# 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
from sklearn.preprocessing import StandardScaler
from tensortrade.features.feature_transformer import FeatureTransformer
[docs]class StandardNormalizer(FeatureTransformer):
"""A transformer for normalizing values within a feature pipeline by removing the mean and scaling to unit variance."""
[docs] def __init__(self, columns: Union[List[str], str, None] = None, feature_min=0, feature_max=1, inplace=True):
"""
Arguments:
columns (optional): A list of column names to normalize.
feature_min (optional): The minimum value in the range to scale to.
feature_max (optional): The maximum value in the range to scale to.
inplace (optional): If `False`, a new column will be added to the output for each input column.
"""
self._feature_min = feature_min
self._feature_max = feature_max
self._inplace = inplace
self.columns = columns
self._history = {}
[docs] def reset(self):
self._history = {}