Use float64 in Jenks natural breaks internals (#1100)#1101
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The Jenks matrices and bin edge array used float32, causing the naive variance formula (sum_squares - sum*sum/w) to lose all significant digits when data had a large offset relative to its spread. Changed lower_class_limits, var_combinations, val cast, and kclass to float64.
test_natural_breaks_large_offset_1100: five tight clusters offset by 100,000 must be separated into 5 distinct classes. With float32 internals, the variance calculation lost all signal and merged clusters.
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Summary
Fixes #1100. The Jenks natural breaks algorithm used float32 for its internal matrices and bin edge array. The naive variance formula
sum_squares - (sum * sum) / wloses all significant digits when data has a large offset relative to its spread (elevations around 1000m, projected coordinates in the millions, etc.).Changed four float32 sites to float64:
lower_class_limitsmatrix dtypevar_combinationsmatrix dtypeval = np.float32(data[i4])cast removedkclassbin edge array dtypeTest plan
test_natural_breaks_large_offset_1100: five tight clusters at offset 100,000 with spread of 10 -- all 5 classes must be separated cleanlytest_classify.pysuite: 85 passed, no regressions