Predicted Time calculation about erpp-rmtpp HOT 8 OPEN

The paper mentions that it uses certain "numerical integration techniques" to solve for 13.
But, I could not find that in the official implementation (C++) at https://github.com/dunan/NeuralPointProcess.

@SZH1230456 Were you able to figure out something?

from erpp-rmtpp.

def next_time(self,tj,hj):
    umax = self.umax #maximum time
    Deltat = umax/self.N
    dt = torch.linspace(0, umax, self.N+1)
    df = dt * self.fstart(dt, hj)
    #normalization factor
    integrand_ = ((df[1:] + df[:-1]) * 0.5) * Deltat
    integral_ = torch.sum(integrand_)
    return tj + integral_

This is the numerical method

from erpp-rmtpp.

Thanks @Kaimaoge .
I see that tf_rmtpp uses scipy.integrate.quad method.

from erpp-rmtpp.

def next_time(self,tj,hj):
    umax = self.umax #maximum time
    Deltat = umax/self.N
    dt = torch.linspace(0, umax, self.N+1)
    df = dt * self.fstart(dt, hj)
    #normalization factor
    integrand_ = ((df[1:] + df[:-1]) * 0.5) * Deltat
    integral_ = torch.sum(integrand_)
    return tj + integral_

This is the numerical method

@Kaimaoge Hi, I‘m confused with the function of self.umax, self.N and self.fstart, could you please explain it?

from erpp-rmtpp.

def next_time(self,tj,hj):
    umax = self.umax #maximum time
    Deltat = umax/self.N
    dt = torch.linspace(0, umax, self.N+1)
    df = dt * self.fstart(dt, hj)
    #normalization factor
    integrand_ = ((df[1:] + df[:-1]) * 0.5) * Deltat
    integral_ = torch.sum(integrand_)
    return tj + integral_

This is the numerical method

@Kaimaoge Hi, I‘m confused with the function of self.umax, self.N and self.fstart, could you please explain it?

umax is the upper limits of integration, N here is the number of units for calculating numerical integration, f_stat is the probability function.

from erpp-rmtpp.

def next_time(self,tj,hj):
    umax = self.umax #maximum time
    Deltat = umax/self.N
    dt = torch.linspace(0, umax, self.N+1)
    df = dt * self.fstart(dt, hj)
    #normalization factor
    integrand_ = ((df[1:] + df[:-1]) * 0.5) * Deltat
    integral_ = torch.sum(integrand_)
    return tj + integral_

This is the numerical method

@Kaimaoge Hi, I‘m confused with the function of self.umax, self.N and self.fstart, could you please explain it?

umax is the upper limits of integration, N here is the number of units for calculating numerical integration, f_stat is the probability function.

@Kaimaoge Thanks, could you please share the code of fstart you used?

from erpp-rmtpp.

Thanks @Kaimaoge .
I see that tf_rmtpp uses scipy.integrate.quad method.

Always this method reports warning:

IntegrationWarning: The occurrence of roundoff error is detected, which prevents
the requested tolerance from being achieved. The error may be
underestimated.

And the f_star always encounters exp() overflow.
Also I am wondering the way they calculate the lambda:
https://github.com/musically-ut/tf_rmtpp/blob/master/src/tf_rmtpp/rmtpp_core.py#L621
it is a little different from the paper

from erpp-rmtpp.