Pes pc uniform tutorial9/24/2023 ![]() choice ( list ( range ( len ( X ))), 4 ) # Define the cost function for optimization cost = lambda _params : - target_alignment ( X, Y, lambda x1, x2 : kernel ( x1, x2, _params ), assume_normalized_kernel = True, ) # Optimization step params = opt. GradientDescentOptimizer ( 0.2 ) for i in range ( 500 ): # Choose subset of datapoints to compute the KTA on. ![]() sum ( T * T )) inner_product = inner_product / norm return inner_product params = init_params opt = qml. ![]() array ( Y ) = 1 ) nminus = len ( Y ) - nplus _Y = np. ![]() square_kernel_matrix ( X, kernel, assume_normalized_kernel = assume_normalized_kernel, ) if rescale_class_labels : nplus = np. Def target_alignment ( X, Y, kernel, assume_normalized_kernel = False, rescale_class_labels = True, ): """Kernel-target alignment between kernel and labels.""" K = qml.
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