def errorInterpreter(self, errCode):
header = "HomotopySolver::"
if errCode & 0b001:
print header, "Max number of iteration reached"
if errCode >> 1 & 0b010:
y = self.yeq
y[self.ndimA - 1] = 1
print header, "Not valid equilibrium::H(x,1)=", linalg.norm(
self._PC_F(y), 2)
def _correctorSteps(self,y0,b):
err = 10
count = 0
while err>self.epsCorr and count<1e2:
F = self._PC_F(y0)
dF = self._PC_DF(y0)
A = np.append(dF,b,axis=0)
B = A.dot(y0) - np.append(F,[[0]],axis=0)
ynew = linalg.solve(A,B)
err = linalg.norm(F,2)
y0 = ynew
count = count + 1
print "Correction err:", err
return (y0,err)
def _predictorSteps(self,y0):
err = 0.0
d = y0
count = 0
y00 = y0
h = self.hStep
while err<self.epsPred:
F = self._PC_F(y0)
dF = self._PC_DF(y0)
d = linalg.null_space(dF) #kernel of the Jacobian
#d = d/linalg.norm(d,2) #Normalization of the kernel
if d[-1,0]<0:
d = -d
ynext = y0 + h * d
err = linalg.norm(F,2)
y0 = ynext
print h,self.epsPred,err
return (y0,np.transpose(d)) # return the prediction step and a vector orthogonal to the kernel
epsPred = 9e-3 #Prediction error
epsCorr = 1e-9 #Correction error
maxSteps = 5e2 #Max number of steps
thrPrune = .01
lstm = LSTMNN(None)
weights = readFromPickle(data_input_file)
weights, pPrune = pruneW(weights, thrPrune)
lstm.updateWeigths(weights)
ndim = 2 * lstm.dimOfOutput()
x0 = np.zeros([ndim, 1])
lstm.setInitStat(x0)
print("dim = ", str(ndim))
(t, x, eCode) = lstm.findEquilibria(epsPred, epsCorr, maxSteps)
xeq = lstm.refineTheEquilibria(x)
xeq = np.array([xeq]).T
lstm.errorInterpreter(eCode)
print("||F(xeq)-xeq|| = ", linalg.norm(xeq - lstm.F(xeq), 2))
tmp_out = {'xeq': xeq, 'prTh': thrPrune}
scipyio.savemat(output_eq_file, tmp_out)
def isValidEquilibrium(self, ErrTol):
y = self.yeq
y[self.ndimA - 1] = 1.
return linalg.norm(self._PC_F(y), 2) < ErrTol
[x[0, 0], x[1, 0]**2]])
x0 = np.array([[0], [0]])
hStep = 1e-3
epsPred = 1e-5
epsCorr = 1e-9
ndim = 2
maxSteps = 1e4
PT = PolyTest(x0, ndim, epsPred, epsCorr, hStep, maxSteps)
(y, errCode) = PT.runSolver()
print errCode
print y
PT.errorInterpreter(errCode)
if ~(errCode >> 1 & 0b010):
y[-1] = 1
print "Valid equilibrium::H(x,1)=", linalg.norm(PT._PC_F(y), 2)
if test == 2:
data_input_file = 'demo/data/PTB_0/save_10v2.pkl'
epsPred = 1e-3
epsCorr = 1e-9
maxSteps = 1e5
lstm = LSTMNN(data_input_file)
#lstm.makeOutputsAsInputs()
ndim = 2 * lstm.dimOfOutput()
#x0 = np.random.uniform(-1,1,[ndim,1])
x0 = np.zeros([ndim, 1])
lstm.setInitStat(x0)
(t, x, eCode) = lstm.findEquilibria(epsPred, epsCorr, maxSteps)