Python aggregationDivide 예제들

예제 #1

파일: base.py 프로젝트: BornOfHope/nupic

        'trainSPNetOnlyIfRequested': False,
    },
}
# end of config dictionary


# Adjust base config dictionary for any modifications if imported from a
# sub-experiment
updateConfigFromSubConfig(config)


# Compute predictionSteps based on the predictAheadTime and the aggregation
# period, which may be permuted over.
if config['predictAheadTime'] is not None:
  predictionSteps = int(round(aggregationDivide(
      config['predictAheadTime'], config['aggregationInfo'])))
  assert (predictionSteps >= 1)
  config['modelParams']['clParams']['steps'] = str(predictionSteps)


# Adjust config by applying ValueGetterBase-derived
# futures. NOTE: this MUST be called after updateConfigFromSubConfig() in order
# to support value-getter-based substitutions from the sub-experiment (if any)
applyValueGettersToContainer(config)


dataPath = os.path.abspath(os.path.join(os.path.dirname(__file__), 
                                        'data.csv'))

control = {
  # The environment that the current model is being run in

예제 #2

파일: description.py 프로젝트: devbeta/nupic

        },
        "trainSPNetOnlyIfRequested": False,
    },
}
# end of config dictionary


# Adjust base config dictionary for any modifications if imported from a
# sub-experiment
updateConfigFromSubConfig(config)


# Compute predictionSteps based on the predictAheadTime and the aggregation
# period, which may be permuted over.
if config["predictAheadTime"] is not None:
    predictionSteps = int(round(aggregationDivide(config["predictAheadTime"], config["aggregationInfo"])))
    assert predictionSteps >= 1
    config["modelParams"]["clParams"]["steps"] = str(predictionSteps)


# Adjust config by applying ValueGetterBase-derived
# futures. NOTE: this MUST be called after updateConfigFromSubConfig() in order
# to support value-getter-based substitutions from the sub-experiment (if any)
applyValueGettersToContainer(config)


control = {
    # The environment that the current model is being run in
    "environment": "nupic",
    # Input stream specification per py/nupicengine/cluster/database/StreamDef.json.
    #

예제 #3

    },
    'claTrainSPNetOnlyIfRequested': True,
    'dataSource': 'fillInBySubExperiment',
}
# end of config dictionary

# Adjust base config dictionary for any modifications if imported from a
# sub-experiment
updateConfigFromSubConfig(config)

# Compute predictionSteps based on the predictAheadTime and the aggregation
# period, which may be permuted over.
if config['predictAheadTime'] is not None:
    predictionSteps = int(
        round(
            aggregationDivide(config['predictAheadTime'],
                              config['aggregationInfo'])))
    assert (predictionSteps >= 1)
    config['modelParams']['clParams']['steps'] = str(predictionSteps)

# Adjust config by applying ValueGetterBase-derived
# futures. NOTE: this MUST be called after updateConfigFromSubConfig() in order
# to support value-getter-based substitutions from the sub-experiment (if any)
applyValueGettersToContainer(config)

# With no TP, there are no columns
if not config['modelParams']['tpEnable']:
    config['modelParams']['clParams']['cellsPerCol'] = 0

################################################################################
control = {
    # The environment that the current model is being run in

예제 #4

파일: description.py 프로젝트: mrcslws/nupic

    },


}
# end of config dictionary


# Adjust base config dictionary for any modifications if imported from a
# sub-experiment
updateConfigFromSubConfig(config)


# Compute predictionSteps based on the predictAheadTime and the aggregation
# period, which may be permuted over.
if config['predictAheadTime'] is not None:
  predictionSteps = int(round(aggregationDivide(
      config['predictAheadTime'], config['aggregationInfo'])))
  assert (predictionSteps >= 1)
  config['modelParams']['clParams']['steps'] = str(predictionSteps)


# Adjust config by applying ValueGetterBase-derived
# futures. NOTE: this MUST be called after updateConfigFromSubConfig() in order
# to support value-getter-based substitutions from the sub-experiment (if any)
applyValueGettersToContainer(config)



# [optional] A sequence of one or more tasks that describe what to do with the
# model. Each task consists of a task label, an input spec., iteration count,
# and a task-control spec per opfTaskSchema.json
#

예제 #5

파일: description.py 프로젝트: Gnomonol/nupic

        },
        'trainSPNetOnlyIfRequested': False,
    },
}
# end of config dictionary

# Adjust base config dictionary for any modifications if imported from a
# sub-experiment
updateConfigFromSubConfig(config)

# Compute predictionSteps based on the predictAheadTime and the aggregation
# period, which may be permuted over.
if config['predictAheadTime'] is not None:
    predictionSteps = int(
        round(
            aggregationDivide(config['predictAheadTime'],
                              config['aggregationInfo'])))
    assert (predictionSteps >= 1)
    config['modelParams']['clParams']['steps'] = str(predictionSteps)

# Adjust config by applying ValueGetterBase-derived
# futures. NOTE: this MUST be called after updateConfigFromSubConfig() in order
# to support value-getter-based substitutions from the sub-experiment (if any)
applyValueGettersToContainer(config)

################################################################################

# [optional] A sequence of one or more tasks that describe what to do with the
# model. Each task consists of a task label, an input spec., iteration count,
# and a task-control spec per opfTaskSchema.json
#
# NOTE: The tasks are intended for OPF clients that make use of OPFTaskDriver.