def __init__(
self, input_slice, minimum_buffer_sdram, receive_buffer_host,
maximum_sdram_for_buffering, using_auto_pause_and_resume,
receive_buffer_port, input_filters, input_n_keys,
time_between_requests, buffer_size_before_receive,
label):
AbstractNengoMachineVertex.__init__(self, label=label)
MachineDataSpecableVertex.__init__(self)
AbstractHasAssociatedBinary.__init__(self)
AbstractAcceptsMulticastSignals.__init__(self)
AbstractReceiveBuffersToHost.__init__(self)
ProvidesProvenanceDataFromMachineImpl.__init__(self)
self._input_slice = input_slice
self._minimum_buffer_sdram = minimum_buffer_sdram
self._maximum_sdram_for_buffering = maximum_sdram_for_buffering
self._using_auto_pause_and_resume = using_auto_pause_and_resume
self._receive_buffer_host = receive_buffer_host
self._receive_buffer_port = receive_buffer_port
self._input_filters = input_filters
self._input_n_keys = input_n_keys
self._time_between_requests = time_between_requests
self._buffer_size_before_receive = buffer_size_before_receive
self._buffered_sdram_per_timestep = [
self.SDRAM_RECORDING_SDRAM_PER_ATOM * input_slice.n_atoms]
def __init__(self, partition_identifier, filter, row_id, constraints=None):
label = "retina filter for row {}".format(row_id)
MachineVertex.__init__(self, label, constraints)
MachineDataSpecableVertex.__init__(self)
AbstractHasAssociatedBinary.__init__(self)
AbstractProvidesOutgoingPartitionConstraints.__init__(self)
self._partition_identifier = partition_identifier
self._filter = filter
self._row_id = row_id
def __init__(self, size_in, input_filters, inputs_n_keys, hostname, label):
AbstractNengoMachineVertex.__init__(self, label=label)
MachineDataSpecableVertex.__init__(self)
AbstractHasAssociatedBinary.__init__(self)
AbstractAcceptsMulticastSignals.__init__(self)
ProvidesProvenanceDataFromMachineImpl.__init__(self)
self._size_in = size_in
self._input_filters = input_filters
self._input_n_keys = inputs_n_keys
self._hostname = hostname
self._output_lock = threading.Lock()
self._output = numpy.zeros(self._size_in)
def __init__(
self, sub_population_id, neuron_slice, input_slice, output_slice,
learnt_slice, resources, encoders_with_gain, tau_rc, tau_refactory,
ensemble_size_in, label, learnt_encoder_filters, input_filters,
inhibitory_filters, modulatory_filters, pes_learning_rules,
ensemble_radius, minimum_buffer_sdram_usage, bias_with_di,
buffered_sdram_per_timestep, overflow_sdram, is_recording,
encoders_with_gain_shape, gain, decoders, learnt_decoders):
AbstractNengoMachineVertex.__init__(self, label=label)
MachineDataSpecableVertex.__init__(self)
AbstractHasAssociatedBinary.__init__(self)
AbstractAcceptsMulticastSignals.__init__(self)
AbstractTransmitsMulticastSignals.__init__(self)
AbstractReceiveBuffersToHost.__init__(self)
ProvidesProvenanceDataFromMachineImpl.__init__(self)
self._resources = resources
self._neuron_slice = neuron_slice
self._input_slice = input_slice
self._output_slice = output_slice
self._learnt_slice = learnt_slice
self._sub_population_id = sub_population_id
self._ensemble_size_in = ensemble_size_in
self._encoders_with_gain = encoders_with_gain
self._tau_rc = tau_rc
self._tau_refactory = tau_refactory
self._learnt_encoder_filters = learnt_encoder_filters
self._input_filters = input_filters
self._inhibitory_filters = inhibitory_filters
self._modulatory_filters = modulatory_filters
self._local_pes_learning_rules = pes_learning_rules
self._ensemble_radius = ensemble_radius
self._bias_with_di = bias_with_di
self._encoders_with_gain_shape = encoders_with_gain_shape
self._gain = gain
self._decoders = decoders
self._learnt_decoders = learnt_decoders
# recording params
self._minimum_buffer_sdram_usage = minimum_buffer_sdram_usage
self._buffered_sdram_per_timestep = buffered_sdram_per_timestep
self._overflow_sdram = overflow_sdram
self._is_recording = is_recording
def __init__(self, outgoing_partition_slice, update_period,
minimum_buffer_sdram, receive_buffer_host,
maximum_sdram_for_buffering, using_auto_pause_and_resume,
receive_buffer_port, is_recording_output, this_cores_matrix,
label):
AbstractNengoMachineVertex.__init__(self, label=label)
MachineDataSpecableVertex.__init__(self)
AbstractHasAssociatedBinary.__init__(self)
AbstractTransmitsMulticastSignals.__init__(self)
ProvidesProvenanceDataFromMachineImpl.__init__(self)
self._outgoing_partition_slice = outgoing_partition_slice
self._minimum_buffer_sdram = minimum_buffer_sdram
self._maximum_sdram_for_buffering = maximum_sdram_for_buffering
self._using_auto_pause_and_resume = using_auto_pause_and_resume
self._receive_buffer_host = receive_buffer_host
self._receive_buffer_port = receive_buffer_port
self._update_period = update_period
self._is_recording_output = is_recording_output
self._output_data = this_cores_matrix
def __init__(self, size_in, output_slice, transform_data, n_keys,
filter_keys, output_slices, machine_time_step, filters, label,
constraints):
AbstractNengoMachineVertex.__init__(self,
label=label,
constraints=constraints)
AbstractHasAssociatedBinary.__init__(self)
AbstractAcceptsMulticastSignals.__init__(self)
MachineDataSpecableVertex.__init__(self)
AbstractTransmitsMulticastSignals.__init__(self)
ProvidesProvenanceDataFromMachineImpl.__init__(self)
self._size_in = size_in
self._output_slice = output_slice
self._transform_data = transform_data
self._n_keys = n_keys
self._filter_keys = filter_keys
self._output_slices = output_slices
self._machine_time_step = machine_time_step
self._filters = filters
# Store which signal parameter slices we contain
self._transmission_params = self._filter_transmission_params()
def __init__(self, outgoing_partition, label):
AbstractNengoMachineVertex.__init__(self, label)
MachineDataSpecableVertex.__init__(self)
AbstractHasAssociatedBinary.__init__(self)
AbstractTransmitsMulticastSignals.__init__(self)
AbstractProvidesNKeysForPartition.__init__(self)
AbstractRecordable.__init__(self)
ProvidesProvenanceDataFromMachineImpl.__init__(self)
# TODO WHY DO WE PARTITION OVER OUTGOING PARTITIONS!!!
self._managing_app_outgoing_partition = outgoing_partition
transform = self._managing_app_outgoing_partition.identifier\
.transmission_parameter.full_transform(
slice_out=self.TRANSFORM_SLICE_OUT)
self._n_keys = transform.shape[0]
self._n_packets_transmitted = 0
# Check n keys size
if self._n_keys > self.MAX_N_KEYS_SUPPORTED:
raise NotImplementedError(
"Connection is too wide to transmit to SpiNNaker. "
"Consider breaking the connection up or making the "
"originating node a function of time Node.")
def __init__(self, mbs, constraint):
self._mbs = mbs * self.SDRAM_READING_SIZE_IN_BYTES_CONVERTER
MachineVertex.__init__(self, label="speed", constraints=[constraint])
MachineDataSpecableVertex.__init__(self)
AbstractHasAssociatedBinary.__init__(self)