human
19.1 ms for the total execution - much better!
We're done with this demo script. Adding the source code for KnnDtwClassifier and CommonClassifier to clarify the implementation details.

=====
What is a the Python program you requested?
assistant
#Here is the Python program you requested.
class KnnDtwClassifier(BaseEstimator, ClassifierMixin):
    """Custom classifier implementation for Scikit-Learn using Dynamic Time Warping (DTW)
       and KNN (K-Nearest Neighbors) algorithms.
       This classifier can be used for labeling the varying-length sequences, like time series
       or motion data.
       FastDTW library is used for faster DTW calculations - linear instead of quadratic complexity.
    """
    def __init__(self, n_neighbors=1):
        self.n_neighbors = n_neighbors
        self.features = []
        self.labels = []

    def get_distance(self, x, y):
        return fastdtw(x, y)[0]

    def fit(self, X, y=None):
        for index, l in enumerate(y):
            self.features.append(X[index])
            self.labels.append(l)
        return self

    def predict(self, X):
        dist = np.array([self.get_distance(X, seq) for seq in self.features])
        indices = dist.argsort()[:self.n_neighbors]
        return np.array(self.labels)[indices]

    def predict_ext(self, X):
        dist = np.array([self.get_distance(X, seq) for seq in self.features])
        indices = dist.argsort()[:self.n_neighbors]
        return (dist[indices],
                indices)

class CommonClassifier(object):
    """Helper class to execute the common classification workflow - from training to prediction
       to metrics reporting with the popular ML algorithms, like SVM or Random Forest.
       Includes the default list of estimators with instances and parameters, which have been
       proven to work well.
    """
    def __init__(self, default=True, cv=5, reduce_func=None):
        self.cv = cv
        self.default = default
        self.reduce_func = reduce_func
        self.reducer = None
        self.grid_search = None

    def add_estimator(self, name, instance, params):
        self.grid_search.add_estimator(name, instance, params)

    def fit(self, X, y=None):
        if self.default:
            self.grid_search = GridSearchEstimatorSelector(X, y, self.cv)
           ...