def load_tslearn_data():
""" Time series data with variable length """
X_train, y_train, X_test, y_test = CachedDatasets().load_dataset("Trace")
X_train = X_train[y_train < 4] # Keep first 3 classes
np.random.shuffle(X_train)
X_train = TimeSeriesScalerMeanVariance().fit_transform(X_train[:50]) # Keep only 50 time series
X_train = TimeSeriesResampler(sz=40).fit_transform(X_train) # Make time series shorter
X_train = X_train.reshape(50,-1)
return X_train
import matplotlib
#matplotlib.use('Agg')
import matplotlib.pyplot as plt
from tslearn.clustering import GlobalAlignmentKernelKMeans
from tslearn.metrics import sigma_gak, cdist_gak
from tslearn.datasets import CachedDatasets
from tslearn.preprocessing import TimeSeriesScalerMeanVariance
import pandas as pd
import numpy as np
from Sloth import cluster
seed = 0
numpy.random.seed(seed)
X_train, y_train, X_test, y_test = CachedDatasets().load_dataset("Trace")
X_train = X_train[y_train < 4] # Keep first 3 classes
numpy.random.shuffle(X_train)
#X_train = TimeSeriesScalerMeanVariance().fit_transform(X_train[:50]) # Keep only 50 time series
X_train = X_train[:50]
sz = X_train.shape[1]
X_train = X_train.reshape((X_train.shape[0], X_train.shape[1]))
#Sloth = Sloth()
eps = 20
min_samples = 2
LOAD = False # Flag for loading similarity matrix from file if it has been computed before
This example uses the KShape clustering method that is based on cross-correlation to cluster time series.
"""
# Author: Romain Tavenard
# License: BSD 3 clause
import numpy
import matplotlib.pyplot as plt
from tslearn.clustering import KShape
from tslearn.datasets import CachedDatasets
from tslearn.preprocessing import TimeSeriesScalerMeanVariance
seed = 0
numpy.random.seed(seed)
X_train, y_train, X_test, y_test = CachedDatasets().load_dataset("Trace")
X_train = X_train[y_train < 4] # Keep first 3 classes
numpy.random.shuffle(X_train)
X_train = TimeSeriesScalerMeanVariance().fit_transform(
X_train[:50]) # Keep only 50 time series
sz = X_train.shape[1]
# Euclidean k-means
ks = KShape(n_clusters=3, verbose=True, random_state=seed)
y_pred = ks.fit_predict(X_train)
plt.figure()
for yi in range(3):
plt.subplot(3, 1, 1 + yi)
for xx in X_train[y_pred == yi]:
plt.plot(xx.ravel(), "k-", alpha=.2)
# Author: Romain Tavenard, Felix Divo
# License: BSD 3 clause
import numpy
import matplotlib.pyplot as plt
from tslearn.barycenters import \
euclidean_barycenter, \
dtw_barycenter_averaging, \
dtw_barycenter_averaging_subgradient, \
softdtw_barycenter
from tslearn.datasets import CachedDatasets
# fetch the example data set
numpy.random.seed(0)
X_train, y_train, _, _ = CachedDatasets().load_dataset("Trace")
X = X_train[y_train == 2]
length_of_sequence = X.shape[1]
def plot_helper(barycenter):
# plot all points of the data set
for series in X:
plt.plot(series.ravel(), "k-", alpha=.2)
# plot the given barycenter of them
plt.plot(barycenter.ravel(), "r-", linewidth=2)
# plot the four variants with the same number of iterations and a tolerance of
# 1e-3 where applicable
ax1 = plt.subplot(4, 1, 1)
