(name="scalar", radius=5, w=5, minval=1, maxval=10,
periodic=False, forced=True)
self.assertEqual(l.__dict__, d)
# -------------------------------------------------------------------------
# Test the input description generation and topDown decoding of a
# non-periodic encoder
v = l.minval
while v < l.maxval:
output = l.encode(v)
decoded = l.decode(output)
(fieldsDict, _) = decoded
self.assertEqual(len(fieldsDict), 1)
(ranges, _) = fieldsDict.values()[0]
self.assertEqual(len(ranges), 1)
(rangeMin, rangeMax) = ranges[0]
self.assertEqual(rangeMin, rangeMax)
self.assertLess(abs(rangeMin - v), l.resolution)
topDown = l.topDownCompute(output)[0]
self.assertTrue(numpy.array_equal(topDown.encoding, output))
self.assertLessEqual(abs(topDown.value - v), l.resolution)
# Test bucket support
bucketIndices = l.getBucketIndices(v)
topDown = l.getBucketInfo(bucketIndices)[0]
self.assertLessEqual(abs(topDown.value - v), l.resolution / 2)
self.assertEqual(topDown.scalar, topDown.value)
self.assertTrue(numpy.array_equal(topDown.encoding, output))
# Next value
v += l.resolution / 4
# Make sure we can fill in holes
decoded = l.decode(numpy.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1]))
(fieldsDict, _) = decoded
self.assertEqual(len(fieldsDict), 1)
(ranges, _) = fieldsDict.values()[0]
self.assertEqual(len(ranges), 1)
self.assertTrue(numpy.array_equal(ranges[0], [10, 10]))
decoded = l.decode(numpy.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1]))
(fieldsDict, _) = decoded
self.assertEqual(len(fieldsDict), 1)
(ranges, _) = fieldsDict.values()[0]
self.assertEqual(len(ranges), 1)
self.assertTrue(numpy.array_equal(ranges[0], [10, 10]))
#Test min and max
l = ScalarEncoder(name="scalar", n=14, w=3, minval=1, maxval=10,
periodic=False, forced=True)
decoded = l.topDownCompute(
numpy.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1]))[0]
self.assertEqual(decoded.value, 10)
decoded = l.topDownCompute(
numpy.array([1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]))[0]
self.assertEqual(decoded.value, 1)
#Make sure only the last and first encoding encodes to max and min, and
#there is no value greater than max or min
l = ScalarEncoder(name="scalar", n=140, w=3, minval=1, maxval=141,
periodic=False, forced=True)
for i in range(137):
iterlist = [0 for _ in range(140)]
for j in range(i, i+3):
iterlist[j] =1
npar = numpy.array(iterlist)
decoded = l.topDownCompute(npar)[0]
self.assertLessEqual(decoded.value, 141)
self.assertGreaterEqual(decoded.value, 1)
self.assertTrue(decoded.value < 141 or i==137)
self.assertTrue(decoded.value > 1 or i == 0)
# -------------------------------------------------------------------------
# Test the input description generation and top-down compute on a small
# number non-periodic encoder
l = ScalarEncoder(name="scalar", n=15, w=3, minval=.001, maxval=.002,
periodic=False, forced=True)
v = l.minval
while v < l.maxval:
output = l.encode(v)
decoded = l.decode(output)
(fieldsDict, _) = decoded
self.assertEqual(len(fieldsDict), 1)
(ranges, _) = fieldsDict.values()[0]
self.assertEqual(len(ranges), 1)
(rangeMin, rangeMax) = ranges[0]
self.assertEqual(rangeMin, rangeMax)
self.assertLess(abs(rangeMin - v), l.resolution)
topDown = l.topDownCompute(output)[0].value
self.assertLessEqual(abs(topDown - v), l.resolution / 2)
v += l.resolution / 4
# -------------------------------------------------------------------------
# Test the input description generation on a large number, non-periodic
# encoder
l = ScalarEncoder(name="scalar", n=15, w=3, minval=1, maxval=1000000000,
periodic=False, forced=True)
v = l.minval
while v < l.maxval:
output = l.encode(v)
decoded = l.decode(output)
(fieldsDict, _) = decoded
self.assertEqual(len(fieldsDict), 1)
(ranges, _) = fieldsDict.values()[0]
self.assertEqual(len(ranges), 1)
(rangeMin, rangeMax) = ranges[0]
self.assertEqual(rangeMin, rangeMax)
self.assertLess(abs(rangeMin - v), l.resolution)
topDown = l.topDownCompute(output)[0].value
self.assertLessEqual(abs(topDown - v), l.resolution / 2)
v += l.resolution / 4
def testNonPeriodicBottomUp(self):
"""Test Non-periodic encoder bottom-up"""
l = ScalarEncoder(name="scalar",
n=14,
w=5,
minval=1,
maxval=10,
periodic=False,
forced=True)
self.assertTrue(
numpy.array_equal(
l.encode(1),
numpy.array([1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],
dtype=defaultDtype)))
self.assertTrue(
numpy.array_equal(
l.encode(2),
numpy.array([0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],
dtype=defaultDtype)))
self.assertTrue(
numpy.array_equal(
l.encode(10),
numpy.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1],
dtype=defaultDtype)))
# Test that we get the same encoder when we construct it using resolution
# instead of n
d = l.__dict__
l = ScalarEncoder(name="scalar",
resolution=1,
w=5,
minval=1,
maxval=10,
periodic=False,
forced=True)
self.assertEqual(l.__dict__, d)
# Test that we get the same encoder when we construct it using radius
# instead of n
l = ScalarEncoder(name="scalar",
radius=5,
w=5,
minval=1,
maxval=10,
periodic=False,
forced=True)
self.assertEqual(l.__dict__, d)
# -------------------------------------------------------------------------
# Test the input description generation and topDown decoding of a
# non-periodic encoder
v = l.minval
while v < l.maxval:
output = l.encode(v)
decoded = l.decode(output)
(fieldsDict, _) = decoded
self.assertEqual(len(fieldsDict), 1)
(ranges, _) = list(fieldsDict.values())[0]
self.assertEqual(len(ranges), 1)
(rangeMin, rangeMax) = ranges[0]
self.assertEqual(rangeMin, rangeMax)
self.assertLess(abs(rangeMin - v), l.resolution)
topDown = l.topDownCompute(output)[0]
self.assertTrue(numpy.array_equal(topDown.encoding, output))
self.assertLessEqual(abs(topDown.value - v), l.resolution)
# Test bucket support
bucketIndices = l.getBucketIndices(v)
topDown = l.getBucketInfo(bucketIndices)[0]
self.assertLessEqual(abs(topDown.value - v), l.resolution / 2)
self.assertEqual(topDown.scalar, topDown.value)
self.assertTrue(numpy.array_equal(topDown.encoding, output))
# Next value
v += l.resolution / 4
# Make sure we can fill in holes
decoded = l.decode(
numpy.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1]))
(fieldsDict, _) = decoded
self.assertEqual(len(fieldsDict), 1)
(ranges, _) = list(fieldsDict.values())[0]
self.assertEqual(len(ranges), 1)
self.assertTrue(numpy.array_equal(ranges[0], [10, 10]))
decoded = l.decode(
numpy.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1]))
(fieldsDict, _) = decoded
self.assertEqual(len(fieldsDict), 1)
(ranges, _) = list(fieldsDict.values())[0]
self.assertEqual(len(ranges), 1)
self.assertTrue(numpy.array_equal(ranges[0], [10, 10]))
#Test min and max
l = ScalarEncoder(name="scalar",
n=14,
w=3,
minval=1,
maxval=10,
periodic=False,
forced=True)
decoded = l.topDownCompute(
numpy.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1]))[0]
self.assertEqual(decoded.value, 10)
decoded = l.topDownCompute(
numpy.array([1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]))[0]
self.assertEqual(decoded.value, 1)
#Make sure only the last and first encoding encodes to max and min, and
#there is no value greater than max or min
l = ScalarEncoder(name="scalar",
n=140,
w=3,
minval=1,
maxval=141,
periodic=False,
forced=True)
for i in range(137):
iterlist = [0 for _ in range(140)]
for j in range(i, i + 3):
iterlist[j] = 1
npar = numpy.array(iterlist)
decoded = l.topDownCompute(npar)[0]
self.assertLessEqual(decoded.value, 141)
self.assertGreaterEqual(decoded.value, 1)
self.assertTrue(decoded.value < 141 or i == 137)
self.assertTrue(decoded.value > 1 or i == 0)
# -------------------------------------------------------------------------
# Test the input description generation and top-down compute on a small
# number non-periodic encoder
l = ScalarEncoder(name="scalar",
n=15,
w=3,
minval=.001,
maxval=.002,
periodic=False,
forced=True)
v = l.minval
while v < l.maxval:
output = l.encode(v)
decoded = l.decode(output)
(fieldsDict, _) = decoded
self.assertEqual(len(fieldsDict), 1)
(ranges, _) = list(fieldsDict.values())[0]
self.assertEqual(len(ranges), 1)
(rangeMin, rangeMax) = ranges[0]
self.assertEqual(rangeMin, rangeMax)
self.assertLess(abs(rangeMin - v), l.resolution)
topDown = l.topDownCompute(output)[0].value
self.assertLessEqual(abs(topDown - v), l.resolution / 2)
v += l.resolution / 4
# -------------------------------------------------------------------------
# Test the input description generation on a large number, non-periodic
# encoder
l = ScalarEncoder(name="scalar",
n=15,
w=3,
minval=1,
maxval=1000000000,
periodic=False,
forced=True)
v = l.minval
while v < l.maxval:
output = l.encode(v)
decoded = l.decode(output)
(fieldsDict, _) = decoded
self.assertEqual(len(fieldsDict), 1)
(ranges, _) = list(fieldsDict.values())[0]
self.assertEqual(len(ranges), 1)
(rangeMin, rangeMax) = ranges[0]
self.assertEqual(rangeMin, rangeMax)
self.assertLess(abs(rangeMin - v), l.resolution)
topDown = l.topDownCompute(output)[0].value
self.assertLessEqual(abs(topDown - v), l.resolution / 2)
v += l.resolution / 4
def testNonPeriodicBottomUp(self):
"""Test Non-periodic encoder bottom-up"""
l = ScalarEncoder(name='scalar', n=14, w=5, minval=1, maxval=10, periodic=False, forced=True)
print "\nTesting non-periodic encoder encoding, resolution of %f..." % \
l.resolution
self.assertTrue((l.encode(1) == numpy.array([1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],
dtype=defaultDtype)).all())
self.assertTrue((l.encode(2) == numpy.array([0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],
dtype=defaultDtype)).all())
self.assertTrue((l.encode(10) == numpy.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1],
dtype=defaultDtype)).all())
# Test that we get the same encoder when we construct it using resolution
# instead of n
d = l.__dict__
l = ScalarEncoder(name='scalar', resolution=1, w=5, minval=1, maxval=10,
periodic=False, forced=True)
self.assertEqual(l.__dict__, d)
# Test that we get the same encoder when we construct it using radius
# instead of n
l = ScalarEncoder(name='scalar', radius=5, w=5, minval=1, maxval=10, periodic=False, forced=True)
self.assertEqual(l.__dict__, d)
# -------------------------------------------------------------------------
# Test the input description generation and topDown decoding of a non-periodic
# encoder
v = l.minval
print "\nTesting non-periodic encoder decoding, resolution of %f..." % \
l.resolution
while v < l.maxval:
output = l.encode(v)
decoded = l.decode(output)
print "decoding", output, "(%f)=>" % v, l.decodedToStr(decoded)
(fieldsDict, fieldNames) = decoded
self.assertEqual(len(fieldsDict), 1)
(ranges, desc) = fieldsDict.values()[0]
self.assertEqual(len(ranges), 1)
(rangeMin, rangeMax) = ranges[0]
self.assertEqual(rangeMin, rangeMax)
self.assertTrue(abs(rangeMin - v) < l.resolution)
topDown = l.topDownCompute(output)[0]
print "topdown =>", topDown
self.assertTrue((topDown.encoding == output).all())
self.assertTrue(abs(topDown.value - v) <= l.resolution)
# Test bucket support
bucketIndices = l.getBucketIndices(v)
print "bucket index =>", bucketIndices[0]
topDown = l.getBucketInfo(bucketIndices)[0]
self.assertTrue(abs(topDown.value - v) <= l.resolution / 2)
self.assertEqual(topDown.scalar, topDown.value)
self.assertTrue((topDown.encoding == output).all())
# Next value
v += l.resolution / 4
# Make sure we can fill in holes
decoded = l.decode(numpy.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1]))
(fieldsDict, fieldNames) = decoded
self.assertEqual(len(fieldsDict), 1)
(ranges, desc) = fieldsDict.values()[0]
self.assertTrue(len(ranges) == 1 and numpy.array_equal(ranges[0], [10, 10]))
print "decodedToStr of", ranges, "=>", l.decodedToStr(decoded)
decoded = l.decode(numpy.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1]))
(fieldsDict, fieldNames) = decoded
self.assertEqual(len(fieldsDict), 1)
(ranges, desc) = fieldsDict.values()[0]
self.assertTrue(len(ranges) == 1 and numpy.array_equal(ranges[0], [10, 10]))
print "decodedToStr of", ranges, "=>", l.decodedToStr(decoded)
#Test min and max
l = ScalarEncoder(name='scalar', n=14, w=3, minval=1, maxval=10, periodic=False, forced=True)
decoded = l.topDownCompute(numpy.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1]))[0]
self.assertEqual(decoded.value, 10)
decoded = l.topDownCompute(numpy.array([1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]))[0]
self.assertEqual(decoded.value, 1)
#Make sure only the last and first encoding encodes to max and min, and there is no value greater than max or min
l = ScalarEncoder(name='scalar', n=140, w=3, minval=1, maxval=141, periodic=False, forced=True)
for i in range(137):
iterlist = [0 for _ in range(140)]
for j in range(i, i+3):
iterlist[j] =1
npar = numpy.array(iterlist)
decoded = l.topDownCompute(npar)[0]
self.assertTrue(decoded.value <= 141)
self.assertTrue(decoded.value >= 1)
self.assertTrue(decoded.value < 141 or i==137)
self.assertTrue(decoded.value > 1 or i == 0)
# -------------------------------------------------------------------------
# Test the input description generation and top-down compute on a small number
# non-periodic encoder
l = ScalarEncoder(name='scalar', n=15, w=3, minval=.001, maxval=.002,
periodic=False, forced=True)
print "\nTesting non-periodic encoder decoding, resolution of %f..." % \
l.resolution
v = l.minval
while v < l.maxval:
output = l.encode(v)
decoded = l.decode(output)
print "decoding", output, "(%f)=>" % v, l.decodedToStr(decoded)
(fieldsDict, fieldNames) = decoded
self.assertEqual(len(fieldsDict), 1)
(ranges, desc) = fieldsDict.values()[0]
self.assertEqual(len(ranges), 1)
(rangeMin, rangeMax) = ranges[0]
self.assertEqual(rangeMin, rangeMax)
self.assertTrue(abs(rangeMin - v) < l.resolution)
topDown = l.topDownCompute(output)[0].value
print "topdown =>", topDown
self.assertTrue(abs(topDown - v) <= l.resolution / 2)
v += l.resolution / 4
# -------------------------------------------------------------------------
# Test the input description generation on a large number, non-periodic encoder
l = ScalarEncoder(name='scalar', n=15, w=3, minval=1, maxval=1000000000,
periodic=False, forced=True)
print "\nTesting non-periodic encoder decoding, resolution of %f..." % \
l.resolution
v = l.minval
while v < l.maxval:
output = l.encode(v)
decoded = l.decode(output)
print "decoding", output, "(%f)=>" % v, l.decodedToStr(decoded)
(fieldsDict, fieldNames) = decoded
self.assertEqual(len(fieldsDict), 1)
(ranges, desc) = fieldsDict.values()[0]
self.assertEqual(len(ranges), 1)
(rangeMin, rangeMax) = ranges[0]
self.assertEqual(rangeMin, rangeMax)
self.assertTrue(abs(rangeMin - v) < l.resolution)
topDown = l.topDownCompute(output)[0].value
print "topdown =>", topDown
self.assertTrue(abs(topDown - v) <= l.resolution / 2)
v += l.resolution / 4
# -------------------------------------------------------------------------
# Test setting fieldStats after initialization
if False:
#TODO: remove all this? (and fieldstats from ScalarEncoder (if applicable) )?
# Modified on 11/20/12 12:53 PM - setFieldStats not applicable for ScalarEncoder
l = ScalarEncoder(n=14, w=3, minval=100, maxval=800, periodic=True, forced=True)
l.setFieldStats("this", {"this":{"min":1, "max":8}})
l = ScalarEncoder(name='scalar', n=14, w=3, minval=100, maxval=800, periodic=True, forced=True)
l.setFieldStats("this", {"this":{"min":1, "max":8}})
self.assertTrue((l.encode(3) == numpy.array([0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],
dtype=defaultDtype)).all())
self.assertTrue((l.encode(3.1) == l.encode(3)).all())
self.assertTrue((l.encode(3.5) == numpy.array([0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],
dtype=defaultDtype)).all())
self.assertTrue((l.encode(3.6) == l.encode(3.5)).all())
self.assertTrue((l.encode(3.7) == l.encode(3.5)).all())
self.assertTrue((l.encode(4) == numpy.array([0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0],
dtype=defaultDtype)).all())
self.assertTrue((l.encode(1) == numpy.array([1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1],
dtype=defaultDtype)).all())
self.assertTrue((l.encode(1.5) == numpy.array([1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
dtype=defaultDtype)).all())
self.assertTrue((l.encode(7) == numpy.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1],
dtype=defaultDtype)).all())
self.assertTrue((l.encode(7.5) == numpy.array([1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1],
dtype=defaultDtype)).all())
l = ScalarEncoder(name='scalar', n=14, w=5, minval=100, maxval=1000, periodic=False, forced=True)
l.setFieldStats("this", {"this":{"min":1, "max":10}})
print "\nTesting non-periodic encoding using setFieldStats, resolution of %f..." % \
l.resolution
self.assertTrue((l.encode(1) == numpy.array([1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],
dtype=defaultDtype)).all())
self.assertTrue((l.encode(2) == numpy.array([0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],
dtype=defaultDtype)).all())
self.assertTrue((l.encode(10) == numpy.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1],
dtype=defaultDtype)).all())
def testNonPeriodicBottomUp(self):
"""Test Non-periodic encoder bottom-up"""
l = ScalarEncoder(name='scalar', n=14, w=5, minval=1, maxval=10, periodic=False, forced=True)
print "\nTesting non-periodic encoder encoding, resolution of %f..." % \
l.resolution
self.assertTrue((l.encode(1) == numpy.array([1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],
dtype=defaultDtype)).all())
self.assertTrue((l.encode(2) == numpy.array([0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],
dtype=defaultDtype)).all())
self.assertTrue((l.encode(10) == numpy.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1],
dtype=defaultDtype)).all())
# Test that we get the same encoder when we construct it using resolution
# instead of n
d = l.__dict__
l = ScalarEncoder(name='scalar', resolution=1, w=5, minval=1, maxval=10,
periodic=False, forced=True)
self.assertEqual(l.__dict__, d)
# Test that we get the same encoder when we construct it using radius
# instead of n
l = ScalarEncoder(name='scalar', radius=5, w=5, minval=1, maxval=10, periodic=False, forced=True)
self.assertEqual(l.__dict__, d)
# -------------------------------------------------------------------------
# Test the input description generation and topDown decoding of a non-periodic
# encoder
v = l.minval
print "\nTesting non-periodic encoder decoding, resolution of %f..." % \
l.resolution
while v < l.maxval:
output = l.encode(v)
decoded = l.decode(output)
print "decoding", output, "(%f)=>" % v, l.decodedToStr(decoded)
(fieldsDict, fieldNames) = decoded
self.assertEqual(len(fieldsDict), 1)
(ranges, desc) = fieldsDict.values()[0]
self.assertEqual(len(ranges), 1)
(rangeMin, rangeMax) = ranges[0]
self.assertEqual(rangeMin, rangeMax)
self.assertTrue(abs(rangeMin - v) < l.resolution)
topDown = l.topDownCompute(output)[0]
print "topdown =>", topDown
self.assertTrue((topDown.encoding == output).all())
self.assertTrue(abs(topDown.value - v) <= l.resolution)
# Test bucket support
bucketIndices = l.getBucketIndices(v)
print "bucket index =>", bucketIndices[0]
topDown = l.getBucketInfo(bucketIndices)[0]
self.assertTrue(abs(topDown.value - v) <= l.resolution / 2)
self.assertEqual(topDown.scalar, topDown.value)
self.assertTrue((topDown.encoding == output).all())
# Next value
v += l.resolution / 4
# Make sure we can fill in holes
decoded = l.decode(numpy.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1]))
(fieldsDict, fieldNames) = decoded
self.assertEqual(len(fieldsDict), 1)
(ranges, desc) = fieldsDict.values()[0]
self.assertTrue(len(ranges) == 1 and numpy.array_equal(ranges[0], [10, 10]))
print "decodedToStr of", ranges, "=>", l.decodedToStr(decoded)
decoded = l.decode(numpy.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1]))
(fieldsDict, fieldNames) = decoded
self.assertEqual(len(fieldsDict), 1)
(ranges, desc) = fieldsDict.values()[0]
self.assertTrue(len(ranges) == 1 and numpy.array_equal(ranges[0], [10, 10]))
print "decodedToStr of", ranges, "=>", l.decodedToStr(decoded)
#Test min and max
l = ScalarEncoder(name='scalar', n=14, w=3, minval=1, maxval=10, periodic=False, forced=True)
decoded = l.topDownCompute(numpy.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1]))[0]
self.assertEqual(decoded.value, 10)
decoded = l.topDownCompute(numpy.array([1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]))[0]
self.assertEqual(decoded.value, 1)
#Make sure only the last and first encoding encodes to max and min, and there is no value greater than max or min
l = ScalarEncoder(name='scalar', n=140, w=3, minval=1, maxval=141, periodic=False, forced=True)
for i in range(137):
iterlist = [0 for _ in range(140)]
for j in range(i, i+3):
iterlist[j] =1
npar = numpy.array(iterlist)
decoded = l.topDownCompute(npar)[0]
self.assertTrue(decoded.value <= 141)
self.assertTrue(decoded.value >= 1)
self.assertTrue(decoded.value < 141 or i==137)
self.assertTrue(decoded.value > 1 or i == 0)
# -------------------------------------------------------------------------
# Test the input description generation and top-down compute on a small number
# non-periodic encoder
l = ScalarEncoder(name='scalar', n=15, w=3, minval=.001, maxval=.002,
periodic=False, forced=True)
print "\nTesting non-periodic encoder decoding, resolution of %f..." % \
l.resolution
v = l.minval
while v < l.maxval:
output = l.encode(v)
decoded = l.decode(output)
print "decoding", output, "(%f)=>" % v, l.decodedToStr(decoded)
(fieldsDict, fieldNames) = decoded
self.assertEqual(len(fieldsDict), 1)
(ranges, desc) = fieldsDict.values()[0]
self.assertEqual(len(ranges), 1)
(rangeMin, rangeMax) = ranges[0]
self.assertEqual(rangeMin, rangeMax)
self.assertTrue(abs(rangeMin - v) < l.resolution)
topDown = l.topDownCompute(output)[0].value
print "topdown =>", topDown
self.assertTrue(abs(topDown - v) <= l.resolution / 2)
v += l.resolution / 4
# -------------------------------------------------------------------------
# Test the input description generation on a large number, non-periodic encoder
l = ScalarEncoder(name='scalar', n=15, w=3, minval=1, maxval=1000000000,
periodic=False, forced=True)
print "\nTesting non-periodic encoder decoding, resolution of %f..." % \
l.resolution
v = l.minval
while v < l.maxval:
output = l.encode(v)
decoded = l.decode(output)
print "decoding", output, "(%f)=>" % v, l.decodedToStr(decoded)
(fieldsDict, fieldNames) = decoded
self.assertEqual(len(fieldsDict), 1)
(ranges, desc) = fieldsDict.values()[0]
self.assertEqual(len(ranges), 1)
(rangeMin, rangeMax) = ranges[0]
self.assertEqual(rangeMin, rangeMax)
self.assertTrue(abs(rangeMin - v) < l.resolution)
topDown = l.topDownCompute(output)[0].value
print "topdown =>", topDown
self.assertTrue(abs(topDown - v) <= l.resolution / 2)
v += l.resolution / 4
# -------------------------------------------------------------------------
# Test setting fieldStats after initialization
if False:
#TODO: remove all this? (and fieldstats from ScalarEncoder (if applicable) )?
# Modified on 11/20/12 12:53 PM - setFieldStats not applicable for ScalarEncoder
l = ScalarEncoder(n=14, w=3, minval=100, maxval=800, periodic=True, forced=True)
l.setFieldStats("this", {"this":{"min":1, "max":8}})
l = ScalarEncoder(name='scalar', n=14, w=3, minval=100, maxval=800, periodic=True, forced=True)
l.setFieldStats("this", {"this":{"min":1, "max":8}})
self.assertTrue((l.encode(3) == numpy.array([0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],
dtype=defaultDtype)).all())
self.assertTrue((l.encode(3.1) == l.encode(3)).all())
self.assertTrue((l.encode(3.5) == numpy.array([0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],
dtype=defaultDtype)).all())
self.assertTrue((l.encode(3.6) == l.encode(3.5)).all())
self.assertTrue((l.encode(3.7) == l.encode(3.5)).all())
self.assertTrue((l.encode(4) == numpy.array([0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0],
dtype=defaultDtype)).all())
self.assertTrue((l.encode(1) == numpy.array([1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1],
dtype=defaultDtype)).all())
self.assertTrue((l.encode(1.5) == numpy.array([1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
dtype=defaultDtype)).all())
self.assertTrue((l.encode(7) == numpy.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1],
dtype=defaultDtype)).all())
self.assertTrue((l.encode(7.5) == numpy.array([1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1],
dtype=defaultDtype)).all())
l = ScalarEncoder(name='scalar', n=14, w=5, minval=100, maxval=1000, periodic=False, forced=True)
l.setFieldStats("this", {"this":{"min":1, "max":10}})
print "\nTesting non-periodic encoding using setFieldStats, resolution of %f..." % \
l.resolution
self.assertTrue((l.encode(1) == numpy.array([1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],
dtype=defaultDtype)).all())
self.assertTrue((l.encode(2) == numpy.array([0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],
dtype=defaultDtype)).all())
self.assertTrue((l.encode(10) == numpy.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1],
dtype=defaultDtype)).all())
class LogEncoder(Encoder):
"""
This class wraps the ScalarEncoder class.
A Log encoder represents a floating point value on a logarithmic scale.
valueToEncode = log10(input)
w -- number of bits to set in output
minval -- minimum input value. must be greater than 0. Lower values are
reset to this value
maxval -- maximum input value (input is strictly less if periodic == True)
periodic -- If true, then the input value "wraps around" such that minval =
maxval For a periodic value, the input must be strictly less than
maxval, otherwise maxval is a true upper bound.
Exactly one of n, radius, resolution must be set. "0" is a special
value that means "not set".
n -- number of bits in the representation (must be > w)
radius -- inputs separated by more than this distance in log space will have
non-overlapping representations
resolution -- The minimum change in scaled value needed to produce a change
in encoding. This should be specified in log space. For
example, the scaled values 10 and 11 will be distinguishable
in the output. In terms of the original input values, this
means 10^1 (1) and 10^1.1 (1.25) will be distinguishable.
name -- an optional string which will become part of the description
verbosity -- level of debugging output you want the encoder to provide.
clipInput -- if true, non-periodic inputs smaller than minval or greater
than maxval will be clipped to minval/maxval
forced -- (default False), if True, skip some safety checks
"""
def __init__(self,
w=5,
minval=1e-07,
maxval=10000,
periodic=False,
n=0,
radius=0,
resolution=0,
name="log",
verbosity=0,
clipInput=True,
forced=False):
# Lower bound for log encoding near machine precision limit
lowLimit = 1e-07
# Limit minval as log10(0) is undefined.
if minval < lowLimit:
minval = lowLimit
# Check that minval is still lower than maxval
if not minval < maxval:
raise ValueError(
"Max val must be larger than min val or the lower limit "
"for this encoder %.7f" % lowLimit)
self.encoders = None
self.verbosity = verbosity
# Scale values for calculations within the class
self.minScaledValue = math.log10(minval)
self.maxScaledValue = math.log10(maxval)
if not self.maxScaledValue > self.minScaledValue:
raise ValueError(
"Max val must be larger, in log space, than min val.")
self.clipInput = clipInput
self.minval = minval
self.maxval = maxval
self.encoder = ScalarEncoder(w=w,
minval=self.minScaledValue,
maxval=self.maxScaledValue,
periodic=False,
n=n,
radius=radius,
resolution=resolution,
verbosity=self.verbosity,
clipInput=self.clipInput,
forced=forced)
self.width = self.encoder.getWidth()
self.description = [(name, 0)]
self.name = name
# This list is created by getBucketValues() the first time it is called,
# and re-created whenever our buckets would be re-arranged.
self._bucketValues = None
############################################################################
def getWidth(self):
return self.width
############################################################################
def getDescription(self):
return self.description
############################################################################
def getDecoderOutputFieldTypes(self):
"""
Encoder class virtual method override
"""
return (FieldMetaType.float, )
############################################################################
def _getScaledValue(self, inpt):
"""
Convert the input, which is in normal space, into log space
"""
if inpt == SENTINEL_VALUE_FOR_MISSING_DATA:
return None
else:
val = inpt
if val < self.minval:
val = self.minval
elif val > self.maxval:
val = self.maxval
scaledVal = math.log10(val)
return scaledVal
############################################################################
def getBucketIndices(self, inpt):
"""
See the function description in base.py
"""
# Get the scaled value
scaledVal = self._getScaledValue(inpt)
if scaledVal is None:
return [None]
else:
return self.encoder.getBucketIndices(scaledVal)
############################################################################
def encodeIntoArray(self, inpt, output):
"""
See the function description in base.py
"""
# Get the scaled value
scaledVal = self._getScaledValue(inpt)
if scaledVal is None:
output[0:] = 0
else:
self.encoder.encodeIntoArray(scaledVal, output)
if self.verbosity >= 2:
print "input:", inpt, "scaledVal:", scaledVal, "output:", output
print "decoded:", self.decodedToStr(self.decode(output))
############################################################################
def decode(self, encoded, parentFieldName=''):
"""
See the function description in base.py
"""
# Get the scalar values from the underlying scalar encoder
(fieldsDict, fieldNames) = self.encoder.decode(encoded)
if len(fieldsDict) == 0:
return (fieldsDict, fieldNames)
# Expect only 1 field
assert (len(fieldsDict) == 1)
# Convert each range into normal space
(inRanges, inDesc) = fieldsDict.values()[0]
outRanges = []
for (minV, maxV) in inRanges:
outRanges.append((math.pow(10, minV), math.pow(10, maxV)))
# Generate a text description of the ranges
desc = ""
numRanges = len(outRanges)
for i in xrange(numRanges):
if outRanges[i][0] != outRanges[i][1]:
desc += "%.2f-%.2f" % (outRanges[i][0], outRanges[i][1])
else:
desc += "%.2f" % (outRanges[i][0])
if i < numRanges - 1:
desc += ", "
# Return result
if parentFieldName != '':
fieldName = "%s.%s" % (parentFieldName, self.name)
else:
fieldName = self.name
return ({fieldName: (outRanges, desc)}, [fieldName])
############################################################################
def getBucketValues(self):
"""
See the function description in base.py
"""
# Need to re-create?
if self._bucketValues is None:
scaledValues = self.encoder.getBucketValues()
self._bucketValues = []
for scaledValue in scaledValues:
value = math.pow(10, scaledValue)
self._bucketValues.append(value)
return self._bucketValues
############################################################################
def getBucketInfo(self, buckets):
"""
See the function description in base.py
"""
scaledResult = self.encoder.getBucketInfo(buckets)[0]
scaledValue = scaledResult.value
value = math.pow(10, scaledValue)
return [
EncoderResult(value=value,
scalar=value,
encoding=scaledResult.encoding)
]
############################################################################
def topDownCompute(self, encoded):
"""
See the function description in base.py
"""
scaledResult = self.encoder.topDownCompute(encoded)[0]
scaledValue = scaledResult.value
value = math.pow(10, scaledValue)
return EncoderResult(value=value,
scalar=value,
encoding=scaledResult.encoding)
############################################################################
def closenessScores(self, expValues, actValues, fractional=True):
"""
See the function description in base.py
"""
# Compute the percent error in log space
if expValues[0] > 0:
expValue = math.log10(expValues[0])
else:
expValue = self.minScaledValue
if actValues[0] > 0:
actValue = math.log10(actValues[0])
else:
actValue = self.minScaledValue
if fractional:
err = abs(expValue - actValue)
pctErr = err / (self.maxScaledValue - self.minScaledValue)
pctErr = min(1.0, pctErr)
closeness = 1.0 - pctErr
else:
err = abs(expValue - actValue)
closeness = err
#print "log::", "expValue:", expValues[0], "actValue:", actValues[0], \
# "closeness", closeness
#import pdb; pdb.set_trace()
return numpy.array([closeness])
@classmethod
def read(cls, proto):
encoder = object.__new__(cls)
encoder.verbosity = proto.verbosity
encoder.minScaledValue = proto.minScaledValue
encoder.maxScaledValue = proto.maxScaledValue
encoder.clipInput = proto.clipInput
encoder.minval = proto.minval
encoder.maxval = proto.maxval
encoder.encoder = ScalarEncoder.read(proto.encoder)
encoder.name = proto.name
encoder.width = encoder.encoder.getWidth()
encoder.description = [(encoder.name, 0)]
encoder._bucketValues = None
return encoder
def write(self, proto):
proto.verbosity = self.verbosity
proto.minScaledValue = self.minScaledValue
proto.maxScaledValue = self.maxScaledValue
proto.clipInput = self.clipInput
proto.minval = self.minval
proto.maxval = self.maxval
self.encoder.write(proto.encoder)
proto.name = self.name
class CategoryEncoder(Encoder):
"""Encodes a list of discrete categories (described by strings), that aren't
related to each other, so we never emit a mixture of categories.
The value of zero is reserved for "unknown category"
Internally we use a ScalarEncoder with a radius of 1, but since we only encode
integers, we never get mixture outputs.
The SDRCategoryEncoder uses a different method to encode categories"""
def __init__(self,
w,
categoryList,
name="category",
verbosity=0,
forced=False):
"""params:
forced (default False) : if True, skip checks for parameters' settings; see encoders/scalar.py for details
"""
self.encoders = None
self.verbosity = verbosity
# number of categories includes "unknown"
self.ncategories = len(categoryList) + 1
self.categoryToIndex = dict()
self.indexToCategory = dict()
self.indexToCategory[0] = UNKNOWN
for i in xrange(len(categoryList)):
self.categoryToIndex[categoryList[i]] = i + 1
self.indexToCategory[i + 1] = categoryList[i]
self.encoder = ScalarEncoder(w,
minval=0,
maxval=self.ncategories - 1,
radius=1,
periodic=False,
forced=forced)
self.width = w * self.ncategories
assert self.encoder.getWidth() == self.width
self.description = [(name, 0)]
self.name = name
# These are used to support the topDownCompute method
self._topDownMappingM = None
# This gets filled in by getBucketValues
self._bucketValues = None
def getDecoderOutputFieldTypes(self):
""" [Encoder class virtual method override]
"""
# TODO: change back to string meta-type after the decoding logic is fixed
# to output strings instead of internal index values.
#return (FieldMetaType.string,)
return (FieldMetaType.integer, )
def getWidth(self):
return self.width
def getDescription(self):
return self.description
def getScalars(self, input):
""" See method description in base.py """
if input == SENTINEL_VALUE_FOR_MISSING_DATA:
return numpy.array([None])
else:
return numpy.array([self.categoryToIndex.get(input, 0)])
def getBucketIndices(self, input):
""" See method description in base.py """
# Get the bucket index from the underlying scalar encoder
if input == SENTINEL_VALUE_FOR_MISSING_DATA:
return [None]
else:
return self.encoder.getBucketIndices(
self.categoryToIndex.get(input, 0))
def encodeIntoArray(self, input, output):
# if not found, we encode category 0
if input == SENTINEL_VALUE_FOR_MISSING_DATA:
output[0:] = 0
val = "<missing>"
else:
val = self.categoryToIndex.get(input, 0)
self.encoder.encodeIntoArray(val, output)
if self.verbosity >= 2:
print "input:", input, "va:", val, "output:", output
print "decoded:", self.decodedToStr(self.decode(output))
def decode(self, encoded, parentFieldName=''):
""" See the function description in base.py
"""
# Get the scalar values from the underlying scalar encoder
(fieldsDict, fieldNames) = self.encoder.decode(encoded)
if len(fieldsDict) == 0:
return (fieldsDict, fieldNames)
# Expect only 1 field
assert (len(fieldsDict) == 1)
# Get the list of categories the scalar values correspond to and
# generate the description from the category name(s).
(inRanges, inDesc) = fieldsDict.values()[0]
outRanges = []
desc = ""
for (minV, maxV) in inRanges:
minV = int(round(minV))
maxV = int(round(maxV))
outRanges.append((minV, maxV))
while minV <= maxV:
if len(desc) > 0:
desc += ", "
desc += self.indexToCategory[minV]
minV += 1
# Return result
if parentFieldName != '':
fieldName = "%s.%s" % (parentFieldName, self.name)
else:
fieldName = self.name
return ({fieldName: (outRanges, desc)}, [fieldName])
def closenessScores(
self,
expValues,
actValues,
fractional=True,
):
""" See the function description in base.py
kwargs will have the keyword "fractional", which is ignored by this encoder
"""
expValue = expValues[0]
actValue = actValues[0]
if expValue == actValue:
closeness = 1.0
else:
closeness = 0.0
if not fractional:
closeness = 1.0 - closeness
return numpy.array([closeness])
def getBucketValues(self):
""" See the function description in base.py """
if self._bucketValues is None:
numBuckets = len(self.encoder.getBucketValues())
self._bucketValues = []
for bucketIndex in range(numBuckets):
self._bucketValues.append(
self.getBucketInfo([bucketIndex])[0].value)
return self._bucketValues
def getBucketInfo(self, buckets):
""" See the function description in base.py
"""
# For the category encoder, the bucket index is the category index
bucketInfo = self.encoder.getBucketInfo(buckets)[0]
categoryIndex = int(round(bucketInfo.value))
category = self.indexToCategory[categoryIndex]
return [
EncoderResult(value=category,
scalar=categoryIndex,
encoding=bucketInfo.encoding)
]
def topDownCompute(self, encoded):
""" See the function description in base.py
"""
encoderResult = self.encoder.topDownCompute(encoded)[0]
value = encoderResult.value
categoryIndex = int(round(value))
category = self.indexToCategory[categoryIndex]
return EncoderResult(value=category,
scalar=categoryIndex,
encoding=encoderResult.encoding)
@classmethod
def read(cls, proto):
encoder = object.__new__(cls)
encoder.verbosity = proto.verbosity
encoder.encoder = ScalarEncoder.read(proto.encoder)
encoder.width = proto.width
encoder.description = [(proto.name, 0)]
encoder.name = proto.name
encoder.indexToCategory = {
x.index: x.category
for x in proto.indexToCategory
}
encoder.categoryToIndex = {
category: index
for index, category in encoder.indexToCategory.items()
if category != UNKNOWN
}
encoder._topDownMappingM = None
encoder._bucketValues = None
return encoder
def write(self, proto):
proto.width = self.width
proto.indexToCategory = [{
"index": index,
"category": category
} for index, category in self.indexToCategory.items()]
proto.name = self.name
proto.verbosity = self.verbosity
self.encoder.write(proto.encoder)
class CategoryEncoder(Encoder):
"""
Encodes a list of discrete categories (described by strings), that aren't
related to each other, so we never emit a mixture of categories.
The value of zero is reserved for "unknown category"
Internally we use a :class:`.ScalarEncoder` with a radius of 1, but since we
only encode integers, we never get mixture outputs.
The :class:`.SDRCategoryEncoder` uses a different method to encode categories.
:param categoryList: list of discrete string categories
:param forced: if True, skip checks for parameters' settings; see
:class:`.ScalarEncoder` for details. (default False)
"""
def __init__(self, w, categoryList, name="category", verbosity=0, forced=False):
self.encoders = None
self.verbosity = verbosity
# number of categories includes "unknown"
self.ncategories = len(categoryList) + 1
self.categoryToIndex = dict()
self.indexToCategory = dict()
self.indexToCategory[0] = UNKNOWN
for i in xrange(len(categoryList)):
self.categoryToIndex[categoryList[i]] = i+1
self.indexToCategory[i+1] = categoryList[i]
self.encoder = ScalarEncoder(w, minval=0, maxval=self.ncategories - 1,
radius=1, periodic=False, forced=forced)
self.width = w * self.ncategories
assert self.encoder.getWidth() == self.width
self.description = [(name, 0)]
self.name = name
# These are used to support the topDownCompute method
self._topDownMappingM = None
# This gets filled in by getBucketValues
self._bucketValues = None
def getDecoderOutputFieldTypes(self):
""" [Encoder class virtual method override]
"""
# TODO: change back to string meta-type after the decoding logic is fixed
# to output strings instead of internal index values.
#return (FieldMetaType.string,)
return (FieldMetaType.integer,)
def getWidth(self):
return self.width
def getDescription(self):
return self.description
def getScalars(self, input):
""" See method description in base.py """
if input == SENTINEL_VALUE_FOR_MISSING_DATA:
return numpy.array([None])
else:
return numpy.array([self.categoryToIndex.get(input, 0)])
def getBucketIndices(self, input):
""" See method description in base.py """
# Get the bucket index from the underlying scalar encoder
if input == SENTINEL_VALUE_FOR_MISSING_DATA:
return [None]
else:
return self.encoder.getBucketIndices(self.categoryToIndex.get(input, 0))
def encodeIntoArray(self, input, output):
# if not found, we encode category 0
if input == SENTINEL_VALUE_FOR_MISSING_DATA:
output[0:] = 0
val = "<missing>"
else:
val = self.categoryToIndex.get(input, 0)
self.encoder.encodeIntoArray(val, output)
if self.verbosity >= 2:
print "input:", input, "va:", val, "output:", output
print "decoded:", self.decodedToStr(self.decode(output))
def decode(self, encoded, parentFieldName=''):
""" See the function description in base.py
"""
# Get the scalar values from the underlying scalar encoder
(fieldsDict, fieldNames) = self.encoder.decode(encoded)
if len(fieldsDict) == 0:
return (fieldsDict, fieldNames)
# Expect only 1 field
assert(len(fieldsDict) == 1)
# Get the list of categories the scalar values correspond to and
# generate the description from the category name(s).
(inRanges, inDesc) = fieldsDict.values()[0]
outRanges = []
desc = ""
for (minV, maxV) in inRanges:
minV = int(round(minV))
maxV = int(round(maxV))
outRanges.append((minV, maxV))
while minV <= maxV:
if len(desc) > 0:
desc += ", "
desc += self.indexToCategory[minV]
minV += 1
# Return result
if parentFieldName != '':
fieldName = "%s.%s" % (parentFieldName, self.name)
else:
fieldName = self.name
return ({fieldName: (outRanges, desc)}, [fieldName])
def closenessScores(self, expValues, actValues, fractional=True,):
""" See the function description in base.py
kwargs will have the keyword "fractional", which is ignored by this encoder
"""
expValue = expValues[0]
actValue = actValues[0]
if expValue == actValue:
closeness = 1.0
else:
closeness = 0.0
if not fractional:
closeness = 1.0 - closeness
return numpy.array([closeness])
def getBucketValues(self):
""" See the function description in base.py """
if self._bucketValues is None:
numBuckets = len(self.encoder.getBucketValues())
self._bucketValues = []
for bucketIndex in range(numBuckets):
self._bucketValues.append(self.getBucketInfo([bucketIndex])[0].value)
return self._bucketValues
def getBucketInfo(self, buckets):
""" See the function description in base.py
"""
# For the category encoder, the bucket index is the category index
bucketInfo = self.encoder.getBucketInfo(buckets)[0]
categoryIndex = int(round(bucketInfo.value))
category = self.indexToCategory[categoryIndex]
return [EncoderResult(value=category, scalar=categoryIndex,
encoding=bucketInfo.encoding)]
def topDownCompute(self, encoded):
""" See the function description in base.py
"""
encoderResult = self.encoder.topDownCompute(encoded)[0]
value = encoderResult.value
categoryIndex = int(round(value))
category = self.indexToCategory[categoryIndex]
return EncoderResult(value=category, scalar=categoryIndex,
encoding=encoderResult.encoding)
@classmethod
def getSchema(cls):
return CategoryEncoderProto
@classmethod
def read(cls, proto):
encoder = object.__new__(cls)
encoder.verbosity = proto.verbosity
encoder.encoder = ScalarEncoder.read(proto.encoder)
encoder.width = proto.width
encoder.description = [(proto.name, 0)]
encoder.name = proto.name
encoder.indexToCategory = {x.index: x.category
for x in proto.indexToCategory}
encoder.categoryToIndex = {category: index
for index, category
in encoder.indexToCategory.items()
if category != UNKNOWN}
encoder._topDownMappingM = None
encoder.ncategories = len(proto.indexToCategory)
encoder._bucketValues = None
encoder.encoders = None
return encoder
def write(self, proto):
proto.width = self.width
proto.indexToCategory = [
{"index": index, "category": category}
for index, category in self.indexToCategory.items()
]
proto.name = self.name
proto.verbosity = self.verbosity
self.encoder.write(proto.encoder)
class LogEncoder(Encoder):
"""
This class wraps the ScalarEncoder class.
A Log encoder represents a floating point value on a logarithmic scale.
valueToEncode = log10(input)
w -- number of bits to set in output
minval -- minimum input value. must be greater than 0. Lower values are
reset to this value
maxval -- maximum input value (input is strictly less if periodic == True)
periodic -- If true, then the input value "wraps around" such that minval =
maxval For a periodic value, the input must be strictly less than
maxval, otherwise maxval is a true upper bound.
Exactly one of n, radius, resolution must be set. "0" is a special
value that means "not set".
n -- number of bits in the representation (must be > w)
radius -- inputs separated by more than this distance in log space will have
non-overlapping representations
resolution -- The minimum change in scaled value needed to produce a change
in encoding. This should be specified in log space. For
example, the scaled values 10 and 11 will be distinguishable
in the output. In terms of the original input values, this
means 10^1 (1) and 10^1.1 (1.25) will be distinguishable.
name -- an optional string which will become part of the description
verbosity -- level of debugging output you want the encoder to provide.
clipInput -- if true, non-periodic inputs smaller than minval or greater
than maxval will be clipped to minval/maxval
forced -- (default False), if True, skip some safety checks
"""
def __init__(
self,
w=5,
minval=1e-07,
maxval=10000,
periodic=False,
n=0,
radius=0,
resolution=0,
name="log",
verbosity=0,
clipInput=True,
forced=False,
):
# Lower bound for log encoding near machine precision limit
lowLimit = 1e-07
# Limit minval as log10(0) is undefined.
if minval < lowLimit:
minval = lowLimit
# Check that minval is still lower than maxval
if not minval < maxval:
raise ValueError(
"Max val must be larger than min val or the lower limit " "for this encoder %.7f" % lowLimit
)
self.encoders = None
self.verbosity = verbosity
# Scale values for calculations within the class
self.minScaledValue = math.log10(minval)
self.maxScaledValue = math.log10(maxval)
if not self.maxScaledValue > self.minScaledValue:
raise ValueError("Max val must be larger, in log space, than min val.")
self.clipInput = clipInput
self.minval = minval
self.maxval = maxval
self.encoder = ScalarEncoder(
w=w,
minval=self.minScaledValue,
maxval=self.maxScaledValue,
periodic=False,
n=n,
radius=radius,
resolution=resolution,
verbosity=self.verbosity,
clipInput=self.clipInput,
forced=forced,
)
self.width = self.encoder.getWidth()
self.description = [(name, 0)]
self.name = name
# This list is created by getBucketValues() the first time it is called,
# and re-created whenever our buckets would be re-arranged.
self._bucketValues = None
############################################################################
def getWidth(self):
return self.width
############################################################################
def getDescription(self):
return self.description
############################################################################
def getDecoderOutputFieldTypes(self):
"""
Encoder class virtual method override
"""
return (FieldMetaType.float,)
############################################################################
def _getScaledValue(self, inpt):
"""
Convert the input, which is in normal space, into log space
"""
if inpt == SENTINEL_VALUE_FOR_MISSING_DATA:
return None
else:
val = inpt
if val < self.minval:
val = self.minval
elif val > self.maxval:
val = self.maxval
scaledVal = math.log10(val)
return scaledVal
############################################################################
def getBucketIndices(self, inpt):
"""
See the function description in base.py
"""
# Get the scaled value
scaledVal = self._getScaledValue(inpt)
if scaledVal is None:
return [None]
else:
return self.encoder.getBucketIndices(scaledVal)
############################################################################
def encodeIntoArray(self, inpt, output):
"""
See the function description in base.py
"""
# Get the scaled value
scaledVal = self._getScaledValue(inpt)
if scaledVal is None:
output[0:] = 0
else:
self.encoder.encodeIntoArray(scaledVal, output)
if self.verbosity >= 2:
print "input:", inpt, "scaledVal:", scaledVal, "output:", output
print "decoded:", self.decodedToStr(self.decode(output))
############################################################################
def decode(self, encoded, parentFieldName=""):
"""
See the function description in base.py
"""
# Get the scalar values from the underlying scalar encoder
(fieldsDict, fieldNames) = self.encoder.decode(encoded)
if len(fieldsDict) == 0:
return (fieldsDict, fieldNames)
# Expect only 1 field
assert len(fieldsDict) == 1
# Convert each range into normal space
(inRanges, inDesc) = fieldsDict.values()[0]
outRanges = []
for (minV, maxV) in inRanges:
outRanges.append((math.pow(10, minV), math.pow(10, maxV)))
# Generate a text description of the ranges
desc = ""
numRanges = len(outRanges)
for i in xrange(numRanges):
if outRanges[i][0] != outRanges[i][1]:
desc += "%.2f-%.2f" % (outRanges[i][0], outRanges[i][1])
else:
desc += "%.2f" % (outRanges[i][0])
if i < numRanges - 1:
desc += ", "
# Return result
if parentFieldName != "":
fieldName = "%s.%s" % (parentFieldName, self.name)
else:
fieldName = self.name
return ({fieldName: (outRanges, desc)}, [fieldName])
############################################################################
def getBucketValues(self):
"""
See the function description in base.py
"""
# Need to re-create?
if self._bucketValues is None:
scaledValues = self.encoder.getBucketValues()
self._bucketValues = []
for scaledValue in scaledValues:
value = math.pow(10, scaledValue)
self._bucketValues.append(value)
return self._bucketValues
############################################################################
def getBucketInfo(self, buckets):
"""
See the function description in base.py
"""
scaledResult = self.encoder.getBucketInfo(buckets)[0]
scaledValue = scaledResult.value
value = math.pow(10, scaledValue)
return [EncoderResult(value=value, scalar=value, encoding=scaledResult.encoding)]
############################################################################
def topDownCompute(self, encoded):
"""
See the function description in base.py
"""
scaledResult = self.encoder.topDownCompute(encoded)[0]
scaledValue = scaledResult.value
value = math.pow(10, scaledValue)
return EncoderResult(value=value, scalar=value, encoding=scaledResult.encoding)
############################################################################
def closenessScores(self, expValues, actValues, fractional=True):
"""
See the function description in base.py
"""
# Compute the percent error in log space
if expValues[0] > 0:
expValue = math.log10(expValues[0])
else:
expValue = self.minScaledValue
if actValues[0] > 0:
actValue = math.log10(actValues[0])
else:
actValue = self.minScaledValue
if fractional:
err = abs(expValue - actValue)
pctErr = err / (self.maxScaledValue - self.minScaledValue)
pctErr = min(1.0, pctErr)
closeness = 1.0 - pctErr
else:
err = abs(expValue - actValue)
closeness = err
# print "log::", "expValue:", expValues[0], "actValue:", actValues[0], \
# "closeness", closeness
# import pdb; pdb.set_trace()
return numpy.array([closeness])
class CategoryEncoder(Encoder):
"""Encodes a list of discrete categories (described by strings), that aren't
related to each other, so we never emit a mixture of categories.
The value of zero is reserved for "unknown category"
Internally we use a ScalarEncoder with a radius of 1, but since we only encode
integers, we never get mixture outputs.
The SDRCategoryEncoder uses a different method to encode categories"""
def __init__(self, w, categoryList, name="category", verbosity=0, forced=False):
"""params:
forced (default False) : if True, skip checks for parameters' settings; see encoders/scalar.py for details
"""
self.encoders = None
self.verbosity = verbosity
# number of categories includes "unknown"
self.ncategories = len(categoryList) + 1
self.categoryToIndex = dict() # check_later: what is the purpose of categoryToIndex and indexToCategory?
self.indexToCategory = dict()
self.indexToCategory[0] = UNKNOWN
for i in xrange(len(categoryList)):
self.categoryToIndex[categoryList[i]] = i+1
self.indexToCategory[i+1] = categoryList[i]
self.encoder = ScalarEncoder(w, minval=0, maxval=self.ncategories - 1,
radius=1, periodic=False, forced=forced)
self.width = w * self.ncategories
assert self.encoder.getWidth() == self.width
self.description = [(name, 0)]
self.name = name
# These are used to support the topDownCompute method
self._topDownMappingM = None
# This gets filled in by getBucketValues
self._bucketValues = None
############################################################################
def getDecoderOutputFieldTypes(self):
""" [Encoder class virtual method override]
"""
# TODO: change back to string meta-type after the decoding logic is fixed
# to output strings instead of internal index values.
#return (FieldMetaType.string,)
return (FieldMetaType.integer,)
############################################################################
def getWidth(self):
return self.width
############################################################################
def getDescription(self):
return self.description
############################################################################
def getScalars(self, input):
""" See method description in base.py """
if input == SENTINEL_VALUE_FOR_MISSING_DATA:
return numpy.array([None])
else:
return numpy.array([self.categoryToIndex.get(input, 0)]) # to_note: returns the scalar value of the input, as stored in the categoryToIndex
# Will return in the format of a numpy array (e.g. [1] or [2]), return [0] if the
# input does not match with any of the key in categoryToIndex dictionary
############################################################################
def getBucketIndices(self, input):
""" See method description in base.py """
# Get the bucket index from the underlying scalar encoder
if input == SENTINEL_VALUE_FOR_MISSING_DATA:
return [None]
else:
return self.encoder.getBucketIndices(self.categoryToIndex.get(input, 0)) # to_note: get the first ON bit from the ScalarEncoder for a given input.
# Unknown value will have the first ON bit at position 1, then other values at k*w
# Value NONE will have all 0s
# problem_with_this_approach: this approach might be fast, but treating
# category encoding as rigid scalar encoding might make it hard for learning
############################################################################
def encodeIntoArray(self, input, output):
# if not found, we encode category 0
if input == SENTINEL_VALUE_FOR_MISSING_DATA:
output[0:] = 0
val = "<missing>"
else:
val = self.categoryToIndex.get(input, 0)
self.encoder.encodeIntoArray(val, output)
if self.verbosity >= 2:
print "input:", input, "va:", val, "output:", output
print "decoded:", self.decodedToStr(self.decode(output))
############################################################################
def decode(self, encoded, parentFieldName=''):
""" See the function description in base.py
"""
# Get the scalar values from the underlying scalar encoder
(fieldsDict, fieldNames) = self.encoder.decode(encoded)
if len(fieldsDict) == 0:
return (fieldsDict, fieldNames)
# Expect only 1 field
assert(len(fieldsDict) == 1)
# Get the list of categories the scalar values correspond to and
# generate the description from the category name(s).
(inRanges, inDesc) = fieldsDict.values()[0] # to_note: dict.values() returns values in [list] form, that's why we need [0]
outRanges = []
desc = ""
for (minV, maxV) in inRanges:
minV = int(round(minV))
maxV = int(round(maxV))
outRanges.append((minV, maxV))
while minV <= maxV:
if len(desc) > 0:
desc += ", "
desc += self.indexToCategory[minV]
minV += 1
"""
## Test with noisy encoding (very likely if such encoding comes from output of the predicting process)
catfish = numpy.zeros(20, 'int')
# catfish[5:10] = 1
# catfish[15:20] = 1
catfish[5:10] = 1
catfish[8] = 1
catfish[11] = 1
catfish[14] = 1
catfish[17] = 1
print "Cat fish =", catfish; print
## Note: this kind of encoding is highly unstable, for a little noisy output like this [0 0 0 0 0 1 1 1 1 1 0 1 0 0 1 0 0 1 0 0],
## it should safely generate 'cat'. However, it generates 'cat', 'dog', 'fish'. To improve this encoding/decoding scheme,
## we might need to replace the filling in process, even might need to think about other way to process information.
## problem_with_this_approach
"""
# Return result
if parentFieldName != '':
fieldName = "%s.%s" % (parentFieldName, self.name)
else:
fieldName = self.name
return ({fieldName: (outRanges, desc)}, [fieldName])
############################################################################
def closenessScores(self, expValues, actValues, fractional=True,):
""" See the function description in base.py
kwargs will have the keyword "fractional", which is ignored by this encoder
"""
expValue = expValues[0]
actValue = actValues[0]
if expValue == actValue:
closeness = 1.0
else:
closeness = 0.0
if not fractional:
closeness = 1.0 - closeness
return numpy.array([closeness])
############################################################################
def getBucketValues(self):
""" See the function description in base.py """
if self._bucketValues is None:
numBuckets = len(self.encoder.getBucketValues())
self._bucketValues = []
for bucketIndex in range(numBuckets):
self._bucketValues.append(self.getBucketInfo([bucketIndex])[0].value) # to_note: list of category corresponding to bucket indices
# each bucket is a number that is spaced (radius/w) each other
return self._bucketValues
############################################################################
def getBucketInfo(self, buckets):
""" See the function description in base.py
"""
# For the category encoder, the bucket index is the category index
bucketInfo = self.encoder.getBucketInfo(buckets)[0]
categoryIndex = int(round(bucketInfo.value))
category = self.indexToCategory[categoryIndex] # to_note: map the bucket index to category
return [EncoderResult(value=category, scalar=categoryIndex,
encoding=bucketInfo.encoding)]
############################################################################
def topDownCompute(self, encoded):
""" See the function description in base.py
"""
encoderResult = self.encoder.topDownCompute(encoded)[0] # to_note: return EncoderResult, which includes the value (depend on ScalarEncoder)
value = encoderResult.value
categoryIndex = int(round(value))
category = self.indexToCategory[categoryIndex]
return EncoderResult(value=category, scalar=categoryIndex,
encoding=encoderResult.encoding)
@classmethod
def read(cls, proto):
encoder = object.__new__(cls)
encoder.verbosity = proto.verbosity
encoder.encoder = ScalarEncoder.read(proto.encoder)
encoder.width = proto.width
encoder.description = [(proto.name, 0)]
encoder.name = proto.name
encoder.indexToCategory = {x.index: x.category
for x in proto.indexToCategory}
encoder.categoryToIndex = {category: index
for index, category
in encoder.indexToCategory.items()
if category != UNKNOWN}
encoder._topDownMappingM = None
encoder._bucketValues = None
return encoder
def write(self, proto):
proto.width = self.width
proto.indexToCategory = [
{"index": index, "category": category}
for index, category in self.indexToCategory.items()
]
proto.name = self.name
proto.verbosity = self.verbosity
self.encoder.write(proto.encoder)
class LogEncoder(Encoder):
"""
This class wraps the :class:`.ScalarEncoder`.
A Log encoder represents a floating point value on a logarithmic scale.
.. code-block:: python
valueToEncode = log10(input)
:param resolution: The minimum change in scaled value needed to produce a
change in encoding. This should be specified in log space.
For example, the scaled values 10 and 11 will be
distinguishable in the output. In terms of the original
input values, this means 10^1 (1) and 10^1.1 (1.25) will be
distinguishable.
:param radius: inputs separated by more than this distance in log space will
have non-overlapping representations
"""
def __init__(self,
w=5,
minval=1e-07,
maxval=10000,
periodic=False,
n=0,
radius=0,
resolution=0,
name="log",
verbosity=0,
clipInput=True,
forced=False):
# Lower bound for log encoding near machine precision limit
lowLimit = 1e-07
# Limit minval as log10(0) is undefined.
if minval < lowLimit:
minval = lowLimit
# Check that minval is still lower than maxval
if not minval < maxval:
raise ValueError("Max val must be larger than min val or the lower limit "
"for this encoder %.7f" % lowLimit)
self.encoders = None
self.verbosity = verbosity
# Scale values for calculations within the class
self.minScaledValue = math.log10(minval)
self.maxScaledValue = math.log10(maxval)
if not self.maxScaledValue > self.minScaledValue:
raise ValueError("Max val must be larger, in log space, than min val.")
self.clipInput = clipInput
self.minval = minval
self.maxval = maxval
self.encoder = ScalarEncoder(w=w,
minval=self.minScaledValue,
maxval=self.maxScaledValue,
periodic=False,
n=n,
radius=radius,
resolution=resolution,
verbosity=self.verbosity,
clipInput=self.clipInput,
forced=forced)
self.width = self.encoder.getWidth()
self.description = [(name, 0)]
self.name = name
# This list is created by getBucketValues() the first time it is called,
# and re-created whenever our buckets would be re-arranged.
self._bucketValues = None
def getWidth(self):
return self.width
def getDescription(self):
return self.description
def getDecoderOutputFieldTypes(self):
"""
Encoder class virtual method override
"""
return (FieldMetaType.float, )
def _getScaledValue(self, inpt):
"""
Convert the input, which is in normal space, into log space
"""
if inpt == SENTINEL_VALUE_FOR_MISSING_DATA:
return None
else:
val = inpt
if val < self.minval:
val = self.minval
elif val > self.maxval:
val = self.maxval
scaledVal = math.log10(val)
return scaledVal
def getBucketIndices(self, inpt):
"""
See the function description in base.py
"""
# Get the scaled value
scaledVal = self._getScaledValue(inpt)
if scaledVal is None:
return [None]
else:
return self.encoder.getBucketIndices(scaledVal)
def encodeIntoArray(self, inpt, output):
"""
See the function description in base.py
"""
# Get the scaled value
scaledVal = self._getScaledValue(inpt)
if scaledVal is None:
output[0:] = 0
else:
self.encoder.encodeIntoArray(scaledVal, output)
if self.verbosity >= 2:
print "input:", inpt, "scaledVal:", scaledVal, "output:", output
print "decoded:", self.decodedToStr(self.decode(output))
def decode(self, encoded, parentFieldName=''):
"""
See the function description in base.py
"""
# Get the scalar values from the underlying scalar encoder
(fieldsDict, fieldNames) = self.encoder.decode(encoded)
if len(fieldsDict) == 0:
return (fieldsDict, fieldNames)
# Expect only 1 field
assert(len(fieldsDict) == 1)
# Convert each range into normal space
(inRanges, inDesc) = fieldsDict.values()[0]
outRanges = []
for (minV, maxV) in inRanges:
outRanges.append((math.pow(10, minV),
math.pow(10, maxV)))
# Generate a text description of the ranges
desc = ""
numRanges = len(outRanges)
for i in xrange(numRanges):
if outRanges[i][0] != outRanges[i][1]:
desc += "%.2f-%.2f" % (outRanges[i][0], outRanges[i][1])
else:
desc += "%.2f" % (outRanges[i][0])
if i < numRanges-1:
desc += ", "
# Return result
if parentFieldName != '':
fieldName = "%s.%s" % (parentFieldName, self.name)
else:
fieldName = self.name
return ({fieldName: (outRanges, desc)}, [fieldName])
def getBucketValues(self):
"""
See the function description in base.py
"""
# Need to re-create?
if self._bucketValues is None:
scaledValues = self.encoder.getBucketValues()
self._bucketValues = []
for scaledValue in scaledValues:
value = math.pow(10, scaledValue)
self._bucketValues.append(value)
return self._bucketValues
def getBucketInfo(self, buckets):
"""
See the function description in base.py
"""
scaledResult = self.encoder.getBucketInfo(buckets)[0]
scaledValue = scaledResult.value
value = math.pow(10, scaledValue)
return [EncoderResult(value=value, scalar=value,
encoding = scaledResult.encoding)]
def topDownCompute(self, encoded):
"""
See the function description in base.py
"""
scaledResult = self.encoder.topDownCompute(encoded)[0]
scaledValue = scaledResult.value
value = math.pow(10, scaledValue)
return EncoderResult(value=value, scalar=value,
encoding = scaledResult.encoding)
def closenessScores(self, expValues, actValues, fractional=True):
"""
See the function description in base.py
"""
# Compute the percent error in log space
if expValues[0] > 0:
expValue = math.log10(expValues[0])
else:
expValue = self.minScaledValue
if actValues [0] > 0:
actValue = math.log10(actValues[0])
else:
actValue = self.minScaledValue
if fractional:
err = abs(expValue - actValue)
pctErr = err / (self.maxScaledValue - self.minScaledValue)
pctErr = min(1.0, pctErr)
closeness = 1.0 - pctErr
else:
err = abs(expValue - actValue)
closeness = err
#print "log::", "expValue:", expValues[0], "actValue:", actValues[0], \
# "closeness", closeness
#import pdb; pdb.set_trace()
return numpy.array([closeness])
@classmethod
def read(cls, proto):
encoder = object.__new__(cls)
encoder.verbosity = proto.verbosity
encoder.minScaledValue = proto.minScaledValue
encoder.maxScaledValue = proto.maxScaledValue
encoder.clipInput = proto.clipInput
encoder.minval = proto.minval
encoder.maxval = proto.maxval
encoder.encoder = ScalarEncoder.read(proto.encoder)
encoder.name = proto.name
encoder.width = encoder.encoder.getWidth()
encoder.description = [(encoder.name, 0)]
encoder._bucketValues = None
return encoder
def write(self, proto):
proto.verbosity = self.verbosity
proto.minScaledValue = self.minScaledValue
proto.maxScaledValue = self.maxScaledValue
proto.clipInput = self.clipInput
proto.minval = self.minval
proto.maxval = self.maxval
self.encoder.write(proto.encoder)
proto.name = self.name
