def __init__(self, nddistr=None, d=None):
super(TwoVarsModel, self).__init__(nddistr, [d])
self.eliminate_other([d])
self.d = d
self.vars = []
self.symvars = []
for var in nddistr.Vars: #self.free_rvs:
self.vars.append(var)
self.symvars.append(var.getSymname())
#print "=====", self.vars
#print self.symvars
#print self.dep_rvs
#print self.rv_to_equation
self.symop = self.rv_to_equation[d]
if len(self.vars) != 2:
raise Exception("use it with two variables")
x = self.symvars[0]
y = self.symvars[1]
z = sympy.Symbol("z")
self.fun_alongx = eq_solve(self.symop, z, y)[0]
self.fun_alongy = eq_solve(self.symop, z, x)[0]
self.lfun_alongx = my_lambdify([x, z], self.fun_alongx, "numpy")
self.lfun_alongy = my_lambdify([y, z], self.fun_alongy, "numpy")
self.Jx = 1 * sympy.diff(self.fun_alongx, z)
#print "Jx=", self.Jx
#print "fun_alongx=", self.fun_alongx
self.Jy = 1 * sympy.diff(self.fun_alongy, z)
self.lJx = my_lambdify([x, z], self.Jx, "numpy")
self.lJy = my_lambdify([y, z], self.Jy, "numpy")
self.z = z
def __init__(self, nddistr=None, d=None):
super(TwoVarsModel, self).__init__(nddistr, [d])
self.eliminate_other([d])
self.d = d
self.vars = []
self.symvars = []
for var in nddistr.Vars: #self.free_rvs:
self.vars.append(var)
self.symvars.append(var.getSymname())
#print "=====", self.vars
#print self.symvars
#print self.dep_rvs
#print self.rv_to_equation
self.symop = self.rv_to_equation[d]
if len(self.vars) != 2:
raise Exception("use it with two variables")
x = self.symvars[0]
y = self.symvars[1]
z = sympy.Symbol("z")
self.fun_alongx = eq_solve(self.symop, z, y)[0]
self.fun_alongy = eq_solve(self.symop, z, x)[0]
self.lfun_alongx = my_lambdify([x, z], self.fun_alongx, "numpy")
self.lfun_alongy = my_lambdify([y, z], self.fun_alongy, "numpy")
self.Jx = 1 * sympy.diff(self.fun_alongx, z)
#print "Jx=", self.Jx
#print "fun_alongx=", self.fun_alongx
self.Jy = 1 * sympy.diff(self.fun_alongy, z)
self.lJx = my_lambdify([x, z], self.Jx, "numpy")
self.lJy = my_lambdify([y, z], self.Jy, "numpy")
self.z = z
def var_change_helper(self, free_var, dep_var):
"""Compute inverse transformation and Jacobian for substituting
dep_var for free_var."""
free_var = self.prepare_var(free_var)
dep_var = self.prepare_var(dep_var)
# inverve transformation
equation = self.rv_to_equation[dep_var] - dep_var.getSymname()
solutions = eq_solve(self.rv_to_equation[dep_var],
dep_var.getSymname(), free_var.getSymname())
var_changes = []
for uj in solutions:
# remove complex valued solutions
vj = uj.atoms(sympy.Symbol)
hvj = {}
for v in vj:
#print self.sym_to_rv[v].range()
hvj[v] = self.sym_to_rv[v].range()[1]
if len(solutions) > 1 and not sympy.im(uj.subs(hvj)) == 0:
continue
uj_symbols = list(sorted(uj.atoms(sympy.Symbol), key=str))
inv_transf = my_lambdify(uj_symbols, uj, "numpy")
inv_transf_vars = [self.sym_to_rv[s] for s in uj_symbols]
if params.models.debug_info:
#print "vars to change: ", free_var.getSymname(), " <- ", dep_var.getSymname(), "=", self.rv_to_equation[dep_var]
#print "equation: ", dep_var.getSymname(), "=", self.rv_to_equation[dep_var]
print("substitution: ",
free_var.getSymname(),
"=",
uj,
end=' ')
#print "variables: ", uj_symbols, inv_transf_vars
# Jacobian
#J = sympy.Abs(sympy.diff(uj, dep_var.getSymname()))
J = sympy.diff(uj, dep_var.getSymname())
print(J.atoms())
J_symbols = list(sorted(J.atoms(sympy.Symbol), key=str))
if len(J_symbols) > 0:
jacobian_vars = [self.sym_to_rv[s] for s in J_symbols]
jacobian = my_lambdify(J_symbols, J, "numpy")
jacobian = NDFun(len(jacobian_vars),
jacobian_vars,
jacobian,
safe=True,
abs=True)
else:
jacobian = NDConstFactor(abs(float(J)))
jacobian_vars = []
if params.models.debug_info:
print("; Jacobian=", J)
#print "variables: ", J_symbols, jacobian_vars
var_changes.append((uj, inv_transf, inv_transf_vars, jacobian))
return var_changes, equation
def var_change_helper(self, free_var, dep_var):
"""Compute inverse transformation and Jacobian for substituting
dep_var for free_var."""
free_var = self.prepare_var(free_var)
dep_var = self.prepare_var(dep_var)
# inverve transformation
equation = self.rv_to_equation[dep_var] - dep_var.getSymname()
solutions = eq_solve(self.rv_to_equation[dep_var], dep_var.getSymname(), free_var.getSymname())
var_changes = []
for uj in solutions:
# remove complex valued solutions
vj = uj.atoms(sympy.Symbol)
hvj = {}
for v in vj:
#print self.sym_to_rv[v].range()
hvj[v]=self.sym_to_rv[v].range()[1]
if len(solutions)>1 and not sympy.im(uj.subs(hvj))==0:
continue
uj_symbols = list(sorted(uj.atoms(sympy.Symbol), key = str))
inv_transf = my_lambdify(uj_symbols, uj, "numpy")
inv_transf_vars = [self.sym_to_rv[s] for s in uj_symbols]
if params.models.debug_info:
#print "vars to change: ", free_var.getSymname(), " <- ", dep_var.getSymname(), "=", self.rv_to_equation[dep_var]
#print "equation: ", dep_var.getSymname(), "=", self.rv_to_equation[dep_var]
print("substitution: ", free_var.getSymname(), "=", uj, end=' ')
#print "variables: ", uj_symbols, inv_transf_vars
# Jacobian
#J = sympy.Abs(sympy.diff(uj, dep_var.getSymname()))
J = sympy.diff(uj, dep_var.getSymname())
print(J.atoms())
J_symbols = list(sorted(J.atoms(sympy.Symbol), key = str))
if len(J_symbols) > 0:
jacobian_vars = [self.sym_to_rv[s] for s in J_symbols]
jacobian = my_lambdify(J_symbols, J, "numpy")
jacobian = NDFun(len(jacobian_vars), jacobian_vars, jacobian, safe = True, abs = True)
else:
jacobian = NDConstFactor(abs(float(J)))
jacobian_vars = []
if params.models.debug_info:
print("; Jacobian=", J)
#print "variables: ", J_symbols, jacobian_vars
var_changes.append((uj, inv_transf, inv_transf_vars, jacobian))
return var_changes, equation
def solveCutsX(self, fun, ay, by):
axc = eq_solve(fun, ay, self.symvars[0])[0]
bxc = eq_solve(fun, by, self.symvars[0])[0]
return (axc, bxc)
def solveCutsX(self, fun, ay, by):
axc = eq_solve(fun, ay, self.symvars[0])[0]
bxc = eq_solve(fun, by, self.symvars[0])[0]
return (axc, bxc)