simulate_experiment with domain error when n_outcomes is not constant #149

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1. PengDauan commented on Nov 18, 2018

   

   PengDauan

   on Nov 18, 2018

   Last edited by PengDauan

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   class FiniteOutcomeModel(Model):

   def domain(self, expparams):
       if self.is_n_outcomes_constant:
           return self._domain if expparams is None else [self._domain for ep in expparams]
       else:
           return [IntegerDomain(min=0,max=n_o-1) for n_o in self.n_outcomes(expparams)]
   
   def simulate_experiment(self, modelparams, expparams, repeat=1):
   
       super(FiniteOutcomeModel, self).simulate_experiment(modelparams, expparams, repeat)
       
       if self.is_n_outcomes_constant:
           all_outcomes = self.domain(None).values
           probabilities = self.likelihood(all_outcomes, modelparams, expparams)
           cdf = np.cumsum(probabilities, axis=0)
           randnum = np.random.random((repeat, 1, modelparams.shape[0], expparams.shape[0]))
           outcome_idxs = all_outcomes[np.argmax(cdf > randnum, axis=1)]
           outcomes = all_outcomes[outcome_idxs]
       else:
           assert(self.are_expparam_dtypes_consistent(expparams))
           dtype = self.domain(expparams[0, np.newaxis])[0].dtype
           outcomes = np.empty((repeat, modelparams.shape[0], expparams.shape[0]), dtype=dtype)
           for idx_experiment, single_expparams in enumerate(expparams[:, np.newaxis]):
               all_outcomes = self.domain(single_expparams).values
               probabilities = self.likelihood(all_outcomes, modelparams, single_expparams)
               cdf = np.cumsum(probabilities, axis=0)[..., 0]
               randnum = np.random.random((repeat, 1, modelparams.shape[0]))
               outcomes[:, :, idx_experiment] = all_outcomes[np.argmax(cdf > randnum, axis=1)]
               
       return outcomes[0, 0, 0] if repeat == 1 and expparams.shape[0] == 1 and modelparams.shape[0] == 1 else outcomes
   
2. PengDauan commented on Nov 18, 2018

   

   PengDauan

   on Nov 18, 2018

   Last edited by PengDauan

   Author

   More actions

   def simulate_experiment(self, modelparams, expparams, repeat=1):
   	# Call the superclass simulate_experiment, not recording the result.
   	# This is used to count simulation calls.
   	super(FiniteOutcomeModel, self).simulate_experiment(modelparams, expparams, repeat)
   	
   	if self.is_n_outcomes_constant:
   		# In this case, all expparams have the same domain
   		all_outcomes = self.domain(None).values
   		probabilities = self.likelihood(all_outcomes, modelparams, expparams)
   		cdf = np.cumsum(probabilities, axis=0)
   		randnum = np.random.random((repeat, 1, modelparams.shape[0], expparams.shape[0]))
   		outcome_idxs = all_outcomes[np.argmax(cdf > randnum, axis=1)]
   		outcomes = all_outcomes[outcome_idxs]
   	else:
   		# Loop over each experiment, sadly.
   		# Assume all domains have the same dtype
   		assert(self.are_expparam_dtypes_consistent(expparams))
   		dtype = self.domain(expparams[0, np.newaxis])[0].dtype
   		outcomes = np.empty((repeat, modelparams.shape[0], expparams.shape[0]), dtype=dtype)
   		for idx_experiment, single_expparams in enumerate(expparams[:, np.newaxis]):
   			all_outcomes = self.domain(single_expparams).values
   			probabilities = self.likelihood(all_outcomes, modelparams, single_expparams)
   			cdf = np.cumsum(probabilities, axis=0)[..., 0]
   			randnum = np.random.random((repeat, 1, modelparams.shape[0]))
   			outcomes[:, :, idx_experiment] = all_outcomes[np.argmax(cdf > randnum, axis=1)]
   			
   	return outcomes[0, 0, 0] if repeat == 1 and expparams.shape[0] == 1 and modelparams.shape[0] == 1 else outcomes
   

   change the code in abstract.FiniteOutcomeModel.simulate_experiment
   all_outcomes= self.domain(single_expparams).values
   to
   all_outcomes= self.domain(single_expparams)[idx_experiment].values
   will solve the problem in this case, but I'm not sure this is the general case
3. PengDauan

   changed the title [-]simulate_experiment with domain error[/-] [+]simulate_experiment with domain error when n_outcomes is not constant[/+] on Nov 18, 2018