def __init__(self, H = 3, d = 1, ny = 1, M = 3, debug_output = False):
"""
H: number of hidden units
d: number of inputs
ny: number of outputs
M: number of mixture components
"""
self.c = ny
ny = 2*M + M * ny # M mixing coefficients + M variances + M*ny means
self.M = M
self.count_fwd = 0
TLP.__init__(self, H, d, ny, linear_output = True, error_function = 'mdn',
debug_output = debug_output)
def __init__(self, H = 3, d = 1, ny = 1, T = 100):
"""
Create a fully-connected neural network with one hidden recurrent layer .
@param H: number of hidden units
@param ny: number of output units
@param T: number of time-steps
"""
TLP.__init__(self, H, d, ny)
self.T = T
z = np.zeros([T, H+1]) # hidden unit activations + bias
self.z = z[None, :]
self.z[:,:,0] = 1 # set extra bias unit to one
dj = np.zeros([T, H+1])
self.dj = dj[None, :]
# init recurrent weights
self.wh = np.random.normal(loc=0.0, scale = 1,size=[H, H]) / np.sqrt(H) # TODO: check?
self.Nwh = H**2
def __init__(self, H = 3, d = 1, ny = 1): TLP.__init__(self, H, d, ny, linear_output = True, error_function = 'bayes')
