# Copyright 2019 The TensorTrade Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License
import pandas as pd
import numpy as np
from gym import Space
from copy import copy
from typing import Union, List, Tuple, Dict
from tensortrade.features.feature_transformer import FeatureTransformer
[docs]class MinMaxNormalizer(FeatureTransformer):
"""A transformer for normalizing values within a feature pipeline by the column-wise extrema."""
[docs] def __init__(self,
columns: Union[List[str], str, None] = None,
feature_min: float = 0,
feature_max: float = 1,
inplace: bool = True):
"""
Arguments:
columns (optional): A list of column names to normalize.
feature_min (optional): The minimum `float` in the range to scale to. Defaults to 0.
feature_max (optional): The maximum `float` in the range to scale to. Defaults to 1.
inplace (optional): If `False`, a new column will be added to the output for each input column.
"""
super().__init__(columns=columns, inplace=inplace)
self._feature_min = feature_min
self._feature_max = feature_max