Exemple #1
0
    def __call__(self, x, regularize=True):
        # TODO: check repeats for basis covariances too.

        # If initial values were passed in, observe on them.
        if self.need_init_obs:
            self._init_obs()

        # Record original shape of x and regularize it.
        orig_shape = shape(x)

        if len(orig_shape) > 1:
            orig_shape = orig_shape[:-1]

        if regularize:
            if any(isnan(x)):
                raise ValueError, 'Input argument to Realization contains NaNs.'
            x = regularize_array(x)

        if x is self.x_sofar:
            return self.f_sofar

        if self.check_repeats:
            # use caching_call to save duplicate calls.
            f, self.x_sofar, self.f_sofar = caching_call(
                self.draw_vals, x, self.x_sofar, self.f_sofar)
        else:
            # Call to self.draw_vals.
            f = self.draw_vals(x)

        if regularize:
            return f.reshape(orig_shape)
        else:
            return f
Exemple #2
0
    def __call__(self, x, regularize=True):

        # TODO: check repeats for basis covariances too.

        # TODO: Do the timing trick down through this method to see where the bottleneck is.

        # Record original shape of x and regularize it.
        orig_shape = shape(x)

        if len(orig_shape)>1:
            orig_shape = orig_shape[:-1]

        if regularize:
            if any(isnan(x)):
                raise ValueError, 'Input argument to Realization contains NaNs.'
            x = regularize_array(x)

        if x is self.x_sofar:
            return self.f_sofar

        if self.check_repeats:
            # use caching_call to save duplicate calls.
            f, self.x_sofar, self.f_sofar = caching_call(self.draw_vals, x, self.x_sofar, self.f_sofar)

        else:
            # Call to self.draw_vals.
            f = self.draw_vals(x)

        if regularize:
            return f.reshape(orig_shape)
        else:
            return f
    def __call__(self, x, regularize=True):
        # TODO: check repeats for basis covariances too.
        
        # If initial values were passed in, observe on them.
        if self.need_init_obs:
            self._init_obs()

        # Record original shape of x and regularize it.
        orig_shape = shape(x)

        if len(orig_shape)>1:
            orig_shape = orig_shape[:-1]

        if regularize:
            if any(isnan(x)):
                raise ValueError, 'Input argument to Realization contains NaNs.'
            x = regularize_array(x)

        if x is self.x_sofar:
            return self.f_sofar

        if self.check_repeats:
            # use caching_call to save duplicate calls.
            f, self.x_sofar, self.f_sofar = caching_call(self.draw_vals, x, self.x_sofar, self.f_sofar)
        else:
            # Call to self.draw_vals.
            f = self.draw_vals(x)

        if regularize:
            return f.reshape(orig_shape)
        else:
            return f