def create_chain_if_net(nunits):
gpunet = GpuNetwork()
instream = ConstantInputStream(1.25, 0.020, 1.0)
gpunet.add_stream(instream)
num_states = -1
num_params = -1
dx = 0.05
dy = 5e-6
dz = 5e-6
for k in range(nunits):
u = IFUnit()
u.position = np.array([0.0, k*dx, 0.0])
num_states = u.num_states
num_params = u.num_params
gpunet.add_unit(u)
u0 = gpunet.units[0]
gpunet.connect_stream(instream, 0, u0, 1.0)
for k,u in enumerate(gpunet.units[1:]):
uprev = gpunet.units[k]
w = 1.00 / float(k+2)
#gpunet.connect_stream(instream, 0, u, w)
gpunet.connect(uprev, u, 10.5)
return gpunet,num_states,num_params
def create_cube_if_net(width, height, depth):
gpunet = GpuNetwork()
max_cube_width = 1.25
mcw_half = max_cube_width / 2.0
dx = max_cube_width / width
dy = max_cube_width / height
dz = max_cube_width / depth
max_len = max_cube_width*np.sqrt( np.sqrt(2*max_cube_width**2) ) #longest distance in cube between two neurons
all_units = np.ndarray([width, height, depth], dtype='object')
#build network
for x in range(width):
for y in range(height):
for z in range(depth):
u = IFUnit()
u.position = np.array([mcw_half - x*dx, mcw_half - y*dy, mcw_half - z*dz])
gpunet.add_unit(u)
all_units[x, y, z] = u
#connect units topographically
print 'Connecting Neurons...'
for x in range(width):
for y in range(height):
for z in range(depth):
u = all_units[x, y, z]
for u2 in all_units.ravel():
d = np.linalg.norm(u.position - u2.position)
p_connect = min(1.0, 1.0 - (d / max_len)*0.2)
w = max(np.random.randn(), -0.25)
print 'd=%0.6f, p_connect=%0.6f, w=%0.6f' % (d, p_connect, w)
if np.random.rand() < p_connect:
gpunet.connect(u, u2, np.random.randn())
#create input streams
nstreams = 10
all_streams = []
for k in range(nstreams):
rs = RandomInputStream(0.0, 1.5, 0.050, 1000.00, positive=True)
gpunet.add_stream(rs)
all_streams.append(rs)
#connect inputs up
max_inputs = 5
for u in all_units[:, :, 0].ravel():
nin = np.random.randint(max_inputs+1)
streams_connected = {}
for k in range(nin):
rsnum = np.random.randint(nstreams)
if rsnum not in streams_connected:
gpunet.connect_stream(all_streams[rsnum], 0, u, 1.0)
streams_connected[rsnum] = True
return gpunet
