def __init__(self, state_word_len, variables):
""" Initialize the internal helper class. """
self.state_word_len = state_word_len
self.variables = variables
variable_names = map(lambda i: 'S%d' % i,
xrange(self.state_word_len))
variable_names += self.variables
self.qm = QM(variable_names)
def __init__(self, state_word_len, variables):
""" Initialize the internal helper class. """
self.state_word_len = state_word_len
self.variables = variables
variable_names = map(lambda i:'S%d'%i, xrange(self.state_word_len))
variable_names += self.variables
self.qm = QM(variable_names)
class InternalOptimizer:
def __init__(self, state_word_len, variables):
""" Initialize the internal helper class. """
self.state_word_len = state_word_len
self.variables = variables
variable_names = map(lambda i: 'S%d' % i,
xrange(self.state_word_len))
variable_names += self.variables
self.qm = QM(variable_names)
def solve(self, f_on, f_off=None):
"""
Returns a function that satisfies the conditions given.
f_on: a list of lambda expressions; if one of the lambda expressions
evaluates to True then the requested function should evaluate to True
f_off: a list of lambda expressions; if one of them evaluates to True
then the requested function whould evaluate to False
returns: a tuple a,b; a is the complexity of the function and b is the
function
"""
self.state_env = self.State()
self.variables_env = self.Variables(self.variables)
c = self.state_word_len
d = len(self.variables)
ones = []
dc = set(i for i in xrange(1 << (d + c)))
for variables_word in xrange(1 << d):
self.variables_env.word = variables_word
for state, state_word in self.state_map.iteritems():
self.state_env.state = state
on = self.evaluate(f_on)
if f_off == None:
off = not on
else:
off = self.evaluate(f_off)
assert not (on and off)
if on:
ones.append(variables_word << c | state_word)
dc.remove(variables_word << c | state_word)
elif off:
dc.remove(variables_word << c | state_word)
dc = list(dc)
return self.qm.solve(ones, dc)
def evaluate(self, f_array):
"""
Evaluates a list of lambda expressions in the state and variables
environment. The lambda expressions are terms of an OR expression.
f_array: a list of lambda expressions
returns: the logical OR after evaluate the lambda expression in the setup
environment
"""
for f in f_array:
if f(self.state_env, self.variables_env):
return True
return False
"""
This class provides access to the state word from the lambda expressions.
"""
class State:
def __getitem__(self, item):
return self.state == item
"""
This class provides access to the input variables from the lambda
expressions.
"""
class Variables:
def __init__(self, variables):
self.variables = {}
for i in xrange(len(variables)):
self.variables[variables[i]] = 1 << i
def __getitem__(self, item):
return bool(self.word & self.variables[item])
def get_function(self, minterms):
""" Retrieve a human readable form of the given function. """
return self.qm.get_function(minterms)
class InternalOptimizer:
def __init__(self, state_word_len, variables):
""" Initialize the internal helper class. """
self.state_word_len = state_word_len
self.variables = variables
variable_names = map(lambda i:'S%d'%i, xrange(self.state_word_len))
variable_names += self.variables
self.qm = QM(variable_names)
def solve(self, f_on, f_off = None):
"""
Returns a function that satisfies the conditions given.
f_on: a list of lambda expressions; if one of the lambda expressions
evaluates to True then the requested function should evaluate to True
f_off: a list of lambda expressions; if one of them evaluates to True
then the requested function whould evaluate to False
returns: a tuple a,b; a is the complexity of the function and b is the
function
"""
self.state_env = self.State()
self.variables_env = self.Variables(self.variables)
c = self.state_word_len
d = len(self.variables)
ones = []
dc = set(i for i in xrange(1<<(d+c)))
for variables_word in xrange(1<<d):
self.variables_env.word = variables_word
for state,state_word in self.state_map.iteritems():
self.state_env.state = state
on = self.evaluate(f_on)
if f_off == None:
off = not on
else:
off = self.evaluate(f_off)
assert not (on and off)
if on:
ones.append(variables_word<<c|state_word)
dc.remove(variables_word<<c|state_word)
elif off:
dc.remove(variables_word<<c|state_word)
dc = list(dc)
return self.qm.solve(ones,dc)
def evaluate(self, f_array):
"""
Evaluates a list of lambda expressions in the state and variables
environment. The lambda expressions are terms of an OR expression.
f_array: a list of lambda expressions
returns: the logical OR after evaluate the lambda expression in the setup
environment
"""
for f in f_array:
if f(self.state_env,self.variables_env):
return True
return False
"""
This class provides access to the state word from the lambda expressions.
"""
class State:
def __getitem__(self, item):
return self.state == item
"""
This class provides access to the input variables from the lambda
expressions.
"""
class Variables:
def __init__(self, variables):
self.variables = {}
for i in xrange(len(variables)):
self.variables[variables[i]] = 1<<i
def __getitem__(self, item):
return bool(self.word & self.variables[item])
def get_function(self, minterms):
""" Retrieve a human readable form of the given function. """
return self.qm.get_function(minterms)
