<｜begin▁of▁sentence｜>### Instruction:
Imagine you're coaching a team and you've been keeping track of your players' performance across 10 different skill areas, with data collected from 50 separate training sessions. Now, you want to streamline your analysis and create a focused game plan by narrowing down these skill areas without losing vital insights into your players' strengths and weaknesses.

We've got a play similar to this in data science, called dimensionality reduction. Specifically, you'd be looking at a technique using the select_svd function from the graspologic toolkit, which is like scouting for the most valuable players among your features. It's about finding a way to reduce the field of play to the essentials, ensuring your strategy is sharp and informed. How can you employ this select_svd function to effectively condense those 10 skill areas, automatically determining the optimal number to keep your game plan solid yet versatile?
### Response:
import numpy as np
from graspologic.embed import select_svd

# Step 1: Create a sample data array
# Here we'll create a synthetic dataset with 50 samples and 10 features.
np.random.seed(42)
X = np.random.rand(50, 10)

# Step 2: Perform SVD with automated dimensionality selection
# We don't specify n_components, so it will select the dimensionality using Zhu & Godsie's algorithm.
U, D, V = select_svd(X, n_elbows=2, algorithm='randomized')

# Step 3: Print the dimensions of the reduced data
print('U (left singular vectors) shape:', U.shape)
print('D (singular values) shape:', D.shape)
print('V (right singular vectors) shape:', V.shape)
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