def _matmul_normal_constant(norm_rv: _Normal,
constant_rv: _Constant) -> _Normal:
if norm_rv.ndim == 1 or (norm_rv.ndim == 2 and norm_rv.shape[0] == 1):
if norm_rv.cov_cholesky_is_precomputed:
cov_cholesky = _utils.linalg.cholesky_update(
constant_rv.support.T @ norm_rv.cov_cholesky)
else:
cov_cholesky = None
return _Normal(
mean=norm_rv.mean @ constant_rv.support,
cov=constant_rv.support.T @ (norm_rv.cov @ constant_rv.support),
cov_cholesky=cov_cholesky,
random_state=_utils.derive_random_seed(norm_rv.random_state,
constant_rv.random_state),
)
elif norm_rv.ndim == 2 and norm_rv.shape[0] > 1:
# This part does not do the Cholesky update,
# because of performance configurations: currently, there is no way of switching
# the Cholesky updates off, which might affect (large, potentially sparse) covariance matrices
# of matrix-variate Normal RVs. See Issue #335.
cov_update = _linear_operators.Kronecker(
_linear_operators.Identity(constant_rv.shape[0]),
constant_rv.support)
return _Normal(
mean=norm_rv.mean @ constant_rv.support,
cov=cov_update.T @ (norm_rv.cov @ cov_update),
random_state=_utils.derive_random_seed(norm_rv.random_state,
constant_rv.random_state),
)
else:
raise TypeError(
"Currently, matrix multiplication is only supported for vector- and "
"matrix-variate Gaussians.")
def _matmul_normal_constant(norm_rv: _Normal, constant_rv: _Constant) -> _Normal:
if norm_rv.ndim == 1 or (norm_rv.ndim == 2 and norm_rv.shape[0] == 1):
return _Normal(
mean=norm_rv.mean @ constant_rv.support,
cov=constant_rv.support.T @ (norm_rv.cov @ constant_rv.support),
random_state=_utils.derive_random_seed(
norm_rv.random_state, constant_rv.random_state
),
)
elif norm_rv.ndim == 2 and norm_rv.shape[0] > 1:
cov_update = _linear_operators.Kronecker(
_linear_operators.Identity(constant_rv.shape[0]), constant_rv.support
)
return _Normal(
mean=norm_rv.mean @ constant_rv.support,
cov=cov_update.T @ (norm_rv.cov @ cov_update),
random_state=_utils.derive_random_seed(
norm_rv.random_state, constant_rv.random_state
),
)
else:
raise TypeError(
"Currently, matrix multiplication is only supported for vector- and "
"matrix-variate Gaussians."
)
def _sub_dirac_normal(dirac_rv: _Dirac, norm_rv: _Normal) -> _Normal:
return _Normal(
mean=dirac_rv.support - norm_rv.mean,
cov=norm_rv.cov,
random_state=_utils.derive_random_seed(dirac_rv.random_state,
norm_rv.random_state),
)
def transpose(self, *axes: int) -> "RandomVariable":
"""
Transpose the random variable.
Parameters
----------
axes : None, tuple of ints, or n ints
See documentation of numpy.ndarray.transpose.
Returns
-------
transposed_rv : The transposed random variable.
"""
return RandomVariable(
shape=np.empty(shape=self.shape).transpose(*axes).shape,
dtype=self.dtype,
random_state=_utils.derive_random_seed(self.random_state),
sample=lambda size: self.sample(size).transpose(*axes),
mode=lambda: self.mode.transpose(*axes),
median=lambda: self.median.transpose(*axes),
mean=lambda: self.mean.transpose(*axes),
cov=lambda: self.cov,
var=lambda: self.var.transpose(*axes),
std=lambda: self.std.transpose(*axes),
entropy=lambda: self.entropy,
as_value_type=self.__as_value_type,
)
def reshape(self, newshape: ShapeArgType) -> "RandomVariable":
"""Give a new shape to a random variable.
Parameters
----------
newshape :
New shape for the random variable. It must be compatible with the original
shape.
"""
newshape = _utils.as_shape(newshape)
return RandomVariable(
shape=newshape,
dtype=self.dtype,
random_state=_utils.derive_random_seed(self.random_state),
sample=lambda size: self.sample(size).reshape(size + newshape),
mode=lambda: self.mode.reshape(newshape),
median=lambda: self.median.reshape(newshape),
mean=lambda: self.mean.reshape(newshape),
cov=lambda: self.cov,
var=lambda: self.var.reshape(newshape),
std=lambda: self.std.reshape(newshape),
entropy=lambda: self.entropy,
as_value_type=self.__as_value_type,
)
def __abs__(self) -> "RandomVariable":
return RandomVariable(
shape=self.shape,
dtype=self.dtype,
random_state=_utils.derive_random_seed(self.random_state),
sample=lambda size: abs(self.sample(size=size)),
)
def _sub_normal_dirac(norm_rv: _Normal, dirac_rv: _Dirac) -> _Normal:
return _Normal(
mean=norm_rv.mean - dirac_rv.support,
cov=norm_rv.cov,
random_state=_utils.derive_random_seed(norm_rv.random_state,
dirac_rv.random_state),
)
def _sub_constant_normal(constant_rv: _Constant, norm_rv: _Normal) -> _Normal:
return _Normal(
mean=constant_rv.support - norm_rv.mean,
cov=norm_rv.cov,
random_state=_utils.derive_random_seed(
constant_rv.random_state, norm_rv.random_state
),
)
def _sub_normal_constant(norm_rv: _Normal, constant_rv: _Constant) -> _Normal:
return _Normal(
mean=norm_rv.mean - constant_rv.support,
cov=norm_rv.cov,
random_state=_utils.derive_random_seed(
norm_rv.random_state, constant_rv.random_state
),
)
def _sub_constant_normal(constant_rv: _Constant, norm_rv: _Normal) -> _Normal:
cov_cholesky = norm_rv.cov_cholesky if norm_rv.cov_cholesky_is_precomputed else None
return _Normal(
mean=constant_rv.support - norm_rv.mean,
cov=norm_rv.cov,
cov_cholesky=cov_cholesky,
random_state=_utils.derive_random_seed(constant_rv.random_state,
norm_rv.random_state),
)
def _add_normal_constant(norm_rv: _Normal, constant_rv: _Constant) -> _Normal:
cov_cholesky = norm_rv.cov_cholesky if norm_rv.cov_cholesky_is_precomputed else None
return _Normal(
mean=norm_rv.mean + constant_rv.support,
cov=norm_rv.cov,
cov_cholesky=cov_cholesky,
random_state=_utils.derive_random_seed(norm_rv.random_state,
constant_rv.random_state),
)
def _dirac_binary_operator(dirac_rv1: Dirac,
dirac_rv2: Dirac) -> Dirac:
return Dirac(
support=operator(dirac_rv1.support, dirac_rv2.support),
random_state=_utils.derive_random_seed(
dirac_rv1.random_state,
dirac_rv2.random_state,
),
)
def _constant_rv_binary_operator(constant_rv1: Constant,
constant_rv2: Constant) -> Constant:
return Constant(
support=operator(constant_rv1.support, constant_rv2.support),
random_state=_utils.derive_random_seed(
constant_rv1.random_state,
constant_rv2.random_state,
),
)
def _mul_normal_dirac(
norm_rv: _Normal, dirac_rv: _Dirac
) -> Union[_Normal, _Dirac, type(NotImplemented)]:
if dirac_rv.size == 1:
if dirac_rv.support == 0:
return _Dirac(
support=np.zeros_like(norm_rv.mean),
random_state=_utils.derive_random_seed(norm_rv.random_state,
dirac_rv.random_state),
)
else:
return _Normal(
mean=dirac_rv.support * norm_rv.mean,
cov=(dirac_rv.support**2) * norm_rv.cov,
random_state=_utils.derive_random_seed(norm_rv.random_state,
dirac_rv.random_state),
)
return NotImplemented
def _rv_binary_op(rv1: _RandomVariable, rv2: _RandomVariable) -> _RandomVariable:
shape, dtype, sample = _make_rv_binary_op_result_shape_dtype_sample_fn(
op_fn, rv1, rv2
)
return _RandomVariable(
shape=shape,
dtype=dtype,
random_state=_utils.derive_random_seed(rv1.random_state, rv2.random_state),
sample=sample,
)
def _matmul_dirac_normal(dirac_rv: _Dirac, norm_rv: _Normal) -> _Normal:
if norm_rv.ndim == 1 or (norm_rv.ndim == 2 and norm_rv.shape[1] == 1):
return _Normal(
mean=dirac_rv.support @ norm_rv.mean,
cov=dirac_rv.support @ (norm_rv.cov @ dirac_rv.support.T),
random_state=_utils.derive_random_seed(dirac_rv.random_state,
norm_rv.random_state),
)
else:
raise TypeError(
"Currently, matrix multiplication is only supported for vector-variate "
"Gaussians.")
def _sub_normal(self, other: "Normal") -> "Normal":
if other.shape != self.shape:
raise ValueError(
"Subtraction of two normally distributed random variables is only "
"possible if both operands have the same shape.")
return Normal(
mean=self._mean - other._mean,
cov=self._cov + other._cov,
random_state=_utils.derive_random_seed(self.random_state,
other.random_state),
)
def _mul_normal_constant(
norm_rv: _Normal, constant_rv: _Constant
) -> Union[_Normal, _Constant, type(NotImplemented)]:
if constant_rv.size == 1:
if constant_rv.support == 0:
return _Constant(
support=np.zeros_like(norm_rv.mean),
random_state=_utils.derive_random_seed(
norm_rv.random_state, constant_rv.random_state
),
)
else:
return _Normal(
mean=constant_rv.support * norm_rv.mean,
cov=(constant_rv.support ** 2) * norm_rv.cov,
random_state=_utils.derive_random_seed(
norm_rv.random_state, constant_rv.random_state
),
)
return NotImplemented
def _generic_rv_add(rv1: _RandomVariable, rv2: _RandomVariable) -> _RandomVariable:
shape, dtype, sample = _make_rv_binary_op_result_shape_dtype_sample_fn(
operator.add, rv1, rv2
)
return _RandomVariable(
shape=shape,
dtype=dtype,
random_state=_utils.derive_random_seed(rv1.random_state, rv2.random_state),
sample=sample,
mean=lambda: rv1.mean + rv2.mean,
)
def __getitem__(self, key: ArrayLikeGetitemArgType) -> "RandomVariable":
return RandomVariable(
shape=np.empty(shape=self.shape)[key].shape,
dtype=self.dtype,
random_state=_utils.derive_random_seed(self.random_state),
sample=lambda size: self.sample(size)[key],
mode=lambda: self.mode[key],
mean=lambda: self.mean[key],
var=lambda: self.var[key],
std=lambda: self.std[key],
entropy=lambda: self.entropy,
as_value_type=self.__as_value_type,
)
def _truediv_normal_dirac(norm_rv: _Normal, dirac_rv: _Dirac) -> _Normal:
if dirac_rv.size == 1:
if dirac_rv.support == 0:
raise ZeroDivisionError
return _Normal(
mean=norm_rv.mean / dirac_rv.support,
cov=norm_rv.cov / (dirac_rv.support**2),
random_state=_utils.derive_random_seed(norm_rv.random_state,
dirac_rv.random_state),
)
return NotImplemented
def _truediv_normal_constant(norm_rv: _Normal, constant_rv: _Constant) -> _Normal:
if constant_rv.size == 1:
if constant_rv.support == 0:
raise ZeroDivisionError
return _Normal(
mean=norm_rv.mean / constant_rv.support,
cov=norm_rv.cov / (constant_rv.support ** 2),
random_state=_utils.derive_random_seed(
norm_rv.random_state, constant_rv.random_state
),
)
return NotImplemented
def __pos__(self) -> "RandomVariable":
return RandomVariable(
shape=self.shape,
dtype=self.dtype,
random_state=_utils.derive_random_seed(self.random_state),
sample=lambda size: +self.sample(size=size),
in_support=lambda x: self.in_support(+x),
mode=lambda: +self.mode,
median=lambda: +self.median,
mean=lambda: +self.mean,
cov=lambda: self.cov,
var=lambda: self.var,
std=lambda: self.std,
as_value_type=self.__as_value_type,
)
def reshape(self, newshape: ShapeArgType) -> "Normal":
try:
reshaped_mean = self.dense_mean.reshape(newshape)
except ValueError as exc:
raise ValueError(
f"Cannot reshape this normal random variable to the given shape: "
f"{newshape}") from exc
reshaped_cov = self.dense_cov
if reshaped_mean.ndim > 0 and reshaped_cov.ndim == 0:
reshaped_cov = reshaped_cov.reshape(1, 1)
return Normal(
mean=reshaped_mean,
cov=reshaped_cov,
random_state=_utils.derive_random_seed(self.random_state),
)
def _matmul_constant_normal(constant_rv: _Constant,
norm_rv: _Normal) -> _Normal:
if norm_rv.ndim == 1 or (norm_rv.ndim == 2 and norm_rv.shape[1] == 1):
if norm_rv.cov_cholesky_is_precomputed:
cov_cholesky = _utils.linalg.cholesky_update(
constant_rv.support @ norm_rv.cov_cholesky)
else:
cov_cholesky = None
return _Normal(
mean=constant_rv.support @ norm_rv.mean,
cov=constant_rv.support @ (norm_rv.cov @ constant_rv.support.T),
cov_cholesky=cov_cholesky,
random_state=_utils.derive_random_seed(constant_rv.random_state,
norm_rv.random_state),
)
else:
raise TypeError(
"Currently, matrix multiplication is only supported for vector-variate "
"Gaussians.")
def _truediv_normal_constant(norm_rv: _Normal,
constant_rv: _Constant) -> _Normal:
if constant_rv.size == 1:
if constant_rv.support == 0:
raise ZeroDivisionError
if norm_rv.cov_cholesky_is_precomputed:
cov_cholesky = norm_rv.cov_cholesky / constant_rv.support
else:
cov_cholesky = None
return _Normal(
mean=norm_rv.mean / constant_rv.support,
cov=norm_rv.cov / (constant_rv.support**2),
cov_cholesky=cov_cholesky,
random_state=_utils.derive_random_seed(norm_rv.random_state,
constant_rv.random_state),
)
return NotImplemented
def transpose(self, *axes: int) -> "Normal":
if len(axes) == 1 and isinstance(axes[0], tuple):
axes = axes[0]
elif (len(axes) == 1 and axes[0] is None) or len(axes) == 0:
axes = tuple(reversed(range(self.ndim)))
mean_t = self.dense_mean.transpose(*axes).copy()
# Transpose covariance
cov_axes = axes + tuple(mean_t.ndim + axis for axis in axes)
cov_t = self.dense_cov.reshape(self.shape + self.shape)
cov_t = cov_t.transpose(*cov_axes).copy()
if mean_t.ndim > 0:
cov_t = cov_t.reshape(mean_t.size, mean_t.size)
return Normal(
mean=mean_t,
cov=cov_t,
random_state=_utils.derive_random_seed(self.random_state),
)
def __getitem__(self, key: ArrayLikeGetitemArgType) -> "Normal":
"""
Marginalization in multi- and matrixvariate normal distributions, expressed by
means of (advanced) indexing, masking and slicing.
We support all modes of array indexing presented in
https://numpy.org/doc/1.19/reference/arrays.indexing.html.
Note that, currently, this method does not work for normal distributions other
than the multi- and matrixvariate versions.
Parameters
----------
key : int or slice or ndarray or tuple of None, int, slice, or ndarray
Indices, slice objects and/or boolean masks specifying which entries to keep
while marginalizing over all other entries.
"""
if not isinstance(key, tuple):
key = (key, )
# Select entries from mean
mean = self.dense_mean[key]
# Select submatrix from covariance matrix
cov = self.dense_cov.reshape(self.shape + self.shape)
cov = cov[key][tuple([slice(None)] * mean.ndim) + key]
if mean.ndim > 0:
cov = cov.reshape(mean.size, mean.size)
return Normal(
mean=mean,
cov=cov,
random_state=_utils.derive_random_seed(self.random_state),
)
def __pos__(self) -> "Normal":
return Normal(
mean=+self._mean,
cov=self._cov,
random_state=_utils.derive_random_seed(self.random_state),
)
def __abs__(self) -> "Constant":
return Constant(
support=abs(self.support),
random_state=_utils.derive_random_seed(self.random_state),
)
