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chi_merge_vector has redundant calculation.

1

codes below is calculated in every while loop, and takes too much time. ``` intervals, unique_intervals = assign_interval_unique(x, unique_intervals[:, 1]) pt_value, pt_column, pt_index = pivot_table_np(intervals[:, 1], y) ``` In my...

cheesebear

chi_merge_vector can't handle data with np.Inf

1

it's because np.quantilehas problem. Quantile function determines boundary by (a+b)/2, but (a+np.Inf)/2 equals np.NaN codes below can solve the problem. boundaries = np.unique( np.quantile(x, np.arange(0, 1, 1/initial_intervals)[1:]) ) # Add...

cheesebear