tfp.substrates.jax.math.psd_kernels.SchurComplement

The SchurComplement kernel.

Inherits From: AutoCompositeTensorPsdKernel, PositiveSemidefiniteKernel

tfp.substrates.jax.math.psd_kernels.SchurComplement(
    base_kernel,
    fixed_inputs,
    fixed_inputs_is_missing=None,
    diag_shift=None,
    cholesky_fn=None,
    validate_args=False,
    name='SchurComplement',
    _precomputed_divisor_matrix_cholesky=None
)

Given a block matrix M = [[A, B], [C, D]], the Schur complement of D in M is written M / D = A - B @ Inverse(D) @ C.

This class represents a PositiveSemidefiniteKernel whose behavior is as follows. We compute a matrix, analogous to D in the above definition, by calling base_kernel.matrix(fixed_inputs, fixed_inputs). Then given new input locations x and y, we can construct the remaining pieces of M above, and compute the Schur complement of D in M (see Mathematical Details, below).

Notably, this kernel uses a bijector (Invert(CholeskyOuterProduct)), as an intermediary for the requisite matrix solve, which means we get a caching benefit after the first use.

Mathematical Details

Suppose we have a kernel k and some fixed collection of inputs Z = [z0, z1, ..., zN]. Given new inputs x and y, we can form a block matrix

   M = [
     [k(x, y), k(x, z0), ..., k(x, zN)],
     [k(z0, y), k(z0, z0), ..., k(z0, zN)],
     ...,
     [k(zN, y), k(z0, zN), ..., k(zN, zN)],
   ]

We might write this, so as to emphasize the block structure,

   M = [
     [xy, xZ],
     [yZ^T, ZZ],
   ],

   xy = [k(x, y)]
   xZ = [k(x, z0), ..., k(x, zN)]
   yZ = [k(y, z0), ..., k(y, zN)]
   ZZ = "the matrix of k(zi, zj)'s"

Then we have the definition of this kernel's apply method:

schur_comp.apply(x, y) = xy - xZ @ ZZ^{-1} @ yZ^T

and similarly, if x and y are collections of inputs.

As with other PSDKernels, the apply method acts as a (possibly vectorized) scalar function of 2 inputs. Given a single x and y, apply will yield a scalar output. Given two (equal size!) collections X and Y, it will yield another (equal size!) collection of scalar outputs.

Examples

Here's a simple example usage, with no particular motivation.

from tensorflow_probability.math import psd_kernels

base_kernel = psd_kernels.ExponentiatedQuadratic(amplitude=np.float64(1.))
# 3 points in 1-dimensional space (shape [3, 1]).
z = [[0.], [3.], [4.]]

schur_kernel = psd_kernels.SchurComplement(
    base_kernel=base_kernel,
    fixed_inputs=z)

# Two individual 1-d points
x = [1.]
y = [2.]
print(schur_kernel.apply(x, y))
# ==> k(x, y) - k(x, z) @ Inverse(k(z, z)) @ k(z, y)

A more motivating application of this kernel is in constructing a Gaussian process that is conditioned on some observed data.

from tensorflow_probability import distributions as tfd
from tensorflow_probability.math import psd_kernels

base_kernel = psd_kernels.ExponentiatedQuadratic(amplitude=np.float64(1.))
observation_index_points = np.random.uniform(-1., 1., [50, 1])
observations = np.sin(2 * np.pi * observation_index_points[..., 0])

posterior_kernel = psd_kernels.SchurComplement(
    base_kernel=base_kernel,
    fixed_inputs=observation_index_points)

# Assume we use a zero prior mean, and compute the posterior mean.
def posterior_mean_fn(x):
  k_x_obs_linop = tf.linalg.LinearOperatorFullMatrix(
      base_kernel.matrix(x, observation_index_points))
  chol_linop = tf.linalg.LinearOperatorLowerTriangular(
      posterior_kernel.divisor_matrix_cholesky())

  return k_x_obs_linop.matvec(
      chol_linop.solvevec(
          chol_linop.solvevec(observations),
          adjoint=True))

# Construct the GP posterior distribution at some new points.
gp_posterior = tfp.distributions.GaussianProcess(
    index_points=np.linspace(-1., 1., 100)[..., np.newaxis],
    kernel=posterior_kernel,
    mean_fn=posterior_mean_fn)

# Draw 5 samples on the above 100-point grid
samples = gp_posterior.sample(5)

base_kernel A PositiveSemidefiniteKernel instance, the kernel used to build the block matrices of which this kernel computes the Schur complement.
fixed_inputs A (nested) Tensor, representing a collection of inputs. The Schur complement that this kernel computes comes from a block matrix, whose bottom-right corner is derived from base_kernel.matrix(fixed_inputs, fixed_inputs), and whose top-right and bottom-left pieces are constructed by computing the base_kernel at pairs of input locations together with these fixed_inputs. fixed_inputs is allowed to be an empty collection (either None or having a zero shape entry), in which case the kernel falls back to the trivial application of base_kernel to inputs. See class-level docstring for more details on the exact computation this does; fixed_inputs correspond to the Z structure discussed there. fixed_inputs (or each of its nested components) is assumed to have shape [b1, ..., bB, N, f1, ..., fF] where the b's are batch shape entries, the f's are feature_shape entries, and N is the number of fixed inputs. Use of this kernel entails a 1-time O(N^3) cost of computing the Cholesky decomposition of the k(Z, Z) matrix. The batch shape elements of fixed_inputs must be broadcast compatible with base_kernel.batch_shape.
fixed_inputs_is_missing A boolean Tensor of shape [..., N]. When is_missing is not None and an element of mask is True, this kernel will return values computed as if the divisor matrix did not contain the corresponding row or column.
diag_shift A floating point scalar to be added to the diagonal of the divisor_matrix before computing its Cholesky.
cholesky_fn Callable which takes a single (batch) matrix argument and returns a Cholesky-like lower triangular factor. Default value: None, in which case make_cholesky_with_jitter_fn is used with the jitter parameter.
validate_args If True, parameters are checked for validity despite possibly degrading runtime performance. Default value: False
name Python str name prefixed to Ops created by this class. Default value: "SchurComplement"
_precomputed_divisor_matrix_cholesky Internal parameter -- do not use.

base_kernel

batch_shape Shape of a single sample from a single event index as a TensorShape.

May be partially defined or unknown.

The batch dimensions are indexes into independent, non-identical parameterizations of this PositiveSemidefiniteKernel.

cholesky_bijector

cholesky_fn

diag_shift

dtype (Nested) dype over which the kernel operates.
feature_ndims The number of feature dimensions.

Kernel functions generally act on pairs of inputs from some space like

R^(d1 x ... x dD)

or, in words: rank-D real-valued tensors of shape [d1, ..., dD]. Inputs can be vectors in some R^N, but are not restricted to be. Indeed, one might consider kernels over matrices, tensors, or even more general spaces, like strings or graphs. Inputs may also be nested structures, in which case feature_ndims is a parallel nested structure containing the feature rank of each component.

fixed_inputs

name Name prepended to all ops created by this class.
parameters Dictionary of parameters used to instantiate this PSDKernel.
trainable_variables

validate_args Python bool indicating possibly expensive checks are enabled.
variables

Methods

apply

View source

apply(
    x1, x2, example_ndims=0, name='apply'
)

Apply the kernel function pairs of inputs.

Args
x1 (Nested) Tensor input to the kernel, of shape B1 + E1 + F, where B1 and E1 may be empty (ie, no batch/example dims, resp.). If nested, B1 and E1 must broadcast across elements of the structure. F (the feature shape) must have rank equal to the kernel's feature_ndims property, or to the corresponding element of the feature_ndims nested structure. Batch shape must broadcast with the batch shape of x2 and with the kernel's batch shape. Example shape must broadcast with example shape of x2. x1 and x2 must have the same number of example dims (ie, same rank).
x2 (Nested) Tensor input to the kernel, of shape B2 + E2 + F, where B2 and E2 may be empty (ie, no batch/example dims, resp.). If nested, B1 and E1 must broadcast across elements of the structure. F (the feature shape) must have rank equal to the kernel's feature_ndims property, or to the corresponding element of the feature_ndims nested structure. Batch shape must broadcast with the batch shape of x2 and with the kernel's batch shape. Example shape must broadcast with example shape of x2. x1 and x2 must have the same number of example dims (ie, same rank).
example_ndims A python integer, the number of example dims in the inputs. In essence, this parameter controls how broadcasting of the kernel's batch shape with input batch shapes works. The kernel batch shape will be broadcast against everything to the left of the combined example and feature dimensions in the input shapes.
name name to give to the op

Returns
Tensor containing the results of applying the kernel function to inputs x1 and x2. If the kernel parameters' batch shape is Bk then the shape of the Tensor resulting from this method call is broadcast(Bk, B1, B2) + broadcast(E1, E2).

Given an index set S, a kernel function is mathematically defined as a real- or complex-valued function on S satisfying the positive semi-definiteness constraint:

sum_i sum_j (c[i]*) c[j] k(x[i], x[j]) >= 0

for any finite collections {x[1], ..., x[N]} in S and {c[1], ..., c[N]} in the reals (or the complex plane). '*' is the complex conjugate, in the complex case.

This method most closely resembles the function described in the mathematical definition of a kernel. Given a PositiveSemidefiniteKernel k with scalar parameters and inputs x and y in S, apply(x, y) yields a single scalar value.

Examples

import tensorflow_probability as tfp; tfp = tfp.substrates.jax

# Suppose `SomeKernel` acts on vectors (rank-1 tensors)
scalar_kernel = tfp.math.psd_kernels.SomeKernel(param=.5)
scalar_kernel.batch_shape
# ==> []

# `x` and `y` are batches of five 3-D vectors:
x = np.ones([5, 3], np.float32)
y = np.ones([5, 3], np.float32)
scalar_kernel.apply(x, y).shape
# ==> [5]

The above output is the result of vectorized computation of the five values

[k(x[0], y[0]), k(x[1], y[1]), ..., k(x[4], y[4])]

Now we can consider a kernel with batched parameters:

batch_kernel = tfp.math.psd_kernels.SomeKernel(param=[.2, .5])
batch_kernel.batch_shape
# ==> [2]
batch_kernel.apply(x, y).shape
# ==> Error! [2] and [5] can't broadcast.

The parameter batch shape of [2] and the input batch shape of [5] can't be broadcast together. We can fix this in either of two ways:

Fix #1

Give the parameter a shape of [2, 1] which will correctly broadcast with [5] to yield [2, 5]:

batch_kernel = tfp.math.psd_kernels.SomeKernel(
    param=[[.2], [.5]])
batch_kernel.batch_shape
# ==> [2, 1]
batch_kernel.apply(x, y).shape
# ==> [2, 5]
Fix #2

By specifying example_ndims, which tells the kernel to treat the 5 in the input shape as part of the "example shape", and "pushing" the kernel batch shape to the left:

batch_kernel = tfp.math.psd_kernels.SomeKernel(param=[.2, .5])
batch_kernel.batch_shape
# ==> [2]
batch_kernel.apply(x, y, example_ndims=1).shape
# ==> [2, 5]

batch_shape_tensor

View source

batch_shape_tensor(
    name='batch_shape_tensor'
)

Shape of a single sample from a single event index as a 1-D Tensor.

The batch dimensions are indexes into independent, non-identical parameterizations of this PositiveSemidefiniteKernel.

Args
name name to give to the op

Returns
batch_shape Tensor.

copy

View source

copy(
    **override_parameters_kwargs
)

Creates a copy of the kernel.

Args
**override_parameters_kwargs String/value dictionary of initialization arguments to override with new values.

Returns
copied_kernel A new instance of type(self) initialized from the union of self.parameters and override_parameters_kwargs, i.e., dict(self.parameters, **override_parameters_kwargs).

divisor_matrix

View source

divisor_matrix()

divisor_matrix_cholesky

View source

divisor_matrix_cholesky(
    fixed_inputs=None, fixed_inputs_is_missing=None
)

matrix

View source

matrix(
    x1, x2, name='matrix'
)

Construct (batched) matrices from (batches of) collections of inputs.

Args
x1 (Nested) Tensor input to the first positional parameter of the kernel, of shape B1 + [e1] + F, where B1 may be empty (ie, no batch dims, resp.), e1 is a single integer (ie, x1 has example ndims exactly 1), and F (the feature shape) must have rank equal to the kernel's feature_ndims property (or to the corresponding element of feature_ndims, if nested). Batch shape must broadcast with the batch shape of x2 and with the kernel's batch shape.
x2 (Nested) Tensor input to the second positional parameter of the kernel, shape B2 + [e2] + F, where B2 may be empty (ie, no batch dims, resp.), e2 is a single integer (ie, x2 has example ndims exactly 1), and F (the feature shape) must have rank equal to the kernel's feature_ndims property (or to the corresponding element of feature_ndims, if nested). Batch shape must broadcast with the batch shape of x1 and with the kernel's batch shape.
name name to give to the op

Returns
Tensor containing the matrix (possibly batched) of kernel applications to pairs from inputs x1 and x2. If the kernel parameters' batch shape is Bk then the shape of the Tensor resulting from this method call is broadcast(Bk, B1, B2) + [e1, e2] (note this differs from apply: the example dimensions are concatenated, whereas in apply the example dims are broadcast together).

Given inputs x1 and x2 of shapes

[b1, ..., bB, e1, f1, ..., fF]

and

[c1, ..., cC, e2, f1, ..., fF]

This method computes the batch of e1 x e2 matrices resulting from applying the kernel function to all pairs of inputs from x1 and x2. The shape of the batch of matrices is the result of broadcasting the batch shapes of x1, x2, and the kernel parameters (see examples below). As such, it's required that these shapes all be broadcast compatible. However, the kernel parameter batch shapes need not broadcast against the 'example shapes' (e1 and e2 above).

When the two inputs are the (batches of) identical collections, the resulting matrix is the so-called Gram (or Gramian) matrix (https://en.wikipedia.org/wiki/Gramian_matrix).

Examples

First, consider a kernel with a single scalar parameter.

import tensorflow_probability as tfp; tfp = tfp.substrates.jax

scalar_kernel = tfp.math.psd_kernels.SomeKernel(param=.5)
scalar_kernel.batch_shape
# ==> []

# Our inputs are two lists of 3-D vectors
x = np.ones([5, 3], np.float32)
y = np.ones([4, 3], np.float32)
scalar_kernel.matrix(x, y).shape
# ==> [5, 4]

The result comes from applying the kernel to the entries in x and y pairwise, across all pairs:

  | k(x[0], y[0])    k(x[0], y[1])  ...  k(x[0], y[3]) |
  | k(x[1], y[0])    k(x[1], y[1])  ...  k(x[1], y[3]) |
  |      ...              ...                 ...      |
  | k(x[4], y[0])    k(x[4], y[1])  ...  k(x[4], y[3]) |

Now consider a kernel with batched parameters with the same inputs

batch_kernel = tfp.math.psd_kernels.SomeKernel(param=[1., .5])
batch_kernel.batch_shape
# ==> [2]

batch_kernel.matrix(x, y).shape
# ==> [2, 5, 4]

This results in a batch of 2 matrices, one computed from the kernel with param = 1. and the other with param = .5.

We also support batching of the inputs. First, let's look at that with the scalar kernel again.

# Batch of 10 lists of 5 vectors of dimension 3
x = np.ones([10, 5, 3], np.float32)

# Batch of 10 lists of 4 vectors of dimension 3
y = np.ones([10, 4, 3], np.float32)

scalar_kernel.matrix(x, y).shape
# ==> [10, 5, 4]

The result is a batch of 10 matrices built from the batch of 10 lists of input vectors. These batch shapes have to be broadcastable. The following will not work:

x = np.ones([10, 5, 3], np.float32)
y = np.ones([20, 4, 3], np.float32)
scalar_kernel.matrix(x, y).shape
# ==> Error! [10] and [20] can't broadcast.

Now let's consider batches of inputs in conjunction with batches of kernel parameters. We require that the input batch shapes be broadcastable with the kernel parameter batch shapes, otherwise we get an error:

x = np.ones([10, 5, 3], np.float32)
y = np.ones([10, 4, 3], np.float32)

batch_kernel = tfp.math.psd_kernels.SomeKernel(params=[1., .5])
batch_kernel.batch_shape
# ==> [2]
batch_kernel.matrix(x, y).shape
# ==> Error! [2] and [10] can't broadcast.

The fix is to make the kernel parameter shape broadcastable with [10] (or reshape the inputs to be broadcastable!):

x = np.ones([10, 5, 3], np.float32)
y = np.ones([10, 4, 3], np.float32)

batch_kernel = tfp.math.psd_kernels.SomeKernel(
    params=[[1.], [.5]])
batch_kernel.batch_shape
# ==> [2, 1]
batch_kernel.matrix(x, y).shape
# ==> [2, 10, 5, 4]

# Or, make the inputs broadcastable:
x = np.ones([10, 1, 5, 3], np.float32)
y = np.ones([10, 1, 4, 3], np.float32)

batch_kernel = tfp.math.psd_kernels.SomeKernel(
    params=[1., .5])
batch_kernel.batch_shape
# ==> [2]
batch_kernel.matrix(x, y).shape
# ==> [10, 2, 5, 4]

Here, we have the result of applying the kernel, with 2 different parameters, to each of a batch of 10 pairs of input lists.

parameter_properties

View source

@classmethod
parameter_properties(
    dtype=tf.float32
)

Returns a dict mapping constructor arg names to property annotations.

This dict should include an entry for each of the kernel's Tensor-valued constructor arguments.

Args
dtype Optional float dtype to assume for continuous-valued parameters. Some constraining bijectors require advance knowledge of the dtype because certain constants (e.g., tfb.Softplus.low) must be instantiated with the same dtype as the values to be transformed.

Returns
parameter_properties A str ->tfp.python.internal.parameter_properties.ParameterPropertiesdict mapping constructor argument names toParameterProperties` instances.

tensor

View source

tensor(
    x1, x2, x1_example_ndims, x2_example_ndims, name='tensor'
)

Construct (batched) tensors from (batches of) collections of inputs.

Args
x1 (Nested) Tensor input to the first positional parameter of the kernel, of shape B1 + E1 + F, where B1 and E1 arbitrary shapes which may be empty (ie, no batch/example dims, resp.), and F (the feature shape) must have rank equal to the kernel's feature_ndims property (or to the corresponding element of feature_ndims, if nested). Batch shape must broadcast with the batch shape of x2 and with the kernel's batch shape.
x2 (Nested) Tensor input to the second positional parameter of the kernel, shape B2 + E2 + F, where B2 and E2 arbitrary shapes which may be empty (ie, no batch/example dims, resp.), and F (the feature shape) must have rank equal to the kernel's feature_ndims property (or to the corresponding element of feature_ndims, if nested). Batch shape must broadcast with the batch shape of x1 and with the kernel's batch shape.
x1_example_ndims A python integer greater than or equal to 0, the number of example dims in the first input. This affects both the alignment of batch shapes and the shape of the final output of the function. Everything left of the feature shape and the example dims in x1 is considered "batch shape", and must broadcast as specified above.
x2_example_ndims A python integer greater than or equal to 0, the number of example dims in the second input. This affects both the alignment of batch shapes and the shape of the final output of the function. Everything left of the feature shape and the example dims in x1 is considered "batch shape", and must broadcast as specified above.
name name to give to the op

Returns
Tensor containing (possibly batched) kernel applications to pairs from inputs x1 and x2. If the kernel parameters' batch shape is Bk then the shape of the Tensor resulting from this method call is broadcast(Bk, B1, B2) + E1 + E2. Note this differs from apply: the example dimensions are concatenated, whereas in apply the example dims are broadcast together. It also differs from matrix: the example shapes are arbitrary here, and the result accrues a rank equal to the sum of the ranks of the input example shapes.

Examples

First, consider a kernel with a single scalar parameter.

import tensorflow_probability as tfp; tfp = tfp.substrates.jax

scalar_kernel = tfp.math.psd_kernels.SomeKernel(param=.5)
scalar_kernel.batch_shape
# ==> []

# Our inputs are two rank-2 collections of 3-D vectors
x = np.ones([5, 6, 3], np.float32)
y = np.ones([7, 8, 3], np.float32)
scalar_kernel.tensor(x, y, x1_example_ndims=2, x2_example_ndims=2).shape
# ==> [5, 6, 7, 8]

# Empty example shapes work too!
x = np.ones([3], np.float32)
y = np.ones([5, 3], np.float32)
scalar_kernel.tensor(x, y, x1_example_ndims=0, x2_example_ndims=1).shape
# ==> [5]

The result comes from applying the kernel to the entries in x and y pairwise, across all pairs:

  | k(x[0], y[0])    k(x[0], y[1])  ...  k(x[0], y[3]) |
  | k(x[1], y[0])    k(x[1], y[1])  ...  k(x[1], y[3]) |
  |      ...              ...                 ...      |
  | k(x[4], y[0])    k(x[4], y[1])  ...  k(x[4], y[3]) |

Now consider a kernel with batched parameters.

batch_kernel = tfp.math.psd_kernels.SomeKernel(param=[1., .5])
batch_kernel.batch_shape
# ==> [2]

# Inputs are two rank-2 collections of 3-D vectors
x = np.ones([5, 6, 3], np.float32)
y = np.ones([7, 8, 3], np.float32)
scalar_kernel.tensor(x, y, x1_example_ndims=2, x2_example_ndims=2).shape
# ==> [2, 5, 6, 7, 8]

We also support batching of the inputs. First, let's look at that with the scalar kernel again.

# Batch of 10 lists of 5x6 collections of dimension 3
x = np.ones([10, 5, 6, 3], np.float32)

# Batch of 10 lists of 7x8 collections of dimension 3
y = np.ones([10, 7, 8, 3], np.float32)

scalar_kernel.tensor(x, y, x1_example_ndims=2, x2_example_ndims=2).shape
# ==> [10, 5, 6, 7, 8]

The result is a batch of 10 tensors built from the batch of 10 rank-2 collections of input vectors. The batch shapes have to be broadcastable. The following will not work:

x = np.ones([10, 5, 3], np.float32)
y = np.ones([20, 4, 3], np.float32)
scalar_kernel.tensor(x, y, x1_example_ndims=1, x2_example_ndims=1).shape
# ==> Error! [10] and [20] can't broadcast.

Now let's consider batches of inputs in conjunction with batches of kernel parameters. We require that the input batch shapes be broadcastable with the kernel parameter batch shapes, otherwise we get an error:

x = np.ones([10, 5, 6, 3], np.float32)
y = np.ones([10, 7, 8, 3], np.float32)

batch_kernel = tfp.math.psd_kernels.SomeKernel(params=[1., .5])
batch_kernel.batch_shape
# ==> [2]
batch_kernel.tensor(x, y, x1_example_ndims=2, x2_example_ndims=2).shape
# ==> Error! [2] and [10] can't broadcast.

The fix is to make the kernel parameter shape broadcastable with [10] (or reshape the inputs to be broadcastable!):

x = np.ones([10, 5, 6, 3], np.float32)
y = np.ones([10, 7, 8, 3], np.float32)

batch_kernel = tfp.math.psd_kernels.SomeKernel(
    params=[[1.], [.5]])
batch_kernel.batch_shape
# ==> [2, 1]
batch_kernel.tensor(x, y, x1_example_ndims=2, x2_example_ndims=2).shape
# ==> [2, 10, 5, 6, 7, 8]

# Or, make the inputs broadcastable:
x = np.ones([10, 1, 5, 6, 3], np.float32)
y = np.ones([10, 1, 7, 8, 3], np.float32)

batch_kernel = tfp.math.psd_kernels.SomeKernel(
    params=[1., .5])
batch_kernel.batch_shape
# ==> [2]
batch_kernel.tensor(x, y, x1_example_ndims=2, x2_example_ndims=2).shape
# ==> [10, 2, 5, 6, 7, 8]

with_precomputed_divisor

View source

@staticmethod
with_precomputed_divisor(
    base_kernel,
    fixed_inputs,
    fixed_inputs_is_missing=None,
    diag_shift=None,
    cholesky_fn=None,
    validate_args=False,
    name='PrecomputedSchurComplement',
    _precomputed_divisor_matrix_cholesky=None
)

Returns a SchurComplement with a precomputed divisor matrix.

This method is the same as creating a SchurComplement kernel, but assumes that fixed_inputs, diag_shift and base_kernel are unchanging / not parameterized by any mutable state. We explicitly read / concretize these values when this method is called, since we can precompute some factorizations in order to speed up subsequent invocations of the kernel.

Args
base_kernel A PositiveSemidefiniteKernel instance, the kernel used to build the block matrices of which this kernel computes the Schur complement.
fixed_inputs A (nested) Tensor, representing a collection of inputs. The Schur complement that this kernel computes comes from a block matrix, whose bottom-right corner is derived from base_kernel.matrix(fixed_inputs, fixed_inputs), and whose top-right and bottom-left pieces are constructed by computing the base_kernel at pairs of input locations together with these fixed_inputs. fixed_inputs is allowed to be an empty collection (either None or having a zero shape entry), in which case the kernel falls back to the trivial application of base_kernel to inputs. See class-level docstring for more details on the exact computation this does; fixed_inputs correspond to the Z structure discussed there. fixed_inputs (or each of its nested components) is assumed to have shape [b1, ..., bB, N, f1, ..., fF] where the b's are batch shape entries, the f's are feature_shape entries, and N is the number of fixed inputs. Use of this kernel entails a 1-time O(N^3) cost of computing the Cholesky decomposition of the k(Z, Z) matrix. The batch shape elements of fixed_inputs must be broadcast compatible with base_kernel.batch_shape.
fixed_inputs_is_missing A boolean Tensor of shape [..., N]. When is_missing is not None and an element of is_missing is True, the returned kernel will return values computed as if the divisor matrix did not contain the corresponding row or column.
diag_shift A floating point scalar to be added to the diagonal of the divisor_matrix before computing its Cholesky.
cholesky_fn Callable which takes a single (batch) matrix argument and returns a Cholesky-like lower triangular factor. Default value: None, in which case make_cholesky_with_jitter_fn is used with the jitter parameter.
validate_args If True, parameters are checked for validity despite possibly degrading runtime performance. Default value: False
name Python str name prefixed to Ops created by this class. Default value: "PrecomputedSchurComplement"
_precomputed_divisor_matrix_cholesky Internal arg -- do not use.

__add__

View source

__add__(
    k
)

__getitem__

View source

__getitem__(
    slices
)

Slices the batch axes of this kernel, returning a new instance.

k = tfpk.ExponentiatedQuadratic(
  amplitude=tf.ones([3, 5, 7, 9]),
  length_scale=tf.ones([3, 5, 7, 9]))
k.batch_shape  # => [3, 5, 7, 9]
k2 = k[:, tf.newaxis, ..., -2:, 1::2]
k2.batch_shape  # => [3, 1, 5, 2, 4]

Args
slices slices from the [] operator

Returns
dist A new PositiveSemidefiniteKernel instance with sliced parameters.

__iter__

View source

__iter__()

__mul__

View source

__mul__(
    k
)

__radd__

View source

__radd__(
    lhs
)