`import numpy as np
import matplotlib.pyplot as plt
from tslearn.clustering import TimeSeriesKMeans

from sklearn.model_selection import train_test_split
import pandas as pd
#from tslearn.datasets import CachedDatasets
from tslearn.preprocessing import TimeSeriesScalerMeanVariance, TimeSeriesResampler
`

时间序列聚类：
tslearn.clustering

时间序列数据预处理：
tslearn.preprocessing
时间序列重新取样
TimeSeriesResampler
对时间序列重新取样，使其达到目标大小。
Resampler for time series. Resample time series so that they reach the target size.

Parameters: sz (int) – Size of the output time series.#参数为整型；输出时间序列的大小。
Example

TimeSeriesResampler(sz=5).fit_transform([[0, 3, 6]]) # doctest: +NORMALIZE_WHITESPACE
array([[[ 0. ],
[ 1.5],
[ 3. ],
[ 4.5],
[ 6. ]]])

Methods

时间周期重新取样。拟合变换（x，**kwargs）[来源]

拟合数据，然后转换它。

参数：x（类似数组）–要重新采样的时间序列数据集。

返回：重新采样的时间序列数据集。

返回类型：numpy.ndarray

时间序列预测源代码
Source code for tslearn.preprocessing

“”"
The :mod:tslearn.preprocessing module gathers time series scalers.
“”"

import numpy
from sklearn.base import TransformerMixin
from scipy.interpolate import interp1d

from tslearn.utils import to_time_series_dataset, check_equal_size, ts_size

author = ‘Romain Tavenard romain.tavenard[at]univ-rennes2.fr’

[docs]class TimeSeriesResampler(TransformerMixin):
“”"Resampler for time series. Resample time series so that they reach the target size.

Parameters
----------
sz : int
    Size of the output time series.

Example
-------
>>> TimeSeriesResampler(sz=5).fit_transform([[0, 3, 6]]) # doctest: +NORMALIZE_WHITESPACE
array([[[ 0. ],
        [ 1.5],
        [ 3. ],
        [ 4.5],
        [ 6. ]]])
"""

[docs] def init(self, sz):
self.sz_ = sz

[docs] def fit_transform(self, X, **kwargs):
“”"Fit to data, then transform it.

    Parameters
    ----------
    X : array-like
        Time series dataset to be resampled.

    Returns
    -------
    numpy.ndarray
        Resampled time series dataset.
    """
    X_ = to_time_series_dataset(X)
    n_ts, sz, d = X_.shape
    equal_size = check_equal_size(X_)
    X_out = numpy.empty((n_ts, self.sz_, d))
    for i in range(X_.shape[0]):
        xnew = numpy.linspace(0, 1, self.sz_)
        if not equal_size:
            sz = ts_size(X_[i])
        for di in range(d):
            f = interp1d(numpy.linspace(0, 1, sz), X_[i, :sz, di], kind="slinear")
            X_out[i, :, di] = f(xnew)
    return X_out

[docs]class TimeSeriesScalerMinMax(TransformerMixin):
“”"Scaler for time series. Scales time series so that their span in each dimension is between min and max.

Parameters
----------
min : float (default: 0.)
    Minimum value for output time series.
max : float (default: 1.)
    Maximum value for output time series.

Note
----
    This method requires a dataset of equal-sized time series.

Example
-------
>>> TimeSeriesScalerMinMax(min=1., max=2.).fit_transform([[0, 3, 6]]) # doctest: +NORMALIZE_WHITESPACE
array([[[ 1. ],
        [ 1.5],
        [ 2. ]]])
"""

[docs] def init(self, min=0., max=1.):
self.min_ = min
self.max_ = max

[docs] def fit_transform(self, X, **kwargs):
“”"Fit to data, then transform it.

    Parameters
    ----------
    X : array-like
        Time series dataset to be rescaled.

    Returns
    -------
    numpy.ndarray
        Rescaled time series dataset.
    """
    X_ = to_time_series_dataset(X)
    for i in range(X_.shape[0]):
        for d in range(X_.shape[2]):
            cur_min = X_[i, :, d].min()
            cur_max = X_[i, :, d].max()
            cur_range = cur_max - cur_min
            X_[i, :, d] = (X_[i, :, d] - cur_min) * (self.max_ - self.min_) / cur_range + self.min_
    return X_

[docs]class TimeSeriesScalerMeanVariance(TransformerMixin):
“”"Scaler for time series. Scales time series so that their mean (resp. standard deviation) in each dimension is
mu (resp. std).

Parameters
----------
mu : float (default: 0.)
    Mean of the output time series.
std : float (default: 1.)
    Standard deviation of the output time series.

Note
----
    This method requires a dataset of equal-sized time series.

Example
-------
>>> TimeSeriesScalerMeanVariance(mu=0., std=1.).fit_transform([[0, 3, 6]]) # doctest: +NORMALIZE_WHITESPACE
array([[[-1.22474487],
        [ 0. ],
        [ 1.22474487]]])
"""

[docs] def init(self, mu=0., std=1.):
self.mu_ = mu
self.std_ = std

[docs] def fit_transform(self, X, **kwargs):
“”"Fit to data, then transform it.

    Parameters
    ----------
    X
        Time series dataset to be rescaled

    Returns
    -------
    numpy.ndarray
        Rescaled time series dataset
    """
    X_ = to_time_series_dataset(X)
    for i in range(X_.shape[0]):
        for d in range(X_.shape[2]):
            cur_mean = X_[i, :, d].mean()
            cur_std = X_[i, :, d].std()
            if cur_std == 0.:
                cur_std = 1.
            X_[i, :, d] = (X_[i, :, d] - cur_mean) * self.std_ / cur_std + self.mu_
    return X_