Remove mean and logprob (inconsistent values due to numerical approxi…

…mations)

mlpp_lib/probabilistic_layers.py

@@ -361,36 +361,6 @@ def _sample_n(self, n, seed=None):
 
                     return chosen_samples
 
-                def _mean(self):
-                    """
-                    Original: X ~ N(mu, sigma)
-                    Censored: Y = X if 0 <= X <= 1 else 0 if X < 0 else 1
-
-                    Law of total expectations: E[Y] = E[Y | X > 1] * P(X > 1) + E[Y | X < 0] * P(X < 0) + E[Y | 0 <= X <= 1] * P(0 <= X <= 1)
-                                                    = P(X > 1) * 1 + P(X < 0) * 0 + E[X | 0 <= X <= 1] * P(0 <= X <= 1)
-                                                    = 1 - Phi((1 - mu) / sigma) + E[Z ~ TruncNormal(mu, sigma, 0, 1)] * (Phi((1 - mu) / sigma) - Phi(-mu / sigma))
-                    Ref for TruncatedNormal mean: https://en.wikipedia.org/wiki/Truncated_normal_distribution
-                    """
-                    original_mean = self.normal.mean()
-                    low_bound_standard = (0 - original_mean) / self.normal.stddev()
-                    high_bound_standard = (1 - original_mean) / self.normal.stddev()
-
-                    self.low_bound_cdf = self.normal.cdf(low_bound_standard)
-                    self.high_bound_cdf = self.normal.cdf(high_bound_standard)
-
-                    self.low_bound_pdf = self.normal.prob(low_bound_standard)
-                    self.high_bound_pdf = self.normal.prob(high_bound_standard)
-
-                    return original_mean + self.normal.stddev() * (
-                        self.low_bound_pdf - self.high_bound_pdf
-                    ) / (self.high_bound_cdf - self.low_bound_cdf + 1e-3)
-
-                def _log_prob(self, value):
-                    original_log_prob = self.normal.log_prob(value)
-
-                    return original_log_prob - tf.math.log(
-                        self.high_bound_cdf - self.low_bound_cdf + 1e-3
-                    )
 
             return independent_lib.Independent(
                 CustomCensored(normal_dist),