# Path: pyrl/networks.py

# 
# Path: pyrl/sgd.py
# class Adam(object):
#     def __init__(self, trainables, accumulators=None):
#         self.trainables = trainables
# 
#         if accumulators is None:
#             self.means = [theanotools.shared(0*x.get_value()) for x in trainables]
#             self.vars  = [theanotools.shared(0*x.get_value()) for x in trainables]
#             self.time  = theanotools.shared(0)
#         else:
#             self.means = [theanotools.shared(x) for x in accumulators[0]]
#             self.vars  = [theanotools.shared(x) for x in accumulators[1]]
#             self.time  = theanotools.shared(accumulators[2])
# 
#     def get_values(self):
#         means  = [x.get_value() for x in self.means]
#         vars_  = [x.get_value() for x in self.vars]
#         time   = self.time.get_value()
# 
#         return [means, vars_, time]
# 
#     def get_updates(self, loss, lr, max_norm=1, beta1=0.9, beta2=0.999,
#                     epsilon=1e-8, grads=None):
#         # Gradients
#         if grads is None:
#             grads = tensor.grad(loss, self.trainables)
# 
#         # Clipping
#         norm  = tensor.sqrt(sum([tensor.sqr(g).sum() for g in grads]))
#         m     = theanotools.clipping_multiplier(norm, max_norm)
#         grads = [m*g for g in grads]
# 
#         # Safeguard against numerical instability
#         new_cond = tensor.or_(tensor.or_(tensor.isnan(norm), tensor.isinf(norm)),
#                               tensor.or_(norm < 0, norm > 1e10))
#         grads = [tensor.switch(new_cond, np.float32(0), g) for g in grads]
# 
#         # Safeguard against numerical instability
#         #cond  = tensor.or_(norm < 0, tensor.or_(tensor.isnan(norm), tensor.isinf(norm)))
#         #grads = [tensor.switch(cond, np.float32(0), g) for g in grads]
# 
#         # New values
#         t       = self.time + 1
#         lr_t    = lr*tensor.sqrt(1. - beta2**t)/(1. - beta1**t)
#         means_t = [beta1*m + (1. - beta1)*g for g, m in zip(grads, self.means)]
#         vars_t  = [beta2*v + (1. - beta2)*tensor.sqr(g) for g, v in zip(grads, self.vars)]
#         steps   = [lr_t*m_t/(tensor.sqrt(v_t) + epsilon)
#                    for m_t, v_t in zip(means_t, vars_t)]
# 
#         # Updates
#         updates  = [(x, x - step) for x, step in zip(self.trainables, steps)]
#         updates += [(m, m_t) for m, m_t in zip(self.means, means_t)]
#         updates += [(v, v_t) for v, v_t in zip(self.vars, vars_t)]
#         updates += [(self.time, t)]
# 
#         return norm, grads, updates


from   collections import OrderedDict
from   theano import tensor
from .         import nptools, tasktools, theanotools, utils
from .debug    import DEBUG
from .networks import Networks
from .sgd      import Adam
import datetime
import sys
import numpy as np
import theano
            self.config = config

            # Time step
            self.dt = dt
            if self.dt is None:
                self.dt = config['dt']
                print("Using config dt = {}".format(self.dt))

            # Leak
            alpha = self.dt/config['tau']

            # Policy network
            K = config['p0']*config['N']
            self.policy_config = {
                'Nin':      config['Nin'],
                'N':        config['N'],
                'Nout':     config['Nout'],
                'p0':       config['p0'],
                'rho':      config['rho'],
                'f_out':    'softmax',
                'Win':      config['Win']*np.sqrt(K)/config['Nin'],
                'Win_mask': config['Win_mask'],
                'fix':      config['fix'],
                'L2_r':     config['L2_r'],
                'L1_Wrec':  config['L1_Wrec'],
                'L2_Wrec':  config['L2_Wrec'],
                'alpha':    alpha
                }

            # Network type

# Path: pyrl/networks.py

# 
# Path: pyrl/sgd.py
# class Adam(object):
#     def __init__(self, trainables, accumulators=None):
#         self.trainables = trainables
# 
#         if accumulators is None:
#             self.means = [theanotools.shared(0*x.get_value()) for x in trainables]
#             self.vars  = [theanotools.shared(0*x.get_value()) for x in trainables]
#             self.time  = theanotools.shared(0)
#         else:
#             self.means = [theanotools.shared(x) for x in accumulators[0]]
#             self.vars  = [theanotools.shared(x) for x in accumulators[1]]
#             self.time  = theanotools.shared(accumulators[2])
# 
#     def get_values(self):
#         means  = [x.get_value() for x in self.means]
#         vars_  = [x.get_value() for x in self.vars]
#         time   = self.time.get_value()
# 
#         return [means, vars_, time]
# 
#     def get_updates(self, loss, lr, max_norm=1, beta1=0.9, beta2=0.999,
#                     epsilon=1e-8, grads=None):
#         # Gradients
#         if grads is None:
#             grads = tensor.grad(loss, self.trainables)
# 
#         # Clipping
#         norm  = tensor.sqrt(sum([tensor.sqr(g).sum() for g in grads]))
#         m     = theanotools.clipping_multiplier(norm, max_norm)
#         grads = [m*g for g in grads]
# 
#         # Safeguard against numerical instability
#         new_cond = tensor.or_(tensor.or_(tensor.isnan(norm), tensor.isinf(norm)),
#                               tensor.or_(norm < 0, norm > 1e10))
#         grads = [tensor.switch(new_cond, np.float32(0), g) for g in grads]
# 
#         # Safeguard against numerical instability
#         #cond  = tensor.or_(norm < 0, tensor.or_(tensor.isnan(norm), tensor.isinf(norm)))
#         #grads = [tensor.switch(cond, np.float32(0), g) for g in grads]
# 
#         # New values
#         t       = self.time + 1
#         lr_t    = lr*tensor.sqrt(1. - beta2**t)/(1. - beta1**t)
#         means_t = [beta1*m + (1. - beta1)*g for g, m in zip(grads, self.means)]
#         vars_t  = [beta2*v + (1. - beta2)*tensor.sqr(g) for g, v in zip(grads, self.vars)]
#         steps   = [lr_t*m_t/(tensor.sqrt(v_t) + epsilon)
#                    for m_t, v_t in zip(means_t, vars_t)]
# 
#         # Updates
#         updates  = [(x, x - step) for x, step in zip(self.trainables, steps)]
#         updates += [(m, m_t) for m, m_t in zip(self.means, means_t)]
#         updates += [(v, v_t) for v, v_t in zip(self.vars, vars_t)]
#         updates += [(self.time, t)]
# 
#         return norm, grads, updates


# Path: pyrl/policygradient.py
from   collections import OrderedDict
from   theano import tensor
from .         import nptools, tasktools, theanotools, utils
from .debug    import DEBUG
from .networks import Networks
from .sgd      import Adam
import datetime
import sys
import numpy as np
import theano

            self.config = config

            # Time step
            self.dt = dt
            if self.dt is None:
                self.dt = config['dt']
                print("Using config dt = {}".format(self.dt))

            # Leak
            alpha = self.dt/config['tau']

            # Policy network
            K = config['p0']*config['N']
            self.policy_config = {
                'Nin':      config['Nin'],
                'N':        config['N'],
                'Nout':     config['Nout'],
                'p0':       config['p0'],
                'rho':      config['rho'],
                'f_out':    'softmax',
                'Win':      config['Win']*np.sqrt(K)/config['Nin'],
                'Win_mask': config['Win_mask'],
                'fix':      config['fix'],
                'L2_r':     config['L2_r'],
                'L1_Wrec':  config['L1_Wrec'],
                'L2_Wrec':  config['L2_Wrec'],
                'alpha':    alpha
                }

            # Network type

            Network = Networks[config['network_type']]