Optimize memory usage in empirical_cross_covariance calculation #214
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If X is a large matrix, it might be stored as a float32 array to save memory. However, if Y is of type float64, the resulting cross-covariance matrix will be float64, potentially doubling the memory usage even if X is float32. To prevent unnecessary memory consumption, we cast Y to the same data type as X before computing the dot product. This ensures that the output cross-covariance matrix uses memory efficiently while retaining the precision dictated by X's data type.
tommyod
approved these changes
Mar 7, 2024
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Resolves: #213
If X is a large matrix, it might be stored as a float32 array to save memory. However, if Y is of type float64,
the resulting cross-covariance matrix will be float64, potentially doubling the memory usage even if X is float32. To prevent unnecessary memory consumption,
we cast Y to the same data type as X before computing the dot product. This ensures that the output cross-covariance matrix uses memory efficiently while retaining the precision dictated by X's data type.