VMware虚拟机 Ubuntu20-LTS
python3.6
tensorflow1.15.0
keras2.3.1
运行截图:
代码:
实验3-1标准化
from sklearn.preprocessing import StandardScaler from sklearn.preprocessing import MinMaxScaler from matplotlib import gridspec import numpy as np import matplotlib.pyplot as plt cps = np.random.random_integers(0, 100, (100, 2)) ss = StandardScaler() std_cps = ss.fit_transform(cps) gs = gridspec.GridSpec(5,5) fig = plt.figure() ax1 = fig.add_subplot(gs[0:2, 1:4]) ax2 = fig.add_subplot(gs[3:5, 1:4]) ax1.scatter(cps[:, 0], cps[:, 1]) ax2.scatter(std_cps[:, 0], std_cps[:, 1]) plt.show()
实验3-2归一化
from sklearn.preprocessing import MinMaxScaler import numpy as np data = np.random.uniform(0, 100, 10)[:, np.newaxis] mm = MinMaxScaler() mm_data = mm.fit_transform(data) origin_data = mm.inverse_transform(mm_data) print('data is ',data) print('after Min Max ',mm_data) print('origin data is ',origin_data)
实验3-3正则化
X = [[1, -1, 2], [2, 0, 0], [0, 1, -1]] # 使用L2正则化 from sklearn.preprocessing import normalize l2 = normalize(X, norm='l2') print('l2:', l2) # 使用L1正则化 from sklearn.preprocessing import Normalizer normalizerl1 = Normalizer(norm='l1') l1 = normalizerl1.fit_transform(X) print('l1:', l1)
标签:mm,特征,print,处理,实验,np,import,cps,data From: https://www.cnblogs.com/liucaizhi/p/18192325