Thanks Subutai, this is I have been using:
MODEL_PARAMS = {
# Type of model that the rest of these parameters apply to.
'model': "CLA",
# Version that specifies the format of the config.
'version': 1,
# Intermediate variables used to compute fields in modelParams and also
# referenced from the control section.
#'aggregationInfo': { 'days': 0,
# 'fields': [(u'c1', 'sum'), (u'c0', 'first')],
# 'hours': 1,
# 'microseconds': 0,
# 'milliseconds': 0,
# 'minutes': 0,
# 'months': 0,
# 'seconds': 0,
# 'weeks': 0,
# 'years': 0},
'predictAheadTime': None,
# Model parameter dictionary.
'modelParams': {
# The type of inference that this model will perform
'inferenceType': 'TemporalAnomaly',
'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,
# Example:
# dsEncoderSchema = [
# DeferredDictLookup('__field_name_encoder'),
# ],
#
# (value generated from DS_ENCODER_SCHEMA)
'encoders': {
u'timestamp_timeOfDay': None,
u'timestamp_dayOfWeek': None,
u'timestamp_weekend': None,
u'channel1': {
'clipInput': True,
'fieldname': u'channel1',
'maxval': 4.0,
'minval': -4.0,
'n': 134,
'name': u'channel1',
'type': 'ScalarEncoder',
'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,
# Spatial Pooler implementation selector.
# Options: 'py', 'cpp' (speed optimized, new)
'spatialImp' : 'cpp',
'globalInhibition': 1,
# Number of columns in the SP (must be same as in TP)
'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.
'potentialPct': 0.8,
# 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.
'synPermConnected': 0.1,
'synPermActiveInc': 0.0001,
'synPermInactiveDec': 0.0005,
'maxBoost': 1.0,
},
# Controls whether TP is enabled or disabled;
# TP is necessary for making temporal predictions, such as
predicting
# the next inputs. Without TP, the model is only capable of
# reconstructing missing sensor inputs (via SP).
'tpEnable' : True,
'tpParams': {
# TP diagnostic output verbosity control;
# 0: silent; [1..6]: increasing levels of verbosity
# (see verbosity in nta/trunk/py/nupic/research/TP.py and
TP10X*.py)
'verbosity': 0,
# Number of cell columns in the cortical region (same number for
# SP and TP)
# (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 TP
# 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 TP
# 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': 9,
# 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': 12,
'outputType': 'normal',
# "Pay Attention Mode" length. This tells the TP 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': 3,
},
# Don't create the classifier since we don't need predictions.
'clEnable': False,
'clParams': None,
'anomalyParams': { u'anomalyCacheRecords': None,
u'autoDetectThreshold': None,
u'autoDetectWaitRecords': 2184},
'trainSPNetOnlyIfRequested': False,
},
}
On Tue, Oct 21, 2014 at 2:10 PM, Subutai Ahmad <[email protected]> wrote:
> Hi Nicolas,
>
> Yeah, 913 years might be too long! Can you email the model params file
> you are using? I could look at that and try to suggest some changes.
>
> However, 10 msecs per data point is not too bad for the HTM. It might be
> hard to get too much faster!
>
> --Subutai
>
> On Tue, Oct 21, 2014 at 1:45 PM, Nicolas Thiébaud <[email protected]>
> wrote:
>
>> Hi,
>>
>> What are the parameters we can tune to train and predict faster. I am
>> seeing ingestion speed without learning at 100 scalar points points per
>> second.
>>
>> If I downsample my input data (EEG) to 100Hz which is a minimum
>> acceptable and using a 16 core amazon instance, I would need 913 years (not
>> corrected for Moore's law).
>>
>> Which model params should I try to tune?
>>
>> Thank you for the help,
>>
>> Nicolas.
>>
>
>
