<｜begin▁of▁sentence｜>### Instruction:
Given a set of 10 points in a 3-dimensional space, how can we use ClassicalMDS from graspologic to find a 2-dimensional representation of these points that preserves their pairwise distances as well as possible?
### Response:
import numpy as np
from graspologic.embed import ClassicalMDS
from sklearn.metrics import pairwise_distances

# Generating synthetic data
X = np.random.rand(10, 3)  # ten points in 3 dimensions

# Computing the pairwise Euclidean distances
precomputed_distances = pairwise_distances(X, metric='euclidean')

# Applying Classical MDS
# Embed these points into 2 dimensions
cmds = ClassicalMDS(n_components=2, dissimilarity='precomputed')
X_transformed = cmds.fit_transform(precomputed_distances)

# The result is the position of the points in 2D space
print(X_transformed)
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