<｜begin▁of▁sentence｜>### Instruction:
In the realm of a Radiation Therapist, consider the scenario where you're presented with an intricate task akin to the precision required in targeting cancer cells. You have a collection of vital data akin to patient samples—10 in total, each characterized by 4 critical indicators or 'features'. In order to streamline the data, much like honing in on the most effective treatment plan, you're considering the application of a technique analogous to reducing the complexity of treatment parameters. This technique is singular value decomposition, a methodology used to concentrate information akin to isolating the most influential factors in a therapy plan. 

Your objective is to distill the data, selecting only the most significant 'dimensions' or features of these samples, to better understand the underlying structure, akin to choosing the most relevant therapy angles. For guidance, you turn to a specialized algorithm embodied in the graspologic library, namely the Zhu & Godsie method, which serves a comparable role to a clinical algorithm determining the optimal radiation dosage. 

Would the select_svd function within this graspologic toolkit serve your needs to perform such data refinement automatically, identifying the ideal number of dimensions much like selecting the appropriate therapy intensity levels? How would this function be executed in such a clinical data setting?
### Response:
import graspologic
import numpy as np

# Sample data matrix X
X = np.random.rand(10, 4)  # 10 samples with 4 features each

# Perform SVD dimensionality reduction
# Using randomized algorithm with an automatic selection of the target dimension
U, D, V = graspologic.embed.select_svd(X, n_components=None, n_elbows=2, algorithm='randomized')

# Print the shapes of the output matrices to confirm dimensionality reduction
print('U shape:', U.shape)
print('D shape:', D.shape)
print('V shape:', V.shape)
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