Hi Team,

I am new to HTM and trying to run HTM model for Anomaly detection use case (wanted to train it as binary classification), took the MNIST example as reference tried to build the model (only difference is being, my dataset is multivariate and I had to concatenate SDRs). My accuracy score is not improving though I have used Optimizers.

here is my sample code snippet

```
default_parameters = {
'potentialRadius': 16,
'boostStrength': 7.0,
'columnDimensions': (32,),
'dutyCyclePeriod': 1000,
'minPctOverlapDutyCycle': 0.001,
'potentialPct': 0.5,
'stimulusThreshold': 12,
'synPermActiveInc': 0.1,
'synPermConnected': 0.1,
'synPermInactiveDec': 0.01
}
rdse_params = RDSE_Parameters()
rdse_params.sparsity = 0.3
rdse_params.size = 2048
rdse_params.radius = 10
numeric_encoder = RDSE(rdse_params)
total_size = (train.shape[1] -1) * numeric_encoder.size
print("total size::",total_size)
# Setup the AI.
sp = SpatialPooler(
inputDimensions = (total_size,),
columnDimensions = (2048,),
potentialRadius = parameters['potentialRadius'],
potentialPct = parameters['potentialPct'],
globalInhibition = True,
stimulusThreshold = int(round(parameters['stimulusThreshold'])),
synPermInactiveDec = parameters['synPermInactiveDec'],
synPermActiveInc = parameters['synPermActiveInc'],
synPermConnected = parameters['synPermConnected'],
minPctOverlapDutyCycle = parameters['minPctOverlapDutyCycle'],
dutyCyclePeriod = int(round(parameters['dutyCyclePeriod'])),
boostStrength = parameters['boostStrength'],
seed = 0, # this is important, 0="random" seed which changes on each invocation
spVerbosity = 99,
wrapAround = False)
columns = SDR( sp.getColumnDimensions() )
columns_stats = Metrics( columns, 99999999 )
sdrc = Classifier()
cols = list(data_numeric.columns)
cols.remove('attack')
spatial_output = SDR(sp.getColumnDimensions())
for index, row in train.iterrows():
# Create an SDR for the concatenated encodings
concatenated_sdr = SDR(total_size)
# Keep track of the current position in the concatenated SDR
position = 0
# Encode each column's value and concatenate
for col in cols :
#print("Encode the value into an SDR")
encoded_sdr = SDR(numeric_encoder.size)
encoded_sdr = numeric_encoder.encode(row[col])
# encoded_sdr = SDR(numeric_encoder.encode(row[col]))
# Concatenate the encoded SDR to the larger SDR
concatenated_sdr.dense[position:position + numeric_encoder.size] = encoded_sdr.dense
position = position + numeric_encoder.size
# Spatial Pooling
sp.compute(concatenated_sdr, True, spatial_output)
lbl= int(row['attack'])
#print(lbl)
#print("ok")
sdrc.learn(spatial_output,lbl)
# Temporal Memory
print(str(sp))
print(str(columns_stats))
# Testing Loop
score = 0
for index, row in test.iterrows():
# Create an SDR for the concatenated encodings
concatenated_sdr = SDR(total_size)
# Keep track of the current position in the concatenated SDR
position = 0
# Encode each column's value and concatenate
for col in cols :
#print("Encode the value into an SDR")
encoded_sdr = SDR(numeric_encoder.size)
encoded_sdr = numeric_encoder.encode(row[col])
# encoded_sdr = SDR(numeric_encoder.encode(row[col]))
# Concatenate the encoded SDR to the larger SDR
concatenated_sdr.dense[position:position + numeric_encoder.size] = encoded_sdr.dense
position = position + numeric_encoder.size
# Spatial Pooling
sp.compute(concatenated_sdr, False, spatial_output)
lbl= int(row['attack'])
#print(lbl)
if lbl == np.argmax( sdrc.infer( spatial_output ) ):
score += 1
score = score / len(test)
print('Score:', 100 * score, '%')
return score
```

Please help me with the issue.

Thanks in advance.