Exemplo n.º 1
0
    def __str__(self):
        s = Parameterized.__str__(self).split('\n')
        #def __str__(self, names=None):
        #    if names is None:
        #        names = self._get_print_names()
        #s = Parameterized.__str__(self, names=names).split('\n')
        # add priors to the string
        if self.priors is not None:
            strs = [str(p) if p is not None else '' for p in self.priors]
        else:
            strs = [''] * len(self._get_params())

    #         strs = [''] * len(self._get_param_names())
    #     name_indices = self.grep_param_names("|".join(names))
    #     strs = np.array(strs)[name_indices]
        width = np.array(max([len(p) for p in strs] + [5])) + 4

        log_like = self.log_likelihood()
        log_prior = self.log_prior()
        obj_funct = '\nLog-likelihood: {0:.3e}'.format(log_like)
        if len(''.join(strs)) != 0:
            obj_funct += ', Log prior: {0:.3e}, LL+prior = {0:.3e}'.format(
                log_prior, log_like + log_prior)
        obj_funct += '\n\n'
        s[0] = obj_funct + s[0]
        s[0] += "|{h:^{col}}".format(h='prior', col=width)
        s[1] += '-' * (width + 1)

        for p in range(2, len(strs) + 2):
            s[p] += '|{prior:^{width}}'.format(prior=strs[p - 2], width=width)

        return '\n'.join(s)
Exemplo n.º 2
0
Arquivo: model.py Projeto: Dalar/GPy
    def __str__(self):
        s = Parameterized.__str__(self).split('\n')
        #def __str__(self, names=None):
        #    if names is None:
        #        names = self._get_print_names()
        #s = Parameterized.__str__(self, names=names).split('\n')
        # add priors to the string
        if self.priors is not None:
            strs = [str(p) if p is not None else '' for p in self.priors]
        else:
            strs = [''] * len(self._get_params())
       #         strs = [''] * len(self._get_param_names())
       #     name_indices = self.grep_param_names("|".join(names))
       #     strs = np.array(strs)[name_indices]
        width = np.array(max([len(p) for p in strs] + [5])) + 4

        log_like = self.log_likelihood()
        log_prior = self.log_prior()
        obj_funct = '\nLog-likelihood: {0:.3e}'.format(log_like)
        if len(''.join(strs)) != 0:
            obj_funct += ', Log prior: {0:.3e}, LL+prior = {0:.3e}'.format(log_prior, log_like + log_prior)
        obj_funct += '\n\n'
        s[0] = obj_funct + s[0]
        s[0] += "|{h:^{col}}".format(h='prior', col=width)
        s[1] += '-' * (width + 1)

        for p in range(2, len(strs) + 2):
            s[p] += '|{prior:^{width}}'.format(prior=strs[p - 2], width=width)

        return '\n'.join(s)