def time_series(net, nodes_list, Nbr_Initial_States, Nbr_States, MAX_TimeStep=20):
'''
Arguments:
1. net
2. Nbr_States
3. MAX_TimeStep
Return:
1. timeSeriesData
'''
#Nbr_Nodes = len(net.nodes())
#Nbr_All_Initial_States = np.power(Nbr_States, Nbr_Nodes)
timeSeriesData = {}
for n in net.nodes():
timeSeriesData[n] = {}
for initState in range(Nbr_Initial_States):
timeSeriesData[n][initState] = []
for initDecState in range(Nbr_Initial_States):
currBiState = decimal_to_binary(nodes_list, initDecState, Nbr_States)
for step in range(MAX_TimeStep):
prevBiState = currBiState.copy()
for n in nodes_list:
timeSeriesData[n][initDecState].append(prevBiState[n])
currBiState = ur.sigmoid_updating(net, prevBiState)
return timeSeriesData
def biological_sequence(net, nodes_list, bio_initStates, fileName, Nbr_States=2):
bioSeq = []
currBiStates = bio_initStates
finished = False
while(not finished):
oneDiff = 0
prevBiStates = currBiStates.copy()
bioSeq.append(prevBiStates)
currBiStates = ur.sigmoid_updating(net, prevBiStates)
for u in nodes_list:
if abs(prevBiStates[u] - currBiStates[u]) > 0:
oneDiff = 1
break
finished = (oneDiff < 1)
OUTPUT_FILE = open(fileName, 'w')
OUTPUT_FILE.write('time step')
for u in nodes_list:
OUTPUT_FILE.write('\t%s'%(u))
OUTPUT_FILE.write('\n')
for i in range(len(bioSeq)):
OUTPUT_FILE.write('%d'%i)
for u in nodes_list:
OUTPUT_FILE.write('\t%d'%(bioSeq[i][u]))
OUTPUT_FILE.write('\n')
def time_series_one(net, nodes_list, Initial_State, Nbr_States, MAX_TimeStep=20):
'''
Description:
-- compute TE for every pair of nodes using distribution from all initial conditions that converge to the primary or biological attractor
Arguments:
-- 1. net
-- 2. nodes_list
-- 3. Initial_States_List
-- 4. Nbr_States
-- 5. MAX_TimeStep
Return:
-- 1. timeSeriesData (only for primary attractor)
'''
timeSeriesData = {}
for n in net.nodes():
timeSeriesData[n] = {}
timeSeriesData[n][0] = []
currBiState = Initial_State
for step in range(MAX_TimeStep):
prevBiState = currBiState.copy()
for n in nodes_list:
timeSeriesData[n][0].append(prevBiState[n])
currBiState = ur.sigmoid_updating(net, prevBiState)
return timeSeriesData
def time_series_all(net, nodes_list, Nbr_Initial_States, Nbr_States, MAX_TimeStep=20):
'''
Description:
-- compute TE for every pair of nodes using distribution from all possible initial conditions or an arbitrary set of initial conditions
Arguments:
-- 1. net
-- 2. nodes_list
-- 3. Initial_States_List
-- 4. Nbr_States
-- 5. MAX_TimeStep
Return:
-- 1. timeSeriesData
'''
#Nbr_Nodes = len(net.nodes())
#Nbr_All_Initial_States = np.power(Nbr_States, Nbr_Nodes)
timeSeriesData = {}
for n in net.nodes():
timeSeriesData[n] = {}
for initState in range(Nbr_Initial_States):
timeSeriesData[n][initState] = []
for initDecState in range(Nbr_Initial_States):
currBiState = decimal_to_binary(nodes_list, initDecState, Nbr_States)
for step in range(MAX_TimeStep):
prevBiState = currBiState.copy()
for n in nodes_list:
timeSeriesData[n][initDecState].append(prevBiState[n])
currBiState = ur.sigmoid_updating(net, prevBiState)
return timeSeriesData
def net_state_transition(net, nodes_list, Nbr_States=2):
'''
Arguments:
1. net
2. Nbr_States
Return:
1. decStateTransMap
'''
Nbr_Nodes = len(net.nodes())
Nbr_All_Initial_States = np.power(Nbr_States, Nbr_Nodes)
decStateTransMap = nx.DiGraph()
for prevDecState in range(0, Nbr_All_Initial_States):
prevBiState = decimal_to_binary(nodes_list, prevDecState, Nbr_States)
currBiState = ur.sigmoid_updating(net, prevBiState)
currDecState = binary_to_decimal(nodes_list, currBiState, Nbr_States)
decStateTransMap.add_edge(prevDecState, currDecState)
return decStateTransMap
#Nbr_Nodes = len(net.nodes())
#Nbr_All_Initial_States = np.power(Nbr_States, Nbr_Nodes)
timeSeriesData = {}
for n in net.nodes():
timeSeriesData[n] = {}
for initState in range(Nbr_Initial_States):
timeSeriesData[n][initState] = []
for initDecState in range(Nbr_Initial_States):
currBiState = decimal_to_binary(nodes_list, initDecState, Nbr_States)
for step in range(MAX_TimeStep):
prevBiState = currBiState.copy()
for n in nodes_list:
timeSeriesData[n][initDecState].append(prevBiState[n])
currBiState = ur.sigmoid_updating(net, prevBiState)
return timeSeriesData
################# END: time_series_en(net, nodes_list, Nbr_States=2, MAX_TimeStep=20) ########################
################# BEGIN: net_state_transition_map(net, nodes_list, Nbr_States=2) ########################
def net_state_transition(net, nodes_list, Nbr_States=2):
'''
Arguments:
1. net
2. Nbr_States
Return:
1. decStateTransMap
'''
