def __init__(self, baseModel, configFile):
self.base = baseModel
# Initialize databases for this extension
self.modelParams = baseModel.modelParams
self.modelParams.addDatabase("proc_id_model")
self.gpuPtrs = baseModel.gpuPtrs
self.gpuPtrs.addDatabase("proc_id_model")
if not self.base.data.isspatial:
log.error(
"SpatialGmmProcessIdModel only supports spatial datasets!")
exit()
# Check whether the data file has hard coded process IDs
if self.base.data.proc_ids_known:
log.warning(
"K and C are present in the data file but the SpatialGmmProcessIdModel ignores them!"
)
# Parse the configuration file to get params
self.parseConfigurationFile(configFile)
pprint_dict(self.params, "Process ID Model Params")
self.initializeGpuKernels()
def __init__(self, baseModel, configFile):
self.base = baseModel
self.base.data = self.base.data
# Check whether the data file has hard coded process IDs
if not self.base.data.proc_ids_known:
log.error("Process IDs are not present in the data!")
# Parse the configuration file to get params
self.parseConfigurationFile(configFile)
pprint_dict(self.params, "Process ID Model Params")
self.initializeGpuKernels()
def __init__(self, baseModel, configFile):
self.base = baseModel
self.base.data = self.base.data
# Check whether the data file has hard coded process IDs
if not self.base.data.proc_ids_known:
log.error("Process IDs are not present in the data!")
# Parse the configuration file to get params
self.parseConfigurationFile(configFile)
pprint_dict(self.params, "Process ID Model Params")
self.initializeGpuKernels()
def __init__(self, baseModel, configFile):
super(StochasticBlockModelCoupledWithW,self).__init__(baseModel, configFile)
# Initialize databases for this extension
self.modelParams.addDatabase("weight_model")
self.gpuPtrs.addDatabase("weight_model")
# Load the GPU kernels necessary for background rate inference
# Allocate memory on GPU for background rate inference
# Initialize host params
self.parseConfigurationFile(configFile)
pprint_dict(self.params, "Weight and Graph Model Params")
self.initializeGpuKernels()
def __init__(self, baseModel, configFile):
super(StochasticBlockModelCoupledWithW,
self).__init__(baseModel, configFile)
# Initialize databases for this extension
self.modelParams.addDatabase("weight_model")
self.gpuPtrs.addDatabase("weight_model")
# Load the GPU kernels necessary for background rate inference
# Allocate memory on GPU for background rate inference
# Initialize host params
self.parseConfigurationFile(configFile)
pprint_dict(self.params, "Weight and Graph Model Params")
self.initializeGpuKernels()
def __init__(self, baseModel, configFile):
# Store pointer to base model
self.base = baseModel
# Initialize databases for this extension
self.modelParams = baseModel.modelParams
self.modelParams.addDatabase("impulse_model")
self.gpuPtrs = baseModel.gpuPtrs
self.gpuPtrs.addDatabase("impulse_model")
# Load the GPU kernels necessary for impulse response param inference
# Allocate memory on GPU for inference
# Initialize host params
self.parseConfigurationFile(configFile)
pprint_dict(self.params, "Impulse Model Params")
def __init__(self, baseModel, configFile):
# Store pointer to base model
self.base = baseModel
# Initialize databases for this extension
self.modelParams = baseModel.modelParams
self.modelParams.addDatabase("weight_model")
self.gpuPtrs = baseModel.gpuPtrs
self.gpuPtrs.addDatabase("weight_model")
# Load the GPU kernels necessary for background rate inference
# Allocate memory on GPU for background rate inference
# Initialize host params
self.parseConfigurationFile(configFile)
pprint_dict(self.params, "Weight Model Params")
self.initializeGpuKernels()
self.initializeGpuMemory()
def __init__(self, dataManager, configFile, data):
"""
Construct a new base model.
"""
self.parseConfigFile(configFile)
pprint_dict(self.params, "Base Model Params")
# Save a pointer to the data and compute the intervals between spikes
self.dm = dataManager
self.data = data
self.prepareData()
# Initialize parameter and GPU pointer databases
self.modelParams = ParamsDatabase()
self.gpuPtrs = ParamsDatabase()
# Initialize model parameters on host and gpu
self.initializeRandomness()
self.initializeGpuKernels()
self.constructModelExtensions(configFile)
self.computeSampleOrder()
def __init__(self, baseModel, configFile):
self.base = baseModel
# Initialize databases for this extension
self.modelParams = baseModel.modelParams
self.modelParams.addDatabase("proc_id_model")
self.gpuPtrs = baseModel.gpuPtrs
self.gpuPtrs.addDatabase("proc_id_model")
if not self.base.data.isspatial:
log.error("SpatialGmmProcessIdModel only supports spatial datasets!")
exit()
# Check whether the data file has hard coded process IDs
if self.base.data.proc_ids_known:
log.warning("K and C are present in the data file but the SpatialGmmProcessIdModel ignores them!")
# Parse the configuration file to get params
self.parseConfigurationFile(configFile)
pprint_dict(self.params, "Process ID Model Params")
self.initializeGpuKernels()
def __init__(self, dataManager, configFile, data):
"""
Construct a new base model.
"""
self.parseConfigFile(configFile)
pprint_dict(self.params, "Base Model Params")
# Save a pointer to the data and compute the intervals between spikes
self.dm = dataManager
self.data = data
self.prepareData()
# Initialize parameter and GPU pointer databases
self.modelParams = ParamsDatabase()
self.gpuPtrs = ParamsDatabase()
# Initialize model parameters on host and gpu
self.initializeRandomness()
self.initializeGpuKernels()
self.constructModelExtensions(configFile)
self.computeSampleOrder()