Here is the model_params.py which I didn’t modify for the sine function:
MODEL_PARAMS = {
# Type of model that the rest of these parameters apply to.
‘model’: “HTMPrediction”,
# Version that specifies the format of the config.
'version': 1,
'predictAheadTime': None,
# Model parameter dictionary.
'modelParams': {
# The type of inference that this model will perform
'inferenceType': 'TemporalMultiStep',
'sensorParams': {
# Sensor diagnostic output verbosity control;
# if > 0: sensor region will print out on screen what it's sensing
# at each step 0: silent; >=1: some info; >=2: more info;
# >=3: even more info (see compute() in py/regions/RecordSensor.py)
'verbosity' : 0,
# CPU usage encoder.
'encoders': {
'cpu': {
'fieldname': u'cpu',
'n': 200,
'name': u'cpu',
'type': 'ScalarEncoder',
'minval': 0.0,
'maxval': 100.0,
'w': 21
}
},
# A dictionary specifying the period for automatically-generated
# resets from a RecordSensor;
#
# None = disable automatically-generated resets (also disabled if
# all of the specified values evaluate to 0).
# Valid keys is the desired combination of the following:
# days, hours, minutes, seconds, milliseconds, microseconds, weeks
#
# Example for 1.5 days: sensorAutoReset = dict(days=1,hours=12),
'sensorAutoReset' : None,
},
'spEnable': True,
'spParams': {
# SP diagnostic output verbosity control;
# 0: silent; >=1: some info; >=2: more info;
'spVerbosity' : 0,
'globalInhibition': 1,
# Number of cell columns in the cortical region (same number for
# SP and TM)
# (see also tpNCellsPerCol)
'columnCount': 2048,
'inputWidth': 0,
# SP inhibition control (absolute value);
# Maximum number of active columns in the SP region's output (when
# there are more, the weaker ones are suppressed)
'numActiveColumnsPerInhArea': 40,
'seed': 1956,
# potentialPct
# What percent of the columns's receptive field is available
# for potential synapses. At initialization time, we will
# choose potentialPct * (2*potentialRadius+1)^2
'potentialPct': 0.5,
# The default connected threshold. Any synapse whose
# permanence value is above the connected threshold is
# a "connected synapse", meaning it can contribute to the
# cell's firing. Typical value is 0.10. Cells whose activity
# level before inhibition falls below minDutyCycleBeforeInh
# will have their own internal synPermConnectedCell
# threshold set below this default value.
# (This concept applies to both SP and TM and so 'cells'
# is correct here as opposed to 'columns')
'synPermConnected': 0.1,
'synPermActiveInc': 0.1,
'synPermInactiveDec': 0.01,
},
# Controls whether TM is enabled or disabled;
# TM is necessary for making temporal predictions, such as predicting
# the next inputs. Without TM, the model is only capable of
# reconstructing missing sensor inputs (via SP).
'tmEnable' : True,
'tmParams': {
# TM diagnostic output verbosity control;
# 0: silent; [1..6]: increasing levels of verbosity
# (see verbosity in nupic/trunk/py/nupic/research/backtracking_tm.py and backtracking_tm_cpp.py)
'verbosity': 0,
# Number of cell columns in the cortical region (same number for
# SP and TM)
# (see also tpNCellsPerCol)
'columnCount': 2048,
# The number of cells (i.e., states), allocated per column.
'cellsPerColumn': 32,
'inputWidth': 2048,
'seed': 1960,
# Temporal Pooler implementation selector (see _getTPClass in
# CLARegion.py).
'temporalImp': 'cpp',
# New Synapse formation count
# NOTE: If None, use spNumActivePerInhArea
#
# TODO: need better explanation
'newSynapseCount': 20,
# Maximum number of synapses per segment
# > 0 for fixed-size CLA
# -1 for non-fixed-size CLA
#
# TODO: for Ron: once the appropriate value is placed in TM
# constructor, see if we should eliminate this parameter from
# description.py.
'maxSynapsesPerSegment': 32,
# Maximum number of segments per cell
# > 0 for fixed-size CLA
# -1 for non-fixed-size CLA
#
# TODO: for Ron: once the appropriate value is placed in TM
# constructor, see if we should eliminate this parameter from
# description.py.
'maxSegmentsPerCell': 128,
# Initial Permanence
# TODO: need better explanation
'initialPerm': 0.21,
# Permanence Increment
'permanenceInc': 0.1,
# Permanence Decrement
# If set to None, will automatically default to tpPermanenceInc
# value.
'permanenceDec' : 0.1,
'globalDecay': 0.0,
'maxAge': 0,
# Minimum number of active synapses for a segment to be considered
# during search for the best-matching segments.
# None=use default
# Replaces: tpMinThreshold
'minThreshold': 12,
# Segment activation threshold.
# A segment is active if it has >= tpSegmentActivationThreshold
# connected synapses that are active due to infActiveState
# None=use default
# Replaces: tpActivationThreshold
'activationThreshold': 16,
'outputType': 'normal',
# "Pay Attention Mode" length. This tells the TM how many new
# elements to append to the end of a learned sequence at a time.
# Smaller values are better for datasets with short sequences,
# higher values are better for datasets with long sequences.
'pamLength': 1,
},
'clParams': {
# Classifier implementation selection.
'implementation': 'cpp',
'regionName' : 'SDRClassifierRegion',
# Classifier diagnostic output verbosity control;
# 0: silent; [1..6]: increasing levels of verbosity
'verbosity' : 0,
# This controls how fast the classifier learns/forgets. Higher values
# make it adapt faster and forget older patterns faster.
'alpha': 0.0001,
# This is set after the call to updateConfigFromSubConfig and is
# computed from the aggregationInfo and predictAheadTime.
'steps': '5',
},
'trainSPNetOnlyIfRequested': False,
},
}
