or not np.isfinite(scale_range)
or np.isclose(scale_range, 0.0)
):
- return values
+ return out
out[mask] = low + (values[mask] - self._min) / value_range * scale_range
elif method == NORMALIZATION_TYPES[1]: # "sigmoid"
or not np.isfinite(scale_range)
or np.isclose(scale_range, 0.0)
):
- return values
+ return out
out[mask] = self._min + (values[mask] - low) / scale_range * value_range
elif method == NORMALIZATION_TYPES[1]: # "sigmoid"
)
arr = np.asarray(X, dtype=float)
+ # Exclude non-finite values and zeros (NEUTRAL extrema label) from transformation
mask = np.isfinite(arr) & ~np.isclose(arr, 0.0)
standardized = self._standardize(arr, mask)
)
arr = np.asarray(X, dtype=float)
+ # Exclude non-finite values and zeros (NEUTRAL extrema label) from transformation
mask = np.isfinite(arr) & ~np.isclose(arr, 0.0)
degammaized = self._inverse_gamma(arr, mask)