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I have a problem with algorithm robustness. When I use the same dataset and run the algorithm twice to impute the data, can I get the completely same result twice?
Thank you.
Look forward to your reply.
The text was updated successfully, but these errors were encountered:
@GeniusYx the results should be almost identical from run to run -- the only difference is in the computation of randomized PCA. However, to get the same result every time, you can set the seed with random_state.
import numpy as np
import magic
X = np.random.poisson(0.2, (5000, 1000))
X_magic1 = magic.MAGIC(random_state=42).fit_transform(X)
X_magic2 = magic.MAGIC(random_state=42).fit_transform(X)
np.testing.assert_equal(X_magic1, X_magic2)
Dear authors:
Thank you for your contribution.
I have a problem with algorithm robustness. When I use the same dataset and run the algorithm twice to impute the data, can I get the completely same result twice?
Thank you.
Look forward to your reply.
The text was updated successfully, but these errors were encountered: