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Predictions without experimental reference #7

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RMeli opened this issue Aug 24, 2020 · 0 comments
Open

Predictions without experimental reference #7

RMeli opened this issue Aug 24, 2020 · 0 comments
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RMeli commented Aug 24, 2020

Predictions should work even if there is no experimental reference provided. This is currently not the case and using 0.00 everywhere fails when numpy tries to compute the correlation coefficient or the kernel density estimate (because the array is constant).

PearsonRConstantInputWarning: An input array is constant; the correlation coefficent is not defined.

UserWarning: Data must have variance to compute a kernel density estimate.
@RMeli RMeli self-assigned this Aug 24, 2020
@RMeli RMeli added enhancement Feature or Enhancement problem Problem labels Aug 24, 2020
@RMeli RMeli changed the title Predictions without Experimental Reference Predictions without experimental reference Aug 27, 2020
@RMeli RMeli mentioned this issue Aug 27, 2020
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