机器学习：使用opencv和python进行智能图像处理

数据特征处理

from sklearn import preprocessing
import numpy as np

x = np.array([[1., -2., 2.], [3., 0., 0.], [0., 1., -1.]])
x_scaled = preprocessing.scale(x)

print(x_scaled)

print('\n')

y = x_scaled.mean(axis=0)
print(y)
print('\n')

z = x_scaled.std(axis=0)
print(z)
print('\n')

x_normalized_l1 = preprocessing.normalize(x, norm='l1')
print(x_normalized_l1)
print('\n')

x_normalized_l2 = preprocessing.normalize(x, norm='l2')
print(x_normalized_l2)
print('\n')

min_max_scaler = preprocessing.MinMaxScaler()
x_min_max = min_max_scaler.fit_transform(x)
print(x_min_max)
print('\n')

min_max_scaler = preprocessing.MinMaxScaler(feature_range=(-10, 10))
x_min_max2 = min_max_scaler.fit_transform(x)
print(x_min_max2)
print('\n')

binarizer = preprocessing.Binarizer(threshold=0.5)
x_binarizer = binarizer.transform(x)
print(x_binarizer)
print('\n')