# 数据的标准化归一化简单例子
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()
# X: numpy array of shape [n_samples, n_features]Training set.
data = np.random.uniform(0, 100, 10)[:, np.newaxis]
ss = StandardScaler()
std_data = ss.fit_transform(data)
origin_data = ss.inverse_transform(std_data)
print('data is ', data)
print('after standard ', std_data)
print('after inverse ', origin_data)
print('after standard mean and std is ', np.mean(std_data), np.std(std_data))
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)
