bridgescaler.deep#
Classes#
Calculate standard scaler scores on an arbitrarily dimensional dataset as long as the last dimension is |
|
Performs a quantile transform on N-dimensional arrays where the variable dimension is the last one. |
Module Contents#
- class bridgescaler.deep.DeepStandardScaler#
Bases:
objectCalculate standard scaler scores on an arbitrarily dimensional dataset as long as the last dimension is the variable dimension.
- mean_ = None#
- sd_ = None#
- fit(x)#
- transform(x)#
- fit_transform(x)#
- inverse_transform(x)#
- class bridgescaler.deep.DeepMinMaxScaler#
Bases:
object- max_ = None#
- min_ = None#
- fit(x)#
- transform(x)#
- fit_transform(x)#
- inverse_transform(x)#
- class bridgescaler.deep.DeepQuantileTransformer(n_quantiles=1000, stochastic=False)#
Bases:
objectPerforms a quantile transform on N-dimensional arrays where the variable dimension is the last one.
- n_quantiles#
number of quantiles to calculate and store
- stochastic#
When transforming to quantile space, whether to take the mean of the left and right interpolation values (False) or to pick a random point in between (True).
- n_quantiles = 1000#
- stochastic = False#
- quantiles_ = None#
- references_ = None#
- fitted_ = False#
- x_column_names_ = None#
- fit(x)#
- transform(x)#
- fit_transform(x)#
- inverse_transform(x)#
- _transform_col(x_col, col_index)#
- _inverse_transform_col(x_col, col_index)#