def __init__(self,N,rates,time_between_rates=1000,sequential=True):
super(PoissonGroup,self).__init__(N,name='PoissonGroup')
self.state=None
if isinstance(rates,basestring): # filename
self.rates=Remember(filename=rates)
elif isinstance(rates,int) or isinstance(rates,float):
self.rates=float(rates)*numpy.ones((1,N))
time_between_rates=-100
elif isinstance(rates,list):
rates=numpy.array(rates)
rates.shape=(len(rates),1)
self.rates=rates*numpy.ones((len(rates),N))
else:
self.rates=rates.reshape((numpy.prod(rates.shape)/N,N))
self.time_between_rates=time_between_rates
self.sequential=sequential
self.connection_groups_to=[]
self.time_to_next_rate=0
self.which_rate=-1
self.rate=self.rates[0,:]
self.N=N
self.spikes=numpy.zeros(N,numpy.byte)
self.old_spikes=numpy.zeros(N,numpy.byte)
self.type=get_neuron_type(self)
self.initialized=True
def __init__(self,qty,time_between_rates=1000,
pics=['new_images12_dog.pics'],
filter=[min_max_filter],
filter_params=[{'min':0.0,'max':50.0}],
rate_range=None):
if isinstance(qty,list):
total_qty=sum(qty)
num_channels=len(qty)
else:
total_qty=qty
qty=[qty]
num_channels=1
if rate_range:
for f in filter_params:
f['min']=rate_range[0]
f['max']=rate_range[1]
if len(pics)!=num_channels:
pics=pics*num_channels
if len(filter)!=num_channels:
filter=filter*num_channels
if len(filter_params)!=num_channels:
filter_params=filter_params*num_channels
super(NaturalImages,self).__init__(total_qty,name='NaturalImages')
self.state=None
self.time_between_rates=time_between_rates
self.time_to_next_rate=0
self.num_channels=num_channels
try:
params=plasticity.utils.default_params()
except:
print "No package 'plastictity' found. NaturalImages will not work."
return
self.params={}
keys=['neuron_offsets','num_neurons',
'pattern_input','noise_input']
for k in keys:
self.params[k]=params[k]
self.params['pattern_input']=[]
self.params['noise_input']=[]
for c in range(num_channels):
self.params['pattern_input'].append(plasticity.utils.get_pattern())
self.params['noise_input'].append(plasticity.utils.get_noise())
for c in range(num_channels):
self.params['pattern_input'][c]['num_inputs']=qty[c]
self.params['pattern_input'][c]['filter']=filter[c]
self.params['pattern_input'][c]['filter_params']=filter_params[c]
if pics[c]:
self.params['pattern_input'][c]['filename']=default_pics_path+pics[c]
else:
self.params['pattern_input'][c]['type']=0
num_neurons=1
num_inputs=total_qty
self.X=numpy.zeros((num_neurons,num_inputs),numpy.float64)
plasticity.train.init_params(self.params)
N=total_qty
self.N=N
self.connection_groups_to=[]
self.spikes=numpy.zeros(N,numpy.byte)
self.old_spikes=numpy.zeros(N,numpy.byte)
self.type=get_neuron_type(self)
self.initialized=True
