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Linear 1-D
interpolation on a regular (constant spacing) grid.
tfp.math.batch_interp_regular_1d_grid(
x,
x_ref_min,
x_ref_max,
y_ref,
axis=-1,
fill_value='constant_extension',
fill_value_below=None,
fill_value_above=None,
grid_regularizing_transform=None,
name=None
)
Given [batch of] reference values, this function computes a piecewise linear
interpolant and evaluates it on a [batch of] of new x
values.
The interpolant is built from C
reference values indexed by one dimension
of y_ref
(specified by the axis
kwarg).
If y_ref
is a vector, then each value y_ref[i]
is considered to be equal
to f(x_ref[i])
, for C
(implicitly defined) reference values between
x_ref_min
and x_ref_max
:
x_ref[i] = x_ref_min + i * (x_ref_max - x_ref_min) / (C - 1),
i = 0, ..., C - 1.
In the general case, dimensions to the left of axis
in y_ref
are broadcast
with leading dimensions in x
, x_ref_min
, x_ref_max
.
Returns | |
---|---|
y_interp
|
Interpolation between members of y_ref , at points x .
Tensor of same dtype as x , and shape [A1, ..., AN, D, B1, ..., BM]
|
Raises | |
---|---|
ValueError
|
If fill_value is not an allowed string.
|
ValueError
|
If axis is not a scalar.
|
Examples
Interpolate a function of one variable:
y_ref = tf.exp(tf.linspace(start=0., stop=10., 20))
batch_interp_regular_1d_grid(
x=[6.0, 0.5, 3.3], x_ref_min=0., x_ref_max=10., y_ref=y_ref)
==> approx [exp(6.0), exp(0.5), exp(3.3)]
Interpolate a batch of functions of one variable.
# First batch member is an exponential function, second is a log.
implied_x_ref = [tf.linspace(-3., 3.2, 200), tf.linspace(0.5, 3., 200)]
y_ref = tf.stack( # Shape [2, 200], 2 batches, 200 reference values per batch
[tf.exp(implied_x_ref[0]), tf.log(implied_x_ref[1])], axis=0)
x = [[-1., 1., 0.], # Shape [2, 3], 2 batches, 3 values per batch.
[1., 2., 3.]]
y = tfp.math.batch_interp_regular_1d_grid( # Shape [2, 3]
x,
x_ref_min=[-3., 0.5],
x_ref_max=[3.2, 3.],
y_ref=y_ref,
axis=-1)
# y[0] approx tf.exp(x[0])
# y[1] approx tf.log(x[1])
Interpolate a function of one variable on a log-spaced grid:
x_ref = tf.exp(tf.linspace(tf.log(1.), tf.log(100000.), num_pts))
y_ref = tf.log(x_ref + x_ref**2)
batch_interp_regular_1d_grid(x=[1.1, 2.2], x_ref_min=1., x_ref_max=100000.,
y_ref, grid_regularizing_transform=tf.log)
==> [tf.log(1.1 + 1.1**2), tf.log(2.2 + 2.2**2)]