<｜begin▁of▁sentence｜>### Instruction:
Imagine you are a cryptographer working on refining a secure communication protocol where you need to align two sets of encoded signals represented by matrices, to ensure that they can be accurately compared. These signals are encoded in matrices X and Y, each with the same dimensions (n, d). Specifically, X is given by [[1, 0], [0, 1], [1, 1]] and Y by [[0, 1], [1, 1], [1, 0]]. Your task is to use a mathematical procedure called Orthogonal Procrustes, which is available in the graspologic package, to discover the best orthogonal matrix that, when applied to X, transforms it to most closely match Y. 

The goal is to calculate this transformation matrix and then apply it to the original matrix X, in order to evaluate the effectiveness of the alignment. Could you provide a statement restructured for this context that describes the need to obtain the transformed version of matrix X, the orthogonal transformation matrix itself, and the resultant measure of alignment between the original X and Y—the objective function value—as facilitated by the OrthogonalProcrustes API from the graspologic toolkit?
### Response:
import numpy as np
from graspologic.align import OrthogonalProcrustes

X = np.array([[1, 0], [0, 1], [1, 1]])
Y = np.array([[0, 1], [1, 1], [1, 0]])

# Initialize the OrthogonalProcrustes model
model = OrthogonalProcrustes()

# Fit the model to align X to Y
model.fit(X, Y)

# Transform X to best match Y
X_prime = model.transform(X)

print('Transformed X:', X_prime)
print('Orthogonal Matrix Q_:', model.Q_array_)
print('Objective function score:', model.score_)
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