标准化数据-StandardScaler
StandardScaler----计算训练集的平均值和标准差，以便测试数据集使⽤相同的变换
官⽅⽂档：
class sklearn.preprocessing.StandardScaler(copy=True, with_mean=True, with_std=True)
Standardize features by removing the mean and scaling to unit variance
通过删除平均值和缩放到单位⽅差来标准化特征
The standard score of a sample x is calculated as:
样本x的标准分数计算如下：
z = (x - u) / s
where u is the mean of the training samples or zero if with_mean=Fal, and s is the standard deviation of the training samples or one if with_std=Fal.
其中u是训练样本的均值，如果with_mean=Fal，则为0
s是训练样本的标准偏差，如果with_std=Fal，则为1
Centering and scaling happen independently on each feature by computing the relevant statistics on the samples in the training t. Mean and standard deviation are then stored to be ud on later data using the transform method.
Standardization of a datat is a common requirement for many machine learning estimators: they might behave badly if the individual features do not more or less look like standard normally distributed data (e.g. Gaussian with 0 mean and unit variance).
For instance many elements ud in the objective function of a learning algorithm (such as the RBF kernel of Support Vector Machines or the L1 and L2 regularizers of linear models) assume that all features are centered around 0 and have variance in the same order. If a feature has a variance that is orders of magnitude larger that others, it might dominate the objective function and make the estimator unable to learn from other features correctly as expected.
This scaler can also be applied to spar CSR or CSC matrices by passing with_mean=Fal to avoid breaking the sparsity structure of the data.
Read more in the .
Parameters:copy : boolean, optional, default True
If Fal, try to avoid a copy and do inplace scaling instead. This is not guaranteed to always work inplace; e.g. if the
data is not a NumPy array or scipy.spar CSR matrix, a copy may still be returned.
with_mean : boolean, True by default
If True, center the data before scaling. This does not work (and will rai an exception) when attempted on spar
matrices, becau centering them entails building a den matrix which in common u cas is likely to be too large to
fit in memory.
with_std : boolean, True by default
If True, scale the data to unit variance (or equivalently, unit standard deviation).
Attributes:scale_ : ndarray or None, shape (n_features,)
Per feature relative scaling of the data. This is calculated using np.sqrt(var_). Equal to None when with_std=Fal.
New in version 0.17: scale_
mean_ : ndarray or None, shape (n_features,)
The mean value for each feature in the training t. Equal to None when with_mean=Fal.
var_ : ndarray or None, shape (n_features,)
The variance for each feature in the training t. Ud to compute scale_. Equal to None when with_std=Fal.
n_samples_en_ : int or array, shape (n_features,)
The number of samples procesd by the estimator for each feature. If there are not missing samples,
the n_samples_en will be an integer, otherwi it will be an array. Will be ret on new calls to fit, but increments
across partial_fit calls.
across partial_fit calls.
See also
Equivalent function without the estimator API.
Further removes the linear correlation across features with ‘whiten=True’.
Notes
NaNs are treated as missing values: disregarded in fit, and maintained in transform.
For a comparison of the different scalers, transformers, and normalizers, e .
Examples
>>>
>>> from sklearn.preprocessing import StandardScaler
>>> data = [[0, 0], [0, 0], [1, 1], [1, 1]]
>>> scaler = StandardScaler()
>>> print(scaler.fit(data))
StandardScaler(copy=True, with_mean=True, with_std=True)
>>> an_)
[0.5 0.5]
>>> ansform(data))
[[-1. -1.]
[-1. -1.]
[ 1. 1.]
[ 1. 1.]]
>>> ansform([[2, 2]]))
[[3. 3.]]
Methods⽅法
(X[, y])Compute the mean and std to be ud for later scaling.
计算⽤于以后缩放的mean和std
(X[, y])Fit to data, then transform it.
适合数据，然后转换它
([deep])Get parameters for this estimator.
(X[, copy])Scale back the data to the original reprentation
(X[, y])Online computation of mean and std on X for later scaling.
(**params)Set the parameters of this estimator.
(X[, y, copy])Perform standardization by centering and scaling
通过居中和缩放执⾏标准化
__init__(copy=True, with_mean=True, with_std=True)
fit(X, y=None)
Compute the mean and std to be ud for later scaling.
Parameters:X : {array-like, spar matrix}, shape [n_samples, n_features]
The data ud to compute the mean and standard deviation ud for later scaling along the features axis.
y
Ignored
fit_transform(X, y=None, **fit_params)
Fit to data, then transform it.
Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.
使⽤可选参数fit_params是变换器适合X和Y，并返回X的变换版本
Parameters:X : numpy array of shape [n_samples, n_features]
Training t.
y : numpy array of shape [n_samples]
Target values.
Returns:X_new : numpy array of shape [n_samples, n_features_new]
Transformed array.
get_params(deep=True)
Get parameters for this estimator.
Parameters:deep : boolean, optional
If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns:params : mapping of string to any
Parameter names mapped to their values.
inver_transform(X, copy=None)
Scale back the data to the original reprentation
Parameters:X : array-like, shape [n_samples, n_features]
The data ud to scale along the features axis.
copy : bool, optional (default: None)
Copy the input X or not.
Returns:X_tr : array-like, shape [n_samples, n_features]
Transformed array.
partial_fit(X, y=None)
Online computation of mean and std on X for later scaling. All of X is procesd as a single batch. This is intended for cas when fit is not feasible due to very large number of n_samples or becau X is read from a continuous stream.
The algorithm for incremental mean and std is given in Equation 1.5a,b in Chan, Tony F., Gene H. Golub, and Randall J. LeVeque.
“Algorithms for computing the sample variance: Analysis and recommendations.” The American Statistician 37.3 (1983): 242-247: Parameters:X : {array-like, spar matrix}, shape [n_samples, n_features]
The data ud to compute the mean and standard deviation ud for later scaling along the features axis.
y
Ignored
t_params(**params)
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.
Returns:lf
transform(X, y=’deprecated’, copy=None)
Perform standardization by centering and scaling
Parameters:X : array-like, shape [n_samples, n_features]
The data ud to scale along the features axis.
y : (ignored)
Deprecated since version 0.19: This parameter will be removed in 0.21. copy : bool, optional (default:
None)
Copy the input X or not.
