def worker(input_worker):
"""Explanations"""
#Global variables:
global numInput,numOutput,numHidden
global dim_hidden_output, dim_hidden_output
global sigma
global env
#Local:
seed = int(input_worker[0])
p = input_worker[1]
env.seed(seed)
np.random.seed(seed)
#Neural Networks:
NN = NeuralNetwork(numInput,numHidden,numOutput, VBN_dict)
NN.wi=p[0]
NN.wo=p[1]
#distortions
epsilon = np.random.multivariate_normal(np.zeros(dim_hidden_output+dim_input_hidden),np.identity(dim_hidden_output+dim_input_hidden))
epsilon_wo= epsilon[0:dim_hidden_output].reshape((numHidden,numOutput))
epsilon_wi= epsilon[dim_hidden_output:dim_hidden_output+dim_input_hidden].reshape((numInput,numHidden))
#epsilon_wo = np.random.multivariate_normal([0 for x in range(dim_hidden_output)],np.identity(dim_hidden_output)).reshape((numHidden,numOutput))
#epsilon_wi = np.random.multivariate_normal([0 for x in range(dim_input_hidden)],np.identity(dim_input_hidden)).reshape((numInput,numHidden))
#parameters update
NN.wo=NN.wo+epsilon_wo*sigma #remark:we should merge the two, and reshape the matrix
NN.wi=NN.wi+epsilon_wi*sigma
#initial_observation=env.reset()
reward_worker=episodeRoute(NN,env,initial_observation,steps=250)
return(reward_worker,epsilon_wi,epsilon_wo)
def worker_train_VBN(input_worker_VBN):
"""Explanations"""
#Global variables:
global numInput,numOutput,numHidden
global dim_hidden_output, dim_hidden_output
global env
#Local:
seed=int(input_worker_VBN[0])
p = input_worker_VBN[1]
env.seed(seed)
#np.random.seed(seed)
VBN_dict = {}
VBN_dict['mu_i']=0
VBN_dict['var_i']=0
VBN_dict['mu_h']=0
VBN_dict['var_h']=0
VBN_dict['mu_o']=0
VBN_dict['var_o']=0
#Neural Networks:
NN = NeuralNetwork(numInput,numHidden,numOutput, VBN_dict)
NN.wi=p[0]
NN.wo=p[1]
steps=250
ai = env.reset()
num_step=steps
for j in range(steps):
ao = NN.feedForward(ai)
#to transfer to the main
# question: how many worker for this ?
sum_zi=[0.] * numInput
sum_zh=[0.] * numHidden
sum_zo=[0.] *numOutput
sum_zi2=[0.] * numInput
sum_zh2=[0.] * numHidden
sum_zo2=[0.] *numOutput
sum_zi=[sum(x) for x in zip(sum_zi, NN.zi)] ### VERY WEIRD !! ALWAYS EQUAL TO 1 ?
sum_zh=[sum(x) for x in zip(sum_zh, NN.zh)]
sum_zo=[sum(x) for x in zip(sum_zo, NN.zo)]
sum_zi2=[sum(x) for x in zip(sum_zi2, square(NN.zi))]
sum_zh2=[sum(x) for x in zip(sum_zh2, square(NN.zh))]
sum_zo2=[sum(x) for x in zip(sum_zo2, square(NN.zo))]
'''
sum_zi=map(add, sum_zi, NN.zi)
sum_zh=map(add, sum_zh, NN.zh)
sum_zo=map(add, sum_zo, NN.zo)
sum_zi=map(add, sum_zi2, square(NN.zi))
sum_zh=map(add, sum_zh2, square(NN.zh))
sum_zo=map(add, sum_zo2, square(NN.zo))
'''
action=np.argmax(ao)
ai, reward, done, info = env.step(action)
if done:
break
num_step=j
return(sum_zi,sum_zh,sum_zo,sum_zi2,sum_zh2,sum_zo2,num_step)
