MinMaxScaler

MinMaxScaler

一、总结

一句话总结：

MinMaxScaler是min、max归一化，使用的话先fit，然后再transform归一化操作，也可以合并为fit_transform

>>> from sklearn.preprocessing import MinMaxScaler
>>> data = [[-1, 2], [-0.5, 6], [0, 10], [1, 18]]
>>> scaler = MinMaxScaler()
>>> print(scaler.fit(data))
MinMaxScaler()
>>> print(scaler.data_max_)
[ 1. 18.]
>>> print(scaler.transform(data))
[[0.   0.  ]
 [0.25 0.25]
 [0.5  0.5 ]
 [1.   1.  ]]
>>> print(scaler.transform([[2, 2]]))
[[1.5 0. ]]

 

1、训练集的归一化方法为 scaler.fit_transform，验证集和测试集的归一化方法为scaler.transform？

壹、training_set_scaled = sc.fit_transform(training_set)  # 求得训练集的最大值，最小值这些训练集固有的属性，并在训练集上进行归一化

贰、test_set = sc.transform(test_set)  # 利用训练集的属性对测试集进行归一化

# 归一化
sc = MinMaxScaler(feature_range=(0, 1))  # 定义归一化：归一化到(0，1)之间
print(sc)

MinMaxScaler(copy=True, feature_range=(0, 1))

-------------

training_set_scaled = sc.fit_transform(training_set)  # 求得训练集的最大值，最小值这些训练集固有的属性，并在训练集上进行归一化
test_set = sc.transform(test_set)  # 利用训练集的属性对测试集进行归一化
print(training_set_scaled[:5,])
print(test_set[:5,])

[[0.011711  ]
 [0.00980951]
 [0.00540518]
 [0.00590914]
 [0.00489135]]
[[0.84288404]
 [0.85345726]
 [0.84641315]
 [0.87046756]
 [0.86758781]]

 

 

二、MinMaxScaler

博客对应课程的视频位置：

 

>>> from sklearn.preprocessing import MinMaxScaler
>>> data = [[-1, 2], [-0.5, 6], [0, 10], [1, 18]]
>>> scaler = MinMaxScaler()
>>> print(scaler.fit(data))
MinMaxScaler()
>>> print(scaler.data_max_)
[ 1. 18.]
>>> print(scaler.transform(data))
[[0.   0.  ]
 [0.25 0.25]
 [0.5  0.5 ]
 [1.   1.  ]]
>>> print(scaler.transform([[2, 2]]))
[[1.5 0. ]]

 

 

training_set_scaled = sc.fit_transform(training_set)  # 求得训练集的最大值，最小值这些训练集固有的属性，并在训练集上进行归一化

Signature: sc.fit_transform(X, y=None, **fit_params)
Docstring:
Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params
and returns a transformed version of X.

Parameters
----------
X : numpy array of shape [n_samples, n_features]
    Training set.

y : numpy array of shape [n_samples]
    Target values.

**fit_params : dict
    Additional fit parameters.

Returns
-------
X_new : numpy array of shape [n_samples, n_features_new]
    Transformed array.

 

 

=================================================================================

training_set = maotai.iloc[0:2426 - 300, 2:3].values  # 前(2426-300=2126)天的开盘价作为训练集,表格从0开始计数，2:3 是提取[2:3)列，前闭后开,故提取出C列开盘价
test_set = maotai.iloc[2426 - 300:, 2:3].values  # 后300天的开盘价作为测试集
print(training_set.shape)
print(test_set.shape)

(2126, 1)
(300, 1)

In [5]:

# 归一化
sc = MinMaxScaler(feature_range=(0, 1))  # 定义归一化：归一化到(0，1)之间
print(sc)

MinMaxScaler(copy=True, feature_range=(0, 1))

In [5]:

training_set_scaled = sc.fit_transform(training_set)  # 求得训练集的最大值，最小值这些训练集固有的属性，并在训练集上进行归一化
test_set = sc.transform(test_set)  # 利用训练集的属性对测试集进行归一化
print(training_set_scaled[:5,])
print(test_set[:5,])

[[0.011711  ]
 [0.00980951]
 [0.00540518]
 [0.00590914]
 [0.00489135]]
[[0.84288404]
 [0.85345726]
 [0.84641315]
 [0.87046756]
 [0.86758781]]