Adaptive Scaling

[csmangum]

To address the need for dynamically or adaptively scaling the input in your ContinuousEvolution project, you can consider several approaches:

1. Adaptive Normalization:

   • Use techniques like Min-Max Scaling or Standardization, but update the scaling parameters periodically based on the most recent data.
   • Keep a rolling window of data to compute the scaling parameters. This way, the scaling adapts to the latest trends.

2. Online Normalization:

   • Implement an online algorithm to update the mean and variance (or min and max) of the input data as new data arrives.
   • Algorithms like Welford's method can be used to update the mean and variance incrementally.

3. Quantile Transformation:

   • Transform the data based on quantiles, which can adapt to the changing distribution of the data.
   • Use an online version of the QuantileTransformer to adjust to new data points.

4. Z-Score Normalization with Decay:

   • Apply Z-Score normalization but introduce a decay factor to give more weight to recent data.
   • This method ensures that old data has less influence, allowing the scaling to adapt over time.

5. Adaptive Binning:

   • Divide the data into bins and scale within each bin.
   • Update the bin boundaries periodically based on the distribution of the most recent data.

Here is a simple implementation of adaptive normalization using a rolling window approach in Python:

import numpy as np
from collections import deque

class AdaptiveScaler:
    def __init__(self, window_size):
        self.window_size = window_size
        self.data_window = deque(maxlen=window_size)
        
    def update(self, new_data):
        self.data_window.extend(new_data)
        current_data = np.array(self.data_window)
        self.min_val = current_data.min(axis=0)
        self.max_val = current_data.max(axis=0)
        
    def transform(self, data):
        return (data - self.min_val) / (self.max_val - self.min_val + 1e-6)

# Example usage:
scaler = AdaptiveScaler(window_size=100)
new_data = np.random.randn(10, 5)  # New batch of data
scaler.update(new_data)
scaled_data = scaler.transform(new_data)

This approach updates the scaling parameters (min and max values) based on the most recent window_size data points, ensuring that the scaling adapts to changes in the input data over time.

Feel free to adjust the window_size or experiment with other techniques mentioned above to best suit your project's requirements.