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# Author: Romain Tavenard
# License: BSD 3 clause

import numpy
from sklearn.metrics import accuracy_score

from tslearn.generators import random_walk_blobs
from tslearn.preprocessing import TimeSeriesScalerMinMax, \
    TimeSeriesScalerMeanVariance
from tslearn.neighbors import KNeighborsTimeSeriesClassifier, \
    KNeighborsTimeSeries

numpy.random.seed(0)
n_ts_per_blob, sz, d, n_blobs = 20, 100, 1, 2

# Prepare data
X, y = random_walk_blobs(n_ts_per_blob=n_ts_per_blob,
                         sz=sz,
                         d=d,
                         n_blobs=n_blobs)
scaler = TimeSeriesScalerMinMax(value_range=(0., 0.0011))  # Rescale time series
X_scaled = scaler.fit_transform(X)

indices_shuffle = numpy.random.permutation(n_ts_per_blob * n_blobs)
X_shuffle = X_scaled[indices_shuffle]
y_shuffle = y[indices_shuffle]

X_train = X_shuffle[:n_ts_per_blob * n_blobs // 2]
X_test = X_shuffle[n_ts_per_blob * n_blobs // 2:]
y_train = y_shuffle[:n_ts_per_blob * n_blobs // 2]
y_test = y_shuffle[n_ts_per_blob * n_blobs // 2:]

# Nearest neighbor search
knn = KNeighborsTimeSeries(n_neighbors=3, metric="dtw")
knn.fit(X_train, y_train)
dists, ind = knn.kneighbors(X_test)
print("1. Nearest neighbour search")
print("Computed nearest neighbor indices (wrt DTW)\n", ind)
print("First nearest neighbor class:", y_test[ind[:, 0]])

# Nearest neighbor classification
knn_clf = KNeighborsTimeSeriesClassifier(n_neighbors=3, metric="dtw")
knn_clf.fit(X_train, y_train)
predicted_labels = knn_clf.predict(X_test)
print("\n2. Nearest neighbor classification using DTW")
print("Correct classification rate:", accuracy_score(y_test, predicted_labels))

# Nearest neighbor classification with a different metric (Euclidean distance)
knn_clf = KNeighborsTimeSeriesClassifier(n_neighbors=3, metric="euclidean")
knn_clf.fit(X_train, y_train)
predicted_labels = knn_clf.predict(X_test)
print("\n3. Nearest neighbor classification using L2")
print("Correct classification rate:", accuracy_score(y_test, predicted_labels))

# Nearest neighbor classification based on SAX representation
metric_params = {'n_segments': 10, 'alphabet_size_avg': 5}
knn_clf = KNeighborsTimeSeriesClassifier(n_neighbors=3, metric="sax",
                                         metric_params=metric_params)
knn_clf.fit(X_train, y_train)
predicted_labels = knn_clf.predict(X_test)
print("\n4. Nearest neighbor classification using SAX+MINDIST")
print("Correct classification rate:", accuracy_score(y_test, predicted_labels))