def testErrorChecks(self):
params1 = RDSE_Parameters()
params1.size = 100
params1.sparsity = .10
params1.radius = 10
R1 = RDSE( params1 )
A = SDR([10, 10])
R1.encode( 33, A )
# Test wrong input dimensions
B = SDR( 1 )
with self.assertRaises(RuntimeError):
R1.encode( 3, B )
# Test invalid parameters, size == 0
params1.size = 0
with self.assertRaises(RuntimeError):
RDSE( params1 )
params1.size = 100
# Test invalid parameters, activeBits == 0
params1.activeBits = 0
params1.sparsity = 0.00001 # Rounds to zero!
with self.assertRaises(RuntimeError):
RDSE( params1 )
# Test missing activeBits
params2 = RDSE_Parameters()
params2.size = 100
params2.radius = 10
with self.assertRaises(RuntimeError):
RDSE( params2 )
# Test missing resolution/radius
params3 = RDSE_Parameters()
params3.size = 100
params3.activeBits = 10
with self.assertRaises(RuntimeError):
RDSE( params3 )
# Test too many parameters: activeBits & sparsity
params4 = RDSE_Parameters()
params4.size = 100
params4.sparsity = .6
params4.activeBits = 10
params4.radius = 4
with self.assertRaises(RuntimeError):
RDSE( params4 )
# Test too many parameters: resolution & radius
params5 = RDSE_Parameters()
params5.size = 100
params5.activeBits = 10
params5.radius = 4
params5.resolution = 4
with self.assertRaises(RuntimeError):
RDSE( params5 )
def testRandomOverlap(self):
""" Verify that distant values have little to no semantic similarity.
Also measure sparsity & activation frequency. """
P = RDSE_Parameters()
P.size = 2000
P.sparsity = .08
P.radius = 12
P.seed = 42
R = RDSE( P )
num_samples = 1000
A = SDR( R.parameters.size )
M = Metrics( A, num_samples + 1 )
for i in range( num_samples ):
X = i * R.parameters.radius
R.encode( X, A )
print( M )
assert(M.overlap.max() < .15 )
assert(M.overlap.mean() < .10 )
assert(M.sparsity.min() > R.parameters.sparsity - .01 )
assert(M.sparsity.max() < R.parameters.sparsity + .01 )
assert(M.sparsity.mean() > R.parameters.sparsity - .005 )
assert(M.sparsity.mean() < R.parameters.sparsity + .005 )
assert(M.activationFrequency.min() > R.parameters.sparsity - .05 )
assert(M.activationFrequency.max() < R.parameters.sparsity + .05 )
assert(M.activationFrequency.mean() > R.parameters.sparsity - .005 )
assert(M.activationFrequency.mean() < R.parameters.sparsity + .005 )
assert(M.activationFrequency.entropy() > .99 )
def testAverageOverlap(self):
""" Verify that nearby values have the correct amount of semantic
similarity. Also measure sparsity & activation frequency. """
P = RDSE_Parameters()
P.size = 2000
P.sparsity = .08
P.radius = 12
P.seed = 42
R = RDSE( P )
A = SDR( R.parameters.size )
num_samples = 10000
M = Metrics( A, num_samples + 1 )
for i in range( num_samples ):
R.encode( i, A )
print( M )
assert(M.overlap.min() > (1 - 1. / R.parameters.radius) - .04 )
assert(M.overlap.max() < (1 - 1. / R.parameters.radius) + .04 )
assert(M.overlap.mean() > (1 - 1. / R.parameters.radius) - .001 )
assert(M.overlap.mean() < (1 - 1. / R.parameters.radius) + .001 )
assert(M.sparsity.min() > R.parameters.sparsity - .01 )
assert(M.sparsity.max() < R.parameters.sparsity + .01 )
assert(M.sparsity.mean() > R.parameters.sparsity - .005 )
assert(M.sparsity.mean() < R.parameters.sparsity + .005 )
assert(M.activationFrequency.min() > R.parameters.sparsity - .05 )
assert(M.activationFrequency.max() < R.parameters.sparsity + .05 )
assert(M.activationFrequency.mean() > R.parameters.sparsity - .005 )
assert(M.activationFrequency.mean() < R.parameters.sparsity + .005 )
assert(M.activationFrequency.entropy() > .99 )
def testSeed(self):
P = RDSE_Parameters()
P.size = 1000
P.sparsity = .08
P.radius = 12
P.seed = 98
R = RDSE( P )
A = R.encode( 987654 )
P.seed = 99
R = RDSE( P )
B = R.encode( 987654 )
assert( A != B )
def testSparsityActiveBits(self):
""" Check that these arguments are equivalent. """
# Round sparsity up
P = RDSE_Parameters()
P.size = 100
P.sparsity = .0251
P.radius = 10
R = RDSE( P )
assert( R.parameters.activeBits == 3 )
# Round sparsity down
P = RDSE_Parameters()
P.size = 100
P.sparsity = .0349
P.radius = 10
R = RDSE( P )
assert( R.parameters.activeBits == 3 )
# Check activeBits
P = RDSE_Parameters()
P.size = 100
P.activeBits = 50 # No floating point issues here.
P.radius = 10
R = RDSE( P )
assert( R.parameters.sparsity == .5 )
def testConstructor(self):
params1 = RDSE_Parameters()
params1.size = 100
params1.sparsity = .10
params1.radius = 10
R1 = RDSE( params1 )
params2 = R1.parameters
params2.sparsity = 0 # Remove duplicate arguments
params2.radius = 0 # Remove duplicate arguments
R2 = RDSE( params2 )
A = SDR( R1.parameters.size )
R1.encode( 66, A )
B = R2.encode( 66 )
assert( A == B )
def testDeterminism(self):
""" Verify that the same seed always gets the same results. """
GOLD = SDR( 1000 )
GOLD.sparse = [
28, 47, 63, 93, 123, 124, 129, 131, 136, 140, 196, 205, 213, 239,
258, 275, 276, 286, 305, 339, 345, 350, 372, 394, 395, 443, 449,
462, 468, 471, 484, 514, 525, 557, 565, 570, 576, 585, 600, 609,
631, 632, 635, 642, 651, 683, 693, 694, 696, 699, 721, 734, 772,
790, 792, 795, 805, 806, 833, 836, 842, 846, 892, 896, 911, 914,
927, 936, 947, 953, 955, 962, 965, 989, 990, 996]
P = RDSE_Parameters()
P.size = GOLD.size
P.sparsity = .08
P.radius = 12
P.seed = 42
R = RDSE( P )
A = R.encode( 987654 )
print( A )
assert( A == GOLD )
def testPickle(self):
"""
The pickling is successfull if pickle serializes and de-serialize the
RDSE object.
Moreover, the de-serialized object shall give the same SDR than the
original encoder given the same scalar value to encode.
"""
rdse_params = RDSE_Parameters()
rdse_params.sparsity = 0.1
rdse_params.size = 100
rdse_params.resolution = 0.1
rdse_params.seed = 1997
rdse = RDSE(rdse_params)
filename = "RDSE_testPickle"
try:
with open(filename, "wb") as f:
pickle.dump(rdse, f)
except:
dump_success = False
else:
dump_success = True
assert(dump_success)
try:
with open(filename, "rb") as f:
rdse_loaded = pickle.load(f)
except:
read_success = False
else:
read_success = True
assert(read_success)
value_to_encode = 69003
SDR_original = rdse.encode(value_to_encode)
SDR_loaded = rdse_loaded.encode(value_to_encode)
assert(SDR_original == SDR_loaded)
def testJSONSerialization(self):
"""
This test is to insure that Python can access the C++ serialization functions.
Serialization is tested more completely in C++ unit tests. Just checking
that Python can access it.
"""
rdse_params = RDSE_Parameters()
rdse_params.sparsity = 0.1
rdse_params.size = 1000
rdse_params.resolution = 0.1
rdse_params.seed = 1997
rdse = RDSE(rdse_params)
filename = 'RDSE_testPickle'
rdse.saveToFile(filename, 'JSON')
rdse_loaded = RDSE()
rdse_loaded.loadFromFile(filename, 'JSON')
value_to_encode = 69003
SDR_original = rdse.encode(value_to_encode)
SDR_loaded = rdse_loaded.encode(value_to_encode)
assert (SDR_original == SDR_loaded)
seqn = np.reshape(seqn, (len(seqn),1))
seqn = seqn.astype('float64')
# scaling must be between 0 and 1 because the line
# "predict.learn(count, tm_actCells, label)" throws an error
# when the label is a negative number
scaler = MinMaxScaler((0,1))
X_train_scaled = scaler.fit_transform(seqn)
train_size = int(np.ceil(len(seqn)*0.70))
train_set = X_train_scaled[0:train_size]
# encode the integer sequence using the pre-built RDSE in htm.core,
# it apparently uses a hash function
params = RDSE_Parameters()
# sparsity is actually recommended to be around 2%
params.sparsity = 0.10
# this radius will change depending on the values of the sequence
# since the integers are scaled to be between 0 and 1, 0.1 seemed like
# a good radius, but could be too large
params.radius = 0.1
# professionals from HTMForum recommended a larger encoder
params.size = 1000
rdseEncoder = RDSE(params)
# set up the spatial pooler
# if your encoded numbers are already sparse,
# the SP isn't really necessary; it is used when the encoder
# does not produce a sparse representation (which happens according
# to resources)
sp = SP(inputDimensions = (rdseEncoder.size,),
columnDimensions = (1000,),
