import numpy as np
[docs]
def rt_svrscore_vol(data, SVRs, caps_labels):
"""
Compute SVR scores using pretrained models.
Parameters
----------
data : np.ndarray
The input data to be used for making predictions.
SVRs : dict
A dictionary of trained Support Vector Regressor (SVR) models, where the keys are
label names and the values are the corresponding SVR models.
caps_labels : list of str
A list of labels corresponding to the SVRs in `SVRs`. The function will use these
labels to predict the values from the respective SVRs.
Returns
-------
np.ndarray
The predicted values from each SVR for each label.
"""
out = []
for cap_lab in caps_labels:
out.append(SVRs[cap_lab].predict(data[:,np.newaxis].T)[0])
return np.array(out)[:,np.newaxis]
[docs]
def rt_maskscore_vol(data, inputs, labels):
out = []
masked_templates = inputs["masked_templates"].item()
masks = inputs["masks"].item()
voxel_counts = inputs["voxel_counts"].item()
for name in labels:
mask = masks[name]
template = masked_templates[name]
masked_data = data[mask]
out.append(np.dot(template, masked_data) / voxel_counts[name])
return np.array(out)[:, np.newaxis]