时间序列数据聚类方法python代码（k-means)

          时间序列数据聚类方法python代码（k-means)

import numpy
import matplotlib.pyplot as plt

from tslearn.clustering import TimeSeriesKMeans
from tslearn.datasets import CachedDatasets
from tslearn.preprocessing import TimeSeriesScalerMeanVariance, TimeSeriesResampler

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 = TimeSeriesResampler(sz=40).fit_transform(X_train) # Make time series shorter
sz = X_train.shape[1]

Euclidean k-means

print(“Euclidean k-means”)
km = TimeSeriesKMeans(n_clusters=3, verbose=True, random_state=seed)
y_pred = km.fit_predict(X_train)

plt.figure()
for yi in range(3):
plt.subplot(3, 3, yi + 1)
for xx in X_train[y_pred == yi]:
plt.plot(xx.ravel(), “k-”, alpha=.2)
plt.plot(km.cluster_centers_[yi].ravel(), “r-”)
plt.xlim(0, sz)
plt.ylim(-4, 4)
if yi == 1:
plt.title(“Euclidean $k$-means”)

DBA-k-means

print(“DBA k-means”)
dba_km = TimeSeriesKMeans(n_clusters=3, n_init=2, metric=“dtw”, verbose=True, max_iter_barycenter=10, random_state=seed)
y_pred = dba_km.fit_predict(X_train)

for yi in range(3):
plt.subplot(3, 3, 4 + yi)
for xx in X_train[y_pred == yi]:
plt.plot(xx.ravel(), “k-”, alpha=.2)
plt.plot(dba_km.cluster_centers_[yi].ravel(), “r-”)
plt.xlim(0, sz)
plt.ylim(-4, 4)
if yi == 1:
plt.title(“DBA $k$-means”)

Soft-DTW-k-means

print(“Soft-DTW k-means”)
sdtw_km = TimeSeriesKMeans(n_clusters=3, metric=“softdtw”, metric_params={“gamma_sdtw”: .01},
verbose=True, random_state=seed)
y_pred = sdtw_km.fit_predict(X_train)

for yi in range(3):
plt.subplot(3, 3, 7 + yi)
for xx in X_train[y_pred == yi]:
plt.plot(xx.ravel(), “k-”, alpha=.2)
plt.plot(sdtw_km.cluster_centers_[yi].ravel(), “r-”)
plt.xlim(0, sz)
plt.ylim(-4, 4)
if yi == 1:
plt.title(“Soft-DTW $k$-means”)

plt.tight_layout()
plt.show()