<commit_before>
# Numenta Platform for Intelligent Computing (NuPIC)
# Copyright (C) 2013, Numenta, Inc.  Unless you have an agreement
# with Numenta, Inc., for a separate license for this software code, the
# following terms and conditions apply:
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero Public License version 3 as
# published by the Free Software Foundation.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
# See the GNU Affero Public License for more details.
#
# You should have received a copy of the GNU Affero Public License
# along with this program.  If not, see http://www.gnu.org/licenses.
#
# http://numenta.org/licenses/
# ----------------------------------------------------------------------

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': [('consumption', 'sum')],
        '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': '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,

            # Include the encoders we use
            'encoders': {
                u'consumption':    {
                    'fieldname': u'consumption',
                    'resolution': 0.88,
                    'seed': 1,
                    'name': u'consumption',
                    'type': 'RandomDistributedScalarEncoder',
                    },

                'timestamp_timeOfDay': {   'fieldname': u'timestamp',
                                           'name': u'timestamp_timeOfDay',
                                           'timeOfDay': (21, 1),
                                           'type': 'DateEncoder'},
                'timestamp_weekend': {   'fieldname': u'timestamp',
                                         'name': u'timestamp_weekend',
                                         'type': 'DateEncoder',
                                         'weekend': 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),
            #
            # (value generated from SENSOR_AUTO_RESET)
            '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 cell columns in the cortical region (same number for
            # SP and TP)
            # (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. 
            'potentialPct': 0.85,

            # 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': 0.04,

            'synPermInactiveDec': 0.005,

            # maxBoost controls the strength of boosting. It should be a
            # a number greater or equal than 1.0. No boosting is applied if
            # maxBoost=1.0. Boosting encourages efficient usage of SP columns.
            'maxBoost': 3.0,
        },

        # 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 nupic/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 Memory implementation selector (see _getTPClass in
            # CLARegion.py).
            'temporalImp': 'cpp',

            # New Synapse formation count<commit_msg>rename maxBoost to boostStrength in hotgym example<commit_after>
# Numenta Platform for Intelligent Computing (NuPIC)
# Copyright (C) 2013, Numenta, Inc.  Unless you have an agreement
# with Numenta, Inc., for a separate license for this software code, the
# following terms and conditions apply:
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero Public License version 3 as
# published by the Free Software Foundation.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
# See the GNU Affero Public License for more details.
#
# You should have received a copy of the GNU Affero Public License
# along with this program.  If not, see http://www.gnu.org/licenses.
#
# http://numenta.org/licenses/
# ----------------------------------------------------------------------

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': [('consumption', 'sum')],
        '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': '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,

            # Include the encoders we use
            'encoders': {
                u'consumption':    {
                    'fieldname': u'consumption',
                    'resolution': 0.88,
                    'seed': 1,
                    'name': u'consumption',
                    'type': 'RandomDistributedScalarEncoder',
                    },

                'timestamp_timeOfDay': {   'fieldname': u'timestamp',
                                           'name': u'timestamp_timeOfDay',
                                           'timeOfDay': (21, 1),
                                           'type': 'DateEncoder'},
                'timestamp_weekend': {   'fieldname': u'timestamp',
                                         'name': u'timestamp_weekend',
                                         'type': 'DateEncoder',
                                         'weekend': 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),
            #
            # (value generated from SENSOR_AUTO_RESET)
            '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 cell columns in the cortical region (same number for
            # SP and TP)
            # (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. 
            'potentialPct': 0.85,

            # 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': 0.04,

            'synPermInactiveDec': 0.005,

            # boostStrength controls the strength of boosting. It should be a
            # a number greater or equal than 0.0. No boosting is applied if
            # boostStrength=0.0. Boosting encourages efficient usage of SP columns.
            'boostStrength': 3.0,
        },

        # 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 nupic/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 Memory implementation selector (see _getTPClass in
            # CLARegion.py).
            'temporalImp': 'cpp',

            # New Synapse formation count
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