A sampler which returns the nearest values between the input samples and the allowed values,
without replacement.
Parameters:
| Name |
Type |
Description |
Default |
samples |
Union[Iterable, Sequence]
|
input conditions |
required
|
allowed_samples |
|
allowed conditions to sample from |
required
|
Returns:
| Type |
Description |
|
|
the nearest values from allowed_samples to the samples |
Source code in autora/experimentalist/sampler/nearest_value.py
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60 | def nearest_values_sampler(
samples: Union[Iterable, Sequence],
allowed_values: np.ndarray,
n: int,
):
"""
A sampler which returns the nearest values between the input samples and the allowed values,
without replacement.
Args:
samples: input conditions
allowed_samples: allowed conditions to sample from
Returns:
the nearest values from `allowed_samples` to the `samples`
"""
if isinstance(allowed_values, Iterable):
allowed_values = np.array(list(allowed_values))
if len(allowed_values.shape) == 1:
allowed_values = allowed_values.reshape(-1, 1)
if isinstance(samples, Iterable):
samples = np.array(list(samples))
if allowed_values.shape[0] < n:
raise Exception(
"More samples requested than samples available in the set allowed of values."
)
if isinstance(samples, Iterable) or isinstance(samples, Sequence):
samples = np.array(list(samples))
if hasattr(samples, "shape"):
if samples.shape[0] < n:
raise Exception(
"More samples requested than samples available in the pool."
)
x_new = np.empty((n, allowed_values.shape[1]))
# get index of row in x that is closest to each sample
for row, sample in enumerate(samples):
if row >= n:
break
dist = np.linalg.norm(allowed_values - sample, axis=1)
idx = np.argmin(dist)
x_new[row, :] = allowed_values[idx, :]
allowed_values = np.delete(allowed_values, idx, axis=0)
return x_new
|