机器学习(四) 机器学习(四) 分类算法--K近邻算法 KNN (下)

六、网格搜索与 K 邻近算法中更多的超参数

七、数据归一化 Feature Scaling

解决方案：将所有的数据映射到同一尺度

八、scikit-learn 中的 Scaler

preprocessing.py

import numpy as np


class StandardScaler:

    def __init__(self):
        self.mean_ = None
        self.scale_ = None

    def fit(self, X):
        """根据训练数据集X获得数据的均值和方差"""
        assert X.ndim == 2, "The dimension of X must be 2"

        self.mean_ = np.array([np.mean(X[:,i]) for i in range(X.shape[1])])
        self.scale_ = np.array([np.std(X[:,i]) for i in range(X.shape[1])])

        return self

    def transform(self, X):
        """将X根据这个StandardScaler进行均值方差归一化处理"""
        assert X.ndim == 2, "The dimension of X must be 2"
        assert self.mean_ is not None and self.scale_ is not None, \
               "must fit before transform!"
        assert X.shape[1] == len(self.mean_), \
               "the feature number of X must be equal to mean_ and std_"

        resX = np.empty(shape=X.shape, dtype=float)
        for col in range(X.shape[1]):
            resX[:,col] = (X[:,col] - self.mean_[col]) / self.scale_[col]
        return resX