def pad_shape_right_with_ones(x, ndims):
"""Maybe add `ndims` ones to `x.shape` on the right.
If `ndims` is zero, this is a no-op; otherwise, we will create and return a
new `Tensor` whose shape is that of `x` with `ndims` ones concatenated on the
right side. If the shape of `x` is known statically, the shape of the return
value will be as well.
Args:
x: The `Tensor` we'll return a reshaping of.
ndims: Python `integer` number of ones to pad onto `x.shape`.
Returns:
If `ndims` is zero, `x`; otherwise, a `Tensor` whose shape is that of `x`
with `ndims` ones concatenated on the right side. If possible, returns a
`Tensor` whose shape is known statically.
Raises:
ValueError: if `ndims` is not a Python `integer` greater than or equal to
zero.
"""
if not (isinstance(ndims, int) and ndims >= 0):
raise ValueError(
'`ndims` must be a Python `integer` greater than zero. Got: {}'.
format(ndims))
if ndims == 0:
return x
x = tf.convert_to_tensor(x)
original_shape = x.shape
new_shape = distribution_util.pad(tf.shape(x),
axis=0,
back=True,
value=1,
count=ndims)
x = tf.reshape(x, new_shape)
x.set_shape(original_shape.concatenate([1] * ndims))
return x
def pad_shape_right_with_ones(x, ndims):
"""Maybe add `ndims` ones to `x.shape` on the right.
If `ndims` is zero, this is a no-op; otherwise, we will create and return a
new `Tensor` whose shape is that of `x` with `ndims` ones concatenated on the
right side. If the shape of `x` is known statically, the shape of the return
value will be as well.
Args:
x: The `Tensor` we'll return a reshaping of.
ndims: Python `integer` number of ones to pad onto `x.shape`.
Returns:
If `ndims` is zero, `x`; otherwise, a `Tensor` whose shape is that of `x`
with `ndims` ones concatenated on the right side. If possible, returns a
`Tensor` whose shape is known statically.
Raises:
ValueError: if `ndims` is not a Python `integer` greater than or equal to
zero.
"""
if not (isinstance(ndims, int) and ndims >= 0):
raise ValueError(
'`ndims` must be a Python `integer` greater than zero. Got: {}'
.format(ndims))
if ndims == 0:
return x
x = tf.convert_to_tensor(x)
original_shape = x.shape
new_shape = distribution_util.pad(
tf.shape(x), axis=0, back=True, value=1, count=ndims)
x = tf.reshape(x, new_shape)
x.set_shape(original_shape.concatenate([1]*ndims))
return x
def _embed_l2_regularization():
"""Adds synthetic observations to implement L2 regularization."""
# `tf.matrix_solve_ls` does not respect the `l2_regularization` argument
# when `fast_unsafe_numerics` is `False`. This function adds synthetic
# observations to the data to implement the regularization instead.
# Adding observations `sqrt(l2_regularizer) * I` is mathematically
# equivalent to adding the term
# `-l2_regularizer ||coefficients||_2**2` to the log-likelihood.
num_model_coefficients = num_cols(model_matrix)
batch_shape = tf.shape(model_matrix)[:-2]
eye = tf.eye(
num_model_coefficients, batch_shape=batch_shape, dtype=a.dtype)
a_ = tf.concat([a, tf.sqrt(l2_regularizer) * eye], axis=-2)
b_ = distributions_util.pad(
b, count=num_model_coefficients, axis=-1, back=True)
# Return l2_regularizer=0 since its now embedded.
l2_regularizer_ = np.array(0, a.dtype.as_numpy_dtype)
return a_, b_, l2_regularizer_
def _uniform_correlation_like_matrix(num_rows, batch_shape, dtype, seed):
"""Returns a uniformly random `Tensor` of "correlation-like" matrices.
A "correlation-like" matrix is a symmetric square matrix with all entries
between -1 and 1 (inclusive) and 1s on the main diagonal. Of these,
the ones that are positive semi-definite are exactly the correlation
matrices.
Args:
num_rows: Python `int` dimension of the correlation-like matrices.
batch_shape: `Tensor` or Python `tuple` of `int` shape of the
batch to return.
dtype: `dtype` of the `Tensor` to return.
seed: Random seed.
Returns:
matrices: A `Tensor` of shape `batch_shape + [num_rows, num_rows]`
and dtype `dtype`. Each entry is in [-1, 1], and each matrix
along the bottom two dimensions is symmetric and has 1s on the
main diagonal.
"""
num_entries = num_rows * (num_rows + 1) / 2
ones = array_ops.ones(shape=[num_entries], dtype=dtype)
# It seems wasteful to generate random values for the diagonal since
# I am going to throw them away, but `fill_triangular` fills the
# diagonal, so I probably need them.
# It's not impossible that it would be more efficient to just fill
# the whole matrix with random values instead of messing with
# `fill_triangular`. Then would need to filter almost half out with
# `matrix_band_part`.
unifs = uniform.Uniform(-ones, ones).sample(batch_shape, seed=seed)
tril = util.fill_triangular(unifs)
symmetric = tril + array_ops.matrix_transpose(tril)
diagonal_ones = array_ops.ones(shape=util.pad(batch_shape,
axis=0,
back=True,
value=num_rows),
dtype=dtype)
return array_ops.matrix_set_diag(symmetric, diagonal_ones)
def auto_correlation(
x,
axis=-1,
max_lags=None,
center=True,
normalize=True,
name="auto_correlation"):
"""Auto correlation along one axis.
Given a `1-D` wide sense stationary (WSS) sequence `X`, the auto correlation
`RXX` may be defined as (with `E` expectation and `Conj` complex conjugate)
```
RXX[m] := E{ W[m] Conj(W[0]) } = E{ W[0] Conj(W[-m]) },
W[n] := (X[n] - MU) / S,
MU := E{ X[0] },
S**2 := E{ (X[0] - MU) Conj(X[0] - MU) }.
```
This function takes the viewpoint that `x` is (along one axis) a finite
sub-sequence of a realization of (WSS) `X`, and then uses `x` to produce an
estimate of `RXX[m]` as follows:
After extending `x` from length `L` to `inf` by zero padding, the auto
correlation estimate `rxx[m]` is computed for `m = 0, 1, ..., max_lags` as
```
rxx[m] := (L - m)**-1 sum_n w[n + m] Conj(w[n]),
w[n] := (x[n] - mu) / s,
mu := L**-1 sum_n x[n],
s**2 := L**-1 sum_n (x[n] - mu) Conj(x[n] - mu)
```
The error in this estimate is proportional to `1 / sqrt(len(x) - m)`, so users
often set `max_lags` small enough so that the entire output is meaningful.
Note that since `mu` is an imperfect estimate of `E{ X[0] }`, and we divide by
`len(x) - m` rather than `len(x) - m - 1`, our estimate of auto correlation
contains a slight bias, which goes to zero as `len(x) - m --> infinity`.
Args:
x: `float32` or `complex64` `Tensor`.
axis: Python `int`. The axis number along which to compute correlation.
Other dimensions index different batch members.
max_lags: Positive `int` tensor. The maximum value of `m` to consider
(in equation above). If `max_lags >= x.shape[axis]`, we effectively
re-set `max_lags` to `x.shape[axis] - 1`.
center: Python `bool`. If `False`, do not subtract the mean estimate `mu`
from `x[n]` when forming `w[n]`.
normalize: Python `bool`. If `False`, do not divide by the variance
estimate `s**2` when forming `w[n]`.
name: `String` name to prepend to created ops.
Returns:
`rxx`: `Tensor` of same `dtype` as `x`. `rxx.shape[i] = x.shape[i]` for
`i != axis`, and `rxx.shape[axis] = max_lags + 1`.
Raises:
TypeError: If `x` is not a supported type.
"""
# Implementation details:
# Extend length N / 2 1-D array x to length N by zero padding onto the end.
# Then, set
# F[x]_k := sum_n x_n exp{-i 2 pi k n / N }.
# It is not hard to see that
# F[x]_k Conj(F[x]_k) = F[R]_k, where
# R_m := sum_n x_n Conj(x_{(n - m) mod N}).
# One can also check that R_m / (N / 2 - m) is an unbiased estimate of RXX[m].
# Since F[x] is the DFT of x, this leads us to a zero-padding and FFT/IFFT
# based version of estimating RXX.
# Note that this is a special case of the Wiener-Khinchin Theorem.
with ops.name_scope(name, values=[x]):
x = ops.convert_to_tensor(x, name="x")
# Rotate dimensions of x in order to put axis at the rightmost dim.
# FFT op requires this.
rank = util.prefer_static_rank(x)
if axis < 0:
axis = rank + axis
shift = rank - 1 - axis
# Suppose x.shape[axis] = T, so there are T "time" steps.
# ==> x_rotated.shape = B + [T],
# where B is x_rotated's batch shape.
x_rotated = util.rotate_transpose(x, shift)
if center:
x_rotated -= math_ops.reduce_mean(x_rotated, axis=-1, keepdims=True)
# x_len = N / 2 from above explanation. The length of x along axis.
# Get a value for x_len that works in all cases.
x_len = util.prefer_static_shape(x_rotated)[-1]
# TODO (langmore) Investigate whether this zero padding helps or hurts. At id:595 gh:596
# the moment is is necessary so that all FFT implementations work.
# Zero pad to the next power of 2 greater than 2 * x_len, which equals
# 2**(ceil(Log_2(2 * x_len))). Note: Log_2(X) = Log_e(X) / Log_e(2).
x_len_float64 = math_ops.cast(x_len, np.float64)
target_length = math_ops.pow(
np.float64(2.),
math_ops.ceil(math_ops.log(x_len_float64 * 2) / np.log(2.)))
pad_length = math_ops.cast(target_length - x_len_float64, np.int32)
# We should have:
# x_rotated_pad.shape = x_rotated.shape[:-1] + [T + pad_length]
# = B + [T + pad_length]
x_rotated_pad = util.pad(x_rotated, axis=-1, back=True, count=pad_length)
dtype = x.dtype
if not dtype.is_complex:
if not dtype.is_floating:
raise TypeError("Argument x must have either float or complex dtype"
" found: {}".format(dtype))
x_rotated_pad = math_ops.complex(x_rotated_pad,
dtype.real_dtype.as_numpy_dtype(0.))
# Autocorrelation is IFFT of power-spectral density (up to some scaling).
fft_x_rotated_pad = spectral_ops.fft(x_rotated_pad)
spectral_density = fft_x_rotated_pad * math_ops.conj(fft_x_rotated_pad)
# shifted_product is R[m] from above detailed explanation.
# It is the inner product sum_n X[n] * Conj(X[n - m]).
shifted_product = spectral_ops.ifft(spectral_density)
# Cast back to real-valued if x was real to begin with.
shifted_product = math_ops.cast(shifted_product, dtype)
# Figure out if we can deduce the final static shape, and set max_lags.
# Use x_rotated as a reference, because it has the time dimension in the far
# right, and was created before we performed all sorts of crazy shape
# manipulations.
know_static_shape = True
if not x_rotated.shape.is_fully_defined():
know_static_shape = False
if max_lags is None:
max_lags = x_len - 1
else:
max_lags = ops.convert_to_tensor(max_lags, name="max_lags")
max_lags_ = tensor_util.constant_value(max_lags)
if max_lags_ is None or not know_static_shape:
know_static_shape = False
max_lags = math_ops.minimum(x_len - 1, max_lags)
else:
max_lags = min(x_len - 1, max_lags_)
# Chop off the padding.
# We allow users to provide a huge max_lags, but cut it off here.
# shifted_product_chopped.shape = x_rotated.shape[:-1] + [max_lags]
shifted_product_chopped = shifted_product[..., :max_lags + 1]
# If possible, set shape.
if know_static_shape:
chopped_shape = x_rotated.shape.as_list()
chopped_shape[-1] = min(x_len, max_lags + 1)
shifted_product_chopped.set_shape(chopped_shape)
# Recall R[m] is a sum of N / 2 - m nonzero terms x[n] Conj(x[n - m]). The
# other terms were zeros arising only due to zero padding.
# `denominator = (N / 2 - m)` (defined below) is the proper term to
# divide by by to make this an unbiased estimate of the expectation
# E[X[n] Conj(X[n - m])].
x_len = math_ops.cast(x_len, dtype.real_dtype)
max_lags = math_ops.cast(max_lags, dtype.real_dtype)
denominator = x_len - math_ops.range(0., max_lags + 1.)
denominator = math_ops.cast(denominator, dtype)
shifted_product_rotated = shifted_product_chopped / denominator
if normalize:
shifted_product_rotated /= shifted_product_rotated[..., :1]
# Transpose dimensions back to those of x.
return util.rotate_transpose(shifted_product_rotated, -shift)
def auto_correlation(
x,
axis=-1,
max_lags=None,
center=True,
normalize=True,
name="auto_correlation"):
"""Auto correlation along one axis.
Given a `1-D` wide sense stationary (WSS) sequence `X`, the auto correlation
`RXX` may be defined as (with `E` expectation and `Conj` complex conjugate)
```
RXX[m] := E{ W[m] Conj(W[0]) } = E{ W[0] Conj(W[-m]) },
W[n] := (X[n] - MU) / S,
MU := E{ X[0] },
S**2 := E{ (X[0] - MU) Conj(X[0] - MU) }.
```
This function takes the viewpoint that `x` is (along one axis) a finite
sub-sequence of a realization of (WSS) `X`, and then uses `x` to produce an
estimate of `RXX[m]` as follows:
After extending `x` from length `L` to `inf` by zero padding, the auto
correlation estimate `rxx[m]` is computed for `m = 0, 1, ..., max_lags` as
```
rxx[m] := (L - m)**-1 sum_n w[n + m] Conj(w[n]),
w[n] := (x[n] - mu) / s,
mu := L**-1 sum_n x[n],
s**2 := L**-1 sum_n (x[n] - mu) Conj(x[n] - mu)
```
The error in this estimate is proportional to `1 / sqrt(len(x) - m)`, so users
often set `max_lags` small enough so that the entire output is meaningful.
Note that since `mu` is an imperfect estimate of `E{ X[0] }`, and we divide by
`len(x) - m` rather than `len(x) - m - 1`, our estimate of auto correlation
contains a slight bias, which goes to zero as `len(x) - m --> infinity`.
Args:
x: `float32` or `complex64` `Tensor`.
axis: Python `int`. The axis number along which to compute correlation.
Other dimensions index different batch members.
max_lags: Positive `int` tensor. The maximum value of `m` to consider
(in equation above). If `max_lags >= x.shape[axis]`, we effectively
re-set `max_lags` to `x.shape[axis] - 1`.
center: Python `bool`. If `False`, do not subtract the mean estimate `mu`
from `x[n]` when forming `w[n]`.
normalize: Python `bool`. If `False`, do not divide by the variance
estimate `s**2` when forming `w[n]`.
name: `String` name to prepend to created ops.
Returns:
`rxx`: `Tensor` of same `dtype` as `x`. `rxx.shape[i] = x.shape[i]` for
`i != axis`, and `rxx.shape[axis] = max_lags + 1`.
Raises:
TypeError: If `x` is not a supported type.
"""
# Implementation details:
# Extend length N / 2 1-D array x to length N by zero padding onto the end.
# Then, set
# F[x]_k := sum_n x_n exp{-i 2 pi k n / N }.
# It is not hard to see that
# F[x]_k Conj(F[x]_k) = F[R]_k, where
# R_m := sum_n x_n Conj(x_{(n - m) mod N}).
# One can also check that R_m / (N / 2 - m) is an unbiased estimate of RXX[m].
# Since F[x] is the DFT of x, this leads us to a zero-padding and FFT/IFFT
# based version of estimating RXX.
# Note that this is a special case of the Wiener-Khinchin Theorem.
with ops.name_scope(name, values=[x]):
x = ops.convert_to_tensor(x, name="x")
# Rotate dimensions of x in order to put axis at the rightmost dim.
# FFT op requires this.
rank = util.prefer_static_rank(x)
if axis < 0:
axis = rank + axis
shift = rank - 1 - axis
# Suppose x.shape[axis] = T, so there are T "time" steps.
# ==> x_rotated.shape = B + [T],
# where B is x_rotated's batch shape.
x_rotated = util.rotate_transpose(x, shift)
if center:
x_rotated -= math_ops.reduce_mean(x_rotated, axis=-1, keepdims=True)
# x_len = N / 2 from above explanation. The length of x along axis.
# Get a value for x_len that works in all cases.
x_len = util.prefer_static_shape(x_rotated)[-1]
# TODO(langmore) Investigate whether this zero padding helps or hurts. At
# the moment is is necessary so that all FFT implementations work.
# Zero pad to the next power of 2 greater than 2 * x_len, which equals
# 2**(ceil(Log_2(2 * x_len))). Note: Log_2(X) = Log_e(X) / Log_e(2).
x_len_float64 = math_ops.cast(x_len, np.float64)
target_length = math_ops.pow(
np.float64(2.),
math_ops.ceil(math_ops.log(x_len_float64 * 2) / np.log(2.)))
pad_length = math_ops.cast(target_length - x_len_float64, np.int32)
# We should have:
# x_rotated_pad.shape = x_rotated.shape[:-1] + [T + pad_length]
# = B + [T + pad_length]
x_rotated_pad = util.pad(x_rotated, axis=-1, back=True, count=pad_length)
dtype = x.dtype
if not dtype.is_complex:
if not dtype.is_floating:
raise TypeError("Argument x must have either float or complex dtype"
" found: {}".format(dtype))
x_rotated_pad = math_ops.complex(x_rotated_pad,
dtype.real_dtype.as_numpy_dtype(0.))
# Autocorrelation is IFFT of power-spectral density (up to some scaling).
fft_x_rotated_pad = spectral_ops.fft(x_rotated_pad)
spectral_density = fft_x_rotated_pad * math_ops.conj(fft_x_rotated_pad)
# shifted_product is R[m] from above detailed explanation.
# It is the inner product sum_n X[n] * Conj(X[n - m]).
shifted_product = spectral_ops.ifft(spectral_density)
# Cast back to real-valued if x was real to begin with.
shifted_product = math_ops.cast(shifted_product, dtype)
# Figure out if we can deduce the final static shape, and set max_lags.
# Use x_rotated as a reference, because it has the time dimension in the far
# right, and was created before we performed all sorts of crazy shape
# manipulations.
know_static_shape = True
if not x_rotated.shape.is_fully_defined():
know_static_shape = False
if max_lags is None:
max_lags = x_len - 1
else:
max_lags = ops.convert_to_tensor(max_lags, name="max_lags")
max_lags_ = tensor_util.constant_value(max_lags)
if max_lags_ is None or not know_static_shape:
know_static_shape = False
max_lags = math_ops.minimum(x_len - 1, max_lags)
else:
max_lags = min(x_len - 1, max_lags_)
# Chop off the padding.
# We allow users to provide a huge max_lags, but cut it off here.
# shifted_product_chopped.shape = x_rotated.shape[:-1] + [max_lags]
shifted_product_chopped = shifted_product[..., :max_lags + 1]
# If possible, set shape.
if know_static_shape:
chopped_shape = x_rotated.shape.as_list()
chopped_shape[-1] = min(x_len, max_lags + 1)
shifted_product_chopped.set_shape(chopped_shape)
# Recall R[m] is a sum of N / 2 - m nonzero terms x[n] Conj(x[n - m]). The
# other terms were zeros arising only due to zero padding.
# `denominator = (N / 2 - m)` (defined below) is the proper term to
# divide by by to make this an unbiased estimate of the expectation
# E[X[n] Conj(X[n - m])].
x_len = math_ops.cast(x_len, dtype.real_dtype)
max_lags = math_ops.cast(max_lags, dtype.real_dtype)
denominator = x_len - math_ops.range(0., max_lags + 1.)
denominator = math_ops.cast(denominator, dtype)
shifted_product_rotated = shifted_product_chopped / denominator
if normalize:
shifted_product_rotated /= shifted_product_rotated[..., :1]
# Transpose dimensions back to those of x.
return util.rotate_transpose(shifted_product_rotated, -shift)