def _log_cdf(self, x): loc = tf.convert_to_tensor(self.loc) scale = tf.convert_to_tensor(self.scale) safe_x = self._get_safe_input(x, loc=loc, scale=scale) log_cdf = np.log(2 / np.pi) + tf.math.log( tf.atan((safe_x - loc) / scale)) return tf.where(x < loc, dtype_util.as_numpy_dtype(self.dtype)(-np.inf), log_cdf)
def _log_cdf(self, x): loc = tf.convert_to_tensor(self.loc) scale = tf.convert_to_tensor(self.scale) with tf.control_dependencies(self._maybe_assert_valid_sample(x, loc)): safe_x = self._get_safe_input(x, loc=loc, scale=scale) log_cdf = np.log(2 / np.pi) + tf.math.log( tf.atan((safe_x - loc) / scale)) return tf.where(x < loc, dtype_util.as_numpy_dtype(self.dtype)(-np.inf), log_cdf)
def _log_cdf(self, x): return tf.math.log1p(2 / np.pi * tf.atan(self._z(x))) - np.log(2)
def _cdf(self, x): return tf.atan(self._z(x)) / np.pi + 0.5
def _log_cdf(self, x): return self._extend_support_with_default_value( x, lambda x: np.log(2 / np.pi) + tf.math.log(tf.atan(self._z(x))), default_value=-np.inf)
def _log_cauchy_cdf(z): return tf.math.log1p(2 / np.pi * tf.atan(z)) - np.log(2)
def _cauchy_cdf(z): return tf.atan(z) / np.pi + 0.5