class_docstr: Classifier for multivariate time series.
  
  It provides a convenient class to classify multivariate time series with
  classifier that can only deal with univariate time series. The labels are
  predicted in a hard voting fashion using the predictions for each feature.
  
  Parameters
  ----------
  estimator : estimator object or list thereof
      Classifier. If one estimator is provided, it is cloned and each clone
      performs prediction for one feature. If a list of estimators is
      provided, each estimator performs prediction for one feature.
  
  weights : array-like, shape = (n_classifiers,) or None (default=None)
      Sequence of weights (`float` or `int`) to weight the occurrences of
      predicted class labels. Uses uniform weights if None.
  
  Attributes
  ----------
  classes_ : array, shape = (n_classes,)
      An array of class labels known to the classifier.
  
  estimators_ : list of estimator objects
      The collection of fitted classifiers.
  
  Examples
  --------
  >>> from pyts.classification import BOSSVS
  >>> from pyts.datasets import load_basic_motions
  >>> from pyts.multivariate.classification import MultivariateClassifier
  >>> X_train, X_test, y_train, y_test = load_basic_motions(return_X_y=True)
  >>> clf = MultivariateClassifier(BOSSVS())
  >>> clf.fit(X_train, y_train)
  MultivariateClassifier(...)
  >>> clf.score(X_test, y_test)
  1.0
class_docstr_tok: ["Classifier","for","multivariate","time","series",".","It","provides","a","convenient","class","to","classify","multivariate","time","series","with","classifier","that","can","only","deal","with","univariate","time","series",".","The","labels","are","predicted","in","a","hard","voting","fashion","using","the","predictions","for","each","feature",".","Parameters","--","--","--","--","--","estimator",":","estimator","object","or","list","thereof","Classifier",".","If","one","estimator","is","provided",",","it","is","cloned","and","each","clone","performs","prediction","for","one","feature",".","If","a","list","of","estimators","is","provided",",","each","estimator","performs","prediction","for","one","feature",".","weights",":","array-like",",","shape","=","(","n_classifiers",",",")","or","None","(","default=None",")","Sequence","of","weights","(","`","float","`","or","`","int","`",")","to","weight","the","occurrences","of","predicted","class","labels",".","Uses","uniform","weights","if","None",".","Attributes","--","--","--","--","--","classes_",":","array",",","shape","=","(","n_classes",",",")","An","array","of","class","labels","known","to","the","classifier",".","estimators_",":","list","of","estimator","objects","The","collection","of","fitted","classifiers",".","Examples","--","--","--","--",">",">",">","from","pyts.classification","import","BOSSVS",">",">",">","from","pyts.datasets","import","load_basic_motions",">",">",">","from","pyts.multivariate.classification","import","MultivariateClassifier",">",">",">","X_train",",","X_test",",","y_train",",","y_test","=","load_basic_motions","(","return_X_y=True",")",">",">",">","clf","=","MultivariateClassifier","(","BOSSVS","(",")",")",">",">",">","clf.fit","(","X_train",",","y_train",")","MultivariateClassifier","(","...",")",">",">",">","clf.score","(","X_test",",","y_test",")","1.0"]
code: def __init__(self, estimator, weights=None):
      self.estimator = estimator
      self.weights = weights
  
file_path: pyts/pyts/multivariate/classification/multivariate.py
imports: import numpy
  from numba import njit, prange
  from sklearn.base import BaseEstimator, clone
  from sklearn.preprocessing import LabelEncoder
  from sklearn.utils.validation import check_is_fitted
  from ...base import MultivariateClassifierMixin
  from ..utils import check_3d_array
label_desc: Use image node_id 1 to create a new MultivariateClassifier object from inherited base classes: BaseEstimator, MultivariateClassifierMixin with example: obj = MultivariateClassifier(estimator, weights)