> from sklearn.preprocessing import StandardScaler > data =, ,, ] > scaler = StandardScaler () > print ( scaler. Variance is zero, we can’t achieve unit variance, and the data is leftĪs-is, giving a scaling factor of 1. Generally this is calculated using np.sqrt(var_). Per feature relative scaling of the data to achieve zero mean and unit Attributes : scale_ ndarray of shape (n_features,) or None If True, scale the data to unit variance (or equivalently, Matrix which in common use cases is likely to be too large to fit in Sparse matrices, because centering them entails building a dense This does not work (and will raise an exception) when attempted on Not a NumPy array or scipy.sparse CSR matrix, a copy may still be This is not guaranteed to always work inplace e.g. If False, try to avoid a copy and do inplace scaling instead. With_mean=False to avoid breaking the sparsity structure of the data. This scaler can also be applied to sparse CSR or CSC matrices by passing Than others, it might dominate the objective function and make theĮstimator unable to learn from other features correctly as expected. If a feature has a variance that is orders of magnitude larger Machines or the L1 and L2 regularizers of linear models) assume thatĪll features are centered around 0 and have variance in the same Gaussian with 0 mean and unit variance).įor instance many elements used in the objective function ofĪ learning algorithm (such as the RBF kernel of Support Vector Individual features do not more or less look like standard normallyĭistributed data (e.g. Machine learning estimators: they might behave badly if the Standardization of a dataset is a common requirement for many Standard deviation are then stored to be used on later data using The relevant statistics on the samples in the training set. Note that order number A164BD is for an optional binder to hold the print version of FFS-1, order number A16416.Where u is the mean of the training samples or zero if with_mean=False,Īnd s is the standard deviation of the training samples or one ifĬentering and scaling happen independently on each feature by computing However, flexibility is provided to the user in the form of an advanced assessment level to handle uncommon situations that may require a more detailed analysis. The procedures are not intended to provide a definitive guideline for every possible situation that may be encountered. The Fitness-For-Service (FFS) assessment procedures in this Standard can be used to evaluate flaws commonly encountered in pressure vessels, piping and tankage. Qualitative and quantitative guidance for establishing remaining life and in-service margins for continued operation of equipment are provided in regards to future operating conditions and environmental compatibility. The Fitness-For-Service (FFS) assessment procedures in this Standard cover both the present integrity of the component given a current state of damage and the projected remaining life. This Standard has broad application since the assessment procedures are based on allowable stress methods and plastic collapse loads for noncrack-like flaws, and the Failure Assessment Diagram (FAD) Approach for crack-like flaws. The assessment procedures in this Standard can be used for Fitness-For-Service assessments and/or rerating of equipment designed and constructed to recognized codes and standards, including international and internal corporate standards. The methods and procedures in this Standard are intended to supplement and augment the requirements in API 510, API 570, API 653, and other post construction codes that reference FFS evaluations such as NB-23.
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