Python3入门机器学习经典算法与应用——knn算法数据归一化 Feature Scaling

数据归一化 Feature Scaling

最值归一化 normalization

把所有数据映射到0-1之间

适⽤用于分布有明显边界的情况；受outlier影响较⼤大

均值方差归一化 standardization

均值方差归一化：把所有数据归⼀一到均值为0⽅方差为1的分布中

对测试数据集如何归一化

测试数据是模拟真实环境
• 真实环境很有可能⽆无法得到所有测试
数据的均值和⽅方差
• 对数据的归⼀一化也是算法的⼀一部分

(x_test - mean_train) / std_train

模型

手写StandardScaler

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